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How to Write a Good Cover Letter for a Research Position

Writing a cover letter can be intimidating, but it doesn’t have to be!

Some people believe cover letters are a science. Others seem to think they are more akin to black magic. Regardless of how you feel about cover letters, they are one of the most important parts of the job application process. Your resume or CV may get you an interview, but a good cover letter is what ensures that the hiring manager reads your resume in the first place.

Writing a cover letter for any job is important, but the art of writing a good cover letter for a research position can make or break your application. While writing a cover letter for a research position, you have to walk a fine line of proving your expertise and passion while limiting jargon and dense language.

In this post, we will explain cover letter writing basics, and then dive into how to write a research specific cover letter with examples of both good and bad practices.

What Is A Cover Letter and Why Do Cover Letters Matter?

A cover letter is your opportunity to tell a story and connect the dots of your resume. Resumes and curriculum vitae (CVs) are often cold and static—they don’t show any sort of character that will give companies a hint about if you will fit in with their culture. 

Your cover letter gives you the chance to demonstrate that you are an interesting, qualified, and intelligent person. Without proving that you are worth the time to interview, a company or research organization will set your application in the rejection pile without giving it a second look. 

So, what is a cover letter, exactly? It is an explanation (written out in paragraph form) of what you can bring to the company that goes beyond the information in your resume. Cover letters give a company a glimpse into the qualities that will make you the ideal candidate for their opening. 

Note that a cover letter is not the same as a letter of intent. A cover letter is written for a specific job opening. For example, if I got an email saying that the University of Colorado was looking for a tenure track faculty member to teach GEO 1001, and I chose to apply, I would write a cover letter. 

A letter of intent, however, is written regardless of the job opening. It is intended to express an interest in working at a particular company or with a particular group. The goal of a letter of intent is to demonstrate your interest in the company (or whatever type of group you are appealing to) and illustrate that you are willing to work with them in whatever capacity they feel is best. 

For example, if I loved the clothing company, Patagonia and wanted to work there, I could write a letter of intent. They may have an opening for a sales floor associate, but after reading my application and letter of intent, decide I would be better suited to a design position. Or, they may not have any positions open at all, but choose to keep my resume on hand for the next time they do. 

Most organizations want a cover letter, not a letter of intent, so it is important to make sure your cover letter caters to the specifics of the job posting. A cover letter should also demonstrate why you want to work at the company, but it should be primarily focused on why you can do the job better than any of the other applicants.

How to Write a Good Cover Letter: The Basics 

Writing a cover letter isn’t hard. Writing a good cover letter, a cover letter that will encourage a hiring manager to look at your application and schedule an interview, is more difficult (but certainly not impossible). Below, we will go over each of the important parts of a cover letter: the salutation, introduction, body, and conclusion, as well as some other best practices.

How to Write a Good Cover Letter Salutation

Don’t start with “Dear Sir/Ma’am” (or any iteration of a vague greeting, including “to whom it may concern”). Avoiding vague greetings is the oldest trick in the book, but it still holds a lot of weight. Starting a cover letter with the above phrase is pretty much stamping “I didn’t bother to research this company at all because I am sending out a million generic cover letters” across your application. It doesn’t look good. 

The best practice is to do your research and use your connections to find a name. “Dear Joe McGlinchy” means a lot more than “Dear Hiring Manager.” LinkedIn is a great tool for this—you can look up the company, then look through the employees until you find someone that seems like they hire for the relevant department. 

The most important thing about the salutation is to address a real human. By selecting someone in the company, you’ve demonstrated that you’ve done some research and are actually interested in this company specifically. Generic greetings aren’t eye-catching and don’t do well.

How to Write a Good Cover Letter Introduction

Once you’ve addressed your cover letter to a real human being, you need a powerful introduction to prove that this cover letter is worth the time it will take to read. This means that you need a hook. 

Your first sentence needs to be a strong starter, something to encourage the hiring manager not only to continue reading the cover letter, but to look at your application as well. If you have a contact in the company, you should mention them in the first sentence. Something along the lines of “my friend, Amanda Rice (UX/UI manager), suggested I apply for the natural language processing expert position after we worked together on a highly successful independent project.” 

The example above uses a few techniques. The name drop is good, but that only works if you actually have a connection in the company. Beyond that, this example has two strengths. First, it states the name of the position. This is important because hiring managers can be hiring for several different positions at a time, and by immediately clarifying which position you are applying for, you make their job a little bit easier.  Next, this sentence introduces concrete skills that apply to the job. That is a good way to start because it begins leading into the body, where you will go into depth about how exactly your experience and skills make you perfect for the job. 

Another technique for a strong lead-in to a cover letter is to begin with an applicable personal experience or anecdote. This attracts more attention than stereotypical intros (like the example above), but you have to be careful to get to the point quickly. Give yourself one or two sentences to tell the story and prove your point before you dive into your skills and the main body of the cover letter.

A more standard technique for introductions is simply expressing excitement. No matter how you choose to start, you want to demonstrate that you are eager about the position, and there is no easier way to do that than just saying it. This could take the form of “When I saw the description for X job on LinkedIn, I was thrilled: it is the perfect job for my Y skills and Z experience.” This option is simple and to-the-point, which can be refreshing for time-crunched hiring managers. 

Since we’ve provided a few good examples, we will offer a bad example, so you can compare and contrast. Don’t write anything along the line of: “My name is John Doe, and I am writing to express my interest in the open position at your company.” 

There are a few issues here. First, they can probably figure out your name. You don’t need that to be in the first sentence (or any of the sentences—the closing is an obvious enough spot). Next, “the open position” and “your company” are too generic. That sounds like the same cover letter you sent to every single employer in a hundred mile radius. Give the specifics! Finally, try to start with a little more spice. Add in some personality, something to keep the hiring manager reading. If you bore them to death in the first line, they aren’t going to look over your resume and application with the attention they deserve. 

How to Write a Good Cover Letter Body

So, you’ve addressed a real human being, and you’ve snagged their attention with a killer opening line. What next? Well, you have to hold on to that attention by writing an engaging and informative cover letter body. 

The body of a cover letter is the core of the important information you want to transmit. The introduction’s job was to snag the attention of the hiring manager. The body’s job is to sell them on your skills.  There are a few formatting things to be aware of before we start talking about what content belongs in the body of the cover letter. First, keep the company culture and standards in mind when picking a format. For example, if I want to work for a tech startup that is known for its wit and company culture, I can probably get away with using a bulleted list or another informal format. However, if I am applying to a respected research institution, using a standard five paragraph format is best. 

In addition, the cover letter should not be longer than a page. Hiring managers are busy people. They may have hundreds of resumes to read, so they don’t need a three page essay per person. A full page is plenty, and many hiring managers report finding three hundred words or less to be the idea length. Just to put that into context, the text from here to the “How to Write a Good Cover Letter Body” header below is about perfect, length-wise. 

Now, on to the more important part: the content. A cover letter should work in tandem with a resume. If you have a list of job experiences on your resume, don’t list them again in the cover letter. Use the valuable space in the cover letter to give examples about how you have applied your skills and experience. 

For example, if I have worked as a barista, I wouldn’t just say “I have worked as a barista at Generic Cafe.” The hiring manager could learn that from my resume. Instead, I could say “Working as a barista at Generic Cafe taught me to operate under pressure without feeling flustered. Once…” I would go on to recount a short story that illustrated my ability to work well under pressure. It is important that the stories and details you choose to include are directly related to the specific job. Don’t ramble or add anything that isn’t obviously connected. Use the job description as a tool—if it mentions a certain skill a few times, make sure to include it!

If you can match the voice and tone of your cover letter to the voice of the company, that usually earns you extra points. If, in their communications, they use wit, feel free to include it in your letter as well. If they are dry, to the point, and serious, cracking jokes is not the best technique.

A Few Don’ts of Writing a Cover Letter Body   

There are a few simple “don’ts” in cover letter writing. Do not: 

• Bad: I am smart, dedicated, determined, and funny.
• Better: When I was working at Tech Company, I designed and created an entirely new workflow that cut the product delivery time in half. 
• Bad: When I was seven, I really loved the monkeys at the zoo. This demonstrates my fun-loving nature. 
• Better: While working for This Company, I realized I was far more productive if I was light-hearted. I became known as the person to turn to in my unit when my coworkers needed a boost, and as my team adopted my ideology, we exceeded our sales goals by 200%. 
• Bad: I would love this job because it would propel me to the next stage of my career.
• Better: With my decade of industry experience communicating with engineers and clients, I am the right person to manage X team. 
• Bad: I know I’m not the most qualified candidate for this job, but…
• Better: I can apply my years of experience as an X to this position, using my skills in Y and Z to… 
• Bad: I am a thirty year old white woman from Denver…
• Better: I have extensive experience managing diverse international teams, as illustrated by the time I…  

The most important part of the cover letter is the body. Sell your skills by telling stories, but walk the razor’s edge between saying too much and not enough. When in doubt, lean towards not enough—it is better for the hiring manager to call you in for an interview to learn more than to bore them.

How to Write a Good Cover Letter Conclusion

 The last lines of a cover letter are extremely important. Until you can meet in-person for an interview, the conclusion of your cover letter will greatly affect the impression the hiring manager has of you. A good technique for concluding your cover letter is to summarize, in a sentence, what value you can bring to the company and why you are perfect for the position. Sum up the most important points from your cover letter in a short, concise manner. 

Write with confidence, but not arrogance. This can be a delicate balance. While some people have gotten away (and sometimes gotten a job) with remarks like, “I’ll be expecting the job offer soon,” most do not. Closing with a courteous statement that showcases your capability and skills is far more effective than arrogance. Try to avoid trite or generic statements in the closing sentence as well. This includes the template, “I am very excited to work for XYZ Company.” Give the hiring manager something to remember and close with what you can offer the company. 

The final step in any cover letter is to edit. Re-read your cover letter. Then, set it aside for a few hours (or days, time permitting) and read it again. Give it to a friend to read. Read it aloud. This may seem excessive, but there is nothing more off-putting than a spelling or grammar error in the first few lines of a cover letter. The hiring manager may power through and ignore it, but it will certainly taint their impression. 

Once the cover letter is as flawless and compelling as it can be, send it out! If you are super stuck on how to get started, working within a template may help. Microsoft Word has many free templates that are aesthetically appealing and can give you a hint to the length and content. A few good online options live here (free options are at the bottom—there is no reason to pay for a resume template).

How to Write a Cover Letter for a Research Position

Writing a cover letter for a research position is the same as writing any other cover letter. There are, however, a few considerations and additions that are worth pointing out. A job description may not directly ask for a cover letter, but it is good practice to send one unless they specifically say not to. This means that even if a cover letter isn’t mentioned, you should send one—it is best practice and gives you an opportunity to expand on your skills and research in a valuable way.

Format and Writing Style for a Research Position Cover Letter

Research and academics tend to appreciate formality more than start-ups or tech companies, so using the traditional five paragraph format is typically a good idea. The five paragraph format usually includes an introduction, three short examples of skills, and a concluding paragraph. This isn’t set in stone—if you’d rather write two paragraphs about the skills and experience you bring to the company, that is fine. 

Keep in mind that concise and to-the-point writing is extremely valuable in research. Anyone who has ever written a project proposal under 300 words knows that every term needs to add value. Proving that you are a skilled writer, starting in your cover letter, will earn you a lot of points. This means that cover letters in research and academia, though you may have more to say, should actually be shorter than others. Think of the hiring manager—they are plowing through a massive stack of verbose, technical, and complex cover letters and CVs. It is refreshing to find an easy to read, short cover letter. 

On the “easy to read” point, remember that the hiring manager may not be an expert in your field. Even if they are, you cannot assume that they have the exact same linguistic and educational background as you. For example, if you have dedicated the last five years of your life to studying a certain species of bacteria that lives on Red-Eyed Tree Frogs, all of those technical terms you have learned (and maybe even coined) have no place in your cover letter. Keep jargon to an absolute minimum. Consider using a tool like the Hemingway Editor to identify and eliminate jargon. While you want to reduce jargon, it is still important to prove that you’ve researched their research. Passion about the research topic is one of the most valuable attributes that a new hire can offer. 

Use your cover letter to prove that you have done your homework, know exactly what the institution or group is doing, and want to join them. If you have questions about the research or want to learn more, it isn’t a bad idea to get in touch with one of the researchers. You can often use LinkedIn or the group’s staff site to learn who is working on the project and reach out.

What Research Information Should be Included in a Cover Letter

A research position cover letter is not the place for your academic history, dissertation, or publications. While it may be tempting to go into detail about the amazing research you did for your thesis, that belongs in your CV. Details like this will make your cover letter too long. While these are valuable accomplishments, don’t include them unless there is something  that pertains to the group’s research, and your CV doesn’t cover it in depth. 

If you do choose to write about your research, write about concrete details and skills that aren’t in your CV. For example, if you have spent the last few years working on identifying the effects of a certain gene sequence in bird migration, include information about the lab techniques you used. Also, try to put emphasis on the aspects of your resume and CV that make you stand out from other candidates. It is likely that you will be competing with many similarly qualified candidates, so if you have a unique skill or experience, make sure it doesn’t get lost in the chaos—a cover letter is the perfect place to highlight these sorts of skills. 

Industry experience is a great differentiator. If you have relevant industry experience, make sure to include it in your cover letter because it will almost certainly set you apart. Another valuable differentiator is a deep and established research network. If you have been working on research teams for years and have deep connections with other scientists, don’t be afraid to include this information. This makes you a very valuable acquisition for the company because you come with an extensive network

Include Soft Skills in Your Cover Letter

Scientific skills aren’t the only consideration for hiring managers. Experience working with and leading teams is incredibly valuable in the research industry. Even if the job description doesn’t mention teamwork, add a story or description of a time you worked with (or, even better, lead) a successful team. Soft skills like management, customer service, writing, and clear communication are important in research positions. Highlight these abilities and experiences in your cover letter in addition to the hard skills and research-based information. 

If you are struggling to edit and polish your letter, give it to both someone within your field and someone who is completely unfamiliar with your research (or, at least, the technical side of it). Once both of those people say that the letter makes sense and is compelling, you should feel confident submitting it.

Cover letters are intended to give hiring managers information beyond what your resume and CV are able to display. Write with a natural but appropriately formal voice, do your research on the position, and cater to the job description. A good cover letter can go a long way to getting you an interview, and with these tips, your cover letters will certainly stand out of the pile.

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Research Assistant Cover Letter Examples and Templates for 2024

• Cover Letter Examples
• Cover Letter Text Examples

How To Write a Research Assistant Cover Letter

To write a good research assistant cover letter, focus on how you can help conduct experiments or surveys for the organization that posted the job. Use your letter to show research-related skills like data gathering, report writing, or laboratory analysis.

This guide will help you write a cover letter that gets you interviews for your next job as a research assistant.

Research Assistant Cover Letter Templates and Examples

• Entry-Level
• Senior-Level

Research Assistant Text-Only Cover Letter Templates and Examples

Jacob Stanton Junior Research Assistant | [email protected] | (503) 555-4512 | 3434 Julip St., Portland, OR 97267

October 9, 2024

Sarah Hibbins HR Manager Contemplative Inc. (503) 555-1212 [email protected]

Dear Ms. Hibbins,

Discovering how positive reinforcement techniques increased the therapeutic benefits of dopamine by 72% was one of my proudest moments as a Junior Research Assistant last year. My supervisor said my testing methods and reward pathway computations were invaluable to the team. I believe these same skills could be beneficial to Contemplative Inc. if I joined your company as the new Junior Research Assistant.

During my 2+ years at Therapeutic Circle, I have improved the efficiency of their data collection system by 52% using the IBM SPSS Statistics platform to optimize production. I enjoy building innovative systems that utilize the latest subgroup variation techniques. Some of my other achievements include:

• Developed system using Python and JavaScript to efficiently troubleshoot production quality code and reduce user interface (UI) costs by 67%
• Decreased reporting needed by reconfiguring quantitative protocols, saving $75,000 in overall testing costs in 2020
• Helped train 25 staff members attending Code Documentation class

I would love to discuss how my communication, qualitative, and analytical skills would be beneficial to your business. Please contact me at your convenience to schedule an interview.

Best regards,

Jacob Stanton

Justin Stewart Research Assistant | [email protected] | (503) 555-1895 | 2367 Sage St., Portland, OR 97267

David Greene HR Manager Critical Learning Institute (503) 555-9924 [email protected]

Dear Mr. Greene,

One of my top achievements in 2020 was streamlining needs assessment data in order to launch a new adaptive learning platform and increase enrollment by 60%. As a Research Assistant at Aspiring Minds, I understand how well executed Agile methodologies ensure an optimal learning experience. I believe similar results could be achieved at the Critical Learning Institute if I were chosen as your new Research Assistant.

Your organization has been at the forefront of science, technology, engineering, and mathematics (STEM)-focused education. For over seven years, I have led our research team to develop STEM competency requirements, quality control procedures, and curriculum reviews. Some of my recent accomplishments include:

• Managed survey rollout for Consortium for the Study of Leadership and Ethics in Education (CSLEE) conference in 2021
• Led the creation of a statistical data library to improve reading and writing testing methods for K-12 students
• Published 20 articles in Social Science Research and received several accolades from peers regarding quantitative methods for integrative learning

I look forward to discussing how my critical thinking and writing skills would benefit your company. Feel free to contact me at your convenience to schedule an interview. Thank you very much for your time and consideration.

Justin Stewart

Jill Sanchez Senior Research Assistant | [email protected] | (503) 555-6767 | 4101 Cyprus Rd., Portland, OR 97267

October 9, 2021

Rick O’Brien HR Manager Lakeview University (503) 555-7783 [email protected]

Dear Mr. O’Brien,

One of my proudest moments last year was studying how synovitis, when treated with anti-inflammatory polysaccharides, resulted in a 36% decrease in rheumatoid arthritis. As a senior research assistant at Westfield College, this success was featured on the cover of Arthritis & Rheumatology as a “breakthrough therapy.” Similar results could be achieved if I were chosen as your new senior research assistant at Lakeview University.

The clinical trials at Lakeview University, featuring the latest immunoelectrophoresis-serum techniques, I believe is the future of immunology. My 15+ years of supervision, including 300 clinical trials, has given me the experience that aligns with your excellent patient care. Other accomplishments that would also be beneficial to Lakeview University include:

• Management: Supervised the testing methodology for over 35,000 clinical trial patients
• Development: Designed a lab program reducing turnaround time from 45 days to a week
• Certifications: Received both my ACRP and SOCRA certifications in 2012

I would like to discuss how my project management, interpersonal, and regulatory skills would be helpful to your immunology department. Please contact me at your earliest convenience for an interview.

Jill Sanchez

A good research assistant cover letter usually has five sections, outlined below. When possible, connect each section back to the employer and its stated hiring needs. The following advice and examples show what to include in your cover letter so it’s optimized for each job application.

At the top of the page, include your resume contact header, the date, and any basic details you have on your recipient (as in the example below). To set a clear professional focus, add the title research assistant to your contact header. You can then modify this title based on each job posting to show hiring managers you’re the right kind of candidate. For instance, if you’re applying to a leadership role and have recent leadership experience, consider using the title senior research assistant.

Sarah Hibbins Human Resources Manager Contemplative Inc. (503) 555-1212 [email protected]

2. Salutation

Address your recipient by name as in the first example below – this is the quickest way to signal you’re sending a job-specific letter and not a boilerplate. If you can’t find the recipient’s name, use a variation of “Dear Hiring Manager” so your greeting is still tailored somewhat to each job opening.

Dear Mr. Greene:

Dear Critical Learning Institute Manager:

3. “Hook” or introduction

Catch the reader’s attention by starting your letter with a clear example or measure of your success as a research assistant. Describe a key way you’ve helped test theories or gather data, and spell out the benefit of that work. If possible, choose a highlight that reflects your background in forms of inquiry or investigation similar to those you’re now pursuing.

4. Body paragraph(s)

Use the main section of your cover letter to tell why this research assistant job interests or suits you. For instance, maybe it centers on preparing data for publication, an area in which you excel. Or maybe the facility performs studies or experiments that spark your curiosity. Also, consider how the organization’s research mission or approach compares to your own .

Following this explanation, cite a few more of your skills or achievements , possibly as bullet points.

5. Call to action

Finally, request an interview and thank the hiring manager for their time. Consider briefly restating your main skills as a research assistant and your eagerness to apply them at the organization. To end your cover letter, use a simple closing like “Sincerely” or “Best regards” and then your name.

Research Assistant Cover Letter Tips

1. highlight your main research skills.

As part of your letter’s body text, add a short list of bullet points to show your success in key work areas for a research assistant. With this section, you can give more examples of your conducting experiments, surveys, interviews, or data analysis. Cite data and metrics to show the value of your contributions to research projects and studies.

Some of my other achievements include:

Whenever you can, start your letter by citing any personal or professional connections you have with the hiring manager. If someone at the organization alerted you to the job, use a line like “I was excited to hear from your colleague [Name] about [Company]’s new research assistant position.” Even better if you’ve already met your recipient – mention that with a line like “It was great speaking with you at the job fair last week.”

3. When in doubt, brainstorm

Any time you’re stuck on a section of your cover letter , take 10 minutes to quickly jot down your thoughts on a separate document or sheet of paper. By pausing to think more creatively, you can jog your memory and find new details to include about your research assistant experience. Brainstorming also helps you figure out the best and most original way to describe that experience, giving your letter the confident tone it needs to catch a hiring manager’s attention.

Research Assistant Cover Letter Frequently Asked Questions

Do i really need a cover letter for my job search -.

Yes, in most cases. Job postings today usually require or allow you to send a cover letter along with your resume or curriculum vitae (CV). While not every hiring manager reads or prioritizes them, a well-crafted letter can only help you stand out from other applicants. It will also clarify what points to emphasize during the interview.

What’s the most important part of a cover letter? -

Any explanation you give for why the specific job opening or employer interests you. These details distinguish the cover letter from your other application materials and can help you get past applicant tracking systems . They also set the stage for a good interview discussion about how you fit the role and the organization’s work culture.

How long should my cover letter be? -

No more than one page, or around 250 words. Unlike a resume or CV, the cover letter is bound by pretty strict expectations for page length. Resist the urge to tell your whole career story, even if you have an extensive background as a research assistant. Give just enough detail to pique hiring managers’ interest so they take a closer look at your application.

Craft a new cover letter in minutes

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Jacob Meade

Certified Professional Resume Writer (CPRW, ACRW)

Jacob Meade is a resume writer and editor with nearly a decade of experience. His writing method centers on understanding and then expressing each person’s unique work history and strengths toward their career goal. Jacob has enjoyed working with jobseekers of all ages and career levels, finding that a clear and focused resume can help people from any walk of life. He is an Academy Certified Resume Writer (ACRW) with the Resume Writing Academy, and a Certified Professional Resume Writer (CPRW) with the Professional Association of Resume Writers & Career Coaches.

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3 Research Assistant Cover Letter Examples for 2024

• Research Assistant Cover Letter
• Clinical Research Assistant Cover Letter
• Graduate Research Assistant Cover Letter
• Writing Your Research Assistant Cover Letter

You’re a pro at researching, analyzing your findings, and drawing useful conclusions that can lead to groundbreaking discoveries. Your analytical mind and impeccable eye for detail help you streamline and innovate the research process.

Whether you’re in an undergraduate program or already have your PhD, a research position can be an excellent way to progress your career. However, to beat the competition you’ll need to create a cover letter that complements your research assistant resume and highlights your strengths.

We’re here to help you with that. Check out our research assistant cover letter examples , expert tips, and free AI cover letter generator to help you prepare an irresistible job application.

Research Assistant Cover Letter Example

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Copy this text for your research assistant cover letter!

123 Fictional Avenue Columbus, OH 43004 (123) 456-7890

August 10, 2023

Lily Nguyen JPMorgan Chase & Co. 123 Fictional Lane Columbus, OH 43004

Dear Ms. Nguyen:

Pursuing my master’s degree in statistics fueled my passion for the intricate mechanisms steering financial institutions. This curiosity, coupled with my eagerness to contribute to the industry, has led me to apply for the research assistant role at JPMorgan Chase & Co. With six years of experience conducting literature reviews, gathering data, and more, I am equipped to contribute to your dynamic environment.

In my recent role at Citizens Bank, I liaised with 11 financial analysts to collect, review, and interpret data from over 1000 client accounts. This data played a pivotal role in identifying emerging market trends, enabling the firm to increase its client base by 37 percent during my tenure.

I have also had the chance to lead a team diverse in skills and experiences. For example, I partnered with 3 financial managers from KeyBank, employing statistical analysis methodologies to cut financial forecast errors by $301,788.

With robust analytical and interpersonal skills, I can adapt swiftly to ever-changing circumstances. My professional competencies and propensity to thrive within dynamic environments make me a strong fit for this role.

It would be an honor to discuss how my skills and enthusiasm for finance can enhance JPMorgan Chase & Co.’s esteemed reputation. Thank you for considering my application.

Lucas Brown

Enclosures: Resume Application 2 letters of recommendation Academic Transcripts

Why this cover letter works

• But again, don’t leave out interpersonal skills; you’ll need them to conduct interviews and communicate your findings effectively.

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Clinical Research Assistant Cover Letter Example

Copy this text for your clinical research assistant cover letter!

123 Fictional Avenue Denton, TX 14201 (123) 456-7890

Noah Smith Horizon Health Services 123 Fictional Lane Denton, TX 14201

Dear Mr. Smith:

I have long been impressed with Horizon Health Services’ reach across western New York along with your institution’s outstanding reputation as a behavioral health and addiction treatment leader. Friends and professional peers have spoken of the remarkable workplace environment at HHS, so I’m thrilled to bring my values and career goals to your team as a clinical research assistant. I believe my skills in data management, EMR systems, and clinical trial documentation will greatly contribute to your exceptional team.

My internship at LabCorp Innovation equipped me with a strong foundation in clinical research and data analysis. I managed data for over 69 patient studies, employing Meditech EMR to maintain high data accuracy and ensuring that records were up-to-date and compliant with stringent regulations. This initiative led to a 35% reduction in data retrieval time and an 18% improvement in record accuracy.

Recognizing the recurring issues in EMR accessibility, I led an initiative to restructure the system workflow, creating an easier interface while safeguarding patient information. This improved the staff’s efficiency in accessing and inputting data by 47%, which directly scaled the monthly number of successfully processed patient data from 750 to more than 987.

I also have hands-on experience managing clinical trial documentation. While with Medix Infusion, I supervised the document control process of 32 clinical trials, ensuring that all required papers were timely and accurately maintained. That enhanced the audit-readiness of trials by 40% and reduced preparation time for regulatory audits by 23%.

I believe my strong dedication to delivering high-quality research and a deep understanding of health data protocols will greatly contribute to your esteemed institute. Thank you for considering my application. I’m keen to discuss further how I could be a great fit for Horizon Health Services.

Malik Farag

• Demonstrate your knowledge and application of data collection, analysis, and management methodologies and skills. More importantly, the hiring manager wants to see the quantified impacts of these proficiencies in your previous roles.

Graduate Research Assistant Cover Letter Example

Copy this text for your graduate research assistant cover letter!

123 Fictional Avenue Salt Lake City, UT 84004 (123) 456-7890

Emma White ARUP Laboratories 123 Fictional Lane Salt Lake City, UT 84004

Dear Ms. White:

Navigating through my degree in Biomedical Sciences, much like the calculated and precise nature of laboratory research, instilled in me an unquenchable thirst for breakthroughs, rapid advancements, and the quest for knowledge in the diagnostic medicine realm. Today, I am thrilled at the prospect of applying my skills and passion as a graduate research assistant at ARUP Laboratories, a leader in academic and diagnostic medicine.

While pursuing my undergraduate degree, I had the opportunity to complete a four-month-long internship at Myriad Genetics. Here, I worked alongside reputable professionals in the field, gaining deep insights into the world of diagnostic research. As an integral part of a team that conducted a groundbreaking study, I facilitated the automation of data collection and analysis procedures, resulting in a 29% increase in lab productivity.

I also accepted a seven-month part-time role at Intermountain Healthcare. I spearheaded an initiative to collate, review, and analyze five years’ worth of patient data. The comprehensive report I generated assisted the clinic in identifying diagnostic trends and has been instrumental in inculcating a data-oriented approach in their operations.

ARUP Laboratories’ commitment to diagnostic innovation and excellence resonates with my passion for pushing the boundaries of medical knowledge. Your prestigious, long-standing reputation in diagnostic medicine, complemented by my knack for pertinent research and comprehensive data analysis, creates a synergy I’m excited to explore. I look forward to further discussing how my experience and passion align with your research objectives.

Freya Nilsen

• Enthusiasm also matters for a beginner role. Research the company and weave its mission or values into your passion for the role.

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How to Write an Excellent Research Assistant Cover Letter

Once you’ve captured the attention of recruiters or professors with your research assistant resume , the next step is to seal the deal with an excellent cover letter. Make sure your cover letter matches the job description but adds a personal flair that goes beyond mere keywords.

Use your cover letter to highlight your passion for your field, your experience in research and data analysis, and, most of all, your excitement at joining that particular company or institution. 

Tailoring your cover letter to match the job is a good way to show dedication and the ability to draw useful insights based on a limited amount of information. As both of those qualities are crucial for a research assistant, it’s a good way to impress the reader.

Write an intro that hooks the reader

If you want to prove that you’re a skilled researcher right off the bat, impress the recipient by addressing them by name. 

This might be easier if you’re applying for a position within your college, as you likely already have connections and may be addressing a professor or another faculty member. However, it’s just as important to do if you’re applying for a job, so be ready to do some digging.

Use the first paragraph of your cover letter to show that you’re familiar not just with the intricacies of your field, but also with the company you’re applying to.

Lastly, make sure to paint yourself as an expert from the get-go. For example, if you’re applying for a role in clinical research, mention your in-depth knowledge of medical studies and how you want to leverage it in a way that aligns with the company’s values.

The following opener fails to tick the boxes we’ve talked about above—it’s not at all personalized.

Better not!

I saw your job listing online and I want to apply for this position. I’m looking for any role that is hiring right away.

Now, the below example is what you want to aim for. It showcases an interest and expertise in a relevant field, and most of all, it explains why they chose this job and not any other.

Elaborate on your expertise in the body paragraphs

Research assistant jobs vary wildly, so use this part of your cover letter to show that you know what you’re about in your chosen field of study. 

Pick the things you’re most proud of for this. It’s okay if you don’t have professional experience yet—talk about your projects and academic background to give employers some insight into your level of knowledge.

Pepper in some useful metrics to make this section stand out even more. For instance, if you’re applying to a role that heavily prioritizes managing and collecting data, talk about how you’ve already analyzed over 50,000 entries in Python to identify crucial patterns, streamlining the process by 39%.

That sounds impressive!

End on a strong note with a closer and signoff

Having covered your background in using Matlab to automate data processing or conducting comprehensive literature reviews to support your research projects, you’ve established yourself as an expert. 

To leave a lasting impression on the reader, pick out a couple of skills that are key to this particular role, such as data visualization and technical writing. Next, describe how you’re excited to put them to good use and contribute to impactful research studies at your new workplace. 

Demonstrate that you’re a pleasant person to work with—a key factor in busy research facilities—and thank the reader for their consideration. Lastly, express how eager you are to join this research team to further seal the deal.

This closing paragraph doesn’t really work. It’s very generic and doesn’t highlight the applicant’s unique blend of expertise.

That won’t work…

I’m not an expert yet, but if you take the time to train me, I will do what I can. I really need a research placement for extra credits so please let me know if I can work with you.

The example below does a much better job. It delves into the applicant’s strengths and clearly shows what they can bring to the role.

You got this!

It would be an honor to discuss how my skills and enthusiasm for finance can enhance JPMorgan Chase & Co.’s esteemed reputation. Thank you for considering my application.

This depends on whether you’re applying for a part-time research role as part of your education or a full-time job. For the former, you’ll likely apply directly to the professor or researcher leading the project or department, so ask a faculty member if you’re unsure. Outside of academia, start by checking LinkedIn and the company website, then call or email the business if all else fails.

Mention the company by name a couple of times, especially in the opening and closing paragraphs. Delve into why you chose it above others—perhaps it’s an industry leader or its mission to introduce new life-saving medicine is close to your heart. Lastly, emphasize your expertise in relevant fields like qualitative and quantitive research.

Lean into your education and discuss relevant coursework and projects you were part of, such as field studies and laboratory work. You can also mention transferable skills from part-time jobs, including attention to detail and database management.

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Research Assistant cover letter example

Cover letter header

Cover letter greeting and introduction, cover letter middle part (body), how to close a research assistant cover letter (conclusion and sign-off).

No matter what your field of research is, describing how you go about your job is a vital part of securing your next role. Scientific success is as much about the process as it is about the result, so use the cover letter to explain how you go about your work. The research assistant cover letter examples below will show you how to do just that.

Many research assistants will come into the role from an academic background, so prove that you are ready for a more practical and commercial role. Start the research conversations that you wish to continue during an interview. The depth of your role can be mind-boggling, so where do you start?

Resume.io helps countless job seekers to find the right approach to their job search. Our resume and cover letter tools provide the backdrop for your story, while cover letter examples and writing guides help with the content. Many research assistant applications will be dry and functional. Allow yourself to show some personality alongside the parameters.

In this cover letter writing guide, you can expect to learn the following: 

• How to best format a cover letter and where to tell various aspects of your story.
• What to include in the intro, middle, and conclusion.
• How to experiment with the right tone of voice to start the right conversations.
• Mistakes to avoid. They are unforgivable in the lab too, after all.

Most research assistant roles are in the scientific, accounting, or finance fields, according to stats by Adzuna . Examine carefully the demands of each role. Have you got the industry experience required? Which of your skills are transferable? Is there a track record of people moving industries? Be ambitious, but don’t waste your time. 

Best format for a research assistant cover letter

Just as you would carefully design an experiment, the structure of your cover letter allows the outcomes to slot into place. When you have some guidelines, decisions about which content to include will be simplified. Follow this standard cover letter format and you won’t go wrong. This is a tried-and-tested formula:

• Greeting (salutation)
• Cover letter introduction
• Middle paragraphs (body)
• The closing paragraph of your cover letter (conclusion and call-to-action)

Although you will only be able to concentrate on a few research stories, hiring managers understand the introductory nature of a cover letter. They know there is much more detail behind your words, so don’t feel like you need to pack in everything. Share a relevant example or two in each research assistant cover letter section – pique their interest.

There is a lot more to writing a cover letter than meets the eye. If writing isn’t your forte, our guide to cover letter writing is worth investigating. Every hiring manager will have read hundreds of cover letters during their careers, so explore the tricks that can help you stand out. Your cover letter will certainly be read if there is an interest in your application. We will help you make it powerful and easy to read.

Have a look at our full research assistant cover letter example:

Re: Research assistant role

Dear Professor McMahon,

For the past four years, I have been working in the psychology department of Miami University as a research assistant, on both field- and laboratory-based research projects around cognitive health across a range of age profiles. 

My primary area of interest is the cognitive health outcomes of patients in a care setting. I understand that the role will look at how dementia is treated in a public health setting. Experienced in administering psychological and cognitive assessments in this patient group, I developed links with over 30 local care homes, sharing my expertise with our partners to aid in improving their care provision.

While I enjoy the academic rigor of research, I am looking for a role with a more immediate impact on society. This position offers the perfect balance. I am in awe of your faculty’s reputation in the community and know that you attract the best academic minds because of the scale of your projects. My blend of practical academia should fit in well.

I am well versed in the latest research practices and am always investigating ways to incorporate the latest advances in technology into my work. AI and Data Science are driving deeper insights into our industry. I am a regular at Data Science seminars and am always on the lookout for the latest research applications. You may be interested in a link to my 10,000-word report on the “Impact of Big Data in Psychology” – over 1,500 industry professionals have shared it on social media over the last six months.

I look forward to the opportunity of discussing your research priorities in more detail should you wish to meet for an interview.

Mark Hitching

The header of a cover letter contains the essentials of your application. If the hiring manager wants to invite you to an interview, your full contact details should be here, as well as on your resume. Include your full name, email address, and mobile number. Use plain text and, if this is an electronic document, hyperlinks where possible. Make it as easy as possible to get in touch with you.

There is no requirement to include your full postal address – there are potential data protection issues. Employers will ask for it at the offer stage. You can also save space by excluding the inside address of the employer unless you wish to be overly formal.

Stick to a simple color scheme and design. Research professionals do not require an appreciation for the niceties of design, so let your career stories do the talking.

The cover letter greeting is not something that any research assistant should struggle with. As you are applying for an academic or scientific role, a reasonable level of formality is expected, so addressing the hiring manager as “Dear Mr./Mrs./Dr. Surname” is normal.

Normally, the name of the hiring manager would be included in the job description. If this is not the case, it is acceptable to phone the company to find out. You might even get the chance to ask the receptionist any basic questions that you might have. If you do not know their name, a “Dear Company Team” is fine. Avoid the cold and impersonal “To whom it may concern” as that conveys a generic vibe.

After the greeting, the introduction is where your story starts.

I would suggest leading with a hyper-specific example from your past research work which will indicate that you have what it takes to ace the role in question. Researchers appreciate detail, so get as granular as possible with your sales pitch. While your competition is left relying on meaningless adjectives to describe their experience, you need to start straight away with the big guns.

Lead with context and quantifiable facts about how your work made a difference. How did your techniques produce the optimal results? Share your attitudes about your work and explain why you are uniquely suited to the role in question. Decision-making is a key aspect of a research role – use examples that demonstrate judgment and a calculated approach.

Resist the temptation to share your most impressive achievements. It is possible that some of them won’t be suitable for the role. Only share what is relevant. At no point do you want the hiring manager to think: “impressive, but that isn’t quite what we do here.”

This cover letter sample introduction offers a potential solution: 

For the past four years, I have been working in the psychology department of Miami University as a research assistant, on both field- and laboratory-based research projects around cognitive health across a range of age profiles.

The middle section of a cover letter is where you get the chance to expand on the factual detail of your resume. This is your chance to add some personality, explore the context of your achievements, and tell the hiring manager exactly why you are the right person for the role. Your understanding of what lies ahead should come across loud and clear.

Now is the time for your research projects to shine. Restrict yourself to one or two sentences for each one, packed with detail about the tasks involved and how you came to the outcomes. Show how your education made a difference to your work and mention your ongoing journey of personal development. Let the hiring manager know what comes next – they would be suspicious if you didn’t have further ambitions.

Analyze the job description and pick out a couple of requirements that are least likely to be fulfilled by your fellow applicants. Be clear about your fit in these areas and present yourself as the unicorn candidate of choice. Give the hiring manager the impression that you will be in demand but be unequivocal in your desire to secure this specific role. Tell them why their research priorities are an ideal fit for your career journey.

The middle part of the cover letter below explores a couple of approaches:

The close of a research assistant cover letter should hit home like a scientific conclusion. You have presented all the evidence to prove that you are a worthy candidate for the role, so here is one last reason to hire you. Build on the case – don’t repeat what has been said.

Close the cover letter with a curious hope to find out more about the role during an interview. Research is a complicated industry, so it is natural that you will have many questions. Avoid any note of presumption. You don’t know who else is going for the role and you cannot know the mind of the hiring manager at this early stage. Consider the tone of our example below:

Mistakes to avoid

When you work in research, attention to detail is a given. When writing a job application, therefore, you can be sure that the eagle eye of the hiring manager will be scanning for any mistakes that might hint at carelessness. Writing might well not be your first love, so do your best to avoid the following obvious errors:

• Grammar issues: Spelling and grammar are not hard to check with an online service such as Grammarly. Consider asking family or friends to proofread and check that the cover letter “sounds” like you. You may be surprised what they pick up on.
• Use academic language: While your cover letter should be personable, you need to make sure that you write in scientific language. Keep sentences short and avoid being overly descriptive.
• Keep it simple: Consider the look of the cover letter and make it an easy read. White space is particularly important in this regard, so don’t pack the page with text. Use bullet-pointed lists and shorter paragraphs where appropriate.

The hiring manager needs to be focused on the positive aspects of your application, not distracted by nagging concerns about minor mistakes.

Key takeaways

• Build your research story around the tried-and-tested cover letter structure.
• Choose the most appropriate of our cover letter templates to give the right visual look.
• Hit the appropriate notes with the tone and register of your scientific language.
• Talk about your personal impact on projects and on those around you.

Some of the following medical and administrative examples may help:

• Lab technician cover letter example
• Physician assistant cover letter sample
• Medical assistant cover letter example
• Healthcare cover letter sample

Free professionally designed templates

13 Professional Researcher Cover Letter Examples for 2024

Your researcher cover letter must showcase your academic excellence and research skills. Highlight your publications or any relevant projects that demonstrate expertise in your field. Connect your past experiences with the potential role, indicating how they make you an ideal candidate. It's crucial to convey your passion for the subject and how you can contribute valuable insights to the team.

All cover letter examples in this guide

UX Researcher

Product Researcher

Design Researcher

Market Researcher

Quantitative Researcher

Lab Researcher

User Researcher

Undergraduate Researcher

Psychology Researcher

Student Researcher

Machine Learning Researcher

Qualitative Researcher

Cover letter guide.

Researcher Cover Letter Sample

Cover Letter Format

Cover Letter Salutation

Cover Letter Introduction

Cover Letter Body

Cover Letter Closing

No Experience Researcher Cover Letter

Key Takeaways

Embarking on the job hunt, you’ve likely discovered the need to complement your resume with a researcher cover letter—a daunting task for many. Surpassing the routine checklist of your resume, your cover letter should weave a compelling narrative around your proudest professional milestone. It must strike the delicate balance between formal tone and original expression, avoiding overused phrases that dull your accomplishments. Keep it concise; this powerful one-pager is your chance to captivate and convince.

• Personalize the greeting to address the recruiter and your introduction that fits the role;
• Follow good examples for individual roles and industries from job-winning cover letters;
• Decide on your most noteworthy achievement to stand out;
• Format, download, and submit your researcher cover letter, following the best HR practices.

Use the power of Enhancv's AI: drag and drop your researcher resume, which will swiftly be converted into your job-winning cover letter.

If the researcher isn't exactly the one you're looking for we have a plethora of cover letter examples for jobs like this one:

• Researcher resume guide and example
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• Research Associate cover letter example
• Lab Assistant cover letter example
• Undergraduate Research Assistant cover letter example
• Lab Technician cover letter example
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• Research Manager cover letter example

Researcher cover letter example

Samuel Moore

Columbus, Ohio

+1-(234)-555-1234

[email protected]

• Demonstration of past experience relevant to the role, such as leading a comprehensive evaluation of digital resources, indicates the candidate's ability to perform similar tasks at Ithaka S+R.
• Quantifiable achievements in previous roles, like improving project efficiency by 25%, show the candidate's potential to add measurable value to Ithaka S+R's projects.
• Alignment with the organization's mission, seen in the candidate's expression of shared goals regarding academic growth and equity, suggests a good fit with the team and its objectives.
• Mention of specific skills, such as advanced qualitative methodologies and strategic project management, matches the skill set required for a successful researcher at Ithaka S+R.

The must-have sections and format of your researcher cover letter

When writing your researcher cover letter, keep in mind that it'll only be read by the recruiters and not the Applicant Tracker System (or software used to assess your profile). That's why you should structure your content with a/an:

• Header (apart from your contact information, include your name, the role you're applying for, and the date);
• Personalized salutation;
• Opening paragraph to win the recruiters over;
• Middle paragraph with key details;
• Closing that starts from clichés;
• Sign off (that's not mandatory).

Industry standards dictate your paragraphs to be single-spaced and to wrap your content in a one-inch margin. Designing your researcher cover letter, refer to one of our templates , which automatically takes care of the spacing and margins.

Choose the same font for your researcher cover letter as you did for your resume : the likes of Lato and Bitter would help you to stand out in a sea of cover letters in Arial or Times New Roman.

Export your whole researcher cover letter from our builder in PDF to keep the same formatting and image quality.

The top sections on a researcher cover letter

• Header: Include your contact information, the date, and the employer's contact information, ensuring you can be easily reached for follow-up and portraying a professional format specific to researchers who value detail orientation.
• Greeting: Address the hiring manager or committee directly, if known, to show you've done your research, which is a critical skill for any research position.
• Introduction: Clearly state the research position you're applying for, mention how you found the job listing, and include a hook that summarizes your enthusiasm and fit for the role, demonstrating your genuine interest and initiative in the field.
• Body: Detail your previous research experience, publications, and how your skills align with the job requirements, showing that you can contribute significantly to the ongoing projects or academic pursuits of the organization.
• Closing: Express your eagerness to discuss further how you can contribute to the team, thank the reader for considering your application, and indicate that you have attached your CV or any relevant publications, establishing a call-to-action and preparation for the next steps.

Key qualities recruiters search for in a candidate’s cover letter

Proven track record in conducting independent research and publishing in peer-reviewed journals: It demonstrates the ability to contribute to the scientific community with original findings.

Expertise in specialized techniques or methodologies unique to the field: This shows the candidate possesses the technical skills necessary to perform and contribute to cutting-edge research.

Successful grant writing experience: Securing funding is critical for research; this skill indicates the candidate can attract the necessary resources to support their work.

Prior involvement in collaborative projects with multidisciplinary teams: Research increasingly requires collaboration across various disciplines, so the ability to work with diverse teams is highly valued.

Evidence of critical thinking and problem-solving abilities: Researchers must be able to tackle complex problems, analyze data, and draw meaningful conclusions that propel the field forward.

Strong communication skills, both written and oral: The ability to effectively communicate research findings to a wide range of audiences, including non-specialists, is essential for disseminating knowledge and advancing one's career in academia or industry.

How to start your researcher cover letter: with a greeting, of course

Have you ever considered just how powerful a personalized salutation can be?

We sure have news for you! Your researcher cover letter should start with the right salutation to recruiters, nurturing a sense of respect and individuality.

Greet recruiters by using their first name (e.g. "Dear Tom" or "Dear Patricia") if you've previously established contact with them.

Otherwise, opt out for the less familiar, "Dear Ms. Peaches" or "Dear Ms Kelsey", if you've found the recruiter's name on LinkedIn or a corporate website.

"To whom it may concern" is never a good option, as it creates a sense that you've been sending out your researcher cover letter to anyone. Instead, use "Dear HR team" or "Dear (company name) recruiter" for a feeling of exclusivity.

List of salutations you can use

• Dear Dr. [Last Name],
• Dear Professor [Last Name],
• Dear Hiring Committee,
• Dear Search Committee,
• Dear [Full Name],
• Dear Mr./Ms. [Last Name],

Using your researcher cover letter intro to show your dedication

We know just how difficult it is to start writing your researcher cover letter introduction .

There are so many great qualities you have as a professional, which one should you choose?

How about writing up to two sentences about your passion and commitment to the work you do or are set to do?

Try to describe exactly what you enjoy about the potential role.

A positive attitude from the get-go will help you stand out as a motivated researcher professional.

Choosing your best achievement for the middle or body of your researcher cover letter

Now that you have the recruiters' attention, it's time to write the chunkiest bit of your researcher cover letter .

The body consists of three to six paragraphs that focus on one of your achievements.

Use your past success to tell a story of how you obtained your most job-crucial skills and know-how (make sure to back these up with tangible metrics).

Another excellent idea for your researcher cover letter's middle paragraphs is to shine a light on your unique professional value.

Write consistently and make sure to present information that is relevant to the role.

Finishing off your researcher cover letter with what matters most

So far, you've done a fantastic job in tailoring your researcher cover letter for the role and recruiter.

Your final opportunity to make a good impression is your closing paragraph.

And, no, a "Sincerely yours" just won't do, as it sounds too vague and impersonal.

End your researcher cover letter with the future in mind.

So, if you get this opportunity, what do you plan to achieve? Be as specific, as possible, of what value you'd bring to the organization.

You could also thank recruiters for their interest in your profile and prompt for follow-up actions (and organizing your first interview).

Researcher cover letter advice for candidates with no experience

If you're worried about writing your Researcher cover letter and have no professional experience , we sure have some advice for you.

Turn recruiters' attention to your transferable or relevant skills gained thanks to your life and work experience.

Instead of writing about past jobs, focus on one achievement (whether from your volunteering experience, education, etc.) and the skills it has helped you build.

Alternatively, you could focus your Researcher cover letter on your career objectives and goals. Always remember to make those relevant to the job you're applying for by detailing how you see yourself growing as part of the company.

Recruiters would be way more impressed with candidates who fit the job profile and can bring about plenty of skills and vision to the table.

Key takeaways

Summarizing the most important aspects in writing your researcher cover letter, remember to:

• Create a personalized researcher cover letter for each role you apply for, that includes the recruiter's name in the salutation;
• Format your researcher cover letter with single-spacing, one-inch margins, and a modern, yet ATS-friendly font;
• Always start off your researcher cover letter with two sentences that reflect what is most important about your application;
• Your researcher cover letter body should feature your biggest accomplishments and the job-relevant skills it has taught you;
• Instead of opting for the "Sincerely yours" ending, close your researcher cover letter with a nod to the future with what you aim to achieve in this potential role.

Researcher cover letter examples

Explore additional researcher cover letter samples and guides and see what works for your level of experience or role.

Cover letter examples by industry

AI cover letter writer, powered by ChatGPT

Enhancv harnesses the capabilities of ChatGPT to provide a streamlined interface designed specifically focused on composing a compelling cover letter without the hassle of thinking about formatting and wording.

• Content tailored to the job posting you're applying for
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How to tailor your cv to the job, how to write a cover letter for an internship, 40+ resume tips and advice for 2024, how to make a great resume outline (including examples), how to put cum laude on your resume.

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How to Write a Research Assistant Cover Letter (With Template)

Gabriele Culot

December 29, 2023

Last Update

August 12, 2024

Table of Contents

A well-tailored cover letter: the key to job application success, cover letter tips, how to structure your research assistant cover letter, key takeaways.

• A cover letter can be the most important element in a job application. Ensuring your profile stands out to recruiters is crucial to your professional success.
• A well-tailored cover letter should provide relevant information clearly and concisely. Focus on detailing your skills and why you are the right person for that specific role.
• The included Research Assistant cover letter template provides an easy starting point to craft your own cover letters. Adapt and personalize it to fit your profile.

A well-written cover letter is key to quickly getting the attention of prospective employers. Among countless job seekers, resumes, and application letters, yours need to stand out on first impression if you want to ensure your job search  translates to a new role .

In this post, you will discover:

• Reasons why a well-crafted cover letter is key to professional success, from entry-level roles to senior positions
• Cover letter do’s and dont’s
• A Research Assistant sample cover letter you can easily adapt and personalize

Ensuring you know how to write a cover letter that is clear, informative, and tailored to the role you are applying to will benefit you in many ways. Well-crafted cover letters have many benefits, which include:

• Showcasing relevance:  Tailoring your cover letter allows you to emphasize the most relevant skills, experiences, and achievements that align with the specific job requirements. This immediately captures the attention of the  talent acquisition  team, recruiters, or human resources reps.
• Demonstrating research:  A good cover letter conveys your understanding of the organization's needs and illustrates how you can contribute to its success, signaling to potential employers that you've done your homework.
• Telling your story:  Each job application is unique, and a tailored cover letter enables you to craft a personalized narrative. It lets you connect your professional journey with the role's specific challenges and opportunities, making your application more compelling.
• Highlighting cultural fit:  Your cover letter allows you to address the company's values, mission, and culture. By aligning your experiences and values with those of the organization, you demonstrate a cultural fit and convey your enthusiasm for being part of the team.
• Addressing specific requirements:  Job postings often include  specific skills or qualifications  the employer is seeking. Tailoring your cover letter enables you to address these requirements directly, showcasing how you possess the desired attributes and can meet the company's expectations.

A great cover letter should reflect your professional profile and personality. However, no matter what your cover letter's content is, the tips below will help ensure the message you want to convey is clear and easily accessible to hiring managers.

• Keep it concise:  Aim for a cover letter length of 250-400 words. Be succinct in presenting your qualifications and experiences.
• Use a clean layout:  Opt for a professional and clean cover letter format with a standard font (e.g., Arial, Calibri, or Times New Roman) and a font size of 10-12 points.
• Include   contact information **:** Provide your contact information at the top of the cover letter, including your name, phone number, and professional email address.
• Use   headers   and sections:  Organize your cover letter into clear sections with headers such as Introduction, Work Experience, and Achievements for easy readability.
• Maintain a professional tone:  Keep the tone of your cover letter professional and upbeat. Avoid overly casual language, and focus on showcasing your skills and experiences.
• Use keywords:  Incorporate relevant keywords from the Agile Project Manager  job description  and company website into your cover letter. This can help your application pass through  applicant tracking systems (ATS)  used by many employers.
• Highlight achievements with bullet points:  Use bullet points to list specific accomplishments or notable projects. This makes it easier for the reader to grasp your accomplishments quickly.
• Use quantifiable data:  Whenever possible, include quantifiable data to demonstrate the impact of your achievements. Numbers provide concrete evidence of your contributions.
• Match company tone:  Adapt your writing style to match the tone of the company and industry. Research the company's culture to strike the right balance between professionalism and personality.
• Showcase company knowledge:  Demonstrate your understanding of the company by referencing its values, mission, or recent achievements. Explain why you're excited about the opportunity to work for this specific organization.
• Address employment gaps (if applicable):  If you have employment gaps, briefly address them in a positive light, focusing on any skills or experiences gained during those periods.
• Proofread   thoroughly:  Eliminate typos and grammatical errors by proofreading your cover letter multiple times. Consider using tools like Grammarly to catch any overlooked mistakes and ensure your English (or any language you use) is correct.
• Include a   call to action **:** Conclude your cover letter with a call to action, expressing your enthusiasm for the opportunity and indicating your readiness for an interview.
• Follow submission instructions:  If there are specific instructions for submitting the cover letter, such as naming conventions or document formats, ensure that you adhere to them.
• Save as a PDF:  Save your cover letter as a PDF before submitting it. This ensures that the formatting remains consistent across different devices and software.

While understanding the correct steps to write a cover letter is crucial to your professional success, knowing what mistakes to avoid is equally important. The best cover letter can easily be made useless by a tiny blunder. Avoid making the mistakes listed below; you will be halfway to your new job.

• Don't use a generic greeting:  Avoid generic salutations like "To whom it may concern," “Dear sir or madam, “ or “Dear hiring manager.“ Whenever possible, address the cover letter to a specific person.
• Don't repeat your resume:  An effective cover letter should complement your resume, not duplicate it. Focus on specific experiences and achievements that showcase your qualifications for the role.
• Don't exaggerate or lie:  Be truthful in your cover letter. Exaggerating your qualifications or providing false information can harm your chances and damage your professional reputation.
• Don't use unprofessional email addresses:  Ensure that the email address you use in your contact information is professional. Avoid using nicknames or unprofessional terms.
• Don't include irrelevant information:  Keep your cover letter focused on the job. Avoid including unrelated personal details or experiences that do not contribute to your suitability for the role.
• Don't use jargon unnecessarily:  While demonstrating your knowledge is essential, avoid unnecessary jargon that may confuse the reader. Use clear and straightforward language.
• Don't sound overly eager:  Expressing enthusiasm is positive but can easily feel unauthentic if overdone.

Remember, the goal of a practical cover letter is to present your qualifications in a clear, organized, and compelling manner while adhering to professional standards.

Express your interest in the Research Assistant position in the opening paragraph. Communicate your passion for research, data analysis, and your eagerness to contribute to a team dedicated to advancing knowledge in a specific field. If applicable, mention any referrals that have influenced your decision to apply for this specific role.

About your current role

Highlight your achievements and effective research strategies that have positively impacted the success of your current projects and team. Emphasize your role in conducting experiments, gathering and analyzing data, and contributing to research projects. Demonstrate your proficiency in research methodologies, data collection tools, and your ability to collaborate with fellow researchers.

Use this section to outline your current responsibilities and ongoing projects, emphasizing how they align with the requirements and objectives of the Research Assistant role.

About your experience

Detail your hands-on experience in research assistant roles, showcasing your ability to conduct literature reviews, design experiments, and contribute to the publication of research findings. Clearly communicate that your research skills and readiness for the role are well-established. This section is also an opportunity to highlight any relevant certifications, software proficiency, or additional skills you've acquired throughout your research career path .

Notable achievements

Highlight notable accomplishments that showcase your effectiveness as a Research Assistant. Whether you played a key role in a groundbreaking research project, contributed to the development of research methodologies, or significantly improved data analysis processes, use this section to concisely mention your achievements, how they were measured, and their impact on the overall success of the research projects you've been involved in.

Why you want to work there

Express your interest in the institution or company by highlighting specific aspects of its research focus, mission, and values related to your field of interest that resonate with you. Convey how these align with your professional goals and how you envision contributing to the organization's success through your expertise as a research assistant. Be concise but articulate about your motivations.

Specific projects or initiatives that motivated you to apply

Demonstrate your understanding of the organization by referencing specific research-related projects or initiatives that have captured your interest. Draw connections between these initiatives and your skills and experiences, emphasizing how your contributions align with the institution or company's goals for advancing research. This shows your genuine interest and proactive approach to aligning with the organization's mission.

In the closing paragraph, reiterate your enthusiasm to contribute to the organization's success as a Research Assistant. Express your eagerness to discuss how your skills align with the organization's research objectives and invite the reader to reach out with any questions they may have. Sign off with a professional salutation.

Research Assistant cover letter template

Dear [Hiring Manager’s name],

I am writing to express my interest in the Research Assistant position at [Institution or Company Name], as advertised. With a solid background in research methodologies and a passion for contributing to meaningful projects, I am confident in my ability to make valuable contributions to your research team.

About my current role

In my current position as a Research Assistant at [Current Institution or Company], I have:

• Assisted in the planning and execution of research projects, ensuring adherence to timelines and methodologies.
• Conducted literature reviews, gathered data, and performed statistical analysis using [specific research tools or software].
• Collaborated with research team members to interpret findings and contribute to the development of research papers.

About my Research Assistant experience

My experience extends to:

• Contributing to the design and implementation of experimental protocols and methodologies.
• Managing and organizing research data, ensuring accuracy and completeness.
• Assisting in the preparation of grant proposals and research grant reporting.

Some of my notable achievements include:

• Co-authoring a research paper published in [specific journal or conference], highlighting my contribution to the research community.
• Successfully coordinating the recruitment and participation of study participants, meeting project enrollment targets.
• Implementing efficient data management practices that resulted in a [percentage] reduction in data processing time.

Why I want to work for [Institution or Company]

I am particularly drawn to [Institution or Company Name] due to its [mention aspects unique to the institution or company such as a reputation for excellence in research, commitment to [specific research focus or area], growth,...]. I am excited about the opportunity to apply my research skills and contribute to [Institution or Company Name]'s ongoing success in advancing knowledge in the field.

Specific research projects or initiatives of [Institution or Company] that motivated me to apply

In researching [Institution or Company Name], I was impressed by your recent projects in [specific research focus or area]. My experience in research aligns seamlessly with your organizational objectives. My dedication to rigorous research practices, coupled with my commitment to contributing to meaningful projects, would make me a valuable addition to your research team.

Thank you for considering my application. I am eager to further discuss how my skills and experiences align with the Research Assistant role at [Institution or Company Name]. I look forward to contributing to your team's success.

[Your Full Name]

Get your career rolling with Deel

Your job application is your chance to tell your professional story, and a well-tailored cover letter is your narrative's opening chapter. Remember that personalization is key. Make each word count, emphasizing how your background uniquely positions you as the ideal candidate, and get your dream job. 

Looking for even more inspiration?  Discover how to write a stellar cover letter in 5 steps .

Discover more tips and tools to help boost your career further and climb the steps to your dream job on  the get-hired content hub .

About the author

Gabriele Culot is a content manager and writer passionate about exploring the future of work and its opportunities. An advocate of remote and flexible work models, he is a strong believer in their power to expand access to opportunities and help build richer and more diverse connections. At Deel, he focuses on worker-related and community content, from immigration guides to workplace innovation, from digital nomad lifestyle to workplace wellbeing.

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Research Assistant cover letter examples

Can you be there to support the primary researcher in collecting data and information?

If so, you need to prove it with an engaging and persuasive cover letter. We want to see those research skills in action as you perfect and tailor your application.

But in case you need a little guidance, we’ve also put together some of our top tips and research assistant cover letter examples in the guide below.

CV templates 

Research Assistant cover letter example 1

Build your CV now 

Research Assistant cover letter example 2

Research Assistant cover letter example 3

These 3 Research Assistant cover letter example s should provide you with a good steer on how to write your own cover letter, and the general structure to follow.

Our simple step-by-step guide below provides some more detailed advice on how you can craft a winning cover letter for yourself, that will ensure your CV gets opened.

How to write a Research Assistant cover letter

Here’s how you can write your own eye-catching cover letter, broken down into simple steps.

Write your cover letter in the body of an email/message

When you send a cover letter with a job application, you should always write your message into the body of your email – or the body of the messaging system if you are sending via a job website.

Why do this?

Simply because you want to get your message seen as soon as the recruiter opens your application.

If you attach the cover letter as a separate item, this means the recipient will have to open it before they can read it – slowing down the process and potentially causing frustration along the way.

So, write your cover note in the body of your email/message to ensure you make an instant connection with the reader.

Start with a friendly greeting

Start you cover letter with a greeting that is professional but friendly.

This will build rapport with the recruiter whilst showing your professionalism.

• Hi, hope you’re well
• Hi [insert recruiter name]
• Hi [insert department/team name]

Avoid overly formal greetings like “Dear sir/madam ” unless applying to very traditional companies.

How to find the contact’s name?

Addressing the recruitment contact by name is an excellent way to start building a strong relationship. If it is not listed in the job advert, try these methods to find it.

• Check out the company website and look at their  About page. If you see a hiring manager, HR person or internal recruiter, use their name. You could also try to figure out who would be your manager in the role and use their name.
• Head to LinkedIn , search for the company and scan through the list of employees. Most professionals are on LinkedIn these days, so this is a good bet.

Identify the role you are applying for

Once you’ve opened up the cover letter with a warm greeting to start building a relationship, it is time to identify which role you want to apply for.

Recruiters are often managing multiple vacancies, so you need to ensure you apply to the correct one.

Be very specific and use a reference number if you can find one.

• I am interested in applying for the position of Research Assistant with your company.
• I would like to apply for the role of Sales assistant (Ref: 406f57393)
• I would like to express my interest in the customer service vacancy within your retail department
• I saw your advert for a junior project manager on Reed and would like to apply for the role.

See also: CV examples – how to write a CV – CV profiles

Highlight your suitability

The bulk of your cover letter should be focused around highlighting your suitability for the job you are applying to.

Doing this will show the recruiter that you are suitable candidate and encourage them to open your CV.

The best way to do this, is by studying the job advert you are applying to, and find out what the most important skills and knowledge are.

Once you know the most important requirements, you then need to highlight your matching skills to the recruiter. In a few sentences, tell them exactly why you are a good fit for the job and what you can offer the company.

Keep it short and sharp

A good cover letter is short and sharp, getting to the point quickly with just enough information to grab the attention of recruiters.

Ideally your cover letter should be around 4-8 sentences long – anything longer will risk losing the attention of time-strapped recruiters and hiring managers .

Essentially you need to include just enough information to persuade the reader to open up your CV, where the in-depth details will sit.

Sign off professionally

To round of your cover letter, add a professional signature to the bottom, giving recruiters your vital contact information.

This not only gives various means of contacting you, it also looks really professional and shows that you know how to communicate in the workplace.

Include the following points;

• A friendly sign off – e.g. “Warm regards”
• Your full name
• Phone number (one you can answer quickly)
• Email address
• Profession title
• Professional social network – e.g. LinkedIn

Here is an example signature;

Warm regards,

Gerald Baker Senior Accountant 07887500404 [email protected] LinkedIn

Quick tip : To save yourself from having to write your signature every time you send a job application email, you can save it within your email drafts, or on a separate document that you could copy in.

What to include in your Research Assistant cover letter

Your Research Assistant cover letter will be unique to your situation, but there are certain content guidelines you should stick to for best results.

To attract and entice recruiters, stick with the following key subjects in your cover letter – adapting them to fit your profession and target jobs.

• Your professional experience – Employers will be keen to know if your experience is suitable for the job you are applying to, so provide a good summary of it in your cover letter.
• Your qualifications and education – Highlight your most relevant and high-level of qualification, especially if they are essential to the job.
• The positive impact you have made – Employers love to hear about the benefits you can bring to them, so shout about anything impressive you have done, such as saving money or improving processes.
• Your reasons for leaving – Use a few words of your cover letter to explain why you are leaving your current job and ensure you avoid any negative reasons.
• Your availability – Let recruiters know when you can start a new job . Are you immediately available, or do you have a month notice period?

Research Assistant cover letter templates

Copy and paste these Research Assistant cover letter templates to get a head start on your own.

Good day Judith

I would like to apply for the Research Assistant position at the University of London. I am eager to contribute my skills and dedication to support the impactful work conducted by your research team.

I have successfully completed my MSc in Clinical Epidemiology from the University of Manchester, with a focus on mental health and disease studies and scientific inquiry. Throughout my coursework, I gained experience in research methodologies, literature reviews, and data collection/analysis. I am drawn to the ground-breaking trials conducted at your institution, especially those related to various forms of dementia. Your department’s commitment to improving patient outcomes and enhancing medical practices aligns perfectly with my own dedication to making a meaningful impact in the medical field.

In my previous role as a Junior Research Assistant at the University of Oxford, I collaborated with a multi-disciplinary team to fuel promising research to end Alzheimer’s, where I contributed towards securing £50K in funding to investigate the role of genetics in Alzheimer’s disease, as well as decreasing costs by 15% through efficiently executing experiments.

Thank you for considering my application. I look forward to attending an interview with you.

Kind regards

Gillian Shaw

Good day Margaret

I am writing to apply for the Senior Research Assistant position at the University of Cambridge. With a strong academic background including a Ph.D. in Dementia Studies from the University of Worcester and significant Dementia with Lewy Bodies research experience, I am eager to contribute my passion for improving the lives of individuals affected by dementia to support the work conducted by your team.

Throughout my career, I have been committed to advancing clinical trials and enhancing our understanding of complex neurodegenerative diseases. As a Research Assistant with 10 years of experience, I have been actively involved in multiple projects which explore aspects of dementia, including risk factors, early detection, and therapeutic interventions. Your institution’s dedication to finding approaches to treat and prevent dementia aligns with my vision of making a significant impact.

In this role I was pivotal in obtaining £3M in grants from government agencies, co-authored five papers in reputable peer-reviewed journals, and identified potential biomarkers associated with early-stage dementia which provided crucial insights into disease progression.

I look forward to attending an interview, where we can discuss my qualifications and experience further.

Paul Hayward, Ph.D.

Good morning, Joseph

I hope my CV reaches you concerning the Junior Research Assistant position advertised by the University of Bristol on LinkedIn. With a genuine passion for Parkinson’s disease clinical trials and a strong academic background, I am eager to contribute my competencies to your institution.

I recently completed my BSc (Hons) in Neuroscience from Plymouth University, where the coursework equipped me with a comprehensive understanding of research methodologies, data evaluation, and the intricacies of Parkinson’s disease. Additionally, I gained practical experience in laboratory techniques through my involvement in two university-led research projects. Some of my contributions included supporting the recruitment of 80 participants for a Parkinson’s disease clinical trial, ensuring timely enrolment and adherence to protocols, as well as ensuring 0% lab incidents during research activities.

I am confident in my ability to adhere to the highest ethical standards in research and maintain strict attention to detail throughout the research process. My solid problem-solving skills will be invaluable in conducting data analysis and interpreting findings.

I look forward to speaking with you to discuss my application further. Thank you for your consideration.

Damian Harris

Writing a strong attention-grabbing cover letter is a vital step in landing a good Research Assistant job.

Use the tips, strategies and examples above to get more responses from you job applications and start lining job interview up.

Good luck with your job search!

Research Assistant Cover Letter Sample

Finding a job as a research assistant is not unattainable if you can compose a perfect cover letter. To help you land that job and start your career, we’ve outlined how to write a cover letter step by step and included a research assistant cover letter sample to get you going. And if you need expert help, use our online resume and cover letter service and tips for simple cover letter writing. Let’s dive in!

Cover Letter for a Research Assistant [Example] 

Ensure that you use the right cover letter format to make it look readable, polished, and professional.

[Your name]

[Your address]

[Your phone and email]

[Today’s Date]

[Hiring Manager’s Name]

[341 Company Address]

Company City, State XXXXX]

(xxx)xxx-xxxx

[[email protected]]

Dear [Mr./ Mrs./Miss.] [Hiring Manager’s Name]

I am writing to apply for the position of [Position Name] at [Company Name] as advertised on [Website Name]. Awaiting graduation for my master’s in the field of biology, I was delighted to see your call for a research assistant. I have hands-on experience developing research techniques, research methodologies, and data analysis, making me the best candidate to join your team. 

Let me list some of my accomplishments during my internship as a research assistant when I was able to accumulate commendable experiences. Here they are:

• initiated a new bacterial strain library and organic extraction organization pattern, which resulted in a 5% increase in research efficiency;
• carried out over 100 mouse surgeries and curated pre- and post-surgical care;
• modified a molecular tool to improve observation of molecules in vivo and attained 200% greater accuracy as a result.

I have attached my resume illustrating my relevant skills, experience, and accomplishments. I appreciate you taking the time to read this, and I hope to hear from you soon.

[Your Name]

This is one of the well-written research assistant cover letter examples you can use to create your own document. Now, let’s move on to the standards of writing cover letters.

Research Assistant Cover Letter Template

Consider using a modern cover letter template and format following the guidelines below:

• maintain a friendly and professional tone throughout your research assistant cover letter;
• be accurate and concise;
• do not exceed 3-4 paragraphs;
• double-space your paragraphs;
• choose Georgia, Calibri, Helvetica, Trebuchet MS, or other respected fonts;
• keep margins 1 inch per edge;
• write an attention-grabbing introduction;
• capture your value;
• demonstrate your work experience.

Ultimate Guide on How to Write a Research Assistant Cover Letter

A cover letter guide is an ultimate way to get a perfect letter for a job offer. It takes time to master writing skills, and not every research assistant expert can write a great cover letter. But our cover letter writers for hire do extensive research to scrutinize all the information and put everything in one place, and you’ll be able to make your cover letter effective, to the point, and concise.

Are you in need of expert help in writing and formatting a cover letter for research assistant? Our proficient resume and cover letter writing service are here to make your job application perfect.

How to Format Your Research Assistant Cover Letter

The correct format makes research assistant cover letters appealing and helps a hiring manager notice your worth quickly. The following is a list of cover letter formatting tips to get you closer to your dream job:

• contact information;
• salutation and introduction;
• body paragraphs;
• call-to-action;

Cover Letter Heading

Your cover letter research assistant should include a well-designed header, which is the first thing a hiring manager sees. If well-designed, the cover letter can convince the hiring manager of your proficiency. It’s simple to use the right cover letter header design. Learn all you need to know from the example below.

Cover Letter Sample for Research Assistant [Heading]

Here is one of the relevant cover letter examples research assistant:

Anne Marrie

512 Bubby Drive

Dublin, TX, 56785 United States

(214) 320- 7890

[email protected]

Date and Company Details on Cover Letter

The date shows how recent your cover letter for research assistant position is, and it should appear between the header and the company details. The company details include the addressed person’s title, company name, address, city, and state zip code.

Example Cover Letter Research Assistant [Date and Company Details]

The date and company details should appear immediately after the header. Here is this part from a sample cover letter for research assistant:

February 10, 2022

Stevenson Roberts

352 Magnolia Dr.,

Greenville, SC 66778

(722) 333-5634

[[email protected]]

Cover Letter Greeting

You should also pay attention to your cover letter greeting. This element of the cover letter research position is an opportunity to show professionalism.

“Dear [Mr./Ms./Mx.] [Hiring Manager’s Surname],” is a good greeting if you know the hiring manager’s name. “Dear Marketing Team,” outperforms the formal “To Whom It May Concern,” salutation when you don’t know the recipient’s name.

Cover Letter Example for Research Assistant [Greeting]

Here is a cover letter greeting from a cover letter example research assistant:

“Dear Mrs. Meghan Johnson,”

“Dear Research Team,”

What to Include in a Research Assistant Cover Letter [Body]

You should be able to compose an excellent body part when learning how to write a cover letter for a research assistant position. Market yourself in two or three body paragraphs. State the position and the company you’re applying to in a captivating opening paragraph. Try and include a bulleted list of your most remarkable accomplishments. Conclude by reaffirming your interest and sharing your phone number and email address details.

Research Assistant Cover Letter Sample [Body]

Let’s see how this part looks in one of the cover letter examples for research assistant: 

“I was excited to see an opening for the position of [Position] at [Company Name] as advertised in [Website Name]. My background is in medical laboratory, and I am awaiting graduation. Here is what I’ve accomplished during my college education: 

• participated in 5 research projects; 
• was praised for exceptional performance and organizational abilities;
• won the award “Best Student Researcher” for my dedication to the most recent project. 

I believe I am the best-suited candidate to join your team and accomplish the organization’s objectives. Let’s discuss how I can contribute to your company. Please feel free to contact me via [phone number] or [email address].”

Closing Paragraph for Cover Letter

No hiring manager likes a bland cover letter closing. Keep it simple but add detailed information. Optionally, you can include a postscript. The key to an effective cover letter conclusion is to stimulate the reader’s interest and boost your chances of getting hired, as seen in one of the research assistant cover letter samples below.

Research Assistant Cover Letter Example [Closing]

The goal of a thrilling finish is to secure the interview. Look at the following research assistant cover letter example:

“If I’m hired for this position, I’ll demonstrate the same dedication that helped me initiate a new organization of bacterial strain library and organic extraction, resulting in a 5% increase in research efficiency.”

Exploit Your Skills

Mentioning your core skills when writing a cover letter for research assistant is crucial. Most hiring managers have an applicant tracking system (ATS) that checks your submitted job application for keywords relevant to the job offer, so make sure you describe soft and hard skills that fit the job description. 

Analytical, organizational, leadership, and interpersonal skills are soft skills, while data analysis and data collection are hard skills for graduates interested in research assistant positions.

Include Relevant Keywords

Keywords are words in a cover letter that link to specific skills, competencies, and other qualities employers search for when screening applications.

In a cover letter sample for research assistant, they can link directly to the position you’re applying for and highlight your experience and qualifications.

Using the right keywords, such as skills-based and result-oriented words, can help your application pass through additional tracking methods organizations use to filter prospects.

The Bottom Line

Let’s summarize the crucial steps of writing a cover letter.

• Before writing, ensure the research assistant letter format is up-to-date.
• Mention your address, the date of writing, and company details.
• Begin with a catchy introduction that excites your reader.
• Write a selling body for your research assistant cover letter, presenting your key skills, qualifications, and experience.
• Make your achievements quantifiable to stand out from other applicants.
• Finish the letter with a strong call to action and, possibly, a postscript. ‍

Do you have any questions about writing a cover letter or using a cover letter research assistant example? Need help tweaking your research assistant cover letter? Use our best cv writing service , and you’ll get what you need with ease.

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• If you've studied at Waterloo previously, and thus have a Waterloo student identification number (8 digits), submit a Visiting Student Readmission Application .
• If this is your first time wanting to study at Waterloo, learn about the application process and deadlines .

Letter of Permission Application submission process

This is a summary of the steps to complete the Letter of Permission Application; ensure to read the entire form before completing it.

• Application information: Complete all the fields under student information, host university information, and request information; be sure to sign and date the form.
• Environment: Carol Knipe or Johanna Wandel
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• If the host university does not use MyCreds TM , request the host university to email the official transcript to [email protected] .

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• Open access
• Published: 06 September 2024

ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

• Tianyi Xia 1 , 2 ,
• Luni Hu 1 , 2 ,
• Lulu Zuo 3 ,
• Lei Cao 1 , 2 ,
• Yunjia Zhang 1 , 2 ,
• Mengyang Xu   ORCID: orcid.org/0000-0002-4487-7088 2 , 4 ,
• Lei Zhang 1 , 2 ,
• Taotao Pan 1 , 2 ,
• Bohan Zhang   ORCID: orcid.org/0000-0001-6654-3567 1 , 2 ,
• Bowen Ma 1 , 2 ,
• Chuan Chen 1 , 2 ,
• Junfu Guo   ORCID: orcid.org/0000-0002-4195-7031 3 ,
• Chang Shi 3 ,
• Mei Li   ORCID: orcid.org/0000-0003-3310-2911 2 ,
• Chao Liu   ORCID: orcid.org/0009-0008-6892-6754 1 , 2 ,
• Yuxiang Li   ORCID: orcid.org/0000-0002-1575-3692 2 , 5 , 6 ,
• Yong Zhang   ORCID: orcid.org/0000-0001-9950-1793 2 , 5 , 6 &
• Shuangsang Fang   ORCID: orcid.org/0000-0002-4126-0074 1 , 2  

Nature Communications volume  15 , Article number:  7806 ( 2024 ) Cite this article

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• Bioinformatics
• Computational models
• Data processing
• Transcriptomics

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable ‘gears’ between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

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Alignment and integration of spatial transcriptomics data

SANTO: a coarse-to-fine alignment and stitching method for spatial omics

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

Introduction.

Spatial transcriptomics (ST) is an omics technology that fuels biological research based on measuring gene expression on each position-recorded spot across sliced tissues 1 , 2 , 3 . Notably, a range of methods has been developed. In vivo sequencing (ISS) 4 platforms such as Barcoded Anatomy Resolved by Sequencing (BARseq) 5 and Spatially-resolved Transcript Amplicon Readout Mapping (STARmap) 6 rely on amplification, hybridization and imaging process to capture gene expression information. Next Generation Sequencing (NGS) 7 platform such as Visium 1 , Stereo-seq 8 and Slide-Seq2 9 uses spatial barcoding and capturing in their implementations. These methods offer various sequencing resolutions ranging from 100 µm 10 , 11 to 500 nm 8 , and can measure thousands 5 to tens of thousands 8 of genes simultaneously.

Single-slice ST studies have unleashed discoveries, and facilitated our understanding in diverse biological and medical fields 9 , 12 , 13 , 14 , 15 . Consequently, numerous processing pipelines and analysis models have been developed for ST data on a single section 16 , 17 , 18 , 19 , 20 , 21 . However, to truly capture transcriptomics in the real-world context, three-dimensional (3D) ST was designed to recover biological states and processes in real-world dimensions, without restriction of the isolated planes in single sectional ST studies. Various research has utilized the power of 3D ST to uncover insights in homeostasis, development, and diseases. Among them, Wang et al. 22 uncovered spatial cell state dynamics of Drosophila larval testis and revealed potential regulons of transcription factors. Mohenska et al. 23 revealed complex spatial patterns in Murine heart and identified novel markers for cardiac subsections. And Vickovic et al. 24 explored cell type localizations in Human rheumatoid arthritis synovium. The vast and large variety of downstream 3D research has posted the need for a reliable and automatic recovery method of in vivo spatial profile.

However, the collection process of ST data casts significant challenges onto the accurate reconstruction of 3D ST and the situation has not been overcome by current explorations. Specifically, in 3D ST experiments, individual slices are cross sectioned in a consistent direction, then manually placed on different chips or slides 14 , 25 . This operation introduces varying geospatial reference systems of distinct sections, and coordinates are distorted compared to their in vivo states. The distortions occur due to squeezing and stretching effects during the picking, holding, and relocation of the sections. Such different geospatial systems and distortions complicates the recovery of in vivo 3D profile. Among current recovery approaches, STUtility 26 realizes multi-section alignment through the registration of histology images, without considering either geospatial or molecular profile of mRNA, which leads to compromised accuracies. Recently published method PASTE 27 , and its second version PASTE2 28 achieve alignment using both gene expression and coordinate information, through optimization of mapping between individual spots across sections. These methods cause inaccurate mappings and produces rotational misalignments due to the nonadaptive regularization factors, and their uniform sum of probability assigned to all spots upon presence of spots without actual anchors. All above approaches only consider rigid alignment, yet neglect the correction of shape distortions, resulting in shape inconsistency across registered sections. Published method Gaussian Process Spatial Alignment (GPSA) 29 considers shape distortions in its alignment, yet it doesn’t involve structural consistency in its loss function, which can cause the model to overfit to local gene expression similarities, leading to mistaken distortions of spatial information. Moreover, its hypothesis space involves readout prediction in addition to coordinates alignment, causing uncertainty in direction of gradient descent, and vulnerabilities to input noises. Another alignment approach, Spatial-linked alignment tool (SLAT) 30 also focuses on anchors construction between sections, yet it doesn’t provide a methodology to construct 3D transcriptomics profile. Other tools focus on analysis and visualization of 3d data, such as Spateo 31 , VT3D 32 and StereoPy 33 .

To address these limitations, we introduce ST-GEARS, a 3D geospatial profile recovery approach designed for ST experiments. By formulating the problem using the framework of Fused Gromov-Wasserstein (FGW) Optimal Transport (OT) 34 , ST-GEARS incorporates both gene expression and structural similarity into the Optimization process to retrieve cross-sectional mappings of spots with the same in vivo planar positions, also referred to as ‘anchors’. During this process, we introduce innovative Distributive Constraints that allow for different emphasis on distinct spot groups. The strategy addresses importance of expression consistent groups and suppresses inconsistent groups from imposing disturbances to optimization. Hence it increases anchor accuracy compared to current approaches. ST-GEARS utilizes the retrieved anchors to initially perform rigid alignment of sections. Subsequently, it introduces Elastic Field guided by the anchors to represent the deformation and knowledge to correct it according to each spot’s location. To enhance the quality of the field, Gaussian Smoothing is applied for denoising purposes. ST-GEARS then applies Bi-sectional Application to correction of each section’s spatial profile based on its denoised fields calculated with its neighboring sections. With validity proved mathematically, Bi-sectional Application eliminates distortions of sections, resulting in the successful recovery of a 3D in vivo spatial profile.

To understand effects of ST-GEARS, we first studied its counterparts with innovations including anchors retrieval and elastic registration, respectively on Human dorsolateral prefrontal cortex (DLPFC) 35 , and Drosophila larva 22 . We found an advanced anchors accuracy of ST-GEARS compared to other available methods involving anchor’s concept and unveiled Distributive Constraints as reason behind the advancement. We validated the effectiveness of elastic registration process of ST-GEARS on both tissue shape smoothness and cross-sectional consistency. Then, we studied output of ST-GEARS and other methods on their reconstruction of Mouse hippocampus tissues 36 , Drosophila embryo individual 22 and a complete Mouse brain 37 . The result was studied on morphological, cell and gene levels. ST-GEARS was found to be the only method that correctly reconstruct on all cases despite of cross-sectioning distance, number of sections, and sequencing platforms, and it was found to output the most accurate spatial information under both annotation type or clustering information, and hybridization evidence.

ST-GEARS algorithm

ST-GEARS uses ST data as its inputs, including mRNA expression, spatial coordinates as well as approximate grouping information such as clustering or annotation of each observation. Then it recovers 3D geospatial profile in following steps (Fig.  1 ).

a The automatic pipeline of ST-GEARS which recovers ST-GEARS 3D in vivo spatial information by ordered steps including Fused Gromov Wasserstein (FGW) Optimal Transport (OT) problem parameter computing, problem formulating and solving which outputs probabilistic anchors across sections, rigid registration through Procrustes Analysis which solves optimal positional alignment using the anchors, and finally elastic registration. The input of the method is Unique molecular identifier (UMI) counts and location of each spot measured by ST technology, along with their annotations or cross-section clustering result. And the output of the method is recovered 3D in vivo spatial information of the experimented tissue, or sample. b FGW OT problem parameter computing, which assigns nonuniform weights to spots in preparation for future problem formulating, based on cross-sectional similarity of annotation types or clusters. c FGW OT problem formulating, whose setting aims to solve probabilistic anchors joining spots with highest in vivo proximity, through optimizing the combination of gene expression and structural similarity 34 . FGW OT problem solving, which is implemented based on Conditional Gradient (CG) method, leading to retrieved probabilistic anchors. d Elastic registration, which utilizes the anchors again to compute and denoise distortion fields which guides the elimination of distortions, then applies the fields bi-sectionally to positionally aligned sections, leading to the recovered 3D in vivo spatial information.

(1) Optimization problem formulation under scheme of FGW OT with enhancement of Distributive Constraints. FGW OT formulation is established to enable solving of ‘anchors’, which are the joining of pair of spots with same in vivo planar positions. Noticeably, each solved anchor is equipped with a probability that describes its strength of connection, and each spot is solved to have zero to multiple anchors. Among each two sections, section-specific groups of spots, and genes are initially excluded from the formulation to avoid causing disturbances to anchors computing. Considering that connected spots are more spatially approximate, and more similar in gene expression because of shared cell identity 38 , 39 , FGW was adopted to combine the gene expression and structural terms in optimization, enabling highest gene expression similarity between mapped spots, at the same time keeping similar spot positions relative to their sections. Moreover, an innovative Distributive Constraints setting is designed and integrated into FGW OT’s formulation, to assign higher emphasis on spots or cells whose annotation or cluster express high similarity across section, and vice versa. Distributive Constraints leads registration to rely more on expression-consistent regions of sections, hence largely enhancing both accuracy of anchors and precision of following rigid and elastic registration.

(2) Optimization problem solving utilizing self-adaptive regularization and conditional gradient descent. Our designed Self-adaptive Regularization strategy automatically determines the relative importance between gene expression and structural terms in the optimization problem. This strategy leads to an optimal regularization factor across different section distances, spot sizes, extent of distortions, and data quality such as level of diffusion. Conditional Gradient 34 is adopted as optimizer, which updates anchors iteratively towards higher expression and structural similarity with each iteration. The efficacy of Conditional Gradient has been demonstrated through its convergence to a local optimal point 40 , thereby ensuring the robustness and effectiveness of our approach.

(3) Rigid registration by Procrustes Analysis 41 . After filtering out anchors with relatively low probabilities, the optimal transformation and rotation of each section are analytically solved through Procrustes Analysis, which minimizes summed spatial distances of spots anchored to each other. With the transformation and rotation applied, sections are positionally aligned.

(4) Elastic registration guided by anchors. Based on rigid registration result and anchors solved by FGW OT, elastic registration is implemented through the process including elastic field inference, 2D Gaussian denoising, and bi-sectional fields application. Based on each rigidly registered section, elastic fields is inferred leveraging the location difference between its own spots and its anchored spots on anterior and posterior neighbor sections. An elastic field is a 2D displacement distribution, describing how displacement values are distributed across different locations. Making use of continuity of deformation at local scales, 2D Gaussian Denoising convolutes all over the fields to reduce noises. With denoised fields, our designed Bi-sectional Fields Application corrects each section’s deformation according to its fields calculated with anterior and posterior neighbor sections. The bi-sectional correction method is mathematically proved to approximately recover each section’s spatial profile to its original state.

Enhancement of anchor retrieval accuracy through distributive constraints

As was unfolded, ST-GEARS is an algorithm flow jointly constituted of probabilistic anchor computation and spatial information recovery. Hence, to validate the effectiveness of our method and demonstrate its underlying design philosophy, we conducted comprehensive studies on the two counterparts using real-world data. To begin, we utilized the DLPFC dataset 35 to study our anchors retrieving accuracy with emphasis on the effect of Distributive Constraints design.

To assess the effects of Distributive Constraints on anchor accuracy, we compared ST-GEARS with and without this setting, and with other constraints involving methods including PASTE, PASTE2 and SLAT. We investigated constraint values assigned by these methods, as well as their solved number of anchors and maximum anchor probability of each spot. Furthermore, we examined the annotation types that were considered connected based on the computed anchors to assess accuracy of anchors. Among the methods we compared, ST-GEARS with Distributive Constraints was found to assign different constraint values to spots within different neuron layers, while the others assigned uniform constraints to all layers (Fig.  2a , Supplementary Fig.  1 ). The results of ST-GEARS showed that both number of anchors and the anchors’ maximum probabilities for each spot were lower in Layer 2 and Layer 4 compared to the thicker layers. However, this pattern was not observed in methods without Distributive Constraints setting (Fig.  2a , Supplementary Fig.  1 ). To illustrate the impact of this strategy on anchor accuracy, we tagged each spot with annotation of its connected spot by anchor with highest probability. We then compared this result to the tagged spot’s original annotation (Fig.  2a , Supplementary Fig.  1 ). Under Distributive Constraints, ST-GEARS achieved a significantly higher proximity between annotations compared to PASTE and our method without Distributive Constraints. PASTE2 also led to approximate annotations, but it anchored multiple spots to spots from neighboring layers, particularly those near layer boundaries. SLAT also mapped multiple spots to spots from different tissue layers, particularly of spots located on layer 2, 4 and 6.

a (from left to right) 1st and 2nd human dorsolateral prefrontal cortex (DLPFC) section of patient #3 by Maynard et al. 35 with their provided annotations and our anchors showcase, (of the same section pair) probabilistic constraints settings in Optimal Transport (OT) problem formulating, no. of anchors computed on each spot, max. anchor probability value computed of each spot, and annotation type mapped back to spots through computed anchors; (from top to bottom) respectively by PASTE, PASTE2, SLAT, ours without distributive constraints setting, and ours. The distinction of different annotation types on the 1st section is marked by dotted lines. Mapping accuracy is used to measure accuracy of anchors and is marked alongside respective annotation type mapping visualizations. b Mapping accuracy measured on anchors of sections pairs used in ( b ) by PASTE, PASTE2, SLAT, and ST-GEARS. c Comparison of no. of anchors histograms between ST-GEARS and ST-GEARS without distributive constraints, of sections pairs of 1st and 2nd, 2nd and 3rd, and 3rd and 4th sections. The Probability Density Function (PDF) estimated by Gaussian kernel was plotted in dotted lines with the same color of histograms, to highlight the distribution differences. Source data are provided as a Source Data file.

To evaluate the precision of anchors, we conducted a comparison with the Mapping accuracy index introduced by PASTE 27 . This index measures the weighted percentage \({\sum}_{i,j,l(i)=l(j)}{\pi }_{{ij}}\) of anchors that connect spots with same annotation. As a result, ST-GEARS outperformed PASTE2 and SLAT, and reached a score that was over 0.5 (out of 1) higher than both PASTE and our method without Distributive Constraints (Fig.  2a , b , Supplementary Fig.  1 ).

To uncover the reasons behind the aforementioned phenomena, as the functional area in between thicker neocortical layers, thinner neocortical layers have comparable transcriptomic similarity with their adjacent layers in gene expression, than with its own annotation type 1 , 35 . This implies that, in contrast to thicker layers, thinner layers tend to introduce more disturbances during anchor computation. However, the Distributive Constraints imposed suppression on these annotation types by assigning a smaller sum of probability to each of their spots. The suppression was reflected in above results where each spot in Layer 2 and Layer 4 has fewer assigned anchors and a lower maximum probability (Fig.  2a , Supplementary Fig.  1 ). Further analysis on all spots in the DLPFC reveals that a certain percentage of spots were suppressed in anchor generation due to the Distributive Constraints (Fig.  2c , Supplementary Fig.  2 ).

Recovery of in vivo shape profile through elastic registration

We then utilized Drosophila larva data to investigate the spatial profile recovery effect of ST-GEARS, with an emphasis on our innovated elastic registration. We first applied rigid registration to Drosophila larva sections and observed a visually aligned configuration of individual sections (Supplementary Fig.  3 ). By further mapping cell annotations back to their previous sections, according to the strongest anchors of each spot, the projected annotations are visually in match with original ones (Supplementary Fig.  4 ). The accuracy of the mapping matching between annotations was quantified by Mapping accuracy (Supplementary Fig.  5 ). The above findings validated that ST-GEARS produced reliable anchors and accurately aligned sections through rigid registration. However, when stacking the sections together, we observed an inconsistency on the edge of lateral cross-section of the rigid result (Supplementary Fig.  6 ). This inconsistency doesn’t conform to the knowledge of intra-tissue and overall structural continuity of Drosophila larvae.

After applying elastic registration to the rigidly-aligned larva, we observed a notable improvement in the continuity of the cross section above, indicating a closer-to-real spatial information being retrieved. To further understand the effect of elastic operation on the dataset, we compared the changes in area of the complete body and three individual tissues (trachea, central nervous system (CNS), and fat body) on all sections. We observed an enhanced smoothness in the curves of elastically registered sections, which aligns with the continuous morphology of the larva as expected by theoretical knowledge. To quantify the smoothing effect, we calculated Scale-independent Standard Deviation of Differences ( \({SI}-{STD}-{DI}={STD}(\{{s}_{i}-{s}_{i-1}:i\in [{\mathrm{1,2}},...,I-1]\})/{|mean}(\{{s}_{i}-{s}_{i-1}:i\in [{\mathrm{1,2}},...,I-1]\})|\) ) onto the curves, which measures the smoothness of area changes along the sectioning direction (Fig.  3a and Methods). A decrease of SI-STD-DI on all tissues and the body provided empirical evidence for the improved smoothness. To further investigate the recovery of internal structures, we introduced Mean Structural Similarity (MSSIM). MSSIM takes structurally consistent sections as input, and measures pairwise internal similarity of reconstructed result using annotations or clustering information (Supplementary Fig.  7 ). (See Methods for details). An improved MSSIM was noticed on all 4 sections, indicating that elastic registration further recovers internal geospatial continuity on basis of rigid operation(Fig.  3b ). By comparing registration effect of individual sections, we also observed that the elastic process successfully rectified a bending flaw along the edge of the third section, (Fig.  3c ). The shape fixing highlighted that ST-GEARS not only yielded a more structurally consistent 3D volume, but also provided a more accurate morphology for single sections. The improved smoothness, the recovered structural continuity, and the shape fixing collectively demonstrate that elastic registration effectively recovers geospatial profile.

a A comparison of area changes of 3 tissues and complete body of Drosophila Larva, between result of rigid registration and result of elastic registration appended to rigid registration. The areas are calculated based on recovered spot position of different tissues along cross-sectioning direction. Standard Deviation of Differences (SI-STD-DI) quantifying the smoothness is marked alongside each curve. b A comparison of structural accuracy, measured by Mean Structural Similarity (MSSIM), of selected section pairs from Drosophila Larva (L3), between result of rigid registration only and result of elastic registration appended to rigid registration. The chosen section pairs are the structurally consistent ones. c Comparison of individual sections recovered by rigid registration only and by elastic registration appended to rigid registration, of 1st to 5th section of Drosophila Larva (L3). Shape correction of bended area in the 3 rd section, and increased cross-sectional consistency on the 4th and 5th section were highlighted by blue arrows. Source data are provided as a Source Data file.

With elastic process validated and applied onto rigid registration result, the recovery of spatial information was completed. Stacking individual sections of the elastic result, a complete geospatial profile of the larva was generated (Supplementary Fig.  8 ), visualizing the ST-GEARS’ ability of in vivo spatial information recovery.

Application to sagittal sections of Mouse hippocampus

After validating the component phases of ST-GEARS, we proceeded to apply the method to multiple real-world problems to recover geospatial profiles. We first focused on two sagittal sections of Mouse hippocampus 36 (Supplementary Fig.  9 ) that were 10 μm apart, accounting for 1–2 layers of Cornu Ammonis (CA) 1 neurons 42 . Considering the proximity of these sections, we assumed no structural differences between them.

To compare the differences of registration effect among methods, we extracted CA fields and dentate gyrus (DG) beads (Supplementary Fig.  10 ), then stacked the two sections for a more obvious contrast (Fig.  4a ). PASTE2 failed in performing the registration, leaving the sections unaligned. By GPSA, the sections’ positions were aligned, yet the 2nd section were squeezed into a narrower region than first one, leading to a contradiction of region’s location. The ‘narrowing’ phenomena may be caused by the overfitting of GPSA model on gene expression similarity, since it doesn’t involve structural similarity between registered sections in loss function. The scale on horizontal and vertical axis was distorted due to the equal scale range strategy adopted in GPSA’s preprocessing. STalign also misaligned the sections, leaving an obvious angle between two slices in registration result. This may be due to the method’s processing of ST data into images which completely relies on gene expression abundance to decide pixel intensities. On the sagittal section of Mouse hippocampus, the abundance difference between regions may not provide sufficient structural information required by registration. In the comparison between PASTE and ST-GEARS, our method demonstrates a more accurate centerline overlapping of CA fields and DG compared to PASTE. This indicated an enhanced recovery of spatial structure consistency and an improved registration effect. To quantitatively evaluate these findings, we utilized the MSSIM index as a measure of structural consistency and compared it among PASTE, PASTE2, GPSA, STalign and ST-GEARS (Fig.  4b ). Consistent with the results of centerline, ST-GEARS achieved a higher MSSIM score than GPSA and PASTE, surpassing PASTE2 and STalign by >0.2 out of 1. By comparing memory efficiency across all methods, ST-GEARS and PASTE used ~1 GB less memory than PASTE2, GPSA and STalign, and the peak memory across ST-GEARS and PASTE was almost the same (Supplementary Fig.  11 ). In perspective of time efficiency, registration utilizing ST-GEARS, STalign, GPSA and PASTE was much faster than PASTE2.

a Stacked projections of Cornu Ammonis (CA) fields and dentate gyrus (DG), of pre-registered and registered result of Mouse hippocampus sagittal sections with 10 µm distance, respectively by PASTE, PASTE2, GPSA, STalign and ST-GEARS. b A comparison of both MSSIM measuring structural accuracy and Mapping accuracy measuring anchor accuracy of the 2 registered sections, across PASTE, PASTE2, GPSA, STalign and ST-GEARS. c Stacked projections of region-specific annotation types including DG, Neurogenesis, subiculum, CA1, CA2 and CA3, registered by ST-GEARS. Each column highlights the stacked projection of a single annotation type. Source data are provided as a Source Data file.

To understand reasons behind our enhancement, we thoroughly examined the anchors generated by PASTE, PASTE2 and ST-GEARS, as well as the effects of our elastic registration. By mapping cluster information of the 2nd section to the 1st, and the 1st to the 2nd through anchors, we found correspondences between the projected and original annotations (Supplementary Fig.  12 ). Accordingly, our Mapping accuracy was over 0.25 higher than PASTE and over 0.45 than PASTE2 (Fig.  4a ), indicating our exceptional anchor accuracy. To understand and further substantiate this advantage, we visualized the probabilistic constraints and its resulted anchors probabilities (Supplementary Fig.  13a ). It is worth noting that ST-GEARS implemented Distributive Constraints, in contrast to the uniform distributions used by PASTE. As a result, a certain percentage of spots were found to be suppressed in anchors connection by ST-GEARS (Supplementary Fig.  13b ) compared to PASTE, leaving the registration to rely more on spots with higher cross-sectional similarity and less computational disturbances, and hence lead to a higher anchor accuracy. We excluded Distributive Constraints from ST-GEARS, and noticed an obvious decrease of mapping accuracy on the hippocampus dataset (Supplementary Fig.  14 ), indicating the contribution of Distributive Constraints on anchors accuracy. In the study of elastic effect, we found an increased overlapping of centerlines by elastic registration than by rigid operation only when overlapping CA fields and DG (Fig.  4b ). Quantitively by MSSIM, the cross-sectional similarity was found to be increased by elastic registration (Supplementary Fig.  15 ). These findings suggest that the combination of Distributive Constraints and elastic process contributed to the enhanced registration of the Mouse hippocampus.

To explore the potential effect of impact of our registration on downstream analysis, we extracted region-specific annotation types from the sections, and analyzed their overlapping through stacking registered sections together (Fig.  4c ). In all annotation types including DG, Neurogenesis, subiculum, CA1, CA2 and CA3, the distribution regions from both sections were nearly identical. The overlapping result unveils that ST-GEARS integrated the spatial profile of same cell subpopulations, enabling a convenient and accurate downstream analysis of multiple sections.

Application to 3D reconstruction of Drosophila embryo

Besides tissue level registration of Mouse hippocampus, to evaluate the performance of ST-GEARS in reconstructing individual with multiple sections, we further tested it on a Drosophila embryo. The transcriptomics of embryo was measured by Stereo-seq, with 7 μm cross-sectioning distance 22 . By quantifying the registration effect of spatial information recovery and comparing it to PASTE, PASTE2, GPSA and STalign, we found that ST-GEARS achieved the highest MSSIM in five out of the six structurally consistent pairs (Fig.  5a ). On the pair where ST-GEARS did not result in highest MSSIM, it surpassed PASTE, and achieved a similar score to PASTE2. By comparing area changes with SI-STD-DI quantification of the complete section, and three individual tissues including epidermis, midgut and foregut, ST-GEARS yielded higher smoothness on all regions than all other approaches, both visually and quantitatively (Fig.  5b ).

a A comparison of Mean Structural Similarity (MSSIM) measuring structural similarity, of section pairs that are structurally consistent from Drosophila Embryo (E14-16h), between reconstruction results of PASTE, PASTE2, GPSA, STalign and ST-GEARS. b A comparison of area changes of 3 tissues and complete body of Drosophila Embryo, along cross-sectioning direction, between reconstruction result of PASTE, PASTE2, GPSA, STalign and ST-GEARS. Standard Deviation of Differences (SI-STD-DI) which measures structural consistency is marked alongside each curve to quantify the smoothness. The smoothness difference of ST-GEARS compared to PASTE, PASTE2 and STalign are highlighted by orange rectangles. c Reconstructed individual sections with recovered spatial location of each spot. In result of PASTE, the incorrect flipping on the 15th section was highlighted in orange. In result of PASTE2, gradual rotations were marked by the 1st, 5th, 9th, 13th and 16th sections’ approximate symmetry axis whereas symmetry axis of the 1st section was replicated onto the 16th for angle comparison. In result of GPSA, mistakenly distorted sections were marked by purple arrows. In result of STalign, the incorrect flipping on the 13th section was highlighted in orange. In result of ST-GEARS, the fix of dissecting area on the 15th section was marked by a blue arrow. d Dorsal view of 3D reconstructed Drosophila embryo by PASTE, PASTE2, GPSA, STalign and ST-GEARS. The inaccurate regionalization of midgut was circled and pointed with arrow in orange. The resulted extruding part of single section by PASTE2 was circled and pointed in blue. e Mapping accuracy of all section pairs by PASTE, PASTE2 and ST-GEARS. f By dorsal view, regionalization of marker gene Cpr56F and Osi7 by PASTE, PASTE2, GPSA, STalign and ST-GEARS, and their comparison with hybridization result from Berkeley Drosophila Genome Project (BDGP) database. The gathering expression regions were highlighted by dotted lines. Source data are provided as a Source Data file.

To compare the reconstruction effect, we studied both registered individual section, and reconstructed 3D volume. Among the methods compared, PASTE produced a wrong flipping on the 15 th section along A-P axis (Fig.  5c ). Stacking sections back to 3D and investigating on dorsal view, the wrong flipping caused a false regionalization of foregut circled in orange (Fig.  5d ). Along the first to last section registered by PASTE2, a gradual rotation was witnessed (Fig.  5c ), leading to over 20 degrees of angular misalignment between the first and the last section. Similar to PASTE, this misalignment also caused the wrong regionalization of foregut in 3D map (Fig.  4d ). Equally induced by the rotation, sections were found to extrude in the 3D result circled in blue, breaking the round overall morphology of the embryo. GPSA caused false distortion of 8 out of 16 sections as pointed by purple arrows (Fig.  5c ) and the stacked sections formed a dorsal view of an isolated circle and an inner region (Fig.  5d ). The phenomena may be due to its overfitting onto expressions, which is caused by the contradiction between its hypothesis of consistent readout across sections, and the large readout variation across 16 sections in this application. Similar to PASTE, STalign also produced a wrong flipping, on the 13 th section along A-P axis (Fig.  5c ). Stacking the projections back to 3D, a mistaken regionalization of foregut, caused by the wrong flipping, was circled in orange (Fig.  5d ). In contrast, ST-GEARS avoided all of these mistakes in its results (Fig.  5c ). From the perspective of individual section profiles, noticeably in the 15 th section, we observed a significant reduction in the dissecting region between two parallel lines, indicating the successful fixation of flaws in the session. By comparing time usage across all methods, ST-GEARS achieved the 2nd lowest time consumption in registration (Supplementary Fig.  11 ). In terms of memory consumption, ST-GEARS, PASTE and STalign used much less memory than PASTE2 and GPSA. The three most memory efficient methods used almost identity peak memory, with the value fluctuation of <7%.

To comprehend the rationale behind our improvement, we analyzed the anchors generated by the three methods and the impact of our elastic registration. In the investigation of anchor accuracy, we discovered that ST-GEARS achieves the highest mapping accuracy among all section pairs (Fig.  5e ), suggesting its advanced ability to generate precise anchors, which forms the basis for precise spatial profile recovery. To understand this advancement, probabilistic constraints and its resulted anchors distributions (Supplementary Fig.  16 , Supplementary Fig.  17 ) were studied. With Distributive Constraints (Supplementary Fig.  16a ), ST-GEARS generated different maximum probabilities on different annotation types (Supplementary Fig.  16b ), which indicates that annotation types with higher cross-sectional consistency were prioritized in anchor generation. This selection led to reduced computational disturbances, and hence higher accuracy of anchors. We also compared anchor accuracy with and without Distributive Constraints adopted, and noticed an increase of mapping accuracy on each pair of sections (Supplementary Fig.  18 ). In final registration result, ST-GEARS without Distributive Constraints failed to fix the experimental flaw on the 15 th section (Supplementary Fig.  19 ), in contrast to effect upon the setting adopted (Fig.  5c ). Above findings validate the contributive effect of Distributive Constraints in our method. In study of elastic registration in shape smoothness, we witnessed an increased level of smoothness of tissue epidermis, foregut, and midgut, as well as the complete section, through area changes quantified by SI-STD-DI index (Supplementary Fig.  20 ). In internal structure aspect, an increased MSSIM of structural consistent pairs were noticed (Supplementary Fig.  21 ). An experimental flaw on the 15 th section was also fixed by elastic registration (Supplementary Fig.  22 ). Above findings point that the enhancement of registration accuracy on Drosophila embryo was induced by Distributive Constraints and elastic process.

By mapping spots back to 3D space, we further investigated the effect of different method on downstream analysis, in the perspective of genes expression (Fig.  5f ). Cpr56F and Osi7 were selected as marker genes, which were found to respectively highly express in foregut, and foregut plus epidermis region 22 . Investigating Cpr56F expression by ST-GEARS from dorsal view, we noticed three highly expressing regions, at anterior end, front region, and posterior end of the embryo. The finding matches the hybridization result of stage 13-16 Drosophila embryo extracted from Berkeley Drosophila Genome Project (BDGP) database. In contrast, none of PASTE, PASTE2, GPSA and STalign presented high expression at all three locations. When analyzing the distribution of Osi7 by PASTE, PASTE2 and STalign, we noticed a sharp decrease in expression from inner region to the outer layer marked by purple arrows, contradicting the prior knowledge of high expression in the epidermis. This is probably because PASTE and PASTE2 do not consider distortion correction as part of their methods, leaving section edges un-coincided and marker genes not obviously highly expressed on the outermost region. Though involving distortion correction, STalign lost certain amount of structural information by transforming ST data to image utilizing only information of regional gene expression abundance. The registration did not adequately correct distortion without support of enough structural messages. Similarly, PASTE2 failed to capture expression in outer layers and instead revealed a high expression in one inter-connected area, which did not correspond to the separate expression regions observed in hybridization result. No spatial pattern was witnessed when analyzing distribution of Osi7 by GPSA, which forms an obvious contrast to its hybridization evidence. Comparably, none of the violations was shown in the result of ST-GEARS. The comparison of spatial distribution indicated our potential capability to better enhance the process of downstream gene-related analysis.

Application to Mouse brain reconstruction

The design of 3D experiments involves various levels of sectioning distances 22 , 36 , 37 . To further investigate the applicability of ST-GEARS on ST data with larger slice intervals, we applied the method to a complete Mouse brain hemisphere dataset, which consists of 40 coronal sections (Supplementary Fig.  23a ), with a sectioning distance of 200 μm 37 . The transcriptomics data was measured by BARseq, which includes sequencing data and its cross-modal histology images. Each observation represents captured transcriptomics surrounded by the boundary of a cell.

Through respectively applying PASTE, PASTE2, GPSA, STalign and ST-GEARS onto the dataset, we observed multiple misaligned sections produced by approaches including PASTE, PASTE2, GPSA and STalign (Supplementary Fig.  23b , Supplementary Fig.  23c , Supplementary Fig.  23d , Fig.  6a ). In PASTE, these misalignments include 2 sections with ~ 180° angular misalignment (Supplementary Fig.  23b ). By PASTE2, 4 rotational misalignments and 8 positional misalignments were noticed (Supplementary Fig.  23d ). By GPSA, 12 sections were observed to be rotationally misaligned, and 3 sections were mistakenly distorted (Supplementary Fig.  23b ), probably due to its overfitting onto expressions discussed in analysis of Drosophila embryo. The scale on horizontal and vertical axis was distorted maybe due to the similar reason analyzed in Mouse hippocampus. And by STalign, 7 rotational misalignments were generated (Supplementary Fig.  23e ). As a clear contrast, our algorithm correctly aligned all 40 sections with 200 μm intervals (Supplementary Fig.  23f ). To more accurately assess the result of our registration, we employed the direction of the cutting lines induced during tissue processing 37 , and compared the consistency of tilt angles of these lines in the 20th, 25th, 26th, 27th, 33rd, 34th and 37th slices where these lines are visible. Notably, neither visual angle differences nor cutting line curving were observed, indicating that the sections were properly aligned by ST-GEARS (Fig.  6a , Supplementary Fig.  23f ). To quantify the registration accuracy in aspect of structural continuity, we calculated MSSIM scores of 11 section pairs that are structural consistent (Fig.  6b ). Consistent with the visual observations, PASTE2 presented a much larger score range than other methods, which reflects its instability across sections in this dataset, and GPSA exhibited the lowest median MSSIM score indicating its suboptimal average performance. By comparison, PASTE yielded a higher median score and a smaller variation, while ST-GEARS resulted in the highest median score and the smallest variation among all methods. In terms of computational efficiency, ST-GEARS achieved the 2nd lowest time consumption and lowest peak memory consumption across all methods (Supplementary Fig.  11 ).

a Reconstructed individual sections with recovered spatial location of each spot from the 25th to 36th section. Positional misalignments are marked by arrows of green, and angular misalignments are marked by arrows of orange. Visible cutting lines by ST-GEARS are marked by dotted lines. b A comparison of Mean Structural Similarity (MSSIM) score of 11 section pairs that are structurally consistent, between result of PASTE, PASTE2, GPSA, STalign and our method. The 11 biological replicates were studied, which were derived from different closest section pairs with each section pair representing smallest unit of study. Non control group was used as a MSSIM close to 1 is assumed to the idealized similarity value of the structurally similar pairs, hence a higher MSSIM value indicates higher reconstruction accuracy. The red lines positions show median score; the box extends from the first quartile (Q1) to the third quartile (Q3) of scores; the lower whisker is at the lowest datum above Q1 − 0.5 * (Q3-Q1), and the upper whisker is at the highest datum below Q3 + 0.5*(Q3-Q1); scores out of whiskers range are marked by circles. c Perspective, Lateral and Anterior view of reconstructed Mouse brain hemisphere. d Anterior view of layer annotation types distribution of reconstructed Mouse brain hemisphere. Source data are provided as a Source Data file.

To understand the reasons behind our progress, we examined anchor accuracy changes with regularization factors during ST-GEARS computation (Supplementary Fig.  24 ). Out of 39 section pairs, we observed a change in mapping accuracy >0.1 (out of 1) in 12 pairs. By Self-adaptive Regularization which was designed to face varying data characteristics which also includes varying section distances, regularization factor that leads to optimal mapping accuracy was selected, leading to an increased anchors accuracy in the 12 section pairs. Notably, among these 12 pairs, pairs 29th & 30th, 31st & 32nd and 32nd & 33rd were correctly aligned by ST-GEARS but misaligned by PASTE, which doesn’t adopt any self-adaptive regularization strategy.

After validating the registration result, we investigated the recovered cell-types’ distribution in the 3D space to assess the effectiveness of the reconstruction and its impact on further analysis. We observed that the complete morphology of hemisphere was recovered by ST-GEARS, with clear distinction of different tissues on perspective, lateral and anterior views (Fig.  6c ). We further studied the distribution of separate annotation types within cortex layers and found that 3D regionalization of each annotation type was recovered by ST-GEARS (Fig.  6d ). The reconstructed result indicated the adaptability of ST-GEARS across various scales of sectioning intervals, and its applicability on both bin-level, and cell-level datasets on which histology information is incorporated.

We introduce ST-GEARS, a 3D geospatial profile recovery approach for ST experiments. Leveraging the formulation of FGW OT, ST-GEARS utilizes both gene expression and structural similarities to retrieve cross-sectional mappings of spots with same in vivo planar coordinates, referred to as ‘anchors’. To further enhance accuracy, it uses our innovated Distributive Constraints to enhance the accuracy. Then it rigidly aligns sections utilizing the anchors, before finally eliminating section distortions using Gaussian-denoised Elastic Fields and its Bi-sectional Application.

We validate counterpart of ST-GEARS including anchors retrieval and elastic registration, respectively on DLPFC and Drosophila larva dataset. In the validation of anchors retrieval, through Mapping accuracy evaluation of retrieved anchors, ST-GEARS consistently outperformed PASTE and PASTE2 across all section pairs. We show Distributive Constraints as reasons behind its distinguished performance, which effectively suppressed the generation of anchors between spot groups with low cross-sectional similarity while enhances their generation among groups with higher similarity. To investigate the effectiveness of the elastic registration process, we evaluate the effects of tissue area changes and cross-sectional similarity using the Drosophila larvae dataset. Both smoother tissue area curves and higher similarity observed between structurally consistent sections confirm the efficacy of the elastic process of ST-GEARS.

We demonstrate ST-GEARS’s advanced accuracy of reconstruction compared to current approaches including PASTE, PASTE2 and GPSA, and its positive impact on downstream analysis compared to existing approaches. Our evaluation encompasses diverse application cases, including registration of two adjacent sections of Mouse hippocampus tissue measured by Slide-seq, reconstruction of 16 sections of Drosophila embryo individual measured by Stereo-seq, and reconstruction of a complete Mouse brain measured by BARseq, including 40 sections with sectioning interval as far as 200 μm. Among the methods, registered result by ST-GEARS exhibited the highest intra-structural consistency measured by MSSIM for two hippocampus sections separated by a single layer of neurons. On 16 sections of a Drosophila embryo individual, our method’s outstanding accuracy is indicated by both MSSIM and smoothness of tissue area changes. Importantly, ST-GEARS provides more reliable embryo morphology, precise tissue regionalization, and accurate marker gene distribution under hybridization evidence compared to existing approaches. This suggests that ST-GEARS provides higher quality tissues, cells, and genes information. On Mouse brain sections with large intervals of 200 μm, ST-GEARS avoided positional and angular misalignments that occur in result of PASTE and PASTE2. The improvement was quantified by a higher MSSIM. Both hemisphere morphology and cortex layer regionalization were reflected in the result of 3D reconstruction by ST-GEARS. The successful representation of important structural and functional features in the aforementioned studies collectively underscores ST-GEARS’ reliability and capability for advancing 3D downstream research, enabling more comprehensive and insightful analysis of complex biological systems.

To further enhance and extend our method, opportunities in various aspects are anticipated to be explored. Firstly, algorithm aspects including hyperparameter sensitivity and scalability can be further explored for a more enhanced method performance. Though recommended values are provided for two of its hyperparameters, method performance is still affected by parameter values, raising the potential issue of overfitting and sensitivity which can be further studied. In scalability aspect, ST-GEARS introduces obvious computational cost increasement when dealing with large-scale datasets. Though strategy of Granularity adjusting is innovated to down-grade complexity, opportunity of improving robustness on increasing scale of data is expected to be further explored. Secondly, tasks aimed at improving data preprocessing, including but not limited to batch effect removal and diffusion correction, are expected to be integrated into our method, considering their coupling property with registration task itself: inaccuracies in input data introduce perturbations to anchors optimization, while recovered spatial information of our method may assist data quality enhancement by providing registered sections. Thirdly, the ST-GEARS’ Distributive Constraint takes rough grouping information as its input, which may potentially introduce computational burden during the reconstruction process. To address this, an automatic step is expected to be developed to reliably cluster spots while maintaining computational efficiency of the overall process. This step can be integrated into our method either as preprocessing, or as a coupling task, similarly to our expectation of data quality enhancement. Finally, we envision incorporating a wider scope of anchors applications into our existing framework. such as information integration of sections across time, across modalities and even across species. With interpretability, robustness and accuracy provided by ST-GEARS, we anticipate its applications and extension in various areas of biological and medical research. We believe that our method can help address a multitude of questions regarding growth and development, disease mechanisms, and evolutionary processes.

FGW OT description

Fused Gromov Wasserstein (FGW) Optimal Transport (OT) is the modeling of spot-wise or cell-wise similarity between two sections, with the purpose of solving optimal mappings between the spots or cells, with mappings also called ‘anchors’. By FGW OT, the optimal group of mappings enables highest gene expression similarity between mapped spots, at the same time keeping similar positions relative to their located sections.

The required input of FGW OT includes genes expression, spot or cell locations before registration, and constraint values which assigns different weight to the optimization on different spots or cells. For gene expression, we introduce \({{\bf{A}}}\in {R}^{{n}_{A},m}\) for section A, to describe normalized count of unique molecular identifiers (UMIs) of different genes of each cell or spot, thereinto n A denotes number of spots in slice A, and m denotes number of genes that are captured in both sections. Similarly, we describe gene expression on section B as \({{\bf{B}}}\in {R}^{{n}_{B},m}\) , with genes arranged in the same order as in A . For spot or cell locations, we introduce \({{{\bf{X}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A},2}\) to describe spots locations of section A, with the 1st column storing horizontal coordinates and the 2nd storing vertical coordinates. Similarly, we have \({{{\bf{X}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B},2}\) to describe spots locations in section B. Spots are arranged in the same order in gene expression and location matrices. Constraint values are discussed in section of Distributive Constraints.

FGW OT solves:

Thereinto, \({{{\bf{M}}}}_{{{\bf{AB}}}}\in {R}^{{n}_{A},{n}_{B}}\) describes the similarity of each pair of spots respectively on section A and B, formulated as \({{{\bf{M}}}}_{{{\bf{i}}},{{\bf{j}}}}^{({{\bf{AB}}})}={KL}({A}_{i,:},{B}_{j,:})\) . Be noted that \({{{\rm{M}}}}_{{{\rm{i}}},{{\rm{j}}}}^{({{\rm{AB}}})}\) still indicates spot-wise similarity M AB , with section code AB being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i, j . KL denotes Kullback-Leibler (KL) divergence 43 . \({{{\bf{C}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A},{n}_{A}}\) describes spot-wise distance within section A, with \({{{\bf{C}}}}_{{{\bf{i}}}{{,}}{{\bf{j}}}}^{({{\bf{A}}})}={dis}({{{\bf{X}}}}_{{{\bf{i}}}{{,}}{{:}}}^{({{\bf{A}}})}{{,}}{{{\bf{X}}}}_{{{\bf{j}}},{{:}}}^{({{\bf{A}}})})\) , and dis denoting Euclidean distance measure. Be noted that \({{{\rm{X}}}}_{{{\rm{i}}},:}^{({{\rm{A}}})}\) and \({{{\rm{X}}}}_{{{\rm{j}}},:}^{({{\rm{A}}})}\) still indicate spot locations X A , with section code A being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i and j . \({{{\rm{C}}}}_{{{\rm{i}}},{{\rm{j}}}}^{({{\rm{A}}})}\) refers to spot-wise distance C A for the same reason. Similarly, \({{{\bf{C}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B},{n}_{B}}\) describes spot-wise distance of section B. \({{\bf{L}}}\in {R}^{{n}_{A},{n}_{B},{n}_{A},{n}_{B}}\) defines the difference between all spot pair distance respectively on section A and B, with \({{{\rm{L}}}}_{{{\rm{i}}},{{\rm{j}}},{{\rm{k}}},{{\rm{l}}}}=|{{{\rm{C}}}}_{{{\rm{i}}},{{\rm{k}}}}^{({{\rm{A}}})}-{{{\rm{C}}}}_{{{\rm{j}}},{{\rm{l}}}}^{({{\rm{B}}})}|\) . ⊗ denotes Kronecker product of two matrices; 〈,〉 denotes matrix multiplication.

Adjacency matrix \({{\mathbf{\pi }}}\in {R}^{({n}_{A},{n}_{B})}\) to be optimized stores strength of anchors between spots from the two sections, with row index representing spots on section A, and column index representing spots on section B. Sum of elements of π is 1. With \({{\langle }}{{{\bf{M}}}}_{{{\bf{AB}}}}^{{{\bf{2}}}}{{,}}{{\mathbf{\pi }}}{{\rangle }}\) , the similarity of mapped spots are measured. With \({{\langle }}{{{\bf{L}}}}^{{{\bf{2}}}}({{{\bf{C}}}}_{{{\bf{A}}}}{{,}}{{{\bf{C}}}}_{{{\bf{B}}}}){{\times}}{{\mathbf{\pi }}}{{,}}{{\mathbf{\pi }}}{{\rangle }}\) , similarity between distance of spot pairs on section A, with its anchored spot pairs on section B, is measured. \(\langle {{{\bf{L}}}}^{{{\bf{2}}}}({{{\bf{C}}}}_{{{\bf{A}}}}{l{{,}}}{{{\bf{C}}}}_{{{\bf{B}}}})\otimes {{\mathbf{\pi }}}{{,}}{{\mathbf{\pi }}}\rangle\) describes similarity between spatial structures under the anchors’ connection. α ∈ [0,1] denotes regularization factor, which specifies the relative importance of structure similarity compared to expression similarity. W A and W B are constraint values that are introduced in section of Distributive Constraints.

With the formulation above, FGW OT solves optimal anchors between the spots, or cells, which enables maximum weighted combination of gene expression similarity and position similarity of mapped spots or cells.

Distributive constraints

As adopted by constraint values in FGW OT, we introduce Distributive Constraints, to assign different emphasis to spots or cells in the optimization. Distributive Constraints utilizes cell type component information to differentiate the emphasis: if an annotation or cluster express high similarity across sections, its corresponding spots or cells will be placed relatively high sum of probability, and vice versa. With higher sum of probability, more anchors and anchors with higher strength are generated, while less anchors are produced on spots with lower sum of probability. This operation leads registration to rely more on expression-consistent regions of sections, hence largely enhancing both accuracy of anchors and precision of following rigid and elastic registration.

The required inputs of Distributive Constraints include \({{{\bf{G}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A}}\) and \({{{\bf{G}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B}}\) , which store the grouping information such as annotation type or cluster of each spot in section A and B. We then summarize the repeated annotations or clusters from G A and G B , and put the unique values in \({{\bf{g}}}\in {R}^{{n}_{{group}}}\) . n group is the number of unique annotation type or clusters. Then implemented in ST-GEARS, for each annotation type or cluster g i , we calculate the average gene expression across spots:

Be noted that \({{{\bf{G}}}}_{{{{\bf{i}}}}^{{\prime} }}^{({{\bf{A}}})}\) and \({{{\bf{G}}}}_{{{{\bf{i}}}}^{{\prime} }}^{({{\bf{B}}})}\) still indicate grouping information G A and G B , with section code A and B being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i ′ and j ′. And \({{\bf{1}}}_{{{{\bf{n}}}}_{{{\bf{A}}}}}\) and \({{\bf{1}}}_{{{{\bf{n}}}}_{{{\bf{B}}}}}\) are both row vectors of ones.

With average gene expression of each annotation type or cluster, with the form of distribution, we measure its difference across sections by KL divergence. Then the calculated distance is mapped by logistic kernel, to further emphasize differences between relatively consistent annotations or clusters.

\({di}{s}_{{map}}={f}_{{logistic}}({dis})\) , where \({f}_{{logistic}}\left(x\right)=\frac{1}{1+{e}^{-x}}-0.5\) . Putting scaler value dis of each annotation or cluster together, we have a vector \({{\bf{DI}}}{{{\bf{S}}}}_{{{\bf{map}}}}\in {R}^{{n}_{{celltype}}}\) . Finally, we transform the distance to similarity, map the similarity result back to each spot:

We further apply normalization on the result:

W A and W B are constraints values applied in (1). Since the values are computed based on similarity measure using cell composition information, weight of FGW OT is automatically redistributed, with higher emphasis on more consistent regions across sections, and less emphasis on less consistent area. Enhanced anchor accuracy hence registration accuracy is then achieved.

Self-adaptive regularization

In FGW OT formulation, a regularization factor is included to specify the relative importance of structural similarity compared to expression similarity during optimization. ST-GEARS includes a self-adaptive regularization method that determines the factor value, that induces highest overall accuracy of anchors despite of varying situations. Situations include but are not limited to section distances, spot sizes, extent of distortions, and data quality such as level of diffusion.

By practice, our method respectively adopts factors on multiple scales including 0.8, 0.4, 0.2, 0.1, 0.05, 0.025, 0.013, and 0.006. The candidate values vary exponentially, for ST-GEARS to find the optimal term regardless of scale differences between expression and structural term in (1). The accuracy of each set of optimized anchors by every regularization factor was evaluated, by measuring weighted percentage \({\sum}_{{{{\bf{G}}}}_{{{\bf{i}}}}^{{{(}}{{\bf{A}}}{{)}}}{{=}}{{{\bf{G}}}}_{{{\bf{j}}}}^{{{(}}{{\bf{B}}}{{)}}}}{{{\boldsymbol{\pi }}}}_{{{\bf{i}}}{{,}}{{\bf{j}}}}\) of anchors that join spots with same annotation types or clusters. Be noted that \({{{\rm{G}}}}_{{{\rm{i}}}}^{({{\rm{A}}})}\) and \({{{\rm{G}}}}_{{{\rm{j}}}}^{({{\rm{B}}})}\) still indicate grouping information G A and G B , respectively, with section code A and B being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i and j . The regularization factor value that achieves highest accuracy is then adopted by our method.

Elastic field inference

Finding spots with highest probability.

After rigid registration, elastic fields are inferred based on the anchors with the highest probability for each spot or cell. For elastic field to be applied on each section, it is calculated using its anchors with closest sections, as well as spatial coordinates of sections after rigid registration. Along cross-sectioning order, each section in the middle has two closest sections, respectively on its anterior and posterior sides. Exceptionally, if a section is on anterior or posterior end, it has only one closest section.

Specifically for a section in the middle with N spots, we calculate \({{{\bf{I}}}}_{{{\bf{pre}}}}\epsilon {Z}^{N}\) and \({{{\bf{I}}}}_{{{\bf{next}}}}\epsilon {Z}^{N}\) which stores the mapped spots on anterior and posterior neighbor section for each of its spots. The calculation takes as input adjacency matrix π pre , which stores anchors with the anterior neighbor section output by FGW OT, and π next storing anchors with posterior section.

Be noted that \({{{\rm{\pi }}}}_{:,{{\rm{n}}}}^{\left({{\rm{pre}}}\right)}\) and \(\,{{{\rm{\pi }}}}_{{{\rm{n}}},:}^{({{\rm{next}}})}\) still indicate adjacency matrix π pre and π next , with direction code pre and next being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index n .

Notably, not every spot in a selected section has its own anchored spot, due to multiple strategies including distributive constraint and anchors filtration, hence their corresponding element in I pre and I next are null. For section located on posterior end, only I next is applicable; and for section located on anterior end, only \({{{\bf{I}}}}_{{{\rm{pre}}}}^{{{\rm{n}}}}\) is applicable.

Elastic field establishment

After specifying spots with highest probability, ST-GEARS calculates location displacements between the spots, then establishes elastic fields for each section. An elastic field is a 2D displacement distribution, describing how displacement values are distributed across different locations. And it is established to enable ST-GEARS to benefit from further denoising functions to reduce elastic operation outliers and improve elastic effect consistency across regions.

For each section located in the middle, 4 elastic fields are generated. Two of those represent the section’s horizontal and vertical displacement distribution compared to anterior neighbor section, denoted as 2D matrix F (x_pre) and F (y_pre) , while the other two represent its horizontal and vertical displacement distribution compared to posterior neighbor, denoted as F (x_next) and F (y_next) . To initialize F (x_pre) , F (y_pre) , F (x_next) and F (y_next) for the section, the shape of the matrix is first decided. Its height denoted by Height and width denoted by Width are calculated by gridding the spot locations using a fixed step. Height and Width are shared across the 4 matrices:

For its input, \({{\bf{X}}}\in {R}^{N,2}\) denotes spots location of current section after rigid registration. For a single section, we prepare \({{{\bf{X}}}}^{{{(}}{{\bf{pre}}}{{)}}}\epsilon {R}^{{N\_pre},2}\) and \({{{\bf{X}}}}^{{{(}}{{\bf{next}}}{{)}}}\epsilon {R}^{{N\_next},2}\) as spots location of its anterior and posterior section after rigid alignment, respectively. psize represents average distance between closest spot or cell centers, and it is to be input by users. The matrix has no filled values to this step.

To fill in the fields, we first transform spot locations into the coordinate system of field. With \({{\bf{X}}}\_{{\bf{shifted}}}\,\epsilon {R}^{N,2}\) and \({{\bf{X}}}\_{{\bf{pixel}}}\,\epsilon {R}^{N,2}\) :

We then calculate location displacements between each of its spots and their anchored spots with highest probability, on both anterior and posterior neighbors. With \({{\bf{X}}}\_{{\bf{corres}}}\,\epsilon {R}^{N,2}\) and \({{\bf{X}}}\_{{\bf{delta}}}\epsilon {R}^{N,2}\) :

With the spot locations in field coordinates and the displacement values above, we fill in corresponding elements of the elastic field:

By the end of Eqs. ( 2 ), 4 elastic fields for each section in the middle is established. However, some elements in the matrix are still empty, because of absence of spots or cells located in the grid of location. To address this problem, 2d nearest interpolation method 44 was adopted, which fills in every empty element, with the displacement value of its neighboring elements:

thereinto \({{\bf{mes}}}{{{\bf{h}}}}_{{{\bf{trans}}}}\epsilon {N}^{{n}_{{grids}}\times 2}\) denotes grid coordinates of the designed field, with \({n}_{{grids}}={Height}\times {Width}\) . And f interp_grid denotes the nearest interpolation method.

For section located on posterior end, only F (x_next) and F (y_next) are applicable; and for section located on anterior end, only F (x_pre) and F (y_pre) are applicable.

2D Gaussian denoising

As caused by exerted force, the displacement or elastic field is expected to have static or smoothly changing values across different locations 45 , 46 , 47 . ST-GEARS makes use of this property, to smoothen the field and to reduce errors in the field caused by any upper stream process, such as raw data noises and inaccuracy in anchor computation. Gaussian filtering 48 , 49 is adopted to implement the denoising, similarly to image denoising processes 50 , 51 . Denoised elastic fields are then generated.

It calculates weighted average across the neighboring region of each element to replace its value:

where f gaussian_filter denotes the method of Gaussian filtering.

Bi-sectional fields application

Bi-sectional fields application plan.

With elastic fields generated and denoised, ST-GEARS uses the fields as a guidance to correct distortion for each section. Through querying the elastic fields with spatial location of each spot, the displacement to be implemented is returned. For a section in the middle, its elastic fields calculated with both anterior and posterior neighbor sections are queried, and guidance provided by both anterior and posterior sections are applied on the rigid aligned result, called ‘Bi-sectional Fields Application’. After the application, the distortion of the section is corrected, and the elastic registration result is generated.

Specifically, the denoised elastic fields are first queried, returning the displacement to be implemented:

Next, average displacement returned by both anterior and posterior sections are applied on the rigid registration result, leading to final elastic registration result \({{\bf{X}}}\_{{\bf{final}}}\in {R}^{N,2}\!\!:\)

For section located on posterior end,

For section located on anterior end,

The validity of this plan is proved in the section: Proof of validity of Bi-sectional Fields Application.

Proof of validity of Bi-sectional fields application

Bi-sectional Fields Application accurately recovers the spatial profile before distortion, by averaging and applying displacement value guided by both anterior and posterior neighbor section. The effect is approved mathematically as following:

Take section A, B, and C as an example of a sequence of sections, with X A , X B and X C denoting their spots’ spatial information after rigid alignment, and X A_insitu , X B_insitu and X C_insitu denoting their in vivo spatial information. The distortion occurred to the slices during experiments are denoted as X A_dis , X B_dis and X C_dis .

According to Bi-sectional Fields Application, the corrected spatial information is:

Based on the in vivo morphological consistency across sections, spatial information of section B can be approximated by an average of information of A and C, written as

Given that X A_dis and X C_dis can be seen as independent and identically distributed sets of variables,

where μ ABC is the universal mean, and Σ ABC is the variance of the 2d displacement information.

Inserting the terms (4) and (5) back to Eq. ( 3 ) gives

indicating the proximity of corrected spatial information to in vivo spatial information.

Evaluation metrix

We evaluated the accuracy of anchors by index of Mapping Accuracy, and measured the reconstruction effect by MSSIM and SI-STD-DI, in both elastic effect study and overall methodology comparison.

Mapping accuracy

Designed and adopted by PASTE 27 , Mapping Accuracy calculates the weighted percentage of anchors joining spots with same annotation.

MSSIM index

MSSIM measures the accuracy of registration, based on the assumption that in some sectioning positions, tissue morphology remains almost consistent across slices. The method quantifies the accuracy, by measuring the similarity of annotation type distribution of such section pairs.

To implement the quantification, first, structurally consistent section pairs are selected among all sections arranged in sequence.

Next, on each section from the pair, transformation from individual spots to a complete image is implemented, by gridding the rectangular area that surrounds the tissue, and assigning each grid of a value that represents the annotation type which occurs most frequently in the grid. The resulted image describes the annotation type distribution of the section.

Finally, similarity between each pair of images is measured, by index of MSSIM 52 . The method generates a window with fixed size, slides the window simultaneously on both images, and compares the two framed parts by windows on their intensity, contrast, and structures. Among those, the intensity difference is measured by difference of average pixel values, the contrast difference is measured by comparing variance of the two sets of framed pixel values, and the structure difference is measured by comparing their covariances. A Structural Similarity of Images (SSIM) index is calculated for each position of the window using \({SSIM}(X,Y)=\frac{(2{\mu }_{x}{\mu }_{y})(2{\sigma }_{{xy}}+{c}_{2})}{({\mu }_{x}^{2}+{\mu }_{y}^{2}+{c}_{1})({\sigma }_{x}^{2}+{\sigma }_{y}^{2}+{c}_{2})}\) , where μ x and μ y denote average pixel values of the frames, σ x and σ y denote variances of the frames, and σ xy denotes covariances of the two frames. c 1 and c 2 are constants to avoid 0 value of the divisor. Averaging the SSIM value across all windows gives the final MSSIM result of the two sections.

SI-STD-DI measures smoothness of area changing across sections along a fixed axis, by calculating the standard deviation of area changes on each pair of adjacent sections and scale the result by dividing it by average area.

Software and code

Data analysis.

All software used to analyze data in this study are open-sourced Python packages, including anndata = 0.9.2, numpy = 1.22.4, pandas = 1.4.3, scipy = 1.10.1, matplotlib = 3.5.2, k3d = 2.15.3.

Statistics and reproducibility

No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Reporting summary

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

Data availability

All data used in this research were collected from published sources. DLPFC data was obtained from the research: Transcriptome-scale Spatial Gene Expression in the Human Dorsolateral Prefrontal Cortex, with data downloading link of http://research.libd.org/spatialLIBD/index.html ; Drosophila embryo and Drosophila larva data were collected from High-resolution 3d Spatiotemporal Transcriptomic Maps of Developing Drosophila Embryos and Larvae, with the dataset link of https://db.cngb.org/stomics/datasets/STDS0000060 . Mouse brain data was collected from research: Modular cell type organization of cortical areas revealed by in vivo sequencing. The download link is: https://data.mendeley.com/datasets/8bhhk7c5n9/1 . All datasets were generated on Spatial Transcriptomics platform, with DLPFC data generated by Visium technology of 10x Genomics, Mouse brain data generated by BARseq of Cold Spring Harbor Laboratory, while Drosophila embryo and larva generated by Stereo-seq technology of BGI.  Source data are provided with this paper.

Code availability

The methods of ST-GEARS is packaged, and distributed as an open-source, publicly available repository at https://github.com/STOmics/ST-GEARS 53 .

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Acknowledgements

This work is part of the “SpatioTemporal Omics Consortium” (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org . We acknowledge the Stomics Cloud platform ( https://cloud.stomics.tech/ ) for providing convenient ways for analyzing spatial omics datasets. We acknowledge the CNGB Nucleotide Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) for maintaining the Drosophila database. This work is supported by National Natural Science Foundation of China (32300526 to S. F., 32100514 to M. X.). We thank Weizhen Xue for the inspirational discussion towards design of Distributive Constraints. We thank Yating Ren for her advice towards a more efficient code implementation. We thank Dr. Xiaojie Qiu and Dr. Yinqi Bai for the discussion on the registration topic and their advice on our work.

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Tianyi Xia, Luni Hu, Lei Cao, Yunjia Zhang, Mengyang Xu, Qin Lu, Lei Zhang, Taotao Pan, Bohan Zhang, Bowen Ma, Chuan Chen, Mei Li, Chao Liu, Yuxiang Li, Yong Zhang & Shuangsang Fang

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Tianyi Xia was responsible of method design, analysis design and implementation, as well as drafting of this manuscript. Dr. Luni Hu participated in structure design of the applications. Lulu Zuo was in part of 3D visualizations design, and she helps maintain our online repository. Tianyi Xia, Lei Cao, Lulu Zuo and Dr. Luni Hu conducted experiments and analysis for reply to peer review. Dr. Yunjia Zhang provided insights in anchors results interpretation of DLPFC dataset, and in accuracy analysis of mouse brain dataset. Dr. Mengyang Xu revised this article. Lei Zhang and Bowen Ma offered numerous suggestions to enhance computational efficiency, in both memory and time. Taotao Pan and Chuan Chen provided suggestions in data preprocessing. Qin Lu, Bohan Zhang, Junfu Guo, Chang Shi and Mei Li provided suggestions for this study. Dr. Shuangsang Fang supervised this study in structure and analysis design, and she revised this article. Chao Liu, Yuxiang Li and Yong Zhang supervised this study.

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Correspondence to Chao Liu , Yuxiang Li , Yong Zhang or Shuangsang Fang .

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Xia, T., Hu, L., Zuo, L. et al. ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery. Nat Commun 15 , 7806 (2024). https://doi.org/10.1038/s41467-024-51935-0

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DOI : https://doi.org/10.1038/s41467-024-51935-0

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Dear Colleague Letter: Research Coordination Network for the National Discovery Cloud for Climate (RCN-NDCC)

September 06, 2024

Dear Colleagues:

With this Dear Colleague Letter (DCL), the U. S. National Science Foundation’s (NSF) Directorate for Computer and Information Science and Engineering (CISE) announces its interest in receiving Research Coordination Network (RCN) proposals to establish a coordinating organization for the National Discovery Cloud for Climate initiative. NSF expects to make one award for up to five years and $500,000/year, subject to the availability of funds and quality of proposals.

Overview: National Discovery Cloud for Climate

The National Discovery Cloud for Climate (NDC-C) is an NSF initiative that will cooperatively advance cyberinfrastructure and climate research through awards that support collaborations between computer scientists, cyberinfrastructure developers and operators, and climate researchers from the geosciences, biosciences, engineering, and other disciplines. NDC-C promotes these collaborations through co-funding of proposals submitted to existing programs and solicitations from the Office of Advanced Cyberinfrastructure (OAC) and the Division of Computer and Network Systems (CNS) in the CISE Directorate and the Division of Research, Innovation, Synergies, and Education (RISE) in the Geosciences directorate. Existing solicitations and programs, such as the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) and Training-based Workforce Development for Advanced Cyberinfrastructure (CyberTraining) programs provide significant opportunities for interdisciplinary proposals that span multiple NSF directorates. NDC-C awards cover the full climate cyberinfrastructure spectrum, including general purpose data management, cataloging, discovery, and transport infrastructure; extensible platforms that provide end-user environments that integrate computing, data, and user interfaces for climate scientists and students; and cyberinfrastructure-enabled climate research that builds on these and other systems. A list of current NDC-C award recipients is available from the NSF NDC-C website .

A well-managed RCN can enable these individual awards to achieve a collective impact that is greater than what they will be able to achieve independently. RCNs are intended to promote inter-disciplinary research, foster new collaborations, coordinate overlapping efforts for broader impact such as student outreach and broadening participation in computing, and identify new opportunities for research and the translation of research to practice. The March 2024 NDC-C workshop report provides an overview and initial identification of opportunities for project-to-project collaborations and the potential for collective impact among the awardees through the creation of a backbone organization.

Proposal Guidance and Submission Instructions

Proposals should be prepared in accordance with the guidance contained in the Research Coordination Networks (RCN) program solicitation . As per the solicitation, prospective proposers must consult first with the cognizant program officer prior to submission and must include an email in the proposal’s Other Supplementary Documents section indicating the cognizant program officer’s approval to submit the RCN proposal. Proposals without approval from the cognizant program officer will be returned without review.

When submitting the proposal in research.gov, select the RCN solicitation and then select the CISE Directorate’s Office of Advanced Cyberinfrastructure (OAC); choose the “CYBERINFRASTRUCTURE” program. Proposal titles should begin with “RCN-NDCC:” followed by a substantive title. RCN-NDCC proposals must be received by December 18, 2024 (due by 5 p.m. submitting organization’s time) for consideration .

Submissions must comply with the instructions contained in the RCN program solicitation, including the seven guidance items outlined in Section II, Program Description. Within the context of the general guidelines provided by the solicitation, RCNs relevant to the NDC-C initiative should address issues such as the following that will help NDC-C awardees achieve collective impact:

• Establish and operate communication channels among NDC-C awarded projects and with the broader climate research community.
• Organize and promote student and researcher exchange programs between NDC-C funded projects.
• Conduct regular online and in-person award recipient meetings, including annual all-hands meetings for NDC-C funded projects, with clear agendas for identifying recipient research collaborations and other concrete outcomes.
• Coordinate undergraduate student education programs among NDC-C funded recipients. This may, for example, define multi-year undergraduate student opportunities that allow students to move through various outreach programs and internship opportunities conducted by award recipients.
• Connect NDC-C recipients with other significant, related investments in cyberinfrastructure, climate research, and climate education by the NSF and other federal agencies.
• Guide NDC-C award recipients on translation of research to practice through public and private sector participation.
• Establish a Web presence for the NDC-C RCN.

Review and Award Information

Proposals should be prepared and submitted to NSF as described above. NSF will manage and conduct the review process of proposals in accordance with standard NSF policies and procedures. NSF anticipates issuing one award for up to five years for up to $500,000/year, subject to the quality of proposals and availability of funds. Proposal budgets are expected to have significant funds set aside to cover participant costs, travel funds, and other activities associated with a research coordination network.

For questions about this DCL, please contact the cognizant program officers at [email protected] .

Gregory Hager Assistant Director for Computer and Information Science and Engineering

Part 1. Overview Information

National Institutes of Health ( NIH )

R01 Research Project Grant

• April 4, 2024  - Overview of Grant Application and Review Changes for Due Dates on or after January 25, 2025. See Notice NOT-OD-24-084 .
• August 31, 2022 - Implementation Changes for Genomic Data Sharing Plans Included with Applications Due on or after January 25, 2023. See Notice  NOT-OD-22-198 .
• August 5, 2022 - Implementation Details for the NIH Data Management and Sharing Policy. See Notice  NOT-OD-22-189 .

See Section III. 3. Additional Information on Eligibility .

This Notice of Funding Opportunity (NOFO) encourages grant applications from investigators interested in conducting basic, mechanistic research into the biological/genetic causes of cancer health disparities. These research project grants (R01) will support innovative studies designed to investigate biological/genetic bases of cancer health disparities, such as (1) mechanistic studies of biological factors associated with cancer health disparities, including those related to basic research in cancer biology or cancer prevention strategies, (2) the development and testing of new methodologies and models, and (3) secondary data analyses. This NOFO is also designed to aid and facilitate the growth of a nationwide cohort of scientists with a high level of basic research expertise in cancer health disparities research who can expand available resources and tools, such as biospecimens, patient derived models, and methods that are necessary to conduct basic research in cancer health disparities.

Not Applicable

All applications are due by 5:00 PM local time of applicant organization. 

Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

It is critical that applicants follow the instructions in the Research (R) Instructions in the  How to Apply - Application Guide , except where instructed to do otherwise (in this NOFO or in a Notice from NIH Guide for Grants and Contracts ).

Conformance to all requirements (both in the How to Apply - Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the How to Apply - Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the How to Apply - Application Guide , follow the program-specific instructions.

Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

• Use the NIH ASSIST system to prepare, submit and track your application online.
• Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.
• Use Grants.gov Workspace to prepare and submit your application and eRA Commons to track your application.

Part 2. Full Text of Announcement

Section i. notice of funding opportunity description.

This notice of funding opportunity (NOFO) encourages grant applications from investigators interested in conducting basic, mechanistic research into the biological/genetic causes of cancer health disparities. These research project grants (R01) will support innovative studies designed to investigate biological/genetic bases of cancer disparities, such as (1) mechanistic studies of biological factors associated with cancer disparities, including those related to basic research in cancer biology or cancer prevention strategies, (2) the development and testing of new methodologies and models, and (3) secondary data analyses. This NOFO is also designed to aid and facilitate the growth of a nationwide cohort of scientists with a high level of basic research expertise in cancer health disparities research who can expand available resources and tools, such as biospecimens, patient-derived models, and methods that are necessary to conduct basic research in cancer health disparities.

Please note that this NOFO will be reissued on the simplified review framework (SRF) template in 2025 to allow for resubmissions and renewals. See Simplified Review Framework for NIH Research Project Grant Applications.

In the United States, several racial/ethnic populations demonstrate increased incidence and/or more aggressive disease for specific cancer types. For example, African American males have higher rates of prostate and lung cancer, compared to their Caucasian-American counterparts, and both males and females exhibit a higher incidence of colorectal cancer and multiple myeloma. Hispanic/Latino individuals have the highest rates of cervical cancer and pediatric acute lymphoblastic leukemia (ALL) and one of the poorest survival rates for ALL. Similarly, Asians and Pacific Islanders have the highest incidence rates for liver and stomach cancer while American Indians and Alaska Natives have both the highest incidence and the highest mortality rates in kidney and renal pelvis cancer.

The causes of these cancer health disparities are multifactorial, including barriers in access to healthcare, cultural barriers, environmental disadvantage, differences in diet and lifestyle, ancestry-related risk factors, persistent co-morbidities, and chronic stress exposure due to discrimination and social isolation. An increasing number of studies demonstrate that even when socioeconomic and access to care factors are accounted for, incidence and mortality gaps persist between racial/ethnic populations for some cancer types, which suggests a role for biological contributors. Such studies have included identification of ancestry-related differences in DNA, RNA, and/or protein expression that are associated with cancer risk and/or progression. Other studies have shown the presence of differential tumor microenvironment components among diverse racial/ethnic populations indicating a potential role for immunity and inflammation in contributing to cancer health disparities.

These complex biological factors may enhance understanding of the differences observed in cancer incidence, prevalence, morbidity, and mortality rates among underrepresented populations. The NCI encourages investigations of such biological factors to increase our understanding of the mechanisms that play a role in cancer health disparities.

Specific Research Objectives

The goal of this NOFO is to stimulate interest in the characterization and functional analysis of biological factors associated with cancer health disparities and to provide funding opportunities in this area. Applications should focus on basic cancer research, consistent with the research interests of the NCI's Division of Cancer Biology (DCB) , Division of Cancer Prevention (DCP) , and Center for Cancer Health Equity (CCHE):

The DCB supports research on the discovery and characterization of basic pathways and mechanisms that regulate the development of a pre-malignant state, initiation of cellular transformation and cancer cell progression, formation of the tumor microenvironment, metastasis, and host responses to cancer, including immunologic or metabolic responses.

The CCHE supports cancer health disparity research focused on basic, hypothesis-driven studies that explicitly address the unequal burden of cancer amongst racial/ethnic minorities or other underserved populations across the cancer continuum (prevention, early detection, diagnosis, treatment, and survivorship).

The DCP supports research that will generate new information about molecular processes that are susceptible to intervention throughout the cancer continuum until invasive cancer and underlying mechanisms of cancer and its sequelae (i.e., mechanistic studies on the prevention or treatment of acute and chronic symptoms and morbidities related to cancer and its treatment), developing effective cancer screening and prevention strategies, discovering early detection biomarkers, and pinpointing mechanistically targeted nutrients in cancer prevention.

This NOFO encourages basic research projects that will develop and test new methodologies and new research technologies focused on specific topics in cancer health disparities. The availability of annotated clinical samples as well as enabling technologies (genomics/epigenomics, proteomics, metabolomics, single-cell analysis, imaging) make it feasible to study biological factors that contribute to cancer health disparities among different racial/ethnic populations.

Research projects must propose to investigate the interplay of race/ethnicity and/or other social determinants with cancer biology to mechanistically explain an unequal burden of cancer among populations. As such, proposed studies are encouraged to use biospecimens, patient-derived models, and/or data sets derived from different racial/ethnic and/or underserved groups. Studies investigating age and/or gender disparities, in the absence of race/ethnicity variables, are not solicited. Research projects using a comparative research design between at least two populations are encouraged, in which one or more is underserved.

Projects that are strictly hypothesis-generating, exploratory, and correlative studies are discouraged. As this NOFO is focused upon basic research, immediate clinically translational potential of the proposed project is NOT a requirement for the proposed projects.

Research topics of interest include but are not limited to :

• Causal drivers of early onset of certain cancer types in specific populations
• Genetic/epigenetic mechanisms of cancer susceptibility differences among racial/ethnic populations, such as epigenetic drivers and or suppressors
• Understanding how race/ethnicity impacts disease penetrance in individuals who inherit a cancer susceptibility gene
• Understanding if race/ethnicity has a role in regression of precancerous lesions
• Understanding if risk factors, including environmental exposures, differ across race/ethnicity to influence the development of precancerous lesions
• Identifying cancer risk and early detection biomarkers among underrepresented populations
• Examining how stress impacts the progression of symptoms across different population groups
• Identifying underlying mechanisms of symptoms that are responsible for altering treatment regimens that increase the risk of mortality for racial/ethnic minority patients with cancer
• Understanding the process through which precision therapies improve symptom management to reduce health disparities
• Detecting similarities and differences in cancer metabolism (e.g. alterations in metabolic fuel sources, fatty acid synthesis, lipid metabolism, glycolysis, nutrient uptake) among racial/ethnic populations
• Utilizing New 3D cellular models, organoids, xenografts, patient-derived models, and microfluidic systems designed to recapitulate and investigate cancer health disparities
• Identifying epithelial and mesenchymal markers in circulating tumor cells in cancer patients of distinct racial/ethnic groups
• Investigating how social health disparities may cause adverse gene expression that confers increased cancer risk and/or aggressiveness
• Understanding the role of microbiota in cancer health disparities during tumorigenesis and cancer progression
• Examining the role of oncogenic pathogens in the development of cancer health disparities during tumorigenesis and cancer progression in different populations groups
• Using computational analysis and modeling for predicting aggressive tumors in distinct racial/ethnic populations
• Understanding the biological mechanisms behind differences in toxicity and symptoms in different population groups
• Understanding the biological mechanisms of how stress impacts the progression of symptoms in racial/ethnic minority groups
• Deciphering the mechanisms of accumulated exposure to environmental toxins across populations
• Understanding the biological processes through which precision interventions improve symptom management to reduce cancer health disparities
• Investigating the biological bases of differences among racial/ethnic populations in response to cancer immunotherapies and/or development of immune-related adverse events induced by cancer immunotherapies.

Non-responsive Applications

The following types of studies are not responsive to this NOFO- applications proposing such studies will be considered non-responsive and will not be reviewed or considered for funding:

• Genome-Wide Association Studies (GWAS);
• Behavioral, social, environmental, or community/population-based studies that are not incorporating biological mechanisms in the specific aims; or
• Studies that do not propose cancer health disparity research.

See Section VIII. Other Information for award authorities and regulations.

Section II. Award Information

Grant: A financial assistance mechanism providing money, property, or both to an eligible entity to carry out an approved project or activity.

The  OER Glossary  and the How to Apply - Application Guide provide details on these application types. Only those application types listed here are allowed for this NOFO.

Not Allowed: Only accepting applications that do not propose clinical trials.

Need help determining whether you are doing a clinical trial?

The number of awards is contingent upon NIH appropriations and the submission of a sufficient number of meritorious applications.

The scope of the proposed project should determine the project period. The maximum project period is 5 years.

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. eligible applicants eligible organizations higher education institutions public/state controlled institutions of higher education private institutions of higher education the following types of higher education institutions are always encouraged to apply for nih support as public or private institutions of higher education: hispanic-serving institutions historically black colleges and universities (hbcus) tribally controlled colleges and universities (tccus) alaska native and native hawaiian serving institutions asian american native american pacific islander serving institutions (aanapisis) nonprofits other than institutions of higher education nonprofits with 501(c)(3) irs status (other than institutions of higher education) nonprofits without 501(c)(3) irs status (other than institutions of higher education) for-profit organizations small businesses for-profit organizations (other than small businesses) local governments state governments county governments city or township governments special district governments indian/native american tribal governments (federally recognized) indian/native american tribal governments (other than federally recognized) federal governments eligible agencies of the federal government u.s. territory or possession other independent school districts public housing authorities/indian housing authorities native american tribal organizations (other than federally recognized tribal governments) faith-based or community-based organizations regional organizations non-domestic (non-u.s.) entities (foreign organizations) foreign organizations non-domestic (non-u.s.) entities (foreign organizations) are eligible to apply. non-domestic (non-u.s.) components of u.s. organizations are eligible to apply. foreign components, as defined in the nih grants policy statement , are allowed.  required registrations applicant organizations applicant organizations must complete and maintain the following registrations as described in the how to apply - application guide to be eligible to apply for or receive an award. all registrations must be completed prior to the application being submitted. registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference nih grants policy statement section 2.3.9.2 electronically submitted applications for additional information system for award management (sam) – applicants must complete and maintain an active registration, which requires renewal at least annually . the renewal process may require as much time as the initial registration. sam registration includes the assignment of a commercial and government entity (cage) code for domestic organizations which have not already been assigned a cage code. nato commercial and government entity (ncage) code – foreign organizations must obtain an ncage code (in lieu of a cage code) in order to register in sam. unique entity identifier (uei) - a uei is issued as part of the sam.gov registration process. the same uei must be used for all registrations, as well as on the grant application. era commons - once the unique organization identifier is established, organizations can register with era commons in tandem with completing their grants.gov registrations; all registrations must be in place by time of submission. era commons requires organizations to identify at least one signing official (so) and at least one program director/principal investigator (pd/pi) account in order to submit an application. grants.gov – applicants must have an active sam registration in order to complete the grants.gov registration. program directors/principal investigators (pd(s)/pi(s)) all pd(s)/pi(s) must have an era commons account.  pd(s)/pi(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in era commons. if the pd/pi is also the organizational signing official, they must have two distinct era commons accounts, one for each role. obtaining an era commons account can take up to 2 weeks. eligible individuals (program director/principal investigator) any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the program director(s)/principal investigator(s) (pd(s)/pi(s)) is invited to work with their organization to develop an application for support. individuals from diverse backgrounds, including underrepresented racial and ethnic groups, individuals with disabilities, and women are always encouraged to apply for nih support. see, reminder: notice of nih's encouragement of applications supporting individuals from underrepresented ethnic and racial groups as well as individuals with disabilities , not-od-22-019 , and notice of nih's interest in diversity, not-od-20-031 . for institutions/organizations proposing multiple pds/pis, visit the multiple program director/principal investigator policy and submission details in the senior/key person profile (expanded) component of the how to apply - application guide . 2. cost sharing.

This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement NIH Grants Policy Statement Section 1.2 Definition of Terms.

3. Additional Information on Eligibility

Number of Applications

Applicant organizations may submit more than one application, provided that each application is scientifically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time, per NIH Grants Policy Statement Section 2.3.7.4 Submission of Resubmission Application . This means that the NIH will not accept:

• A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
• A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
• An application that has substantial overlap with another application pending appeal of initial peer review (see  NIH Grants Policy Statement 2.3.9.4 Similar, Essentially Identical, or Identical Applications ).

Section IV. Application and Submission Information

1. requesting an application package.

The application forms package specific to this opportunity must be accessed through ASSIST, Grants.gov Workspace or an institutional system-to-system solution. Links to apply using ASSIST or Grants.gov Workspace are available in Part 1 of this NOFO. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

2. Content and Form of Application Submission

It is critical that applicants follow the instructions in the Research (R) Instructions in the  How to Apply - Application Guide  except where instructed in this notice of funding opportunity to do otherwise. Conformance to the requirements in the How to Apply - Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

Page Limitations

All page limitations described in the How to Apply – Application Guide and the Table of Page Limits must be followed.

The following section supplements the instructions found in the How to Apply – Application Guide and should be used for preparing an application to this NOFO.

SF424(R&R) Cover

All instructions in the How to Apply - Application Guide must be followed.

SF424(R&R) Project/Performance Site Locations

Sf424(r&r) other project information, sf424(r&r) senior/key person profile, r&r or modular budget, r&r subaward budget, phs 398 cover page supplement, phs 398 research plan.

All instructions in the  How to Apply - Application Guide must be followed, with the following additional instructions:

Resource Sharing Plan : Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the  How to Apply - Application Guide .

Other Plan(s): 

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions:

• All applicants planning research (funded or conducted in whole or in part by NIH) that results in the generation of scientific data are required to comply with the instructions for the Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

Appendix:  Only limited Appendix materials are allowed. Follow all instructions for the Appendix as described in the How to Apply - Application Guide .

• No publications or other material, with the exception of blank questionnaires or blank surveys, may be included in the Appendix.

PHS Human Subjects and Clinical Trials Information

When involving human subjects research, clinical research, and/or NIH-defined clinical trials (and when applicable, clinical trials research experience) follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply - Application Guide , with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or Delayed Onset Study record.

Study Record: PHS Human Subjects and Clinical Trials Information

Delayed Onset Study

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start). All instructions in the How to Apply - Application Guide must be followed.

PHS Assignment Request Form

Foreign organizations.

Foreign (non-U.S.) organizations must follow policies described in the NIH Grants Policy Statement , and procedures for foreign organizations described throughout the How to Apply Application Guide.

3. Unique Entity Identifier and System for Award Management (SAM)

See Part 2. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

4. Submission Dates and Times

Part I.  contains information about Key Dates and times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday , the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies). Applicants must then complete the submission process by tracking the status of the application in the eRA Commons , NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time.  If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications .

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in the How to Apply – Application Guide .

5. Intergovernmental Review (E.O. 12372)

This initiative is not subject to intergovernmental review.

6. Funding Restrictions

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Pre-award costs are allowable only as described in the NIH Grants Policy Statement Section 7.9.1 Selected Items of Cost .

7. Other Submission Requirements and Information

Applications must be submitted electronically following the instructions described in the How to Apply - Application Guide . Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit How to Apply – Application Guide . If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Dealing with System Issues guidance. For assistance with application submission, contact the Application Submission Contacts in Section VII.

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile form . Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH. See Section III of this NOFO for information on registration requirements.

The applicant organization must ensure that the unique entity identifier provided on the application is the same identifier used in the organization’s profile in the eRA Commons and for the System for Award Management. Additional information may be found in the How to Apply - Application Guide .

See more tips for avoiding common errors.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review and responsiveness by  components of participating organizations , NIH. Applications that are incomplete, non-compliant and/or nonresponsive will not be reviewed.

Requests of $500,000 or more for direct costs in any year

Applicants requesting $500,000 or more in direct costs in any year (excluding consortium F&A) must contact a Scientific/ Research Contact at least 6 weeks before submitting the application and follow the Policy on the Acceptance for Review of Unsolicited Applications that Request $500,000 or More in Direct Costs as described in the SF424 (R&R) Application Guide.

Recipients or subrecipients must submit any information related to violations of federal criminal law involving fraud, bribery, or gratuity violations potentially affecting the federal award. See Mandatory Disclosures, 2 CFR 200.113 and NIH Grants Policy Statement Section 4.1.35 .

Send written disclosures to the NIH Chief Grants Management Officer listed on the Notice of Award for the IC that funded the award and to the HHS Office of Inspector Grant Self Disclosure Program at [email protected] . 

Post Submission Materials

Applicants are required to follow the instructions for post-submission materials, as described in the policy

Section V. Application Review Information

1. criteria.

Only the review criteria described below will be considered in the review process.  Applications submitted to the NIH in support of the NIH mission are evaluated for scientific and technical merit through the NIH peer review system.

For this particular announcement, note the following:

Immediate clinically translational potential of the proposed project is NOT a requirement for this FOA.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following review criteria and additional review criteria (as applicable for the project proposed).

Reviewers will consider each of the review criteria below in the determination of scientific merit and give a separate score for each. An application does not need to be strong in all categories to be judged likely to have major scientific impact. For example, a project that by its nature is not innovative may be essential to advance a field.

Does the project address an important problem or a critical barrier to progress in the field? Is the prior research that serves as the key support for the proposed project rigorous? If the aims of the project are achieved, how will scientific knowledge, technical capability, and/or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?

Specific to this NOFO : Does the proposed research project have the potential to advance the understanding of biological mechanisms contributing to cancer health disparities in underrepresented populations?

Are the PD(s)/PI(s), collaborators, and other researchers well suited to the project? If Early Stage Investigators or those in the early stages of independent careers, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the project is collaborative or multi-PD/PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance, and organizational structure appropriate for the project?

Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?

Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project? Have the investigators included plans to address weaknesses in the rigor of prior research that serves as the key support for the proposed project? Have the investigators presented strategies to ensure a robust and unbiased approach, as appropriate for the work proposed? Are potential problems, alternative strategies, and benchmarks for success presented? If the project is in the early stages of development, will the strategy establish feasibility and will particularly risky aspects be managed? Have the investigators presented adequate plans to address relevant biological variables, such as sex, for studies in vertebrate animals or human subjects? 

If the project involves human subjects and/or NIH-defined clinical research, are the plans to address 1) the protection of human subjects from research risks, and 2) inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion or exclusion of individuals of all ages (including children and older adults), justified in terms of the scientific goals and research strategy proposed?

Specific to this NOFO : Is the scientific approach proposed adequate to effectively study cancer health disparities between at least two populations, in which one or more are underserved?

Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment, and other physical resources available to the investigators adequate for the project proposed? Will the project benefit from unique features of the scientific environment, subject populations, or collaborative arrangements?

As applicable for the project proposed, reviewers will evaluate the following additional items while determining scientific and technical merit, and in providing an overall impact score, but will not give separate scores for these items.

For research that involves human subjects but does not involve one of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate the justification for involvement of human subjects and the proposed protections from research risk relating to their participation according to the following five review criteria: 1) risk to subjects, 2) adequacy of protection against risks, 3) potential benefits to the subjects and others, 4) importance of the knowledge to be gained, and 5) data and safety monitoring for clinical trials.

For research that involves human subjects and meets the criteria for one or more of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate: 1) the justification for the exemption, 2) human subjects involvement and characteristics, and 3) sources of materials. For additional information on review of the Human Subjects section, please refer to the Guidelines for the Review of Human Subjects .

When the proposed project involves human subjects and/or NIH-defined clinical research, the committee will evaluate the proposed plans for the inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion (or exclusion) of individuals of all ages (including children and older adults) to determine if it is justified in terms of the scientific goals and research strategy proposed. For additional information on review of the Inclusion section, please refer to the Guidelines for the Review of Inclusion in Clinical Research .

The committee will evaluate the involvement of live vertebrate animals as part of the scientific assessment according to the following three points: (1) a complete description of all proposed procedures including the species, strains, ages, sex, and total numbers of animals to be used; (2) justifications that the species is appropriate for the proposed research and why the research goals cannot be accomplished using an alternative non-animal model; and (3) interventions including analgesia, anesthesia, sedation, palliative care, and humane endpoints that will be used to limit any unavoidable discomfort, distress, pain and injury in the conduct of scientifically valuable research. Methods of euthanasia and justification for selected methods, if NOT consistent with the AVMA Guidelines for the Euthanasia of Animals, is also required but is found in a separate section of the application. For additional information on review of the Vertebrate Animals Section, please refer to the Worksheet for Review of the Vertebrate Animals Section.

Reviewers will assess whether materials or procedures proposed are potentially hazardous to research personnel and/or the environment, and if needed, determine whether adequate protection is proposed.

For Resubmissions, the committee will evaluate the application as now presented, taking into consideration the responses to comments from the previous scientific review group and changes made to the project.

For Renewals, the committee will consider the progress made in the last funding period.

As applicable for the project proposed, reviewers will consider each of the following items, but will not give scores for these items, and should not consider them in providing an overall impact score.

Reviewers will assess whether the project presents special opportunities for furthering research programs through the use of unusual talent, resources, populations, or environmental conditions that exist in other countries and either are not readily available in the United States or augment existing U.S. resources.

Reviewers will assess the information provided in this section of the application, including 1) the Select Agent(s) to be used in the proposed research, 2) the registration status of all entities where Select Agent(s) will be used, 3) the procedures that will be used to monitor possession use and transfer of Select Agent(s), and 4) plans for appropriate biosafety, biocontainment, and security of the Select Agent(s).

Reviewers will comment on whether the Resource Sharing Plan(s) (e.g., Sharing Model Organisms ) or the rationale for not sharing the resources, is reasonable.

For projects involving key biological and/or chemical resources, reviewers will comment on the brief plans proposed for identifying and ensuring the validity of those resources.

Reviewers will consider whether the budget and the requested period of support are fully justified and reasonable in relation to the proposed research.

2. Review and Selection Process Applications will be evaluated for scientific and technical merit by (an) appropriate Scientific Review Group(s) convened by the Center for Scientific Review, in accordance with NIH peer review policy and procedures , using the stated review criteria . Assignment to a Scientific Review Group will be shown in the eRA Commons. As part of the scientific peer review, all applications will receive a written critique. Applications may undergo a selection process in which only those applications deemed to have the highest scientific and technical merit (generally the top half of applications under review) will be discussed and assigned an overall impact score. Applications will be assigned on the basis of established PHS referral guidelines to the appropriate NIH Institute or Center. Applications will compete for available funds with all other recommended applications submitted in response to this NOFO. Following initial peer review, recommended applications will receive a second level of review by the appropriate national Advisory Council or Board. The following will be considered in making funding decisions: Scientific and technical merit of the proposed project as determined by scientific peer review. Availability of funds. Relevance of the proposed project to program priorities. If the application is under consideration for funding, NIH will request "just-in-time" information from the applicant as described in the  NIH Grants Policy Statement Section 2.5.1. Just-in-Time Procedures . This request is not a Notice of Award nor should it be construed to be an indicator of possible funding. Prior to making an award, NIH reviews an applicant’s federal award history in SAM.gov to ensure sound business practices. An applicant can review and comment on any information in the Responsibility/Qualification records available in SAM.gov.  NIH will consider any comments by the applicant in the Responsibility/Qualification records in SAM.gov to ascertain the applicant’s integrity, business ethics, and performance record of managing Federal awards per 2 CFR Part 200.206 “Federal awarding agency review of risk posed by applicants.”  This provision will apply to all NIH grants and cooperative agreements except fellowships. 3. Anticipated Announcement and Award Dates

After the peer review of the application is completed, the PD/PI will be able to access his or her Summary Statement (written critique) via the  eRA Commons . Refer to Part 1 for dates for peer review, advisory council review, and earliest start date.

Information regarding the disposition of applications is available in the  NIH Grants Policy Statement Section 2.4.4 Disposition of Applications .

Section VI. Award Administration Information

1. award notices.

A Notice of Award (NoA) is the official authorizing document notifying the applicant that an award has been made and that funds may be requested from the designated HHS payment system or office. The NoA is signed by the Grants Management Officer and emailed to the recipient’s business official.

In accepting the award, the recipient agrees that any activities under the award are subject to all provisions currently in effect or implemented during the period of the award, other Department regulations and policies in effect at the time of the award, and applicable statutory provisions.

Recipients must comply with any funding restrictions described in  Section IV.6. Funding Restrictions . Any pre-award costs incurred before receipt of the NoA are at the applicant's own risk.  For more information on the Notice of Award, please refer to the  NIH Grants Policy Statement Section 5. The Notice of Award and NIH Grants & Funding website, see  Award Process.

Institutional Review Board or Independent Ethics Committee Approval: Recipient institutions must ensure that protocols are reviewed by their IRB or IEC. To help ensure the safety of participants enrolled in NIH-funded studies, the recipient must provide NIH copies of documents related to all major changes in the status of ongoing protocols.

2. Administrative and National Policy Requirements

The following Federal wide and HHS-specific policy requirements apply to awards funded through NIH:

• The rules listed at 2 CFR Part 200 , Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards.
• All NIH grant and cooperative agreement awards include the NIH Grants Policy Statement as part of the terms and conditions in the Notice of Award (NoA). The NoA includes the requirements of this NOFO. For these terms of award, see the NIH Grants Policy Statement Part II: Terms and Conditions of NIH Grant Awards, Subpart A: General and Part II: Terms and Conditions of NIH Grant Awards, Subpart B: Terms and Conditions for Specific Types of Grants, Recipients, and Activities .
• HHS recognizes that NIH research projects are often limited in scope for many reasons that are nondiscriminatory, such as the principal investigator’s scientific interest, funding limitations, recruitment requirements, and other considerations. Thus, criteria in research protocols that target or exclude certain populations are warranted where nondiscriminatory justifications establish that such criteria are appropriate with respect to the health or safety of the subjects, the scientific study design, or the purpose of the research. For additional guidance regarding how the provisions apply to NIH grant programs, please contact the Scientific/Research Contact that is identified in Section VII under Agency Contacts of this NOFO.

All federal statutes and regulations relevant to federal financial assistance, including those highlighted in  NIH Grants Policy Statement Section 4 Public Policy Requirements, Objectives and Other Appropriation Mandates.

Recipients are responsible for ensuring that their activities comply with all applicable federal regulations.  NIH may terminate awards under certain circumstances.  See  2 CFR Part 200.340 Termination and  NIH Grants Policy Statement Section 8.5.2 Remedies for Noncompliance or Enforcement Actions: Suspension, Termination, and Withholding of Support . 

3. Data Management and Sharing

Consistent with the 2023 NIH Policy for Data Management and Sharing, when data management and sharing is applicable to the award, recipients will be required to adhere to the Data Management and Sharing requirements as outlined in the NIH Grants Policy Statement . Upon the approval of a Data Management and Sharing Plan, it is required for recipients to implement the plan as described.

4. Reporting

When multiple years are involved, recipients will be required to submit the  Research Performance Progress Report (RPPR)  annually and financial statements as required in the NIH Grants Policy Statement Section 8.4.1 Reporting.  To learn more about post-award monitoring and reporting, see the NIH Grants & Funding website, see Post-Award Monitoring and Reporting .

A final RPPR, invention statement, and the expenditure data portion of the Federal Financial Report are required for closeout of an award, as described in the NIH Grants Policy Statement Section 8.6 Closeout . NIH NOFOs outline intended research goals and objectives. Post award, NIH will review and measure performance based on the details and outcomes that are shared within the RPPR, as described at 2 CFR Part 200.301.

Section VII. Agency Contacts

We encourage inquiries concerning this funding opportunity and welcome the opportunity to answer questions from potential applicants.

eRA Service Desk (Questions regarding ASSIST, eRA Commons, application errors and warnings, documenting system problems that threaten submission by the due date, and post-submission issues)

Finding Help Online:  https://www.era.nih.gov/need-help  (preferred method of contact) Telephone: 301-402-7469 or 866-504-9552 (Toll Free)

General Grants Information (Questions regarding application instructions, application processes, and NIH grant resources) Email:  [email protected]  (preferred method of contact) Telephone: 301-480-7075

Grants.gov Customer Support (Questions regarding Grants.gov registration and Workspace) Contact Center Telephone: 800-518-4726 Email:  [email protected]

Anu Sharman, Ph.D. National Cancer Institute (NCI) Telephone: 240-276-6250 Email: [email protected]

Tiffany Wallace, Ph.D. National Cancer Institute (NCI) Telephone: 240-276-5114 Email: [email protected]

Asad Umar, D.V.M., Ph.D. National Cancer Institute (NCI) Telephone: 240-276-7070 Email: [email protected]  

Amy Rubinstein, Ph.D. Center for Scientific Review (CSR) Telephone: 301-408-9754 Email: [email protected]

Shane Woodward National Cancer Institute (NCI) Telephone: 240-276-6303 Email: [email protected]

Section VIII. Other Information

Recently issued trans-NIH policy notices may affect your application submission. A full list of policy notices published by NIH is provided in the NIH Guide for Grants and Contracts . All awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Awards are made under the authorization of Sections 301 and 405 of the Public Health Service Act as amended (42 USC 241 and 284) and under Federal Regulations 42 CFR Part 52 and 2 CFR Part 200.