what are the three research approaches

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Chapter 3: Developing a Research Question

3.5 Quantitative, Qualitative, & Mixed Methods Research Approaches

Generally speaking, qualitative and quantitative approaches are the most common methods utilized by researchers. While these two approaches are often presented as a dichotomy, in reality it is much more complicated. Certainly, there are researchers who fall on the more extreme ends of these two approaches, however most recognize the advantages and usefulness of combining both methods (mixed methods). In the following sections we look at quantitative, qualitative, and mixed methodological approaches to undertaking research. Table 2.3 synthesizes the differences between quantitative and qualitative research approaches.

Quantitative Research Approaches

A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. Therefore, asking the “right” questions is key. Further, this perspective favours observable causes and effects and is therefore outcome-oriented. Typically, aggregate data is used to see patterns and “truth” about the phenomenon under study. True understanding is determined by the ability to predict the phenomenon.

Qualitative Research Approaches

On the other side of research approaches is the qualitative approach. This is generally considered to be the opposite of the quantitative approach. Qualitative researchers are considered phenomenologists, or human-centred researchers. Any research must account for the humanness, i.e., that they have thoughts, feelings, and experiences that they interpret of the participants. Instead of a realist perspective suggesting one reality or truth, qualitative researchers tend to favour the constructionist perspective: knowledge is created, not discovered, and there are multiple realities based on someone’s perspective. Specifically, a researcher needs to understand why, how and to whom a phenomenon applies. These aspects are usually unobservable since they are the thoughts, feelings and experiences of the person. Most importantly, they are a function of their perception of those things rather than what the outside researcher interprets them to be. As a result, there is no such thing as a neutral or objective outsider, as in the quantitative approach. Rather, the approach is generally process-oriented. True understanding, rather than information based on prediction, is based on understanding action and on the interpretive meaning of that action.

Table 3.3 Differences between quantitative and qualitative approaches (from Adjei, n.d).

Note: Researchers in emergency and safety professions are increasingly turning toward qualitative methods. Here is an interesting peer paper related to qualitative research in emergency care.

Qualitative Research in Emergency Care Part I: Research Principles and Common Applications by Choo, Garro, Ranney, Meisel, and Guthrie (2015)

Interview-based Qualitative Research in Emergency Care Part II: Data Collection, Analysis and Results Reporting.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 3, 2023 3:14 PM
URL: https://guides.lib.berkeley.edu/researchmethods

How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

Uses an inductive approach
Is used to build theories
Takes a subjective approach
Adopts an open and flexible approach
The researcher is close to the respondents
Interviews and focus groups are oftentimes used to collect word-based data.
Generally, draws on small sample sizes
Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
Uses a deductive approach
Is used to test theories
Takes an objective approach
Adopts a closed, highly planned approach
The research is disconnected from respondents
Surveys or laboratory equipment are often used to collect number-based data.
Generally, requires large sample sizes
Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job.

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

The nature of your research aims, objectives and research questions
The methodological approaches taken in the existing literature
Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
Confirmatory – confirming a potential theory or hypothesis by testing it empirically
A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

Data access
Equipment and software
Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon.

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

Software costs – e.g. survey hosting services, analysis software, etc.
Promotion costs – e.g. advertising a survey to attract respondents
Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
Equipment rental costs – e.g. recording equipment, lab equipment, etc.
Travel costs
Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

Am I more of a “numbers person” or a “words person”?
How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
How much do I know about the software and/or hardware that I’ll potentially use?
How excited am I to learn new research skills and gain new knowledge?
How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

Exploratory
Confirmatory
Combination
Research area norms
Hardware and software
Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Very useful and informative especially for beginners

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Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

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Research Approach

In the field of science different researchers may assign different meanings for the team research approach. In some publications you may see that research approach may imply methods of data collection and data analysis in general and differences between qualitative and quantitative methods in particular.

However, in our view research approach is best seen as a general plan and procedure for conducting the study. Accordingly, approach for the research can be divided into three categories:

Deductive approach
Inductive approach
Abductive approach

The relevance of hypotheses to the study is the main distinctive point between deductive and inductive approaches. Deductive approach tests the validity of assumptions (or theories/hypotheses) in hand, whereas inductive approach contributes to the emergence of new theories and generalizations. Abductive research, on the other hand, starts with ‘surprising facts’ or ‘puzzles’ and the research process is devoted their explanation. [1]

The following table illustrates the major differences between deductive, inductive and abductive research approaches in terms of logic, generaliability, use of data and theory. [2]

Differences between deductive, inductive and abductive approaches

Discussion of research approach is a vital part of any scientific study regardless of the research area. Within the methodology chapter of your dissertation, you need to explain the main differences between inductive, deductive and abductive approaches. Also, you need to specify the approach you have adopted for your research by breaking down your arguments into several points.

Let me explain the research approach for a following study:

Effects of labour migration within the EU on the formation of multicultural teams in Dutch private sector organizations

Deductive Approach

If you have formulated a set of hypotheses for your dissertation that need to be confirmed or rejected during the research process you would be following a deductive approach. In deductive approach, the effects of labour migration within the EU are assessed by developing hypotheses that are tested during the research process.

Dissertations with deductive approach follow the following path:

Deductive process in research approach

The following hypotheses can be developed in order to assess the effects of labour migration within the EU on the formation of multicultural teams in Dutch private sector organizations using a deductive approach:

Hypothesis: Labour migration within the EU contributes to the formation of multicultural teams in Dutch private sector organizations

The whole research process will be devoted to testing this hypothesis. The hypothesis will be proved right or wrong by the end of the research process.

Inductive Approach

Alternatively, inductive approach does not involve formulation of hypotheses. It starts with research questions and aims and objectives that need to be achieved during the research process.

Inductive studies follow the route below:

Inductive process in research approach

Referring to the example above, the effects of labour migration within the EU on the formation of multicultural teams in Dutch private sector organizations can be assessed through finding answers to the following research questions:

Research question: How does labour migration within the EU effect the formation of multicultural teams in Dutch private sector organizations ?

The research process will focus on finding answer to this research question. Answer to the research question to be found by the end of the research process will imply generating a new theory related to the research problem.

Abductive Approach

In abductive approach, the research process is devoted to explanation of ‘incomplete observations’, ‘surprising facts’ or ‘puzzles’ specified at the beginning of the study. Referring to the same research topic, you may observe that labour migration within the EU was actually decreasing the extent of cross-cultural differences within teams in Dutch private sector organizations.

In this case your study can be devoted to the explanation of this phenomenon by using qualitative and/or quantitative methods of data collection and data analysis in an integrated manner.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research approaches. The e-book also explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy , research design , methods of data collection , data analysis and sampling are explained in this e-book in simple words.

John Dudovskiy

[1] Bryman A. & Bell, E. (2015) “Business Research Methods” 4 th edition, Oxford University Press, p.27

[2] Source: Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th edition, Pearson Education Limited

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Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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There are no conflicts of interest.

Introduction to Research Methods

3 types of research.

In the last chapter we talked about the ways that research is all around us. You do it yourself almost every day in small and big ways, but we’re not really here to help you become more rigorous in your search for the best tacos in town. Looking at yelp is research, it’s just not really the type of research we’re going to talk about today. In the first section of this chapter we’ll talk about different types of research. Then we’ll describe different fields of research within social sciences, and finally we’ll discuss the steps of doing research.

I’m going to break types of research into three categories, which probably don’t match the way they’re described in other textbooks.

A lot of the research you do in your daily life could probably be called secondary research . You have a question (“where are the best tacos?”, “when did the Civil War start?”, “is coffee bad for my heart?”) and so you seek an answer. That’s still research, it just doesn’t involve the collection of new data or a lot of detailed steps. Google and other search engines are incredible tools that will direct you towards an answer to your questions. What you’re doing there is secondary research, using the research of others to answer your question. Your collecting, reviewing, or synthesizing existing research, not creating new data to answer a question.

You can be better at secondary research by identifying reputable sources, accessing multiple opinions, and understanding how they produced their findings. That’s part of the research we’re talking about in this class, but only a small part. We’ll return to secondary research in a later chapter, because in order to be really good at it you have to understand how to do the original research yourself. Secondary research thus involves reviewing the research of others and is motivated by getting an answer to a question.

You can only do secondary research if someone has already researched the question you have. Another type of research people do is what could be called applied research, or research that is intended for immediate public dissemination. The idea of applied research is that there is a very clear connection between the research question and the importance of the research. Imagine you’re in a sorority and you’re planning dinner for the new pledges, so you poll everyone to ask which of three options they’d pick. You gather the data and you get an answer – the most common answer is tacos. Why do you care about the answer? Because you needed to know where to go for dinner.

Would people be open to changing the colors on the United States flag? I don’t know, and based on a quick Google search no one has answered the question. No one has polled Americans to find out whether they think red white and blue is a little dated (or maybe just too similar to France). I don’t know why we’d want to change the colors, maybe so everyone has to go buy a new flag. I can’t get an answer to that question based on secondary research though. I have to collect original data if I want an answer.

Polls are a great example of applied research. Who is currently winning the race for president? How do people feel about policies designed to slow climate change? How much trust do citizens have in their government? Those are all questions you can find written about in wonky news sources like the [NY Times], Vox, 538, or others. Why do we care? Because we want to know who is winning the race, or peoples views towards certain policies.

Take another example. A radio station wants to know the demographics of its listeners so that it can make sure the commercials they run are matched to who listens. There isn’t an esoteric question to answer, but they need to collect data to improve their business operations.

The research question and the importance are very directly linked. Thus, applied research involves original research, not just reviewing what others have done, but like secondary research it is motivated to get an answer.

The third type is the least common, but is also generally the focus of a textbook like this. Academic research is the type of research that your professors do most of the time. What differentiates public research from academic research? Public research is concerned with providing new facts, academic research is concerned with testing theories and seeking explanations.

I could spend thousands of dollars to run a new poll with a very rigorous research design to understand exactly what percentage of Americans would support new taxes. If I did that research I might be able to get it published in popular sources like the New York Times, but I could never get it published in an academic journal – and those are the papers that get professors tenure.

Why? Good polls tell us something about the world at this moment, but sciences goal, both the social and hard sciences, is to tell us something about the world beyond this moment. More accurately, it’s concerned with explaining the causes of the phenomena we see. Scientists weren’t just concerned with tracking that rocks fell from buildings, but wanted to identify the force that explained why that occurred (gravity). Similarly, social scientists aren’t just concerned with knowing what percentage of people are in poverty (although that is important) they want to identify the cause of poverty so that those conditions can be changed.

My poll might find that 46% of Americans plan to vote. What academic research is concerned with is the ‘why?’ Why did 46% say that, why did one person say yes and the other no, what does that help us to understand about the society? What we want to understand is the causes of the phenomena we see every day so that we can better understand the world of tomorrow.

Let’s say we did a study and found that 32% of elementary age children are significantly overweight. That’s good to know, it gives everyone an idea of the status of the health of children at this moment. What would be more important to know is why. If we know why 32% of children are significantly overweight and the other 68% aren’t, we can make changes that affect the future. Is it a lack of recess in schools, do children not have enough access to fruits and vegetable, are the foods they’re eating changing – understanding why is just as important as knowing the what, so that we know where to make changes.

Change is the only constant. I do most of my research on urban policy, which sort of means I study cities and the changes they undergo. If I looked at data on all of the neighborhoods in a city a decade ago and look again a decade later some would have gotten richer and others would have gotten poorer.

Change in Income in Nashville 2000 to 2018

That map might look interesting, and it might be important for people living in those communities to know. But unless we can provide an explanation for the change, we haven’t really learned anything. Neighborhoods change. People change. Demographics change. Everything changes. Are the neighborhoods shaded darker going to keep getting richer, or will there be a return to the mean and they’ll get poorer in the next decade? Did something happen that changed the fate of those neighborhoods, or was it just random decisions by a lot of different actors that lead to a new geography of the city? Those questions might seem unnecessary. Some neighborhoods are getting poorer, they need more support! Who cares about the why!? But if we’re going to try and figure out what neighborhoods will get poorer in the future, or want to change the future, we have to understand the underlying causes of those changes. That’s what academic research is trying to untangle. Not just what’s going on, but why, so that we can try to get more control of the future. Thus, academic research involves original research, like applied research, but is focused on developing theories as much as it is getting an answer to a question.

3.2 What’s theory got to do with it?

Let’s first define theory , because the way it’s used in science and the way it’s used in everyday conversation are slightly different. In everyday conversation you might hear the word theory used as the equivalent of “hunch” or “idea”: “oh, that’s just your theory”. In the sciences it means a bit more. A theory in the sciences is a well-substantiated explanation for a set of observations. A law is accepted as true by scientists, it is confirmed fact. A theory is on its way to becoming a law, it just needs more observations to be fully accepted.

The social sciences have plenty of theories, and fewer explanations that can be accepted as laws because as we discussed in the previous chapter humans just make it hard to get consistent findings.

For instance, researchers in in political science and public administration often use:

elite theory which posits that a small minority of elites be they the wealthy or those that drive the creation of policy, holds most of the power in society even within democratic systems.
democratic peace theory argues that democracies generally do not go to war or have armed conflicts with other democracies
representative bureaucracy argues that governing bodies throughout society should be representative of the community they serve or govern.

Those theories and others get applied in different studies to continue testing them and refining them. For instance, I might want to study whether counties with growing Latinx/Latino/Latina populations see changes in who is elected for county wide offices. If I just do that research and report the results it could just be applied research. The public has an interest in knowing who their elected representatives are. If I use the research to test representative bureaucracy as a way of explaining my results, my research is now venturing into academic territory. The theoretical argument is all about explaining whatever I find, whether representation changes in those counties or not.

3.3 Inputs to research

The most difficult thing to accept and internalize in developing a research project is that it is iterative, not linear. We like linear processes like following a recipe. You can follow those steps and you get the end and then you have cookies. Following the research recipe isn’t that clean. It will be a lot of one step forward two steps back, which is progress, but can be frustrating.

Research values novelty. One should not spend a lot of time gathering original data in order to answer a question that has already been answered. So in order to develop a research question worth researching, it is really important to understand what has already been studied on that topic. I’ve learned this from experience, both in my own research and teaching, but you really can’t develop a good research question without doing a lot of reading.

If you’re starting to develop a research project, start with the things you care about. You’re going to spend a lot of time studying it and reading about the subject – it should be something you enjoy. Think about the things you observe in the world, the odd processes or changes you see around you. And think about the things you know a lot about. Whatever your interests are, whether they’re video games or hiking or reading, try to embed that interest in the research.

But again, you have to do a lot of reading. If I was walking down the street and someone stopped me and forced me to come up with a research question in sociology I would surely stumble and I might eventually stammer something out like “why do people leave online communities?” I don’t know much about that, it sounds kind of interesting. And online communities are a somewhat recent development (in comparison to say churches) so maybe the research will be interesting. Almost certainly not. I can guarantee a lot of research has been done on that question. That doesn’t mean I have to abandon the idea, it just means I need to start by reading all the research that has been done, and continue to refine my question. As I read thorough the literature on online group membership I’ll probably find answers to questions that never occurred to me, and as I read I might find questions that haven’t been answered yet.

A similar pattern occurred as I began my PhD. I wanted to study big important questions, and when I got to my program I was given a lot of freedom to decide what I would do research on. I decided I’d start by answering a question I constantly heard debated by policymakers: do sports stadiums create economic activity. I was going to be the researcher that answered the question. But pretty immediately I discovered that, actually, about 100 other researchers had already answered that question (it’s a definitive no, stadiums don’t create any economic activity, they’re a really bad deal for cities). The fact that the public didn’t regard it as a settled question doesn’t mean that researchers hadn’t already answered it. Did I give up? No, I kept reading the literature and I started to uncover related questions that hadn’t been answered yet. I ended up doing my dissertation on minor league baseball stadiums and their impact on the neighborhoods where they are located, which wasn’t the most important question ever but it hadn’t been answered before.

One recommendation I would make as you start reading the literature in a given area is to keep an annotated bibliography . As you read new articles to down a few sentences summarizing them - those few sentences can often be gleaned just from the abstract of the article. That way you’ll have a record of what you read, and as the project changes you can go back and wont have to search through the literature over and over. As your project begins to gain focus, you can pull the relevant articles from your annotated bibliography and begin to build out your paper. I would also recommend using a computer program like Zotero where you can save the details of articles and generate the bibliography of papers later. I don’t know the difference between MLA or APA or any system, because I make the computer do it for me. In the video below I walk through these things with a brief demonstration for anyone starting out a new research project.

One problem you will face in reading about your topic is accessing the articles that are relevant to your topic. If you go to a journals webpage, you’ll see that you can buy the article for probably $30. $30! That’s as much as a book, and you probably won’t even be sure if the article is good before buying it. There have been a lot of arguments made against these paywalls particularly given that taxpayers fund most of the research that is then sent to these journals. Researchers aren’t paid directly for the researcher they publish, which we give the copyright over to journals because it helps us to get tenure; journals then charge for people to read the research, and universities pay subscription fees so their researchers have access. It’s a circular economy, with me working for free, and journals making out like bandits. When you find an article you want to read see if you can access it through your library, but you can also check a few different websites where people post articles in order to “free” them. You can also just reach out to the author of the article and request a copy. Authors generally have the right to share the article even though they’ve given the journal the copyright over its contents, and most researchers are just happy to see someone engage with their work.

Once you’ve got a research question that’s worth studying and hasn’t been answered before, it’s time to answer it yourself. That’ll mean collecting data though, to answer that question. I should probably start by trying to figure out if data already exists that was collected by someone else first. You can do a lot of research in political science based on surveys that are posted online by Gallup or Pew. It’ll be easier to do the research if I can find the data from the start. If I can’t find the data, I’ll be faced with the choice of changing my research question to match the data that’s available or collecting my own data. Collecting your own data can be expensive and difficult, but if you’re interested in breaking new ground in your research it might be necessary.

The two steps are thus iterative. Knowing the literature on a topic will help you to develop new questions and lead you towards data necessary to answer them. But looking at data may help you to generate new questions and lead you back to the literature to understand how it can be used.

As you settle on a research question, and begin to look around for data to answer it, it is good to be explicit about your unit of analysis . The unit of analysis is whatever entity of body you wish to be able to answer your question about at the end of your study. Related, there is also the unit of observation , which is whatever unit you are measuring phenomena at. The unit of analysis and observation can be the same (they often are), but they can also be different.

Your unit of analysis (and observation) can be nations, cities, neighborhoods, individuals, or any other such grouping. Let’s use a few examples. If our research question is ‘why are some nations rich?’ we can answer that by collecting data for different nations, or we could use survey data about the individuals within different countries to make a comparison. Our unit of analysis is the same (countries), but our unit of observation (countries in the first, individuals in the second) can change.

Often we can study similar subjects using different units of observation or analysis. If I want to broadly study volunteering, I could collect data to understand volunteering rates for individuals, cities, states, or countries. The unit of observation will all depend upon what data I either collect or is available. And the unit of analysis can change as well, because I might want to study predictors of why some individuals volunteer and others don’t, or I may want to understand why different countries have different rates of volunteering. Which is all to say that it’ll depend on what you’re actually studying, but you should be explicit from the start about who you are studying.

3.5 Writing research

This is sort of where I get frustrated with myself as a teacher, or more specifically frustrated at myself for you. I just laid out three types of research. One you definitely do, which is secondary research. The second, applied research, is something you’ll see in the world all around you, and there’s some chance you might end up doing in your professional life. And the third, academic research, is generally inaccessible, uncommon, and probably not something you’ll ever do in your life. And yet, here we are, in a class on research, about to start talking about how to write a highly structure research paper using a format you’ll never use again.

Why? Why am I going to do that? In part it’s so that you can better understand the field you’re studying. It’s important to understand what it means not just to be curious about politics or sociology, but to understand what it means to study that field.

Beyond that, the best way to learn something new is to break it apart. This will be a bit like learning to drive by first building a car from parts. You could just move straight to secondary research or driving the car, but for you to really understand why things are working or where a breakdown might be, you have to understand the underlying parts. Each part of your car is important for getting you from point A to B, and each step that goes into research is important to getting you from your question to the right answer.

So when you see a headline in the future like

You’ll be able to better understand how that headline was made. The people that wrote that headline were using some academic research that is being translated to the public to use as secondary research. Where did it come from though, what did the researchers do to know that was the right answer? If it’s good research, they probably followed a process like what we’re about to lay out.

3.5.1 Introduction

If you look at published research in an academic journal it will typically follow a basic structure with 5 sections. The introduction explains the subject of your research and clearly identifies your research question. It provides a bit of background about the subject the current relevancy of it or maybe recent events that heightened its importance.

A good introduction thus has two purposes. First, it should explain to the reader what the paper is about. The thing you learn as you continue to write is the value of being clear in the introduction. Tell the reader what you do in the paper, the order of information you’re going to present, and what they takeaway is going to be. There shouldn’t be any surprise endings or twists. Just give them everything up front.

You can see that is done in the article excerpted and annotated below. This is from an article I published in 2020 Evaluating the impact of short-term rental regulations on Airbnb in New Orleans . The title should generally give you an idea of what the paper is about. It’s not the best paper I’ve ever written, but it’s short and so it’s easy to identify the structure of what I’m describing there. Read the complete introduction below to see how I describe the purpose of the paper in a direct manner with some background to prepare the reader.

3.5.2 Literature Review

There are two themes that should be described in any literature review . The two aren’t separate sections, they’re both intertwined.

What has been done in the area of your research before
What I need to know as a reader to understand what you’re going to do.

You want to prove to the reader that you’re aware of what’s been done in the area of your research before so they’ll believe your research is informed and new. The worst feedback you can get from a reviewer is that someone has already done the same study you’re attempting to publish. With any research question you identify you’ll find that a lot has been done before, and that’s fine. But describe what has been done so that I can better understand what makes yours different or unique.

And you’re also trying to make sure I can understand the background of your topic. What are the key words you’re going to be using? How have other people studied the issue? It’s all the background I need to understand your new contribution. Imagine you’re explaining a movie to someone so that they can see the sequel with you. Who are the main characters, what was the story, where did it leave off? Get them excited to see the sequel because you’re going to finally answer the question that was left lingering by all those past researchers!

You’ll want to use the literature you review to build hypotheses for your article. A hypothesis is a statement of what you expect to find. Hypothesis: toddlers that drink milk will be taller as adults.

That statement about toddlers and milk might be right or wrong, and that’s okay! That’s what the paper is building towards, proving whether the hypothesis is correct or not. Because right or wrong, if that hypothesis hasn’t been tested before we’re learning something new. But the hypothesis will be a lot better and more reasonable if it’s based on existing literature. Why would I think milk would help toddlers grow? I’d want to base that prediction on studies about milks effect on the bodies and what non-milk drinkers might consume and anything else that would be relevant.

The literature review for the paper below is 10 paragraphs in all, but I want to just pull out two. What the literature review is trying to do is just get the reader ready. Again, it’s the “previously on…” intro to your favorite show.

3.5.3 Methodology

The introduction and literature review are all used to set up your new study. Now you can explain how you’ll do whatever new and impressive thing you’re about to do in the methodology section. Describe the data you collected and how you’ll analyze it. Essentially you want to draw the reader a road map so they can understand exactly how to redo your study. It’s similar to the chain of custody in evidence for criminal cases. How did you find this information, where did it come from, why should we trust that this data is good? You don’t just wake up and find data on the side of the road, it had be collected somehow and the way it’s collected could impact whether we trust it or not.

One of the big concerns in science is replicability. We’ll talk about that later, but the study design section is a nod towards it. If I wanted to redesign your study, recreate the experiment with similar subjects in a similar setting, how would I do that? In science, we don’t just trust your word for how you generated your results. Tell us how you generated them, so that we can consider whether there were any potential problems present.

You can start writing the methodology section as you begin the research. As you start you should have a design in mind at the beginning of any project, including what data you are going to collect, how will you collect it, and how you will analyze it. Answers to those questions might evolve as you conduct the research, but you can begin by setting it out as a research design , describing your reserach plan, and then revise it as you write the paper. Regardless, your collection and analysis should be guided by a research design, whether formally written or just a mental plan.

Where did you get the data, and what are you gonna do with it now that you have it? What’s written below may or may not make sense at this stage, but I’m including it just to illustrate the way that researchers attempt to write clearly and directly in describing their studies.

3.6 Results

Once you’ve explained how you conducted your study, you can go ahead and tell the reader what you found in the results section. Exactly what you’ll say here will differ based on what you studied, but there isn’t a lot more to say at this stage.

3.6.1 Discussion

The paper then concludes with a discussion of the significance of the results and their implications. You found something, why do we care? How does that change the field? Should policymakers react? Should scientists react? You’ll often start with an overview of what the paper found, before launching into some of the more specific takeaways you want readers to get.

3.7 Summary

This chapter has covered some of the different ways we do research, and one way (a formal paper) that we report our research. It might seem a little overwhelming to think about how to write up your research results before you even know how to do research. And that’s fine, this is something of getting a fly over of the forest before we start to look more closely at the trees. It’s good to have an idea of what your final paper might look like, before we get started. Now we can begin to get a little more detailed about how we fill in all those words between the title and the final period on a research paper.

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Introduction to Research Methods in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

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Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

Case studies
Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In: Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Methods: What are research methods?

What are research methods?
Searching specific databases

What are research methods

Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic.

There are different types of research methods which use different tools for data collection.

Types of research

Qualitative Research
Quantitative Research
Mixed Methods Research

Qualitative Research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Mixed Methods Research integrates both Q ualitative and Quantitative Research . It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables Triangulation, or verification, of the data from two or more sources.

Finding Mixed Methods research in the Databases

“mixed model*” OR “mixed design*” OR “multiple method*” OR multimethod* OR triangulat*

Data collection tools

Sage research methods.

SAGE research methods online This link opens in a new window Research methods tool to help researchers gather full-text resources, design research projects, understand a particular method and write up their research. Includes access to collections of video, business cases and eBooks,

Help and Information

Next: Finding qualitative research >>
Last Updated: Apr 18, 2024 11:16 AM
URL: https://libguides.newcastle.edu.au/researchmethods

Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Published: 24 April 2024

Spatiotemporally resolved colorectal oncogenesis in mini-colons ex vivo

L. Francisco Lorenzo-Martín ORCID: orcid.org/0000-0003-4717-9338 1 na1 ,
Tania Hübscher ORCID: orcid.org/0000-0002-2376-712X 1 na1 ,
Amber D. Bowler ORCID: orcid.org/0000-0002-9439-2839 2 , 3 ,
Nicolas Broguiere ORCID: orcid.org/0000-0001-9934-4505 1 ,
Jakob Langer ORCID: orcid.org/0000-0002-0095-1936 1 ,
Lucie Tillard 1 ,
Mikhail Nikolaev ORCID: orcid.org/0000-0001-5955-900X 4 ,
Freddy Radtke ORCID: orcid.org/0000-0003-4315-4045 2 , 3 &
Matthias P. Lutolf ORCID: orcid.org/0000-0002-5898-305X 1 , 4

Nature ( 2024 ) Cite this article

Metrics details

Cancer models
Gastrointestinal cancer
Lab-on-a-chip
Tissue engineering

Three-dimensional organoid culture technologies have revolutionized cancer research by allowing for more realistic and scalable reproductions of both tumour and microenvironmental structures 1 , 2 , 3 . This has enabled better modelling of low-complexity cancer cell behaviours that occur over relatively short periods of time 4 . However, available organoid systems do not capture the intricate evolutionary process of cancer development in terms of tissue architecture, cell diversity, homeostasis and lifespan. As a consequence, oncogenesis and tumour formation studies are not possible in vitro and instead require the extensive use of animal models, which provide limited spatiotemporal resolution of cellular dynamics and come at a considerable cost in terms of resources and animal lives. Here we developed topobiologically complex mini-colons that are able to undergo tumorigenesis ex vivo by integrating microfabrication, optogenetic and tissue engineering approaches. With this system, tumorigenic transformation can be spatiotemporally controlled by directing oncogenic activation through blue-light exposure, and emergent colon tumours can be tracked in real-time at the single-cell resolution for several weeks without breaking the culture. These induced mini-colons display rich intratumoural and intertumoural diversity and recapitulate key pathophysiological hallmarks displayed by colorectal tumours in vivo. By fine-tuning cell-intrinsic and cell-extrinsic parameters, mini-colons can be used to identify tumorigenic determinants and pharmacological opportunities. As a whole, our study paves the way for cancer initiation research outside living organisms.

High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays

Three-dimensional culture models mimic colon cancer heterogeneity induced by different microenvironments

Engineering organoids

Cancer arises through the accumulation of genetic lesions that confer unrestrained cell growth potential. Over the past 70 years, both two-dimensional (2D) and three-dimensional (3D) in vitro culture models have been developed to make simplified, animal-free versions of cancers readily available for research 4 . These models successfully portray and dissect a wide range of relatively simple cancer cell behaviours, such as proliferation, motility, invasiveness, survival, cell–cell and cell–stroma interactions, and drug responses, among others 1 , 2 , 4 . However, modelling more complex processes that involve multiple cell (sub)types and tissue-level organization remains a challenge, as is the case for cancer initiation.

The cellular transition from healthy to cancerous is an intricate evolutionary process that is still largely obscure due to the insufficient topobiological complexity of the available in vitro cell culture systems, which precludes de novo tumour generation and the establishment of pathophysiologically relevant tumorigenic models 5 , 6 . Even the current gold-standard organoid-based 3D models, which are often postulated as a bridge between in vitro and in vivo 1 , 3 , 7 , are too simplified for modelling cancer development ex vivo. This is mostly due to (1) their closed cystic structure instead of an in vivo-like apically open architecture 8 ; (2) their short lifespan that requires breaking up the culture every few days for passaging 9 ; (3) their lack of topobiological stability and consistency owing to their stochastic growth in 3D matrices 8 ; and (4) their inability to generate hybrid tissues composed of healthy and cancer cells in a balanced and integrated manner 10 . Various next-generation approaches such as bioprinting and microfabrication technologies have been recently implemented to partially address some of these issues 11 , 12 ; however, none have been able to fully recreate intratumour and intertumour complexity. Consequently, cancer research is still inevitably bound to animal experimentation, which provides a pathophysiologically relevant setting, but forbids high-resolution and real-time analyses of cellular dynamics during oncogenesis. Moreover, these models are economically and ethically costly. Thus, while there is the widespread consensus that animal use in research should be reduced, replaced and refined (the 3 Rs 13 ), this commitment is severely hindered by the insufficient physiological complexity displayed by classical in vitro systems.

Here we postulated that a 3D system able to solve the existing limitations of in vitro cultures could be engineered by leveraging scaffold-guided organoid morphogenesis and optogenetics. Specifically, we developed miniature colon tissues in which cells could (1) be cultured for long durations (several weeks) without the need for breaking the culture through passaging; (2) reproduce the stem-differentiated cell patterning axis in a stable and anatomically relevant topology; (3) be easily mutated and tracked in a spatiotemporally controlled manner; and (4) create a biomechanically dynamic system that allows for tumour emergence while preserving the integrity of the surrounding healthy tissue. These features permit the development of biologically complex tumours ex vivo, bridging the gap between in vitro and in vivo models by providing a high-resolution system that can be used to dissect the molecular factors orchestrating cancer initiation.

Spatiotemporally regulated tumorigenesis

We focused on colorectal cancer (CRC) as it is one of the most prominent cancer types worldwide and its malignant transformation can be readily engineered genetically 14 , 15 . To first achieve spatiotemporal control of oncogenic DNA recombination, we developed a doxycycline-sensitive blue-light-regulated Cre system (hereafter, OptoCre), which we then introduced into inducible Apc fl/fl Kras LSL-G12D/+ Trp53 fl/fl (AKP) healthy colon organoids (Extended Data Fig. 1a–c ). A fluorescent Cre reporter was also incorporated to track cells that undergo oncogenic recombination (Extended Data Fig. 1b,c ). We initially tested the system in conventional organoid cultures, in which OptoCre efficiently induced recombination in the presence of blue light and doxycycline (Extended Data Fig. 1d,e ). Dosage optimization prevented unwanted activation by coupling high efficiency with low leakiness (~1.6%) (Extended Data Fig. 1d,e ). To confirm successful oncogenic transformation, we removed growth factors (EGF, noggin, R-spondin, WNT3A) from the organoid medium and observed that only cells with an activated OptoCre were able to grow, a well-known hallmark of mutated AKP colon organoids 16 (Extended Data Fig. 1f ). The presence of the expected mutations at the Apc, Kras and Trp53 loci was confirmed by PCR and exome sequencing (see below; Extended Data Fig. 3f,g ).

On the basis of previous evidence that small intestine cells can form stable tube-shaped epithelia through scaffold-guided organoid morphogenesis in microfluidic devices 9 , we next aimed to establish a ‘mini-colon’ constituted by OptoCre-AKP cells. By seeding colon cell suspensions in hydrogel-patterned microfluidic devices, we generated single-layered colonic epithelia spatially arranged into crypt- and lumen-like domains (Extended Data Fig. 2a ). This spatial arrangement recapitulated the spatial distribution found in vivo, with stem and progenitor (SOX9 + ) cells located at the bottom of the crypt domains and more differentiated colonocytes (FABP1 + ) located in the upper crypt and lumen areas 17 , 18 (Extended Data Fig. 2b ). In contrast to conventional colon organoids, the lumen of these mini-colons was readily perfusable with fresh medium, enabling the removal of cell debris and extending their lifespan to several weeks without the need for passaging or tissue disruption (Extended Data Fig. 2a ).

Once the healthy mini-colon system was established, we investigated its potential to capture tumour biology by inducing oncogenic recombination through blue-light illumination (Fig. 1a ). To mimic the scenario found in vivo, we fine-tuned OptoCre activation to mutate only a small number of cells (<0.5% of the total population). Due to the stability and defined topology of the mini-colon, we easily detected the acquisition of AKP mutations at the single-cell level (GFP + cells) and tracked their evolution over time (Extended Data Fig. 2c,d ). This revealed that cell death is one of the earliest responses to oncogenic recombination, as mutated mini-colons displayed higher cell shedding rates compared with the controls (Extended Data Fig. 2e ), with a large fraction of the mutated cells undergoing apoptosis (Supplementary Video 1 ). Nevertheless, some mutated cells escaped apoptosis and, after a quiescent period (24–72 h), started dividing at an accelerated pace (Extended Data Fig. 2d ). In conventional organoid cultures, these fast-proliferating mutated cells did not lead to any overt tissular rearrangements (Fig. 1b ), whereas, in the mini-colon system, they developed neoplastic structures over 5–10 days (Fig. 1b ). Furthermore, these mini-colon neoplasias evolved from polyp-like to full-blown tumours, recapitulating in vivo tumorigenesis (Fig. 1b,c and Supplementary Videos 2 and 3 ).

a , Schematic of the experimental workflow followed to induce tumorigenesis in mini-colons. CC, colonocyte; ISC, intestinal stem cell; TA, transit-amplifying cell. b , Bright-field and fluorescence images of time-course tumorigenesis experiments in conventional organoids and mini-colons. Fluorescence signal indicates oncogenic recombination. Scale bars, 200 μm (left) and 75 μm (right). c , Bright-field and fluorescence close-up images of a mini-colon tumour. The red and green signals correspond to healthy and mutated cells, respectively. Scale bar, 25 μm. d , Immunofluorescence images of a mini-colon tumour showing the presence of CD44 (top, green), FABP1 (top, magenta) and nuclei (bottom). Scale bar, 35 μm. e , Multiplicity of tumours emerged in mini-colons of the indicated genotypes after light-mediated oncogenic induction. Statistical analysis was performed using two-way analysis of variance (ANOVA) with Sidak’s multiple-comparison test; ** P = 0.024 (day 6, AKP), ** P = 0.0021 (day 24, A), *** P < 0.0001 (all other conditions). n = 5, 4, 3 and 10 mini-colons for the control, light-induced A, light-induced AK and light-induced AKP conditions, respectively. Data are mean ± s.e.m.

Source Data

Immunostaining analyses revealed that these tumours stemmed from CD44 high cells—a bona fide marker for cancer stem cells in vivo 19 —at the base of the epithelium (Fig. 1d , Extended Data Fig. 2f and Supplementary Video 4 ). Conversely, the bulk of the tumours was composed of cells with different degrees of differentiation, as revealed by the downregulation and upregulation of CD44 and FABP1, respectively (Fig. 1d and Supplementary Video 5 ). This indicated the existence of intratumour heterogeneity in the mini-colon, resembling the in vivo scenario 20 . Consistent with this, histopathological studies showed that these tumours displayed the histological organization characteristic of tubular adenomas (Extended Data Fig. 3a ). To validate their cancerous nature, we performed transplantation experiments in immunodeficient mice and found that mini-colon-derived cancer cells formed tumours in vivo with undistinguishable efficiency from bona fide tumour-derived cancer cells (Extended Data Fig. 3b,c ). Moreover, their histopathological structure was also comparable to the one displayed by primary tumours developed in the colon of AKP mice (Extended Data Fig. 3d ) and included the presence of locally invasive nodules and areas with adenocarcinoma-like features (Extended Data Fig. 3e ).

We confirmed through PCR and exome sequencing that tumour development in the mini-colon was directly associated with the expected mutations at the Apc, Kras and Trp53 loci (Extended Data Fig. 3f,g ). Consistent with this, using organoid lines with a reduced mutational burden ( Apc fl/fl Kras LSL-G12D/+ (hereafter, AK) and Apc fl/fl (hereafter, A)) produced longer latencies in tumour development in a dosage-dependent manner (Fig. 1e and Extended Data Fig. 3h ), demonstrating that mini-colon tumorigenesis can be modulated by the number of oncogenic driver mutations. Collectively, these data show that the mini-colon system enables spatiotemporally controlled in vitro modelling of CRC tumorigenesis with a considerable degree of topobiological complexity.

Context-dependent tumorigenic plasticity

Careful examination of induced mini-colons revealed consistent morphological differences among tumours according to their initiation site, with prominent dense or cystic internal structures arising from the crypt and the luminal epithelium, respectively (see below; Fig. 2b (top)). As mini-colons comprise different types of cells along the crypt–lumen axis (Extended Data Fig. 2b ), we leveraged the spatial resolution provided by OptoCre to investigate whether the initiating cell niche conditioned the morphological and functional features of nascent tumours. To spatially control AKP mutagenesis, we coupled the mini-colon to a photomask restricting blue-light exposure to specific regions of the colonic epithelium (Fig. 2a ), which provided low off-target recombination rates (around 8.5%) (Fig. 2b,c and Extended Data Fig. 4a ). Here again, dense and cystic tumours developed when crypt and lumen epithelia, respectively, were mutationally targeted by blue light (Fig. 2b ). To confirm that this was associated with the differentiation status of the tumour-initiating cell, we cultured mini-colons in either low- or high-differentiation medium before oncogenic induction to shift the proportions of (un)differentiated cells. Low-differentiation conditions produced mini-colons with thicker epithelia, early tumour development and a reduced fraction of cystic tumours (Fig. 2d and Extended Data Fig. 4b,c ). Conversely, high-differentiation conditions produced mini-colons with thinner epithelia, delayed tumour formation and increased cystic tumour frequency (Fig. 2d and Extended Data Fig. 4b,c ). These results indicate that the different environments of the mini-colon can shape tumour fate.

a , Schematic of the experimental workflow followed to spatiotemporally target tumorigenesis in mini-colons. b , Bright-field images of mini-colons that have undergone untargeted (top), crypt-targeted (middle) and lumen-targeted (bottom) tumorigenesis. Targeted areas are indicated by dashed blue lines. The black and white arrows indicate tumours with compact and cystic morphologies, respectively. Scale bar, 75 μm. c , The oncogenic recombination efficiency in targeted and off-target areas in mini-colons. Statistical analysis was performed using two-tailed t -tests; *** P < 0.0001. n = 6 mini-colons per condition. Each point represents one mini-colon. d , Bright-field images of induced mini-colons cultured in low-differentiation (top, WENRNi) and high-differentiation (bottom, ENR) conditions. The black and white arrows indicate tumours with compact and cystic morphologies, respectively. Scale bar, 75 μm. e , Schematic of the different colon organoid lines generated in this work. f , Bright-field images of the indicated colon organoid lines cultured for 2 days in basal medium. Scale bar, 200 μm. g , Metabolic activity (measured using resazurin) of the indicated colon organoid lines cultured in basal medium for the indicated time. Numerical labelling (1–8) was used to facilitate cell line identification. Statistical analysis was performed using two-way ANOVA with Sidak’s multiple-comparison test; *** P = 0.0004 (control), *** P < 0.0001 (all other conditions). n = 3 cultures for each line. For c and g , data are mean ± s.e.m.

To evaluate the functional repercussions of the tumour-initiating niche, we isolated cancer cells from mini-colons enriched in either crypt- or lumen-derived tumours and established organoid cell lines (termed mini-colon AKP) (Fig. 2e ). As a control, we generated AKP mutant organoids by shining blue light onto inducible organoids and kept these mutants in parallel with their mini-colon equivalents, doing the required passages on confluency (termed organoid AKP) (Fig. 2e ). We also established organoid cultures from AKP colon tumours extracted from tamoxifen-treated Cdx2-cre ERT2 AKP mice (termed in vivo AKP) (Fig. 2e ). Notably, in contrast to mini-colons, none of these three types of mutant AKP lines were morphologically distinguishable from healthy non-mutated cells when cultured as organoids (Fig. 1b and Extended Data Fig. 4d ). When we cultured these organoids in basal medium depleted of growth factors (BM; Methods ), both in vivo and crypt tumour-derived mini-colon AKP organoids preserved their proliferative potential (Fig. 2f,g ). Conversely, organoid and lumen tumour-enriched mini-colon AKP lines displayed significantly reduced proliferation rates (Fig. 2f,g ). This was not due to intrinsic cycling defects in any of the organoid lines tested, as these differences were not observed in standard cancer organoid medium (BMGF; Methods and Extended Data Fig. 4e ). As expected, healthy organoids did not grow in any of these conditions (Fig. 2f,g and Extended Data Fig. 4e ). Collectively, these results show that there are context-dependent factors aside from the founding AKP mutations that condition the growth potential of AKP cells. They also indicate that the cells derived from mini-colon crypt tumours recapitulate the growth properties of in vivo CRC cells more faithfully than conventional organoids.

To investigate the molecular programs underpinning these observations, we profiled the transcriptome of the different AKP lines using RNA sequencing (RNA-seq). We first characterized the differences between the two AKP lines derived from conventional systems, in vivo and organoid AKP cells, which also had the biggest disparity in growth potential (Fig. 2g ). According to our previous experiments, in vivo AKP cells upregulated many genes involved in canonical cancer pathways and the promotion of cell growth (Extended Data Fig. 4f,g ). Conversely, these cells downregulated genes associated with cell differentiation, patterning and transcriptional regulation (Extended Data Fig. 4f,g ). To evaluate whether mini-colon AKP cells recapitulated this in vivo AKP transcriptional signature, we performed single-sample gene set enrichment analysis (GSEA) across all of the cell lines. Here, most of the mini-colon AKP lines outscored their organoid AKP counterparts, especially those derived from crypt tumours (Extended Data Fig. 4h ). To investigate the transcriptional divergence between crypt- and lumen-enriched mini-colon AKP cells, we compared the lines with the highest (#v, crypt-enriched) and lowest (#i, lumen-enriched) in vivo AKP signature score (Extended Data Fig. 4h ). These analyses revealed that crypt-derived mini-colon AKP cells upregulated genes involved in WNT signalling, stem cell pluripotency, lipid metabolism and other pathways involved in cancer (Extended Data Fig. 4i ). To identify the potential drivers of growth factor independence among these, we searched for overlaps between AKP lines with high growth potential in BM (in vivo AKP, mini-colon AKP #v). We found that the latter overexpressed a collection of genes that is involved in the activation of MAPK cascades, including receptor tyrosine kinases (RTKs), G-protein-coupled receptors and soluble factors (Extended Data Fig. 5a ). We therefore theorized that these cells were engaging a surplus of MAPK signalling that gave them a greater fitness under growth-factor-poor conditions. To validate this idea, we tested their response to a panel of inhibitors, which confirmed that the growth of AKP lines in BM heavily relied on signals from RTKs (Extended Data Fig. 5b,c ; regorafenib), including KIT (Extended Data Fig. 5b,c ; ripretinib) and FGF receptors (Extended Data Fig. 5b,c ; infigratinib). Corroborating this, the ligands for these RTKs (SCF, FGF2) and others involved in colonocyte clonogenicity (IGF1) 21 could enhance the growth of the AKP lines with poor proliferation potential in BM (Extended Data Fig. 5d,e ). Importantly, all of these dependencies were either reduced or not detectable in conventional CRC organoid medium (BMGF) (Extended Data Figs. 4e and 5b,c ). Taken together, these data indicate that the mini-colon is a plastic system in which context-dependent factors can drive different functional features in CRC cells, including the engagement of ancillary RTK signals that boost their growth potential in challenging environments.

Intra- and intertumour heterogeneity

We hypothesized that the diversity observed in tumour morphology and growth potential reflected clonally distinct tumour types being initiated in the mini-colon. To validate this idea, we performed single-cell transcriptomic profiling of tumour-bearing mini-colons incorporating a genetic cell barcoding system 22 to preserve clonal information (Fig. 3a ). On the basis of bona fide transcriptional markers, mini-colons comprised eight major cell types that were segregated into undifferentiated, absorptive and secretory lineages (Fig. 3b ). Undifferentiated ( Krt20 − ) cells included stem ( Lgr 5 + ), actively proliferating ( Mki67 + ) and progenitor ( Sox9 + Cd44 + ) cells (Fig. 3b,c and Extended Data Fig. 6a ). Mature ( Krt20 + ) absorptive colonocytes constituted the largest fraction of the mini-colon, and included bottom, middle and top colonocytes based on zonation markers 23 (such as Aldob , Iqgap2 and Clca4a ) (Fig. 3b,c and Extended Data Fig. 6a ). Mucus-producing goblet cells ( Muc2 + ) and hormone-releasing enteroendocrine cells ( Neurod1 + ) constituted the secretory compartment (Fig. 3b,c and Extended Data Fig. 6a ). Collectively, this diverse in vivo-like cell composition indicates that mini-colons provide a physiologically relevant context for conducting oncogenesis studies.

a , Schematic of the experimental workflow followed for single-cell and lineage-tracing analysis of mini-colons. b , Unsupervised uniform manifold approximation and projection (UMAP) clustering of the main cell types in mini-colons 7 days after tumorigenic induction. c , The expression (Exp.) of representative cell-type-specific markers in the different cell populations comprising mini-colons. d , Unsupervised clustering (UMAP) of healthy (top) and tumour (bottom) clonal populations in mini-colons. The cell type (left; colour coded as in b ) and clonal identity (right) are indicated. e , The relative cell type abundance in healthy and tumour mini-colon clonal populations. Data are mean ± s.e.m. n = 16 and 18 for healthy and tumour clones, respectively. f , Healthy and tumour mini-colon clonal population sizes. Statistical analysis was performed using two-tailed Mann–Whitney U -tests; ** P = 0.0011. n = 16 and 18 for healthy and tumour clones, respectively. The box plots show the median (centre lines), the first and third quartiles (box limits) and the minimum and maximum values (whiskers). Each point represents one clonal population. g , The correlation between Gpx2 expression and cancer stem cell transcriptional signature enrichment ( Cd44 , Lgr5 , Sox9 ). Statistical analysis was performed using two-sided Pearson correlation tests; P < 0.0001. n = 540 cells. Each point represents one cell. CSC, cancer stem cell; ES, enrichment score. h , Bright-field and immunofluorescence images showing the abundance of GPX2 (magenta) and nuclei (cyan) in healthy (right) and tumour (left, indicated by arrows) crypts in a mini-colon. Scale bar, 35 μm. i , Expression of the indicated genes in the indicated tumour clones. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests; *** P = 1.77 × 10 −17 ( Il1a , clone 1), 1.00 × 10 −78 ( Cdkn2a , clone 14), 3.67 × 10 −22 ( Cdkn2a , clone 48). n = 540 cells. Each point represents one cell.

To determine the clonal identities across the mini-colon, we compared the genetic barcodes among cells and detected 83 clonal populations. We then discarded small (<5 cells) clones and identified cells containing reads corresponding to the mutated versions of Apc and Trp53 (Extended Data Fig. 6b,c ). These bona fide tumour cells distinguished tumour clonal populations (18 classified) from healthy counterparts (16 classified) ( Methods and Extended Data Fig. 6d ). On average, healthy clonal populations consisted of around 18% undifferentiated cells, which gave rise to the remaining approximately 82% of absorptive colonocytes and secretory cells (Fig. 3d,e ). Conversely, mini-colon tumours were mostly formed by undifferentiated cells (~92%), with sparsely present colonocytes and secretory cells (Fig. 3d,e ). Tumour cells also formed larger clonal populations compared with their healthy counterparts (Fig. 3f ). These cell proportions are well aligned with the ones commonly observed in vivo 24 , 25 .

Analyses of the internal structure of single clonal tumours showed that they comprised a non-homogeneous collection of cells with diverse proliferation, stemness and differentiation markers (Extended Data Fig. 7a ). Such intratumour heterogeneity reflects the complexity of mini-colon tumours, consistent with our immunostaining data (Fig. 1d ). To investigate the mechanisms orchestrating cancer stemness and tumour development, we analysed the transcriptional differences between differentiated ( Krt20 + Apoc2 + Fabp2 + ) and stem ( Lgr5 + Cd44 + Sox9 + ) cancer cells within tumours. We found that Gpx2 , a glutathione peroxidase recently linked to CRC malignant transformation 24 , strongly correlated with the stemness potential of mini-colon cancer cells (Fig. 3g ). Consistent with this, we observed that GPX2 protein was particularly enriched in the basal cells of mini-colon tumours (Fig. 3h ).

To examine whether mini-colons could produce different types of tumours, we next compared the transcriptional profiles of the different tumour clones. Even though all tumour-initiating cells carried the same founding AKP mutations and shared many molecular features, we found clear diversity across mini-colon tumours (Extended Data Fig. 7b ). For example, the expression of the interleukin Il1a and leukocyte peptidase inhibitor Slpi revealed the presence of tumours with an inflammatory-like profile (Fig. 3i and Extended Data Fig. 7b,c ). Cdkn2a (encoding tumour suppressors p14 and p16) and Prdm16 were exclusively expressed by tumours seemingly insensitive to these cell cycle arrest genes given their Ki67 + nature (Fig. 3i and Extended Data Figs. 6a and 7b,c ). Aqp5 , an aquaporin inductor of gastric and colon carcinogenesis 26 , marked specific tumours able to produce the oncogenesis-promoting fibroblast growth factor FGF13 (Extended Data Fig. 7b,c ). Together with other markers (Extended Data Fig. 7b ) and corroborations at the protein level (Extended Data Fig. 7d ), these data indicate that a variety of tumour subtypes can be generated in the mini-colon, arguably due to tumour-niche-intrinsic and/or environmental factors. This probably accounts for the observed differences among mini-colon AKP cell lines (Fig. 2g and Extended Data Fig. 4h ). Importantly, we found that this diversity was relatable to the human context. For example, mini-colons generated tumours with transcriptional profiles representing both iCMS2- and iCMS3-like subtypes 27 (Extended Data Fig. 8a,b ) that were associated with a wide range of aggressiveness profiles (Extended Data Fig. 8c ) and correlated with different extents of lymph node colonization (Extended Data Fig. 8d,e ) when cross-compared with transcriptomic data from the TCGA collection of patients with CRC. Collectively, these findings demonstrate that the mini-colon is a complex cellular ecosystem that recreates both healthy and cancer cell diversity.

Screening of tumorigenic factors

The longevity, experimental flexibility and tumour formation dynamics of mini-colons provides an unparalleled in vitro set-up for conducting tumorigenesis assays. We therefore next used mini-colons as screening tools for identifying molecules with a prominent role in tumour development. As our single-cell RNA-seq (scRNA-seq) analyses revealed Gpx2 overexpression in cancer stem cells (Fig. 3g,h ), we probed its functional relevance by adding the glutathione peroxidase inhibitor tiopronin 28 to the basal medium reservoirs of mini-colons right after blue-light-induced AKP mutagenesis (Fig. 4a ). Basal application of the drug provides ubiquitous exposure on the mini-colon basolateral domain, mimicking a systemic therapy model (Fig. 4a ). By the time control mini-colons developed full-blown tumours, tiopronin-treated counterparts were largely tumour-free with a healthy colonic epithelium (Fig. 4b and Extended Data Fig. 9a ). This was not due to the mere reduction in proliferative activity, as tiopronin had a minor impact on organoid growth (Extended Data Fig. 9b,c ). As tiopronin targets several glutathione peroxidases, we corroborated the specific implication of GPX2 in tumour initiation by knocking down its transcript (Extended Data Fig. 9d ). These knockdown cells showed no detectable defects in terms of organoid morphology or proliferation in unchallenged conditions (Extended Data Fig. 9e,f ). However, after blue-light-mediated oncogenic recombination, GPX2-deficient mini-colons developed tumours with reduced kinetics and multiplicity (Fig. 4c and Extended Data Fig. 9g ), recapitulating the results obtained with tiopronin (Fig. 4b and Extended Data Fig. 9a ). Importantly, mini-colons were instrumental for these findings, as conventional organoid cultures cannot reveal differences in tumour-forming abilities (Extended Data Fig. 9b,h ).

a , The experimental workflow for systemic therapy modelling. b , Bright-field images of mini-colons treated with vehicle or tiopronin after tumorigenic recombination. Images correspond to 6 days after induction. Scale bar, 75 μm. c , The multiplicity of tumours emerged in mini-colons of the indicated genotype after oncogenic induction. Statistical analysis was performed using two-way ANOVA with Sidak’s multiple-comparison test; ** P = 0.0034, *** P = 0.0007 (days 6 and 9, sh Gpx2 1), *** P < 0.0001 (all other conditions). n = 5, 5 and 4 mini-colons for control, shGpx2 1 and shGpx2 3, respectively. d , Differentially expressed genes after Gpx2 knockdown in light-induced AKP tumour cells. e , Expression of the indicated genes in colonocytes of the indicated genotypes before and after oncogenic recombination. The colour scale shows the z score. f , The main enriched functional terms after Gpx2 knockdown in light-induced AKP tumour cells. Significant terms are highlighted in blue or red, as determined using one-sided Fisher’s exact tests, with gene expression adjusted P -values (Benjamini-Hochberg correction). g , The multiplicity of tumours emerged in mini-colons of the indicated genotype under the indicated pretreatment (2 days before oncogenic induction). Statistical analysis was performed using two-way ANOVA with Sidak’s multiple-comparison test; * P = 0.0274, ** P = 0.0033 (days 9 and 10), *** P < 0.0001 (days 7 and 8). n = 3 mini-colons for each condition. h , The experimental workflow for microbiota and dietary pattern modelling. BL, blue light. i , Bright-field images of mini-colons treated with the indicated metabolites. Images correspond to 7 days after tumorigenic induction. Scale bar, 75 μm. j , The multiplicity of tumours emerged in mini-colons treated with the indicated metabolites. Statistical analysis was performed using two-way ANOVA with Sidak’s multiple-comparison test; ** P = 0.0080, *** P = 0.0008 (days 7 and 8), *** P < 0.0001 (day 6). n = 3 mini-colons for each condition. For c , g and j , data are mean ± s.e.m.

To gain molecular insights into the mechanism engaged by GPX2, we performed RNA-seq analysis of Gpx2 -knockdown cells both before and after oncogenic recombination. These analyses revealed that GPX2 deficiency remodels the colonocyte transcriptome in both healthy (Extended Data Fig. 9i ) and tumorigenic (Fig. 4d ) conditions (Supplementary Tables 1 and 2 ). This included the downmodulation of canonical markers associated with both healthy and cancer cell stemness, such as Lgr5 and Cd44 (Fig. 4e ). By contrast, markers of proliferative progenitor cells, such as Sox9 , remained unchanged (Fig. 4e ). Consistent with this, Gpx2 abrogation led to the repression of transcriptional programs implicated in stem cell pluripotency, including the WNT, Hippo–YAP and TGFβ pathways, as well as epithelial–mesenchymal transition and other processes involved in cancer cell fitness (Fig. 4f and Extended Data Fig. 9j,k ). Conversely, transcriptional programs associated with proliferation were not affected, consistent with our observations in cell culture (Extended Data Fig. 9e,f,k ). These findings indicate that the inhibition of GPX2 downmodulates colonocyte stemness, which probably accounts for the reduced tumorigenic potential observed in the mini-colon after oncogenic recombination. Supporting this, we found that non-transformed GPX2-deficient cells displayed reduced clonogenic capacity in medium deprived of exogenous WNT signals (Extended Data Fig. 9l,m ). Furthermore, the enhancement of WNT signalling through pretreatment of mini-colons with CHIR99021 for 2 days before oncogenic induction rescued the tumorigenic potential of Gpx2- knockdown cells (Fig. 4g and Extended Data Fig. 9n ). Collectively, these data uncover GPX2 as a key regulator of colon stemness and tumorigenesis, shedding light on lingering questions spurred by the recent discovery of its association with the malignant progression of human CRC 24 .

Besides cell-intrinsic factors, colon tumorigenesis in vivo is heavily modulated by a myriad of environmental molecules that continuously contact the luminal side of colonocytes, such as the metabolites produced by colon-residing microbiota 29 . The impact of these molecules cannot be faithfully evaluated in conventional organoid cultures, as their lumen is not accessible. As mini-colons address this limitation, we also investigated whether they could model the role of bacterial metabolites of which the tumorigenic function has been corroborated in vivo. To that end, we administered specific metabolites exclusively in the luminal side of healthy mini-colons and, after a conditioning period of 2 days, induced oncogenic recombination (Fig. 4h ). When luminally exposed to deoxycholic acid, a tumour-promoting metabolite 29 , 30 , 31 , mini-colons developed tumours with fast kinetics and high multiplicity (Fig. 4i,j ). Conversely, both tumour-suppressive butyrate 29 , 32 and β-hydroxybutyrate 33 slowed tumour development and reduced multiplicity (Fig. 4i,j ). These results demonstrate that mini-colons faithfully recapitulate the in vivo pathophysiological responses to bacterial metabolites, whereas conventional organoid cultures do not provide informative data on their tumorigenic relevance (Extended Data Fig. 10a ).

Dietary components also constitute a relevant source of luminal factors conditioning colon tumorigenesis 34 . We therefore performed analogous experiments modelling diets with different caloric contents (Fig. 4h and Extended Data Fig. 10b ). These revealed that calorie restriction in the luminal space effectively reduced tumour burden when compared to calorie-enriched medium (Extended Data Fig. 10c,d ), consistent with in vivo evidence 35 . To show the relevance of luminal accessibility, we placed the same amount of dietary medium in the basal medium reservoirs instead of the luminal space (Extended Data Fig. 10b ). Here, no differences were observed between the two dietary patterns (Extended Data Fig. 10e,f ), therefore indicating that an accessible lumen—a forbidden feature in conventional organoids—is decisive for the physiologically relevant modelling of colon biology. Collectively, these findings demonstrate that the mini-colon is a versatile tool that enables faithful in vitro recapitulation of CRC tumorigenesis and its environmental determinants.

Here we show that the mini-colon model shifts the paradigm of cancer initiation research, allowing ex vivo tumorigenesis with unparalleled pathophysiological intricacy. Coupled with spatiotemporal control of oncogenesis, real-time single-cell resolution and broad experimental flexibility, this system opens new perspectives for in vitro screening of cellular and molecular determinants of cancer development. Supporting this, mini-colons faithfully reflect in vivo-like responses to microbiota-derived metabolites and dietary patterns. Likewise, our model can help in the discovery and validation of genetic targets and tumour-suppressive drugs, as illustrated by the finding that glutathione peroxidase inhibition abrogates CRC tumour development. This constitutes a major advance over conventional 3D culture systems like organoids and Transwell models, which can recapitulate isolated aspects of colon biology such as histopathological features 36 or apical accessibility 37 , respectively, but lack the all-round topobiological complexity required to allow tumour formation ex vivo. Although such complexity demands bioengineering expertise to generate mini-colons, we have provided a detailed protocol that makes this system widely adoptable across laboratories that are already familiar with conventional organoid cultures (Protocol Exchange 38 ; see Methods ).

As for most genetic models of CRC, our system is based on the simultaneous acquisition of several mutations, which does not fully recapitulate the sequential tumorigenic process that occurs in vivo 39 . We therefore acknowledge that adopting a stepwise mutational system will enhance the relevance of the mini-colon as a cancer initiation model. We are also aware that spatial transcriptomics approaches will improve our understanding of tumour heterogeneity in the mini-colon. In the same lines, we envision the incorporation of additional regulatory layers in our OptoCre system, such as the fusion with the oestrogen receptor ligand-binding domain for subcellular localization control 40 , as a promising way to achieve finer spatiotemporal regulation of recombination.

Although mini-colons cannot be considered to be a general replacement for animals in all contexts of cancer research, they offer the possibility to reduce animal use in a wide range of experimental applications. Importantly, the pathophysiological relevance of the mini-colon can be readily enhanced by including stromal cells in the surrounding biomimetic extracellular matrix, which condition both tumour dynamics and invasiveness (Extended Data Fig. 10g–k ). Current lines of work that will be made available in ensuing publications have also proved that this model can be applied to patient-derived colorectal cancer specimens. Lastly, we anticipate that, by adapting its biomechanical properties, topology and culture conditions, it will be possible to expand the system to other prominent epithelial cancer types, such as lung, breast or prostate, bringing an important experimental resource to multiple fields.

Apc fl/fl mice (a gift from T. Petrova) were crossed to Cdx2-cre ERT2 mice (The Jackson Laboratory). Apc fl/fl Cdx2-cre ERT2 mice (termed A) were then crossed with Kras LSL-G12D/+ Trp53 fl/fl mice (a gift from E. Meylan) to generate Apc fl/fl Kras LSL-G12D/+ Trp53 fl/fl Cdx2-cre ERT2 mice (termed AKP). AKP mice were then back-crossed with C57BL6/J (The Jackson Laboratory) to generate Apc fl/fl Kras LSL-G12D/+ Cdx2-cre ERT2 mice (termed AK).

To induce tumorigenesis in vivo, Cre ERT2 recombinase was activated at the age of 8–10 weeks by a single intraperitoneal injection of 18 mg kg –1 tamoxifen (Sigma-Aldrich, T5648) in sunflower oil. Tumours were allowed to develop for 6 weeks. Mice were then sacrificed for tissue and cell isolation. See also below the specific section for transplantation of organoids in immunocompromised mice.

All animal work was conducted in accordance with Swiss national guidelines, reviewed and approved by the Service Veterinaire Cantonal of Etat de Vaud (VD3035.1 and VD3823). These regulations established 800 mm 3 as the maximal subcutaneous tumour volume allowed, which was not exceeded in any of the experiments. In experiments in which tumorigenesis was induced in vivo, the locomotion, appearance, body condition and intestinal function of the mice were monitored twice weekly and assigned numerical scores to allow quantitative decision making in case humane end points were necessary before the predefined end point of the experiment (6 weeks). All of the mice in this study reached the predefined end point. Mice were kept in the animal facility under EPFL animal care regulations. They were housed in individual cages at 23 ± 1 °C and 55 ± 10% humidity under a 12 h–12 h light–dark cycle. All of the animals were supplied with food and water ad libitum.

OptoCre module plasmid generation

The OptoCre module was designed by integrating the following constructs: (1) FUW-M2rtTA, which constitutively expresses the reverse tetracycline transactivator (rtTA); (2) FUW-tetO-GAVPO, which expresses the light-switchable trans-activator GAVPO after rtTA binding in the presence of doxycycline; and (3) FUW-OptoCre, which expresses Cre recombinase after GAVPO binding in the presence of blue light (Extended Data Fig. 1a ). FUW-M2rtTA was purchased from Addgene (20342). Vectors containing GAVPO and the GAVPO-binding promoter (UASG) 5 -P min , developed previously 41 , were a gift from M. Thomson 42 . For FUW-tetO-GAVPO generation, GAVPO was subcloned into the doxycycline-responsive FUW-TetO backbone (Wernig Lab, Stanford) using the EcoRI and NheI restriction sites (Extended Data Fig. 1a ). For FUW-OptoCre generation, (UASG) 5 -P min was inserted into the FUW-TetO backbone from which the TetO promoter had been removed (Wernig Lab, Stanford) using the BstBI and BamHI restriction sites. We then introduced the Cre recombinase (Addgene, 25997) downstream of (UASG) 5 -P min using the Pac1 restriction sites (Extended Data Fig. 1a ).

Isolation of colon cells

Healthy colon or tumour pieces were finely chopped using a scalpel and transferred to a gentle-MACS C-tube (Miltenyi, 130-093-237) containing 4 ml of digestion medium (RPMI (Thermo Fisher Scientific, 22400089), 1 mg ml –1 collagenase type IV (Life Technologies, 9001-12-1), 0.5 mg ml –1 dispase II (Life Technologies, 17105041) and 10 μg ml –1 DNase I (Applichem, A3778)). Tissues were then digested using the 37C_m_TDK_1 program on the gentle-MACS Octo Dissociator with heaters (Miltenyi). After the program was complete, the cell suspension was passed through a 70-μm strainer (Corning, 431751) and centrifugated at 400 g for 5 min.

Organoid and stromal cell culture

To establish organoids, colon cells were embedded in growth-factor-reduced Matrigel (Corning, 356231) (~2 × 10 4 cells per 20 μl dome) and cultured in Advanced DMEM/F-12 (Thermo Fisher Scientific, 12634028) supplemented with 1× GlutaMax (Thermo Fisher Scientific, 35050038), 10 mM HEPES (Thermo Fisher Scientific, 15630056), 100 μg ml −1 penicillin–streptomycin (Thermo Fisher Scientific, 15140122), 1× B-27 supplement (Thermo Fisher Scientific, 17504001), 1× N2 supplement (Thermo Fisher Scientific, 17502001), 1 mM N -acetylcysteine (Sigma-Aldrich, A9165), 50 μg ml −1 primocin (InvivoGen, ant-pm-2), 50 ng ml −1 EGF (Peprotech, 315-09), 100 ng ml −1 noggin (produced at EPFL Protein Production and Structure Core Facility), 500 ng ml −1 R-spondin (produced at EPFL Protein Production and Structure Core Facility), 50 ng ml –1 WNT3A (Time Bioscience, rmW3aL-010), 10 mM nicotinamide (Calbiochem, 481907) and 2.5 μM Thiazovivin (Stemgen, AMS.04-0017). This full medium is termed ‘WENRNi’. The base version of this medium without EGF, noggin, R-spondin, WNT3A and nicotinamide is referred to as BMGF and was used for the expansion of colon tumour organoids since they do not require the additional growth factors. The base version of BMGF without B-27, N2 and N -acetylcysteine is termed BM or basal medium, and was used for growth-factor deprivation experiments. A detailed protocol describing organoid culture can be found elsewhere 9 . Where indicated, organoids were treated with the following compounds or growth factors: regorafenib (8 μM, Selleckchem, S1178), ripretinib (1 μM, Selleckchem, S8757), infigratinib (1 μM, Selleckchem, S2183), SCF (100 ng ml –1 , PeproTech, 250-03), FGF2 (50 ng ml –1 , Thermo Fisher Scientific, PMG0035) and IGF1 (100 ng ml –1 , R&D Systems, 291-G1-200). Stromal cells were derived from cell suspensions from the primary tissue cultured in EGM-2 MV Microvascular Endothelial Cell Growth Medium-2 (Lonza, CC-3202) on conventional cell culture flasks. This medium selection strategy was followed by magnetic-activated cell sorting (MACS) on EPCAM (Miltenyi Biotec, 130-105-958) according to the manufacturer’s instructions to discard epithelial cells. The presence of stromal cells was further confirmed by immunofluorescence analyses of vimentin expression (see below). Cells were tested for mycoplasma before cryopreservation and in randomized routine checks using the MycoAlert PLUS Mycoplasma Detection Kit (Lonza, LT07-705).

Generation of light-inducible cells

Lentiviral particles carrying the three components of the OptoCre module (see above; Extended Data Fig. 1b ) and a Cre recombination reporter were produced at the EPFL Gene Therapy Platform by transfecting HEK293 cells with each plasmid of the OptoCre module and pLV-CMV-LoxP-DsRed-LoxP-eGFP (Addgene, 65726) plasmids. Lentivirus-containing supernatants were collected and concentrated by centrifugation (1,500 g for 1 h at 4 °C). Lentiviral titration was performed using the p24-antigen ELISA (ZeptoMetrix, 0801111). For transduction, colon organoids (around 2 × 10 5 cells) were dissociated into single cells by incubating in TrypLE Express Enzyme (Thermo Fisher Scientific, 12605028) at 37 °C for 5 min. Cells were then washed with basal medium supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher Scientific, 10500064) and resuspended in WENRNi medium containing 8 μg ml −1 polybrene (Sigma-Aldrich, TR-1003-G) and the following amounts of viral particles: ~10 ng of p24 FUW-M2rtTA per ml, ~80 ng of p24 FUW-tetO-GAVPO per ml, ~80 ng of p24 FUW-OptoCre per ml and ~1,000 ng of p24 CMV-LoxP-DsRed-LoxP-eGFP per ml. These cells were plated in a 24-well plate, centrifuged at 600 g for 60 min at room temperature, and incubated for 6 h at 37 °C. After incubation, the cells were collected, centrifuged, plated in 20 μl Matrigel domes in a 24-well plate and cultured in WENRNi medium. Cells expressing the Cre recombination reporter were selected by supplementing WENRNi medium with 8 μg ml −1 puromycin (InvivoGen, ant-pr-1).

Light-mediated oncogenic recombination

The OptoCre module requires (1) doxycycline to induce rtTA-mediated GAVPO expression and (2) blue light to induce GAVPO-mediated Cre recombinase expression (Extended Data Fig. 1a,b ). At the desired time of oncogenic induction, 2 μg ml −1 doxycycline hydrochloride (Sigma-Aldrich, D3072) was added to the culture medium of either the organoids or mini-colons. Light induction was then performed using a custom-made LightBox built by Baur SA and the Instant Lab at EPFL. The LightBox consisted of an Acqua A5 System (Acme Systems) that could be remotely parametrized using a custom-made web-based application. Communication between the Acqua A5 System and the microcontroller (PJRC, Teensy 3.2) was done through Blocky programming, which allowed for control of the LED drivers (Sparkfun, PicoDuck). The LEDs (Cree LEDs, XLamp XP-C Blue LEDs) were placed into a custom multilayer 24-well plate holder made of black anodized aluminium and polyphenylsulfone; the height was optimized for homogeneous light distribution within each well. The entire LightBox, plate-holder, LEDs and cables were made to be placed in the incubator (watertight and heat resistant). Diffusive elements (Luminit, Light Shaping Diffuser 80°) were used to render the illumination more homogeneous inside each well. The intensity of the blue light (450–465 nm, peak at 455 nm) was optimized, set to 100 μW cm −2 and shined on the cells for 3 h. After blue-light exposure, doxycycline was removed by washing the cultures with fresh medium. In experiments targeting the light to specific regions of the mini-colon, work was carried out in the dark using a near infrared light (Therabulb, NIR-A) to prevent leaky Cre expression. Light-targeting was performed using a photomask that was adapted to the dimensions of the mini-colon and that was created from a photoresist and chrome-coated standard 5 × 5 inch silica plate (Nanofilm) with an automated machine (VPG200 Heidelberg Instrument, 2.0 µm resolution). Once the exposed photoresist was developed, the chrome layer was wet-etched and the remaining photoresist was stripped using a mask processor (Hamatech HMR900) 9 .

Microdevice design, fabrication and loading

The microfluidic device used for mini-colon cultures was designed using Clewin 3.1 (Phoenix Software) and fabricated as previously described 9 . It was composed of three main compartments: (1) a hydrogel chamber for cell growth in the centre; (2) two basal medium reservoirs flanking the hydrogel compartment; and (3) inlet and outlet channels for luminal perfusion 9 . An extracellular matrix containing 80% (v/v) type I collagen (5 mg ml −1 , Reprocell, KKN-IAC-50) and 20% (v/v) growth–factor-reduced Matrigel was loaded into the hydrogel compartment. The microchannels constituting the mini-colon architecture within the hydrogel were designed using Adobe Illustrator CC 2019 and Wolfram Mathematica 11.3. They were then read by PALM RoboSoftware 4.6 (Zeiss) and ablated using a nanosecond laser system (1 ns pulses, 100 Hz frequency, 355 nm; PALM Micro-Beam laser microdissection system, Zeiss). The dimensions of the mini-colon architecture were described previously 9 . A detailed description of all the key steps required for the generation and maintenance of mini-guts is available at Protocol Exchange ( https://doi.org/10.21203/rs.3.pex-903/v1 ) 38 .

Mini-colon culture, development and tumorigenesis

Colon organoids were dissociated into single cells by incubating in TrypLE Express Enzyme for 5 min at 37 °C followed by vigorous pipetting. This cell suspension was washed in 5 volumes of Advanced DMEM/F-12 supplemented with 10% FBS and passed through 40 μm cell strainers (Corning, 431750). After centrifugation at 400 g for 5 min, cells were resuspended in WENRNi medium at around 10 6 cells per ml. The mini-colon luminal microchannel was filled with 10 μl of this cell suspension. Cells were allowed to settle down in the mini-colon crypt-shaped cavities for 5 min, and the leftover unadhered cells were washed out from the microchannel by medium perfusion. The basal medium reservoirs were filled with 100 μl of WENRNi. Unless otherwise indicated, once the healthy colonic epithelium was fully formed (around 2 days after seeding), the medium in the luminal channel was switched to BM, while WENRNi was kept in the basal medium reservoirs. This gradient of growth factor from basal medium reservoirs to luminal space favours colonocyte differentiation across the crypt–lumen axis. For low-differentiation conditions of the differentiation experiments, WENRNi was kept in both the lumen and basal medium reservoirs. Conversely, high-differentiation mini-colons were cultured in WENRNi medium without WNT3A and nicotinamide (termed ENR). Unless otherwise stated, once the colonic epithelium was fully formed, oncogenic induction in the mini-colons was performed as stated above. Where indicated, tiopronin (5 mM, Selleckchem, S2062) or CHIR99021 (3 μM, StemCell Technologies, 100-1042) was added to the basal medium reservoirs after or before oncogenic induction, respectively. For co-culture experiments, ~500 stromal cells were seeded in each hydrogel before the laser-mediated ablation of the mini-colon pattern. The rest of the culture conditions and procedures remained unchanged. To avoid potential unspecific results derived from the small (but non-zero; Extended Data Fig. 1d,e ) leakiness of the optogenetic system, each replication across all studies was performed using independent OptoCre organoid lines freshly generated before each experiment. In all cases, the mini-colons were incubated at 37 °C in 5% CO 2 humidified air, with daily luminal perfusions and medium changes every other day.

Mini-colon whole-mount immunofluorescence staining

Mini-colons were rinsed with phosphate-buffered saline (PBS) and fixed in 4% paraformaldehyde (Thermo Fisher Scientific, 15434389) overnight at 4 °C. After rinsing with PBS, the hydrogels were extracted from the PDMS scaffold using a scalpel, placed into a 48-well plate, permeabilized with 0.1% Tween-20 (Sigma-Aldrich, P9416) in PBS (10 min at 4 °C) and blocked in 2 mg ml −1 bovine serum albumin (Sigma-Aldrich, A3059) in PBS containing 0.1% Triton X-100 (Sigma-Aldrich, T8787) (blocking buffer) for at least 45 min at 4 °C. The samples were subsequently incubated overnight at 4 °C in blocking buffer with the corresponding following primary antibodies: CD44 (1:200; Abcam, ab157107), FABP1 (1:100; R&D Systems, AF1565), SOX9 (1:200; Abcam, ab185966), GPX2 (1:200; Bioss Antibodies, BS-13396R), IL-1α (1:200; R&D Systems, AF-400-SP), CDKN2A (1:100; Abcam, ab211542), E-cadherin (1:100; Abcam, ab11512) and vimentin (1:200; Abcam, ab92547). After three washes in blocking buffer for a total of 6 h at room temperature, the samples were incubated overnight at 4 °C in blocking buffer with the following corresponding secondary antibodies: Alexa Fluor 488 anti-goat (1:400, Thermo Fisher Scientific, A-11055), Alexa Fluor 488 anti-rat (1:400, Thermo Fisher Scientific, A-21208) and Alexa Fluor 647 anti-rabbit (1:400, Thermo Fisher Scientific, A-31573). After 3 washes in blocking buffer for a total of 6 h at room temperature, the samples were incubated with DAPI (1 μg ml −1 ; Tocris Bioscience, 5748) for 10 min at room temperature in blocking buffer. Before imaging, the hydrogels were mounted onto 35 mm glass bottom dishes (Ibidi, 81218-200) in Fluoromount-G (SouthernBiotech, 0100-01).

Mini-colon sectioning and histochemistry

Mini-colons were fixed and extracted from the PDMS scaffold as indicated above and were prepared for cryosectioning by incubating in 30% (w/v) sucrose (Sigma-Aldrich, S1888) in PBS until the sample sank. Subsequently, the samples were incubated for 12 h in a mixture of Cryomatrix (Epredia, 6769006) and 30% sucrose (mixing ratio 50/50) followed by a 12 h incubation in pure Cryomatrix. The samples were then embedded in a tissue mould, frozen on dry ice, and cut into 40-µm-thick sections at −20 °C using the CM3050S cryostat (Leica). Haematoxylin and eosin staining was performed at the EPFL Histology Core Facility using the Ventana Discovery Ultra automated slide preparation system (Roche).

Microscopy and image analysis

Bright-field and fluorescence imaging of living organoids and mini-colons was performed using the Nikon Eclipse Ti2 inverted microscope with ×4/0.13 NA, ×10/0.30 NA and ×40/0.3 NA air objectives and a DS-Qi2 camera (Nikon Corporation). Time lapses were taken in a Nikon Eclipse Ti inverted microscope system equipped with ×4/0.20 NA and ×10/0.30 NA air objectives and DS-Qi2 (Nikon Corporation) and Andor iXon Ultra 888 (Oxford Instruments) cameras. Both systems were controlled using the NIS-Elements AR software (Nikon Corporation). The extended depth of field (EDF) of bright-field images was calculated using a built-in NIS-Elements function. Fluorescence confocal imaging of fixed mini-colons was performed using the Leica SP8 STED 3X inverted microscope system equipped with ×10/0.30 NA air and ×25/0.95 NA water objectives, 405 nm diode and supercontinuum 470–670 nm lasers, and the system was controlled by the Leica LAS-X software (v.3.5.7, Leica microsystems). Histological sections were imaged using a Leica DM5500 upright microscope with ×10/0.30 NA and ×20/0.75 NA air objectives, a ×40/1.0 NA oil objective and a DMC 2900 Color camera, and the system was controlled by the Leica LAS-X software. Image processing was performed using standard contrast- and intensity-level adjustments in ImageJ (NIH). For oncogenic recombination analyses, the GFP-positive area was measured from 16-bit EDF images by subtracting the background, sharpening the images, and applying a signal threshold and a mask. The ratio between GFP-positive area and total organoid area was used for analyses. Recombined cells were segmented using StarDist with the default parameters ( https://github.com/stardist ) on the GFP channel of mini-colon images. Cell debris was discarded from segmentation analyses by setting an empirically established size threshold. For tumour quantification in the mini-colon, neoplastic structures with at least three times the thickness of the surrounding healthy epithelium were considered to be tumours. Videos of immunostainings were rendered using Imaris (Oxford Instruments).

Mini-colon shedding evaluation

The medium from the luminal compartments of the mini-colons, together with an additional luminal perfusion of 10 μl of basal medium, was collected every day for 4 days after the blue-light-induced oncogenic recombination. The protein content in these extracts was analysed using conventional Bradford assays (Bio-Rad, 5000006) and used as an indicator of cell shedding.

Mini-colon cell line derivation

Mini-colon-containing hydrogels were extracted from their microfluidic devices with a scalpel as indicated above and incubated with 0.1% (w/v) collagenase I (Thermo Fisher Scientific, 17100-017) at 37 °C for 10 min. Once the hydrogel was fully digested, the mini-colon was washed with PBS and digested with TrypLE Express Enzyme for 5 min at 37 °C. The resulting cell suspension was washed with Advanced DMEM/F-12 supplemented with 10% FBS, pelleted, embedded in Matrigel and cultured as indicated above for regular colon organoids.

Transplantation of organoids in immunocompromised mice

Organoid lines were established as indicated above from either in vivo colon tumours (reference AKP) or tumour-bearing mini-colons (mini-colon AKP). These organoids were dissociated into single cells using TrypLE Express Enzyme for 5 min at 37 °C, washed with Advanced DMEM/F-12 supplemented with 10% FBS, pelleted and embedded in Matrigel at 2.5 × 10 6 cells per ml. A total of 100 μl of this suspension was inoculated by subcutaneous injection into the right flank of NOD. Cd-Prkdz scid Il2rg tm1Wjl /Szj (NSG) mice (Jackson laboratories). Tumour growth was monitored using callipers twice per week until the end point at 18 days after inoculation. Length ( L ) and width ( W ) were measured and used to approximate the volume ( V ) of the tumour in mm 3 using the modified ellipsoid formula: V = ( L × W 2 )/2. After euthanasia, tumours were resected from the graft location and measured once more with callipers.

Graft sectioning and histochemistry

Tumour samples were fixed overnight in 4% paraformaldehyde at 4 °C, dehydrated in graded ethanol baths, cleared with xylene, embedded in paraffin and cut into 4-µm-thick sections using the HM 325 Rotary Microtome (Thermo Fisher Scientific). These sections were mounted onto Superfrost plus slides (Epredia, J1800AMNZ) and allowed to dry for 2 days at room temperature. Haematoxylin and eosin staining was performed at the EPFL Histology Core Facility using the Ventana Discovery Ultra automated slide preparation system (Roche).

Mutational screening in colon organoids

Genomic DNA was isolated from colon cells using the PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific, K182001) according to the manufacturer’s instructions. Recombination of the LSL (LoxP-Stop-LoxP) cassette controlling Kras G12D expression was confirmed by PCR using the protocol and oligos described by the Tyler Jacks laboratory ( https://jacks-lab.mit.edu/ , Kras G12D Conditional PCR). Apc and Trp53 recombinations were confirmed through exome sequencing performed at BGI Genomics at 100× coverage using DNBSEQ sequencing technology. DNA reads were mapped to the mouse GRCm39 genome assembly using BWA-MEM (v.0.7.17), filtered using samtools (v.1.9) and visualized using IGV (Integrative Genomics Viewer, Broad Institute, v.2.12.3).

Organoid proliferation assays

Single-cell suspensions of colon cells were generated as indicated above and embedded in 10 μl Matrigel domes at around 10 4 cells per dome in a 48-well plate. For each of the following 4 days, 220 μM resazurin (Sigma-Aldrich, R7017) was added to the culture medium and incubated for 4 h at 37 °C. Next, the resazurin-containing medium was collected and replaced with regular medium. Organoid proliferation was estimated by measuring the reduction of resazurin to fluorescent resorufin in the medium each day using the Tecan Infinite F500 microplate reader (Tecan) with 560 nm excitation and 590 nm emission filters. In the case of colony-formation assays, seeding was performed at around 10 3 cells per dome and the resulting colonies were counted after 3 days.

Organoid RNA extraction and bulk transcriptome profiling

Before RNA isolation, organoids were cultured for 3 days as indicated above and starved for 24 h in BM for the evaluation of growth-factor dependence. In the case of the Gpx2- knockdown experiments, 2 timepoints were analysed: 0 and 2 weeks after blue-light-induced activation (before and after oncogenic recombination, respectively). In all cases, cells were collected using TrypLE Express Enzyme as indicated above and lysed in RLT buffer (Qiagen, 74004), and the RNA was extracted using the QIAGEN RNeasy Micro Kit (Qiagen, 74004) according to the manufacturer’s instructions. Purified RNA was quality checked using a TapeStation 4200 (Agilent), and 500 ng was used for QuantSeq 3′ mRNA-seq library construction according to the manufacturer’s instructions (Lexogen, 015.96). Libraries were quality checked using a Fragment Analyzer (Agilent) and were sequenced in the NextSeq 500 (Illumina) system using NextSeq vm2.5 chemistry with Illumina protocol 15048776. Reads were aligned to the mouse genome (GRCm39) using star (v.2.7.0e) 43 . R (v.4.1.2) was used to perform the differential expression analyses. Count values were imported and processed using edgeR 44 . Expression values were normalized using the trimmed mean of M values (TMM) method 45 and low-expressed genes (<1 counts per million) were filtered out. Differentially expressed genes were identified using linear models (Limma-Voom) 46 and P values were adjusted for multiple comparisons using the Benjamini–Hochberg correction method 47 . Volcano plots and heat maps were generated using the EnhancedVolcano ( https://github.com/kevinblighe/EnhancedVolcano ) and heatmap3 ( https://github.com/slzhao/heatmap3 ) packages, respectively. The in vivo AKP signature was established from the differentially expressed genes between in vivo and organoid AKP lines with a log 2 -transformed fold change of at least |2|. To evaluate the enrichment of the in vivo AKP gene expression program across samples, the enrichment scores for both the upregulated and downregulated signatures were calculated using single-sample GSEA (ssGSEA) 48 . The difference between the two normalized enrichment scores yielded the fit score. ssGSEA was also used to analyse the enrichment of the MSigDB curated Hallmark gene set 49 in Gpx2 -knockdown organoids. Functional annotation was performed using DAVID 50 on the genes with a log 2 -transformed fold change of at least |1|. GOplot 51 was used for the integration of expression and functional annotation data. Known functional interactions among relevant genes were obtained through STRING 52 . Cytoscape 53 was used to perform network data integration and visualization.

Single-cell transcriptome profiling and lineage tracing

Lineage tracing was performed using the CellTag system 22 (V1 pooled barcode library, Addgene, 115643-LVC). In brief, we co-transduced inducible colon organoids with the CellTag barcode library (multiplicity of infection of around 5) and the OptoCre module as indicated above. These cells were then introduced and induced in the mini-colon system as indicated before. After 7 days in the system and when mini-colon tumours were clearly visible, we extracted the cells from mini-colons as indicated above. After pooling and filtering (40 μm) the cell suspensions from two mini-colons, the single-cell sequencing library was constructed using 10x Genomics Chromium 3′ reagents v3.1 according to the manufacturer’s instructions (10x Genomics, PN-1000269, PN-1000127, PN-1000215). Sequencing was performed using NovaSeq 6000 v1.5 reagents (Illumina protocol #1000000106351 v03) for around 100,000 reads per cell. The reads were aligned using Cell Ranger (v.6.1.2) 54 to the mouse genome (mm10) carrying artificial chromosomes for both GFP and CellTag UTR genes, as recommended by CellTag developers for facilitating barcode identification 55 . Raw count matrices were imported into R and analysed using Seurat (v.4.2.0) 56 . Dead cells were discarded on the basis of the number of detected genes (less than 3,000) and the percentage of mitochondrial genes (more than 20%), leading to 2,429 cells after filtering. The data were log-normalized and scaled, and dimensionality reduction was conducted using UMAP with 10 dimensions. Louvain clustering yielded 17 clusters that were merged and named on the basis of canonical cell type markers. Stem, cycling, progenitor, goblet and enteroendocrine cell scoring was based on published signatures in mini-intestines and in vivo 9 . Gene sets highlighting bottom, middle and top colonocytes were taken from enterocyte zonation studies 23 . Cancer stemness was scored based on the expression of Lgr5 , Cd44 and Sox9 . Intrinsic consensus molecular subtype (iCMS) signatures for colorectal cancer were obtained from published work 27 . Signature scoring was performed using burgertools ( https://github.com/nbroguiere/burgertools ). Visual representations of the data were generated using Seurat internal functions. For lineage-tracing analyses, CellTag detection, quantification and clone calling were performed as indicated by CellTag developers 55 , excluding cells expressing fewer than 2 or more than 30 CellTags. After filtering, 83 clonal populations were identified, from which only those with a minimum size of 5 cells were considered for further analyses. To identify clonal populations belonging to tumour cells, we looked for cells expressing transcripts carrying the genetically engineered Apc and Trp53 mutations, that is, deletions of exons 15 and 2–10, respectively (Extended Data Figs. 3g and 6b,c ). Note that this approach could not be performed for Kras , as the mutation is also present in the transcripts from WT cells (but not expressed). As scRNA-seq provides low coverage on exon junctions and therefore the presence of mutations can be assessed only in a small fraction of cells, we used both the cell-type composition and size distributions of bona fide mutationally confirmed tumour clonal populations to classify the rest of clones. Those falling within plus or minus 2 s.d. of the mean cell composition and size of bona fide tumours were classified as tumour clonal populations. Healthy clones were defined as those with a clearly distinct (outside the aforementioned range) cell type composition and the same upper limit size as was observed for tumour clones. After filtering and classification, 16 healthy and 18 tumour clonal populations were obtained and used for further analyses (Extended Data Fig. 6d ). To define the most robust tumour-clone-specific markers, the gene expression from cells in each clone was compared to that from cells in each other clone using the Wilcoxon rank-sum test. We considered only the positive markers and selected those with adjusted P < 10 −5 . The association of these markers with clinical parameters in patients with CRC (survival, lymph node staging) was performed through cBioPortal ( https://www.cbioportal.org/ ) using the 640-sample CRC TCGA dataset ( https://www.cancer.gov/tcga ) and a differential expression threshold equal or greater than |2 |. Further information is provided in the Data availability and Code availability sections.

shRNA-mediated transcript knockdown

Organoids were transduced as indicated above with lentiviral particles encoding Gpx2 shRNAs obtained from Sigma-Aldrich (TRCN0000076529, TRCN0000076531 and TRCN0000076532; sh Gpx2 1, sh Gpx2 2 and sh Gpx2 3, respectively) or, as a control, shRNA-free counterparts (Addgene, 65726). Transduced cells were selected with puromycin (5 μg ml −1 ; InvivoGen, ant-pr-1). Proper transcript knockdown was assessed using quantitative PCR with reverse transcription (RT–qPCR) and RNA-seq.

Analysis of mRNA abundance

Organoids were cultured and collected as indicated above. Cells were then lysed in RLT buffer and RNA was extracted using the QIAGEN RNeasy Micro Kit as indicated above. RT–qPCR was performed using the iTaq Universal SYBR Green One-Step Kit (Bio-Rad Laboratories, 1725150) and the QuantStudio 7 Flex Real-Time PCR System (Thermo Fisher Scientific, 4485701). Raw data were analysed using Design & Analysis Software (v.2.6.0, Thermo Fisher Scientific). We used the abundance of the endogenous Gapdh mRNA as internal normalization control. The following primers were used for transcript quantification: 5′-AGTTCGGACATCAGGAGAACTG-3′ (forward, Gpx2 ), 5′-GATGCTCGTTCTGCCCATTG-3′ (reverse, Gpx2 ), 5′-ATCCTGCACCACCAACTGCT-3′ (forward, Gapdh ) and 5′-GGGCCATCCACAGTCTTCTG-3′ (reverse, Gapdh ).

Microbiota and diet modelling

Inducible mini-colons were generated as indicated above. Once the epithelium was formed and before oncogenic induction, mini-colons were subjected to a conditioning period of 2 days in which luminal medium was (1) supplemented with 100 μM deoxycholate (Sigma-Aldrich, D2510), 10 mM butyrate (Sigma-Aldrich, B5887) or 10 mM β-hydroxybutyrate (Sigma-Aldrich, 54965); or (2) replaced with MEMα (calorie-restricted condition, Thermo Fisher Scientific, 22561-021) or Advanced DMEM/F12 supplemented with 30 μM palmitic acid (calorie-enriched condition, Sigma-Aldrich, P0500). The same concentrations were used in organoid control experiments, but these were added to the full culture medium as the luminal compartment is not accessible in organoids. To assess the relevance of luminal exposure to these factors in the mini-colon, the same total amounts were added in the basal medium reservoirs instead of the luminal channel. In all cases, after conditioning, oncogenic recombination was performed and the mini-colon was cultured as indicated above. The different medium compositions were replenished every day during luminal perfusion.

Statistics and reproducibility

The number of biological replicates ( n ), the type of statistical tests performed and the statistical significance for each experiment are indicated in the corresponding figure legend. For images associated with quantification charts (Fig. 1b,c with Fig. 1e ; Fig. 2b with Fig. 2c ; Fig. 2d with Extended Data Fig. 4b ; Fig. 2f with Fig. 2g ; Fig. 4b with Extended Data Fig. 9a ; Fig. 4i with Fig. 4j ; Extended Data Fig. 2a with Fig. 1e ; Extended Data Fig. 3d,e with Extended Data Fig. 3b ; Extended Data Fig. 3h with Fig. 1e ; Extended Data Fig. 5b with Extended Data Fig. 5c ; Extended Data Fig. 5d with Extended Data Fig. 5e ; Extended Data Fig. 9b with Extended Data Fig. 9c ; Extended Data Fig. 9e with Extended Data Fig. 9f ; Extended Data Fig. 9g with Fig. 4c ; Extended Data Fig. 9l with Extended Data Fig. 9m ; Extended Data Fig. 9n with Fig. 4g ; Extended Data Fig. 10c with Extended Data Fig. 10d ; Extended Data Fig. 10e with Extended Data Fig. 10f ), the number of replicates is the same as for the corresponding chart and is indicated in the figure legend of the latter. For the rest of representative images (Figs. 1d and 3h and Extended Data Figs. 1f , 2b,c,f , 3a , 4a , 7d , 9h and 10a,g–k ), three independent experiments were performed. scRNA-seq (Fig. 3a ) and exome sequencing with matched PCR (Extended Data Fig. 3f,g ) were performed with two independent sets of samples. Bulk RNA-seq was performed with at least three independent sets of samples. Unless otherwise indicated, statistical analyses were performed using GraphPad Prism v.9 (GraphPad). Data normality and equality of variances were analysed with Shapiro–Wilk and Bartlett’s tests, respectively. Parametric distributions were analysed using the Student’s t -test (when comparing two experimental groups) or ANOVA followed by either Dunnett’s test (when comparing more than two experimental groups with a single control group) or Tukey’s HSD test (when comparing more than two experimental groups with every other group). Nonparametric distributions were analysed using either Mann–Whitney U -tests (for comparisons of two experimental groups) or the Kruskal–Wallis followed by Dunn’s test (for comparisons of three or more than three experimental groups) tests. Sidak’s multiple-comparison test was used when comparing different sets of means. χ 2 tests were used to determine the significance of the differences between expected and observed frequencies. In all cases, values were considered to be significant when P ≤ 0.05. Data obtained are given as the mean ± s.e.m.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Bulk and single-cell RNA-seq data reported in this paper have been deposited at the Gene Expression Omnibus (GEO) public repository under accession number GSE221163 . The association analysis with clinical parameters in patients with CRC was performed through cBioPortal ( https://cbioportal.org ) using the 640-sample CRC TCGA dataset ( https://cancer.gov/tcga ). Source data are provided with this paper.

Code availability

The code used for data analysis is available at GitHub ( https://github.com/LorenzoLF/Mini-colon_bioengineering ) 57 and Zenodo ( https://doi.org/10.5281/zenodo.10057882 ) 58 .

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Acknowledgements

We thank M. Thomson for the original light-inducible plasmids; M. Wernig and G. Neumayer for the initial idea and work on the doxycycline- and light-inducible system; C. Baur for discussing, designing and building the custom illumination device; A. Chrisnandy for assistance on photomask fabrication; O. Mitrofanova, B. Elci and Y. Tinguely for assistance on microdevice fabrication; D. Dutta and S. Li for input on organoid work; and J. Prébandier for administrative assistance. We acknowledge support from the following EPFL core facilities: CMi, CPG, PTBTG, HCF, BIOP, FCCF, BSF and GECF. This work was funded by the Swiss Cancer League (KFS-5103-08-2020), the Personalized Health and Related Technologies (PHRT) Initiative from the ETH Board and the EPFL.

Open access funding provided by EPFL Lausanne.

Author information

These authors contributed equally: L. Francisco Lorenzo-Martín, Tania Hübscher

Authors and Affiliations

Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

L. Francisco Lorenzo-Martín, Tania Hübscher, Nicolas Broguiere, Jakob Langer, Lucie Tillard & Matthias P. Lutolf

Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Amber D. Bowler & Freddy Radtke

Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland

Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland

Mikhail Nikolaev & Matthias P. Lutolf

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Contributions

L.F.L.-M. conceived the study, designed experiments, performed the experimental and bioinformatic work, analysed the data, performed artwork design and wrote the manuscript. T.H. generated the OptoCre module and blue-light-associated systems, designed experiments, performed experimental work and analysed data. A.D.B. performed mouse-related work and isolated primary cells. N.B. performed bioinformatic work and analysed data. J.L. produced the microfluidic devices, optimized hydrogel patterning and generated mini-colon histological sections. L.T. performed experimental work. M.N. designed and developed the first mini-gut system. F.R. helped to conceive the work. M.P.L. conceived the work, designed experiments and edited the manuscript.

Corresponding authors

Correspondence to L. Francisco Lorenzo-Martín or Matthias P. Lutolf .

Ethics declarations

Competing interests.

The EPFL has filed for patent protection (EP16199677.2, PCT/EP2017/079651, US20190367872A1) on the scaffold-guided organoid technology used here, and M.P.L. and M.N. are named as inventors on those patents. M.P.L. is shareholder in Doppl, which is commercializing those patents. The other authors declare no competing interests.

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Extended data figures and tables

Extended data fig. 1 generation of blue–light-inducible akp colon organoids..

a , Schematic of the plasmids comprising the OptoCre module. The promoter, gene, and restriction sites used to generate the plasmids are indicated. b , Schematic of the integration of the different genetic elements that allow spatiotemporal control of oncogenic recombination in colon organoids. c , Schematic of the experimental workflow used to test and optimize the OptoCre system. d , Brightfield and fluorescence images of OptoCre-carrying inducible colon organoids exposed to the indicated conditions. Red and green signals correspond to healthy and mutated cells, respectively. Images were taken 48 h after induction. Scale bar, 200 μm. e , Recombination efficiency in inducible colon organoid exposed to the conditions indicated in panel d. *** P = 0.0001 (Kruskal-Wallis and Dunn’s multiple comparisons test; n = 9 cultures for each condition). Each point represents one well of organoids. Data represent mean ± SEM. f , Brightfield and fluorescence images of inducible colon organoids exposed to control (top) and activation (bottom) conditions, dissociated into single cells and replated in the absence of growth factors (BMGF medium). Green signal corresponds to mutated cells. Images were taken 24 h after replating. Scale bar, 200 μm.

Extended Data Fig. 2 Oncogenic mutations induce neoplastic growth in mini-colons.

a , Time-course brightfield and fluorescence images of non-induced healthy colon cells grown as conventional organoids and mini-colons. Absence of fluorescence signal indicates absence of oncogenic recombination. Scale bars, 200 μm (organoids) and 75 μm (mini-colons). b , Immunofluorescence images showing the expression of Fabp1 (left, green), and Sox9 (right, magenta) in healthy mini-colons cultured for 7 days. Scale bar, 100 μm. c , Fluorescence image (left) showing the presence of mutated cells 36 h after the blue–light-mediated induction of a mini-colon. The segmentation of each mutated cell is shown (right). Scale bar, 120 μm. d , Evolution of the number of mutated cells in an inducible mini-colon after blue–light-mediated activation. Each dot represents a measurement every 15 min. The 2 nd order smoothing of the data is shown. e , Time-course quantitation of cell shedding (total protein content) into the lumen of OptoCre and control mini-colons after blue-light induced oncogenic recombination. * P = 0.0156; ** P = 0.0033; *** P = 0.0003 (24 h), <0.0001 (96 h) (two-way ANOVA and Sidak’s multiple comparisons test; n = 3 mini-colons for each condition). Each point represents one mini-colon. Data represent mean ± SEM. f , Low- (left) and high-magnification (right) immunofluorescence images showing the presence of CD44 (magenta) and nuclei (blue) in a tumour-bearing mini-colon. White and grey arrowheads indicate early and advanced tumorigenic events, respectively. Scale bars, 120 μm (left) and 50 μm (right).

Extended Data Fig. 3 Oncogenic mutations induce full blown tumours in mini-colons.

a , Hematoxylin and eosin staining of a mini-colon tumour section. Scale bar, 25 μm. b , Time-course growth of tumours produced by cells derived from mini-colon tumours upon subcutaneous transplantation in immunodeficient mice ( n = 5 mice). As a reference, bona fide cancer cells from primary colon tumours are included. c , Image of the tumours at the endpoint of the experiment shown in panel b. d , Hematoxylin and eosin stainings of sections from the indicated tumour types. Zoomed-in areas (right) are indicated with a dashed square (left). Scale bar, 100 μm. e , Hematoxylin and eosin stainings of sections from mini-colon AKP implant tumours showing the presence of invading cancer cells (left, black arrowheads) and areas of cellular atypia (right, white arrowhead). Scale bar, 200 μm. f , Electrophoretic separation of PCR-amplified KRAS LSL locus in the indicated samples. See Methods for more details on PCR design. g , Whole exome sequencing coverage in the indicated loci and cells. Missing exons in recombined cells are indicated. h , Brightfield images of mini-colons of the indicated genotypes 23 days after blue light exposure. Neoplastic and tumour structures are indicated with black and white arrowheads in A and AK mini-colons, respectively. By that time tumours have extended throughout the whole mini-colon tissue in the case of the AKP model, forming a dense mass of cancer cells. Scale bar, 75 μm.

Extended Data Fig. 4 Mini-colon tumours display in vivo -like functional and transcriptional features.

a , Brightfield and fluorescence images of a mini-colon where blue light exposure has been targeted to a specific area (dashed blue line). Red and green signals correspond to healthy and mutated cells, respectively. Images were taken 36 h after induction. Scale bar, 75 μm. b , Multiplicity of tumours emerged in mini-colons cultured in the indicated conditions. * P = 0.0122; ** P = 0.0035; *** P = 0.0002 (two-way ANOVA and Sidak’s multiple comparisons test; n = 4 mini-colons for each condition). c , Distribution of tumour morphologies in mini-colons cultured in the indicated conditions. ** P = 0.0024 (two-way ANOVA and Sidak’s multiple comparisons test; n = 4 mini-colons for each condition). d , Brightfield images of the indicated colon organoid lines cultured for 3 days in full organoid medium. Scale bar, 200 μm. e , Metabolic activity (measured using resazurin) of the indicated colon organoid lines cultured in BMGF medium for the indicated time. Numeric labelling (1-8) is used to facilitate cell line identification. *** P = 0.0002 (mini-colon AKP #iii), <0.0001 (all other conditions) (two-way ANOVA and Sidak’s multiple comparisons test; n = 3 cultures for each line). f , Volcano plot of the differentially expressed genes between “in vivo” and “organoid AKP” cell lines. g , Top enriched functional clusters in the differentially expressed genes identified in panel f. h , Enrichment of the “in vivo AKP” transcriptional signature identified in panel f across the different “mini-colon” and “organoid AKP” lines. * P = 0.0137; ** P = 0.0032 (#iii), 0.0075 (#iv); *** P = 0.0001 (Brown-Forsythe ANOVA and Dunnett’s T3 multiple comparisons test (two-sided), n = 6 and 3 cultures for “organoid AKP” and the rest of cell lines, respectively). Each dot represents one culture. i , Main enriched functional terms in the differentially expressed genes between “mini-colon AKP” lines # i and # v. Significant terms are highlighted in red (one-sided Fisher’s exact test, adjusted P values). In b , c , e , and h , data represent mean ± SEM. GPL, glycerophospholipid; EL, ether lipid; PG, proteoglycans; SC, stem cell.

Extended Data Fig. 5 Tumorigenesis in the mini-colon leads to enhanced RTK signalling promoting growth factor independence.

a , Gene interaction network of the overlapping genes that are upregulated in AKP lines with high-proliferation potential in BM (“in vivo AKP”, “mini-colon AKP” #v) when compared to low-growth counterparts (“organoid AKP”, “mini-colon AKP” #i). Network hubs are highlighted with circles. b , Brightfield images of “mini-colon AKP” #v organoids cultured for 3 days with the indicated media and compounds. Scale bar, 200 μm. c , Metabolic activity (measured using resazurin) of “mini-colon AKP” #v organoids cultured in the indicated conditions. * P = 0.0236; *** P < 0.0001 (two-way ANOVA and Sidak’s multiple comparisons test; n = 4 cultures for infigranib and 8 for the rest of conditions). d , Brightfield images of “organoid AKP” cells cultured for 3 days in BM with the indicated growth factors. Scale bar, 200 μm. e , Metabolic activity (measured using resazurin) of “organoid AKP” cells cultured in the indicated conditions. *** P < 0.0001 (two-way ANOVA and Sidak’s multiple comparisons test; n = 8 cultures for each condition). In c and e , each point represents one well of organoids and data represent mean ± SEM.

Extended Data Fig. 6 Mini-colons comprise a complex cellular ecosystem.

a , Expression distribution of cell–type-specific markers across mini-colon cells. Cell-type labels can be found in Fig. 3b . b , Examples of single-cell RNA reads capturing exon-exon junctions that reveal the expected oncogenic recombination in Apc . c , Examples of single-cell RNA reads capturing exon-exon junctions that reveal the expected oncogenic recombination in Trp53 . d , Unsupervised UMAP clustering of the main cell types found in each of the healthy and tumour clonal populations found within the mini-colon. Tumour clones carry the “CRC” label. UMAP structure corresponds to the one shown in Fig. 3b .

Extended Data Fig. 7 Mini-colons display intra- and inter-tumour diversity.

a , Expression distribution of proliferation ( Mki67 ), stemness ( Cd44 ), and differentiation ( Krt20 ) markers within a single clonal tumour population. b , Heatmap of the genes showing the strongest ( P < 10 −5 ) differential expression across mini-colon tumours. The tumour clonal population is indicated on top. c , Expression of the indicated genes in the indicated tumour clones. *** P = 2.74·10 −14 ( Slpi , clone #1), 4.06·10 −50 ( Prdm16 , clone #14), 1.05·10 −13 ( Aqp5 , clone #25) (two-sided Wilcoxon rank-sum test; n = 540 cells). Each point represents one cell. d , Immunofluorescence images showing the expression of Il1a (left, green), Cdkn2a (right, red), and the presence of nuclei (cyan) in tumour-bearing and control mini-colons. White and grey arrowheads indicate positive and negative tumours, respectively, in terms of marker expression. Scale bar, 100 μm.

Extended Data Fig. 8 The tumour heterogeneity in the mini-colon is relatable to the human context.

a , Expression distribution of intrinsic consensus molecular subtype (iCMS) signatures across tumour cells in the mini-colon. Cell-type labels can be found in Fig. 3d . Exp, expression. b , Fraction of cells within each tumour clone in the mini-colon classified in each iCMS group. c , Survival of CRC patients from the TCGA database according to the expression of the indicated tumour clone-specific markers. The logrank test P value is indicated ( n = 375 patients). d , Presence of cancer cells in lymph nodes from CRC patients from the TCGA database according to the expression of the indicated tumour clone-specific markers. *** P = 2.714·10 −5 (two-sided Wilcoxon test; n = 375 patients). e , Lymph node staging in CRC patients from the TCGA database according to the expression of the indicated tumour clone-specific markers. * P = 0.0222; ** P = 4.923·10 −3 (two-sided Chi-squared test; n = 375 patients).

Extended Data Fig. 9 Gpx2 regulates colonocyte stemness and tumorigenesis.

a , Multiplicity of tumours emerged in mini-colons treated with the indicated compound upon oncogenic induction. *** P = 0.0001 (day 5), <0.0001 (all other conditions) (two-way ANOVA and Sidak’s multiple comparisons test; n = 6 mini-colons for each condition). b , Brightfield images of colon organoids treated with the indicated compound after tumorigenic recombination. Images correspond to 3 days after induction. Scale bar, 200 μm. c , Metabolic activity (measured using resazurin) of organoids cultured in the indicated conditions and times after oncogenic recombination. No significant differences (two-way ANOVA and Sidak’s multiple comparisons test; n = 4 cultures for each condition). d , qRT-PCR based quantitation of Gpx2 mRNA in the indicated cell lines. *** P < 0.0001 (one-way ANOVA and Tukey’s multiple comparisons test; n = 6, 4, and 3 organoid cultures for parental, sh Gpx2 #1, and the rest of the lines, respectively). e , Brightfield images of non-induced colon organoids of the indicated genotypes after 3 days of culture. Scale bar, 200 μm. f , Metabolic activity (measured using resazurin) of non-induced colon organoids of the indicated genotypes at the indicated times. No significant differences (two-way ANOVA and Sidak’s multiple comparisons test; n = 6 cultures for each condition). g , Brightfield images of mini-colons of the indicated genotypes after tumorigenic recombination. Images correspond to 6 days after induction. Scale bar, 75 μm. h , Brightfield images of colon organoids of the indicated genotypes after tumorigenic recombination. Images correspond to 6 days after induction. Scale bar, 200 μm. i , Volcano plot showing the differentially expressed genes upon Gpx2 knockdown in non-transformed colon cells. j , Bubble plot showing the main enriched functional terms in the differentially expressed genes upon Gpx2 knockdown in non-transformed colon cells. Significant terms are highlighted in either blue (downmodulated) or red (upmodulated) (one-sided Fisher’s exact test, adjusted P values). k , Enrichment of the indicated hallmark signatures from the MSigDB in the indicated cell lines. ** P = 0.0013; *** P = 0.0008 (Wnt), 0.0003 (EMT, before recombination), <0.0001 (all other conditions); NS, not significant (one-way ANOVA and Tukey’s multiple comparisons test; n = 3 cultures for each condition). l , Colony assay images of non-induced colon organoids of the indicated genotypes after 3 days of culture in the indicated media conditions. Scale bar, 200 μm. m , Clonogenic capacity of non-induced colon organoids of the indicated genotypes after 3 days of culture in the indicated media conditions. * P = 0.0219; ** P = 0.0012 (EN, sh Gpx2 #3), 0.0036 (BMGF, sh Gpx2 #1); *** P < 0.0001 (two-way ANOVA and Dunnet’s multiple comparisons test; n = 3 cultures for each condition). n , Brightfield images of Gpx2 knockdown mini-colons that had undergone the indicated pre-treatment before tumorigenic recombination. Images correspond to 7 days after tumour induction. Scale bar, 75 μm. In a , c , d , f , k , and m , each point represents one well of organoids and data represent mean ± SEM.

Extended Data Fig. 10 Mini-colons provide experimental versatility and resolution to tumorigenic studies.

a , Brightfield images of colon organoids treated with the indicated bacterial metabolites. Images correspond to 5 days after oncogenic induction. Scale bar, 200 μm. b , Schematic of the experimental setup used to evaluate the relevance of luminal access in tumorigenic studies. c , Brightfield images of mini-colons treated with the indicated diets according to experimental setup displayed in panel b (left). Images correspond to 6 days after tumorigenic induction. Scale bar, 75 μm. d , Multiplicity of tumours emerged in mini-colons treated with the indicated diets according to experimental setup displayed in panel b (left). *** P = 0.0001 (day 7), <0.0001 (days 6 and 8) (two-way ANOVA and Sidak’s multiple comparisons test; n = 4 and 3 mini-colons for calorie-restricted and -enriched diets, respectively). e , Brightfield images of mini-colons treated with the indicated diets according to experimental setup displayed in panel b (right). Images correspond to 6 days after tumorigenic induction. Scale bar, 75 μm. f , Multiplicity of tumours emerged in mini-colons treated with the indicated diets according to experimental setup displayed in panel b (right). Differences are not significant (two-way ANOVA and Sidak’s multiple comparisons test; n = 4 and 3 mini-colons for calorie-restricted and -enriched diets, respectively). g , Brightfield image of a healthy (non-transformed) mini-colon with integrated stromal cells in the extracellular matrix. Scale bar, 75 μm. h , Immunofluorescence image showing the presence of E-cadherin (green) and Vimentin (magenta) in the mini-colon shown in panel g. Scale bar, 75 μm. i , Brightfield images of mini-colons in the indicated mono- (left) and co-culture (right) setups. Images correspond to 6 days after tumorigenic induction. Scale bar, 75 μm. j , Brightfield image of an invasive front (arrowhead) formed in response to the presence of stromal cells in a mini-colon. The image corresponds to 6 days after tumorigenic induction (zoomed-in from panel i). Scale bar, 30 μm. k , Immunofluorescence image showing the presence of E-cadherin (green) and Vimentin (magenta) in the invasive front from panel j 23 days after tumorigenic induction. Scale bar, 20 μm. In d and f , data represent mean ± SEM.

Supplementary information

Reporting summary, supplementary table 1.

Differentially expressed genes in shGpx2 colon organoids before oncogenic recombination.

Supplementary Table 2

Differentially expressed genes in shGpx2 colon organoids after oncogenic recombination

Supplementary Video 1

Early response to oncogenic activation within a mini-colon. 46 h time-lapse video of mutated cells in a mini-colon 24 h after oncogenic recombination.

Supplementary Video 2

Hyperplasia and early tumour development in a mini-colon. 36 h time-lapse video of a mini-colon with multiple tumour-initiating events 5 days after oncogenic recombination.

Supplementary Video 3

Ex vivo tumour development in a mini-colon. 38 h time-lapse video of tumour development in a mini-colon 9 days after oncogenic recombination.

Supplementary Video 4

Cancer stem cells initiate tumour development in mini-colons. 3D visualization of cancer stem cell marker CD44 overexpression in early tumorigenic sites.

Supplementary Video 5

Intratumour complexity in mini-colons. 3D visualization of CD44 (cancer stem cell marker) and FABP1 (mature colonocyte marker) expression in mini-colon tumours and epithelium.

Source data

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Lorenzo-Martín, L.F., Hübscher, T., Bowler, A.D. et al. Spatiotemporally resolved colorectal oncogenesis in mini-colons ex vivo. Nature (2024). https://doi.org/10.1038/s41586-024-07330-2

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Published: 24 April 2024

Designing a tool ensuring older patients the right medication at the right time after discharge from hospital– the first step in a participatory design process

Thorbjørn Hougaard Mikkelsen 1 , 2 , 3 ,
Jens Søndergaard 3 ,
Niels Kristian Kjær 3 ,
Jesper Bo Nielsen 3 ,
Jesper Ryg 4 , 5 ,
Lene Juel Kjeldsen 6 &
Christian Backer Mogensen 1 , 2

BMC Health Services Research volume 24 , Article number: 511 ( 2024 ) Cite this article

Metrics details

On average, older patients use five or more medications daily, increasing the risk of adverse drug reactions, interactions, or medication errors. Healthcare sector transitions increase the risk of information loss, misunderstandings, unclear treatment responsibilities, and medication errors. Therefore, it is crucial to identify possible solutions to decrease these risks. Patients, relatives, and healthcare professionals were asked to design the solution they need.

We conducted a participatory design approach to collect information from patients, relatives, and healthcare professionals. The informants were asked to design their take on a tool ensuring that patients received the correct medication after discharge from the hospital. We included two patients using five or more medications daily, one relative, three general practitioners, four nurses from different healthcare sectors, two hospital physicians, and three pharmacists.

The patients’ solution was a physical location providing a medication overview, including side effects and interactions. Healthcare professionals suggested different solutions, including targeted and timely information that provided an overview of the patient’s diagnoses, treatment and medication. The common themes identified across all sub-groups were: (1) Overview of medications, side effects, and diagnoses, (2) Sharing knowledge among healthcare professionals, (3) Timely discharge letters, (4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional?

All study participants describe the need for a more concise, relevant overview of information. This study describes elements for further elaboration in future participatory design processes aimed at creating a tool to ensure older patients receive the correct medication at the correct time.

Peer Review reports

Healthcare sector transitions increase the risk of information loss, misunderstandings, unclear treatment responsibilities, and medication errors [ 1 , 2 , 3 ]. Medication of older patients following hospital visits is often seen as particularly complex [ 4 ]. Polypharmacy adds significantly to this complexity due to the uncertainty about how often and for how long medication is needed, challenges in sharing information in sector transitions with different healthcare professionals, and the patients’ and relatives’ cognitive ability and motivation to follow medication plans [ 5 ]. During hospitalisation, 60% of patients receive three or more changes to their medication, and the risk of a harmful event increases significantly with each prescription change [ 6 , 7 ]. Older patients often use five or more prescription medications daily [ 8 , 9 ], but polypharmacy is not always beneficial for the patient [ 10 , 11 , 12 , 13 , 14 , 15 ], and some older patients experience severe side effects [ 16 , 17 , 18 , 19 , 20 ] often due to drug-drug interactions [ 21 , 22 ]. In addition, previous parts of this study have shown that older patients are often concerned about drug-drug interactions and side effects as well as confused about aspects such as names, labels, and when to take the medication [ 23 ]. Therefore, the discharge of elderly patients from the hospital is a complex process where robust tools are needed to support the correct medication at the correct time. For international readers it is important to know a particular artefact in the Danish healthcare system. When the shared Medication Record (SMR) was established to document prescribed medications for a patient over ten years, a new word, “ordineret medicin,” was introduced, which translated means non-prescription medication. This phrase was introduced to distinguish between an active prescription and a passive non-prescription medication. The SMR is a continuously updated and accessible online overview for patients and healthcare professionals regardless of sector, and gives healthcare professionals, and patients access to view current medications, including dose and prescription redemption [ 24 , 25 ]. SMR also enables healthcare professionals to see the patient’s medication history and register changes [ 26 ]. Upon discharge, GPs receive a discharge summary from the hospital describing the treatment and suggesting follow-up. If home care is needed, the municipality receives a patient treatment- and care plan from the hospital so the municipality can prepare for the patient’s return home. The patient treatment- and care plan will among other things include information regarding the hospitalization, diagnoses, medication, and required nursing and homecare support after discharge [ 27 ]. This knowledge is important to understand some of the results of this study. Despite these systems enabling sharing of information improvements are needed to ensure the right medication for older patients [ 23 ].

To develop a solution for solving major medication challenges facing polypharmacy patients when discharged from the hospital, we invited relevant actors to design their vision of the most suitable and robust tool. In this study, we will explore the first step in this design process of a future hopefully robust tool to be used, when patients cross healthcare sectors. Previous studies were typically based on input from only one stakeholder, whereas our study invited both clinicians from both healthcare sectors and patients into the same participatory design process with the purpose of developing a tool to be shared, appreciated and seen as useful for all stakeholders.

This study aims to provide knowledge about key elements a future solution should include to ensure correct medication treatment for older patients transitioning between secondary and primary healthcare sectors.

Participatory design

This study focuses on older patients treated with five or more medications and we used a participatory design (PD) process including patients, relatives and healthcare professionals. PD is beneficial when exploring informants’ wishes and creating new solutions [ 28 ].

PD studies combine the use of different methods and activities running simultaneously during the entire process: Literature studies, field studies, design and development, and testing [ 29 ]. Within the health sciences, PD is typically broadly divided into 3 phases. In phase 1, the users’ needs are identified and discussed in this study using FGIs. In phase 2, a prototype is developed and designed through workshops. Mock-ups and proposed solutions are designed, tested, and retested to develop a prototype that can be pilot-tested [ 29 , 30 , 31 ]. In phase 3, the prototype is tested [ 29 , 30 ]. This study reports the first step in phase 2 aiming at laying the foundation for developing prototypes and Mock-ups in future studies if financed.

We asked participants to create a tool or solution, to enhance adherence. We let the participants think, discuss, and report their reflections on the best solution [ 28 , 32 ].

Our object was not defined a priori. Hence, we began the process with a brainstorm, where the participants were asked to list essential aspects to ensure all patients received the correct medication. The factors identified from the brainstorm were discussed in smaller groups of peer participants. The small groups were asked to design a solution, later presenting to the group how it would work [ 28 , 32 ].

Brainstorming generates ideas emphasizing many solutions without consideration for practicalities [ 33 , 34 ]. The participants were instructed not to be critical of ideas but to describe any additional ideas that come to mind, no matter how wild. It was emphasized that the brainstorm aimed to generate many ideas, and participants were encouraged to be innovative after hearing other’s ideas [ 34 ]. Many studies described brainstorming in groups as suboptimal in productivity compared to brainstorming individually beforehand [ 34 , 35 , 36 ]. Hence, individual brainstorming was conducted before participants shared ideas and initiated the design process.

Setting and participants

Participant recruitment aimed at achieving rich and diverse perspectives [ 29 ]. GPs and nurses were invited to the participatory design process through one of the co-authors (NK) professional network and GPs associated with Hospital Sønderjylland, University Hospital of Southern Denmark. Homecare nurses were invited through their local municipality and hospital nurses through their departments. Pharmacists were invited through a local pharmacist. Patients and relatives were invited following admission to the emergency department if 72 years or older and managing five or more medications themselves or with the help of a relative and able to transport themselves to the PD process at the hospital. Patients with dementia were excluded. The participating patients and relatives have previously participated in focus group interviews (FGI) reported elsewhere [ 23 ] and were subsequently invited to participate in the participatory design process. The inclusion of patients invited to the FGIs was based on consecutive sampling among patients admitted to the Emergency Department at Hospital Sønderjylland. The patients were invited while admitted to the department during ten days in April, May and June 2021 [ 23 ]. Overall 31 patients were eligible for the FGIs. A total of 10 patients, here of three with a spouse, accepted the invitation to the FGIs. One died before the FGI and another did not show up [ 23 ]. Patients and relatives participating in the FGIs were invited to participate in the making process. The three pharmacists could not attend the participatory design process on the same day as the other actors and were invited to participate on an alternative day. All participants, except one hospital physician and one pharmacist, were Danish by ethnicity (ethnicity not stated due to anonymity aspects).

Six following groups of similar participants were created: (1) Three GPs, (2) Two chief physicians, (3) Three pharmacists, (4) Two nurses employed in general practice, (5) One participating hospital nurse was grouped with the two homecare nurses. (6) Two patients aged 73 and 78 years and one relative. In total, 16 informants with different backgrounds participated.

Data collection

The participatory design process took place at the hospital. The first author (THM) prepared a generative toolkit (Picture 1), in addition, the participants had access to a wide range of other remedies such as paper and cardboard in many colors.

The material was presented at the beginning of the participatory design process and included a short statement about the ambition of the process, which was also stated in the invitation. In addition, the informants were informed verbally and in writing about the study’s details and asking them to sign a consent form highlightning that participating was voluntary and anonymous and that their participation would have no influence on their subsequent treatment as well as explaining that the purpose of the research study.

The participatory design process

Firstly the Informants were welcomed individually and seated in groups with peer participants, e.g. GPs together, nurses together. The agenda was as follows:

Short outline of the workshop.

Presentation of the program.

Brainstorm about important aspects of ensuring the right medication at all times.

The task as presented to the participants and visible on PowerPoint during the whole process: “Your task is to design the perfect tool to ensure you always get the right medication in the right place. Focus on the solutions and functions of the tool. Build the tool with the remedies we have gathered here. The things you decide to add to the solution must have a function corresponding to a need you or others have- how it looks doesn’t matter, but remember the function of the different parts because we will ask you to present your new tool to the larger group.”

Presentation of the generative toolkit.

Making a “thing” that can ensure the right medication at all times.

All groups present their solution to the other groups.

The workshop was facilitated by THM and lasted 2 h and 15 min. There was approximately 1 ½ hours for the making process and half an hour for the presentation of the solutions. The participants had access to refreshments during and after the workshop. The atmosphere was good and empathic addressing the participants own everyday problems and at the same time acknowledging other participants’ situations and working conditions during the presentations.

During the participatory design process, the informants undisturbed generated, tested and elaborated on ideas until the presentation of the models. Data was captured during the presentations to the larger group and were recorded and subsequently transcribed in full, coded, and sorted by THM, JS, and CBM.

The analysis of data follows methods often applied in participatory design studies [ 28 , 29 , 31 ]. We applied an inductive approach focusing on the informants’ descriptions, perceptions, understandings, and ideas. We also applied a deductive analytic strategy based on the themes presented by other informants and identified through the literature. The group discussions were analyzed phenomenally, focusing on the informants’ experiences and perceptions [ 37 , 38 ].

The participants were asked to design a tool to illustrate how to ensure patients always get the right medication at the right time. Their solutions were diverse. The patients built a health centre (Additional file 2 ), the chief physicians a health card containing all key information about the patient (Additional file 3), the general practitioners a communication channel to the hospital (Additional file 4 ), the nurses employed in general a solution ensuring that the same information is available to all health professionals (Additional file 5 ), the pharmacists designed a combined database and communication channel (Additional file 6 ) and hospital- and homecare nurses design the good discharge process (Additional file 7 ). However, the common factor for all solutions was the focus on an overview of the patient’s diagnoses and treatment. During the analysis, the following themes were identified: (1) Overview of medications, side effects, and diagnoses, (2) Sharing knowledge among healthcare professionals, (3) Timely discharge letters, (4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional?

Overview of medications, side effects, and diagnoses

All participants strived for solutions that created an overview. The patients asked for an overview of their medication, side effects, and interactions. The healthcare professionals aimed for an overview of the patient’s diagnoses and elements important for treatment, such as the presence of a pacemaker. This information should be available in a single solution.

Chief physician: We are affected by the same fatigue as the other groups have expressed, we do not have the information we need, not even from you (general practice ed.) when you send the patients in, then we face fragmented knowledge and we need to collate and update the information. Is it possible to summarise the information using one solution, preferably a solution that the patient has e.g. a chip or something?

For patients, the most important thing is to get an overview of the medication, the associated diagnosis, and interactions. Therefore, the patients/relative group suggested a healthcare centre to provide answers regarding medications and health issues.

Patient: Medication is a huge issue. I’m so uneasy about being sent from one hospital to another. Every time you talk to a doctor, you get a new medication. How does the new medication affect the other medications?

The patients request contact with a physician responsible for an overview:

Spouse: there are many people who need to know about the medication, how to take medication, how to act if you get the wrong medication because you can also experience adverse drug reactions.

In this way, the patients request access to a central healthcare information centre with profound knowledge of the patient’s diagnosis and medications, including side effects and interactions with other medications, and responsibility for the patient’s treatment.

The patients built a health centre that collated information, provided an overview of diagnoses and medications, and gave knowledge about side effects and interactions.

Patient: When you come to this house, you get an answer you can understand. When you are discharged from hospital, you are often left with new medications, and you are left to your own devices or you have to contact your GP. We request closer cooperation between the hospital and the general practitioners or health care centres. Because sometimes, when you come home, you realise it is difficult to understand the mixed medication you have been given.

Thus, patients ask for a solution that collates information about diagnoses, medications, and interactions and can explain it to the patients. However, it is a prerequisite for healthcare professionals to be able to create an overview of diagnoses and treatments.

Sharing knowledge among healthcare professionals

All participating healthcare professionals asked for additional information from other parts of the healthcare sector. All of them have access to SMR, showing the patients’ current prescriptions and medications prescribed within the last ten years, giving profound information regarding the patient’s medication.

The participating homecare and hospital-employed nurses build an illustration of the good discharge illustrating their principal wishes:

Hospital-employed nurse: We have looked into the available communication tools to see how they can ensure that the medication and the medication management are handled in the best way. […] We have tried to illustrate the path to a good discharge. And the cornerstones […] were that the SMR is updated and were the patient given a sufficient amount of medication to take hom e until the new medication could be retrieved or delivered from the pharmacy, […] and that there are prescriptions for the new and previous medication […], and then; who collects it (at the pharmacy ed.) […] - we have our treatment and care plans, we can send them out to each other, but (the homecare nurses ed.): It’s fine that you (the physician ed.) prescribe a new medication, but we also need to know the indication/purpose… .

As the quote shows, there are many aspects regarding a good discharge. An important part is that the SMR is updated, ensuring primary healthcare the relevant and updated information regarding the medication. It is also important to ensure that the patient has the right and sufficient amount of medication at home and if not, a plan to ensure how the patient can access more or new medication, as well as a plan for a follow-up consultation when needed. Finally, they request information about the diagnoses leading to a new prescription.

Timely discharge summaries

The GP receives a discharge summary from the hospital when a patient is discharged. However, the GPs also requested more information such as diagnosis, what information was given to the patients, and timely discharge summaries:

GP: What we lack in this communication channel is that the discharge summay arrives on time and contains the necessary information. If there have been changes in medication, we need to know why. […] The medication that may have been prescribed; is the patient informed well enough about it? […] If they receive dose dispensing, […] then we must also have a home nurse over so that we can get them dosed up as a supplement to their usual medication.

The GPs ask for different types of information, including that the discharge letters are received quickly. However, this can be logistically difficult for hospital doctors as hospital secretaries are given three days to prepare discharge letters.

The participatory design process shows that the discharge letters are important for the GPs and that it is important that they are received shortly after discharge so that they can contribute to ensuring that the patient always receives the right medication at the right time.

4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional?

As described above, SMR contains all medication prescribed to the patient within the last ten years. The diagnosis is stated in the discharge letter, although the citations below indicate they don’t always fully meet the wishes of the GPs.

Chief physician: Do you receive discharge letters that you find informative and make you feel well-prepared (for resuming the treatment of the patient ed)? GP: The problem is if they are the standardized ones, then there will be far too much unnecessary information, and then we will go straight to the conclusion. And then, unfortunately, you may sometimes overlook some important information.

Thus, too little but also too much irrelevant information can be problematic. The challenge with too much irrelevant information is that the general practitioner cannot form a quick overview of the patient’s treatment at the hospital. Likewise, the chief physician does not want to provide too much information. As a chief physician said when presenting their model:

Chief physician: That is also why we propose… you have to define what is common because there is no reason for us to know everything that happens out there, because it will not be relevant and focused, and it will require too much sorting work. But there are some common things of mutual benefit that we all should all know.

In summary, all participating groups request targeted information. They did not request the same information showing that some information should be available to all the participating groups while other information should target specific groups. In this way, a solution/tool to ensure that the patient always gets the right medication should collect the relevant information to allow an overview, and ensure targeted information to the relevant actors to prevent information overload and loss for the healthcare professionals, but also avoid insecurity and confusion for the patients.

The participating healthcare professionals requested targeted information corresponding to the patient’s preferences and expectations. The patients requested one integrated service or a healthcare professional who has the overview of the patient’s diagnosis and medications, including side effects and interactions with other medications, and responsibility for the patient’s treatment. This could be a physician, clinical pharmacist at the hospital, or GP. This corresponds with a systematic review of interventions to increase medication adherence showing that verbal and verbal/written information was the most effective [ 39 ]. This study adds that even though the different stakeholders ask for different information, this different information can be contained in one shared tool to be developed ensuring useful and targeted information to all stakeholder groups.

All the informants want a better overview of the patient’s treatment, medication, and diagnoses despite the fact that that medications prescribed to patients are already accessible online to all groups of informants in SMR [ 24 , 25 , 26 ] and that GPs already receive a discharge summary from the hospital with suggested follow-up. If needed, the municipalities homecare, receive a patient treatment- and care plan from the hospital typically including information regarding the hospitalization, diagnoses, medication, and required nursing and homecare support after discharge [ 27 ]. In summary, all groups already have a large degree of access to information. To ensure the right medication at the right time these data need to be targeted and presented in a way that makes it easy to ensure the patient the right medication at the right time, targeting the different groups and their responsibilities. Hence it may be more important to be able to provide the right information to different groups at the right time, rather than synthesizing the results at this point and, risking not addressing some of the issues presented in further PD processes. Although the participatory design process was about how to ensure that the patient always gets the right medication, the process showed that the stakeholders also want to know and share other related health information, including the diagnosis / medical indication for medication, also described elsewhere [ 40 , 41 ] and, in the case of the participating patients, side effects, and interactions, aspects also described elsewhere [ 23 , 42 , 43 ]. The GPs also requested what information was given to the patients, as described elsewhere [ 40 ], which also should be in a language understandable to the patient, also described elsewhere [ 17 , 44 ]. The different groups of participants built solutions related to their tasks and their tools. For example, nurses focused on the perfect patient discharge using tools in place with additional elements for improvement. This example demonstrates the relevance and strength of PD and why it is important to invite many different actors in an iterative PD process [ 31 ] before a final solution is fully developed which also will allow other possible aspects and solutions to emerge.

Perspectives

Despite many years of research in sharing information between healthcare professionals [ 40 , 41 , 44 , 45 , 46 ] and the fact, that it is already possible to gather and share information within the framework of Danish legislation through SMR and the discharge summary to the GPs as well as the patient treatment and care plan to the homecare nurses [ 27 ] this study show, that targeted information is still requested by the participants. There is a need to ensure that the information is present, that it is easy to find, and does not disappear in an irrelevant information overload.

These findings can strengthen the focus on cross-sectoral communication when combined with other available experiences such as further studies on the use and shortcomings of discharge summaries. The findings can be used for the future process of optimizing existing communication channels between healthcare sectors. Hopefully, the findings can also contribute to developing a “tool” or platform that provides a fast, sufficient, and safe overview for all the health professionals engaged in the individual patient’s care.

The first step towards a solution ensuring that the patient always receives the right medication is to create an overview of what information the different actors want, especially the patients and relatives. This study is the first in a repetitive, iterative PD process to find a solution. The fact that the participants built different solutions shows that different needs can coincide. Therefore, future PD processes must be split between professionals and patients in parallel paths to focus on the professionals’ wishes for an online solution, in combination with a solution that can support citizens’ wishes for a physical location or a possible app that may be of interest among younger and future older generations as described elsewhere [ 47 , 48 ]. This knowledge can be used to develop a solution during future repeated iterative PD processes developing several prototypes, testing, and developing the common solution gradually [ 31 ].

Strength and limitations

It is a limitation that a relatively small number of different participants attended. However, it is a strength that all central actors are represented; GPs, the hospital, and the municipality as well as some patients and a relative participated in the PD process. We recognize the limited number of patients in our sample, but the Danish society especially among older citizens is quite homogeneous, all have free access to health care, and data was sampled though interviews, so we are not particularly worried about representability among the patient group. In addition PD processes are often conducted with a rather small number of participants, including patients [ 30 , 49 , 50 ] and is known as a reliable method [ 31 , 49 , 50 ].

Bias may occur if the informants do not express their actual attitudes if they feel insecure in the setting. Hence the actors were grouped with like-minded participants to ensure an environment where they did not restrain themselves out of respect for others. All the participants, including the patients and the relative participated and spoke freely, and the atmosphere was friendly and relaxed. The participation of patients and a relative is considered a strength as they enriched the discussion. The first author (THM) is a trained researcher in qualitative methods and ensured that all voices, experiences, and opinions were heard and presented. As a sociologist, THM had no prior knowledge regarding patients’ medications and the problems facing older patients after discharge from the hospital or knowledge of the problems of healthcare professionals.

A further strength of this study is that the participating patients and relatives managed multiple medications daily and were well-functioning. The participants had a high degree of knowledge about their illness and were willing to discuss central issues about managing the disease. Patients are probably the best informants to highlight the factors preoccupying this target group. A further limitation of the study is that frail senior citizens may be underrepresented, and patients taking no particular interest in their medication might be expected to decline participation in the focus group interviews. However, the participation of healthcare professionals enabled the perspectives related to frail patients or patients with no particular interest in their medication to be included.

All participants in this study state that they lack an overview of patient-related information. Patients lack an overview of their medication, side effects, and interactions. Health professionals lack an overview of the patient’s diagnoses and other factors of importance for the treatment. While the patients wish that the service are available in one physical location, the healthcare professionals wish that important information is gathered, sorted, and accessible to the relevant healthcare professionals online at all times. These two wishes are not mutually exclusive, but important elements should be elaborated upon in future PD processes to ensure that older patients receive the right medication at the right time.

Data availability

The datasets are not publicly available due to regulations from The Danish Data Protection Agency.

Abbreviations

General practitioner

Odense University Hospital

an online Shared Medication Record that can be accessed by the patient and healthcare professionals across sectors. In Danish called Fælles Medicinkort (FMK)

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Acknowledgements

The authors thank the participating informants for participating and sharing their knowledge, experiences, and ideas.

A grant from VELUX FONDEN supported this study (grant no. 34175). VELUX FONDEN did not participate in or influence the design of the study as well as the collection, analysis, and interpretation of data or in writing the manuscript.

Open access funding provided by University of Southern Denmark

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T.H.M. designed the study, collected the data, analyzed and interpreted the data, and drafted the manuscript. J.S. and C.B.M. designed the study, analyzed and interpreted the data, and commented critically on the manuscript. J.B.N., J.R., L.K. and N.K. contributed to the design of the study, the interpretation of data, and commented critically on the manuscript. All authors reviewed the manuscript.

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This study was conducted according to the guidelines of the Declaration of Helsinki. The project was sent to the Regional Committee of Health Ethics in the Region of Southern Denmark, Denmark, for approval (case no. 20212000-69). According to the committee, the project falls outside the scope of a notifiable Health Science research project as it is based on interviews. Therefore, the principles of consolidated criteria for reporting qualitative research [ 51 ] were followed as well as the guidelines of the Declaration of Helsinki. Storage management of the data fulfilled the European General Data Protection Regulations. All Informants gave informed consent and signed a consent form. Informants were informed that they were free to withdraw their consent at any time and that the findings would be anonymous.

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Mikkelsen, T.H., Søndergaard, J., Kjær, N.K. et al. Designing a tool ensuring older patients the right medication at the right time after discharge from hospital– the first step in a participatory design process. BMC Health Serv Res 24 , 511 (2024). https://doi.org/10.1186/s12913-024-10992-3

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Polypharmacy
Older people
Adverse drug reactions
Medication errors
Continuity of care
Qualitative research

The YOU FIRST Approach to Quality Measurement

April 18, 2024 · Andy Reynolds

A new kind of quality measure is gaining traction: Person-Centered Outcome (PCO) measures that focus on measuring what matters to patients.
Mounting evidence shows PCO measures are feasible and that providers and patients like them.
A recent NCQA webinar discussed encouraging findings from our latest round of PCO testing, prompting us to begin recruiting Special Needs Plans to test PCO measures.

The Value of PCO Measures

PCO measures are personalized, structured, measurable goals for adults who require complex care because of one or more chronic conditions due to medical, behavioral health or social needs.

Most quality measures focus on specific diseases or health conditions. PCO measures are different and more flexible, and they relate to what patients care about—often they are practical, specific aspirations like “Walking my daughter down the aisle at her wedding in 3 months.”

Standardizing PCO measures comes from translating patient preferences into a SMART sequence of three measures:

A goal that matters to the patient.
Patient-Reported Outcome Measure : A standardized questionnaire or survey.
Goal Attainment Scaling : Comparing progress against a continuum of possible outcomes.
A plan to reach the goal.
Measure 2 – Goal Follow-up. Providers follow up on the goal, 2 weeks to 6 months after the patient and provider agreed on it.
Measure 3 – Goal Achievement. The “outcome” part of PCO: Was the goal met?

PCO measures have long been in geriatric care. Now, they’re proving useful in more populations and places.

Generous support from The John A. Hartford , SCAN and Gordon and Betty Moore Foundations has allowed NCQA to study PCO measures:

In 30 practices across 17 states.
Among more than 180 clinicians and 5,000 clients or patients.
In varied settings, including Certified Community Behavioral Health Clinics (CCBHC), behavioral health homes, home-based primary care practices, home and community-based services programs and serious illness programs.

Testing shows PCO measures work!

Our March webinar on PCO measures featured clinic managers who helped us test PCO measures in a multi-state learning collaborative.

Feedback was positive:

“It was a good way to engage and hit an on-ramp to get to what members wanted to work on.”
“This approach made it easier to focus and think about what patients wanted.”
“These measures are great. It sets a visual on progress made.”

Our research also shows that the PCO approach helps patients feel heard and more involved in their care.

PCO Measurement Supports Other Goals

HEALTH EQUITY

Everyone deserves the chance to be as healthy as possible, no matter who they are or where they come from.

PCO measures advance health equity by:

Translating people’s priorities into care plans.
Making goal achievement part of quality assessment.
Giving providers a common language for tracking services patients care about.

The CMS National Quality Strategy emphasizes health equity and person-centered care, creating a mutually reinforcing relationship.

BEHAVIORAL HEALTH

Behavioral health is a NCQA priority , so it’s no surprise that our March webinar celebrated the successful test of PCO measures in CCBHCs:

PCO measures are for people with complex care needs.
Behavioral healthcare is complex.
Standardizing outcome measures is hard.

PCO measures’ flexibility means they’re a good match for assessing behavioral healthcare.

See our free PCO toolkit and courses , which include modules about behavioral health. (Details below.)

What’s Next

We’re expanding usage of PCO measures into other areas:

Care delivery. PCO measurement is now an elective in our Patient-Centered Medical Home and Patient-Centered Specialty Practice Recognition programs.(Using PCO is part of our interest in adapting HEDIS for the delivery system—a goal we’ve covered in Future of HEDIS webinars since 2019 .)
Long-Term Services and Supports. Starting next year, our LTSS programs will include PCO elements.
HEDIS. Expect to see PCO measures in our main measure set by 2027.

How You Can Use PCO

SPECIAL NEEDS PLANS

ANY HEALTH PLANS

Use our PCO Toolkit . FREE resources for implementing PCO measures: Examples from primary care, LTSS and behavioral health; patient information in seven languages to help you tailor care for diverse people and groups.
Identifying and Developing a Person-Centered Outcome Goal .
Using Patient-Reported Outcome Measures to Document and Track SMART Person-Centered Outcome Goals .
Using Goal Attainment Scaling to Document and Track SMART Person-Centered Outcome Goals .

ANYONE INTERESTED IN PERSONALIZED CARE

Contact our PCO team . We can answer your questions, and we want to hear your ideas about this promising way to measure quality.

Three Stories Illuminate How to Improve Health Equity

Cross-Sector Collaboration: New Research and Recommendations

Top takeaways from the health equity forum.

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Got tinnitus a device that tickles the tongue helps this musician find relief.

Allison Aubrey

After using the Lenire device for an hour each day for 12 weeks, Victoria Banks says her tinnitus is "barely noticeable." David Petrelli/Victoria Banks hide caption

After using the Lenire device for an hour each day for 12 weeks, Victoria Banks says her tinnitus is "barely noticeable."

Imagine if every moment is filled with a high-pitched buzz or ring that you can't turn off.

More than 25 million adults in the U.S., have a condition called tinnitus, according to the American Tinnitus Association. It can be stressful, even panic-inducing and difficult to manage. Dozens of factors can contribute to the onset of tinnitus, including hearing loss, exposure to loud noise or a viral illness.

There's no cure, but there are a range of strategies to reduce the symptoms and make it less bothersome, including hearing aids, mindfulness therapy , and one newer option – a device approved by the FDA to treat tinnitus using electrical stimulation of the tongue.

The device has helped Victoria Banks, a singer and songwriter in Nashville, Tenn., who developed tinnitus about three years ago.

"The noise in my head felt like a bunch of cicadas," Banks says. "It was terrifying." The buzz made it difficult for her to sing and listen to music. "It can be absolutely debilitating," she says.

Shots - Health News

Tinnitus bothers millions of americans. here's how to turn down the noise.

Banks tried taking dietary supplements , but those didn't help. She also stepped up exercise, but that didn't bring relief either. Then she read about a device called Lenire, which was approved by the FDA in March 2023. It includes a plastic mouthpiece with stainless steel electrodes that electrically stimulate the tongue. It is the first device of its kind to be approved for tinnitus.

"This had worked for other people, and I thought I'm willing to try anything at this point," Banks recalls.

She sought out audiologist Brian Fligor, who treats severe cases of tinnitus in the Boston area. Fligor was impressed by the results of a clinical trial that found 84% of participants who tried Lenire experienced a significant reduction in symptoms. He became one of the first providers in the U.S. to use the device with his patients. Fligor also served on an advisory panel assembled by the company who developed it.

"A good candidate for this device is somebody who's had tinnitus for at least three months," Fligor says, emphasizing that people should be evaluated first to make sure there's not an underlying medical issue.

Tinnitus often accompanies hearing loss, but Victoria Banks' hearing was fine and she had no other medical issue, so she was a good candidate.

Banks used the device for an hour each day for 12 weeks. During the hour-long sessions, the electrical stimulation "tickles" the tongue, she says. In addition, the device includes a set of headphones that play a series of tones and ocean-wave sounds.

The device works, in part, by shifting the brain's attention away from the buzz. We're wired to focus on important information coming into our brains, Fligor says. Think of it as a spotlight at a show pointed at the most important thing on the stage. "When you have tinnitus and you're frustrated or angry or scared by it, that spotlight gets really strong and focused on the tinnitus," Fligor says.

"It's the combination of what you're feeling through the nerves in your tongue and what you're hearing through your ears happening in synchrony that causes the spotlight in your brain to not be so stuck on the tinnitus," Fligor explains.

A clinical trial found 84% of people who used the device experienced a significant reduction in symptoms. Brian Fligor hide caption

A clinical trial found 84% of people who used the device experienced a significant reduction in symptoms.

"It unsticks your spotlight" and helps desensitize people to the perceived noise that their tinnitus creates, he says.

Banks says the ringing in her ears did not completely disappear, but now it's barely noticeable on most days.

"It's kind of like if I lived near a waterfall and the waterfall was constantly going," she says. Over time, the waterfall sound fades out of consciousness.

"My brain is now focusing on other things," and the buzz is no longer so distracting. She's back to listening to music, writing music, and performing music." I'm doing all of those things," she says.

When the buzz comes back into focus, Banks says a refresher session with the device helps.

A clinical trial found that 84% of people who tried Lenire , saw significant improvements in their condition. To measure changes, the participants took a questionnaire that asked them to rate how much tinnitus was impacting their sleep, sense of control, feelings of well-being and quality of life. After 12 weeks of using the device, participants improved by an average of 14 points.

"Where this device fits into the big picture, is that it's not a cure-all, but it's quickly become my go-to," for people who do not respond to other ways of managing tinnitus, Fligor says.

One down-side is the cost. Banks paid about $4,000 for the Lenire device, and insurance doesn't cover it. She put the expense on her credit card and paid it off gradually.

Fligor hopes that as the evidence of its effectiveness accumulates, insurers will begin to cover it. Despite the cost, more than 80% of participants in the clinical trial said they would recommend the device to a friend with tinnitus.

But, it's unclear how long the benefits last. Clinical trials have only evaluated Lenire over a 1-year period. "How durable are the effects? We don't really know yet," says audiologist Marc Fagelson, the scientific advisory committee chair of the American Tinnitus Association. He says research is promising but there's still more to learn.

Fagelson says the first step he takes with his patients is an evaluation for hearing loss. Research shows that hearing aids can be an effective treatment for tinnitus among people who have both tinnitus and hearing loss, which is much more common among older adults. An estimated one-third of adults 65 years of age and older who have hearing loss, also have tinnitus.

"We do see a lot of patients, even with very mild loss, who benefit from hearing aids," Fagelson says, but in his experience it's about 50-50 in terms of improving tinnitus. Often, he says people with tinnitus need to explore options beyond hearing aids.

Bruce Freeman , a scientist at the University of Pittsburgh Medical Center, says he's benefitted from both hearing aids and Lenire. He was fitted for the device in Ireland where it was developed, before it was available in the U.S.

Freeman agrees that the ringing never truly disappears, but the device has helped him manage the condition. He describes the sounds that play through the device headphones as very calming and "almost hypnotic" and combined with the tongue vibration, it's helped desensitize him to the ring.

Freeman – who is a research scientist – says he's impressed with the results of research, including a study published in Nature, Scientific Reports that points to significant improvements among clinical trial participants with tinnitus.

Freeman experienced a return of his symptoms when he stopped using the device. "Without it the tinnitus got worse," he says. Then, when he resumed use, it improved.

Freeman believes his long-term exposure to noisy instruments in his research laboratory may have played a role in his condition, and also a neck injury from a bicycle accident that fractured his vertebra. "All of those things converged," he says.

Freeman has developed several habits that help keep the high-pitched ring out of his consciousness and maintain good health. "One thing that does wonders is swimming," he says, pointing to the swooshing sound of water in his ears. "That's a form of mindfulness," he explains.

When it comes to the ring of tinnitus, "it comes and goes," Freeman says. For now, it has subsided into the background, he told me with a sense of relief. "The last two years have been great," he says – a combination of the device, hearing aids and the mindfulness that comes from a swim.

This story was edited by Jane Greenhalgh

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U.S. centenarian population is projected to quadruple over the next 30 years

A WWII Coast Guard veteran celebrates her 100th birthday in Boston, Massachusetts, on Aug. 19, 2023. (John Tlumacki/The Boston Globe via Getty Images)

The number of Americans ages 100 and older is projected to more than quadruple over the next three decades, from an estimated 101,000 in 2024 to about 422,000 in 2054, according to projections from the U.S. Census Bureau. Centenarians currently make up just 0.03% of the overall U.S. population, and they are expected to reach 0.1% in 2054.

A line chart showing that the U.S. centenarians projected to quadruple in number by 2054.

The number of centenarians in the United States has steadily ticked up since 1950, when the Census Bureau estimates there were just 2,300 Americans ages 100 and older. (The Census Bureau uses calculated estimates for years prior to the 1990 census because it has identified large errors in the census counts of centenarians for those years.)

In the last three decades alone, the U.S. centenarian population has nearly tripled. The 1990 census counted around 37,000 centenarians in the country.

Pew Research Center conducted this analysis to understand how the population of Americans ages 100 and older looks today, and how it is expected to change in the next 30 years. U.S. population estimates come from the U.S. Census Bureau , and global projections are drawn from the United Nations’ population projections under its medium variant scenario .

All racial groups are single-race and non-Hispanic. Hispanics are of any race.

Today, women and White adults make up the vast majority of Americans in their 100s. This trend is largely projected to continue, though their shares will decrease:

A bar chart showing that the vast majority of Americans in their 100s are women, White.

In 2024, 78% of centenarians are women, and 22% are men. In 30 years, women are expected to make up 68% of those ages 100 and older, while 32% will be men.
77% of today’s centenarians are White. Far fewer are Black (8%), Asian (7%) or Hispanic (6%). And 1% or fewer are multiracial; American Indian or Alaska Native; or Native Hawaiian or other Pacific Islander. By 2054, White and Asian adults are projected to make up smaller shares of centenarians (72% and 5%, respectively), while the shares who are Hispanic (11%) or Black (10%) will be larger. (All racial categories here are single-race and non-Hispanic. Hispanics are of any race.)

The U.S. population overall is expected to trend older in the coming decades as life expectancies increase and the birth rate declines. There are currently roughly 62 million adults ages 65 and older living in the U.S., accounting for 18% of the population. By 2054, 84 million adults ages 65 and older will make up an estimated 23% of the population.

Even as the 65-and-older population continues to grow over the next 30 years, those in their 100s are projected to roughly double as a percentage of that age group, increasing from 0.2% of all older Americans in 2024 to 0.5% in 2054.

Centenarians around the world

A chart showing the five countries with the largest centenarian populations.

The world is home to an estimated 722,000 centenarians, according to the United Nations’ population projections for 2024. The U.S. centenarian population is the world’s second largest – the UN estimates it at 108,000, slightly larger than the Census Bureau’s estimate.

Japan is the country with the greatest number of people in their 100s, at 146,000. China (60,000), India (48,000) and Thailand (38,000) round out the top five.

In each of these countries, centenarians make up less than 1% of the overall population, but combined, they account for more than half (55%) of the world’s population ages 100 and older.

Looked at another way, centenarians make up a bigger proportion of the total population in Japan, Thailand and the U.S., and smaller shares in China and India, which have large but relatively young populations. There are about 12 centenarians for every 10,000 people in Japan, five for every 10,000 in Thailand and three for every 10,000 in the U.S. That compares with fewer than one centenarian for every 10,000 people in China and India.

By 2054, the global centenarian population is projected to grow to nearly 4 million. China is expected to have the largest number of centenarians, with 767,000, followed by the U.S., India, Japan and Thailand. As a proportion, centenarians are projected to account for about 49 out of every 10,000 people in Thailand, 40 of every 10,000 in Japan and 14 of every 10,000 in the U.S. Six out of every 10,000 people in China will be centenarians, as will about two of every 10,000 in India.

A map showing that publics in North America, Europe and Asia are projected to see large growth in centenarian populations by 2054.

Older Adults & Aging

Katherine Schaeffer is a research analyst at Pew Research Center

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PDF CHAPTER 1 The Selection of a Research Approach
designs); and specific research methods of data collection, analysis, and interpretation. The selection of a research approach includes the research Learning Objectives 1. Define major research terms used in this book so that you can incorporate them into your projects. 2. Describe the three major methodologies and their differences to
Research Approach
The Three main research approaches are deductive, inductive, and abductive. Deductive Approach. The deductive approach starts with a theory or a hypothesis, and the researcher tests the hypothesis through the collection and analysis of data. The researcher develops a research design and data collection methods based on the theory or hypothesis.
3.5 Quantitative, Qualitative, & Mixed Methods Research Approaches
A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. ...
PDF The Selection of a Research Approach
and interpretation. The selection of a research approach is also based on the nature of the research problem or issue being addressed, the researchers' personal experiences, and the audiences for the study. Thus, in this book, research approaches, research designs, and research methods are three key terms that represent a perspective about
Research Methods--Quantitative, Qualitative, and More: Overview
About Research Methods. This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. As Patten and Newhart note in the book Understanding Research Methods, "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge.
Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
What are Different Research Approaches? Comprehensive Review of
These three research approaches are quantitative, qualitative, and mixed methods that are commonly used by researchers in various research studies. However, with many options regarding the research design, it seems challenging for researchers to select the most appropriate
How To Choose The Right Research Methodology
To choose the right research methodology for your dissertation or thesis, you need to consider three important factors. Based on these three factors, you can decide on your overarching approach - qualitative, quantitative or mixed methods. Once you've made that decision, you can flesh out the finer details of your methodology, such as the ...
What are Different Research Approaches? Comprehensive Review of ...
These three research approaches are quantitative, qualitative, and mixed methods that are commonly used by researchers in various research studies. However, with many options regarding the research design, it seems challenging for researchers to select the most appropriate approach based on the study and realize differences. This study provides ...
Research Approach
Accordingly, approach for the research can be divided into three categories: Deductive approach. Inductive approach. Abductive approach. The relevance of hypotheses to the study is the main distinctive point between deductive and inductive approaches. Deductive approach tests the validity of assumptions (or theories/hypotheses) in hand, whereas ...
Research Methods
Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
(Pdf) Research Approach: an Overview
According to Grover (2015), the three main research approaches are quantitative, qualitative and mixed methods. Tewksbury (2009), describes the qualitative approach as one that seeks to provide ...
What Is Qualitative Research?
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...
Choosing a Qualitative Research Approach
In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question (s), the method (s) of data collection, and how data are analyzed. 4, 5. Go to:
Qualitative Methods in Health Care Research
The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the ...
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
What are Different Research Approaches? Comprehensive Review of
These three research approaches are quantitative, qualitative, and mixed methods that are commonly used by researchers in various research studies. However, with many options regarding the ...
3 Types of research
Thus, applied research involves original research, not just reviewing what others have done, but like secondary research it is motivated to get an answer. The third type is the least common, but is also generally the focus of a textbook like this. Academic research is the type of research that your professors do most of the time.
PDF Chapter 1 The Selection of a Research Approach Do not copy, post or
Three Components Involved in an Approach . Two important components in each definition are that the approach to research involves philosophical assumptions as well as distinct methods or procedures. The broad research approach is the . plan or proposal to conduct research, involves the intersection of philosophy, research designs, and specific ...
Introduction to Research Methods in Psychology
Psychology research can usually be classified as one of three major types. 1. Causal or Experimental Research. When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables.
Research Methods
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Research Methods: What are research methods?
What are research methods. Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic. There are different types of research methods which use different tools for data collection.
Research Methodology
Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect, analyze, and interpret data to answer research questions or solve research problems.
Spatiotemporally resolved colorectal oncogenesis in mini ...
Three-dimensional organoid culture technologies have revolutionized cancer research by allowing for more realistic and scalable reproductions of both tumour and microenvironmental structures1-3.
Designing a tool ensuring older patients the right medication at the
Background On average, older patients use five or more medications daily, increasing the risk of adverse drug reactions, interactions, or medication errors. Healthcare sector transitions increase the risk of information loss, misunderstandings, unclear treatment responsibilities, and medication errors. Therefore, it is crucial to identify possible solutions to decrease these risks. Patients ...
The YOU FIRST Approach to Quality Measurement
Person-Centered Care The YOU FIRST Approach to Quality Measurement. April 18, 2024 · Andy Reynolds. A new kind of quality measure is gaining traction: Person-Centered Outcome (PCO) measures that focus on measuring what matters to patients. Mounting evidence shows PCO measures are feasible and that providers and patients like them.; A recent NCQA webinar discussed encouraging findings from our ...
Minerals
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
An FDA approved device offers a new treatment for ringing in the ears
Fligor was impressed by the results of a clinical trial that found 84% of participants who tried Lenire experienced a significant reduction in symptoms. He became one of the first providers in the ...
Number of people 100 and older is growing in US ...
The number of Americans ages 100 and older is projected to more than quadruple over the next three decades, from an estimated 101,000 in 2024 to about 422,000 in 2054, according to projections from the U.S. Census Bureau. Centenarians currently make up just 0.03% of the overall U.S. population, and they are expected to reach 0.1% in 2054.