spatial organization in an essay

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9.3 Organizing Your Writing

Learning objectives.

Understand how and why organizational techniques help writers and readers stay focused.
Assess how and when to use chronological order to organize an essay.
Recognize how and when to use order of importance to organize an essay.
Determine how and when to use spatial order to organize an essay.

The method of organization you choose for your essay is just as important as its content. Without a clear organizational pattern, your reader could become confused and lose interest. The way you structure your essay helps your readers draw connections between the body and the thesis, and the structure also keeps you focused as you plan and write the essay. Choosing your organizational pattern before you outline ensures that each body paragraph works to support and develop your thesis.

This section covers three ways to organize body paragraphs:

Chronological order
Order of importance
Spatial order

When you begin to draft your essay, your ideas may seem to flow from your mind in a seemingly random manner. Your readers, who bring to the table different backgrounds, viewpoints, and ideas, need you to clearly organize these ideas in order to help process and accept them.

A solid organizational pattern gives your ideas a path that you can follow as you develop your draft. Knowing how you will organize your paragraphs allows you to better express and analyze your thoughts. Planning the structure of your essay before you choose supporting evidence helps you conduct more effective and targeted research.

Chronological Order

In Chapter 8 “The Writing Process: How Do I Begin?” , you learned that chronological arrangement has the following purposes:

To explain the history of an event or a topic
To tell a story or relate an experience
To explain how to do or to make something
To explain the steps in a process

For example, if you are writing an essay about the history of the airline industry, you would begin with its conception and detail the essential timeline events up until present day. You would follow the chain of events using words such as first , then , next , and so on.

Writing at Work

At some point in your career you may have to file a complaint with your human resources department. Using chronological order is a useful tool in describing the events that led up to your filing the grievance. You would logically lay out the events in the order that they occurred using the key transition words. The more logical your complaint, the more likely you will be well received and helped.

Choose an accomplishment you have achieved in your life. The important moment could be in sports, schooling, or extracurricular activities. On your own sheet of paper, list the steps you took to reach your goal. Try to be as specific as possible with the steps you took. Pay attention to using transition words to focus your writing.

Keep in mind that chronological order is most appropriate for the following purposes:

Writing essays containing heavy research
Writing essays with the aim of listing, explaining, or narrating
Writing essays that analyze literary works such as poems, plays, or books

When using chronological order, your introduction should indicate the information you will cover and in what order, and the introduction should also establish the relevance of the information. Your body paragraphs should then provide clear divisions or steps in chronology. You can divide your paragraphs by time (such as decades, wars, or other historical events) or by the same structure of the work you are examining (such as a line-by-line explication of a poem).

On a separate sheet of paper, write a paragraph that describes a process you are familiar with and can do well. Assume that your reader is unfamiliar with the procedure. Remember to use the chronological key words, such as first , second , then , and finally .

Order of Importance

Recall from Chapter 8 “The Writing Process: How Do I Begin?” that order of importance is best used for the following purposes:

Persuading and convincing
Ranking items by their importance, benefit, or significance
Illustrating a situation, problem, or solution

Most essays move from the least to the most important point, and the paragraphs are arranged in an effort to build the essay’s strength. Sometimes, however, it is necessary to begin with your most important supporting point, such as in an essay that contains a thesis that is highly debatable. When writing a persuasive essay, it is best to begin with the most important point because it immediately captivates your readers and compels them to continue reading.

Some key transitional words you should use with this method of organization are most importantly , almost as importantly , just as importantly , and finally .

During your career, you may be required to work on a team that devises a strategy for a specific goal of your company, such as increasing profits. When planning your strategy you should organize your steps in order of importance. This demonstrates the ability to prioritize and plan. Using the order of importance technique also shows that you can create a resolution with logical steps for accomplishing a common goal.

On a separate sheet of paper, write a paragraph that discusses a passion of yours. Your passion could be music, a particular sport, filmmaking, and so on. Your paragraph should be built upon the reasons why you feel so strongly. Briefly discuss your reasons in the order of least to greatest importance.

Spatial Order

As stated in Chapter 8 “The Writing Process: How Do I Begin?” , spatial order is best used for the following purposes:

Helping readers visualize something as you want them to see it
Evoking a scene using the senses (sight, touch, taste, smell, and sound)
Writing a descriptive essay

Spatial order means that you explain or describe objects as they are arranged around you in your space, for example in a bedroom. As the writer, you create a picture for your reader, and their perspective is the viewpoint from which you describe what is around you.

The view must move in an orderly, logical progression, giving the reader clear directional signals to follow from place to place. The key to using this method is to choose a specific starting point and then guide the reader to follow your eye as it moves in an orderly trajectory from your starting point.

Pay attention to the following student’s description of her bedroom and how she guides the reader through the viewing process, foot by foot.

Attached to my bedroom wall is a small wooden rack dangling with red and turquoise necklaces that shimmer as you enter. Just to the right of the rack is my window, framed by billowy white curtains. The peace of such an image is a stark contrast to my desk, which sits to the right of the window, layered in textbooks, crumpled papers, coffee cups, and an overflowing ashtray. Turning my head to the right, I see a set of two bare windows that frame the trees outside the glass like a 3D painting. Below the windows is an oak chest from which blankets and scarves are protruding. Against the wall opposite the billowy curtains is an antique dresser, on top of which sits a jewelry box and a few picture frames. A tall mirror attached to the dresser takes up most of the wall, which is the color of lavender.

The paragraph incorporates two objectives you have learned in this chapter: using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two work together.

The following are possible transition words to include when using spatial order:

Just to the left or just to the right
On the left or on the right
Across from
A little further down
To the south, to the east, and so on
A few yards away
Turning left or turning right

On a separate sheet of paper, write a paragraph using spatial order that describes your commute to work, school, or another location you visit often.

Collaboration

Please share with a classmate and compare your answers.

Key Takeaways

The way you organize your body paragraphs ensures you and your readers stay focused on and draw connections to, your thesis statement.
A strong organizational pattern allows you to articulate, analyze, and clarify your thoughts.
Planning the organizational structure for your essay before you begin to search for supporting evidence helps you conduct more effective and directed research.
Chronological order is most commonly used in expository writing. It is useful for explaining the history of your subject, for telling a story, or for explaining a process.
Order of importance is most appropriate in a persuasion paper as well as for essays in which you rank things, people, or events by their significance.
Spatial order describes things as they are arranged in space and is best for helping readers visualize something as you want them to see it; it creates a dominant impression.

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Spatial Order Organization: What Is It and How to Use it in Essay Writing

Table of contents

According to Merriam-Webster , spatial order organization is the process of relating to space and the objects in it. In writing, it refers to a method in which ideas and elements are laid down logically according to the sequence of their location or their relationship.

Spatial order organization is like putting a disarray of elements into one specific order. It's like fitting pieces of a puzzle together to create a compelling story.

Yes, something like that.

In this article, you will learn more about what spatial order organization is and how you can use it while writing essays for high school and college.

What is Spatial Order Organization

For any kind of essay assignment, grammar, choice of words, information, and also research findings are the main characteristics that students focus on. Similarly, laying down the information in a logical order is also equally indispensable and shouldn’t be sidelined.

Unlike chronological order and other methods of writing, spatial order does not rely on time but is instead solely dependent on the location of the objects. To implement spatial order in your essays and other writing assignments, your information needs to be in an organized format and should showcase the ideas of the writer in a logical flow. It should convey the exact situation as it was observed the first time. This writing format is mostly used for descriptive essays.

Let’s take a look at a few advantages and disadvantages of spatial order organization.

Advantages of Spatial Order Organization in Writing

There might be times when you would have to describe and involve a lot of objects in your scene setting. In the absence of spatial order, this could lead to confusion for you and your professors, who would not be able to follow the sequence of your description. Spatial order results in clear writing.
A scene can be completely described using logical order. Through this logic, your professors will understand the content of your essay, no matter how complex the event or scene is.
The use of transition and signal words in spatial order essays is highly necessary as they connect all the ideas and details of the essay and enhance the readability of your paper.

Disadvantages of Spatial Order Organization in Writing

Due to the usage of sequence and order in the writing pattern of your essays, reading it can become monotonous. Hence, using it for long texts and essays should be avoided.
When the writer showcases all the elements of the spatial order essay in a particular arrangement, equal weight is distributed to all these elements. So, if the writer wants to emphasize on a specific aspect of the scene, or draw attention to a particular subject in the essay, they would not be able to frame that essay easily.

How to Effectively Use Spatial Order Organization in Writing?

‘Spatial order organization’ sounds fancy, but how do you use it appropriately while writing your essays? Here are six essential tips to help you use them correctly to enhance your writing.

1. Select an interesting topic

To write an attention-grabbing paragraph, you need to write on a subject that converges with the spatial organization. Brainstorming is an excellent method to come up with various topic ideas and figure out a basic content structure.

When you write your ideas down, you visually explore the concepts before making a final decision. Ask yourself a few questions:

What do you want to write about?
How will this be relevant to whoever reads it?
Will this topic be of interest to your readers?

2. Structure an outline

Creating an outline should be your next step, as this will make your content flow, give it direction and make it look more organized.

Don't digress from your chosen topic. Digressing will make you lose focus on your spatial structure and content, which will end up creating a negative impression of your effort.

3. Share references and examples

If you’re finding it difficult to write the content for your chosen topic, use references from the internet that will assist in forming your spatial essay.

In these types of essays, visual cues are extremely important. Since these are descriptive in nature, you would majorly be describing an event, a place, a thing, or a situation. Giving examples and references within the body will guide your readers in understanding and literally visualizing what you want to say.

Ensure that you pick a starting point and an ending point so that you can arrange all the details properly for your body paragraph.

Here’s a useful video by IELTS Liz on how to use examples in your essay

4. Construct the body paragraphs

Structuring your body paragraphs is vital. In spatial-order essays, writers have the freedom to choose any sequence, side, or process.

They can choose not to write the essay from the main character’s point of view as well as start the essay from the ending. But remember that the body paragraphs should provide clear divisions or chronological steps.

To make it easier for your readers to understand the sequence of the essay, transitional words in the body paragraphs are used to create a relationship between the full-blown information present and the concepts that are to be expanded. That gives you a high-scoring essay .

5. Use transition and signal words

The usage of transition words and signal words in spatial-order essays is very crucial. They are necessary to link the ideas of the writer and make the paragraph more coherent.

Transition words and spatial order essays go hand in hand, as they make it easier to interpret the entire scenario that the writers are trying to describe to their readers. They also help in assisting the readers in reading the essay in a spatial, logical order.

Here are some examples of transition words:

In spite of
In contrast to
At the same time

Signal words, on the other hand, are prepositions that denote the location, place, and position of the subject in view. Here are a few examples of spatial order signal words:

Across from
On the left
On the right
Attached to
At the top of
At that point

6. Personalize and make it self-explanatory

As you craft your spatial order essay, add a note of personalization to your paragraphs. Since these kinds of essays are laid down in a particular order and can get predictable, including personal tones and phrases can help your essay become relatable and more enjoyable.

For example, if you are writing about your college dorm room, you need to add a bit of your personality in your writing. Through this, the readers will latch on to your emotions and that personal touch, making it possible for them to visualize every detail from the writer’s point of view.

Thus, they could conjure up a visual description of your dorm room.

Now that you know the methods to keep in mind while creating a spatial order organization essay, here is an example of the same to get a clearer idea:

“ When you walk into my room, the first thing you’ll see is a queen-sized bed with its back against the wall. As you go further, you will notice an indigo carpet on the floor, right at the foot of the bed.

On the right-hand side of the bed, on the wall, you will see eight photographs of my family and friends and a large wall hanging beside it. When you look over to the left side of the bed, you will see a beautiful antique clock and my guitar placed right underneath it.

Facing the exit, there are two brown cupboards on your right and a full-body mirror leaning next to it. ”

In this passage, the reader can clearly visualize where all the items in the room are placed as they have been enlisted in the order of their location. Spatial order essays thus give structure to the description and paint a picture for the readers, making it easier to comprehend.

Spatial order organization is a primal factor for writing descriptive, logically sequenced essays.

There are many who don't focus on the sequence of their essay, and there are many who have never heard of spatial order.

These essential tips will help you use spatial order organization in your essay writing, giving you an edge over other students in class. The logical sequencing will enhance the quality of your essay and is sure to leave your teachers impressed.

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What Is Spatial Order And How To Use It In Essay?

Table of Contents

When it comes to effective writing, high school and college students typically think about aspects like good story content, convincing evidence, appropriate word choice, correct grammar, etc.

But the clear and logical organization is an essential part of effective writing, too.

There are several patterns of logical organization that can be used in writing academic essays and term papers :

Chronological order.
Compare-contrast pattern.
Order of importance.
spatial order.
Problem-solution method.
Cause-effect pattern.
Advantages-disadvantages pattern.
Topical pattern.

There are no strict rules which pattern should be used for an effective conveying of ideas. Think carefully of patterns that make sense, and would help your readers get a better understanding of the information.

Besides, paper organization principles have many variations, and very often, different methods are combined.

Let’s talk of the most commonly used patterns: the spatial order meaning and its function.

Spatial order as a principle of organization

Writing an impressive high school or college academic essay depends on the logical organization of the content. Pro essay writing websites recommend organizing your thoughts is as important as the choice of relevant facts to prove your point of view.

For example, in a description of a scene or setting, a specific logical order helps the audience visualize the scene.

… So, what is spatial order?

It is a type of logical organization pattern that is used in descriptive writing. Its main feature is that things are described as they appear when observed.

This approach makes it easier for writers to paint a vivid picture for readers.

Let’s discuss this concept in detail and begin with spatial order definition.

Spatial order definition

…How would we define spatial order?

It’s a principle of descriptive writing when items are arranged in the order of their physical location or correlation.

This structural order in descriptive paragraphs determines the readers’ perspective and how details are perceived. Focus is on location; time is ignored.

How may this organizational arrangement be used?

In fiction , it is used for descriptions of objects and places; in nonfiction – for describing physical or social phenomena.
Technical writers can use this method for explaining how a mechanism works or how parts of a machine fit together.
Food critics apply this pattern when they review a new restaurant for a description of the dining area.
Architects utilize this order to describe designs of buildings.

What is the spatial order?

…What does spatial order mean?

If talking about descriptions, this method means that the writer explains or describes objects as they are arranged in space. It creates a picture for readers, and the audience’s perspective is the viewpoint from which the writer describes what’s around.

First, the writer has to choose a specific starting point and then create an orderly logical progression by providing readers with directional signals they should follow from place to place.

If the description is complicated, you can use a simple chart or diagram to help your readers understand it quickly.

Spatial writing: the details

A spatial organization of information in paragraphs is also called descriptive writing. It is often used when a narrator wants to describe how something looks. For example, you can use this approach in descriptive essays about a person when you describe someone’s appearance, starting from the feet and moving up to the head.

Take a look at spatial order example which describes various layers of a volcano.

A volcano is a beautiful and dangerous natural phenomenon. The magma chamber, an extremely hot part which people rarely see, is under the Earth surface beneath the bed’s rock. The conduit is running from the volcano’s magma chamber to the crater which is at the top of the volcano.

Logical order of location in space can be used in some narrations, classifications, comparisons, and other forms of expository writings.

Have a look at this description paragraph.

According to annual weather patterns, there are 6 major climate regions. In the extreme northern latitudes, the climate is polar without any plant life. Next to it, there is cold tundra in the extremes of the north of Asia and North America where we can find shrubs and grasses. In the adjacent temperate regions, there are forests with a rich diversity of plants.

Don’t worry if you still have some trouble with a proper understanding of what the spatial order is. You can always count on us and order essay from experienced writers.

What are spatial order signal words?

Here is a short list of spatial order signal words which might indicate that a speaker or a writer is following a spatial organization pattern.

Many of these words are prepositions that are placed at the beginning of sentences to connect them with the ideas expressed in the preceding sentences.

On the left hand
On the right hand
Attached to
At the top of
To the side of
In front of

These words are used to build a vivid picture that the audience can grasp.

Spatial organization in writing: how to..?

The spatial pattern works well when writers want to create mental pictures of something that has various parts distinguished by physical location. You may start by describing a scene, object, or a location as a whole and then focus on specific details in the setting.

This type of organization is the best for describing a setting and scene, but writers can also utilize it for giving directions or instructions.

If you wish to apply this method of organization in your description, you need to make a series of decisions.

First of all, decide what scene you want to describe first. You should take the perspective of your readers and determine what details are the most important and relevant for them.
Then you should think about the aspects of the scene you want to include in your description and describe all the elements in a logical order, choosing a starting point. When utilizing this pattern in essays describing items, writers can start from the left and move gradually to the right or go from top to bottom, from inside to outside or from west to east.
It’s essential to use transition words and phrases to show logical relationships between the details.

Read this description of a room where all the details are described in the order of their location in space.

When you walk in the door of my bedroom, the first thing you notice is a large bed on the wall. In front of my bed, there is a grey carpet on the floor. Above the bed, there is a beautiful picture and an old clock. As you turn to the left, you will see a wardrobe and a big mirror.

This approach to the logical organization has some disadvantages as well. When a writer describes elements in such a way, all of them get equal weight.

But if you want to emphasize one specific aspect of a scene and draw particular attention to something, a spatial pattern makes it hard to achieve that goal so you’d better choose another organization method.

Without a clear organizational pattern, your readers could become confused.

A solid organizational pattern helps your audience see connections and allows them to stay focused. Spatial organization in writing a descriptive essay allows readers visualize something as the writer wants them to see it, by evoking a scene using five senses (sight, taste, smell, touch, and sound).

But you shouldn’t use spatial writing in long text because they may sound monotonous and your readers will be bored. Think about other methods of organizing your ideas.

Sometimes, it is better to use a chronological pattern or even just stream-of-consciousness method to communicate a specific idea in a clear, intelligible manner.

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Spatial Order

Spatial order is best used for the following purposes:

Helping readers visualize something as you want them to see it
Evoking a scene using the senses (sight, touch, taste, smell, and sound)
Writing a descriptive essay

Spatial order means that you explain or describe objects as they are arranged around you in your space, for example in a bedroom. As the writer, you create a picture for your readers, and their perspective is the viewpoint from which you describe what is around you. The view must move in an orderly, logical progression, giving the reader clear directional signals to follow from place to place. The key to this method is to choose a specific starting point and then guide the reader to follow your eye as it moves in an orderly fashion from your starting point. Pay attention to the following student’s description and how she guides the reader through the viewing process, foot by foot.

Sample Spatial Order Paragraph

Attached to my back-bedroom wall is a small wooden rack dangling with red and turquoise necklaces that shimmer as I enter. Just to the right of the rack, billowy white curtains frame a large window with a sill that ends just six inches from the floor. The peace of such an image is a stark contrast to my desk, sitting to the right of the window, layered in textbooks, crumpled papers, coffee cups, and an overflowing ashtray. Turning my head to the right, I see a set of two bare windows that frame the trees outside the glass like a three-dimensional painting. Below the windows is an oak chest from which blankets and scarves are protruding. Against the wall opposite the billowy curtains is an antique dresser, on top of which sits a jewelry box and a few picture frames. A tall mirror attached to the dresser takes up much of the lavender wall.

The paragraph incorporates two objectives covered in this chapter: using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two objectives work together.

The following are possible transition words to include when using spatial order:

Just to the left or just to the right
On the left or on the right
Across from
A little further down
To the south, to the east, and so on
A few yards away
Turning left or turning right

On a separate sheet of paper, write a paragraph using spatial order that describes your commute to work, school, or another location you visit often. Collaboration: Please share with a classmate and compare your answers.

KEY TAKEAWAYS

The way you organize your body paragraphs ensures you and your readers stay focused on and draw connections to your thesis statement.
A strong organizational pattern allows you to articulate, analyze, and clarify your thoughts.
Planning the organizational structure for your essay before you begin to search for supporting evidence helps you conduct more effective and directed research.
Chronological order is most commonly used in expository writing. It is useful for explaining the history of your subject, for telling a story, or for explaining a process.
Order of importance is most appropriate in a persuasion paper as well as for essays in which you rank things, people, or events by their significance.
Spatial order describes things as they are arranged in space and is best for helping readers visualize something as you want them to see it; it creates a dominant impression.

The information in this section comes from Successful College Composition p.24-29: Crowther, Kathryn; Curtright, Lauren; Gilbert, Nancy; Hall, Barbara; Ravita, Tracienne; and Swenson, Kirk, “Successful College Composition” (2016). Galileo: English Open Textbooks . Successful College Composition is a transformation of Writing for Success, a text adapted by The Saylor Foundation under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License without attribution as requested by the work’s original creator or licensee. Kathryn Crowther, Lauren Curtright, Nancy Gilbert, Barbara Hall, Tracienne Ravita, and Kirk Swenson adapted this text under a grant from Affordable Learning Georgia to Georgia Perimeter College (GPC) in 2015.

Spatial Order by Cheryl McCormick; Sue Hank; and Ninna Roth is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is Spatial Organization in Writing? (Spatial Order Explained)

by Antony W

September 5, 2021

There’s more to writing an essay than just doing research, creating an outline, and developing a solid argument around a topic.

Even the logical organization of your thoughts and an accurate sequence of events are significant in coming up with a strong storyline for the topic in question.

So in addition to understanding the basic rules for writing comprehensive essays, it also helps to learn and understand the writing patterns used in essay writing.

There are about eight patterns of organization in essay writing. And the one we’re going to look in this case is the spatial organization.

In this guide, you will learn everything there is to know about spatial organization, from what it is and signal words used to its uses and how to apply it in essay writing.

Let’s get started.

What is Spatial Order in Writing?

Spatial order organization in writing is where an author looks at all the important elements of a given scenario and then orders information based on how people or objects fit together in a given physical space.

In spatial organization, you order the information in a particular order, and this can be either from top to bottom or from left to right.

If anything, spatial organization in writing works as a visual tour guide, giving your readers a clear mental image that allows them to know the exact location of objects.

It’s important to understand that spatial organization doesn’t fit in all types of essay writing. More often than not, it’s a powerful tool to use when writing descriptive essays, especially where you want to convey the relationship between an object to another.

Why is Spatial Order Organization Important?

Spatial organization in writing is important because it allows you to create scenic description, making it easy for your target audience to visualize a concept.

Since spatial organization allows you to use senses, from touch and sound to smell and taste, to make a concept look real, it becomes quite easy for a reader to find the concept more descriptive.

4 Steps to Writing a Spatial Order Essay

Start by brainstorming your topic. This part is a no brainer, so it shouldn’t take an entire afternoon.

You might want to write down your thoughts on paper or a digital notepad. It helps a lot to organize your thoughts in a series. Also, make sure the purpose of the writing is clear from the very beginning.

Research is an important part of writing a spatial essay. You still want to know what other people have written about the subject in question, even if you think you understand it very well.

Take references from previous write-ups, magazines, and articles. Check to make sure that the sources from which you get your information are credible.

College essays must have outlines. Even if you feel like your spatial order assignment is easy to pull off, your instructor expects you to use a standard outline for the assignment.

Again, it’s important to create an outline because it helps to organize your thoughts in a logical order.

Before you create an outline for your spatial order essay, determine how you’d like your information to flow. In spatial writing, you can organize your events to flow from past to present – or include a mix of flashback or surprises.

From brainstorming to research to outlining your thoughts, you now have everything you need to write your spatial organization assignment.

Simply use the information collected from your research to write your paper. Make sure the sources you use in writing are credible.

If you are not sure about the trustworthiness of a source or the accuracy of the information shared, either look into a completely different source or ignore it altogether.

How Many Parts Does a Spatial Order Essay Have?

A spatial order essay has three parts. These are the opening paragraph, the body paragraphs, and the conclusion. As you can see, the outline isn’t any different from that of regular essays. Which means that the writing approach isn’t so much different either.

The introduction should grab the attention of your reader from the get go. Follow the hook with some background information, which should clearly explain the reason for choosing the topic.

The last sentence of your introduction should be the thesis statement of the essay , which explains what the spatial order essay intends to cover.

You will have a minimum of four paragraphs in the body section of the essay. Each paragraph should describe an object in details. And just like paragraph rules in essay states, every paragraph in the body section should cover only one object.

Each paragraph should start with a topic sentence, followed by supporting ideas, and end with a closing link that easily transition to the next subsequent paragraph.

End the essay with a strong conclusion paragraph. There are at least four ways to tackle this section.

First, you can give concluding remarks about the topic without introducing new ideas. Second, you can end the essay with a quote , provided it’s relevant to the topic. Third, you can write a short summary of the whole essay. Or you can simply restate the thesis statement.

Words Used in Spatial Order Essays

There are many signal word that you can use in spatial order writing. Below are some common examples of signal words to use in spatial essay writing.

To the left
To the right
Adjacent to
Parallel to
To the side
Beyond center

This by no means exhausted list of the signal words used in spatial essay writing, but they are the most common ones that you will come across.

Let Us Help You Write Your Spatial Order Essay

Are you struggling to get your spatial order essay written? Or maybe you’re about to run out of time to submit the essay to your instructor for review? You simply have to take advantage of our essay writing service and let us help you complete the assignment in time. Also, don’t forget to contact our support team if you have any question.

About the author

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

Organizing Your Writing

Writing for Success

Learning Objectives

Understand how and why organizational techniques help writers and readers stay focused.
Assess how and when to use chronological order to organize an essay.
Recognize how and when to use order of importance to organize an essay.
Determine how and when to use spatial order to organize an essay.

This section covers three ways to organize body paragraphs:

Chronological order
Order of importance
Spatial order

CHRONOLOGICAL ORDER

Chronological arrangement (also called “time order,”) has the following purposes:

To explain the history of an event or a topic
To tell a story or relate an experience
To explain how to do or to make something
To explain the steps in a process

WRITING AT WORK

Keep in mind that chronological order is most appropriate for the following purposes:

Writing essays containing heavy research
Writing essays with the aim of listing, explaining, or narrating
Writing essays that analyze literary works such as poems, plays, or books

ORDER OF IMPORTANCE

Order of importance is best used for the following purposes:

Persuading and convincing
Ranking items by their importance, benefit, or significance
Illustrating a situation, problem, or solution

Some key transitional words you should use with this method of organization are most importantly, almost as importantly, just as importantly, and finally.

SPATIAL ORDER

Spatial order is best used for the following purposes:

Helping readers visualize something as you want them to see it
Evoking a scene using the senses (sight, touch, taste, smell, and sound)
Writing a descriptive essay

Pay attention to the following student’s description of her bedroom and how she guides the reader through the viewing process, foot by foot.

The paragraph incorporates two objectives you have learned in this chapter: using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two work together.

The following are possible transition words to include when using spatial order:

Just to the left or just to the right
On the left or on the right
Across from
A little further down
To the south, to the east, and so on
A few yards away
Turning left or turning right

Key Takeaways

The way you organize your body paragraphs ensures you and your readers stay focused on and draw connections to, your thesis statement.
A strong organizational pattern allows you to articulate, analyze, and clarify your thoughts.
Planning the organizational structure for your essay before you begin to search for supporting evidence helps you conduct more effective and directed research.
Chronological order is most commonly used in expository writing. It is useful for explaining the history of your subject, for telling a story, or for explaining a process.
Order of importance is most appropriate in a persuasion paper as well as for essays in which you rank things, people, or events by their significance.
Spatial order describes things as they are arranged in space and is best for helping readers visualize something as you want them to see it; it creates a dominant impression.

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Spatial Order: What and How to Use it in Essay Writing

Before reading about spatial order , let’s get into the basics. When it comes to powerful writing, high school and university students usually consider many aspects. Like proper story content, convincing proof, suitable word choice, accurate grammar, etc. But the clear and logical agency is an important part of powerful writing, too.

1 What Is Spatial Order?
2 Advantages of Spatial Order Organization in Writing
3 Disadvantages of Spatial Order Organization in Writing
4.1 1. Select an interesting topic
4.2 2. Structure an outline
4.3 3. Share references and examples
4.4 4. Construct the body paragraphs
4.5 5. Use transition and signal words
4.6 6. Personalize and make it self-explanatory
5 Key Takeaways
6 Conclusion

What Is Spatial Order?

Spatial order definition is that you explain or describe objects as they’re organized around you in your space, for example in a bedroom. As the author, you create a photo for your readers, and their angle is the point of view from that you describe what’s around you.

The view needs to move in an orderly, logical progression, giving the reader clear directional alerts to observe from place to place. The key to this approach is to pick a particular starting point. And then guide the reader to follow your eye because it moves in an orderly style from your starting point.

Pay close attention to the subsequent student’s description and the way she guides the reader through the viewing process, foot by foot. Now you realize what is spatial order . So let’s check some benefits and downsides of spatial order companies.

Advantages of Spatial Order Organization in Writing

There are probably times when you’ll have to describe and involve a number of items in your scene-setting. In the absence of spatial order, this can result in confusion for you and your professors. They could not be capable of following the sequence of your description. Spatial order results in clear writing.
A scene may be absolutely defined using logical order. Through this logic, your professors will recognize the content of your essay. No matter how complicated the occasion or scene is.
The use of transition and signal words in spatial order essays is especially important. Because they join all of the ideas and information of the essay and beautify the readability of your paper.

Disadvantages of Spatial Order Organization in Writing

Due to the use of series and order in the writing sample of your essays, studying it may turn out to be monotonous. Hence, using it for lengthy texts and essays must be avoided.
When the author showcases all of the elements of the spatial order essay in a certain arrangement, the same weight is sent to these kinds of elements. So, if the author desires to emphasize a certain component of the scene, or draw interest to a selected subject in the essay, they could not be able to structure that essay easily.

How to Use Spatial Order Effectively in Writing?

Spatial order definition and organization’ sure sounds fancy, however, how do you operate it as it should be even when writing your essays.

Here are six important recommendations that will help you use them efficaciously to beautify your writing.

How to Use Spatial Order Effectively in Writing

1. Select an interesting topic

To write an attention-grabbing paragraph, you need to write down a topic that converges with the spatial organization. Brainstorming is a fantastic approach to give you various topic ideas and figure out a simple content structure.

When you write your thoughts down, you visually discover the concepts before creating a final decision. Ask yourself some questions:

What do you need to put in writing?
How will this be applicable to whoever reads it?
Will this topic be a hobby for your readers?

2. Structure an outline

Creating an outline must be your subsequent step as this could make your content flow. It offers it a path and makes it appear more organized. Don’t digress from your selected topic thinking about what is spatial order.

Digressing will make you lose attention to your spatial structure and content, which will become creating a bad impact on your effort.

3. Share references and examples

If you’re finding it hard to write down the content for your selected topic, use references from the net in order to assist in forming your spatial essay. In those types of essays , visual cues are extremely important.

Since those are descriptive in nature, you’ll majorly be describing an occasion, a place, an element, or a situation. Giving spatial order examples and references in the body will help your readers in understanding and actually visualize what you need to say.

Ensure that you choose a starting and a finishing point, so you can set up all of the information properly for your body paragraph.

4. Construct the body paragraphs

Structuring your body paragraphs is vital. In spatial order essays, writers have the liberty to pick any sequence, aspect, or process.

They can pick to not write the essay from the principal character’s point of view in addition to starting the essay from the ending. But take into account that the body paragraphs must offer clear divisions or chronological steps.

To make it simpler for your readers to recognize the sequence of the essay, transitional words in the body paragraphs are used to create a connection between the full-blown data present and the standards which are to be expanded. That offers you a high-scoring essay.

5. Use transition and signal words

The utilization of transition and signal words in spatial order essays may be very crucial. They are vital to connect the thoughts of the author and make the paragraph more coherent.

Transition words and spatial order essays move hand in hand, as they make it simpler to interpret the whole situation that the writers are trying to describe to their readers. They additionally assist in supporting the readers to examine the essay in a spatial, logical order.

Here are a few spatial order examples of transition words:

In spite of
In contrast to
At the same time

Signal words however are prepositions that denote the location, place, and position of the issue in view. Here are some examples of spatial order signal words:

At that point
On the left
On the right
Attached to
At the top of
Across from

6. Personalize and make it self-explanatory

As you craft your spatial order essay, add a note of personalization for your paragraphs. Since those types of essays are laid down in a selected order and might get predictable, which includes personal tones and terms can assist your essay to become relatable and more enjoyable.

For instance, in case you are writing about your university dorm room, you want to add a piece of your character to your writing. Through this, the readers will latch on to your feelings. And that personal touch, making it feasible for them to visualize each element from the author’s point of view.

Thus, they could be capable of conjuring up a visual description of your dorm room. Now that you understand the techniques to keep in mind, even as creating a spatial order agency essay, right here is an example of the same to get a clearer idea:

“When you walk into my room, the first thing you’ll see is a queen-sized bed, with its back against the wall. As you cross ahead, you may observe an indigo carpet on the floor, right at the foot of the bed. On the right-hand side of the bed, on the wall, you may see eight pictures of my family and friends and a huge wall hanging beside it. When you look over to the left side of the bed, you may see a lovely vintage clock, and my guitar placed right under it. Facing the exit, there are brown cabinets on your right and a full-frame mirror leaning next to it.”

In this passage, the reader can without a doubt visualize wherein all of the items in the room are located as they were enlisted in the order of their location. Spatial order essays accordingly deliver structure to the description and paint a photograph for the readers, making it simpler to comprehend.

Key Takeaways

The manner you arrange your body paragraphs guarantees you and your readers stay focused and draw connections to your thesis statement .
A solid organizational pattern permits you to articulate, analyze, and clarify your thoughts.
Planning the organizational structure for your essay earlier than you start to look for assisting evidence helps you conduct greater effective and directed research.
Chronological order is most typically utilized in expository writing . It is beneficial for explaining the records of your concern, for telling a tale, or for explaining a process.
Order of significance is most suitable in a persuasion paper in addition to for essays in which you rank things, people, or occasions by their significance.
Spatial order describes things as they’re arranged in space and is fine for supporting readers to visualize something as you need them to see it; it creates a dominant effect.

Spatial order agency is a primal element for writing descriptive, logically sequenced essays. There are many that do not focus on the sequence in their essay, and there are numerous who’ve by no means heard of spatial order.

These important recommendations will assist you to use spatial order agency to write my essay , providing you with an edge over other college students in class. The logical sequencing will beautify the quality of your essay and is sure to leave your instructors impressed.

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V. Process and Organization

5.1 Methods of Organizing Your Writing

Kathryn Crowther; Lauren Curtright; Nancy Gilbert; Barbara Hall; Tracienne Ravita; and Terri Pantuso

Now that you’ve identified your topic, it’s time to focus on how to best organize the information. Keep in mind that the method of organization for essays and paragraphs is just as important as content. When you begin to draft an essay or paragraph, your ideas may seem to flow from your mind in a seemingly random manner. However, your readers, who bring to the table different backgrounds, viewpoints, and ideas, need you to clearly organize these ideas to help them draw connections between the body and the thesis . A solid organizational pattern not only helps readers to process and accept your ideas, but also gives your ideas a path that you can follow as you develop your essay (or paragraph). Knowing how you will organize your paragraphs allows you to better express and analyze your thoughts. In addition, planning the structure of your essay before you choose supporting evidence helps you conduct more effective and targeted research. This section covers three ways to organize both essays and paragraphs: chronological order, order of importance, and spatial order.

Chronological Order

Chronological arrangement has the following purposes:

To explain the history of an event or a topic;
To tell a story or relate an experience;
To explain how to do or to make something;
To explain the steps in a process.

Chronological order is used mostly in expository writing which is a form of writing that narrates, describes, informs, or explains a process. When using chronological order, arrange the events in the order that they actually happened, or will happen if you are giving instructions. This method requires you to use words such as first , second , then , after that , later , and finally . These transitional words guide you and your reader through the paper as you expand your thesis. For example, if you are writing an essay about the history of the airline industry, you would begin with its conception and detail the essential timeline events up until present day. You would follow the chain of events using words such as first, then, next, and so on.

Keep in mind that chronological order is most appropriate for the following purposes:

Writing essays containing heavy research;
Writing essays with the aim of listing, explaining, or narrating;
Writing essays that analyze literary works such as poems, plays, or books.

When using chronological order, your introduction should indicate the information you will cover and should also establish the relevance of the information. Your body paragraphs should then provide clear divisions or steps in chronology. You can divide your paragraphs by time (such as decades, wars, or other historical events) or by the same structure of the work you are examining (such as a line-by-line explication of a poem).

Order of Importance

Order of importance is best used for the following purposes:

Persuading and convincing;
Ranking items by their importance, benefit, or significance;
Illustrating a situation, problem, or solution.

Most essays move from the least to the most important point, and the paragraphs are arranged in an effort to build the essay’s strength. Sometimes, however, it is necessary to begin with the most important supporting point, such as in an essay that contains a thesis that is highly debatable. When writing a persuasive essay, it is best to begin with the most important point because it immediately captivates your readers and compels them to continue reading.

For example, if you were supporting your thesis that homework is detrimental to the education of high school students, you would want to present your most convincing argument first, and then move on to the less important points for your case. During your career, you may be required to work on a team that devises a strategy for a specific goal of your company, such as increasing profits. When planning your strategy you should organize your steps in order of importance. This demonstrates the ability to prioritize and plan. Using the order of importance technique also shows that you can create a resolution with logical steps for accomplishing a common goal.

Spatial Order

Spatial order is best used for the following purposes:

Helping readers visualize something as you want them to see it;
Evoking a scene using the senses (sight, touch, taste, smell, and sound);
Writing a descriptive essay.

Spatial order means that you explain or describe objects as they are arranged around you in your space, for example in a bedroom. As the writer, you create a picture for your readers, and their perspective is the viewpoint from which you describe what is around you. The view must move in an orderly, logical progression, giving the reader clear directional signals to follow from place to place. The key to using this method is to choose a specific starting point and then to guide the reader to follow your eye as it moves in an orderly trajectory from your starting point.

Pay attention to the following student’s description of her bedroom and how she guides the reader through the viewing process, foot by foot.

Example of Spatial Order Organization

Attached to my back bedroom wall is a small wooden rack dangling with red and turquoise necklaces that shimmer as I enter. Just to the right of the rack, billowy white curtains frame a large window with a sill that ends just six inches from the floor. The peace of such an image is a stark contrast to my desk, sitting to the right of the window, layered in textbooks, crumpled papers, coffee cups, and an overflowing ashtray. Turning my head to the right, I see a set of two bare windows that frame the trees outside the glass like a three-dimensional painting. Below the windows is an oak chest from which blankets and scarves are protruding. Against the wall opposite the billowy curtains is an antique dresser, on top of which sits a jewelry box and a few picture frames. A tall mirror attached to the dresser takes up much of the lavender wall.

The paragraph incorporates two objectives: using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two objectives work together.

The following are possible transition words to include when using spatial order.

Table 5.1.1: Spatial Order Transition Words

This section contains material from:

Crowther, Kathryn, Lauren Curtright, Nancy Gilbert, Barbara Hall, Tracienne Ravita, and Kirk Swenson. Successful College Composition . 2nd edition. Book 8. Georgia: English Open Textbooks, 2016. http://oer.galileo.usg.edu/english-textbooks/8 . Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

A statement, usually one sentence, that summarizes an argument that will later be explained, expanded upon, and developed in a longer essay or research paper. In undergraduate writing, a thesis statement is often found in the introductory paragraph of an essay. The plural of thesis is theses .

5.1 Methods of Organizing Your Writing Copyright © 2022 by Kathryn Crowther; Lauren Curtright; Nancy Gilbert; Barbara Hall; Tracienne Ravita; and Terri Pantuso is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Spatial Order: What It Is and How to Use It While Writing an Essay

by Robert Pattinson | Apr 8, 2023 | Student Guide | 0 comments

Initially, the meaning of spatial order in writing could be challenging to understand. Spatial order is how we organize information in space (e.g., a house or an event as a space), from top to bottom, from left to right, chronologically, etc. We also call it space structure or order of place. So, spatial order is the way we observe them appearing in space. It is one of the organizational methods that aid your writing while describing specific arrangements in space.

We regard it as being entirely rational and aids the reader in better analyzing a scene or circumstance. There is more to completing an essay than merely conducting research, which is drafting an outline and developing a perfect thesis statement.

You will discover every detail you need to know about spatial order in this article, from the definition of spatial order and how to use spatial order writing .

Table of Contents

What is the Meaning of Spatial Order?

It becomes simple for authors to glance around and convey information in spatial sequencing when presenting their experience in words. Also, this technique enables readers to visualize explanations logically.

What is Spatial Organization in Writing?

While writing descriptive essays, spatial organization or spatial order is employed. The primary goal is to stimulate the reader’s senses and give them a solid understanding of the essay’s ideas and information.

Writing with spatial order organization opens up possibilities, sequencing, and logical progression, making it simple for readers to understand the directional cues.

In descriptive writing, a writer can use various writing styles. These include:

Detailed descriptions
categorizations
evaluations
expository writing

All styles involve a logical order of spatial organization.

Besides spatial order, you can get more details on Fellowship Vs Internship .

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What is the Spatial Order of Signal Words or Transition Words?

In a spatial-order essay, the use of transition words or signal words is essential. They are necessary to connect the writer’s thoughts and improve the paragraph’s flow. Several spatial order transition words serve as prepositions, which link sentences to the concepts represented in the ones before them.

A speaker or writer describes a spatial organization pattern if they use any of the terms on this list of spatial order signal words.

At the top of

In front of

To the side of

Attached to

On the left hand

On the right hand

How to Write a Spatial Order Paragraph?

One of the most searched queries nowadays is How to get WebAssign Answers . If you need help with the answers, let us assist you.

What is Spatial Order in Speech?

The spatial speech pattern arranges data based on how objects fit together in space. This style works well when your primary points are directed to several places that may exist independently. The fundamental indication of spatial order demonstrates that the crucial issues are located in prominent places, just like you can explore more information on Nodal Analysis in this post.

The speech is organized using spatial patterns considering the subject’s physical location. A speaker could discuss the first level of a building and then proceed to the second and third floors.

How to Write a Spatial-Order Essay?

Choose an Interesting Subject

An interesting subject is where good writing begins. A topic might alter the overall impression, whether you are writing for academic purposes or a professional audience. To begin, generate ideas for your subject. Writing down your thoughts allows you to visually examine them before deciding on a course of action. Think about the following questions.

What are you planning to write?
Will the reader find it helpful?
Will your readers be interested in this subject?

Create an Outline

Provide Instances and References

Build Your Body Paragraphs in The Right Way

Use Transition and Signal Words

Concluding Each Paragraph

Make It Unique and Self-Explanatory

Ensure you include some of your personality in each paragraph while planning the spatial order essays. Using personal touch may assist in making your essay more engaging and fascinating, given that such essays are formatted in a specific aspect and may quickly become predictable.

Things to Remember while Writing a Spatial Order Essay

You may express, evaluate, and make sense of your thoughts with the help of a solid organizational structure.
Your body paragraph structure helps you and your audience stay on topic.
Preparing your essay’s organizational structure enables more efficient and targeted research.
Order of significance works best in persuasive essays and essays where you rank events and individuals or describe objects according to their importance.
The chronological sequence is helpful, whether narrating a story, outlining a method, or expressing your concern’s records.

The Benefits of Ordering Spatial Order Essays from Gotakemyonlineclass

Many students do not notice their essays’ logical order and have never heard of spatial order. You may employ spatial order structure in your essay writing with the assistance of these crucial ideas, giving you an advantage over all other students in the classroom.

Students will receive the Take my online class assistance and essay writing services from gotakemyonlineclass.com. If you have questions about producing an essay about spatial order, you can get them from our expert essay writing services.

Frequently Asked Questions

What is spatial order structure.

In spatial order structure, each central point is organized in a directed structure that links each main point to the overall structure. This structure is employed for informative speeches and written essays when the subject is arranged by place, geography, or going through an area.

What Are The Types Of Spatial Data?

Geometric and geographic data are the two major categories of spatial data. Anything capable of being projected to a sphere is considered geographic data. Anything capable of being translated into a flat, two-dimensional surface is known as geometric data.

What Is Spatial And Temporal Order?

The spatial order of objects explains how they look under observation. Temporal order is the inherent order of the cosmos, as seen by the way forces like gravity are ideal for supporting life.

What Is Spatial Vs Topical Order?

Significant points are arranged in a spatial order based on their physical and geographic ties. As opposed to Topical Order, when a topic is dissected into its component pieces and then organized in a specific order that the speaker specifies for a particular reason.

What Is Spatial Vs Chronological Order?

A chronologically ordered speech pattern arranges its critical points following a timeline of events or happenings. For an introduction and informational talks, this approach is highly effective. In contrast to how each primary point is arranged in a spatial pattern of organization , a directional structure connects each main point to the entire.

What Is An Example Of Spatial Order Speech?

The home was in good condition. High hills rose behind and not far on either side; some were open outages, while others were farmed and wooded. The view in front was more expansive. In this example of spatial order speech , the facts are organized using specific starting and ending points. The reader may better visualize the area in this explanation around the cottage with the help of these spatial order examples .

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Most Common Book or Essay Organization Patterns

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M.Ed., Education Administration, University of Georgia
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To improve your ability to understand a difficult book or passage, you might start by finding the organization pattern. This may sound more difficult than it is. There are a few ways that writers can choose to organize their work , and the organization depends very much on the topic.

If you were writing a description of your bedroom, for example, you would most likely use a spatial organization pattern. In other words, you would most likely start by describing one "space" and move on to another space, and keep going until you've covered the entire room.

Spatial organization would be a suitable type of pattern for real estate professionals to use when describing a property.

Then again, if you were required to describe the events that led up to a certain event in history, your most likely organization pattern would be chronological . Chronological refers to the order that things happen in time. You might describe the legislation that set the stage for a particular event, followed by the public response to that legislation, and followed again by social conditions that changed because of the previous events.

So, one of the first things you should do when trying to understand a difficult text is to figure out the particular organization pattern. This helps you frame the entire work in your brain or on paper, as when you're writing an outline.

Chronological Organization

A chronological organization is used by writers when they want to describe what happened or happens in a particular order. Your entire history book is most likely written in a chronological patter. Some of the types of work that might follow this patter include the following. You can see that this type of organization is best when describing things that happen over time.

History chapters
Biographies
Summer vacation essays
Legal case studies

Logical Organization

A logical organization might be used in many ways. Logical organization refers to works that express a point or position using evidence.

Argument essays
Comparison essays

Functional Organization

A functional organization system is used to explain how or why things work. The following types of writing might use this organization pattern most effectively.

How-to essays
Step-by-step essays
Instruction manuals

Spatial Organization

Spatial organization is used in essays that describe or give direction concerning a physical location.

Descriptions
Anatomy essay
Descriptions in fiction

The purpose of developing and understanding organization patters is to help our brains set the stage and know what to expect. These patterns help us to build a framework in our minds and place information in the correct "places" on that framework. Once you determine the overall organization of any text, you'll be better equipped to process information as you read.

When writing your essays and chapters, you should keep your intended organizational pattern in mind as you work, to provide your readers with a clear message that is easily processed.

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How to Use Spatial Order in Essays and Assignments

Spatial order definition

Using the Spatial Order in Essay Writing

How do i use spatial order in an essay, the importance of spatial order in essay writing.

You could be struggling to understand the meaning of spatial order in writing. Let us, in this article, demystify the tenets of spatial order. Good command of English alone won't make a difference in writing the best quality essays. Instead, combining the command of English with good organization of your essay makes you an effective writer.

Regarding organization, a good essay has to have the best presentation and logical development of ideas.

Your tutors or professors do not tell you that these are some of the areas they asses when marking your essays.

Gradecrest.com through its R&D department has worked hard to ensure that our clients get the best.

That makes us the best fast essay writing service . When the writers set out on their writing journey, they usually observe the text structure as well as cohesive elements to ensure our clients get quality papers.

A good essay structure means the ideas are woven together in the best manner possible. However, apart from the structure, a cohesive organization glues the details together to make a good essay.

Spatial order is an example of a cohesive organization that writers can use for excellent essays.

Spatial Order Definition

A spatial order organizational style is also known as the order of place or space structure.

It helps in the presentation of ideas or things as is in their locations.

Compared to the chronological order that lists ideas from top to bottom based on their date of occurrence, spatial order focuses on the description of the locations of things.

Ordinarily, spatial ordering makes the use of phrases such as: o n top of, in front of, besides, alongside, beyond, further from, further along, behind, centric, elevated from, and under, just to mention a few.

Spatial ordering or spatial organization is used when writing descriptive essays.

The main agenda here is to invoke the senses of the reader and help them have a good grip on the concepts and facts presented in the essay.

There are different steps to follow when using the spatial order:

Decide on the scenarios that need to be described in the beginning : Always start with the most important ideas, especially when you have two or more scenarios, like when writing a descriptive essay . Space the ideas based on their relevance, weight, and importance in the essay and make up your mind on what, to begin with.
This sets the mood for your audience and keeps them glued to your paper. Should spatial order reflect in the first paragraph? Yes, this would be good as your thesis statement will lead the audience into the body and keep them glued to the end.
Want quality custom written essays by the experts? You should order it with us. Gradecrest.com has writers who are well versed in spatial ordering. Tutors from the world over like spatial ordering: it is a gateway to scoring high in your essays.
Details of the Scene : Once you have decided on the scenarios, proper planning is inevitable. While planning is quintessential in any form of writing assignment whether it be essays, research papers, speech, and coursework, spatial ordering means going further than the initial plan. It means setting every detail when describing a scene, to capture the finer details, the physical location of everything as you begin the writing process.
Have a logical flow when describing the elements : Spatial order is all about logic. When writing in a logical order, the elements flow seamlessly. As a rule of thumb, spatial order entails the description of events from top-bottom or the furthest right to the furthest left; the decision is purely on you as a writer. When ideas are closer to the rights of your readers, they interact with it first hand and do not struggle to get the gist of your writing. Forming logical relationships is a tough endeavor. However, the use of transitions and avoiding too many adjectives in your writing can spark the minds of the audience. You must also display high levels of critical thinking and creativity to maintain an audience to the end.

If you are writing a descriptive essay about a place or a narrative essay, spatial ordering can come in handy.

The spatial order has some benefits besides assuring you of the best grades.

It simplifies the complex processes and scenes and thus increasing the flow and understanding of ideas in the essay. A reader easily relates to ideas and facts in a spatially ordered essay.
It improves the originality of your essay as you express creativity and critical thinking more than using logical order or other ordering approaches.
It evokes the senses of the reader and makes it easy for them to visualize the gist of the essay. You would want to please that hard professor to get the best grades. Trust us, our team of experts can write you the best essay with flawless grammar, well researched, spatial order arrangement, and plagiarism free. Place an order and enjoy our special

Note that using too much spatial arrangement in your essay is discouraged as it might give some unimportant scenes importance.

Therefore we recommend only using spatial ordering when seeking to spice up an essay but be wise when using spatial order.

It is always best to get academic writing help if you do not understand how to.

List of Transitional Words to use in Spatial Order Essays

When writing descriptive essays using the spatial order approach, certain signal words can help you bring the best out of the paper. Use these signal words in your spatial order essays and wow your professor, lecturer, or teacher for the best grades.

To the left
Slightly below
Parallel to
Alternate to
Adjacent to
To the side
On the right hand
On the left hand
At the top of
At the bottom of
At a depth of

These transition words are like power words to incorporate in your essay when you decide to use a spatial order. They help give a positional placement of an object when describing its features. For instance, when describing a character, the writer can describe the placement of the television on the scene. They will have to explain it in relation to where the character is seated or standing.

Spatial Essay Order Outline Example

To reiterate, spatial order is mostly used in compositions to present the details of a given subject depending on the location it is in. It is an approach used mostly in constructive writing. Majorly, it helps frame the object relative to its place and create an ideal mental image to the reader. When writing a spatial order essay or composition, like for instance, a profile essay of a person or place, you must first begin by drafting an outline.

Mostly, spatial order essays are five-paragraph essays. They can be anything between 500 words or 1000 words, depending on the essay prompt.

Below are some of the components of the outline:

Spatial Order Essay Introduction

Essay hook or grabber to lure the attention of the readers
Background of the essay, covering the reasons for the topic of object choice
Thesis statement, which outlines what the spatial order essay is all about.

The Body of the Spatial Order Essay

Description of the first object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence).
Description of the second object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence)
Description of the third object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence)
Description of the nth object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence)

Note that every single body paragraph should contain only one idea or object description.

Conclusion of a Spatial Order Essay

Concluding transitions and sentence
Summary of the entire essay
Restated thesis statement
Final words or concluding remarks

We hope that our advice helps.

Gradecrest is a professional writing service that provides original model papers. We offer personalized services along with research materials for assistance purposes only. All the materials from our website should be used with proper references. See our Terms of Use Page for proper details.

Module 6: Organizing and Outlining Your Speech

Chronological, step-by-step, and spatial organization, learning objectives.

Explain the chronological, step-by-step, and spatial patterns for speeches and identify which topics work best for these types.

We can think of speeches organized chronologically, step by step, or spatially as following a “natural” or self-evident structure. When you’re talking about a process, for instance, walking the audience through the process step by step seems like a logical or natural choice.

Remember, though, that even if your speech is structured in a “natural” sequence, you still need an introduction that helps the listener understand why they’re listening to this story. Imagine that a friend is going to tell you a story about something that happened to them that day. First, let’s imagine that they start the story with “Something really funny happened to me . . . .” What are you listening for in the story? Now imagine if they started the story with “I’m really upset because of something that happened today.” Or “I really need your advice. Here’s what happened . . . .” With each of these different beginnings, we listen in a different way. In the first case, we’re primed to laugh; in the second, we get ready to offer comfort and sympathy; in the third, we’re prepared to problem solve. The same is true of the beginning of your speech: by setting the stage with the introduction or the “hook,” you’re letting the listener know what they’re listening for and how they should listen.

Chronological

A persuasive speech about Climate Change might describe the predicted effects of global warming in chronological order.

A chronologically organized speech pattern organizes its main points following a sequence of events or occurrences according to the time they took place. This structure works particularly well for informative and introductory speeches. For example, an introductory speech about the life events that lead you to attend your college could be organized chronologically starting with the first meeting with your guidance counselor, which lead to filling out an application a few weeks later to then drafting an essay, going on a campus tour a few months after that, having an interview with the department, and then finally getting the acceptance letter. Another example of a chronological speech topic would be a speech about a historical event, such as the Triangle Shirtwaist Factory Fire, the Boxer Rebellion, or the Arab Spring, which covers the events that occurred in the order in which they happened.

The advantages of a chronological speech pattern are that it is very easy to follow and it creates a clean, clear order. The timeline does the organizational work for the speaker and makes it easy to use. The cons are that there may be many events that occurred, such as in a speech topic about women getting the right to vote, which may not fit into the speech delivery time limit. So, consider the time allotted in addition to whether the time sequence of events is the most effective way to present the material before selecting this pattern.

Step-by-Step

A simple diagram showing instructions two basic salsa dance patterns.

An informative speech about Salsa dancing might give step-by-step instructions.

This pattern presents the steps involved in doing something and is useful for “how-to” or demonstration speeches where you are teaching or showing how to do a task. It follows the order of a process. For example, the steps involved in baking a cake, a speech demonstrating the dance steps required to do the Macarena, or how to create a PowerPoint presentation would use a step-by-step structure.

The advantage of this organizational pattern is that it breaks the task into small pieces for the audience. It allows them to see the process of doing something so that they may be able to do it themselves. The disadvantage of this pattern is that it can be tedious or repetitious if listeners are already very familiar with some of the steps in the process. With this organizational pattern, it’s particularly important to know how much prior knowledge of the process your audience already has.

A detailed poster showing the different parts of the International Space Station.

An informative speech about the International Space Station might use a spatial organization pattern, giving the listeners a tour of each part of the station as though they were moving through it.

A spatial pattern organizes each main point in a directional structure, connecting each main point to a whole. This structure is used for informative speeches where the topic is organized by location, geography, or moving through a space (“spatial” is the adjective form of “space”). For example, a speech about the parts of a resume might move in order from the top section to the bottom section. A speech about the regional cuisine of Germany might move from the Northwest region in a clockwise direction around the country. A speech about a building might start at the front doors and end on the roof. A speech about the pathway of Hurricane Sandy would include the geography showing the path moving from south to north east.

The spatial pattern is particularly useful if you want your listeners to be able to visualize an entire place or a complex object, since it moves between the part and the whole in a visual way. If you want your audience to visualize the Statue of Liberty, for instance, you might describe it spatially from top to bottom, rather than telling the story of its construction (chronological) or talking about the various things it has come to symbolize (topical).

To Watch: Beth Harris and Steven Zucker, “Diego Rivera, Man Controller of the Universe”

Spatial organizational patterns are often used to describe artworks and architecture. In this short video, art historians Dr. Beth Harris and Dr. Steven Zucker discuss Diego Rivera’s 1934 fresco mural Man, Controller of the Universe in the Palacio de Bellas Artes, Mexico City. The first part of the video is organized topically and chronologically, covering some of the major themes of the mural and the circumstances surrounding its creation. At around 3:25 in the video, Zucker says, “Let’s take a closer look at [the mural]”, and the two art historians discuss each part of the mural in sequence starting with the figure in the center. The spatial organization of their description is based on the visual structure of the painting; since the painting is largely symmetrical, Harris and Zucker describe the center, then the upper left and upper right, then the middle left and middle right, then the bottom left and bottom right.

You can view the transcript for “Diego Rivera, Man Controller of the Universe” here (opens in new window) .

What to watch for:

Note how Harris and Zucker end their tour of the artwork with broader thoughts about what we can learn from it: “We’re still very much at these crossroads. Technology is ever more important in our lives. What will technology bring us? A more egalitarian society, a world where everyone can be educated? Or will it bring greater inequality? These are still things debated today. We are still grappling with the increasing power of the tools that we have built, the power that technology has given us, and the choices that we make in terms of how we wield that power.” Whatever organizational pattern you use, it’s always crucial to bring the discussion around to something the audience can take away—a new insight, a new perspective, or a new way of framing a problem.

ISS blueprint. Authored by : Daniel Molybdenum/NASA/Roscosmos, with the help of John Chryslar and others. Located at : https://en.wikipedia.org/wiki/International_Space_Station#/media/File:ISS_blueprint.png . License : CC0: No Rights Reserved
Salsa steps. Authored by : Florian Hoffmann. Located at : https://en.wikipedia.org/wiki/Salsa_(dance)#/media/File:Salsa_Basic_Steps,_LA-style.png . License : CC BY: Attribution
Acqua alta in Piazza San Marco. Authored by : Wolfgang Moroder. Located at : https://en.wikipedia.org/wiki/Climate_change#/media/File:Acqua_alta_in_Piazza_San_Marco-original.jpg . License : CC BY-SA: Attribution-ShareAlike
Diego Rivera, Man Controller of the Universe. Authored by : Smarthistory. Located at : https://youtu.be/1mzQDfK3A5Q . License : Other . License Terms : Standard YouTube License
Chronological, Step by Step, and Spatial Organization. Authored by : Susan Bagley-Koyle with Lumen Learning. License : CC BY: Attribution
Chronological, Step by Step, and Spatial Organization. Authored by : Misti Wills with Lumen Learning. License : CC BY: Attribution

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What is a spatial order in speech and in writing?

Speech spatial order is a type of logical organization pattern that describes things according to their actual location in space. Definition: this order is also called the order of place and spatial structure. And in speech, it means that the narrator describes objects using an ordered logical sequence, describing things as they look when actually observed. An example is the following model: the narrator sets himself the task of describing the room, and in the description, he uses the spatial order, so his story will begin with the entrance to the room and with a description of those objects that first catches his eye. This will be followed by a description of the middle part of the room and only at the end – its distant elements. In this way, the narrator can observe the principle of spatial order and helps the listeners create a clear and vivid idea in their minds about the object of the story.

For what type of writing is spatial order usually best?

As a rule, the principle of spatial order is best suited for descriptive writing . For example:

In fiction or non-fiction, when a description is required to evoke certain feelings in the reader or to tell in great detail about the location of certain objects and places. And also, in order to describe physical or social phenomena.
In technical and scientific literature, when a specific explanation of the operation of various mechanisms, systems and devices is required. Or if you need to make it clear to readers what the structure consists of and how its various elements interact with each other.
In the case of interior descriptions by designers or critics. Here, they are faced with the task of conjuring up in the imagination of readers an idea of the arrangement of furniture, decor, and everything that fills the room. Spatial order will help a lot to achieve the best result.
Architects and designers also often resort to using this principle.
Of course, spatial order is also used when writing an essay.

How to write a spatial order essay?

To write an essay corresponding to the spatial order, it is necessary to describe objects in accordance with their actual position relative to you. Or, if you want to describe what this or that person looks like, then your description should begin with the legs and further up, ending with the head. In addition, before you start writing such an essay, you need to have an accurate idea of what is the main purpose of this description? You must decide what result you need to achieve so as not to deviate too much from the given topic in the process of writing an essay. And in order not to miss anything while writing, you can create a structured plan that will help you stay on course, because organization is the most important element of spatial order essay.

Of course, if you are a first-time writer, then it will be quite useful for you to have the help of a specialist or professional writer who will teach you and tell you what to look for when working with spatial order. You can find such an assistant on our site Writingstatement.com , and the Internet also is a great source of inspiration and filling ideas for writing an essay. On educational portals, you can read examples of written essays in spatial order to get an idea of how to do it right. And, finally, be sure to check the finished text for semantic and logical mistakes.

Define spatial order signal words

Many of these words and phrases are prepositions and are at the beginning of a sentence, and they often act as connecting conjunctions, combining the previous sentence with the new one. The main role of signal words is to create a brighter and more understandable picture for the reader, which the author is trying to convey through words and sentences, in addition, they serve to clarify and create greater order in the entire text as a whole. When writing an essay in spatial order, the use of signal words should never be neglected.

What is a chronology? Why is it used in literature?

By itself, the concept of chronology means a list of events in their time sequence. In writing, authors usually tell a story, describing successive events from beginning to the end, so that a certain structured picture is formed in the imagination of readers, which helps them better understand the essence of what they are reading about. This order in literature is called chronological order, and is the most popular among writers. However, one can often find a work in which the chronological order is not observed at all, on the contrary, it is completely turned upside down, that is, the author begins his story from the end and gradually moves towards the beginning. In this order, understanding the big picture can be a little difficult, but often this style makes the work unique and unusual.

3 types of chronology in writing

Distinguishing the chronological type of writing from the rest is quite simple, since it basically includes very specific time periods in a logical sequence. The most popular types of chronology are the following three types:

Linear chronology . If the author uses this type of chronology, then all the events he describes occur according to an increasing time scale. For example: “Mr. Gray woke up at 9 o’clock in the morning, at 10 am he left for work, at 3 pm he had lunch in a restaurant, and by 6 pm he returned home.” This is a typical chronological linear sequence model.
Reverse chronology . This is a way of storytelling in which the plot of the story is revealed in reverse order, that is, the first scene described is actually the end of the plot, and the last scene is the first in chronological order. An example is the following plot: the main character of the novel goes to bed and falls asleep, the next event described will be what preceded her falling asleep and then right up to the moment when she woke up in the morning of the day described.
Nonlinear chronology . This type of chronology is not similar to either the first or the second; there is no clear sequence and order in it. There are many digressions and disparate events in this way of writing, which sometimes helps the author to create a lively, interesting and extraordinary work. However, when choosing a non-linear chronology as a writing style, it is necessary to understand that it is quite easy to lose the thread of the narrative here or go astray, therefore this style is quite difficult.

Thus, we see that each type of chronology has its own nuances and its own individual structure, which must be followed from the beginning to the very end of the work. It makes no sense to mix and intertwine types of chronology with each other, since the narrative will lose its basis and become too vague and difficult to understand.

3 rules for writing a spatial order essay

So, having dealt with the three types of chronology, signal words and the concept of spatial order, we can derive three basic rules that must be followed when writing an essay in spatial order.

Choosing the right topic

The first thing you need to do before you start working on an essay is to choose a topic and create a structured plan that answers in detail all the questions related to your topic. These questions may sound like this: What is the main goal I pursue when writing this essay? What chronological structure do I want to use? How would it be better to formulate the topic of my essay so that it clearly reflects the intention of my work? There can be a huge number of such questions, however, the clearer your idea of the topic is, the better and richer your essay will turn out.

Arrangement of information

A very important role is played by how correctly and logically you arrange the information blocks in your essay. The narration should be consistent and coherent, and thoughts should be clearly and understandably formulated in such a way that it is convenient for the reader to receive and remember the information that you present. Map out for yourself a visual diagram of what is your starting point (the beginning of the description), and which is the end point (the end of the description).

Transitional words

The third point, which is also one of the most important. The use of transitional words is an essential attribute when writing almost any descriptive essay of a spatial order, here are some of them:

nevertheless
in the same way
in spite of
in contrast
at the same time
while this might be true
on the other hand
for example
for instance
specifically
to illustrate, etc.

But it is important to note one more thing, transitional words should be appropriate, they should not be too much in sentences, they should not interfere with the general understanding of the text, otherwise your work will have too much water, which will make it difficult to read.

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How to Write a Spatial Essay

Maureen green.

An essay about what homes will be like in the future is an example of one kind of topic that might not work well in conventional patterns of essay organization. Rather than chronological or sequential patterns, for example, you might discover that using a spatial essay organization pattern is the most effective way to go. To use spatial order in your essay, emphasize how objects, people, places or concepts relate to each other in a particular manner.

Explore this article

Choose a Subject
Brainstorm Using a Diagramming Activity
Construct an Outline
Construct Body Paragraphs

1 Choose a Subject

First, as with any essay, you need to choose a subject that lends well to spatial organization. If your instructor has provided a specific one for you, this part is easy. If not, think about subjects that interest you. You might choose to describe the parts of a combustion engine and how they work together, or the people in your family and how they are related to each other.

Spatial essays are also great for going on a trip. Think of a place you've always wanted to visit and where you'll go when traveling there. For example, imagine you'll be visiting San Francisco for a few days. You're going to describe the tourist sites you want to see and how they are situated in relationship to one another.

2 Brainstorm Using a Diagramming Activity

You can brainstorm any number of ways, but for spatial order essays, some type of diagramming (also known as clustering, bubbling, webbing or mapping) activity is most effective. A diagramming activity does not replace an outline in the prewriting process; it is designed to allow you to visually explore concepts before you make decisions about the order in which you will write about them.

Going back to our San Francisco example: In the middle of your diagram, you would start by writing "San Francisco." As offshoots from the center of your diagram, you have some options. You could break your visit down by day (Day 1, Day 2, Day 3) or major attraction (Alcatraz, Conservatory of Flowers, Mission murals). For our purposes, let's choose three neighborhoods: the Embarcadero, Haight-Ashbury and Chinatown.

3 Construct an Outline

After fleshing out your diagram, construct an outline for your essay. Beneath each topic, outline two or three subtopics. In our example, the subtopics are the tourist attractions to visit in each neighborhood. If you decide to outline the subtopics even further, you could explain how the hot spots within each district are spatially related, as well. For example, under the Haight-Ashbury topic, note that the world-famous Amoeba Music is just a few blocks from Golden Gate Park.

4 Construct Body Paragraphs

Finally, it's time to write your body paragraphs. Because spatial order does not depend on a prescribed process or sequence, you have the freedom to choose which spatial element will begin your essay. Remember that the essay needs to explain how your topics relate to each other. Therefore, prepositional phrases will be useful. Noting landmarks and the proximity among them -- for example, that Chinatown is next to the Financial District and that Haight-Ashbury is to the west of the Mission District -- helps your reader visualize how to navigate the terrain and stay engaged in the journey you're taking them on.

1 Online Sources: University of Richmond Writing Center: Writer's Web: Getting Started

About the Author

Maureen Green has been a writer and editor for more than 10 years. She has worked with print publications such as "Script" magazine, as well as various websites and small businesses. Green has also been teaching composition online to adult, military and ESL learners since 2008.

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1.5: Methods of Organizing Your Writing

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Kathryn Crowther et al.
Georgia Perimeter College via GALILEO Open Learning Materials

The method of organization for essays and paragraphs is just as important as content. When you begin to draft an essay or paragraph, your ideas may seem to flow from your mind in a seemingly random manner; however, your readers, who bring to the table different backgrounds, viewpoints, and ideas, need you to clearly organize these ideas to help them draw connections between the body and the thesis. A solid organizational pattern not only helps readers to process and accept your ideas, but also gives your ideas a path that you can follow as you develop your essay (or paragraph). Knowing how you will organize your paragraphs allows you to better express and analyze your thoughts. In addition, planning the structure of your essay before you choose supporting evidence helps you conduct more effective and targeted research. This section covers three ways to organize both essays and paragraphs: chronological order, order of importance, and spatial order.

Chronological Order

Chronological arrangement has the following purposes:

To explain the history of an event or a topic
To tell a story or relate an experience
To explain how to do or to make something
To explain the steps in a process

Chronological order is mostly used in expository writing, which is a form of writing that narrates, describes, informs, or explains a process. When using chronological order, arrange the events in the order that they actually happened, or will happen if you are giving instructions. This method requires you to use words such as first, second, then, after that, later, and finally . These transition words guide you and your reader through the paper as you expand your thesis. For example, if you are writing an essay about the history of the airline industry, you would begin with its conception and detail the essential timeline events up until present day. You would follow the chain of events using words such as first, then, next, and so on. Keep in mind that chronological order is most appropriate for the following purposes:

Writing essays containing heavy research
Writing essays with the aim of listing, explaining, or narrating
Writing essays that analyze literary works such as poems, plays, or books

Exercise 12

Exercise 13

Order of Importance

Order of importance is best used for the following purposes:

Persuading and convincing
Ranking items by their importance, benefit, or significance
Illustrating a situation, problem, or solution

Most essays move from the least to the most important point, and the paragraphs are arranged in an effort to build the essay’s strength. Sometimes, however, it is necessary to begin with the most important supporting point, such as in an essay that contains a thesis that is highly debatable. When writing a persuasive essay, it is best to begin with the most important point because it immediately captivates your readers and compels them to continue reading.

Exercise 14

Spatial Order

Spatial order is best used for the following purposes:

Helping readers visualize something as you want them to see it
Evoking a scene using the senses (sight, touch, taste, smell, and sound)
Writing a descriptive essay

Spatial order means that you explain or describe objects as they are arranged around you in your space, for example in a bedroom. As the writer, you create a picture for your readers, and their perspective is the viewpoint from which you describe what is around you. The view must move in an orderly, logical progression, giving the reader clear directional signals to follow from place to place. The key to using this method is to choose a specific starting point and then guide the reader to follow your eye as it moves in an orderly trajectory from your starting point. Pay attention to the following student’s description of her bedroom and how she guides the reader through the viewing process, foot by foot.

Attached to my back bedroom wall is a small wooden rack dangling with red and turquoise necklaces that shimmer as I enter. Just to the right of the rack, billowy white curtains frame a large window with a sill that ends just six inches from the floor. The peace of such an image is a stark contrast to my desk, sitting to the right of the window, layered in textbooks, crumpled papers, coffee cups, and an overflowing ashtray. Turning my head to the right, I see a set of two bare windows that frame the trees outside the glass like a three-dimensional painting. Below the windows is an oak chest from which blankets and scarves are protruding. Against the wall opposite the billowy curtains is an antique dresser, on top of which sits a jewelry box and a few picture frames. A tall mirror attached to the dresser takes up much of the lavender wall.

The paragraph incorporates two objectives covered in this chapter: using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two objectives work together. The following are possible transition words to include when using spatial order:

Just to the left or just to the right
On the left or on the right
Across from
A little further down
To the south, to the east, and so on
A few yards away
Turning left or turning right

Exercise 15

On a separate sheet of paper, write a paragraph using spatial order that describes your commute to work, school, or another location you visit often.

Collaboration : Please share with a classmate and compare your answers.

key takeaways

The way you organize your body paragraphs ensures you and your readers stay focused on and draw connections to your thesis statement.
A strong organizational pattern allows you to articulate, analyze, and clarify your thoughts.
Planning the organizational structure for your essay before you begin to search for supporting evidence helps you conduct more effective and directed research.
Chronological order is most commonly used in expository writing. It is useful for explaining the history of your subject, for telling a story, or for explaining a process.
Order of importance is most appropriate in a persuasion paper as well as for essays in which you rank things, people, or events by their significance.
Spatial order describes things as they are arranged in space and is best for helping readers visualize something as you want them to see it; it creates a dominant impression.

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Organization and Structure

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There is no single organizational pattern that works well for all writing across all disciplines; rather, organization depends on what you’re writing, who you’re writing it for, and where your writing will be read. In order to communicate your ideas, you’ll need to use a logical and consistent organizational structure in all of your writing. We can think about organization at the global level (your entire paper or project) as well as at the local level (a chapter, section, or paragraph). For an American academic situation, this means that at all times, the goal of revising for organization and structure is to consciously design your writing projects to make them easy for readers to understand. In this context, you as the writer are always responsible for the reader's ability to understand your work; in other words, American academic writing is writer-responsible. A good goal is to make your writing accessible and comprehensible to someone who just reads sections of your writing rather than the entire piece. This handout provides strategies for revising your writing to help meet this goal.

Note that this resource focuses on writing for an American academic setting, specifically for graduate students. American academic writing is of course not the only standard for academic writing, and researchers around the globe will have different expectations for organization and structure. The OWL has some more resources about writing for American and international audiences here .

Whole-Essay Structure

While organization varies across and within disciplines, usually based on the genre, publication venue, and other rhetorical considerations of the writing, a great deal of academic writing can be described by the acronym IMRAD (or IMRaD): Introduction, Methods, Results, and Discussion. This structure is common across most of the sciences and is often used in the humanities for empirical research. This structure doesn't serve every purpose (for instance, it may be difficult to follow IMRAD in a proposal for a future study or in more exploratory writing in the humanities), and it is often tweaked or changed to fit a particular situation. Still, its wide use as a base for a great deal of scholarly writing makes it worthwhile to break down here.

Introduction : What is the purpose of the study? What were the research questions? What necessary background information should the reader understand to help contextualize the study? (Some disciplines include their literature review section as part of the introduction; some give the literature review its own heading on the same level as the other sections, i.e., ILMRAD.) Some writers use the CARS model to help craft their introductions more effectively.
Methods: What methods did the researchers use? How was the study conducted? If the study included participants, who were they, and how were they selected?
Results : This section lists the data. What did the researchers find as a result of their experiments (or, if the research is not experimental, what did the researchers learn from the study)? How were the research questions answered?
Discussion : This section places the data within the larger conversation of the field. What might the results mean? Do these results agree or disagree with other literature cited? What should researchers do in the future?

Depending on your discipline, this may be exactly the structure you should use in your writing; or, it may be a base that you can see under the surface of published pieces in your field, which then diverge from the IMRAD structure to meet the expectations of other scholars in the field. However, you should always check to see what's expected of you in a given situation; this might mean talking to the professor for your class, looking at a journal's submission guidelines, reading your field's style manual, examining published examples, or asking a trusted mentor. Every field is a little different.

Outlining & Reverse Outlining

One of the most effective ways to get your ideas organized is to write an outline. A traditional outline comes as the pre-writing or drafting stage of the writing process. As you make your outline, think about all of the concepts, topics, and ideas you will need to include in order to accomplish your goal for the piece of writing. This may also include important citations and key terms. Write down each of these, and then consider what information readers will need to know in order for each point to make sense. Try to arrange your ideas in a way that logically progresses, building from one key idea or point to the next.

Questions for Writing Outlines

What are the main points I am trying to make in this piece of writing?
What background information will my readers need to understand each point? What will novice readers vs. experienced readers need to know?
In what order do I want to present my ideas? Most important to least important, or least important to most important? Chronologically? Most complex to least complex? According to categories? Another order?

Reverse outlining comes at the drafting or revision stage of the writing process. After you have a complete draft of your project (or a section of your project), work alone or with a partner to read your project with the goal of understanding the main points you have made and the relationship of these points to one another. The OWL has another resource about reverse outlining here.

Questions for Writing Reverse Outlines

What topics are covered in this piece of writing?
In what order are the ideas presented? Is this order logical for both novice and experienced readers?
Is adequate background information provided for each point, making it easy to understand how one idea leads to the next?
What other points might the author include to further develop the writing project?

Organizing at the sentence and paragraph level

Signposting.

Signposting is the practice of using language specifically designed to help orient readers of your text. We call it signposting because this practice is like leaving road signs for a driver — it tells your reader where to go and what to expect up ahead. Signposting includes the use of transitional words and phrasing, and they may be explicit or more subtle. For example, an explicit signpost might say:

This section will cover Topic A and Topic B.

A more subtle signpost might look like this:

It's important to consider the impact of Topic A and Topic B.

The style of signpost you use will depend on the genre of your paper, the discipline in which you are writing, and your or your readers’ personal preferences. Regardless of the style of signpost you select, it’s important to include signposts regularly. They occur most frequently at the beginnings and endings of sections of your paper. It is often helpful to include signposts at mid-points in your project in order to remind readers of where you are in your argument.

Questions for Identifying and Evaluating Signposts

How and where does the author include a phrase, sentence, or short group of sentences that explains the purpose and contents of the paper?
How does each section of the paper provide a brief summary of what was covered earlier in the paper?
How does each section of the paper explain what will be covered in that section?
How does the author use transitional words and phrases to guide readers through ideas (e.g. however, in addition, similarly, nevertheless, another, while, because, first, second, next, then etc.)?

WORKS CONSULTED

Clark, I. (2006). Writing the successful thesis and dissertation: Entering the conversation . Prentice Hall Press.

Davis, M., Davis, K. J., & Dunagan, M. (2012). Scientific papers and presentations . Academic press.

Spatial transcriptomics in development and disease

Open access
Published: 09 October 2023
Volume 4 , article number 32 , ( 2023 )

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Ran Zhou 1 ,
Gaoxia Yang 1 , 2 na1 ,
Yan Zhang 2 &
Yuan Wang ORCID: orcid.org/0000-0002-6324-6134 1

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The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

Rna-seq data analysis in galaxy.

From sequence to consequence: Deciphering the complex cis-regulatory landscape

Greg Jude Dsilva & Sanjeev Galande

Avoid common mistakes on your manuscript.

Introduction

Just as no man is an island, a cell’s behavior and function are not isolated, but rather influenced by neighboring cells organized in a three-dimensional space to form a tissue or organ [ 1 ]. Spatial organization is crucial for organogenesis during development, and its disruption/reorganization is implicated in the onset, progression, and treatment of diseases [ 2 ]. Thus, it is important to investigate cellular function in a spatial context, which could offer a more comprehensive and holistic view of complex biological systems, reveal novel mechanisms of organ development and diseases, and lead to new strategies for diagnosis and therapy.

Transcriptomics offers a high-throughput method to examine gene expression differences during development and disease. However, conventional methods for transcriptomic analysis, such as bulk RNA sequencing (RNA-seq) or single-cell RNA sequencing (scRNA-seq) have limitations in preserving the spatial information of the cells within a tissue [ 3 ]. Bulk RNA sequencing averages the gene expression across all cells in a tissue sample, losing the cellular heterogeneity and spatial context. Single-cell RNA sequencing dissociates the cells from the tissue, allowing for high-resolution analysis of individual cell types, but at the cost of losing the positional information of the cells. Furthermore, without spatial information, conventional methods for transcriptomic analysis do not enable easy integration of transcriptomic data with other types of data, such as histological images, proteomics or metabolomics. Collectively, this underscores the necessity for more extensive in situ transcriptional analysis to gain insights into the functioning of intricate biological systems.

Lately, numerous spatial gene profiling technologies have emerged with the goal of uncovering molecular alterations in two-dimentional (2D) and three-dimentional (3D) tissue samples at specific locations, and even achieving resolutions down to single cells or subcellular levels. The modern age technologies are originated from historical tools that enable simultaneous quantification of gene expression while retaining spatial information. The initial concept of in situ hybridization (ISH) dates back to 1969, when it was first employed for detecting DNA-RNA hybrids [ 4 ]. Subsequently, various iterations of this method have been extensively used to visualize gene expression at the single-cell and subcellular levels across space. Evolving further, in situ sequencing (ISS) generates a spatial transcriptome through sequencing by ligation (SBL), gene barcoding, or sequencing by oligonucleotide ligation and detection (SOLiD). Additionally, spatial information can be obtained by targeting and isolating specific regions of interest (ROIs) using methods like physical or optical marking. Following this, the isolated ROIs can be subjected to analysis through approaches like microarray, RNA-seq, or they can be dissociated for single-cell RNA-seq analysis.

While both in situ and ROI-based methods offer spatially resolved gene profiling, they fail to meet the requirement for rapid and high-throughput spatial profiling. More recently, an advanced technique called Spatial Transcriptomics (ST) has emerged, capable of mapping gene expression in tissues by integrating molecular profiling with spatial information [ 5 ]. It overcomes the limitations of conventional transcriptomic methods by assigning cell types (identified by their mRNA signatures) to their precise locations in histological sections, and can even determine subcellular localization of mRNA molecules. This enables us to uncover cellular heterogeneity and intercellular communications within a complex tissue/organ, providing an atlas for organ development and disease progression. Its power and potential have been recognized by Nature Methods , which declared ST as the method of the year 2020 [ 6 ].

Currently, this field is undergoing rapid expansion, driven by several factors including the decreasing costs associated with sequencing, collaborative initiatives undertaken by international consortiums, and notable progress in computing and imaging technologies. Consequently, gene expression landscapes with spatial resolution across a range of tissues, species, developmental stages, and disease conditions have been constructed. In this review, we aim to put everything in context and provide a comprehensive overview of the historical, technical, and application aspects of ST. We categorize and compare different types of ST methods based on their underlying principles and workflows, and we present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also showcase the applications of ST in various domains of biomedical research, such as developmental biology, neuroscience, immunology, and oncology. Finally, we discuss the current limitations and challenges in the field, and we envision the future directions of spatial transcriptomics.

Putting ST in a historical context: the development of a tapestry of methods

Although ST has only become the buzz word in recent years, the idea of visualizing and quantify RNA expression in space is not entirely new. It can be traced back to the 1970s, when radioactive in situ hybridization (ISH) and laser capture microdissection (LCM) were first developed [ 5 ] (Fig. 1 ). These two methods, along with their various derivatives, have been used for decades to study spatial gene expression. They represent two schools of thoughts to obtain spatial information: to detect RNA molecules/sequences in their original location and match them with histological images, or to isolate ROIs within a defined spatial location to perform subsequent RNA analysis. These led to two categories of current ST methods: ROI-based approaches and in situ image-based approaches [ 5 ] (Fig. 2 , Table 1 ). The advent of next generation sequencing (NGS) further expands the 2D ROI-based approaches to 3D tomography-based approaches, allowing for high-throughput whole tissue/organ profiling (Fig. 2 , Table 1). In contrast, spatial barcoding approaches and spatial multi-omics are relatively new additions to the ST toolbox, which integrates localized spatial barcoding of RNA molecules in situ and NGS sequencing [ 5 ] (Fig. 2 , Table 1 ).

Timeline of the major published methods mentioned in this review. Every approach is categorized according to its foundational methodology, primarily segmented into the subsequent classifications: ROI-based methods (purple), image-based methods including FISH (yellow) and ISS (green), tomography-based methods (orange), and spatial barcoding-based methods (blue)

Overview of technologies for spatial transcriptomics. a Physical dissection or optical marking-based approaches involve the selection or marking of regions of interest. Following ROI marking, samples can be individually collected for RNA-seq or dissociated into single cells for scRNA-seq. b Image-based technologies achieve spatially resolved gene expression through decoding fluorescence signals. In situ sequencing and single molecule fluorescence in situ hybridization detect molecular abundance by directly reading transcript sequences within the tissue or target RNA barcodes, respectively. c Tomography-based methods, such as RNA tomo-seq, utilize a frozen section technique to linearly amplify cDNA from single tissue samples. Three identical biological samples are systematically frozen and sliced in three different directions, allowing for the completion of 2D transcriptional reconstruction through overlapping data. d Spatial barcoding-based methods generate spatial transcriptomes using reverse transcription primers with unique positional barcodes

ROI-based approaches

ROI-based approaches involve isolating specific ROIs within a sample through physical microdissection or optical marking. Once the ROIs are collected, they can be subjected to bulk RNA-seq, or dissociated into single cells for scRNA-seq [ 7 , 8 , 9 ]. Physical microdissection methods can isolate ROIs within samples using different techniques, such as LCM and micro-dissection punching system. LCM is a histology-based technique that uses either ultraviolet (UV) cutting or infrared (IR) capture systems to collect the desired ROI from tissue sections. SRS microdissection and sequencing (SMD-seq) [ 10 ] takes a step further to combine LCM with stimulated Raman scattering (SRS) microscopy, which provides chemical contrast to reveal histological tissue architecture without staining to guide the dissection of ROIs. Another approach for isolating ROIs within tissues is the micro-dissection punching system, which uses a punching unit to automatically collect tissue samples, allowing for quick and efficient collection of ROIs.

Optical marking methods can label ROIs using photoactivatable cell tagging or photoactivatable oligonucleotides. One way is to use genetically engineered mice expressing photoactivatable fluorescent markers in specific ROIs or cell types, such as NICHE-seq [ 11 ] and TATTOO-seq [ 12 ]. In contrast, SPACECAT [ 13 ] and OpTAG-seq [ 14 ] enable optical tagging of live cells for scRNA-seq without exogenous genes. Another approach is using oligonucleotide-antibody conjugates to put the spatial barcodes onto cell surface. ZipSeq [ 15 ] attaches the photo-uncaging barcodes onto the cell surface by the antibody or lipid DNA conjugate, and the spatial barcodes are uncaged and captured by 10 × Genomics scRNA-seq pipeline. Alternatively, Merritt et al. provide an integrated commercial system (GeoMX DSP) that enables highly multiplex spatial profiling of protein or RNA in FFPE tissue by oligonucleotide-conjugated RNA probes [ 16 ].

Instead of labeling cells directly, TIVA-tag [ 17 ] is an engineered method that allows for the annealing of mRNA from ROIs in live tissue through photoactivation. The mRNA tagged with TIVA can then be purified for downstream analysis. Along this line, Photo-isolation chemistry (PIC) [ 18 ] and Light-Seq [ 19 ] combine in situ reverse transcription (RT) with caged RT primers that are uncaged after UV irradiation of ROIs to generate spatially indexed sequencing libraries to integrate spatial information with gene expression.

Tomography-based approaches

Tomography, a technique widely applied in medical imaging, entails the reconstruction of three-dimensional structures using a sequence of two-dimensional images captured from varying angles. In the context of spatial genomics, innovative methods like Tomo-seq [ 20 ], Cryo-Sliced [ 21 ], and Transcriptome Tomography [ 22 ] employ tomography principles to divide the entire tissue, organ, or embryo into multiple thin slices along different axes. Subsequently, RNA-seq analysis is conducted on each of these sections to gain insights into gene expression patterns. Sequential image optimization along different sectioning axes and iterative proportional fitting analysis are performed to mathematically reconstruct 3D expression images. Geo-seq [ 23 ] further integrates Tomo-Seq with LCM to achieve 3D spatial transcriptome by sequencing ROIs from different geographical positions. More recently, STRP-seq [ 24 ] offers a new imaging-free framework by slicing adjacent sections into multi-angle sections and reconstruct complex spatial patterns with an associated algorithm. The tomography-based approach offers a convenient and imaging-free technique for exploring spatial genomics. Nonetheless, this method is often characterized by its comparatively lower resolution in comparison to imaging-based approaches. Additionally, it may not fully capture intricate patterns like discontinuous or checkboard-like structures.

Image-based approaches

Despite the power of ROI-based ST methods, they may introduce biases or errors in the spatial information due to the choice or quality of the ROIs. Moreover, ROI-based methods may not capture the continuous or gradual changes in gene expression across space, but rather discretize them into discrete ROIs. In situ image-based approaches overcome these limitations by probing or sequencing RNA molecules in situ on tissue sections to better preserve the spatial information, including single-molecular FISH (smFISH) and ISS.

SmFISH is a technique that uses fluorescent probes to image specific RNA transcripts in tissue sections. However, the number of genes that can be simultaneously imaged by conventional smFISH is limited by the number of spectrally distinct dyes. To overcome this limitation, several methods have been developed that use sequential barcoding or binary encoding schemes to increase the multiplexing capacity of smFISH [ 25 , 26 , 27 ]. For example, sequential fluorescent in situ hybridization (seqFISH) and its improved version seqFISH + use the same FISH probes for hybridization, but each round is labeled with a different fluorescent dye. With eight rounds of hybridization and four dyes, they can cover the entire transcriptome of human or mouse cells (4 8 = 65,536). Another method, multiplexed error-robust FISH (MERFISH) [ 28 ], uses a binary encoding scheme in which the valid encoding words must have at least four Hamming distance from each other. This allows single-bit errors to be detected and corrected by comparing the observed words with a predefined cookbook. However, as the number of hybridization rounds increases, dropout events and spectral overlap may occur more frequently, which may affect the accuracy of target identification. To address this issue, some methods have integrated smFISH with expansion microscopy (ExM), which physically expands the tissue sample and increases the RNA density limit. For instance, ExM-MERFISH [ 29 ] and seqFISH + combined with ExM can substantially increase the number of molecules that can be detected without imaging crowding.

ISS is a technique that uses padlock probes and rolling circle amplification (RCA) to amplify and sequence gene barcodes or short fragments of cDNA directly in tissue sections [ 30 ]. Several methods have been developed based on ISS to achieve spatially resolved gene profiling. For example, ISS and barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses (BOLORAMIS) [ 31 ] used sequencing by ligation to read short segments of RNA from clonally amplified rolling-circle products (RCPs). The improved versions of ISS increased the efficiency of target detection by using different probe designs and signal amplification strategies [ 32 , 33 ]. The spatially resolved transcript amplicon readout mapping (STARmap) [ 34 ] as well as STARmap PLUS [ 35 ] integrated hydrogel-tissue chemistry, targeted signal amplification, and in situ sequencing by dynamic annealing and ligation (SEDAL). A later adaptation with tri-probes (splint, primer and padlock) to detect metabolically-labeled RNAs was called TEMPOmap (temporally resolved in situ sequencing and mapping) [ 36 ].

Instead of detecting pre-designed gene barcodes, fluorescent in situ sequencing (FISSEQ) [ 37 ] as well as ExSeq [ 38 ], used SOLiD method to sequence circularized and RCA-amplified cDNAs. The next-generation of FISSEQ, termed INSTA-seq [ 39 ], efficiently imaged transcript-specific barcodes in situ and then assembled longer RNA molecules by using NGS. Although ISS was commercialized by Cartana, ISS-based methods are often technically challenging to implement due to the complex postprocessing and costly equipment.

Spatial barcoding-based approaches

Although ROI- and image-based provide spatially resolved gene profiling, it can’t satisfy the demand of rapid and high-throughput spatial profiling. Spatial barcoding approach is a high-throughput technique that uses arrays of barcoded oligonucleotides to capture and sequence RNA molecules from tissue sections while preserving their spatial coordinates.

Several methods have been developed based on spatial barcoding to achieve high-throughput and genome-wide spatially resolved gene profiling. In 2016, Ståhl et al. first introduced spatial barcoding-based ST by positioning histological sections on arrayed reverse transcription primers with unique positional barcodes to generate RNA-seq data with two-dimensional positional information [ 40 ]. Its commercial version, Visium, has 55 µm-diameter spots in a hexagonal array, with center-to-center distance at 100 µm. Similarly, Slide-seq [ 41 ] and Slide-seqV2 [ 42 ] used uniquely DNA-barcoded 10 μm microparticles to capture RNA from tissue sections, and High-Definition Spatial Transcriptomics (HDST) deposited barcoded poly(d)T oligonucleotides into 2 μm wells [ 43 ].

More recently, spatial barcoding-based approaches have achieved subcellular resolution. PIXEL-seq achieved a spot diameter of about 1.22 μm [ 44 ], while Stereo-seq, scStereo-seq, Seq-Scope further reduced the spot sizes to sub-micrometer resolution [ 45 , 46 , 47 ]. Other spatial barcoding ST methods such as DBiT-seq [ 48 ], Matrix-seq [ 49 ], MISAR-seq [ 50 ], xDbit [ 51 ] and CBSST-Seq [ 52 ] use microfluidic-based methods to generated the spatial barcodes, allowing for spatial sequencing at pixel-size resolution. Still, none of these methods could ascertain that only a single cell was labelled by each barcode. XYZeq [ 53 ] and sci-Space [ 54 ] partially overcome this limitation by first spatially barcoding thousands of cells on the slide, and then subjecting them to scRNA-seq, revealing spatially expressed genes across cell types.

Transcriptomic analysis only reveals one aspect of cellular function. Spatial multi-omics is a technique that combines spatial transcriptomics with other omics modalities to reveal the multiple layers of cell function. Several methods have been developed to achieve comprehensive spatially resolved molecular profiling. For example, some methods have integrated antibody panels with transcriptomics, such as DBiT-Seq [ 48 ], GeoMX SPG [ 55 ], SM-Omics [ 56 ], spatial-CITE-seq [ 57 ], SPOTS [ 58 ] and Xenium [ 59 ]. These methods can simultaneously measure protein and RNA expression in tissue sections. For chromatin accessibility, some methods have adapted spatial transcriptomics with assay for transposase-accessible chromatin using sequencing (ATAC-seq), such as MISAR-Seq [ 50 ] and Spatial ATAC–RNA-seq [ 60 ]. These methods can simultaneously measure RNA expression and chromatin accessibility in tissue sections. For T-cell receptor (TCR) sequencing, a method called Slide-TCR-seq has combined RNase H-dependent PCR-enabled TCR sequencing (rhTCR-seq) with Slide-seq to simultaneously profile whole transcriptomes and TCRs within intact tissues [ 61 ].

Putting the data together in a spatial context: analytical approaches for ST

High-throughput spatial transcriptomics data pose new computational challenges and opportunities that require novel methods and tools. Some of the key problems include data preprocessing, cell-type deconvolution, identification of spatially variable genes, and inference of cell–cell interactions (Fig. 3 ).

Overview of spatial transcriptomics tasks and analysis tools. a The analysis framework standardizes diverse spatial molecular datasets into a consistent data format, followed by detecting spatial domains, deconvoluting spots, and analyzing cell–cell communication. b Spatial analysis tool timeline showcasing task categories through color coding, with dot shapes representing the language used by each tool

Pre-processing spatial transcriptomic data

The pre-processing of the spatial transcriptomic data usually involves converting the raw signal into spatially resolved expression matrix and the steps required vary between technologies. For imaging-based spatial transcriptome data such as smFISH and ISS, several steps are involved in transforming imaging-based spatial transcriptome data into an expression matrix. Initially, the 5-dimensional images (including x, y, z, rounds, and channels) undergo pre-processing, including filtering to improve the signal-to-noise ratio and image registration. The transcripts are next recognized within every image using spot detection and signal decoding. This generates 3-dimensional data that contains the x and y coordinates and their respective intensity. Lastly, the cell boundaries are identified, and the readouts are assigned to the corresponding cells. As there are variations across different smFISH-based protocols, lots of analysis in published research rely on in-house code that has limited documentation. To address this issue, the starfish package [ 62 ] and an enhanced version called PIPEFISH [ 63 ] have been introduced to offer a detailed and broader analysis framework, and these help in the conversion of raw images into spatially resolved gene expression. In addition, the commercial platform named MERSCOPE and Xenium also provides a dedicated pre-processing pipeline.

For spatial barcoding-based methods such as Visium and other commercial platforms, they provide a graphical user interface (GUI) which allows for spatial barcode registration and spot pre-filtering and then user-friendly pre-processing pipeline is subsequently used to generate spatial gene matrix. Once the users obtain the expression matrix using method-specific pipelines, they can conduct downstream analysis using popular packages like Giotto [ 64 ], Seurat [ 65 ], and Squidpy [ 66 ]. These packages offer a unified format and an in-depth analysis platform including quality control (e.g., filtering poorly expressed gene or spots), comprehensive visualization, unbiased clustering as well as dimensionality reductions.

Cell-type deconvolution

The lack of single-cell resolution in many existing spatially resolved methods makes it difficult to determine the spatial distributions of different cell types. Although a few methods have been achieved for deconvolution of bulk RNA-seq [ 67 ], there are some limitations for directly applying it to spatial transcriptomic data. Despite the pixel size of some methods can approach the size of mammalian cells, an individual spot of spatial transcriptomic data could overlap with multiple cells. Numerous tools have been developed to address this issue. For example, NMFreg [ 41 ], SpatialDWLS [ 68 ] and SPOTlight [ 69 ] decomposed spot transcriptomes into cell abundance by non-negative matrix factorization. Alternative tools based on probabilistic models such as stereoscope [ 70 ], SpatialDecon [ 71 ], RCTD [ 72 ], DestVI [ 73 ], STRIDE [ 74 ], SpaCET [ 75 ], Spatial-LDA [ 76 ], Cell2location [ 77 ] and Cottrazm [ 78 ] deconvolute the cellular proportion of the spots in cooperating with the gene signatures from scRNA-seq datasets. Some other tools like Spatial-ID [ 79 ], CellTrek [ 80 ], Tangram [ 81 ], DSTG [ 82 ] and CytoSPACE [ 83 ] utilize graph network or deep learning model to reconstruct the spatial cellular map by integrating single-cell and spatial transcriptomic data. Two recent studies evaluated the performance of various methods for cellular deconvolution of spatial transcriptomic data, providing users with the necessary information to select the method that best meets their needs [ 84 , 85 ].

Identification of spatial variable features

An initial step to link spatial features with biological significance is to identify genes that are enriched in specific spatial domains. Several methods have been developed to identify spatially variable genes, such as trendsceek [ 86 ], SpatialDE [ 87 ]/SpatialDE2 [ 88 ], SPARK [ 89 ]/SPARK-X [ 90 ], sepal [ 91 ], MULTILAYER [ 92 ], gliss [ 93 ], GPcounts [ 94 ], BOOST-GP [ 95 ], CoSTA [ 96 ], SOMDE [ 97 ], FISHFactor [ 98 ] and MEFISTO [ 99 ], these methods evaluate each gene individually and provide a p -value to indicate the spatial variability of a gene. Typically, these widely used tools involve unbiased spatial domain detection based on the intrinsic variance of gene expression. However, these approaches do not account for the neighboring similarity of cells or spots in spatial domains. As a result, the spatial expression patterns of the genes identified by these methods are not always guaranteed.

Several new methods have emerged for detecting spatial domains from spatial transcriptomic data. BayesSpace [ 100 ], SC-MEB [ 101 ] and SPRUCE [ 102 ] enhance the resolution of clustering analysis by bayesian mixture model-based methods. FICT [ 103 ], SpiceMix [ 104 ], stPlus [ 105 ], ClusterMap [ 106 ] and SpatialPCA [ 107 ] enhance the resolution of clustering analysis by incorporating spatial neighborhoods. SpaGCN [ 108 ], STAGATE [ 109 ] and MAPLE [ 110 ] implement a deep learning approach that integrate gene expression, spatial location as well as histology to identify spatial domains. stLearn [ 111 ] detects spatial domains using spatial location and morphological features to normalize gene expression data.

Due to lack of single-cell resolution among some widely used spatial protocols, the identification of spatially variable genes or domains will be biased by spatial cellular abundance. While deconvolution analysis can determine the cellular abundance in spatial transcriptomic data, current methods do not consider cellular proportions for searching spatially variable genes. In the context of mixtures of cell types, a recently developed tool called cell type-specific inference of DE (C-SIDE) employs a general parametric statistical model to estimate spatially variable genes with stratifying by cell type [ 112 ].

Deciphering cell-to-cell communication

Ligand-receptor-mediated intercellular interactions play a critical role in organismal development and homeostasis. Typically, these cell-to-cell interaction tools infer the potential interaction based on the ligand-receptor pairs such as CellPhoneDB [ 113 ] as well as NicheNet [ 114 ]. However, existing models for inferring intercellular communication in tissues rely on molecular profiles of dissociated cells, without considering their spatial proximity. Soon afterward, GCNG [ 115 ] and node-centric expression models (NCEM) [ 116 ] utilize graph convolutional neural networks (GCNs) to modeling cellular neighborhood graph in cooperating with the pairwise expression of genes to predict cellular interaction across space. An alternative approach named COMMOT (COMMunication analysis by Optimal Transport) use the collective optimal to infer the cell-to-cell communications based on ligand-receptor pairs [ 117 ].

Putting ST in the development and disease context

The rapid development of ST methods and analytic tools has enabled us to study the gene expression and cell function in 3D tissue/organs at an unprecedented resolution. We can now measure the spatial coordinates and molecular profiles of thousands of cells or even single cells within a tissue/organ, and reveal how they are organized and interact with each other. This allows us to better understand the complex biological processes that occur during development and disease.

Embryonic development

Organogenesis is a complex process that involves dramatic changes in gene expression and frequent shifts in cell fate within a short time span, resulting in the formation of various organs and cell types. Errors in this critical stage can cause serious birth defects, so it is important to examine the spatiotemporal expression patterns during organogenesis to better understand the mechanisms of disease (Fig. 4 ).

Spatial transcriptomics provides new insights into the molecular mechanisms underlying organ and embryonic development and human pathological tissues. a Stereo-seq maps spatiotemporal transcriptome dynamics in developing mouse embryos, detailing tissue-specific identities at different stages. b Using scRNA-seq and ST, the spatiotemporal landscape of the mouse stomach and intestine was established, unveiling distinct cell clusters and their interactions responsible for gastric compartmentalization. c ST provides a detailed transcriptional map of cell types within the developing heart at three stages, pinpointing cell-type-specific gene expression within distinct anatomical regions. d By utilizing coronal brain sections covering the entire anterior–posterior axis, ST generates a comprehensive molecular map of the mouse brain. e Combining various ST techniques using both mouse and human tissue samples unveiled widespread changes in the transcriptome and co-expression networks caused by amyloid plaques in (AD). f Employing ST into tissue repair uncovers molecular compartmentalization and transcriptome alterations in both steady state and mucosal healing. g ST elucidate the dynamic gene expression in the tissue during pathogen infection. h The complex cellular structure and heterogeneity of cutaneous lupus erythematosus has been characterized by integrating scRNA-seq and ST analysis in autoimmune diseases

To explore the spatial coordination of embryo development, researchers have developed Stereo-seq [ 118 ], a method that preserves single cell resolution while capturing spatial heterogeneity at larger scales. Stereo-seq was used to generate the first spatiotemporal “whole organism” profile of mouse embryogenesis, revealing the tissue- and location-specific transcriptional regulation and cell fate determination over time. Similarly, Asp et al. obtained the first spatiotemporal resource of human developing heart, which comprehensively characterized the dynamic gene expression across time and space at an organ-wide level during human cardiac morphogenesis [ 119 ]. Zhao et al. constructed a comprehensive spatiotemporal transcriptome map of the tissues developing along the gut axis during mouse embryonic development from E9.5 to E15.5, and showed that mesenchymal-epithelial interactions regulate key developmental events and cell fate decisions [ 120 ], providing new insights into genetic defects in neonatal disease and gut development. Zeng et al. elucidated the evolving molecular and cellular terrain of early gastrulation and nervous system development. Through an analysis of spatial transcriptomic profiles of human embryos, they unveiled processes such as cell diversification, spatial arrangement of neural tube cells, and crucial signaling pathways implicated in cellular transformation [ 121 ]. Arutyunyan et al. offer an in-depth investigation into postimplantation trophoblast differentiation using spatial multiomics analysis, providing insights that can guide the development of experimental models for studying early pregnancy in the human placenta [ 122 ].

Likewise, spatiotemporal profile of developmental tissue from human [ 119 , 123 , 124 , 125 , 126 ], mouse [ 118 , 120 , 127 , 128 ], zebra fish [ 129 ], fruit fly [ 130 ], and chicken [ 131 ] have been generated, which offer valuable resources for developmental biology and facilitate the understanding of abnormal mammalian organogenesis.

Homeostatic tissue architecture

Once developed, tissues/organs under homeostasis maintain an ordered spatial architecture. ST is widely used to analyze the molecular spatial structure of tissues and to create biomolecular maps for clinical and biological research (Fig. 4 ). ST-based approaches can map the entire brain [ 132 , 133 , 134 ] or specific regions such as olfactory bulb [ 40 , 135 ], dorsolateral prefrontal cortex [ 136 , 137 ], hippocampus [ 138 ] and arcuate nucleus [ 139 ] without tissue dissociation of delicate neurons. Ortiz et al. created a molecular map of the adult mouse brain by hybridizing 75 coronal sections from one hemisphere onto the ST array [ 140 ]. Mapping human dorsolateral prefrontal cortex uncovered spatial patterns in genes associated with schizophrenia and autism [ 141 ], suggesting possible mechanisms of genetic susceptibility to these disorders. Recently, Chen et al. created an extensive 3D single-cell atlas of the cynomolgus monkey cortex. This atlas offers insights into the cellular and molecular underpinnings of primate brain evolution, development, and pathogenesis [ 142 ].

To explore the spatial coordination of human immune development, Suo et al. presented a spatial atlas of human immune system across prenatal hematopoietic organs and characterized the developing immune system [ 143 ]. They also functionally validated the properties of human prenatal innate-like B and T cells. Likewise, Gao et al. constructed a spatial profile of mouse fetal liver to dissect hematopoietic stem cell and multipotent progenitor development and expansion in fetal liver [ 128 ]. They discovered novel “pocket-like” units of hematopoietic stem and multipotent progenitor cells, which may affect the efficacy of stem cell therapies. ST-based approaches have also been applied to assess the homeostasis of healthy tissues such as prostate [ 144 ], lung [ 145 , 146 , 147 , 148 ], liver [ 149 , 150 , 151 , 152 ], kidney [ 153 , 154 , 155 ], intestine [ 156 , 157 ], heart [ 158 , 159 , 160 , 161 ], endometrium [ 162 ], embryo [ 163 , 164 ], muscle [ 165 ], adipose [ 166 ] and bone [ 167 ]. These valuable data resources will enhance our understanding of how cell populations collaboratively shape tissue morphology. With expected future advances in the spatial genomics field, the increased resolution and capture size will enable detailed investigation of rare cell populations across spatial domains.

Disease/pathological conditions

ST is also a powerful tool for elucidating the molecular mechanisms of pathogenesis by dissecting localized gene expression in normal and abnormal regions of tissue (Fig. 4 ). Alzheimer’s disease (AD) is a devastating neurological disorder characterized by progressive loss of mental skills, cognition, and physical function. To understand the gene alterations associated with amyloid plaques in AD, several studies used ST to reveal genome-wide transcriptomic changes and co-expression networks induced by amyloid plaques [ 168 , 169 ]. Similarly, Navarro et al. generated spatial profiles of AD mouse models in early phase and improved our understanding of gene expression perturbation in hippocampus and olfactory bulb during disease progression [ 170 ]. ST has also been applied to other neurological diseases, such as brain [ 171 , 172 , 173 ] or spinal cord [ 174 ] injury as well as neurodegenerative diseases [ 175 , 176 , 177 ]. For example, Maniatis et al. conducted a spatiotemporal profile of mouse spinal cord over the course of amyotrophic lateral sclerosis [ 175 ], revealing the molecular mechanisms regulating sub-populations of microglia and astrocytes involved in each stage of disease progression. The spatial profile of dysfunctional brains facilitates the discovery of novel mechanisms of brain diseases, leading to the development of new molecular biomarkers/targets.

In addition to the central nervous system, spatially resolved analysis reveals spatial heterogeneity and region-specific cellular crosstalk in tissue regeneration. To uncover the molecular regionalization of the colon repair process, Sara et al. exploited ST to characterize the transcriptomic landscape of colon at steady state and mucosal healing [ 178 ]. They revealed that drastic transcriptomic changes occur in the distal colon such as the JAK-STAT and TNF-α pathway, but not in the proximal colon. Ben-Moshe et al. analyzed the spatial and temporal dynamics of the coordinated response of multiple cell types during liver regeneration [ 179 ]. Frede et al. discovered the expansion of an IFN-induced B cell subset during mucosal healing, with the depletion of this B cell population leading to improved mucosal healing following intestinal injury [ 180 ]. Likewise, ST tracks the interactions between lymphocytes and wound edge epithelium of the skin, identifying RORγt + γδ T cell-derived IL-17A as an essential mediator for skin repair [ 181 ].

Similarly, ST analysis reveals the regional heterogeneous muscle pathology during injury and generated the pilot datasets to investigate the molecular dynamics of muscle regeneration [ 165 , 182 , 183 , 184 ]. Remarkably, McKellar et al. dveloped a spatial total RNA-sequencing approach that captures coding, noncoding and viral RNAs, and identified spatially expressed noncoding RNAs in skeletal muscle regeneration [ 182 ].

ST also maps the pathogenesis of infectious diseases. To characterize and understand the host-microbe interaction across space, Gracia Villacampa et al. performed spatial genome-wide RNA analysis on the lung of the SARS-CoV-2 patient and identified distinct spatial expression modules and the coordinated enrichment of specific cells associated with infection by NNMF method [ 173 ]. Furthermore, Mothes et al. revealed the significance of activated adventitial niches in driving prolonged lung immunopathology by ST, linked to chemokine up-regulation, endothelial-to-mesenchymal transition, tissue fibrosis, CCR7-expressing exhausted T cell accumulation, and the formation of lymphoid aggregates and ectopic lymphoid structures [ 185 ]. In another study, Boyd et al. used scRNA-seq and ST to elucidate the dynamic gene expression of lung under infection and revealed that lung fibroblasts are critical for coordinating immune response at the site of infection by producing extracellular matrix remodeling enzymes ADAMTS4 [ 186 ].

Most studies on spatial host-microbe interactions focus on the gene expression of host due to the inability to capture the non-polyadenylated transcripts. To overcome this limitation, McKellar et al. demonstrated a spatial total RNA-seq by adding poly(A) tails to RNAs in situ and profiled the coordinated heterogeneity of heart under viral-induced myocarditis [ 182 ]. Moreover, Saarenpää et al. [ 187 ] and Lötstedt et al. [ 188 ] developed spatial host-microbiome sequencing by simultaneously capturing mRNA and 16S sequences. These improvement versions of ST reveal the spatial organization of microbes within the hosts as well as the coordinated immune response of host upon infection.

The ability of ST to investigate the signatures of disease-driving cells versus cells from the normal anatomical regions offers insights into the pathogenic microenvironment in inflammatory disease. For example, Ferreira and colleagues utilized ST to identify localized renal cell types and immune cell to uncover potential chemotactic signals underlying the pathogenesis [ 189 , 190 ]. Billi et al. performed integrated scRNA-seq and ST analysis on skin of cutaneous lupus erythematosus (CLE) patients, and demonstrated that the accumulation of CD16 + DCs drives CLE pathogenesis [ 191 ]. Krausgruber et al. conducted spatial transcriptomics on patient-derived granulomas, uncovering a network of pathogenic macrophages, T cells, and fibroblasts within these structures [ 192 ]. Li et al. conducted a spatially resolved multiomics analysis on primary biliary cholangitis (PBC) samples, revealing the presence of a distinctive population of DUOX2 + ACE2 + small cholangiocytes that play a role in PBC pathogenesis [ 193 ]. To understand oral chronic disease pathogenesis, Caetano et al. performed spatial transcriptomic analysis on human and mouse oral mucosa, and defined highly specialised epithelial and stromal compartments describing location-specific immune programs [ 194 ]. ST analysis was also conducted in other inflammatory disorders such as arthritis [ 195 ], periodontitis [ 196 ], IgG4-related disease [ 197 ] and psoriasis [ 198 , 199 ].

ST is also improving our understand of other disease with anatomically distinct regions such as fatty liver disease [ 200 ], kidney with diabetic disease [ 164 ], heart failure and myocardial infarction [ 201 , 202 ]. Taken together, ST expanded our understanding of spatially localized disease mechanisms, and identified distinct cell populations driving or being activated by disease.

ST probably has the widest application in cancer research (Fig. 5 ). Tumor is a complex ecosystem composed of heterogeneous molecules, cells, and tissues. Intratumor heterogeneity across time and space is a major challenge to cancer therapy, which can be investigated by ST.

Spatial transcriptomics techniques facilitate the study of tumor microenvironment heterogeneity and tumor heterogeneity. a ST examines the diversity within cancer-associated fibroblasts and immunosuppressive molecules within the microenvironment of breast cancer. b ST enables the identification and comprehensive exploration of unique tumor microenvironment regions, such as the tumor interface and tertiary lymph nodes. c ST analyzes the spatial distribution of PDAC-associated heterogeneity, identifying highly heterogeneous and transitional PDAC subpopulations. d ST applied to breast cancer biopsy identified that tumor cells harboring GATA3 mutations became more invasive, revealing the spatial heterogeneity of breast cancer

The first layer of intratumor heterogeneity is the gene expression and phenotypical variations of tumor cells themselves. Nagasawa et al. dissected the spatial heterogeneity of ductal carcinoma of the breast cells by ST, and identified that GATA3 mutation-harboring tumor cells underly the progression to invasive cancer [ 203 ]. Similarly, ST on breast cancer biopsies identified ductal carcinoma in situ (DCIS) areas with extensive spatial heterogeneity and distinct subclones that cannot be detected by conventional transcriptome analysis [ 40 , 204 ]. In pancreatic ductal adenocarcinoma (PDAC), deconvolution of spatial transcriptome data identified highly heterogeneous and transitional PDAC subpopulations exhibiting signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition [ 205 ]. Hao et al. analyzed the spatial distribution of PDAC hypoxia-related heterogeneity based on spatial transcriptomics (ST), revealing the localization of highly aggressive subgroups and their changes in hypoxia-related genes [ 206 ]. ST on FFPE human prostate cancer revealed that luminal cells in tumors are greatly expanded in the invasive carcinoma region and did not co-localize with basal cells [ 207 , 208 ]. ST also provides new insights into drug resistance tumor cells. ST analysis of paired primary and recurrent prostate cancer samples demonstrated that treatment-resistant subpopulations are interspersed in apparently benign tissues with unique molecular features [ 209 ], driving prostate cancer relapse.

The second layer of intratumor heterogeneity involves the complex interplay between tumor cells and tumor microenvironmental (TME) cells. One area that draws a lot of interest is the tumor-immune cell interactions, which have implications in cancer immunotherapy. In breast cancer, the spatial profiling of the stromal immune niche in tumors provides insights into the regulation of antitumor immunity. Spatially distinct subclasses of cancer-associated fibroblasts colocalize with and may be involved in direct regulation of immune cells [ 210 ]. Lipid-associated macrophages and CXCL10 hi macrophages as a major source of immunosuppressive cells in the TME, and spatial analysis revealed their juxtaposition with PD-1 + lymphocytes [ 210 ]. Within lung adenocarcinoma, spatial transcriptomics unveils an upregulation of genes associated with VEGF and CCR2 signaling in response to Treg cell depletion. Notably, short-term VEGF blockade significantly enhances control over the progression of PD-1 blockade-resistant lung adenocarcinoma [ 211 ]. Likewise, Ozato et al. demonstrated that tumor cells trigger the expression of human leukocyte antigen G (HLA-G), leading to the generation of secreted phosphoprotein 1 (SPP1) + macrophages, which in turn bestow colorectal cancer cells with anti-tumor immune properties [ 212 ].

Aside from tumor-immune cell interactions, ST also reveal the interplay between tumor and stromal cells. In esophageal squamous-cell carcinoma (ESCC), Chen et al. employed spatial transcriptomics to characterize the progression of ESCC tumorigenesis across different stages, highlighting the gradual depletion of ANXA1 in epithelial cells along the tumorigenesis process, which in turn promotes ESCC by triggering the formation of cancer-associated fibroblasts [ 213 ]. In cutaneous squamous cell carcinoma, Khavari group orthogonally integrated single-cell and high-dimensional spatial data of normal and diseased tissues, revealing that tumor-specific keratinocytes as hubs of intercellular communication and observed multiple hallmarks of potential immunosuppression [ 214 ]. In colorectal cancer, tumor-specific FAP-positive fibroblasts and SPP1-positive macrophages are closely localized, which may contribute to poor patient survival [ 215 ]. ST on PDAC also revealed colocalization of stress-responsive cancer cells and inflammatory fibroblasts [ 216 ]. Similar cancer-TME interactions have been revealed by ST in ESCC [ 217 ], neuroblastoma [ 218 ], and malignant gliomas [ 219 , 220 , 221 ].

The third layer of intratumor heterogeneity involves special spatial regions such as the tumor-normal interface and tertiary lymph like structures (TLS), forming unique tumor niches implicated in cancer progression. In gastric cancer, Sun et al. performed spatially resolved multi-omics to identify an immune cell-dominated 'tumor-normal interface' region characterized by unique transcriptional signatures and notable immunometabolic changes [ 222 ]. In liver cancer, Wu et al. investigated the tumor ecosystems and cell interactions within an "invasive zone" around the liver tumor border, and identified the damaged hepatocytes with high expression of serum amyloid A1 and A2 which was associated with a worse prognosis [ 223 ]. In kidney cancer, scRNA-seq and ST analysis of cells at the tumor-normal interface versus the tumor core revealed an epithelial-mesenchymal transition meta-program highly enriched at the tumor-normal interface that co-localizes with IL1B-expressing macrophages [ 224 ]. Anderson et al. performed spatial profiling of HER2-positive breast tumors, mapped tumor-associated cell types to find TLS, and constructed a predictive model to infer presence of tertiary lymphoid-like structures [ 225 ]. Similarly, Wu et al. identified a TLS-50 signature to accurately locate TLS in primary liver cancer [ 226 ].

Conclusion and perspective

In summary, ST is a powerful method for mapping gene expression in tissues/organs that can provide new insights into the mechanisms of development, homeostasis and disease (Fig. 6 ).

The future directions of spatial genomics. The ongoing advancement of spatial genomics techniques is driving research in areas such as tissue homeostasis, diseases, tumor and embryo development, and tumor heterogeneity. These advancements hold the potential to offer valuable insights into both biological understanding and clinical applications

Despite the explosion of technological innovation of ST in recent years, some of the technical limitations of current ST approaches remain. The first challenge is how to improve resolution without compromising throughput. The spatial resolution of current methods can vary from subcellular to regional level, and there’s often a trade-off between resolution and throughput. Microdissection or in situ image-based sequencing can achieve high spatial resolution but at the expense of low throughput and high complexity. Conversely, techniques like spatial barcoding-based approachs offer improved scalability and throughput, albeit at the expense of diminished spatial resolution. The development of high-throughput, enhanced resolution spatial barcoding techniques such as Stereo-seq is a step in the right direction, but there is still room for improvement in terms of the spot size, spot distance, and transcripts detected per spot to achieve single-cell, subcellular spatial profile.

The second challenge is the diverse range of methodologies that leads to a multitude of file formats and data structures. This complexity makes data and protocol sharing more difficult. Therefore, there is a growing need to develop universally applicable file formats and establish standardized pipelines for data storage, access, and cross-dataset integration.

The third challenge involves harmonizing spatially resolved data across both intra-omics and cross-omics layers. When considering the integration of spatially resolved omics data within the same layer, a range of complexities emerges due to technical concerns during sample collection. These challenges encompass issues like possible tissue gaps, distortions introduced during sectioning, tears, and the presence of variations in both biological and technical facets, such as differing resolutions across various ST methodologies. In the context of cross-omics layers, the integration of multi-omics data poses a significant challenge. This is particularly pronounced due to the considerable variation in feature counts between different modalities (for instance, proteins versus transcripts) and the existence of distinct statistical distributions.

These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Therefore, researchers need to carefully consider the trade-offs between different methods and choose the most suitable one for their specific biological questions and experimental settings.

Accordingly, the future directions of ST are to improve the spatial resolution, gene coverage, sensitivity, and reducing complexity of existing methods. We could develop new probes, arrays, sequencing strategies and imaging systems to increase the accuracy and throughput of ST. We also need to develop new computational tools to improve data processing, integration, visualization and interpretation, as well as inferring cell–cell interactions, spatial patterns and regulatory networks. Integrating ST with other omics methods, such as proteomics, metabolomics or epigenomics, could obtain a more comprehensive view of biological systems at multiple levels of resolution. Ultimately, we could develop spatiotemporal single-cell omics methods that can capture the dynamics and positioning of molecular profiles in living tissues.

Availability of data and materials

Not applicable.

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Acknowledgements

We thank Bin Chen for technical support, and Biorender.com for image resources.

This work is supported by the National Natural Science Foundation of China (82303975 to R.Z., 82273117 to Y.W. and 82173179 to Y.Z), the National Key Research and Development Program of China, Stem Cell and Translational Research (2022YFA1105200 to Y.W.), the China Postdoctoral Science Foundation (2022TQ0226 to R.Z.), and West China Hospital, Sichuan University (National Clinical Research Center for Geriatrics Z2021JC006 to Y.Z. and Post-Doctor Research Project 2023HXBH100 to R.Z.).

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Ran Zhou and Gaoxia Yang contributed equally to this study.

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Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China

Ran Zhou, Gaoxia Yang & Yuan Wang

National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China

Gaoxia Yang & Yan Zhang

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R.Z. and G.Y. wrote the manuscript and drew the figures. Y.W. and Y.Z. outlined and substantially revised the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Yan Zhang or Yuan Wang .

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Zhou, R., Yang, G., Zhang, Y. et al. Spatial transcriptomics in development and disease. Mol Biomed 4 , 32 (2023). https://doi.org/10.1186/s43556-023-00144-0

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Spatial Order and How to Use It in Your Essay

Author Matt Carlson
Published October 15, 2022

You could be struggling to understand the meaning of spatial order in writing. Let us, in this article, demystify the tenets of spatial order. Good command of English alone won’t make a difference in writing the best quality essays. Instead, combining the command of English with good organization of your essay makes you an effective writer.

Regarding organization, a good essay has to have the best presentation and logical development of ideas.

Your tutors or professors do not tell you that these are some of the areas they asses when marking your essays.

allwriting.net through its R&D department has worked hard to ensure that our clients get the best.

A good essay structure means the ideas are woven together in the best manner possible. However, apart from the structure, a cohesive organization glues the details together to make a good essay.

Spatial order is an example of a cohesive organization that writers can use for excellent essays.

Spatial Order Definition

A spatial order organizational style is also known as the order of place or space structure.

It helps in the presentation of ideas or things as is in their locations.

Compared to the chronological order that lists ideas from top to bottom based on their date of occurrence, spatial order focuses on the description of the locations of things.

Using the Spatial Order in Essay Writing

Spatial ordering or spatial organization is used when writing descriptive essays.

The main agenda here is to invoke the senses of the reader and help them have a good grip on the concepts and facts presented in the essay.

There are different steps to follow when using the spatial order:

Decide on the scenarios that need to be described in the beginning : Always start with the most important ideas, especially when you have two or more scenarios, like when writing a descriptive essay . Space the ideas based on their relevance, weight, and importance in the essay and make up your mind on what, to begin with.
This sets the mood for your audience and keeps them glued to your paper. Should spatial order reflect in the first paragraph? Yes, this would be good as your thesis statement will lead the audience into the body and keep them glued to the end.
Want quality custom written essays by the experts? You should order it with us. allwriting.net has writers who are well versed in spatial ordering. Tutors from the world over like spatial ordering: it is a gateway to scoring high in your essays.

How do I use Spatial Order in an Essay?

Details of the Scene : Once you have decided on the scenarios, proper planning is inevitable. While planning is quintessential in any form of writing assignment whether it be essays, research papers, speech, and coursework, spatial ordering means going further than the initial plan. It means setting every detail when describing a scene, to capture the finer details, the physical location of everything as you begin the writing process.
Have a logical flow when describing the elements : Spatial order is all about logic. When writing in a logical order, the elements flow seamlessly. As a rule of thumb, spatial order entails the description of events from top-bottom or the furthest right to the furthest left; the decision is purely on you as a writer. When ideas are closer to the rights of your readers, they interact with it first hand and do not struggle to get the gist of your writing. Forming logical relationships is a tough endeavor. However, the use of transitions and avoiding too many adjectives in your writing can spark the minds of the audience. You must also display high levels of critical thinking and creativity to maintain an audience to the end.

If you are writing a descriptive essay about a place or a narrative essay, spatial ordering can come in handy.

The Importance of Spatial Order in Essay Writing

The spatial order has some benefits besides assuring you of the best grades.

It simplifies the complex processes and scenes and thus increasing the flow and understanding of ideas in the essay. A reader easily relates to ideas and facts in a spatially ordered essay.
It improves the originality of your essay as you express creativity and critical thinking more than using logical order or other ordering approaches.
It evokes the senses of the reader and makes it easy for them to visualize the gist of the essay. You would want to please that hard professor to get the best grades. Trust us, our team of experts can write you the best essay with flawless grammar, well researched, spatial order arrangement, and plagiarism free. Place an order and enjoy our special

Note that using too much spatial arrangement in your essay is discouraged as it might give some unimportant scenes importance.

Therefore we recommend only using spatial ordering when seeking to spice up an essay but be wise when using spatial order.

It is always best to get academic writing help if you do not understand how to.

List of Transitional Words to use in Spatial Order Essays

To the left
Slightly below
Parallel to
Alternate to
Adjacent to
To the side
On the right hand
On the left hand
At the top of
At the bottom of
At a depth of

Spatial Essay Order Outline Example

Mostly, spatial order essays are five-paragraph essays. They can be anything between 500 words or 1000 words, depending on the essay prompt.

Below are some of the components of the outline:

Spatial Order Essay Introduction

Essay hook or grabber to lure the attention of the readers
Background of the essay, covering the reasons for the topic of object choice
Thesis statement, which outlines what the spatial order essay is all about.

The Body of the Spatial Order Essay

Description of the first object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence).
Description of the second object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence)
Description of the third object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence)
Description of the nth object (topic sentence, supporting ideas and facts (in-text citations and signal words), and concluding sentence)

Note that every single body paragraph should contain only one idea or object description.

Conclusion of a Spatial Order Essay

Concluding transitions and sentence
Summary of the entire essay
Restated thesis statement
Final words or concluding remarks

We hope that our advice helps.

Posted by Matt Carlson

How to Write an Informative Essay + 3 informative essay examples

An informative essay is a piece of writing that teaches the...

Writing a Five Page Essay Paper Fast

Hacks to Write a Five Page Essay Paper in a Day or Less

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Published: 02 April 2024

Bento: a toolkit for subcellular analysis of spatial transcriptomics data

Clarence K. Mah 1 , 2 , 3 na1 ,
Noorsher Ahmed 2 , 3 na1 ,
Nicole A. Lopez 2 ,
Dylan C. Lam 2 , 3 , 4 ,
Avery Pong 2 ,
Alexander Monell 2 , 5 ,
Colin Kern 6 ,
Yuanyuan Han 6 ,
Gino Prasad 2 , 5 ,
Anthony J. Cesnik 7 ,
Emma Lundberg 7 , 8 , 9 ,
Quan Zhu 6 ,
Hannah Carter 1 &
Gene W. Yeo ORCID: orcid.org/0000-0002-0799-6037 2 , 3 , 10 , 11

Genome Biology volume 25 , Article number: 82 ( 2024 ) Cite this article

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The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell–cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene–gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

Introduction

The spatial organization of molecules in a cell is essential for performing their functions. While protein localization [ 1 ] and disease-associated mislocalization are well appreciated [ 2 , 3 ], the same principles for RNA have begun to emerge. For instance, the spatial and temporal regulation of RNA play a crucial role in localized cellular processes such as cell migration and cell division [ 4 , 5 ], as well as specialized cell functionalities like synaptic plasticity [ 6 , 7 , 8 ]. Mislocalization of RNA has been associated with diseases such as Huntington’s disease (HD), where defects in axonal mRNA transport and subsequent translation in human spiny neurons lead to cell death and neurodegeneration [ 9 , 10 , 11 , 12 ].

The study of subcellular RNA localization necessitates single-molecule measurements. Since the development of single-molecule fluorescent in situ hybridization (smFISH), recent advances in multiplexed methods such as MERFISH [ 13 ], seqFISH + [ 14 ], HybISS [ 15 ], and Ex-Seq [ 16 ] have enabled RNA localization measurements at near transcriptome scales, while maintaining single-molecule resolution. A number of computational toolkits, such as Squidpy [ 17 ], stLearn [ 18 ], Giotto [ 19 ], Seurat [ 20 ], and Scanpy [ 21 ] enabled the characterization of tissue architecture, cell–cell interactions, and spatial expression patterns. Despite the single-molecule measurements in spatial transcriptomics, these analytical approaches are limited to investigating spatial variation at the multicellular scale and lack the ability to investigate subcellular organization. To further our understanding of RNA localization and its function in normal and abnormal cell activity, we need to expand our analytical capacity to the subcellular scale.

Recent methods such as FISH-quant-v2 [ 22 ] and FISHFactor [ 23 ] identify subcellular patterns describing the spatial distribution of RNA species, but are unable to annotate more than a single gene per cell or are limited to analyze at most 20,000 molecules on accessible computing resources. In contrast, a single spatial transcriptomics experiment measures at least hundreds to thousands of genes across hundreds of thousands of cells. Additionally, methods such as ClusterMap [ 24 ] and Baysor [ 25 ] highlight the potential for transcript locations alone to inform meaningful domains such as cell and nuclear regions. Using spatial proteomics data, CAMPA [ 26 ] and Pixie [ 27 ] utilize subcellular spatial variation in protein abundance to identify subcellular regions and annotate pixel-level features.

Building on these promising approaches, we present Bento, an open-source Python toolkit for scalable analysis of spatial transcriptomics data at the subcellular resolution. Bento ingests single-molecule resolution data and segmentation masks, utilizing geospatial tools (GeoPandas [ 28 ], Rasterio [ 29 ]) for spatial analysis of molecular imaging data, and data science tools including SciPy [ 30 ] and Tensorly [ 31 ] for scalable analysis of high-dimensional feature matrices. Furthermore, Bento is a member of the Scverse ecosystem [ 32 ], enabling integration with Scanpy [ 21 ], Squidpy [ 17 ], and more than 30 other single-cell omics analysis tools.

Overview of Bento data infrastructure for subcellular analysis

In order to facilitate a flexible workflow, Bento is generally compatible with molecule-level resolution spatial transcriptomics data (Fig. 1 A), such as datasets produced by MERFISH [ 13 ], seqFISH + [ 14 ], CosMx (NanoString) [ 33 ], Xenium (10 × Genomics) [ 15 , 34 ], and Molecular Cartography (Resolve Biosciences) [ 35 ]. Bento’s workflow takes as input (1) 2D spatial coordinates of transcripts annotated by gene and (2) segmentation boundaries (e.g., cell membrane, nuclear membrane, and any other regions of interest) (Fig. 1 B). While 3D molecular coordinates are commonly included, 3D segmentation information is limited to z-stacked 2D segmentation, limiting its usability. If available, Bento can also handle arbitrary sets of segmentations for other subcellular structures or regions of interest. These inputs are stored in the AnnData data format [ 36 ], which links cell and gene metadata to standard count matrices, providing compatibility with standard single-cell RNA-seq quality control and analysis tools in the Scverse ecosystem [ 32 ]. With a data structure for segmentation boundaries and transcript coordinates in place, Bento can easily compute spatial statistics and measure spatial phenotypes to build flexible multidimensional feature sets for exploratory subcellular analysis and utilize these spatial metrics to augment quality control (Fig. 1 C).

Workflow and functionality of the Bento toolkit. A Single-molecule resolved spatial transcriptomics data from commercial or custom platforms are ingested into Bento where it is converted to the AnnData format ( B ), where it can be manipulated with Bento as well as a wide ecosystem of single-cell omics tools. C Geometric statistics are illustrated for the seqFISH + dataset, including metrics describing cell and nuclear geometries and cell density to assess overall data quality. D Bento has a standard interface to perform a wide variety of subcellular analyses

Bento offers a precise yet flexible palette of novel complementary subcellular analyses (Fig. 1 D). We introduce RNAforest, a multilabel approach for annotating RNA localization patterns adapted from FISH-quant v2 [ 22 ]. We find that many RNAs are spatially distributed according to gene function. We then implement RNAcoloc, a context-specific approach to quantify colocalization to characterize how genes colocalize with each other in a compartment-specific manner. Having established systematic patterning and organization of RNA transcripts, we demonstrate RNAflux, an unsupervised method for semantic segmentation of subcellular domains. RNAflux first quantifies subcellular expression gradients at pixel resolution before identifying consistent subcellular domains via unsupervised clustering. We demonstrate the utility of Bento’s tools by applying them to identifying critical localization changes in human iPSC-derived cardiomyocytes upon drug treatment with doxorubicin, a widely used chemotherapeutic known to cause cardiotoxicity [ 37 ].

RNAforest: utilizing subcellular landmarks to predict RNA subcellular localization

In computer vision, key points or landmarks are commonly used for tasks like facial recognition [ 38 ] and object detection. Analogous to these classical applications, we derive spatial features using cell and nucleus boundaries as landmarks to predict RNA localization patterns from spatial summary statistics. Building on the summary statistics used for classifying smFISH data in FISH-quant v2 [ 39 ], RNAforest consists of an ensemble of five binary random forest classifiers rather than a single multi-classifier model to assign one or more labels. These pattern labels, adapted from several high-throughput smFISH imaging experiments in HeLa cells [ 40 , 41 , 42 , 43 ], are broadly applicable to eukaryotic cells: (i) nuclear (contained in the volume of the nucleus), (ii) cytoplasmic (diffuse throughout the cytoplasm), (iii) nuclear edge (near the inner/outer nuclear membrane), (iv) cell edge (near the cell membrane), and (v) none (complete spatial randomness). It is important to note, as was done previously in FISH-quant v2 [ 39 ] that because of the 2D nature of the dataset, RNA that is in truth cytoplasmic but above or below the nucleus will still appear as though in the nucleus when collapsed in the z -dimension. As we use the FISH-quant v2 pattern simulation framework, this is accounted for in the training dataset.

We used the FISH-quant v2 simulation framework to generate realistic ground-truth data [ 42 ]. Each sample is defined as a set of points with coordinates in two dimensions, representing the set of observed transcripts for a gene in a particular cell. In total, we simulated 2000 samples per class for a total of 10,000 samples (see the “ Methods ” section). We used 80% of the simulated data for training and held out the remaining 20% for testing (Additional File 1 : Fig. S1A). Each sample is encoded by a set of 13 input features, describing characteristics of its spatial point distribution, including proximity to cellular compartments and extensions (features 1–3), measures of symmetry about a center of mass (features 4–6), and measures of dispersion and point density (features 7–13) (Fig. 2 A). These features are normalized to morphological properties of the cell to control for variability in cell shape. A detailed description of every feature is described in Additional File 1 : Table S1, and model architectures and hyperparameters are described in Additional File 2 : Table S2 (see the “ Methods ” section).

Subcellular localization pattern identification with RNAforest. A Thirteen spatial summary statistics are computed for every gene-cell pair describing the spatial arrangement of molecules and boundaries in relation to one another. The features (Supp. Table 1 ) are inputs for RNAforest, a multilabel ensemble classifier that assigns one or more subcellular localization labels: cell edge, cytoplasmic, nuclear, nuclear edge, and none. The colors for each label are used consistently throughout to figure. Top 10 genes for each label visualized for each label other than “none” in B U2-OS cells and C 3T3 cells. D and E are UpSet plots showing the proportion of measured transcripts assigned to each label. F and G show the relative label proportion across cells for each gene and are colored by the majority label ( F and G ). H Top 5 consistent genes for each label. I ssGEA identifies the enrichment of GO cellular component domains for each label in the 3T3 cell dataset. Stars denote p -values under thresholds defined in the legend. P -values are derived from ssGSEA permutation tests with Benjamini–Hochberg correction controlling for false discovery rate

We applied RNAforest on two datasets from different spatial platforms, cell types, and gene panel sizes: a MERFISH dataset in U2-OS cells and a seqFISH + dataset in 3T3 cells. Validation performance on manual annotation of subsets of both datasets shows that RNAforest generalizes well despite biological and technical differences (see the “ Methods ” section, Additional File 1 : Fig. S1B). The MERFISH dataset measured 130 genes (low plexity) with high detection efficiency per gene (111 molecules per gene per cell on average), while the seqFISH + dataset measured 10,000 genes (very high plexity) with lower detection efficiency (8 molecules per gene per cell on average) (Fig. 2 B, C, Additional File 1 : Fig. S1C-F). In agreement with previous work characterizing RNA localization of 411 genes [ 43 ], we find that genes commonly exhibit variability in localization across cells. This suggests that heterogeneity in localization likely generalizes to the entire transcriptome. Of the localization patterns besides “none,” “nuclear” was the most common (22.1%) in the U2-OS osteosarcoma cells (Fig. 2 D, F), while “cell edge” was the most common (15.9%) in the 3T3 fibroblast cells (Fig. 2 E, G).

In the U2-OS cells, we found many genes to have preferential localization in different subcellular compartments (Fig. 2 H). In agreement with our RNAflux findings, we find genes known to localize to the nucleus [ 44 , 45 ] to be frequently labeled “nucleus” (MALAT1, SOD2) and genes encoding secreted extracellular proteins [ 13 ] to be frequently labeled “nuclear edge” (FBN1, FBN2). As expected, we find genes preferentially “nuclear” and “nuclear edge” localized to mirror nucleus and endoplasmic reticulum genes found in a 10 k genes MERFISH study of U2-OS cells that included ER staining [ 46 ] (Additional File 1 : Fig. S2; see the “ Methods ” section). Leveraging the 3T3 seqFISH + dataset’s higher plexity, we were able to ask whether genes with similar localization preferences are functionally related. We applied gene set enrichment analysis to gene localization frequencies to identify enriched gene ontology terms [ 47 ] (Fig. 2 I; see the “ Methods ” section). Secretory processes were enriched in the nucleus and nuclear edge, which may be linked to increased transcription of fibroblast-related functions. Cell edge enriched pathways consisted of those with the cell membrane as their site of function (e.g., endocytosis and tight junction suggesting local translation of these genes). Additionally, the term for cell cycle was significantly enriched in the cytoplasm only. Genes without strong localization preference (most frequently “none”) were not significantly associated with any pathways. These genes likely do not undergo active transport and are functionally independent of local translation.

In summary, RNAforest gives a user a facile method for annotating RNA localization patterns and quantifying heterogeneity in a transcriptome-wide manner independent of RNA abundance. Beyond known RNA localizations, we find that transcript location is generally associated with known gene function, alluding to the systematic spatial regulation of RNA transport. We foresee RNAforest will be a valuable addition to characterize RNA localization across diverse spatial transcriptomics datasets.

RNAcoloc: an approach for context-specific RNA colocalization

In geospatial information processing, a fundamental feature that is often gleaned from large datasets is the colocation of objects (e.g., gleaning socialization metrics from cell phone colocation data in Singapore [ 48 ]). Colocation is similarly valuable in understanding co-translation and interaction networks of genes in a biological context [ 49 ]. Recent spatial transcriptomics approaches have used a number of colocalization metrics from the geographic information systems and ecology fields, e.g., the bivariate versions of Ripley’s K function (also known as cross-k-function) [ 50 ], Moran’s I [ 51 ], and the join count statistic [ 52 ]. These metrics are designed to measure spatial associations between two populations, i.e., gene A transcripts and gene B transcripts. However, it is more appropriate to think of all transcripts in a single cell from a single population; after all, RNA transcription and localization are not completely stochastic. We have shown that the subcellular distribution of RNA is highly structured with RNAforest. As such, we developed RNAcoloc, an approach that combines the Colocation Quotient (CLQ) [ 53 ] metric and tensor decomposition for context-specific RNA colocalization (see the “ Methods ” section). The CLQ is a colocalization statistic that is capable of accounting for the biophysical properties of RNA spatial distributions. First, the CLQ considers how clustered the overall RNA population is in a cell and measures whether specific pairs of genes are more clustered than expected given the spatial pattern of the overall population. Second, the CLQ is inherently asymmetric and captures the direction of attraction, i.e., the attraction of gene A to gene B is not the same as the attraction of gene B to gene A. This is most common when gene A and gene B have very different expression levels, which is prevalent due to overdispersion in gene expression data.

RNAcoloc calculates CLQ scores for each gene per cell in a compartment-specific manner, such that each sample has 2 scores, a nucleus and cytoplasm CLQ score. An initial comparison in the U2-OS dataset of global colocalization between nuclear and cytoplasmic fractions unsurprisingly found that transcripts from the same gene tend to cluster more tightly with themselves than with transcripts from other genes (Fig. 3 B). Additionally, self-colocalization is significantly stronger in the cytoplasm than in the nucleus. In conjunction with our findings from RNAforest analysis that genes of the same localization pattern tend to have similar functions, this suggests that the RNAs are more tightly spatially regulated once exported from the nucleus.

Compartment-specific RNA colocalization with RNAcoloc. A Transcripts are separated by compartment (nucleus and cytoplasm) before CLQ scores are calculated for every gene pair across all cells. This yields a cell × gene pair × compartment tensor. B Pairwise comparison of log CLQ distributions for gene pairs and self-pairs, further categorized by compartment. The Mann–Whitney U test was used for comparisons. Stars denote p -values below the legend threshold with Benjamini–Hochberg correction controlling for false-discovery rate. From top to bottom, group sizes are 12,254,430 (cytoplasm gene pairs), 115,187 (nucleus gene pairs), 6,778,402 (cytoplasm self-pairs), and 86,474 (nucleus self-pairs). C Tensor decomposition yields 4 factors. From left to right, the three heatmaps show the loadings of each factor for each dimension—compartments, cells, and gene pairs. Only the top 5 associated gene pairs for each factor are shown. D Top examples of compartment-specific colocalized gene pairs. Black scale bars denote 10 μm

By calculating CLQ scores for every gene–gene pair across compartments, RNAcoloc constructs a tensor of shape P × C × S where P , C , and S represent the number of gene–gene pairs, cells, and compartments, respectively (Fig. 3 A; see the “ Methods ” section).

RNAcoloc then applies tensor decomposition — specifically, non-negative parallel factor analysis — a data-driven, unsupervised approach for discovering substructure in high-dimensional data [ 31 , 54 ] to decompose the tensor into k “factors”. The number of factors is determined using the elbow method heuristic, optimizing for the root mean squared error (RMSE) reconstruction loss (see the “ Methods ” section). Unlike matrix dimensionality reduction methods, such as PCA, the order of the components (factors) is unassociated with the amount of variance explained. Each factor is composed of 3 loading vectors, which correspond to the compartments, cells, and gene pairs. Higher values denote a stronger association with that factor. Crucially for interpretation, factors derived from tensor decomposition are not mutually exclusive and can share overlapping sets of associated compartments, cells, and gene pairs.

Applied to the U2-OS dataset, RNAcoloc decomposes RNA colocalization into 4 factors. Examining factor loadings indicates two distinct subpopulations of cells with compartment-specific colocalization behaviors; cluster 1 cells exhibit uniform (Factor 0) and cytoplasmic (Factor 3) colocalization, while cluster 2 cells show nuclear (Factor 1) and cytoplasmic colocalization (Factor 2) (Fig. 3 C, D). Factor 3 describes the colocalization of gene pairs in the cytoplasm of cluster 1 cells, especially a number of genes that attract PIK3CA transcripts While little is known about PIK3CA RNA interactions, the PI3K pathway regulates mitotic organization, including the regulation of dynein and dynactin motor proteins. DYNC1H1 is among the top genes attracting PI3KCA and specifically encodes cytoplasmic dynein, a motor protein critical for spindle formation and chromosomal segregation in mitosis [ 55 ]. This hints that not only is compartmental localization of RNA linked to the cell cycle [ 46 ], but RNA-RNA interactions may play a role as well. In cluster 2 cells, MALAT1 attracts CNR2 transcripts more than expected in the cytoplasm. Even though MALAT1 is canonically abundantly localized to the nucleus, this demonstrates that the CLQ score identifies gene pairs colocalizing more than expected despite the disproportionate expression of MALAT1 relative to CNR2, whereas other approaches seem confounded by large differences in expression [ 45 ].

We demonstrate the ability of RNAcoloc to quantify compartment-specific gene-pair colocation by exploring cytoplasmic vs. nuclear colocalization. As we found separately with RNAforest, RNAcoloc analysis finds evidence that RNA transport is spatially regulated, especially after nuclear export. We highlight several examples of colocalization suggesting how RNA localization allows the same gene to have multiple functions in a spatially dependent fashion, i.e., depending on its molecular neighbors and local environment [ 56 , 57 ]. We foresee RNAcoloc will be increasingly relevant as many spatial technologies are beginning to image proteins along with RNA, which can be used to delineate more granular compartments, such as cell organelles or distinct regions, e.g., neuron cell bodies vs dendrites.

RNAflux: unsupervised semantic segmentation of subcellular domains in single cells

To build on RNAforest, we overcame the restricted number of localization patterns defined by the supervised method by framing RNA localization as an unsupervised embedding problem. RNAflux looks at local neighborhoods within the space of a cell and builds a normalized gene composition per neighborhood. Differences in neighborhood compositions can be leveraged to identify distinct subcellular domains in a manner that is entirely unsupervised and independent of cell geometry.

We applied this embedding procedure to compute a gene composition vector for every pixel in 2D coordinate space, generating a spatially coherent embedding across entire cells (Fig. 4 A; see the “ Methods ” section).

RNAflux finds distinct subcellular domains with consistent spatial organization and local gene composition. A Flowchart of RNAflux and fluxmap computation. Local neighborhoods of a fixed radius are arrayed across a cell and a normalized gene composition is computed for each pixel coordinate, producing an RNAflux embedding. The first three principal components of the RNAflux embedding are visualized for U2-OS cells coloring RGB values by PC1, PC2, and PC3 values respectively for each pixel. Fluxmap domains are computed from each RNAflux embedding to create semantic segmentation masks of each subcellular domain. B The left panel shows a field of view of U2-OS cells, dots denoting individual molecules colored by gene species, nuclei, and cell boundaries outlined in white. For the same field of view of cells, the center panel shows RNAflux embeddings and the right panel shows fluxmap domains. C The scatter plot shows how the composition of each gene is distributed across fluxmap domains. The position of each point denotes the relative bias of a given gene’s composition across fluxmaps. D Heatmap showing the fraction of pixels with a positive enrichment value for each APEX-seq location for each fluxmap domain. E – I The most highly enriched location is shown for each fluxmap domain. Domain boundaries are denoted by white lines within each cell

Applied to a MERFISH dataset with a target panel of 130 genes across over 1153 U2-OS cells, we demonstrate that RNAflux embeddings can detect transcriptionally distinct subcellular domains. Performing dimensional reduction of the embeddings showed that the top sources of variation spatially correspond to the nucleus, the nuclear periphery, and cytoplasmic regions consistently across cells (Fig. 4 B; see the “ Methods ” section) confirming that RNAflux measures intracellular transcriptional variation, as opposed to intercellular variation. To delineate compositionally similar domains in a data-driven manner, we cluster pixel embeddings using self-organizing maps (SOMs), effectively performing unsupervised semantic segmentation (see the “ Methods ” section). We denote the resulting clusters as “fluxmap domains.” We found that this assigned pixels to 5 fluxmap domains, consistently highlighting spatial regions across every cell (e.g., fluxmap 2 is always nuclear while the remaining domains constitute the cytoplasm) (Fig. 4 B). By considering the spatial distribution of molecules across fluxmap domains, we can quantify the composition of molecules for each gene across fluxmaps (Fig. 4 C), e.g., nuclear-localized MALAT1 [ 44 ].

We sought to characterize the fluxmap domains with known information about RNA localization. We used data from a previous study that measured gene expression at “distinct subcellular locales” via APEX-seq, a technique for proximity labeling and sequencing of RNA [ 58 ]. Of the 3288 genes differentially enriched to one or more locales, 63 overlapped with the 130 MERFISH genes. The location enrichment score for each pixel is calculated by taking the weighted sum of its RNAflux embedding and the measured relative enrichment, i.e., log fold change measured by APEX-seq loadings for a given organelle-specific geneset (see the “ Methods ” section). Visualizing each pixel’s location-specific enrichment scores from the APEX-seq dataset highlights the subcellular localization of these compartments, including the cytosol, nucleus, nucleolus, nuclear pore, nuclear lamina, endoplasmic reticulum lumen (ER lumen), ER membrane (ERM), and the outer mitochondrial membrane (OMM) (Fig. 4 D). We find the nuclear compartments have high scores in domain 2, while the cytoplasm scores rank highest in domains 4 and 5. Both the ERM and OMM scores are the strongest in domain 1 (Fig. 4 E).

The most common application for spatial transcriptomics is in mapping heterogeneous cell types in large tissue samples. This presents several challenges. First, the panels for these experiments are weighted heavily towards cell type markers determined by single-nuclei RNA-seq, potentially reducing the intracellular variability in the expression on which Bento relies. Second, substantial intercellular heterogeneity can skew the low-rank embedding by introducing too much variance in requisite cell radii information. To explore the applicability of RNAflux on tissue, we applied it to a previously published breast cancer tissue dataset generated by 10 × Xenium [ 59 ]. We successfully reproduced the identification of unique cell types that reflect canonical expression markers (Fig. S 4 A and B). We apply RNAflux to cell type-disaggregated subsets of the full datasets in fields of view of interest with at least 100 cells of interest and find that fluxmaps 1–3 show different enrichment scores for Nucleus and Endoplasmic Reticulum (ER; combination of ERM and ER lumen). We note that despite the successful delineation of discrete regions in the form of fluxmaps when looking at different regions of tissue that are enriched for different cell types, nuclear and ER enrichment scores change for each fluxmap (Fig. S 4 C).

In summary, RNAflux finds distinct subcellular domains with consistent spatial organization and local gene composition. As an unsupervised method, RNAflux can be applied to any cell type for inferring subcellular domains from transcript locations and functionally annotated with biological enrichment analysis. This process is best performed on cell type-separated data to guard low-rank embeddings from being generated by cells of vastly different morphologies. This disaggregation is important because, unlike uniform cell lines, heterogeneous tissue composed of functionally diverse stromal cells and leukocytes should indeed be expected to have different distributions and complements of subcellular domains.

Doxorubicin-induced stress in cardiomyocytes depletes RNA from the endoplasmic reticulum

Having established Bento’s utility to characterize RNA localization in U2OS cells, we applied Bento to quantify changes in localization in the context of perturbations in cells. Specifically, we performed single-molecule spatial transcriptomics on doxorubicin-treated and untreated cardiomyocytes to identify RNA localization changes as a result of treatment.

Doxorubicin (DOX) was once one of the most effective broad-spectrum anti-cancer anthracycline antibiotics [ 60 , 61 ] with particular efficacy against solid malignancies such as lung and breast cancer, as well as hematologic neoplasia [ 62 , 63 ]. However, DOX’s propensity to cause cardiac damage in patients has led to significant limitations in its clinical use [ 64 ]. The exact mechanism by which DOX induces heart failure is unclear, but significant evidence suggests cardiomyocyte injury driven by oxidative stress is a major factor [ 62 , 65 , 66 , 67 , 68 ]. Specifically, DOX causes stress and dysfunction in multiple cellular compartments in cardiomyocytes such as mitochondria, Sarco/endoplasmic reticulum (SER), deficiencies in calcium signaling, and lipid degradation at the cellular membrane [ 69 ]. We reasoned that by measuring the localization of the RNA transcripts of 100 genes crucial to cardiomyocyte health and function (Additional File 2 : Table S2) and leveraging the tools developed within Bento, we could recapitulate known dysfunction of subcellular domains in cardiomyocytes upon DOX stress and measure novel RNA localization phenotypes.

We utilized a chemically defined protocol to differentiate human induced pluripotent stem cells (iPSCs) into beating cardiomyocytes and treated them with either DMSO (vehicle) or 2.5 μM DOX for 12 h before fixation (see the “ Methods ” section). Single-molecule spatial transcriptomes were measured by Resolve Bioscience using Molecular Cartography. The resulting data was segmented using ClusterMap [ 24 ] for cell boundaries and Cellpose [ 70 ] for nuclei boundaries (Fig. 5 A). Non-myocytes were filtered out using SLC8A1 as a canonical marker for cardiomyocytes (see the “ Methods ” section, Additional File 1 : Fig. S3A). Comparing vehicle and DOX-treated cardiomyocytes, we found NPPA, a classic marker for cardiac stress [ 71 , 72 ], to be upregulated in DOX-treated cells (Fig. 5 B). We identified subcellular domains in the vehicle and DOX-treated cardiomyocytes using RNAflux, clustering the domains into four fluxmap domains (Fig. 5 C, D, Additional File 1 : Fig. S3B). Enrichment of location-specific gene expression aligned domains to the nucleus (nuclear pore, nucleolus, and nucleus), ERM and OMM, ER lumen, and cytosol respectively (Fig. 5 E, Additional File 1 : Fig. S3C). Comparing the gene composition in each domain, we observe an overall localization bias towards both the nucleus and ERM/OMM in vehicle-treated cells (Fig. 5 E top), in agreement with prior poly(A) smFISH studies [ 73 ]. However, RNA in the DOX-treated cardiomyocytes demonstrated a shift in average RNA localization away from the ERM/OMM and towards the nucleus (Fig. 5 E bottom). There was no correlation when comparing the logFC in expression and the difference in nuclear composition of genes after treatment, indicating that localization towards the nucleus is not driven by transcript abundance (Spearman r = 0.07, p = 0.4944). There is evidence that 90% of genes have a half-life of less than 260 min [ 74 ], far less than the 12-h DOX treatment, indicating that the shift in RNA localization is likely due to reduced nuclear export of newly synthesized RNA from the nucleus to the ERM/OMM. Indeed, even low concentrations of DOX have been demonstrated to alter structural fibrous proteins as well as mitochondrial depolarization and fragmentation [ 75 ]. Of particular note, the RNA binding protein RBM20—a critical regulator of mRNA splicing of genes encoding key structural proteins associated with cardiac development and function—had a pronounced depletion of RNA transcripts outside of the nucleus upon DOX treatment (Fig. 5 G left). With further validation, this may indicate nuclear retention and or degradation of nuclear exported RBM20 mRNA as a potential mechanism of DOX-induced cardiomyopathy. Similarly, we found the mRNA of calcium voltage-gated channel subunit CACNB2 to also deplete outside of the nucleus (Fig. 5 G middle). The loss of CACNB2 translation outside of the nucleus may impact calcium signaling crucial to cardiomyocyte function [ 76 ]. Most genes only showed weak shifts in localization, similar to LAMP2 (Fig. 5 G right).

Subcellular RNA localization changes upon Doxorubicin treatment in iPSC-derived cardiomyocytes. A Cardiomyocytes derived from human iPSCs were treated with DMSO or 2.5 μM DOX for 12 h. The localizations of 100 genes relevant to cardiomyocyte health and function were measured using Molecular Cartography. Cell boundaries were determined using ClusterMap and nuclei were segmented using Cellpose. B Top 10 differentially upregulated and downregulated genes in vehicle versus treatment. T -test was used for comparisons. All genes shown are significant given an adjusted p -value threshold of p < 0.01. Benjamini–Hochberg correction was used to control for the false discovery rate. Vehicle and treatment conditions have n = 7159 and 6260 cells respectively. C APEX-seq location-specific gene enrichment of fluxmap domains for the cytosol, endoplasmic reticulum membrane (ERM), endoplasmic reticulum lumen (ER Lumen), nuclear lamina, nucleus, nucleolus, nuclear pore, and outer mitochondrial matrix (OMM). D Fluxmap domains visualized for a representative field of view of cardiomyocytes for vehicle and treatment respectively highlighting cellular nuclei, ERM/OMM, ER Lumen, and cytosol. E RNAflux fluxmap enrichment of each gene averaged across vehicle and treatment cardiomyocytes captures changes in subcellular RNA localization. Top 10 genes are labeled and ranked by the largest shifts between compartment compositions. Shifts are quantified by Wasserstein distance. F Average gene enrichment in each fluxmap across vehicle and treatment conditions colored by log-fold expression demonstrates population-level shifts in transcript subcellular localization. G Visualization of RBM20, CACNB2, and LAMP2 transcripts confirms the depletion of transcripts from the perinuclear and cytosolic compartments of cardiomyocytes upon DOX treatment

Bento seeks to interrogate biology via its “subcellular first” approach to spatial analysis, complementary to “cell-type or tissue first” spatial analysis methods. The toolkit enables quantitative, reproducible, and accessible analysis agnostic to spatial technology platforms in a standardized framework. We implement three novel methods to interrogate subcellular RNA organization: RNAforest for supervised annotation of localization patterns, RNAcoloc for compartment-aware colocalization analysis, and RNAflux for identifying transcriptionally distinct subcellular domains. We showed that with RNAflux, we were able to quantify RNA localization in a variety of contexts, including domain-specific gene localization, drug-induced changes in localization, and cell type-specific localization. With both RNAflux and RNAforest, we find that subcellular mRNA localization reflects gene function. With RNAcoloc, we explore the use of CLQ scores to quantify pairwise gene colocalization in the context of asymmetric associations.

From these results, we found three main factors to limit the effectiveness of subcellular-resolution analysis: molecule density, segmentation quality, and target panel composition. In particular, RNAflux becomes uninformative if too few molecules are detected per cell or if the number of molecules per gene is too sparse. We found that datasets with higher density, i.e., molecules per micrometer 2 , are less noisy and inform more coherent gradients and domains. Both the U2-OS dataset and cardiomyocyte datasets had high enough molecule density to identify consistent fluxmaps. Notably, RNAflux robustly highlights domains corresponding to the nucleus and ER despite some poor cell segmentation and partially unannotated nuclei in the cardiomyocytes dataset (Fig. 5 D). As most commercial target panels are largely composed of marker genes for cell type identification, RNAflux embeddings should be interpreted carefully, especially if transcripts show little spatial variation in subcellular localization. In contrast, RNAforest performs reliably beyond a minimum of 5–10 molecules per sample but is sensitive to accurate segmentation for calculating cell morphology-dependent features (Additional File 1 : Fig. SE-F). The 3T3 cells were manually segmented and the U2-OS cells had relatively accurate segmentation and were therefore amenable to applying RNAforest. We found that the segmentation in the cardiomyocytes is accurate enough for single-cell gene expression analysis, but lacked the precision needed to apply RNAforest. In the case of RNAcoloc, the limiting factor to identifying relevant biology is the target panel composition. The current focus of most target panels typically includes cell type markers and highly expressed genes, whereas it would be more informative to identify colocalizing members of protein complexes, functional pathways, or ligand-receptor pairs. Our ability to characterize relevant pathways with our curated cardiomyocyte gene panel shows how gene panel design focused on function enables discovery. Ultimately, non-targeted transcriptome-scale technologies will be necessary to unlock the full potential of subcellular biology.

A dimensional limitation of Bento is its current inability to process three-dimensional spatial transcriptomic data. While some commercially available spatial transcriptomic methods yield RNA molecular coordinates in 3D, the nuclear and cell segmentation is inevitably still two-dimensional making it difficult to interpret z-dimensional positions lacking the context of cellular geometry in 3D. However, the algorithms behind RNAforest, RNAcoloc, RNAflux, and the plethora of feature calculation functions in Bento are inherently extensible to leveraging three-dimensionality. When three-dimensional cell segmentation improves, we intend to extend Bento to support three-dimensional analysis.

Conclusions

Conventionally, RNA is treated as an intermediary vehicle encoding genomic information for protein synthesis. We began our investigation of RNA localization with the hope of understanding how the spatial organization of RNA functions as a mechanism for post-transcriptional regulation. However, RNAflux conceptually introduces using RNA molecular coordinates as a latent layer of information encoding cellular space–time. Here, we used that latent layer of information to identify subcellular domains. As spatial omic technologies improve to capture more and more information, the potential applications of such latent embeddings will grow as well. Indeed at the tissue level, this concept is already being leveraged with a recent tool, TensionMap, using RNA localization information to predict mechanical tension [ 77 ]. As applications for spatial transcriptomics grow in popularity and complexity, we envision that Bento is a platform for the next generation of tools needed to quantify the complex molecular dynamics governing normal and abnormal cellular processes.

MERFISH and seqFISH + data preprocessing

For the seqFISH + dataset, we limited the scope of our analysis to the set of genes for which at least 10 molecules were detected in at least one cell. This helped reduce sparsity in the data, resulting in 3726 genes remaining. Because pattern classification requires nuclear segmentation masks, we removed all cells lacking annotated nuclei for the remainder of 179 cells. Because the MERFISH data had a much higher number of molecules detected per gene, no gene needed to be removed. Again, cells without annotated nuclei were removed, leaving 1022 cells for downstream analysis.

Preprocessing cardiomyocytes datasets

Single-cell expression matrices of both vehicle replicates and both DOX treatment samples were concatenated as a single expression matrix. Cells were projected into two dimensions with UMAP dimensional reduction. No significant batch effects were detected. Leiden clustering was performed at resolution = 0.5 to isolate and filter out a non-myocyte population depleted in SLC8A1 expression (Additional File 1 : Fig. S3A). All described preprocessing steps were performed in Scanpy [ 21 ].

RNAforest: model selection and training

We evaluated 4 base models for the multilabel classifier including random forests (RF), support vector machines (SVM), feed-forward fully-connected neural networks (NN), and convolutional neural networks (CNN). While all other models use the 13 spatial features for input (Additional File 1 : Table S1), the CNN takes 64 × 64 image representations of each sample as input. Each multilabel classifier consists of 5 binary classifiers with the same base model. We used the labeled 10,000 simulated samples for training, stratifying 80% of the simulated data for training and holding out the remaining 20% for testing. To select the best hyperparameters for each multilabel classifier, we sampled from a fixed hyperparameter space with the Tree-structured Parzen Estimator algorithm and evaluated performance with fivefold cross-validation (Additional File 2 : Table S2). We retrained the final model (random forest base model) on all training data with the best-performing set of hyperparameters (Additional File 1 : Fig. S1E). Exact steps can be found and reproduced in notebooks stored in the GitHub repository: https://github.com/ckmah/bento-manuscript .

RNAforest: image rasterization of molecules and segmentation masks for CNN

To generate an image for a given sample, point coordinates, the cell segmentation mask, and the nuclear segmentation mask are used. The area of the cell is tiled as a 64 × 64 grid, where each bin corresponds to a pixel in the final image. Values are stored in a single channel to render a grayscale image. Pixels inside the cell are encoded as 20 and inside the nucleus encoded as 40. Bins with molecules are encoded as (40 + 20 × n ) where n is the number of molecules. Finally, values are divided by 255 and capped to be between 0 and 1.

RNAforest: simulating training data

We trained a multilabel classifier to assign each gene in every cell labels from five categories: (i) nuclear (contained in the volume of the nucleus), (ii) cytoplasmic (diffuse throughout the cytoplasm), (iii) nuclear edge (near the inner/outer nuclear membrane), (iv) cell edge (near the cell membrane), and (v) none (complete spatial randomness). These categories are a consolidation of those observed in several high-throughput smFISH imaging experiments in HeLa cells [ 40 , 41 , 42 , 43 ]. We used the FISH-quant simulation framework ( https://code.google.com/archive/p/fish-quant/ ) to generate realistic ground-truth images using empirically derived parameters from the mentioned high-throughput smFISH imaging experiments in HeLa cells [ 42 ]. In total, we simulate 2000 samples per class for a total of 10,000 training samples.

Cell shape : Cell morphology varies widely across cell types, and for classifier generalizability, it is important to include many different morphologies in the training set. We use a catalog of cell shapes for over 300 cells from smFISH images in HeLa cells that capture nucleus and cell membrane shape [ 42 ]. Cell shapes were obtained by cell segmentation with CellMask and nuclear segmentation was obtained from DAPI staining.

mRNA abundance : We simulated mRNA abundance at three different expression levels (40, 100, and 200 mRNA per average-sized cell) with a Poisson noise term. Consequently, total mRNA abundance per cell was between 5 and 300 transcripts.

Localization pattern : We focused on 5 possible 2D localization patterns, including cell edge, cytoplasmic, none, nuclear, and nuclear edge. Each pattern was further evaluated at 3 different degrees—weak, moderate, and strong. Moderate corresponds to a pattern typically observed in a cell, whereas weak is close to spatially random. These 5 classes aim to capture biologically relevant behavior generalizable to most cell types; there is room for additional classes describing other biologically relevant localization patterns so long as they can be accurately modeled.

RNAforest: manual annotation of true biological validation data

Using 3 individual annotators, we annotated the same 600 samples across both datasets, keeping samples with 2 or more annotator agreements as true annotations, resulting in 165 annotated seqFISH + samples and 238 annotated MERFISH samples (403 total). We used Cohen’s kappa coefficient [ 78 ] to calculate agreement between pairs of annotators for each label yielding an overall coefficient of 0.602. We found that pairwise agreement between annotators across labels was fairly consistent ranging between 0.588 and 0.628, while label-specific agreement varied more, ranging between 0.45 and 0.72 (Additional File 4 : Table S4).

RNAforest: functional enrichment of gene pattern distributions

For enrichment of compartment-specific expression from Xia et al. [ 46 ], scores are calculated by taking the weighted sum of gene pattern frequencies and published compartment log fold-change values (Additional File 1 : Fig. S2). The Benjamini–Hochberg correction was used to correct p -values for multiple hypothesis testing.

For the seqFISH + dataset, we performed a single-sample Gene Set Enrichment Analysis [ 79 , 80 ] on gene pattern frequencies to compute enrichment scores (Fig. 3 I). ssGSEA was performed with the GSEApy Python package and the “GO_Cellular_Component_2021” gene set library curated by Enrichr [ 81 ]. Gene sets with a minimum size of 50 and a maximum size of 500 were analyzed.

Colocation quotient for RNA colocalization analysis

Pairwise colocalization of genes was determined for each compartment of every cell separately. In this case, each cell was divided into compartments, cytoplasm, and nucleus. The colocation quotient (CLQ) was calculated for every pair of genes \({\varvec{A}}\) and \({\varvec{B}}\) . The CLQ is defined as an odds ratio of the observed to expected proportion of \({\varvec{B}}\) transcripts among neighbors of \({\varvec{A}}\) for a fixed radius r ; it is formulated as:

Here \({{\varvec{C}}}_{{\varvec{A}}\to {\varvec{B}}}\) denotes the number of \({\varvec{A}}\) transcripts of which \({\varvec{B}}\) transcripts are considered a neighbor. \({{\varvec{N}}}_{{\varvec{A}}}\) denotes the total number of \({\varvec{A}}\) transcripts, while \({\varvec{N}}{\boldsymbol{^{\prime}}}_{{\varvec{B}}}\) stands for the total number of \({\varvec{B}}\) transcripts. In the case that \({\varvec{A}}={\varvec{B}}\) , \({\varvec{N}}{\boldsymbol{^{\prime}}}_{{\varvec{B}}}\) equals the total number of \({\varvec{B}}\) transcripts minus 1. \({\varvec{N}}\) denotes the total number of transcripts in the cell. Following statistical recommendations from the original formulation of the colocation quotient (CLQ), genes with fewer than 10 transcripts were not considered to reduce sparsity and improve testing power [ 53 ].

Tensor decomposition for compartment-specific colocalization

We begin by calculating the CLQ for every pair of genes within each compartment of every cell. We structure our data as follows:

Cells set: Denote the set of cells as \({\varvec{C}}\boldsymbol{ }=\boldsymbol{ }\{{{\varvec{c}}}_{1},{{\varvec{c}}}_{2},\boldsymbol{ }...,{{\varvec{c}}}_{{\varvec{n}}}\}\) , where \({{\varvec{c}}}_{{\varvec{i}}}\) represents the \({{\varvec{i}}}^{{\varvec{t}}{\varvec{h}}}\) cell and \({\varvec{n}}\) is the total number of cells.

Compartments set: Every cell has the same set of compartments, represented as \({\varvec{K}}=\{{{\varvec{k}}}_{1},{{\varvec{k}}}_{2},\boldsymbol{ }...,{{\varvec{k}}}_{{\varvec{m}}}\}\) , where \({{\varvec{k}}}_{{\varvec{j}}}\) is the \({{\varvec{j}}}^{{\varvec{t}}{\varvec{h}}}\) compartment within a cell.

Gene pairs set: The gene pairs are represented by \({\varvec{G}}=\{{{\varvec{g}}}_{1},{{\varvec{g}}}_{2},\boldsymbol{ }...,\boldsymbol{ }{{\varvec{g}}}_{{\varvec{p}}}\}\) where \({{\varvec{g}}}_{{\varvec{p}}}\) is the \({{\varvec{p}}}^{{\varvec{t}}{\varvec{h}}}\) gene pair.

By computing the CLQ for every combination of cells in \({\varvec{C}}\) , compartments in \({\varvec{K}}\) , and gene pairs in \({\varvec{G}}\) , we populate a three-dimensional tensor \({\varvec{X}}\) with dimensions corresponding to these sets.

We then apply non-negative parallel factor analysis (PARAFAC) as implemented in Tensorly [ 31 ] to reduce the dimensionality of our dataset and capture the underlying patterns. For tensor decomposition, we employed non-negative parallel factor analysis as implemented in Tensorly [ 31 ]. The tensor \({\varvec{X}}\) is decomposed into the sum of \({\varvec{R}}\) factors; each factor is a three-dimensional tensor expressed as the outer product of three vectors: \({{\varvec{x}}}_{{\varvec{r}}}\) (compartment loadings), \({{\varvec{y}}}_{{\varvec{r}}}\) (cell loadings), and \({{\varvec{z}}}_{{\varvec{r}}}\) (gene pair loadings). This is denoted as follows:

The optimal rank- \({\varvec{R}}\) decomposition of \({\varvec{X}}\) is determined by minimizing the error between \({\varvec{X}}\) and the reconstructed tensor \(\widehat{{\varvec{X}}}\) . We use the elbow function heuristic to select the best-fit rank from a range of 2–12 factors. This approach seeks to balance the complexity of the model against the fidelity of reconstruction. Missing values in \({\varvec{X}}\) are ignored when calculating the loss.

RNAflux: unsupervised spatial embedding and subcellular domain quantization

To generate RNAflux embeddings, first, a set of query coordinates are generated tiling across the cell area on a uniform grid. This effectively downsamples the original data units (pixels) resulting in much fewer samples to compute embeddings. Let \(Q = \{{q}_{1},{q}_{2},...,{q}_{n}\}\) be the set of query coordinates and \({q}_{i}\) denote the \({i}^{th}\) query coordinate. For the MERFISH U2-OS dataset, a step size of 10 data units (pixels) was used to generate the uniform grid. For the iPSC-derived cardiomyocytes, a step size of 5 data units was used. Each query point is assigned an expression vector, counting the abundance of each gene within a fixed radius of 40 and 50 data units respectively. Each expression vector is normalized to sum to one, converting the expression vector to a composition vector, denoted as \({v}_{i}\) . Similarly, the cell composition vector \({x}_{j}\) for the \({j}^{th}\) cell is calculated by normalizing the total cell expression to sum to one. The RNAflux embedding \({r}_{i}\) at query coordinate \({q}_{i}\) is defined as the difference between the \({v}_{i}\) and \({x}_{j}\) divided by \({\sigma }_{v}\) , the vector of standard deviation of composition vectors \(v\) .

The RNAflux embedding serves as an interpretable spatial gene embedding that quantifies highly local fluctuations in gene composition. Dimensional reduction of the embeddings is performed using truncated singular value decomposition (SVD). Truncated SVD was chosen over PCA to better handle large but sparse data. Embeddings were reduced to the top 10 components. To assign domains, self-organizing maps (SOM) as implemented in MiniSom [ 82 ] were used for low-rank quantization of query embeddings. In an analysis of the MERFISH dataset, SOMs of size 1 × k were fit across a range of 2 to 12; the best model was determined using the elbow method heuristic to evaluate quantization error. Similarly, domains were determined for the cardiomyocyte spatial transcriptomics data by fitting the vehicle and treatment samples separately, for k across a range of 2 to 8. The elbow method heuristic determined an optimal k of 6; subsequently, a k of 4 was used for further analysis for ease of interpretation.

RNAflux: visualizing spatial embeddings

The top 3 principal components of the RNAflux embeddings are transformed to map red, green, and blue values respectively. Embeddings are first quantile normalized and scaled to a minimum of 0.1 and 0.9 to avoid mapping extreme quantiles to white and black. These values are then used for red, green, and blue color channels. To map the downsampled grid back to the original data units, linear interpolation was used to rescale the computed color values and fill the space between the uniform grid points.

RNAflux: enrichment of locale-specific transcriptomes derived by APEX-seq

The enrichment score for each pixel is calculated by first taking the weighted sum of its RNAflux embedding and locale-specific log fold-change values as implemented by the decoupler tool [ 83 ]. Scores for pixels within a given cell are normalized against a null distribution constructed via random permutations of the input embeddings, to produce z -scaled enrichment scores. Fluxmap domain enrichment scores are simply obtained by taking the mean score of all pixels within the boundary of each domain. Fluxmap domain overlaps are computed by counting the fraction of pixels within the boundary of each domain with a positive enrichment score.

MERFISH of U2-OS cells

MERFISH sample preparation. MERFISH measurements of 130 genes with five non-targeting blank controls were done as previously described, using the published encoding [ 44 ] and readout probes [ 84 ]. Briefly, U2-OS cells were cultured on 40 mm #1.5 coverslips that are silanized and poly-L-lysine coated [ 44 ] and subsequently fixed in 4% (vol/vol) paraformaldehyde in 1 × PBS for 15 min at room temperature. Cells were then permeabilized in 0.5% Triton X-100 for 10 min at room temperature and washed in 1 × PBS containing Murine RNase Inhibitor (NEB M0314S). Cells were preincubated with hybridization wash buffer (30% (vol/vol) formamide in 2 × SSC) for 10 min at room temperature with gentle shaking. After preincubation, the coverslip was moved to a fresh 60-mm petri dish and residual hybridization wash buffer was removed with a Kimwipe lab tissue. In the new dish, 50 uL of encoding probe hybridization buffer (2 × SSC, 30% (vol/vol) formamide, 10% (wt/vol) dextran sulfate, 1 mg ml −1 yeast tRNA, and a total concentration of 5 uM encoding probes and 1 μM of anchor probe: a 15-nt sequence of alternating dT and thymidine-locked nucleic acid (dT +) with a 5′-acrydite modification (Integrated DNA Technologies). The sample was placed in a humidified 37 °C oven for 36 to 48 h, then washed with 30% (vol/vol) formamide in 2 × SSC for 20 min at 37 °C, 20 min at room temperature. Samples were post-fixed with 4% (vol/vol) paraformaldehyde in 2 × SSC and washed with 2 × SSC with murine RNase inhibitor for 5 min. The samples were finally stained with an Alexa 488-conjugated anchor probe-readout oligo (Integrated DNA Technologies) and DAPI solution at 1 μg/ml.

MERFISH imaging

MERFISH measurements were conducted on a home-built system as described in Huang et al. [ 84 ].

MERFISH spot detection

Individual RNA molecules were decoded in MERFISH images using MERlin v0.1.6 [ 85 ]. Images were aligned across hybridization rounds by maximizing phase cross-correlation on the fiducial bead channel to adjust for drift in the position of the stage from round to round. Background was reduced by applying a high-pass filter and decoding was then performed per-pixel. For each pixel, a vector was constructed of the 16 brightness values from each of the 16 rounds of imaging. These vectors were then L2 normalized and their Euclidean distances to each of the L2 normalized barcodes from the MERFISH codebook were calculated. Pixels were assigned to the gene whose barcode they were closest to, unless the closest distance was greater than 0.512, in which case the pixel was not assigned a gene. Adjacent pixels assigned to the same gene were combined into a single RNA molecule. Molecules were filtered to remove potential false positives by comparing the mean brightness, pixel size, and distance to the closest barcode of molecules assigned to blank barcodes to those assigned to genes to achieve an estimated misidentification rate of 5%. The exact position of each molecule was calculated as the median position of all pixels consisting of the molecule.

MERFISH image segmentation

Cellpose v1.0.2 [ 70 ] was used to perform image segmentation to determine the boundaries of cells and nuclei. The nuclei boundaries were determined by running Cellpose with the “nuclei” model using default parameters on the DAPI stain channel of the pre-hybridization images. Cytoplasm boundaries were segmented with the “cyto” model and default parameters using the polyT stain channel. RNA molecules identified by MERlin were assigned to cells and nuclei by applying these segmentation masks to the positions of the molecules.

iPSC cardiac differentiation and doxorubicin treatment

Matrigel (Corning, cat # 354,277)-coated plates were used to culture iPSCs with mTESR Plus human iPSC medium (StemCell Technologies, cat # 100–0276) in a humidified incubator at 37 °C with 5% CO 2 . iPSCs were dissociated with Gentle Cell Dissociation Reagent (StemCell Technologies, cat # 100–0485) and passaged with mTESR Plus medium and 10uM ROCK inhibitor (Tocris, cat #1254) at a ratio of 1:12. mTESR plus medium was replaced every other day until the cells reached 80% confluency for maintenance and replating, or 90% confluency for cardiac differentiation utilizing a chemically defined protocol [ 86 ]. On day 0 of cardiac differentiation, cells were treated with 6 μM CHIR99021 (Selleck Chem, cat # S1263) in RPMI 1640 media (Gibco, cat # 11,875) and B27 minus insulin supplement (Thermo Fisher, cat # A1895601). On day 2, CHIR was removed, and cells were cultured with RPMI 1640 media and B27 minus insulin supplement (Thermo Fisher, cat # A18956). On day 3, media was replaced with RPMI media containing B27 minus insulin supplement and 5 μM Wnt-C59 (Cellagen Technologies, cat # C7641-2 s). On days 5, 7, and 9, media was replaced with RPMI media containing B27 insulin supplement (Thermo Fisher, cat # 17,504). On days 11 and 13, media was replaced with RPMI 1640 media without glucose (Thermo Fisher, cat # 11,879,020) containing B27 insulin supplement for purification of cardiomyocytes. From days 15 onward, the cells were cultured in RPMI 1640 media containing B27 supplement which was changed every other day until the cells reached day 30 for replating. For replating, cells were incubated in 10 × TrypLE (Thermo Fisher, cat # A1217701) for 12 min at 37 °C, neutralized with equal volumes of RPMI 1640 media containing B27 supplement with 20% FBS (Gibco, cat # 26,140–079), gently dissociated by pipetting, then spun down and resuspended for replating in RPMI 1640 media containing B27 supplement with 20% FBS. The next day, the cell media was replaced with RPMI 1640 media containing B27 supplement which was replaced with fresh media every other day. On day 48, the cells were replated onto chamber slides (Ibidi, cat # 80,826) as described above and recovered for 10 days before doxorubicin treatments began (MedChemExpress, cat # HY-15142). On day 60, doxorubicin treatments concluded, and the cells underwent methanol fixation.

Molecular cartography

Cultured cell processing.

After doxorubicin treatment, cardiomyocytes were washed with PBS (1 ×) twice and fixed in methanol (− 20 °C) for 10 min. After fixation, Methanol was aspirated and cells were dried and stored at − 80 °C until use. The samples were used for Molecular Cartography™ (100-plex combinatorial single-molecule fluorescence in-situ hybridization) according to the manufacturer’s instructions Day 1: Molecular Preparation Protocol for cells, starting with the addition of buffer DST1 followed by cell priming and hybridization. Briefly, cells were primed for 30 min at 37 °C followed by overnight hybridization of all probes specific for the target genes (see below for probe design details and target list). Samples were washed the next day to remove excess probes and fluorescently tagged in a two-step color development process. Regions of interest were imaged as described below and fluorescent signals removed during decolorization. Color development, imaging, and decolorization were repeated for multiple cycles to build a unique combinatorial code for every target gene that was derived from raw images as described below.

Probe design

The probes for 100 genes were designed using Resolve’s proprietary design algorithm. Briefly, the probe design was performed at the gene level. For every targeted gene, all full-length protein-coding transcript sequences from the ENSEMBL database were used as design targets if the isoform had the GENCODE annotation tag “basic” [ 87 , 88 ]. To speed up the process, the calculation of computationally expensive parts, especially the off-target searches, the selection of probe sequences was not performed randomly, but limited to sequences with high success rates. To filter highly repetitive regions, the abundance of k-mers was obtained from the background transcriptome using Jellyfish [ 89 ]. Every target sequence was scanned once for all k-mers, and those regions with rare k-mers were preferred as seeds for full probe design. A probe candidate was generated by extending a seed sequence until a certain target stability was reached. A set of simple rules was applied to discard sequences that were found experimentally to cause problems. After these fast screens, the remaining probe candidates were mapped to the background transcriptome using ThermonucleotideBLAST [ 90 ] and probes with stable off-target hits were discarded. Specific probes were then scored based on the number of on-target matches (isoforms), which were weighted by their associated APPRIS level [ 91 ], favoring principal isoforms over others. A bonus was added if the binding site was inside the protein-coding region. From the pool of accepted probes, the final set was composed by picking the highest-scoring probes. Probes with catalog numbers can be found in Additional File 3 : Table S3.

Samples were imaged on a Zeiss Celldiscoverer 7, using the 50 × Plan Apochromat water immersion objective with an NA of 1.2 and the 0.5 × magnification changer, resulting in a 25 × final magnification. Standard CD7 LED excitation light source, filters, and dichroic mirrors were used together with customized emission filters optimized for detecting specific signals. Excitation time per image was 1000 ms for each channel (DAPI was 20 ms). A z-stack was taken at each region with a distance per z-slice according to the Nyquist-Shannon sampling theorem. The custom CD7 CMOS camera (Zeiss Axiocam Mono 712, 3.45 μm pixel size) was used. For each region, a z-stack per fluorescent color (two colors) was imaged per imaging round. A total of 8 imaging rounds were done for each position, resulting in 16 z-stacks per region. The completely automated imaging process per round was realized by a custom Python script using the scripting API of the Zeiss ZEN software (Open application development).

Image processing and spot segmentation

As a first step, all images were corrected for background fluorescence. A target value for the allowed number of maxima was determined based on the area of the slice in μm 2 multiplied by the factor 0.5. This factor was empirically optimized. The brightest maxima per plane were determined, based upon an empirically optimized threshold. The number and location of the respective maxima were stored. This procedure was done for every image slice independently. Maxima that did not have a neighboring maximum in an adjacent slice (called z-group) were excluded. The resulting maxima list was further filtered in an iterative loop by adjusting the allowed thresholds for (Babs-Bback) and (Bperi-Bback) to reach a feature target value (Babs: absolute brightness, Bback: local background, Bperi: background of periphery within 1 pixel). These feature target values were based on the volume of the 3D image. Only maxima still in a group of at least 2 after filtering were passing the filter step. Each z-group was counted as one hit. The members of the z-groups with the highest absolute brightness were used as features and written to a file. They resemble a 3D-point cloud. To align the raw data images from different imaging rounds, images had to be registered. To do so, the extracted feature point clouds were used to find the transformation matrices. For this purpose, an iterative closest point cloud algorithm was used to minimize the error between two point clouds. The point clouds of each round were aligned to the point cloud of round one (reference point cloud). The corresponding point clouds were stored for downstream processes. Based upon the transformation matrices, the corresponding images were processed by a rigid transformation using trilinear interpolation. The aligned images were used to create a profile for each pixel consisting of 16 values (16 images from two color channels in 8 imaging rounds). The pixel profiles were filtered for variance from zero normalized by the total brightness of all pixels in the profile. Matched pixel profiles with the highest score were assigned as an ID to the pixel. Pixels with neighbors having the same ID were grouped. The pixel groups were filtered by group size, number of direct adjacent pixels in group, and number of dimensions with a size of two pixels. The local 3D-maxima of the groups were determined as potential final transcript locations. Maxima were filtered by the number of maxima in the raw data images where a maximum was expected. The remaining maxima were further evaluated by the fit to the corresponding code. The remaining maxima were written to the results file and considered to resemble transcripts of the corresponding gene. The ratio of signals matching to codes used in the experiment and signals matching to codes not used in the experiment were used as estimation for specificity (false positives). The algorithms for spot segmentation were written in Java and are based on the ImageJ library functionalities. Only the iterative closest point algorithm is written in C + + based on the libpointmatcher library ( https://github.com/ethz-asl/libpointmatcher ).

Image segmentation

Cellpose v1.0.2 [ 70 ] was used to perform image segmentation to determine the boundaries of nuclei. The nuclei boundaries were determined by running Cellpose with the “nuclei” model using default parameters on the DAPI stain channel of the pre-hybridization images. Cytoplasm boundaries were determined with ClusterMap [ 24 ] using spot coordinates.

Review history

The review history is available as Additional file 5 .

Peer review information

Kevin Pang was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Availability of data and materials

The datasets (seqFISH + , MERFISH, and Molecular Cartography datasets) supporting the conclusions of this article are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.c.6564043.v1 [ 92 ], and are also accessible through the Bento Python package.

The source code for Bento is available on the GitHub repository: https://github.com/ckmah/bento-tools [ 93 ]. Analysis code for generating figures can be found at https://github.com/ckmah/bento-manuscript [ 94 ] and is archived at https://doi.org/10.5281/zenodo.10815484 [ 95 ]. Documentation for Bento can be found here: http://bento-tools.readthedocs.io .

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Acknowledgements

We thank members of the Yeo lab, Carter lab, Michelle Franc Ragsac, Erick Armingol, and Nate Lewis for helpful discussions and feedback on the manuscript.

C.K.M. is supported by the National Science Foundation Graduate Research Fellowship under Grant No. (DGE-2038238). N.A. was partially supported by NIH Training Grant T32 GM008666. This work was partially supported by National Institutes of Health grants NS103172, MH107367, AI132122, AI123202, AG069098, HG004659, and HG009889 to G.W.Y. G.W.Y. is also supported by an Allen Distinguished Investigator Award, a Paul G. Allen Frontiers Group advised grant of the Paul G. Allen Family Foundation. A.J.C. and E.L. acknowledge support from the Chan Zuckerberg Initiative (CZF2019-002448) and the Knut and Alice Wallenberg Foundation (KAW 2021.0346) to E.L.

Author information

Clarence K. Mah and Noorsher Ahmed are co-first authors and contributed equally.

Authors and Affiliations

Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA

Clarence K. Mah & Hannah Carter

Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA

Clarence K. Mah, Noorsher Ahmed, Nicole A. Lopez, Dylan C. Lam, Avery Pong, Alexander Monell, Gino Prasad & Gene W. Yeo

Sanford Stem Cell Institute Innovation Center, La Jolla, CA, USA

Clarence K. Mah, Noorsher Ahmed, Dylan C. Lam & Gene W. Yeo

Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA

Dylan C. Lam

Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

Alexander Monell & Gino Prasad

Center for Epigenomics, University of California San Diego, La Jolla, CA, USA

Colin Kern, Yuanyuan Han & Quan Zhu

Department of Bioengineering, Stanford University, Stanford, CA, USA

Anthony J. Cesnik & Emma Lundberg

Department of Pathology, Stanford University, Stanford, CA, USA

Emma Lundberg

Chan-Zuckerberg Biohub, San Francisco, CA, USA

Stem Cell Program, University of California San Diego, La Jolla, CA, USA

Gene W. Yeo

Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

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Contributions

C.K.M, N.A., and G.W.Y. conceptualized the project. C.K.M. and N.A. co-developed the software. C.K.M. and D.L. trained the classification model for subcellular localization. C.K.M., N.A., and D.L. manually annotated data for benchmarking model performance. C.K.M., N.A., and G.P. performed data preprocessing and analysis. A.M., C.K., Y.H., and Q.Z. generated the MERFISH experiment. N.L. designed the gene panel and cultured the cardiomyocytes. A.C. and E.L. aided multimodal spatial analyses. C.K.M., N.A., H.C., and G.W.Y. wrote the manuscript. H.C. and G.W.Y. supervised the project.

Corresponding author

Correspondence to Gene W. Yeo .

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Competing interests.

G.W.Y. is a co-founder, member of the board of directors, equity holder, and paid consultant for Locanabio (until 12/31/2023) and Eclipse Bioinnovations, and a Scientific Adviser and paid consultant to Jumpcode Genomics. G.W.Y. is a Distinguished Visiting Professor at the National University of Singapore. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict-of-interest policies. The authors declare no other competing interests.

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Supplementary Information

Additional file 1:.

Supplementary Figures and Table S1. Supplementary figures and machine learning classifier feature descriptions.

Additional file 2:

Table S2. Hyperoptimization parameters and classifier architectures.

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Table S3. Cardiomyocyte gene panel for Molecular Cartography experiment.

Additional file 4:

Table S4. Manual annotation data for RNA localization patterns

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Mah, C.K., Ahmed, N., Lopez, N.A. et al. Bento: a toolkit for subcellular analysis of spatial transcriptomics data. Genome Biol 25 , 82 (2024). https://doi.org/10.1186/s13059-024-03217-7

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DOI : https://doi.org/10.1186/s13059-024-03217-7

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9.3 Organizing Your Writing
The method of organization you choose for your essay is just as important as its content. Without a clear organizational pattern, your reader could become confused and lose interest. ... using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two work together. The following are possible transition words to ...
Spatial Order Organization: What Is It and How to Use it in Essay Writing
According to Merriam-Webster, spatial order organization is the process of relating to space and the objects in it. In writing, it refers to a method in which ideas and elements are laid down logically according to the sequence of their location or their relationship. Spatial order organization is like putting a disarray of elements into one ...
What Is Spatial Order And How To Use It In Essay?
A spatial organization of information in paragraphs is also called descriptive writing. It is often used when a narrator wants to describe how something looks. For example, you can use this approach in descriptive essays about a person when you describe someone's appearance, starting from the feet and moving up to the head.
9.3 Organizing Your Writing
The method of organization you choose for your essay is just as important as its content. Without a clear organizational pattern, your reader could become confused and lose interest. ... using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two work together. The following are possible transition words to ...
Spatial Order
Spatial order is best used for the following purposes: Helping readers visualize something as you want them to see it. Evoking a scene using the senses (sight, touch, taste, smell, and sound) Writing a descriptive essay. Spatial order means that you explain or describe objects as they are arranged around you in your space, for example in a bedroom.
What is Spatial Organization in Writing? (Spatial Order Explained)
Spatial order organization in writing is where an author looks at all the important elements of a given scenario and then orders information based on how people or objects fit together in a given physical space. In spatial organization, you order the information in a particular order, and this can be either from top to bottom or from left to right.
Organizing Your Writing
The method of organization you choose for your essay is just as important as its content. Without a clear organizational pattern, your reader could become confused and lose interest. ... using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two work together. The following are possible transition words to ...
Spatial Order: What and How to Use it in Essay Writing
Giving spatial order examples and references in the body will help your readers in understanding and actually visualize what you need to say. Ensure that you choose a starting and a finishing point, so you can set up all of the information properly for your body paragraph. 4. Construct the body paragraphs.
Spatial Organization
Spatial organization is generally pretty easy to identify, but be aware that spatial organization is used in both fiction and nonfiction texts. Most fictional passages are organized chronologically, but in paragraphs where the narrator is describing a setting or the appearance of a character, the information may be organized spatially.
5.3: Organizing Your Writing
The method of organization you choose for your essay is just as important as its content. Without a clear organizational pattern, your reader could become confused and lose interest. ... using an implied topic sentence and applying spatial order. Often in a descriptive essay, the two work together. The following are possible transition words to ...
Spatial Order: How To Use It In Essay Writing
Are you struggling to organize your ideas in an essay? Do you want to learn how to use spatial order to create a logical progression of events? Then this vid...
5.1 Methods of Organizing Your Writing
In addition, planning the structure of your essay before you choose supporting evidence helps you conduct more effective and targeted research. This section covers three ways to organize both essays and paragraphs: chronological order, order of importance, and spatial order. Chronological Order. Chronological arrangement has the following purposes:
Spatial Order: Best Guide on How To Use It In Essay Writing
In a spatial-order essay, the use of transition words or signal words is essential. They are necessary to connect the writer's thoughts and improve the paragraph's flow. Several spatial order transition words serve as prepositions, which link sentences to the concepts represented in the ones before them. A speaker or writer describes a ...
Most Common Book or Essay Organization Patterns
Spatial organization would be a suitable type of pattern for real estate professionals to use when describing a property. Then again, if you were required to describe the events that led up to a certain event in history, your most likely organization pattern would be chronological. Chronological refers to the order that things happen in time.
Spatial Order and How to Use It in Your Essay
Using the Spatial Order in Essay Writing. Spatial ordering or spatial organization is used when writing descriptive essays. The main agenda here is to invoke the senses of the reader and help them have a good grip on the concepts and facts presented in the essay. There are different steps to follow when using the spatial order:
Chronological, Step-by-Step, and Spatial Organization
A spatial pattern organizes each main point in a directional structure, connecting each main point to a whole. This structure is used for informative speeches where the topic is organized by location, geography, or moving through a space ("spatial" is the adjective form of "space"). For example, a speech about the parts of a resume ...
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Spatial Order And Chronology In Writing, Speech And Essay
Speech spatial order is a type of logical organization pattern that describes things according to their actual location in space. Definition: this order is also called the order of place and spatial structure. And in speech, it means that the narrator describes objects using an ordered logical sequence, describing things as they look when ...
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An essay about what homes will be like in the future is an example of one kind of topic that might not work well in conventional patterns of essay organization. Rather than chronological or sequential patterns, for example, you might discover that using a spatial essay organization pattern is the most effective way to ...
1.5: Methods of Organizing Your Writing
The method of organization for essays and paragraphs is just as important as content. When you begin to draft an essay or paragraph, your ideas may seem to flow from your mind in a seemingly random manner; however, your readers, who bring to the table different backgrounds, viewpoints, and ideas, need you to clearly organize these ideas to help ...
Organization and Structure
Organization and Structure. There is no single organizational pattern that works well for all writing across all disciplines; rather, organization depends on what you're writing, who you're writing it for, and where your writing will be read. In order to communicate your ideas, you'll need to use a logical and consistent organizational ...
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Abstract. Spatial organization, as one of the basic themes of geography, focuses on how to recognize and organize geographic space in which human activities occur, giving rise to spatial structures. The patterns of spatial structure can be classified into three types: contiguous structures (including uniform regions and functional regions ...
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The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of ...
Spatial Order and How to Use It in Your Essay
Ordinarily, spatial ordering makes the use of phrases such as: on top of, in front of, besides, alongside, beyond, further from, further along, behind, centric, elevated from, and under, just to mention a few., Using the Spatial Order in Essay Writing, Spatial ordering or spatial organization is used when writing descriptive essays.,
Bento: a toolkit for subcellular analysis of spatial transcriptomics
The spatial organization of molecules in a cell is essential for performing their functions. While protein localization [] and disease-associated mislocalization are well appreciated [2, 3], the same principles for RNA have begun to emerge.For instance, the spatial and temporal regulation of RNA play a crucial role in localized cellular processes such as cell migration and cell division [4, 5 ...
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The anti-tumor function of CD8+ T cells is dependent on their proximity to tumor cells. Current studies have focused on the infiltration level of CD8+ T cells in the tumor microenvironment, while further spatial information, such as spatial localization and inter-cellular communication, have not been defined. In this study, co-detection by indexing (CODEX) was designed to characterize PDAC ...