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Abstract Data Types (ADT): Defining ‘What’ not ‘How’

Abstract data types define the conceptual framework for 'what' needs to be accomplished but leaves the 'how' of it all up to developers. in this way, adts are very much like upper management—complete with a lack of regard for your personal time..

Abstract Data Types (ADT) are high-level abstractions characterized by a set of objects and related operations. ADTs do not define implementation guidance and therefore afford programmers much freedom while still adhering to general design requirements.

• 1 Highlights
• 2 Introduction
• 3 Characteristics of ADTs
• 4 ADT Operation Types
• 5 ADT Creation
• 6 ADT Examples
• 7 ADT Implementations
• 8 Custom Queue ADT Implementation
• 9 ADTs vs. Objects
• 10 Final Thoughts
• 11 References

ADTs are fundamental underpinnings of Computer Science and direct the development of many modern programming language features. This article will introduce the ADT as a concept, illustrate how ADTs manipulate encapsulated data, and direct development via focusing on high-level outcomes rather than the nitty-gritty details of implementation.

• Defining an ADT by considering their characteristics
• Understanding the algebraic form through which an ADT is defined
• Common ADTs such as Queue, List, Stack, and Tree
• Implementing ADTs as Data Structures
• Custom implementation of a Queue in Python
• Comparing ADTs to Objects

Introduction

ADTs are among the most foundational concepts of computer science and have a wide range of applications. They describe the what of a program’s functionality and leave the how of it all up to developers. In seeking the definition of an abstract data type we can consider each part of the term as follows:

• Abstract: Conceptual rather than concrete
• Data:  Information formatted for use by a computer
• Type: A category of objects with shared characteristics

The terms  Data  and  Type are immediately recognized as related as their use as  Data Type is common among all programming languages. This term is used to describe integers, booleans, floating-point values, and similar built-in features of most modern programming languages. Given this; let us borrow the following definition of abstract data type (Dale, 1996):

A class of objects whose logical behavior is defined by a set of values and a set of operations.

Abstract data types are almost poetically simplistic but can be difficult to grasp without some contrast to other concepts. In this article, we’ll consider the common characteristics of ADTs, how they are defined, implemented, and some examples of a custom implementation. Through this process, the underlying concepts of ADTs should become clearer.

Characteristics of ADTs

ADTs are contrasted by Data Types (DT) such as Integers, Booleans, and Chars. These structures are characterized by their representation of values without operations. A key feature in defining ADTs is their abstraction of operational details from users and developers.

• Encapsulates a Data Type and set of operations to manipulate the Type
• Operations are only defined by inputs and outputs
• Operation details are hidden from the user through encapsulation
• Theoretical in nature
• Define  what is getting done not  how it is getting done
• Allows internal implementation details to change without

An ADT may provide a complex implementation of a basic function of which a developer would be allowed access to the function without necessitating an awareness of its underlying logic.

ADT Operation Types

Abstract Data Types define necessary operations for implementation. These operations fall into three primary categories—and several less common ones. These categories may be referred to by several names depending on context, language, or the mood of a developer on a particular day. Such categories include the following (Nell, 2010):

• Constructors – Initializes an instance of an ADT
• Transformers – Changes the state of an ADT (a.k.a.  mutator )
• Observers – Allows read-only access to ADT values without changing ADT State. a.k.a.  accessors .
• Destructor – Clears the State of an ADT before freeing of memory space
• Iterator  – Allows the processing of ADT components in a one-by-one fashion.

These are by no means an exhaustive list of possible operation types for ADTs. These are, however, quite common. It is also worth noting that ADT operators can fall into multiple categories. For example, an operator that merges two existing lists would be both a constructor and an observer (Thalmann, 1979.)

ADT Creation

With all this talk of ADTs, one might begin to wonder how to create one. Perhaps the best way to discern between ADTs, Data Structures, Objects, and varying implementations is to consider the syntax by which they are created.

The abstraction of ADTs is clearest when examining the syntax—often expressed in algebraic form. Consider the following logical definition of a Queue ADT using algebraic syntax that describes the Signature and the Axioms (Dale, 1996):

The conceptual nature of ADTs is clear here—no common programming language can do anything with this other than throw a syntax error. Between the name, signature, and axioms, however—a developer can implement a language-specific Data Structure in accord with the Queue ADT expressed above.

ADT Examples

Abstract Data Types can be difficult to grasp initially because—well, they’re so  abstract . They come in many forms intended for many purposes and some ADT implementation may differ ever-so-slightly that one can hardly discern them as separate Data Structure types.

To make the collective ADT waters even muddier—there are sub-categories of ADTs such as AGDTs where specific application contrasts them from broader concepts (Thalmann, 1979.) Some common ADTs that are implanted in the standard libraries of many modern programming languages include the following:

These are by no means an exhaustive list and ADT types and implementations can easily number into the dozens , if not hundreds —being separated only by slight nuances. To get a better feel for how an ADT becomes a Data Structure through implementation, let’s consider how we might go about implementing the Queue ADT outlined above in conceptual algebraic form.

ADT Implementations

An ADT is a concept that does not reveal the inner workings or make demands about how these workings are implemented. For example, a Queue is an ADT in concept only though the Java Queue is considered a Data Structure—a.k.a. an implementation of an ADT.

Early computer languages only provided built-in data types. The constructs, such as an int, char, and double allowed developers to create routines that generated desired output by utilizing these features. In many cases, these routines involved an excess of syntax and complex logical flow.

ADTs brought developers the ability to define their own data structures based on what was deemed convenient to their use case. This helped reduce syntax, increase extensibility, and provide a framework for the development of modern software engineering practices such as SOLID .

Custom Queue ADT Implementation

Queue objects can also provide removal access for the last object similar to Stacks. Through such usage, a Queue is considered a First-In-First-Out (FIFO) design. Other variations of the Queue include such ADTs as the Double-Ended Queue (DeQue) where items can be removed from the beginning or end of the contained collection. Let’s take a look at how the Queue ADT from above would be implemented in Python.

Note : This code is available for download via GitHub

Here we add each person to our list, view the list, then proceed to remove each person again—this is where the Queue is notably different from a Stack. The first person added to our Queue object (Bob) is now the first person removed. This illustrates the First-In-First-Out characteristic of our Queue as opposed to a Last-in-First-Out  characteristic of Stacks.

The Python standard library includes a Queue module that provides a robust, thread-safe (appropriate for multithreading) Queue class. This ADT implementation is much more robust than the basic example shown here. Check out the documentation for a better idea of how a Queue is used out in the wild.

Note : We are only implementing the isEmpty() , pop() , and add() aspects here for brevity. The __init__ and __str__ are Python-specific details outside of the scope of our ADT.

ADTs vs. Objects

ADTs are not objects—this is an important point to understand. Both concepts are data abstractions but there are notable differences. The separation of these terms is often argued as being purely semantic reflected by the synonymous use of each in many textbooks.

Generally; ADTs are suited for defining Types where conceptually simple but computationally complex calculations are required and objects are used for more complex concepts are needed with less efficient computation (Cook, 2009.)

In Java, interfaces are generally used to construct ADTs while classes are used to construct Objects. This is not strictly enforced but the abstraction common to interfaces dually represents the conceptual intent of ADTs. The interchangeability of these terms in many textbooks reflects—if nothing else—the nuance by which their separation is defined.

Final Thoughts

Abstract Data Types (ADTs) are high-level concepts that encapsulate a Type or Class and dictate a set of operations or procedures through which data can be manipulated. The ability to define the  what of implementation and not the how allows designers to focus on the big picture rather than be distracted by lower-level details.

ADTs can be a bit heady in definition but in practice, they are almost beautiful in the simplicity through which they direct such immense expression. The Queue, Stack, List, and Tree are found throughout the world of Computer Science and Software Engineering as countless different implementations—yet all find form from the same ADTs.

• Dale, Nell, and Chip Weems. Programming and Problem Solving with C++: Brief Edition . 6th ed., Jones & Bartlett Learning, 2010.
• Thalmann, D Magnenat-Thalmann, N. Design and implementation of abstract graphical data types,” COMPSAC 79. Proceedings. Computer Software and The IEEE Computer Society’s Third International Applications Conference , 1979., 1979, pp. 519-524, doi: 10.1109/CMPSAC.1979.762551.
• Cook, William R. “On Understanding Data Abstraction, Revisited.” Proceeding of the 24th ACM SIGPLAN Conference on Object-Oriented Programming Systems Languages and Applications – OOPSLA 09 , 2009. doi:10.1145/1640089.1640133.
• Dale, Nell, and Henry Walker. Abstract Data Types: Specifications, Implementations, and Applications . D.C. Heath & Company, 1996.

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Abstract Data Types in Object-Capability Systems

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In this article, we will learn about ADT but before understanding what ADT is let us consider different in-built data types that are provided to us. Data types such as int, float, double, long, etc. are considered to be in-built data types and we can perform basic operations with them such as addition, subtraction, division, multiplication, etc. Now there might be a situation when we need operations for our user-defined data type which have to be defined. These operations can be defined only as and when we require them. So, in order to simplify the process of solving problems, we can create data structures along with their operations, and such data structures that are not in-built are known as Abstract Data Type (ADT).

Abstract Data type (ADT) is a type (or class) for objects whose behavior is defined by a set of values and a set of operations. The definition of ADT only mentions what operations are to be performed but not how these operations will be implemented. It does not specify how data will be organized in memory and what algorithms will be used for implementing the operations. It is called “abstract” because it gives an implementation-independent view. 

The process of providing only the essentials and hiding the details is known as abstraction. 

The user of data type does not need to know how that data type is implemented, for example, we have been using Primitive values like int, float, char data types only with the knowledge that these data type can operate and be performed on without any idea of how they are implemented. 

So a user only needs to know what a data type can do, but not how it will be implemented. Think of ADT as a black box which hides the inner structure and design of the data type. Now we’ll define three ADTs namely List ADT, Stack ADT, Queue ADT.

1. List ADT

Vies of list

• The data is generally stored in key sequence in a list which has a head structure consisting of count , pointers and address of compare function needed to compare the data in the list.
• The data node contains the pointer to a data structure and a self-referential pointer which points to the next node in the list.
• The List ADT Functions is given below:
• get() – Return an element from the list at any given position.
• insert() – Insert an element at any position of the list.
• remove() – Remove the first occurrence of any element from a non-empty list.
• removeAt() – Remove the element at a specified location from a non-empty list.
• replace() – Replace an element at any position by another element.
• size() – Return the number of elements in the list.
• isEmpty() – Return true if the list is empty, otherwise return false.
• isFull() – Return true if the list is full, otherwise return false.

2. Stack ADT

View of stack

• In Stack ADT Implementation instead of data being stored in each node, the pointer to data is stored.
• The program allocates memory for the data and address is passed to the stack ADT.
• The head node and the data nodes are encapsulated in the ADT. The calling function can only see the pointer to the stack.
• The stack head structure also contains a pointer to top and count of number of entries currently in stack.
• push() – Insert an element at one end of the stack called top.
• pop() – Remove and return the element at the top of the stack, if it is not empty.
• peek() – Return the element at the top of the stack without removing it, if the stack is not empty.
• size() – Return the number of elements in the stack.
• isEmpty() – Return true if the stack is empty, otherwise return false.
• isFull() – Return true if the stack is full, otherwise return false.

3. Queue ADT

View of Queue

• The queue abstract data type (ADT) follows the basic design of the stack abstract data type.
• Each node contains a void pointer to the data and the link pointer to the next element in the queue. The program’s responsibility is to allocate memory for storing the data.
• enqueue() – Insert an element at the end of the queue.
• dequeue() – Remove and return the first element of the queue, if the queue is not empty.
• peek() – Return the element of the queue without removing it, if the queue is not empty.
• size() – Return the number of elements in the queue.
• isEmpty() – Return true if the queue is empty, otherwise return false.
• isFull() – Return true if the queue is full, otherwise return false.

Features of ADT:

Abstract data types (ADTs) are a way of encapsulating data and operations on that data into a single unit. Some of the key features of ADTs include:

• Abstraction: The user does not need to know the implementation of the data structure only essentials are provided.
• Better Conceptualization: ADT gives us a better conceptualization of the real world.
• Robust: The program is robust and has the ability to catch errors.
• Encapsulation : ADTs hide the internal details of the data and provide a public interface for users to interact with the data. This allows for easier maintenance and modification of the data structure.
• Data Abstraction : ADTs provide a level of abstraction from the implementation details of the data. Users only need to know the operations that can be performed on the data, not how those operations are implemented.
• Data Structure Independence : ADTs can be implemented using different data structures, such as arrays or linked lists, without affecting the functionality of the ADT.
• Information Hiding: ADTs can protect the integrity of the data by allowing access only to authorized users and operations. This helps prevent errors and misuse of the data.
• Modularity : ADTs can be combined with other ADTs to form larger, more complex data structures. This allows for greater flexibility and modularity in programming.

Overall, ADTs provide a powerful tool for organizing and manipulating data in a structured and efficient manner.

Abstract data types (ADTs) have several advantages and disadvantages that should be considered when deciding to use them in software development. Here are some of the main advantages and disadvantages of using ADTs:

Advantages:

• Encapsulation : ADTs provide a way to encapsulate data and operations into a single unit, making it easier to manage and modify the data structure.
• Abstraction : ADTs allow users to work with data structures without having to know the implementation details, which can simplify programming and reduce errors.
• Data Structure Independence : ADTs can be implemented using different data structures, which can make it easier to adapt to changing needs and requirements.
• Information Hiding : ADTs can protect the integrity of data by controlling access and preventing unauthorized modifications.
• Modularity : ADTs can be combined with other ADTs to form more complex data structures, which can increase flexibility and modularity in programming.

Disadvantages:

• Overhead : Implementing ADTs can add overhead in terms of memory and processing, which can affect performance.
• Complexity : ADTs can be complex to implement, especially for large and complex data structures.
• Learning Curve: Using ADTs requires knowledge of their implementation and usage, which can take time and effort to learn.
• Limited Flexibility: Some ADTs may be limited in their functionality or may not be suitable for all types of data structures.
• Cost : Implementing ADTs may require additional resources and investment, which can increase the cost of development.

Overall, the advantages of ADTs often outweigh the disadvantages, and they are widely used in software development to manage and manipulate data in a structured and efficient way. However, it is important to consider the specific needs and requirements of a project when deciding whether to use ADTs.

From these definitions, we can clearly see that the definitions do not specify how these ADTs will be represented and how the operations will be carried out. There can be different ways to implement an ADT, for example, the List ADT can be implemented using arrays, or singly linked list or doubly linked list. Similarly, stack ADT and Queue ADT can be implemented using arrays or linked lists. 

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Non-linear ADTs—Trees

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• Manoochehr Azmoodeh 2  

Part of the book series: Macmillan Computer Science Series

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In linear abstract data types, there exists exactly one previous and one next element for each element of the ADT (except the first and last elements). In non-linear structures, such a linear ordering does not exist among the components of the structure. The first non-linear structure which we shall study is the ADT tree.

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Bibliographic Notes and Further Reading

ACM (1984). ACM-SIGMOID , Vol. 14, No. 2.

Google Scholar  

Adel’son-Velskii, G. M. and Landis, Y. M. (1962). ‘An algorithm for the representation of information’, Soviet Math. , Vol. 3, pp. 1259–1262.

Aho, A. V., Hopcroft, J. E. and Ullman, J. D. (1983). Data Structures and Algorithms , Addison-Wesley, Reading, Massachusetts.

Aho, A V., Sethi, R. and Ullman, J. P. (1986) Compilers — Principles, Techniques and Tools , Addison-Wesley, Reading, Massachusetts.

Bayer, R. and McCreight, C. (1972). ‘Organisation and maintenance of large ordered indexes’, Acta Informatica , Vol. 1, No. 3, pp. 173–189.

Article   Google Scholar  

Comer, D. (1979). ‘The ubiquitous B-tree ’, ACM Computing Surveys , Vol. 11, No. 2, June, p. 121.

Date, C. J. (1985). Introduction to Database Systems, Vol 1 , 4th edition, Addison-Wesley, Reading, Massachusetts.

Eppinger, J. (1983). ‘An empirical study of insertion and deletion in binary search trees’, CACM , Vol. 26, No. 9, September, pp. 663–670.

Gonnet, G. H. (1983). ‘Balancing binary trees by internal path reductions’, CACM , Vol. 26, No. 12, December, pp. 1074–1082.

Stout, Q. F. and Warren, B. L. (1986). ‘Tree rebalancing in optimal time and space’, CACM , Vol. 29, No. 9 pp. 902–908.

Wirth, N. (1976). Algorithms + Data Structures = Programs , Prentice-Hall, Englewood Cliffs, New Jersey.

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Azmoodeh, M. (1990). Non-linear ADTs—Trees. In: Abstract Data Types and Algorithms. Macmillan Computer Science Series. Palgrave, London. https://doi.org/10.1007/978-1-349-21151-7_4

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Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

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• Open access
• Published: 19 April 2024

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia

• Paolo Parini 2 , 3 ,
• Roman Tremmel 4 , 5 ,
• Joseph Loscalzo 6 ,
• Volker M. Lauschke 4 , 5 , 7 ,
• Bradley A. Maron 6 ,
• Paola Paci 8 ,
• Ingemar Ernberg 9 ,
• Nguan Soon Tan 10 , 11 ,
• Zehuan Liao 10 , 9 ,
• Weiyao Yin 1 ,
• Sundararaman Rengarajan 12 ,
• Xuexin Li   ORCID: orcid.org/0000-0001-5824-9720 13 , 14 on behalf of

The SCA Consortium

Genome Biology volume  25 , Article number:  104 ( 2024 ) Cite this article

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Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

The human body is a highly complex system with dynamic cellular infrastructures and networks of biological events. Thanks to the rapid evolution of single-cell technologies, we are now able to describe and quantify different aspects of single cellular activities using various omics techniques [ 1 , 2 , 3 , 4 ]. Observing or integrating multiple molecular layers of single cells has promoted profound discoveries in cellular mechanisms [ 5 , 6 , 7 , 8 ]. To accommodate the exponential growth of single-cell data [ 9 , 10 ] and to provide comprehensive reference catalogs of human cells [ 11 ], many have dedicated to single-cell database or repository constructions [ 9 , 11 , 12 , 13 , 14 , 15 ]. These databases vary in purpose and scope: some served as data repositories for raw/processed data retrieval [ 11 , 12 , 14 ]; quick references to cell type compositions and cellular molecular phenotypes across tissues [ 11 , 16 , 17 ]; summarized published study findings for global cellular queries across tissues or diseases [ 9 , 13 , 18 ]; or simply web-indexed published results [ 19 ]. The aim of these resources is to provide immediate information sharing among the scientific communities and real-time queries of diverse cellular phenotypes, which, in turn, to accelerate research progress and to provide additional research opportunities.

However, majority of these databases often provide simple cellular overviews or signature profiles largely based on single-cell RNA-sequencing (scRNA-seq) data confined to limited multi-omics landscape [ 9 , 11 , 13 , 20 ]. The need for a database capable of conducting in-depth, real-time rapid queries of several single-cell omics at a time across almost all human tissues has not yet been met. This limitation has motivated us to build a one-stop single-cell multi-omics queryable database on top of constructing the multi-tissue and multi-omics human atlas.

Here, we present the Single Cell Atlas (SCA), a single-cell multi-omics map of human tissues, through a comprehensive characterization of molecular phenotypic variations across 125 healthy adult and fetal tissues and eight omics, including five single-cell (sc) omics modalities, i.e., scRNA-seq [ 21 ], scATAC-seq [ 22 ], scImmune profiling [ 23 ], mass cytometry (CyTOF) [ 24 , 25 ], and flow cytometry [ 26 , 27 ]; alongside spatial transcriptomics [ 28 ]; and two bulk omics, i.e., RNA-seq [ 29 ] and whole-genome sequencing (WGS) [ 30 ]. Prior to quality control (QC) filtering, we have collected 67,674,775 cells from scRNA-Seq, 1,607,924 cells from scATAC-Seq, 526,559 clonotypes from scImmune profiling, and 330,912 cells from multimodal scImmune profiling with scRNA-Seq, 95,021,025 cells from CyTOF, and 334,287,430 cells from flow cytometry; 13 tissues from spatial transcriptomics; and 17,382 samples from RNA-seq and 837 samples from WGS. We demonstrated through case studies the inter-/intra-tissue and cell-type variabilities in molecular phenotypes between adult and fetal tissues, immune repertoire variations across different T and B cell types in various tissues, and the interplay between multiple omics in adult and fetal colon tissues. We also exemplified the extensive effects of monocyte chemoattractant family ligands (i.e., the CCL family) [ 31 ] on interactions between fibroblasts and other cell types, which demonstrates its key regulatory role in immune cell recruitment for localized immunity [ 32 , 33 ].

Construction and content

An overview of the multi-omics healthy human map.

We conducted integrative assessments of eight omics types from 125 adult and fetal tissues from published resources and constructed a comprehensive single-cell multi-omics healthy human map termed SCA (Fig.  1 ). Each tissue consisted of at least two omics types, with the colon having the full spectrum of omics layers, which allowed us to investigate extensively the key mechanisms in each molecular layer of colonic tissue. Organs and tissues with at least five omics layers included colon, blood (whole blood and PBMCs), skin, bone marrow, lung, lymph node, muscle, spleen, and uterus (Additional file 2 : Table S1). Overall, the scRNA-seq data set contained the highest number of matching tissues between adult and fetal groups, which allowed us to study the developmental differences between their cell types. For scRNA-seq data, majority of the sample matrices retrieved from published studies have already undergone filtering to eliminate background noise, including low-quality cells which are most probable empty droplets. However, some samples downloaded retained their raw matrix form, which contained a significant amount of background noise. Consequently, before proceeding with any additional QC filtering, we standardized all scRNA-seq data inputs to the filtered matrix format, ensuring that all samples underwent the removal of background noise before further processing (Additional file 2 : Table S2). This preprocessing step resulted in the removal of 61,774,307 cells out of the original 67,674,775 cells in the downloaded scRNA-seq dataset, leaving us with 5,900,468 cells for subsequent QC filtering. Strict QC was then carried out to filter debris, damaged cells, low-quality cells, and doublets for single-cell omics data [ 34 ], as well as low-quality samples for bulk omics data. After QC filtering, 3,881,472 high-quality cells were obtained for scRNA-Seq; 773,190 cells for scATAC-Seq; 209,708 cells for multimodal scImmune profiling with scRNA-seq data; 2,278,550 cells for CyTOF; and 192,925,633 cells for flow cytometry data. For scImmune profiling alone, clonotypes with missing CDR3 sequences and amino acid information were filtered, leaving 167,379 unique clonotypes across 21 tissues in the TCR repertoires and 16 tissues in the BCR repertoires. For RNA-seq and WGS, 163 severed autolysis samples were removed, leaving 16,704 samples for RNA-seq and 837 for genotyping data.

A multi-omics healthy human single-cell atlas. Circos plot depicting the tissues present in the atlas. Tissues belonging to the same organ were placed under the same cluster and marked with the same color. Circles and stars represent adult and fetal tissues, respectively. The size of a circle or a star indicates the number of its omics data sets present in the atlas. The intensity of the heatmap in the middle of the Circos plot represents the cell count for single-cell omics or the sample count for bulk omics. The bar plots on the outer surface of the Circos represent the number of cell types in the scRNA-seq tissues (in blue) or the number of samples in bulk RNA-seq tissues (in red)

Single-cell RNA-sequencing analysis of adult and fetal tissues revealed cell-type-specific developmental differences

In total, out of the 125 adult and fetal tissues from all omics types, the scRNA-seq molecular layer in the SCA consisted of 92 adult and fetal tissues (Additional file 1 : Fig. S1, Additional file 2 : Additional file 2 : Table S1), spanning almost all organs and tissues of the human body. We profiled all cells from scRNA-seq data and annotated 417 cell types at fine granularity, in which we categorized them into 17 major cell type classes (Fig.  2 A). Comparing across tissues, most of them contained stromal cells, endothelial cells, monocytes, epithelial cells, and T cells (Fig.  2 A). Comparing across the cell type classes, epithelial cells constituted the highest cell count proportions, followed by stromal cells, neurons, and immune cells (Fig.  2 A). For adult tissues, most of the cells were epithelial cells, immune cells, and endothelial cells; whereas in fetal tissues, stromal cells, epithelial cells, and hematocytes constituted the largest cell type class proportions. Of these 92 tissues from the scRNA-seq data, we carried out integrative assessments of these tissues (Figs. 2 and 3 ) to study cellular heterogeneities in different developmental stages of the tissues.

scRNA-seq integrative analysis revealed similarity and heterogeneity between adult and fetal tissues. A Clustering of the 417 cell types from scRNA-seq data, consisting of 92 tissues based on their cell type proportion within each tissue group. Cell types were colored based on the cell type class indicated in the legend. The numbers in the bracket represent the cell number within the tissue group. B UMAP of the cells present in the 94 adult and fetal tissues from scRNA-seq data, colored based on their cell type class. C Phylogenetic tree of the adult (left) and fetal (right) cell types. Clustering was performed based on their top regulated genes. The color represents the cell type class. Distinct clusters are outlined in black and labeled

In-depth assessment of the integrated scRNA-seq further revealed inter-and intra-group similarities between adult and fetal tissues. A Chord diagrams of the highly correlated (AUROC > 0.9) adult and fetal cell types. Each connective line in the middle of the diagrams represents the correlation between two cell types. The color represents the cell type class. B Top receptor-ligand interactions between cell type classes in adult tissues (left) and fetal tissues (right). Color blocks on the outer circle represent the cell type class, and the color in the inner circle represents the receptor (blue) and ligand (red). Arrows indicate the direction of receptor-ligand interactions. C 3D tSNE of the integrative analysis between scRNA-seq and bulk RNA-seq tissues. The colors of the solid dots represent cell types in scRNA-seq data, and the colors of the spheres represent tissues of the bulk data. T indicates the T cell cluster, and B indicates the B cell cluster. D Heatmap showing the top DE genes in each cell type class of the adult and fetal tissues. Scaled expression values were used. Color blocks on the top of the heatmap represent cell type classes. Red arrows indicate the selected cell type classes for subsequent analyses. E Top significant GO BP and KEGG pathways for the cell type classes in adult and fetal tissues. The size of the dots represents the significance level. The color represents the cell type class

For each cell type, we performed differential expression (DE) analysis for each tissue to obtain the DE gene (DEG) signature for each cell type. We assessed the global gene expression patterns between cell types across the tissues based on their upregulated genes (Additional file 2 : Table S3) for adult and fetal tissues (Fig.  2 C, Additional file 1 : Fig. S2). In adult tissues, immune cells (i.e., B, T, monocytes, and NK cells) with hematocytes, stromal cells, neurons, endothelial cells, and epithelial cells formed distinct cellular clusters (Fig.  2 C, Additional file 1 : Fig. S2A), demonstrating highly similar DEG signatures within each of these cell type classes, consistent with the clustering patterns in the previous scRNA-seq atlas [ 35 ]. In fetal tissues, segregation is comparatively less distinctive such that only a subgroup of epithelial cells formed a distinct cell type cluster, cells from the immune cell type classes as well as hematocytes coalesced to form another cluster, and stromal cells formed small clusters between other fetal cell types (Fig.  2 C, Additional file 1 : Fig. S2B), which could represent the similarity in gene expression with other cell types during lineage commitment of stromal cell differentiation [ 36 ].

We next investigated the underlying gene regulatory network (GRN) of the transcriptional activities of cell types across adult and fetal tissues [ 37 ]. We identified active transcription factors (TFs) detected for cell types within each tissue (AUROC > 0.1), and based on these TF signatures, we measured similarities between cell types for adult and fetal tissues (Additional file 1 : Fig. S3). For adult tissues, clustering patterns similar to Additional file 1 : Fig. S1A were observed (Fig.  2 C, Additional file 1 : Fig. S3A). In fetal tissues, two unique clusters, including immune cells with hematocytes and stromal cells, were observed (Additional file 1 : Fig. S3B). Higher similarity in transcription regulatory patterns of stromal cells was observed compared to their gene expression patterns. The concordance between gene expression and transcription regulatory patterns within adult and fetal tissues demonstrated a direct and uniform interplay between the two molecular activities. In terms of the varying TF and DEG clustering patterns between adult and fetal tissues, the adult cell types demonstrated more similar transcriptional activities within the cell type classes than the less-differentiated fetal cell types, which shared more common transcriptional activities.

We dissected the correlation pattern of the clusters shown in Fig.  2 C by drawing inferences from their highly correlated (AUROC > 0.9) cell-type pairs (Fig.  3 A). Specifically, for the immune cluster in adult tissues, monocytes accounted for most of the high correlations within the immune cell cluster, followed by T cells (Fig.  3 A). For fetal tissues, a high number of correlations was observed between the immune cells (i.e., mostly monocytes and T cells) and hematocytes (Fig.  3 A), which explained the clustering pattern observed in fetal tissues (Fig.  2 C). For fetal stromal cells, other than with their own cell types, large coexpression patterns were observed with the hematocytes and the epithelial cells, and a smaller proportion of correlations with other clusters (Fig.  3 A), which accounted for the small clusters of stromal cells formed between other cell types (Fig.  2 C, Additional file 1 : Fig. S2B).

To describe possible cellular networking between the cell type class clusters in Fig.  2 C, we inferred cell–cell interactions [ 38 ] based on their gene expression (Additional file 2 : Table S4), and variations between adult and fetal tissues were observed (Fig.  3 B). In adult tissues, many cell type classes displayed interactions with the neurons, in which they networked with epithelial cells through UNC5D/NTN1 interaction; with stromal cells through SORCS3/NGF; with T cells through LRRC4C/NTNG2; etc. (Fig.  3 B). Among the top interactions of fetal tissues, among the top interactions, monocytes actively network with other cells, such as via CCR1/CCL7 with hematocytes, CSF1R/CSF1 with stromal cells, and FPR1/SSA1 with epithelial cells.

We performed a pseudobulk integrative analysis of the cell types of the scRNA-seq data from 19 tissues found in both adult and fetal tissues, with the 54 tissues from the bulk RNA-seq data (Fig.  3 C) to compare single-cell tissues with the corresponding tissues in the bulk datasets. For cell types of scRNA-seq data, adult cell types formed distinct clusters of T cells, B cells, hematocytes, stromal cells, epithelial cells, endothelial cells, and neurons (Fig.  3 C). Fetal cell types, by comparison, formed a unique cluster of cell types separating themselves from adult cell types. Internally, a gradient of cell types from brain tissues to cell types from the digestive system was observed in this fetal cluster. Fusing the bulk tissue-specific RNA-seq data sets with the pseudobulk scRNA-seq cell types gave close proximities of the bulk brain tissues with the pseudobulk brain-specific cell types, such as neurons and astrocytes (Fig.  3 C). Bulk whole blood clustered with pseudobulk hematocytes, and bulk EBV-transformed lymphocytes clustered with pseudobulk B cells. Other distinctive clusters included bulk colon and small intestine clustered with pseudobulk colon- and small intestine-specific epithelial cells, and bulk heart clustered with pseudobulk cardiomyocytes and other muscle cells (Fig.  3 C).

Next, we conducted gene ontology (GO) of biological processes (BPs) and KEGG pathway analyses [ 39 , 40 , 41 , 42 ] of the top upregulated genes of each cell type class cluster (Fig.  3 D) found in Fig.  2 C. Multiple testing correction for each cell type class was performed using Benjamini & Hochberg (BH) false discovery rate (FDR) [ 43 ]. At 5% FDR and average log2-fold-change > 0.25 (ranked by decreasing fold-change), the top three most significant genes of the remaining cell type classes were each scanned through the phenotypic traits from 442 genome-wide association studies (GWAS) and the UK Biobank [ 44 , 45 ] to seek significant genotypic associations of the top genes with diseases and traits. Notably, for GO pathways, the most significant BPs for B and T cells in both adult and fetal tissues were similar (Fig.  3 E). In contrast, epithelial cells and neurons differ in their associated BPs between adult and fetal tissues. For KEGG pathways, adult and fetal tissues shared common top pathways in T cells and in epithelial cells (Fig.  3 E). Among the top genotype–phenotype association results of the top genes (Additional file 1 : Fig. S4), SNP rs2239805 in HLA-DRA of adult monocytes has a high-risk association with primary biliary cholangitis, which is consistent with previous studies showing associations of HLA-DRA or monocytes with the disease [ 46 , 47 , 48 , 49 , 50 ].

Multimodal analysis of scImmune profiling with scRNA-sequencing in multiple tissues

To decipher the immune landscape at the cell type level in the scImmune profiling data, we carried out an integrative in-depth analysis of the immune repertoires with their corresponding scRNA-seq data. The overall landscape of the cell types mainly included clusters of naïve and memory B cells, naïve T/helper T/cytotoxic T cells, NK cells, monocytes, and dendritic cells (Fig.  4 A) and mainly comprised immune repertoires from the blood, cervix, colon, esophagus, and lung (Additional file 1 : Fig. S5). On a global scale, we examined clonal expansions [ 51 , 52 ] in both T and B cells across all tissues. Here, we defined unique clonal types as unique combinations of VDJ genes of the T cell receptor (TCR) chains (i.e., alpha and beta chains) and immunoglobin (Ig) chains on T cells and B cells, respectively. Integrating clonal type information from both the T and B cell repertoires with their scRNA-seq revealed sites of differential clonal expansion in various cell types (Fig.  4 B and C, Additional file 1 : Fig. S5). In T cell repertoires, high proportions of large or hyperexpanded clones were found in terminally differentiated effector memory cells reexpressing CD45RA (Temra) CD8 T cells [ 53 , 54 ] and cytotoxic T cells, and a large proportion of them was found in the lung (Fig.  4 C, Additional file 1 : Fig. S5), which interplays with the highly immune regulatory environment of the lungs to defend against pathogen or microbiota infections [ 55 , 56 ]. MAIT cells [ 57 , 58 ] have also demonstrated their large or high expansions across tissues, especially in the blood, colon, and cervix (Additional file 1 : Fig. S5A), with their main function to protect the host from microbial infections and to maintain mucosal barrier integrity [ 58 , 59 ]. In contrast, single clones were present mostly in naïve helper T cells and naïve cytotoxic T cells. (Additional file 1 : Fig. S5B) and were almost homogeneously across tissues (Fig.  4 C). This observation ensures the availability of high TCR diversity to trigger sufficient immune response for new pathogens [ 60 ]. For the B cell repertoire in blood, most of these immunocytes remained as single clones or small clones, with a small subset of naïve B cells and memory B cells exhibiting medium clonal expansion (Additional file 1 : Fig. S5B).

Multi-modal analysis of scImmune profiling with scRNA-seq revealed a clonotype expansion landscape in six tissues. A tSNE of cell types from the multi-modal tissues of the scImmune-profiling data. Colors represent cell types. Cell clusters were outlined and labeled. B tSNE of cell types from the multi-modal tissues of the scImmune-profiling data. Colors indicate clonal-type expansion groups of the cells. Cells not present in the T or B repertoires are shown in gray (NA group). C Stacked bar plots revealing the clonal expansion landscapes of the T and B cell repertoires across 6 tissues. Colors represent clonal type groups. D Alluvial plot showing the top clonal types in T cell repertoires and their proportions shared across the cell types. Colors represent clonotypes. E Alluvial plot showing the top clonal types in B cell repertoires and their proportions shared across the cell types. Colors represent clonotypes

Among the top clones (Fig.  4 D), TRAV17.TRAJ49.TRAC_TRBV6-5.TRBJ1-1.TRBD1.TRBC1 was present mostly in Temra CD8 T cells and shared the same clonal type sequence with cytotoxic T and helper T cells (Additional file 2 : Table S5). This top clone was found to be highly represented in the lung, and comparatively, other large clones of CD8 T cells were found in the blood (Additional file 1 : Fig. S5C). The top ten clones were found in Temra CD8 T cells of blood and lung tissues and cytotoxic T cells and helper T cells from blood, cervix, and lung tissues (Additional file 1 : Fig. S5C). Some of them exhibited a high prevalence of cell proportions in Temra CD8 T cells (Fig.  4 D). In the B cell repertoire of blood, the top clones were found only in naïve and memory B cells, with similar proportions for each of the top clones (Fig.  4 E).

Multi-omics analysis of colon tissues across five omics data sets

To examine the phenotypic landscapes and interplays between different omics methods and data sets, we carried out an interrogative analysis of colon tissue across five omics data sets, including scRNA-Seq, scATAC-Seq, spatial transcriptomics, RNA-seq, and WGS, to examine the phenotypic landscapes across omics layers and the interplays and transitions between omics layers. In the overview of the transcriptome landscapes in adult and fetal colons (Fig.  5 A and B), the adult colon consisted of a large proportion of immune cells (such as B cells, T cells, and macrophages) and epithelial cells (such as mucin-secreting goblet cells and enterocytes) (Fig.  5 A). In contrast, the fetal colon contained a substantial number (proportion) of mesenchymal stem cells (MSCs), fibroblasts, smooth muscle cells, neurons, and enterocytes and a very small proportion of immune cells (Fig.  5 B).

In-depth scRNA-seq analysis revealed distinct variations between adult and fetal colons. A tSNE of the adult colon; colors represent cell types. B tSNE of the fetal colon; colors represent cell types. C Heatmap showing the correlations of the cell types of the MSC lineage from adult and fetal colons based on their top upregulated genes. The intensity of the heatmap shows the AUROC level between cell types. Color blocks on the top of the heatmap represent classes (first row from the top), cell types (second row), and cell type classes (third row). D Heatmap showing the correlations of the cell types of the MSC lineage from adult and fetal colons based on the expression of the TFs. The intensity of the heatmap shows the AUROC level between cell types. Color blocks on the top of the heatmap represent classes (first row from the top), cell types (second row), and cell type classes (third row). E Pseudotime trajectory of the MSC lineage in the adult colon. The color represents the cell type, and the violin plots represent the density of cells across pseudo-time. F Pseudo-time trajectory of the MSC lineage in the fetal colon. The color represents the cell type, and the violin plots represent the density of cells across pseudotime. G Heatmap showing the pseudotemporal expression patterns of TFs in the lineage transition of MSCs to enterocytes in both adult and fetal colons. Intensity represents scaled expression data. The top 25 TFs for MSCs or their differentiated cells are labeled. H Pseudotemporal expression transitions of the top TFs in the MSC-to-enterocyte transitions for both adult and fetal colons. I Heatmap showing the pseudotemporal expression patterns of TFs in the lineage transition of MSCs to fibroblasts in both adult and fetal colons. Intensity represents scaled expression data. The top 25 TFs for MSCs or their differentiated cells are labeled. J Pseudotemporal expression transitions of the top TFs in the MSC-to-fibroblast transitions for both adult and fetal colons

As there were fewer immune cells observed in the fetal colon as compared to the adult colon, we compared the MSC lineage cell types between the two groups. Based on their differential gene expression signatures (Fig.  5 C) and their TF expression (Fig.  5 D), the highly specialized columnar epithelial cells, enterocytes, for both molecular layers correlated well between adult and fetal colons, unlike other cell types, which did not demonstrate high correlations between their adult and fetal cells. Other than the enterocytes, adult and fetal fibroblasts were highly similar to MSCs in both transcriptomic and regulatory patterns (Fig.  5 C and D). We modeled pseudo-temporal transitions of MSC lineage cells, and similar phenomena were observed (Fig.  5 E and F). Both adult and fetal fibroblasts were pseudotemporally closer to MSCs, and the transitions were much earlier than other cells. Analysis across regulatory, gene expression, and pseudotemporal patterns showed in both adult and fetal colons that fibroblasts were more similar to MSCs phenotypically, as shown in prior literature reports [ 61 , 62 , 63 ] and recently with therapeutic implications [ 64 , 65 ]. In addition, transient phases of cells along the MSC lineage trajectory were observed for enterocytes and goblet cells (Fig.  5 E and F), which demonstrated that these high plasticity cells were at different cell-state transitions before their full maturation, as evident in the literature [ 66 , 67 ]. By contrast, the fetal intestine was more primitive than the adult intestine during fetal development, and as a key cell type in extracellular matrix (ECM) construction [ 68 ], fibroblasts displayed transitional cell stages of cells along the pseudotime trajectory (Fig.  5 F).

Comparing regulatory elements of these transitions demonstrated similarities and differences (Fig.  5 G–J, Additional file 1 : Fig. S6). For MSC-to-enterocyte transitions (Fig.  5 G, Additional file 2 : Table S6), the leading TFs with significant pseudotemporal changes were labeled. The expression E74 Like ETS transcription factor 3, ELF3, which belongs to the epithelium-specific ETS (ESE) subfamily [ 69 ], increased during the transition for both adult and fetal enterocytes (Fig.  5 H, Additional file 2 : Table S6) and as previously demonstrated is important in intestinal epithelial differentiation during embryonic development in mice [ 69 , 70 ]. Conversely, high mobility group box 1, HMGB1 [ 71 ], decreased pseudotemporally for both adult and fetal enterocytes (Fig.  5 H, Additional file 2 : Table S6) and has been shown to inhibit enterocyte migration [ 72 ]. The nuclear orphan receptor, NR2F6, a non-redundant negative regulator of adaptive immunity, [ 73 , 74 ], displayed a comparative decline in expression halfway through the pseudotime transition for adult enterocytes but continued to increase for fetal enterocytes (Fig.  5 H, Additional file 2 : Table S6). Another TF from the ETS family, Spi-B transcription factor, SPIB, also showed differential expression during the transition between adult and fetal enterocytes (Fig.  5 H), which was up-regulated in fetal enterocytes and down-regulated in adult enterocytes, suggesting its potential bi-functional role in enterocyte differentiation in fetal-to-adult transition.

For MSC-to-fibroblast transitions (Fig.  5 I, Additional file 2 : Table S6), TFs such as ARID5B, FOS, FOSB, JUN, and JUNB displayed almost identical trajectory patterns between adult and fetal fibroblasts (Fig.  5 J, Additional file 2 : Table S6). Of these TFs, FOS, FOSB, JUN, and JUNB were shown to be absent in the healthy mucosa transcriptional networks [ 75 ], in line with their observations in Fig.  5 J. By contrast, Bcl-2-associated transcription factor 1, BCLAF1, was pseudotemporally up-regulated in fetal fibroblasts but downregulated in adult fibroblasts. Prior studies showed that knocking out BCLAF1 is embryonic lethal [ 76 , 77 ] and yet could be oncogenic in colon cancer [ 78 ], which could explain the trajectory difference of it in fetal and adult. Other cell types also displayed varying degrees of similarities and differences (Additional file 1 : Fig. S5, Additional file 2 : Table S6).

In scATAC-Sequencing, we examined the contributions of cis -regulatory elements in the adult colon. We identified DA peaks for cell clusters and identified corresponding genes closest to these DA peak regions. Cell type identities were postulated based on the gene activities of the scATAC-Seq data (GSEA) [ 79 , 80 ] (Fig.  6 A). Common cell types were detected in scATAC-Seq compared to scRNA-seq (Figs. 5 A and 6 A). We performed sequence motif analysis to detect regulatory sequences unique to each cell type based on their leading DA peaks; among the top enriched motifs, many of the Myocyte Enhancer Factors such as MEF2B, MEF2C, and MEF2D from cells such as smooth muscle cells and pericytes, were found to be significantly enriched (Fig.  6 B), which were also up-regulated in the scRNASeq findings shown earlier (Additional file 2 : Table S6).

Multi-omics analysis of adult and fetal colon tissues revealed distinct variations between adults and fetuses as well as across omics. A UMAP of cell types present in the scATAC-Seq of the adult colon. Colors represent cell types. B Top enriched motif sequences in cell types of the adult colon scATAC-Seq data. C , D Spatial transcriptomic profiles of adult colon sample 1 ( C ) and sample 2 ( D ). The top TFs were selected, and their spatial expressions were mapped onto the slide images. E , F Top receptor-ligand interactions between cell type classes in colon 1 ( E ) and colon 2 ( F ) of the spatial transcriptomics data. Color blocks on the outer circle represent the cell type class, and the color in the inner circle represents the receptor (blue) and ligand (red). Arrows indicate the direction of receptor-ligand interactions. G , H Top receptor-ligand interactions between cell type classes in the adult colon ( G ) and fetal colon ( H ) of the scRNA-seq data. Color blocks on the outer circle represent the cell type class, and the color in the inner circle represents the receptor (blue) and ligand (red). Arrows indicate the direction of receptor-ligand interactions

We examined the physical landscape of the leading TFs (found in scRNA-Seq and scATAC-Seq) in spatial transcriptomics data from two adult colons [ 5 ]. TFs ELF3 and NR2F6 were expressed generally in many locations in colonic tissue and displayed similar expression patterns for both of the adult colons (Fig.  6 C and D), consistent with significant up-regulation in almost all MSC lineage cell types in the pseudotemporal transitions (Additional file 2 : Table S6). In contrast, SPIB was not up-regulated in general, while displaying higher expression in B cells (Fig.  6 C and D), consistent with its role in adaptive immunity, as previously discussed. For other leading TFs, such as BCLAF1, EPAS1, and PLAG1, there were no clear discrete patterns of expression among the cell types.

To examine how cells interact with one another in spatial transcriptomics of the adult colon, we performed receptor-ligand interaction analysis [ 38 ]. Leading interactions included VIP/VIPR2 and ADCYAP1/VIPR2 interactions between neurons and fibroblasts, the NCAM1/GFRA1 interaction between neuronal cells, as well as LTB/CD40 and LY86/CD180 interactions between B cells (Fig.  6 E, Additional file 2 : Table S7). In colon 2, leading interactions occurred between the B cells and between the B cells and enterocytes or fibroblasts. These included LTB/CD40, APOE/LRP8, LY86/CD180, and VCAM1/ITGB7 between B cells; APOE/VLDLR between B cells (APOE) and enterocytes (VLDLR); and CXCL12/CXCR4, FN1/CD79A, CD34/SELL, and ICAM2/ITGAL between fibroblasts and B cells (Fig.  6 F, Additional file 2 : Table S7).

The same type of analysis was performed on both scRNA-seq from both adult and fetal colons. In the adult colon in scRNA-seq (Fig.  6 G), the fibroblasts comprised the leading interactions with cells such as CD8 T cells (CCL8-ACKR2), with (other) fibroblasts (CCL13-CCR9), goblet cells (CCL13-CCR3), and mast cells (PROC-PROCR). In the fetal colon, leading interaction pairs were derived mostly from fibroblasts and macrophages with other cells (Fig.  6 H, Additional file 2 : Table S7), including C4BPA-CD40 between fibroblasts (C4BPA) and endothelial cells (CD40); CCL24-CCR2 between neuronal cells (CCL24) and macrophages (CCR2); CCL13-CCR1 and MUC7-SELL between goblet cells (CCL13 and MUC7) and macrophages (CCR1 and SELL); and IL21-IL21R between smooth muscle cells (IL21) and macrophages (IL21R). In scRNA-seq of both adult and fetal colons, the active interactions of fibroblasts with other cells based on CCL family ligand-receptor interactions seemed to suggest its key regulatory role in immune cell recruitment in the colon (via the active interaction and activation of monocyte chemoattractants, i.e., the CCL family), consistent with prior publications [ 32 , 33 ].

Comparing the two omics data sets, both colon samples from spatial transcriptomics data shared leading interactions with that of the scRNA-seq from adult and fetal colons (Additional file 2 : Table S7). Between spatial colon 1 and the scRNA-seq fetal colon, common interaction pairs were found between neuronal cells, enterocytes with neurons, and neurons with fibroblasts (Additional file 2 : Table S7). For spatial colon 2, 25 of its 95 top unique interactions were shared with the scRNA-seq adult colon, and 10 were shared with the scRNA-seq fetal colon (Additional file 2 : Table S7). For the scRNA-seq adult colon, 445 of its 852 top unique interactions were found in the scRNA-seq fetal colon. For example, CLEC3A-CLEC10A interactions between macrophages (CLEC10A) and enterocytes (CLEC3A), goblet cells (CLEC3A), or smooth muscle cells (CLEC3A), as well as between macrophages. Among them, the scRNA-seq fetal colon seemed to share the greatest number of cell-type-specific interactions with the other three groups (Additional file 2 : Table S7).

At 1% BH FDR and log2FC > 0.25 for the bulk RNA-seq data in adult transverse colon data, we compared these upregulated genes with the top genes in scRNA-seq and the top genes in expression quantitative trait loci (eQTL) (eGenes) and splicing QTL (sQTL) (sGenes) of WGS of the corresponding transverse colon data (Additional file 1 : Fig. S6). Comparing the top 10 genes of eGenes and sGenes, no common genes were found (Additional file 1 : Figs. S7A and S7B). Comparing the overlapping patterns in bulk transcriptomics with scRNA-seq data, there was a much higher number of overlaps in scRNA-seq with eGenes and sGenes compared to bulk RNA-seq (Additional file 1 : Fig. S7C). We grouped the overlapping genes according to their cell types in scRNA-seq (Additional file 1 : Fig. S7D). In particular, the goblet cells and enterocytes in eGenes were similar in proportion within eGenes for bulk RNA-seq compared to scRNA-Seq. Similar phenomena were observed in sGenes (Additional file 1 : Fig. S7D).

Utility and discussion

User interface (ui) overview.

SCA offers an intuitive, user-friendly interface designed to facilitate seamless navigation and efficient phenotype retrieval by researchers across eight single-cell and bulk omics from 125 healthy adult and fetal tissues. Designed with a focus on user experience, the UI offers intuitive and simple navigations for users to explore complex layers of multi-omics multi-tissue resources. Here is an overview of the SCA UI, (I) Home Page: Landing page of the database to serve as the gateway to the comprehensive features of the SCA, offering users a starting point to dive into the wealth of multi-omics data. (II) About: This section offers a thorough description of the portal, complemented by an introductory video summarizing the key features of the database to provide guidance to new users. (II) Overview: Here, we highlight the diversity of omics data available, providing a snapshot of the various omics types and summarizing key information about each. (IV) Atlas: Features interactive representations of human adult and fetal anatomies, and a gateway for users to explore each tissue in-depth with detailed phenotypes specific to each tissue and their corresponding omics. (V) Query: While the Atlas tab is to showcase comprehensive features in each tissue, the Query tab is dedicated to exploring key phenotypic features across all tissues for different omics types, such as regulon search, receptor-ligand interactions, and clonotype abundance, etc. (VI) Demo: Offers a comprehensive walkthrough of the database, using the adult colon transverse tissue as an illustrative example, to demonstrate the capability of the platform and how users can extract meaningful insights. (VII) Analyze: Provides an extensive suite of tools tailored to assist users in performing single-cell analyses across a wide array of omics, along with rapid plotting tools that allow for the creation of customizable plots quickly and efficiently. (VIII) Download: Provides the option for batch downloads, enabling users to conveniently download the data utilized within the database based on their specific selections. (IX) Sources: Offers detailed information about the origins of the raw data used to construct the database, ensuring transparency and trust in the data provided. (X) Discussion: Facilitates a collaborative community space where users can interact, offer assistance, pose questions, and share feedback and suggestions, enhancing the collective utility of the platform. (XI) News: Keeps users informed about the latest updates, additions, and enhancements to the database, ensuring the SCA community stays abreast of new developments.

Intended uses of the database and envisioned benefits

SCA is crafted to serve as a comprehensive resource in the burgeoning field of single-cell and multi-omics research. Its primary intention is to facilitate a deeper understanding of the cellular complexity and diversity inherent in healthy adult and fetal tissues through simultaneous exploration of multiple omics. Beyond this, SCA aims to serve as a robust analysis platform to support post-quantification analysis of high-throughput single-cell sequencing data. As such, researchers can leverage SCA for comparative studies, hypothesis generation, and validation purposes. The integration of multi-omics data facilitates a deeper understanding of cellular mechanisms, potentially accelerating discoveries in cellular mechanisms, developmental biology, and potential therapeutic targets.

Explicitly, SCA enables scientists to quickly derive insights that would otherwise require extensive time and resources to uncover, thereby speeding up the cycle of hypothesis, experimentation, and conclusion. The database will significantly enhance data accessibility and integration, allowing researchers to easily combine data from different omics types and tissues to obtain a holistic view of cellular functions. This integrative approach is crucial for understanding complex biological systems and for the development of comprehensive models of human health and disease. By cataloging cellular characteristics across a range of tissues and conditions, SCA empowers precision medicine initiatives. It provides a detailed cellular context for phenotypic variations and potential markers at the single-cell level and with bulk level for comparative assessments, supporting the development of potential personalized treatment plans based on cellular profiles.

SCA fosters a collaborative research environment by providing a common platform for scientists from diverse backgrounds with research specialties across tissues, diseases, and omics analysis. It encourages interdisciplinary approaches, connecting researchers from diverse fields and promoting the exchange of knowledge and methodologies. This collaborative ethos is expected to drive forward innovations in research and technology.

Benchmarking with existing databases

Here, we evaluated our SCA database against other existing databases [ 9 , 11 , 13 , 20 , 81 ], emphasizing the distinctive attributes that make SCA stand out (Additional file 2 : Table S8). SCA integrates eight distinct omics types, surpassing the scope of Single Cell Portal (SCP) [ 20 ], Human Cell Atlas (HCA) [ 11 ], GTEx Portal [ 81 ], DISCO [ 9 ], and Panglaodb [ 13 ] in providing a wide-ranging multi-omics platform for exhaustive single-cell omics research. Data accessibility is publicly available for all these platforms, except that GTEx Portal encompassing both public and protected datasets (Additional file 2 : Table S8). SCA is noteworthy for its extensive coverage of eight single-cell and bulk omics over 125 differentiated tissues, established a significant lead over the other portals in terms of omics types. Furthermore, SCA sets a new standard with its unmatched capabilities. Other than the typical representations of cell type proportions and visualizing basic features in cell types, features that are notably limited or absent in SCP, HCA, DISCO, and Panglaodb, such as cell–cell interactions, transcription factor activities, the visualization of regulon modules, motif enrichments, clonotype abundance, detailed repertoire profiles, etc., are areas unaddressed by other databases. SCA is the sole provider of specialized queries targeting various phenotypes across multiple omics (Additional file 2 : Table S8). This specificity of analysis remains unparalleled when juxtaposed with other databases in our comparative cohort. Ultimately, SCA stands out as a premier, all-encompassing resource for the omics research community.

Future development and maintenance

In an effort to ensure the platform remains relevant, up-to-date, and increasingly valuable to the broad spectrum of researchers, we will be implementing annual updates. These will incorporate findings from newly published studies and novel phenotypic analyses gathered over the year. As we strive to continually enrich our platform, these updates will address gaps in tissue representation for each omics type, and simultaneously expand the sample size within each tissue. Our commitment to transparency and traceability is reflected in our approach to versioning. We will systematically denote improvements to the database, including new features and datasets, in an accessible point-form format. Updates will be marked by adjustments to the database accession number, with the current version designated as SCA V1.0.0. In addition to serving as a resource for data and phenotypic features, our ultimate aim is for SCA to function as a user-friendly platform, facilitating rapid access to multi-omics data resources and enabling cross-comparison of user datasets with our own.

Conclusions

Our study establishes a comprehensive evaluation of the healthy human multi-tissue and multi-omics landscape at the single-cell level, culminating in the construction of a multi-omics human map and its accompanying web-based platform SCA. This innovative platform streamlines the delivery of multi-omics insights, potentially reducing costs and accelerating research by obviating the need for extensive data consolidation. The big data framework of SCA facilitates the exploration of a broad spectrum of phenotypic features, offering a more representative snapshot of the study population than traditional single omics or bulk analysis could achieve. This multi-omics approach is poised to be instrumental in unraveling the complexities of multidimensional biological systems, offering a holistic perspective that enhances our understanding of biological phenomena.

Despite its robust capabilities, SCA faces challenges associated with the technological limitations of flow cytometry and CyTOF modalities, which restrict the number of detectable proteins. These constraints complicate the integration of data from different studies. We have consciously chosen not to pursue the imputation of expression values across these datasets due to concerns about reliability. Moving forward, we aim to refine tissue stratification within the portal by introducing more detailed sample classifications, such as sampling sites, age groups, genders across tissues, and for fetal tissues, different developmental stages. This advancement depends on the acquisition of comprehensive data to support more precise and accurate analyses.

SCA is designed not only as a database but as a catalyst for a paradigm shift towards a multi-omics-focused research approach. It encourages the scientific community to embrace a multi-omics perspective in their research, facilitating the generation of new hypotheses and the discovery of novel insights. This platform is expected to foster an environment rich in intellectual exploration, propelling forward the development of groundbreaking research trajectories. In essence, SCA emerges as a pioneering open-access, single-cell multi-omics atlas, offering an in-depth view of healthy human tissues across a wide array of omics disciplines and 125 diverse adult and fetal tissues. It unlocks new avenues for exploration in multi-omics research, positioning itself as a vital tool in advancing our understanding of life sciences. SCA is set to become an invaluable asset in the research community, significantly contributing to advancements in biology and medicine by facilitating a deeper comprehension of complex biological systems.

Availability of data and materials

This paper used and analyzed publicly available data sets and their resource references are available at http://www.singlecellatlas.org . Codes used for the construction of the database, data analysis, and visualization have been deposited on GitHub and can be accessed via https://github.com/eudoraleer/sca and is under the MIT License [ 82 ], and is also on Zenodo at https://zenodo.org/records/10906053 [ 83 ]. Web-based platforms hosting the interactive atlas and database queries are available at https://www.singlecellatlas.org .

Aldridge S, Teichmann SA. Single cell transcriptomics comes of age. Nat Commun. 2020;11:4307.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zhu C, Preissl S, Ren B. Single-cell multimodal omics: the power of many. Nat Methods. 2020;17:11–4.

Article   CAS   PubMed   Google Scholar  

Mimitou EP, Lareau CA, Chen KY, Zorzetto-Fernandes AL, Hao Y, Takeshima Y, Luo W, Huang T-S, Yeung BZ, Papalexi E, et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol. 2021;39:1246–58.

Li X. Harnessing the potential of spatial multiomics: a timely opportunity. Signal Transduct Target Ther. 2023;8:234.

Article   PubMed   PubMed Central   Google Scholar  

Fawkner-Corbett D, Antanaviciute A, Parikh K, Jagielowicz M, Gerós AS, Gupta T, Ashley N, Khamis D, Fowler D, Morrissey E, et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell. 2021;184:810-826.e823.

Miao Z, Humphreys BD, McMahon AP, Kim J. Multi-omics integration in the age of million single-cell data. Nat Rev Nephrol. 2021;17:710–24.

Chappell L, Russell AJC, Voet T. Single-Cell (Multi)omics Technologies. Annu Rev Genomics Hum Genet. 2018;19:15–41.

Li H, Qu L, Yang Y, Zhang H, Li X, Zhang X. Single-cell transcriptomic architecture unraveling the complexity of tumor heterogeneity in distal cholangiocarcinoma. Cell Mol Gastroenterol Hepatol. 2022;13(1592–1609): e1599.

Google Scholar  

Li M, Zhang X, Ang KS, Ling J, Sethi R, Lee NYS, Ginhoux F, Chen J. DISCO: a database of Deeply Integrated human Single-Cell Omics data. Nucleic Acids Res. 2022;50:D596-d602.

Pan L, Mou T, Huang Y, Hong W, Yu M, Li X. Ursa: A comprehensive multiomics toolbox for high-throughput single-cell analysis. Mol Biol Evol. 2023;40(12):msad267.

Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, Bodenmiller B, Campbell P, Carninci P, Clatworthy M, et al. The Human Cell Atlas eLife. 2017;6: e27041.

PubMed   Google Scholar  

Clough E, Barrett T. The gene expression omnibus database. Statistical Genomics: Methods and Protocols. 2016:93–110.

Franzén O, Gan L-M, Björkegren JLM: PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, 2019.

Cummins C, Ahamed A, Aslam R, Burgin J, Devraj R, Edbali O, Gupta D, Harrison PW, Haseeb M, Holt S, et al. The European Nucleotide Archive in 2021. Nucleic Acids Res. 2022;50:D106-d110.

Pan L, Shan S, Tremmel R, Li W, Liao Z, Shi H, Chen Q, Zhang X, Li X. HTCA: a database with an in-depth characterization of the single-cell human transcriptome. Nucleic Acids Res. 2022;51:D1019–28.

Article   PubMed Central   Google Scholar  

Elmentaite R, Domínguez Conde C, Yang L, Teichmann SA. Single-cell atlases: shared and tissue-specific cell types across human organs. Nat Rev Genet. 2022;23:395–410.

Quake SR: A decade of molecular cell atlases. Trends in Genetics 2022.

Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, Bu C, Zhang Z, Zhang Z, Li Y, et al. CancerSCEM: a database of single-cell expression map across various human cancers. Nucleic Acids Res. 2022;50:D1147-d1155.

Ner-Gaon H, Melchior A, Golan N, Ben-Haim Y, Shay T. JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets. J Immunol. 2017;198:3375–9.

Tarhan L, Bistline J, Chang J, Galloway B, Hanna E, Weitz E: Single Cell Portal: an interactive home for single-cell genomics data. bioRxiv 2023.

Kolodziejczyk Aleksandra A, Kim JK, Svensson V, Marioni John C, Teichmann Sarah A. The Technology and Biology of Single-Cell RNA Sequencing. Mol Cell. 2015;58:610–20.

Schwartzman O, Tanay A. Single-cell epigenomics: techniques and emerging applications. Nat Rev Genet. 2015;16:716–26.

Gomes T, Teichmann SA, Talavera-López C. Immunology Driven by Large-Scale Single-Cell Sequencing. Trends Immunol. 2019;40:1011–21.

Cheung RK, Utz PJ. CyTOF—the next generation of cell detection. Nat Rev Rheumatol. 2011;7:502–3.

Spitzer Matthew H, Nolan Garry P. Mass Cytometry: Single Cells. Many Features Cell. 2016;165:780–91.

CAS   PubMed   Google Scholar  

Tian Y, Carpp LN, Miller HER, Zager M, Newell EW, Gottardo R. Single-cell immunology of SARS-CoV-2 infection. Nat Biotechnol. 2022;40:30–41.

McKinnon KM: Flow Cytometry: An Overview. Current Protocols in Immunology 2018, 120:5.1.1–5.1.11.

Rao A, Barkley D, França GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature. 2021;596:211–20.

Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet. 2019;20:631–56.

Ng PC, Kirkness EF. Whole Genome Sequencing. In: Barnes MR, Breen G, editors. Genetic Variation: Methods and Protocols. Totowa, NJ: Humana Press; 2010. p. 215–26.

Chapter   Google Scholar  

Hughes CE, Nibbs RJB. A guide to chemokines and their receptors. Febs j. 2018;285:2944–71.

Stadler M, Pudelko K, Biermeier A, Walterskirchen N, Gaigneaux A, Weindorfer C, Harrer N, Klett H, Hengstschläger M, Schüler J, et al. Stromal fibroblasts shape the myeloid phenotype in normal colon and colorectal cancer and induce CD163 and CCL2 expression in macrophages. Cancer Lett. 2021;520:184–200.

Davidson S, Coles M, Thomas T, Kollias G, Ludewig B, Turley S, Brenner M, Buckley CD. Fibroblasts as immune regulators in infection, inflammation and cancer. Nat Rev Immunol. 2021;21:704–17.

Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573-3587.e3529.

Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, Chen H, Wang J, Tang H, Ge W, et al. Construction of a human cell landscape at single-cell level. Nature. 2020;581:303–9.

Kariminekoo S, Movassaghpour A, Rahimzadeh A, Talebi M, Shamsasenjan K, Akbarzadeh A. Implications of mesenchymal stem cells in regenerative medicine. Artificial Cells, Nanomedicine, and Biotechnology. 2016;44:749–57.

Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine J-C, Geurts P, Aerts J, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14:1083–6.

Cillo AR, Kürten CHL, Tabib T, Qi Z, Onkar S, Wang T, Liu A, Duvvuri U, Kim S, Soose RJ, et al. Immune Landscape of Viral- and Carcinogen-Driven Head and Neck Cancer. Immunity. 2020;52:183-199.e189.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47–e47.

Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49:D545-d551.

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.

The Gene Ontology resource. enriching a GOld mine. Nucleic Acids Res. 2021;49:D325-d334.

Article   Google Scholar  

Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc: Ser B (Methodol). 1995;57:289–300.

Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, Paul DS, Freitag D, Burgess S, Danesh J, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32:3207–9.

Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, Butterworth AS, Staley JR. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35:4851–3.

Ballardini G, Bianchi F, Doniach D, Mirakian R, Pisi E, Bottazzo G. ABERRANT EXPRESSION OF HLA-DR ANTIGENS ON BILEDUCT EPITHELIUM IN PRIMARY BILIARY CIRRHOSIS: RELEVANCE TO PATHOGENESIS. The Lancet. 1984;324:1009–13.

Hirschfield GM, Liu X, Xu C, Lu Y, Xie G, Lu Y, Gu X, Walker EJ, Jing K, Juran BD, et al. Primary Biliary Cirrhosis Associated with HLA, IL12A, and IL12RB2 Variants. N Engl J Med. 2009;360:2544–55.

Peng A, Ke P, Zhao R, Lu X, Zhang C, Huang X, Tian G, Huang J, Wang J, Invernizzi P, et al. Elevated circulating CD14(low)CD16(+) monocyte subset in primary biliary cirrhosis correlates with liver injury and promotes Th1 polarization. Clin Exp Med. 2016;16:511–21.

Chen Y-Y, Arndtz K, Webb G, Corrigan M, Akiror S, Liaskou E, Woodward P, Adams DH, Weston CJ, Hirschfield GM. Intrahepatic macrophage populations in the pathophysiology of primary sclerosing cholangitis. JHEP Reports. 2019;1:369–76.

Olmos JM, García JD, Jiménez A, de Castro S. Impaired monocyte function in primary biliary cirrhosis. Allergol Immunopathol (Madr). 1988;16:353–8.

Britanova OV, Putintseva EV, Shugay M, Merzlyak EM, Turchaninova MA, Staroverov DB, Bolotin DA, Lukyanov S, Bogdanova EA, Mamedov IZ, et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. J Immunol. 2014;192:2689–98.

Borcherding N, Bormann NL, Kraus G. scRepertoire: An R-based toolkit for single-cell immune receptor analysis. F1000Research. 2020;9.

Larbi A, Fulop T. From “truly naïve” to “exhausted senescent” T cells: When markers predict functionality. Cytometry A. 2014;85:25–35.

Article   PubMed   Google Scholar  

Lee S-W, Choi HY, Lee G-W, Kim T, Cho H-J, Oh I-J, Song SY, Yang DH, Cho J-H. CD8⁺ TILs in NSCLC differentiate into TEMRA via a bifurcated trajectory: deciphering immunogenicity of tumor antigens. J Immunother Cancer. 2021;9: e002709.

Chen K, Kolls JK. T Cell-Mediated Host Immune Defenses in the Lung. Annu Rev Immunol. 2013;31:605–33.

Mowat AM, Agace WW. Regional specialization within the intestinal immune system. Nat Rev Immunol. 2014;14:667–85.

Godfrey DI, Koay H-F, McCluskey J, Gherardin NA. The biology and functional importance of MAIT cells. Nat Immunol. 2019;20:1110–28.

Nel I, Bertrand L, Toubal A, Lehuen A. MAIT cells, guardians of skin and mucosa? Mucosal Immunol. 2021;14:803–14.

Legoux F, Salou M, Lantz O. MAIT Cell Development and Functions: the Microbial Connection. Immunity. 2020;53:710–23.

van den Broek T, Borghans JAM, van Wijk F. The full spectrum of human naive T cells. Nat Rev Immunol. 2018;18:363–73.

Soundararajan M, Kannan S. Fibroblasts and mesenchymal stem cells: Two sides of the same coin? J Cell Physiol. 2018;233:9099–109.

Muzlifah AH, Matthew PC, Christopher DB, Francesco D. Mesenchymal stem cells: the fibroblasts’ new clothes? Haematologica. 2009;94:258–63.

Lendahl U, Muhl L, Betsholtz C. Identification, discrimination and heterogeneity of fibroblasts. Nat Commun. 2022;13:3409.

Steens J, Unger K, Klar L, Neureiter A, Wieber K, Hess J, Jakob HG, Klump H, Klein D. Direct conversion of human fibroblasts into therapeutically active vascular wall-typical mesenchymal stem cells. Cell Mol Life Sci. 2020;77:3401–22.

Ichim TE, O’Heeron P, Kesari S. Fibroblasts as a practical alternative to mesenchymal stem cells. J Transl Med. 2018;16:212.

Beumer J, Clevers H. Cell fate specification and differentiation in the adult mammalian intestine. Nat Rev Mol Cell Biol. 2021;22:39–53.

Moor AE, Harnik Y, Ben-Moshe S, Massasa EE, Rozenberg M, Eilam R, Bahar Halpern K, Itzkovitz S. Spatial Reconstruction of Single Enterocytes Uncovers Broad Zonation along the Intestinal Villus Axis. Cell. 2018;175:1156-1167.e1115.

Kendall RT, Feghali-Bostwick CA. Fibroblasts in fibrosis: novel roles and mediators. Front Pharmacol. 2014;5:123.

Oliver JR, Kushwah R, Wu J, Pan J, Cutz E, Yeger H, Waddell TK, Hu J. Elf3 plays a role in regulating bronchiolar epithelial repair kinetics following Clara cell-specific injury. Lab Invest. 2011;91:1514–29.

Ng AYN, Waring P, Ristevski S, Wang C, Wilson T, Pritchard M, Hertzog P, Kola I. Inactivation of the transcription factor Elf3 in mice results in dysmorphogenesis and altered differentiation of intestinal epithelium. Gastroenterology. 2002;122:1455–66.

Chen R, Kang R, Tang D. The mechanism of HMGB1 secretion and release. Exp Mol Med. 2022;54:91–102.

Dai S, Sodhi C, Cetin S, Richardson W, Branca M, Neal MD, Prindle T, Ma C, Shapiro RA, Li B, et al. Extracellular High Mobility Group Box-1 (HMGB1) Inhibits Enterocyte Migration via Activation of Toll-like Receptor-4 and Increased Cell-Matrix Adhesiveness 2. J Biol Chem. 2010;285:4995–5002.

Klepsch V, Gerner RR, Klepsch S, Olson WJ, Tilg H, Moschen AR, Baier G, Hermann-Kleiter N. Nuclear orphan receptor NR2F6 as a safeguard against experimental murine colitis. Gut. 2018;67:1434–44.

Klepsch V, Hermann-Kleiter N, Baier G. Beyond CTLA-4 and PD-1: Orphan nuclear receptor NR2F6 as T cell signaling switch and emerging target in cancer immunotherapy. Immunol Lett. 2016;178:31–6.

Sanz-Pamplona R, Berenguer A, Cordero D, Molleví DG, Crous-Bou M, Sole X, Paré-Brunet L, Guino E, Salazar R, Santos C, et al. Aberrant gene expression in mucosa adjacent to tumor reveals a molecular crosstalk in colon cancer. Mol Cancer. 2014;13:46.

McPherson JP, Sarras H, Lemmers B, Tamblyn L, Migon E, Matysiak-Zablocki E, Hakem A, Azami SA, Cardoso R, Fish J, et al. Essential role for Bclaf1 in lung development and immune system function. Cell Death Differ. 2009;16:331–9.

Aw S. Sun H, Geng Y, Peng Q, Wang P, Chen J, Xiong T, Cao R, Tang J: Bclaf1 is an important NF-κB signaling transducer and C/EBPβ regulator in DNA damage-induced senescence. Cell Death Differ. 2016;23:865–75.

Zhou X, Li X, Cheng Y, Wu W, Xie Z, Xi Q, Han J, Wu G, Fang J, Feng Y. BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells. Nat Commun. 2014;5:4581.

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545–50.

Liberzon A, Subramanian A, Pinchback R. Thorvaldsdottir H, Tamayo P, Mesirov JP: Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27:1739–40.

GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318–30.

Pan L, Parini P, Tremmel R, Loscalzo J, Lauschke VM, Maron BA, Paci P, Ernberg I, Tan NS, Liao Z, Yin W, Rengarajan S, Li X: Single Cell Atlas: a single-cell multi-omics human cell encyclopedia. Github. https://github.com/eudoraleer/sca/ ; 2024.

Pan L, Parini P, Tremmel R, Loscalzo J, Lauschke VM, Maron BA, Paci P, Ernberg I, Tan NS, Liao Z, Yin W, Rengarajan S, Wang ZN, Li X: Single Cell Atlas: a single-cell multi-omics human cell encyclopedia. Zenodo. https://zenodo.org/doi/10.5281/zenodo.10906053 ; 2024.

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Acknowledgements

The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Rackham, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. We would like to thank Vladimir Kuznetsov for his advice on the manuscript, and Liming Zhang and Xueqiang Peng for their help in data handling.

Members of The SCA Consortium

Lu Pan 1 , Paolo Parini 2,3 , Roman Tremmel 4,5 , Joseph Loscalzo 6 , Volker M. Lauschke 4,5,7 , Bradley A. Maron 6 , Paola Paci 8 , Ingemar Ernberg 9 , Nguan Soon Tan 10,11 , Zehuan Liao 9,10 , Weiyao Yin 1 , Sundararaman Rengarajan 12 , Xuexin Li 13,14,*

1 Institute of Environmental Medicine, Karolinska Institutet, Solna, 171 65, Sweden.

2 Cardio Metabolic Unit, Department of Medicine, and Department of Laboratory Medicine, Karolinska Institutet, Stockholm, 141 86, Sweden.

3 Medicine Unit, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, 141 86, Sweden.

4 Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, 70376, Germany.

5 University of Tuebingen, Tuebingen, 72076, Germany.

6 Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.

7 Department of Physiology and Pharmacology, Karolinska Institutet, Solna, 171 65, Sweden.

8 Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, 00185, Italy.

9 Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, 171 65, Sweden.

10 School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.

11 Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore 308232, Singapore.

12 Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, 02115, USA.

13 Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang 110032, China.

14 Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, 171 65, Sweden.

Review history

The review history is available as Additional File 4 .

Peer review information

Veronique van den Berghe was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Open access funding provided by Karolinska Institute. This work is supported by the Karolinska Institute Network Medicine Global Alliance (KI NMA) collaborative grant C24401073 (X.L., L.P.), C62623013 (X.L., L.P.), and C331612602 (X.L., L.P.).

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Institute of Environmental Medicine, Karolinska Institutet, 171 65, Solna, Sweden

Lu Pan & Weiyao Yin

Cardio Metabolic Unit, Department of Medicine, and, Department of Laboratory Medicine , Karolinska Institutet, 141 86, Stockholm, Sweden

Paolo Parini

Theme Inflammation and Ageing, Medicine Unit, Karolinska University Hospital, 141 86, Stockholm, Sweden

Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376, Stuttgart, Germany

Roman Tremmel & Volker M. Lauschke

University of Tuebingen, 72076, Tuebingen, Germany

Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA

Joseph Loscalzo & Bradley A. Maron

Department of Physiology and Pharmacology, Karolinska Institutet, 171 65, Solna, Sweden

Volker M. Lauschke

Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185, Rome, Italy

Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 171 65, Solna, Sweden

Ingemar Ernberg & Zehuan Liao

School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore

Nguan Soon Tan & Zehuan Liao

Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, 308232, Singapore

Nguan Soon Tan

Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, 02115, USA

Sundararaman Rengarajan

Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang, 110032, China

Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 65, Solna, Sweden

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• , Paolo Parini
• , Roman Tremmel
• , Joseph Loscalzo
• , Volker M. Lauschke
• , Bradley A. Maron
• , Paola Paci
• , Ingemar Ernberg
• , Nguan Soon Tan
• , Zehuan Liao
• , Weiyao Yin
• , Sundararaman Rengarajan
•  & Xuexin Li

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Conceptualization, X.L., L.P., and J.L.; methodology, X.L. and L.P.; investigation, X.L., L.P., V.M.L., R.T., and J.L.; analysis and visualization, L.P.; cross-checking and validation, X.L. and L.P.; website construction, L.P., X.L., and R.T.; funding acquisition, X.L. and L.P.; project administration, X.L., L.P., P.P., and V.M.L.; supervision, X.L. and J.L.; writing, L.P. and X.L. All authors edited and reviewed the manuscript.

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Correspondence to Xuexin Li .

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VML is CEO and shareholder of HepaPredict AB, co-founder, and shareholder of PersoMedix AB, and discloses consultancy work for Enginzyme AB. The other authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:.

Figure S1. Sample count in fetal and adult groups across tissues and omics types. Figure S2. Correlations between cell types based on gene expression signatures revealed distinct cell type class clusters. (A-B) Heatmap showing the correlations of the cell types from adult (A) and fetal (B) cell types based on the expression of their top upregulated genes. The intensity of the heatmap shows the AUROC level between cell types. Colour blocks on the top of the heatmap represent tissues (first row from the top), biological systems (second row), cell types (third row) and cell type classes (fourth row). Figure S3. Correlations between cell types based on TF signatures revealed similar clustering patterns. (A-B) Heatmap showing the correlations of the cell types from adult (A) and fetal (B) cell types based on the expression of the TF signatures of each cell type. The intensity of the heatmap shows the AUROC level between cell types. Colour blocks on the top of the heatmap represent tissues (first row from the top), biological systems (second row), cell types (third row) and cell type classes (fourth row). Figure S4. Phenotype or disease trait associations. Forest plot showing the associations of phenotype or disease traits in selected cell type classes of scRNA-seq data for both adult and fetal tissues. The X-axis displays the odds ratio of each trait, and the colors of the points represent cell type classes. Figure S5. Landscape of clonal expansion patterns across tissues. (A) tSNE of the tissues from the multi-modal tissues of the scImmune-profiling data. Colors indicate clonal type expansion groups of the cells. Cells not present in the T or B repertoires are colored gray (NA group). Tissues with too few cells present in the T or B repertoires were filtered (i.e., bile duct and kidney) in the main analysis. (B) Stacked bar plots revealing the overall clonal expansion landscapes of the T and B cell repertoires. Colors represent clonal type groups. (C) Alluvial plot showing the top clonal types in T cell repertoires and their proportions shared across tissues containing these clonotypes. Colors represent clonotypes. Figure S6. Pseudotime heatmaps of MSC lineage cell types in the adult and fetal colon. (A-B) Pseudotime trajectory of each cell type in the MSC lineage of adult (A) and fetal (B) colons. The color represents the cell type, and the violin plots represent the density of cells across pseudotime. Figure S7. Comparison of DE gene overlaps between bulk RNA-seq, scRNA-seq and WGS. (A) Chromosomal positions of the top 10 eGenes in colon transverse bulk RNA-seq data. Gene names and their SNP rsid are shown. (B) Chromosomal positions of the top 10 sGenes in colon transverse bulk RNA-seq data. Gene names and their SNP rsid are shown. (C) Stacked bar plot showing the number of shared DE genes of the bulk RNA-seq data and the scRNA-seq data with the genes of the top eQTLs and sQTLs. The color represents the omics type. (D) Stacked bar plot showing the number of shared DE genes across the bulk RNA-seq data, the scRNA-seq data, genes of the top eQTLs and sQTLs. Colors represent the cell types to which the genes belonged with reference to the DE genes of the cell types in the scRNA-seq data. Fig. S8. Comprehensive workflow for scATAC-Seq data analyses in SCA V1.0.0.

Additional file 2:

Table S1. Cell counts of the adult and fetal tissue groups at each omics level. Table S2. Filtered matrix raw read counts for scRNA-Seq across tissues in both fetal and adult groups. Cell_Count_Filtered_Matrix column represents raw read counts initially obtained from published studies or after filtering for the removal of background noises. Table S3. Statistics of the upregulated genes from adult and fetal tissues, filtered by average Log2FoldChange > 0.25 and adjusted P of 0.05. Clusters represent cell types. Genes were ranked by average log2-fold-change. Table S4. Top receptor–ligand interaction profiles of the cell types in the 38 matching adult and fetal tissues. Interaction analysis was done separately for each tissue, and information on the interaction pairs can be viewed from the first column. Table S5: Top clonotypes (VDJ gene combinations) of each cell type present in the T and B cell repertoires. Table S6. Top TFs in the pseudotime transitions of adult and fetal colon cell types. Table S7 . Top receptor-ligand pairs in spatial transcriptomics of adult colons (colon 1 and colon 2) as well as in scRNA-seq adult and fetal colons. The first column represents the data type to which the interactions belong. Table ranked by decreasing interaction ratios. Table S8 . Comparison of SCA with other single-cell omics databases. Green tick indicates a yes and a red cross indicates a no. Table S9. List of public resources included in the SCA database portal. SCA_PID refers to SCA-designated project identity number (PID).

Additional file 3.

Supplementary Methods.

Additional file 4.

Review history.

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Pan, L., Parini, P., Tremmel, R. et al. Single Cell Atlas: a single-cell multi-omics human cell encyclopedia. Genome Biol 25 , 104 (2024). https://doi.org/10.1186/s13059-024-03246-2

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Data-driven solitons dynamics and parameters discovery in the generalized nonlinear dispersive mKdV-type equation via deep neural networks learning

• Wang, Xiaoli
• Han, Wenjing
• Yan, Zhenya

In this paper, we study the dynamics of data-driven solutions and identify the unknown parameters of the nonlinear dispersive modified KdV-type (mKdV-type) equation based on physics-informed neural networks (PINNs). Specifically, we learn the soliton solution, the combination of a soliton and an anti-soliton solution, the combination of two solitons and one anti-soliton solution, and the combination of two solitons and two anti-solitons solution of the mKdV-type equation by two different transformations. Meanwhile, we learn the data-driven kink solution, peakon solution, and periodic solution using the PINNs method. By utilizing image simulations, we conduct a detailed analysis of the nonlinear dynamical behaviors of the aforementioned solutions in the spatial-temporal domain. Our findings indicate that the PINNs method solves the mKdV-type equation with relative errors of O (10 -3 ) or O (10 -4 ) for the multi-soliton and kink solutions, respectively, while relative errors for the peakon and periodic solutions reach O (10 -2 ) . In addition, the tanh function has the best training effect by comparing eight common activation functions (e.g., ReLU(x ) , ELU(x ) , SiLU(x ) , sigmoid(x ) , swish(x ) , sin(x ) , cos(x ) , and tanh(x ) ). For the inverse problem, we invert the soliton solution and identify the unknown parameters with relative errors reaching O (10 -2 ) or O (10 -3 ) . Furthermore, we discover that adding appropriate noise to the initial condition enhances the robustness of the model. Our research results are crucial for understanding phenomena such as interactions in travelling waves, aiding in the discovery of physical processes and dynamic features in nonlinear systems, which have significant implications in fields such as nonlinear optics and plasma physics.

• Nonlinear dispersive equation;
• Physics-informed neural networks;
• Activation functions;
• Data-driven soliton solutions;
• Data-driven parameter discovery

• Open access
• Published: 24 April 2024

Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran

• Shabnam Iezadi 1 ,
• Kamal Gholipour 1 ,
• Jabraeil Sherbafi 2 ,
• Sama Behpaie 3 ,
• Nazli soltani 2 ,
• Mohsen Pasha 2 &
• Javad Farahishahgoli 2  

BMC Health Services Research volume  24 , Article number:  517 ( 2024 ) Cite this article

Metrics details

This study aimed to assess the service quality (SQ) for Type 2 diabetes mellitus (T2DM) and hypertension in primary healthcare settings from the perspective of service users in Iran.

The Cross-sectional study was conducted from January to March 2020 in urban and rural public health centers in the East Azerbaijan province of Iran. A total of 561 individuals aged 18 or above with either or both conditions of T2DM and hypertension were eligible to participate in the study. The study employed a two-step stratified sampling method in East Azerbaijan province, Iran. A validated questionnaire assessed SQ. Data were analyzed using One-way ANOVA and multiple linear regression statistical models in STATA-17.

Among the 561 individuals who participated in the study 176 (31.3%) were individuals with hypertension, 165 (29.4%) with T2DM, and 220 (39.2%) with both hypertension and T2DM mutually. The participants’ anthropometric indicators and biochemical characteristics showed that the mean Fasting Blood Glucose (FBG) in individuals with T2DM was 174.4 (Standard deviation (SD) = 73.57) in patients with T2DM without hypertension and 159.4 (SD = 65.46) in patients with both T2DM and hypertension. The total SQ scores were 82.37 (SD = 12.19), 82.48 (SD = 12.45), and 81.69 (SD = 11.75) for hypertension, T2DM, and both conditions, respectively. Among people with hypertension and without diabetes, those who had specific service providers had higher SQ scores (b = 7.03; p  = 0.001) compared to their peers who did not have specific service providers. Those who resided in rural areas had lower SQ scores (b = -6.07; p  = 0.020) compared to their counterparts in urban areas. In the group of patients with T2DM and without hypertension, those who were living in non-metropolitan cities reported greater SQ scores compared to patients in metropolitan areas (b = 5.09; p  = 0.038). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score ( P  = 0.018). In the group of people with both hypertension and T2DM, those who had specific service providers had higher SQ scores (b = 8.32; p  < 0.001) compared to the group without specific service providers.

Study reveals gaps in T2DM and hypertension care quality despite routine check-ups. Higher SQ correlates with better self-care. Improving service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

Peer Review reports

Diabetes and hypertension, recognized as major contributors to premature mortality, stand as primary risk factors for heart attacks, strokes, and kidney diseases [ 1 , 2 ]. Diabetes, in particular, may result in blindness and lower limb amputations [ 1 ]. The prevalence of diabetes is on the rise globally, especially in low- and middle-income countries (LMICs), where approximately two-thirds of individuals with hypertension reside [ 3 , 4 ]. Existing literature underscores the high prevalence of Type 2 Diabetes Mellitus (T2DM) and/or hypertension in Iran, akin to other LMICs, posing substantial threats to patients and healthcare systems if not effectively managed [ 4 , 5 , 6 ]. Alarmingly, evidence indicates that the rates of treatment and control for both T2DM and hypertension in Iran are notably lower than in higher-income countries, magnifying the potential for severe consequences [ 4 , 7 ].

The global healthcare community has increasingly emphasized the importance of quality of care since the Institute of Medicine’s landmark publication, “Crossing the Quality Chasm,” urging essential changes to bridge the quality gap by the end of the 21st century [ 8 ]. Despite these efforts, many health systems, particularly those in LMICs, grapple with low-quality care [ 8 ]. Poor quality of care stands out as a significant factor contributing to inadequate control of hypertension and T2DM [ 9 , 10 ]. Studies have consistently shown a positive correlation between receiving high-quality care for diabetic or hypertensive conditions and achieving better health outcomes [ 9 , 10 , 11 ]. Consequently, gaining a deeper understanding of the quality of care provided to patients with T2DM and/or hypertension is crucial for effective community management.

Assessing quality is a foundational step toward enhancing care for individuals with chronic health conditions [ 12 ]. Quality of care can be assessed from various perspectives, including technical and service quality. Technical quality assesses the adherence of services to established guidelines [ 13 ], while service quality examines the overall quality of services provided to patients [ 14 ]. SQ primarily describes how the received care is perceived and influenced by various factors such as physical, social, and cultural contexts, as well as aspects like accessibility, respect, and confidentiality [ 14 ]. Most studies examining the quality of T2DM and/or hypertension care have predominantly focused on technical aspects, with only a handful exploring service quality [ 15 , 16 ]. Notably, despite the higher prevalence of T2DM and hypertension in LMICs, the majority of studies examining service quality for these conditions originate from high-income countries, underscoring the imperative for additional research in LMICs [ 3 , 4 , 17 ]. This study aims to fill this gap by assessing service quality for T2DM and hypertension in primary healthcare settings from the perspective of service users in Iran.

Study design

This cross-sectional study was conducted from January to March 2020 in the East Azerbaijan province of Iran. We adhered to The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines to prepare our study report [ 18 ].

Study settings and participants

The target population included individuals seeking healthcare from health centers in the East-Azerbaijan province of Iran. Eligible participants were aged 18 or above, diagnosed with T2DM and/or hypertension at least 12 months before data collection.

We employed a two-step stratified sampling method. Initially, all 20 districts in East-Azerbaijan province were categorized into metropolitan, densely populated urban, and predominantly rural areas. Subsequently, we randomly selected districts (Tabriz, Marand, Bostanabad, Varzaqhan, Ajabshir) and health centers within those districts. Participants were then randomly selected from lists of eligible individuals in each selected health center.

Sample size calculation

Using the G-Power program (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), we calculated a sample size of 637 based on 95% power, 0.05 α and an effect size of 0.07 to consider the stratified sampling, considering a linear regression test based on a fixed model.

Participants’ recruitment

Health workers in selected centers communicated with potential participants during routine care visits. They explained the study’s purpose, introduced the research team, and obtained written consent from willing participants. To safeguard privacy, participants could complete the anonymous questionnaire in a separate room.

Data collection

Data was collected from January to March 2020 using a standard SQ questionnaire (the validity and reliability were already approved in similar contexts) [ 19 , 20 , 21 ]. The questionnaire included four main parts. The first part consisted of the demographic characteristics (age, gender, place of birth, current residency, language, employment status, health insurance status, and education level). The second part encompassed questions related to disease conditions (medical history, type of treatment, complications, and smoking status), and the third part contained questions related to self-management conditions. The final part included 37 questions in 13 dimensions of service quality (SQ), including choice of care provider (2 questions), communication (4 questions), autonomy (4 questions ), availability of support groups (3 questions), continuum of care (2 questions), basic amenities (4 questions), dignity (4 questions), timeliness (4 questions), safety (2 questions), prevention services (2 questions), accessibility of services (2 questions), confidentiality (2 questions) and dietary counseling (2 questions).

Despite previous validation, the face validity of the questionnaire was reviewed and confirmed by health management specialists and cardiologists at Tabriz University of Medical Sciences, and its reliability was confirmed according to the Cronbach’s alpha coefficient (α = 0.81) in a pilot study on 30 participants. We recruited 13 participants from urban and 17 participants from rural center in pilot study. Cronbach’s alpha coefficient ranged from (α = 0.67) for “timeliness”, to (α = 0.83) for “dietary counseling”. Additionally, according to previous studies, an SQ score of less than “nine” indicates a failure in the quality of care and a significant gap for improvement [ 19 , 20 , 21 ]. We excluded the participants of the pilot study from the main sample size to avoid any bias.

Data analysis

For each question, participants were asked to report the importance of the item and their perception of the quality of care they had received about that item (performance) during the last 12 months. Questions related to the importance of the SQ items were scored on a four-point Likert scale, which was then scaled from 1 to 10 (1 = not important, 3 = somehow important, 6 = important, and 10 = very important). Questions related to the perceived performance of services were also scored on a four-point scale ranging from ‘‘never, sometimes, usually, and always’’ or ‘‘poor, fair, good, and excellent’’. For analysis, this scale was dichotomized, say, 0 = usually/always or good/excellent and 1 = never/sometimes or poor/fair [ 22 , 23 ].

An overall measure of SQ was calculated for each SQ dimension by combining the importance and performance scores using the following formula [ 22 , 23 ]:

Service Quality = 10 – (importance × performance).

SQ score ranges from 0 (worse) to 10 (best). The SQ score of each dimension was calculated as mean SQ scores of that dimension’s questions and total SQ was calculated as mean SQ scores of all 37 questions. Finally, the service quality score was reported on a scale of 0-100.We assessed and confirmed the normality of data by one sample Kolmogorov–Smirnov test ( n  = 561, Z = 0.07, P _Value = 0.06). We reported frequencies and percentages for categorical variables and mean and standard deviation for the numerical variables, including, age and SQ score and its dimensions. We used the One-Way ANOVA test to analyze the differences between the anthropometric indices and biochemical characteristics and dimensions of SQ in categorical variables.

We employed a two-step linear regression analysis as the entering method for our data analysis. Variables identified as related with Service Quality (SQ) in the univariate analysis were included in the multiple linear regression model. The significance thresholds for the entry and removal of variables in the stepwise regression model were set at 0.05 and 0.25, respectively. Additionally, age, education, continuous care by specialists, and self-evaluation of disease were included as control variables.

To ensure the validity of our regression analysis, we conducted several checks. Normality of residuals was assessed and confirmed through the normal probability plot, while the homogeneity of residual variances was verified via the residual versus predicted values plot. We further ensured residual independence and addressed multicollinearity by employing Durbin-Watson Statistics and Variance Inflation Factor, respectively. These steps were taken to fulfill all assumptions of multiple linear regression. Also, reference categories in regression analysis were selected based on the research team’s theoretical interest and previous studies.

Statistical significance was determined at a p -value threshold of < 0.05. The data were meticulously analyzed using the STATA version 17 (StatsCorp, College Station, TX, USA).

Among the 637 contacted patients, an impressive 561 individuals participated in the study, reflecting a robust response rate of 91.1%. The majority of participants were female (69%), hailing from metropolitan areas (36%), predominantly speaking Azeri (94%), unemployed (74%), lacking supplementary health insurance (65%), and reporting illiteracy (41%) (Table  1 ).

he anthropometric indices and biochemical characteristics of the participants revealed a predominant occurrence of overweight status. Notably, the mean Fasting Blood Glucose (FBG) levels in individuals with Type 2 Diabetes Mellitus (T2DM) were elevated, measuring 174.4 (73.57) in patients with T2DM without hypertension and 159.4 (65.46) in patients with both T2DM and Hypertension. Additional details regarding the participants’ anthropometric indices and biochemical characteristics can be found in Table  2 .

Statement of principal findings

In this study, the evaluation of service quality (SQ) for Type 2 Diabetes Mellitus (T2DM) and hypertension in primary healthcare settings in Iran revealed that SQ scores for participants with T2DM without Hypertension, those with hypertension without T2DM, and those with both conditions were at an average level. The primary weaknesses identified in SQ were related to the availability of support groups, self-care training, and dietary counseling.

In our study, participants reported higher scores for “dignity” and “confidentiality” items in service provision compared to the other dimensions of the SQ, while the lowest score was reported for the availability of support groups. The significant role of the support groups in controlling patients with T2DM and/or hypertension, especially in LMICs with a rising burden of diabetes, is well documented. For example, studies have reported that support groups can enhance diabetes knowledge and psychosocial functioning [ 24 , 25 ], improve diabetes outcomes [ 26 , 27 ], and enhance self-management behaviors [ 27 , 28 ]. Therefore, it is of fundamental importance to take advantage of support groups when providing services for patients with diabetes or other chronic health conditions. However, this principle component of care seems to be ignored in the care process of patients with T2DM and/or hypertension in Iran.

In addition to access to support groups, the dimensions of “nutrition counseling”, “disease prevention services”, and “the right to choose service providers” had the lowest scores among all dimensions of SQ in all three groups of patients. However, a strong body of evidence has shown that due to the important role of nutrition interventions in improving glucose metabolism, weight, BMI, and waist circumference in T2DM [ 29 ], nutrition counseling is essential for patients with T2DM [ 30 ]. Other studies, on the other hand, have highlighted the role of social interactions in the effective control of T2DM and/or hypertension and in guiding the self-management tasks. For instance, one study showed that risks of uncontrolled hypertension are lower among those with higher social interactions who discuss their health issues with others in a social group [ 31 ]. Due to the importance of these elements in care process of the patients with T2DM and/or hypertension, it is critical for the health system to employ plans to monitor the performance of the healthcare provider with regard to service quality of chronic health conditions.

To achieve desirable outcomes in treating patients with T2DM and/or hypertension, healthcare providers need to be very concrete about providing self-management and dietary counseling. Moreover, considering the progressive nature of T2DM and hypertension and the need for constant monitoring of progress and any complications of the disease, it is necessary to provide them with accurate training and self-management advice by the service providers. In addition, the authorities of the health system should take measures to continuously evaluate the status of these services and care in the healthcare center.

Based on the results of this study, the patients’ self-care status was not favorable. Poor performance in implementing self-care programs indicates that healthcare providers may have failed to achieve care goals for patients with chronic conditions. The results of the study also showed that generally the better the self-care status the higher the SQ score. This finding may imply that empowered patients can receive better care from service providers [ 32 ].

The results of our study identified that people who received their services from a specific provider reported significantly higher scores for SQ than those without a specific service provider. This highlights the need for stability in service providers, especially when dealing with chronic situations, which require long-term coordination between the patient and the service provider. Receiving services from a specific healthcare provider for chronic health conditions is one of the main elements of the continuum of care [ 33 ]. Studies have shown that continuum of care is connected to greater glycemic control [ 34 , 35 ], improvement of health-related quality of life [ 36 ], and lower odds for mortality in patients with T2DM [ 35 ].

Additionally, based on the results of the current study, patients in small cities reported a higher quality of services than those in rural areas. Aligning with our results, several studies have shown that patients with diabetes in rural areas are less likely to receive adequate and high-quality care compared to their non-rural counterparts [ 37 , 38 ]. A systematic review has summarized several interventions targeting patients, professionals, and health systems to improve the quality of care for patients with diabetes in rural areas, including patient education, clinician education, and electronic patient registry [ 39 ]. Recent studies from LMICs also have reported the improvement of diabetes and/or hypertension care as a result of interventions such as patient education by health workers/nurses [ 40 ] and professionals’ and patients’ joint advocacy for health system reform to improve the access to medication and disease management/prevention services in rural areas [ 41 ].

Implications for policy, practice, and research

The results of this study are crucial for enhancing health authorities’ understanding of the quality of healthcare services for patients with Type 2 Diabetes Mellitus (T2DM) and/or hypertension, along with identifying determinant factors. This knowledge is foundational for initiating improvements in service quality and addressing the specific needs of patients with chronic health conditions. A deep understanding of the healthcare service landscape for patients with chronic health conditions is deemed monumental. This understanding serves as the initial step towards implementing targeted interventions and strategies to enhance the overall quality of services provided to patients. It lays the groundwork for addressing challenges and optimizing care delivery. The emphasis of the World Health Organization (WHO) on universal health coverage and the management of chronic diseases, particularly in developing countries, aligns with the significance of this study’s results. The holistic views presented on the quality of services for individuals with T2DM and/or hypertension, encompassing both rural and urban areas in a Low- and Middle-Income Country (LMIC), contribute to global health priorities. In summary, the study’s implications extend to informing policy decisions, guiding practice improvements, and shaping the trajectory of future research endeavors. The holistic perspective provided by this research contributes to the ongoing global efforts to enhance healthcare services for individuals with chronic conditions, particularly in LMICs.

Limitations

We acknowledge that there are some limitations to this study. First, the main health outcomes of T2DM and hypertension, such as Hemoglobin HA1c and blood pressure, were missed from patients’ medical records and, therefore, were not included in the data analysis. Second, the samples were patients with T2DM and/or hypertension who received healthcare services from the public sector and those who were visited by physicians in their private offices were not included in the study. As a result, we were not able to compare the SQ in the private and public sectors. Despite these limitations, this study could provide more insight into how SQ of T2DM and hypertension may be varied among patients with different characteristics and different geographical residencies.

The results of the current study revealed that even though the primary health system has initiated delivering routine check-ups for patients with T2DM and/or hypertension in primary health centers a decade ago, there is a gap in the quality of services provided. While SQ scores across participant groups were generally average, significant weaknesses were identified in the availability of support groups, self-care training, and dietary counseling. Notably, higher SQ scores correlated with better self-care status, suggesting the importance of patient empowerment in improving care outcomes. Stability in healthcare providers was also highlighted as essential for continuity of care, particularly in managing chronic conditions like T2DM and hypertension. Notably, higher SQ scores correlated with better self-care status, suggesting the importance of patient empowerment in improving care outcomes. Furthermore, disparities in service quality between small cities and rural areas were evident, with rural populations facing greater challenges in accessing adequate care. Addressing these disparities requires targeted interventions such as patient and clinician education initiatives, as well as health system reforms to improve access to medication and disease management services in rural areas. Overall, enhancing service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

The findings regarding self-reported hypertension self-management status indicated that among individuals with hypertension without Type 2 Diabetes Mellitus (T2DM), the majority adhered to the “regular use of prescription drugs” (approximately 94%). Conversely, “regular blood pressure measurement at home” was the least adhered-to item, with an adherence rate of around 61%. In contrast, among patients with both T2DM and hypertension, a substantial proportion reported adherence to a “recommended diet” (approximately 90%) and being “aware of the side effects of high blood pressure” (roughly 88%). The results of Fisher’s Exact Test and Independent Samples Test demonstrated no statistically significant relationship between hypertension self-management status and the presence of T2DM among individuals with hypertension, neither for sub-items nor for the total score. Comprehensive details on the hypertension self-management status of participants are presented in Table  3 .

The self-reported Type 2 Diabetes Mellitus (T2DM) self-management status revealed that the majority of participants adhered to the “regular use of prescription drugs” (approximately 97%). Conversely, “regular glucose measurement at home” emerged as the least adhered-to items, with adherence rates of approximately 58% among patients with T2DM without hypertension and 47% among patients with both T2DM and hypertension. A comprehensive overview of the T2DM self-management status of patients is presented in Table  4 .

Among all 13 dimensions of Service Quality (SQ), confidentiality and dignity exhibited the highest scores across all groups. The total SQ scores were 82.37 (12.19), 82.48 (12.45), and 81.69 (11.75) for hypertension, Type 2 Diabetes Mellitus (T2DM), and both conditions (Hypertension & T2DM), respectively. Notably, there were no statistically significant differences in total SQ scores between the groups ( P  = 0.780). Detailed results of SQ scores for each group are presented in Table  5 .

The Multiple Regression model results unveiled several relationships with Service Quality (SQ) scores in different patient groups. Among individuals with hypertension and without diabetes, those with specific service providers demonstrated higher SQ scores (b = 7.03; p  < 0.001) compared to those without specific service providers. Moreover, individuals in rural areas with hypertension and without diabetes exhibited lower SQ scores (b = -6.07; p  < 0.05) than their urban counterparts.

In the group of patients with Type 2 Diabetes Mellitus (T2DM) and without hypertension, those residing in non-metropolitan cities reported higher SQ scores compared to patients in metropolitan areas (b = 5.09; p  < 0.05). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score ( P  < 0.05).

For people with both hypertension and T2DM, those with specific service providers demonstrated higher SQ scores (b = 8.32; p  < 0.001) compared to those without specific service providers. Patients with both conditions who had a diabetes history of over 10 years exhibited higher SQ scores than those with less than two years of diabetes history (b = 4.47; p  < 0.05).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Body Mass Index

Mean Fasting Blood Glucose

Service Quality

Standard Deviation

Type2 Diabetes Mellitus

Gholipour K, Asghari-Jafarabadi M, Iezadi S, Jannati A, Keshavarz S. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J. 2018;24(8):770–7.

Article   PubMed   Google Scholar  

Khanijahani A, Akinci N, Iezadi S, Priore D. Impacts of high-deductible health plans on patients with diabetes: a systematic review of the literature. Prim Care Diabetes. 2021;15(6):948–57.

World Health Organization. Diabetes: World Health Organization (WHO); 2021 [cited 2022]. Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes .

Aghaei Meybodi HR, Khashayar P, Rezai Homami M, Heshmat R, Larijani B. Prevalence of hypertension in an Iranian population. Ren Fail. 2014;36(1):87–91.

Esteghamati A, Larijani B, Aghajani MH, Ghaemi F, Kermanchi J, Shahrami A, et al. Diabetes in Iran: prospective analysis from First Nationwide Diabetes Report of National Program for Prevention and Control of Diabetes (NPPCD-2016). Sci Rep. 2017;7(1):13461.

Article   PubMed   PubMed Central   Google Scholar  

Undén A-L, Elofsson S, Andréasson A, Hillered E, Eriksson I, Brismar K. Gender differences in self-rated health, quality of life, quality of care, and metabolic control in patients with diabetes. Gend Med. 2008;5(2):162–80.

Zhang Y, Moran AE. Trends in the prevalence, awareness, treatment, and Control of Hypertension among Young Adults in the United States, 1999 to 2014. Hypertension. 2017;70(4):736–42.

Article   CAS   PubMed   Google Scholar  

Committee on Quality of Health Care in America; Institute of Medicine USA. Crossing the Quality Chasm: a New Health System for the 21st Century. Washington (DC): National Academies; 2001.

Google Scholar  

Teh XR, Lim MT, Tong SF, Husin M, Khamis N, Sivasampu S. Quality of hypertension management in public primary care clinics in Malaysia: an update. PLoS ONE. 2020;15(8):e0237083.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Arch G, Mainous I, Koopman RJ, Gill JM, Baker R, Pearson WS. Relationship between continuity of Care and Diabetes Control: evidence from the Third National Health and Nutrition Examination Survey. Am J Public Health. 2004;94(1):66–70.

Article   Google Scholar  

De Berardis G, Pellegrini F, Franciosi M, Belfiglio M, Di Nardo B, Greenfield S, et al. Quality of diabetes care predicts the development of cardiovascular events: results of the QuED study. Nutr Metabolism Cardiovasc Dis. 2008;18(1):57–65.

Moosavi A, Sadeghpour A, Azami-Aghdash S, Derakhshani N, Mohseni M, Jafarzadeh D, et al. Evidence-based medicine among health-care workers in hospitals in Iran: a nationwide survey. J Educ Health Promot. 2020;9:365.

Gholipour K, Tabrizi JS, Asghari Jafarabadi M, Iezadi S, Farshbaf N, Farzam Rahbar F, et al. Customer’s self-audit to improve the technical quality of maternity care in Tabriz: a community trial. EMHJ-Eastern Mediterranean Health J. 2016;22(5):309–17.

Article   CAS   Google Scholar  

Gholipour K, Tabrizi JS, Asghari Jafarabadi M, Iezadi S, Mardi A. Effects of customer self-audit on the quality of maternity care in Tabriz: a cluster-randomized controlled trial. PLoS ONE. 2018;13(10):e0203255.

Arah OA, Roset B, Delnoij DMJ, Klazinga NS, Stronks K. Associations between technical quality of diabetes care and patient experience. Health Expect. 2013;16(4):e136–45.

Corrao G, Rea F, Di Martino M, Lallo A, Davoli M, DlE PlAlma R, et al. Effectiveness of adherence to recommended clinical examinations of diabetic patients in preventing diabetes-related hospitalizations. Int J Qual Health Care. 2019;31(6):464–72.

Konerding U, Bowen T, Elkhuizen SG, Faubel R, Forte P, Karampli E, et al. The impact of accessibility and service quality on the frequency of patient visits to the primary diabetes care provider: results from a cross-sectional survey performed in six European countries. BMC Health Serv Res. 2020;20(1):800.

von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The strengthening the reporting of Observational studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495–9.

Karimi S, Mottaghi P, Shokri A, Yarmohammadian MH, Tabrizi JS, Gholipour K, et al. Service quality for people with rheumatoid arthritis: Iranian patients’ perspective. Int J Health Syst Disaster Manage. 2013;1(4):243.

Tabrizi J, Gholipour K, Alipour R, Farahbakhsh M, Asghari-Jafarabadi M, Haghaei M. Service quality of maternity care from the perspective of pregnant women in tabriz health centers and health posts–2010–2011. Hospital. 2014;12(4):9–18.

Tabrizi J, O’Rourke P, Wilson AJ, Coyne ET. Service quality for type 2 diabetes in Australia: the patient perspective. Diabet Med. 2008;25(5):612–7.

Sixma HJ, Kerssens JJ, Campen Cv, Peters L. Quality of care from the patients’ perspective: from theoretical concept to a new measuring instrument. Health Expect. 1998;1(2):82–95.

van der Eijk I, Sixma H, Smeets T, Veloso FT, Odes S, Montague S, et al. Quality of health care in inflammatory bowel disease: development of a reliable questionnaire (QUOTE-IBD) and first results. Am J Gastroenterol. 2001;96(12):3329–36.

Gilden JL, Hendryx MS, Clar S, Casia C, Singh SP. Diabetes support groups improve Health Care of Older Diabetic patients. J Am Geriatr Soc. 1992;40(2):147–50.

Fongwa MN, dela Cruz FA, Hays RD. African American women’s perceptions of the meaning of support groups for improving adherence to hypertension treatment: a conceptual model. Nurs Open. 2019;6(3):860–70.

Park PH, Wambui CK, Atieno S, Egger JR, Misoi L, Nyabundi JS, et al. Improving Diabetes Management and Cardiovascular Risk factors through peer-led self-management support groups in Western Kenya. Diabetes Care. 2015;38(8):e110–1.

Ozemek C, Phillips SA, Popovic D, Laddu-Patel D, Fancher IS, Arena R, et al. Nonpharmacologic management of hypertension: a multidisciplinary approach. Curr Opin Cardiol. 2017;32(4):381–8.

Tejada-Tayabas LM, Lugo MJR. The role of mutual support groups for the control of diabetes in a Mexican City: achievements and limitations from the patients’ perspective. Health. 2014;6(15):1984.

Razaz JM, Rahmani J, Varkaneh HK, Thompson J, Clark C, Abdulazeem HM. The health effects of medical nutrition therapy by dietitians in patients with diabetes: a systematic review and meta-analysis: Nutrition therapy and diabetes. Prim Care Diabetes. 2019;13(5):399–408.

Mohd Yusof B-N, Yahya NF, Hasbullah FY, Wan Zukiman WZHH, Azlan A, Yi RLX, et al. Ramadan-focused nutrition therapy for people with diabetes: a narrative review. Diabetes Res Clin Pract. 2021;172:108530.

Cornwell EY, Waite LJ. Social Network Resources and Management of Hypertension. J Health Soc Behav. 2012;53(2):215–31.

Derakhshani N, Doshmangir L, Ahmadi A, Fakhri A, Sadeghi-Bazargani H, Gordeev VS. Monitoring process barriers and Enablers towards Universal Health Coverage within the Sustainable Development Goals: a systematic review and content analysis. Clinicoecon Outcomes Res. 2020;12:459–72.

Jafarabadi MA, Gholipour K, Shahrokhi H, Malek A, Ghiasi A, Pourasghari H, et al. Disparities in the quality of and access to services in children with autism spectrum disorders: a structural equation modeling. Archives Public Health. 2021;79(1):58.

Mainous AG, Koopman RJ, Gill JM, Baker R, Pearson WS. Relationship between continuity of Care and Diabetes Control: evidence from the Third National Health and Nutrition Examination Survey. Am J Public Health. 2004;94(1):66–70.

Lustman A, Comaneshter D, Vinker S. Interpersonal continuity of care and type two diabetes. Prim Care Diabetes. 2016;10(3):165–70.

Hänninen J, Takala J, Keinänen-Kiukaanniemi S. Good continuity of care may improve quality of life in type 2 diabetes. Diabetes Res Clin Pract. 2001;51(1):21–7.

Lutfiyya MN, Patel YR, Steele JB, Tetteh BS, Chang L, Aguero C, et al. Are there disparities in diabetes care? A comparison of care received by US rural and non-rural adults with diabetes. Prim Health Care Res Dev. 2009;10(4):320–31.

Chen CC, Chen LW, Cheng SH. Rural–urban differences in receiving guideline-recommended diabetes care and experiencing avoidable hospitalizations under a universal coverage health system: evidence from the past decade. Public Health. 2017;151:13–22.

Ricci-Cabello I, Ruiz-Perez I, Rojas-García A, Pastor G, Gonçalves DC. Improving Diabetes Care in Rural areas: a systematic review and Meta-analysis of Quality Improvement interventions in OECD Countries. PLoS ONE. 2013;8(12):e84464.

Gill GV, Price C, Shandu D, Dedicoat M, Wilkinson D. An effective system of nurse-led diabetes care in rural Africa. Diabet Med. 2008;25(5):606–11.

Chang H, Hawley NL, Kalyesubula R, Siddharthan T, Checkley W, Knauf F, et al. Challenges to hypertension and diabetes management in rural Uganda: a qualitative study with patients, village health team members, and health care professionals. Int J Equity Health. 2019;18(1):38.

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Acknowledgements

We are deeply grateful for the contributions of district health centers’ employees and urban health centers’/posts’ on data collection and we appreciate the financial support of Tabriz University of Medical Sciences, the Office of the Vice-Chancellor for Health at Tabriz University of Medical Sciences. Also, special thanks to all participants for their patience and participation in this study.

This study was funded by Tabriz University of medical science, Approval ID IR.TBZMED.REC.1398.428 (grant number: 61696).

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All authors developed the study design. KG, JS, SB, NS, and MP participated in data collection. SI and KG performed the data synthesis. SI, KG, and SB drafted the manuscript. SI and KG edited the manuscript grammatically. All authors conducted a literature review. All authors read the manuscript and approved it after any comments.

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Correspondence to Kamal Gholipour .

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The Research & Ethics Committee of the Tabriz University of Medical Sciences approved the design and procedure of the study (ethic code: IR.TBZMED.REC.1398.428). Written informed consents were required for all subjects in this study in accordance with the institutional requirements. All participants signed an informed consent form before enrolling into the study. In cases where the participant was illiterate, the content of the form was explained to him and his legally authorized representative or the guardians of the illiterate participants, and in case of consent, the form was signed by them. All methods were performed in accordance with the Ethics Committee requirements.

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Iezadi, S., Gholipour, K., Sherbafi, J. et al. Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran. BMC Health Serv Res 24 , 517 (2024). https://doi.org/10.1186/s12913-024-10854-y

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Received : 14 February 2023

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DOI : https://doi.org/10.1186/s12913-024-10854-y

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