qualitative research yin

We’re fighting to restore access to 500,000+ books in court this week. Join us!

Internet Archive Audio

• This Just In
• Grateful Dead
• Old Time Radio
• 78 RPMs and Cylinder Recordings
• Audio Books & Poetry
• Computers, Technology and Science
• Music, Arts & Culture
• News & Public Affairs
• Spirituality & Religion
• Radio News Archive

• Flickr Commons
• Occupy Wall Street Flickr
• NASA Images
• Solar System Collection
• Ames Research Center

• All Software
• Old School Emulation
• MS-DOS Games
• Historical Software
• Classic PC Games
• Software Library
• Kodi Archive and Support File
• Vintage Software
• CD-ROM Software
• CD-ROM Software Library
• Software Sites
• Tucows Software Library
• Shareware CD-ROMs
• Software Capsules Compilation
• CD-ROM Images
• ZX Spectrum
• DOOM Level CD

• Smithsonian Libraries
• FEDLINK (US)
• Lincoln Collection
• American Libraries
• Canadian Libraries
• Universal Library
• Project Gutenberg
• Children's Library
• Biodiversity Heritage Library
• Books by Language
• Additional Collections

• Prelinger Archives
• Democracy Now!
• Occupy Wall Street
• TV NSA Clip Library
• Animation & Cartoons
• Arts & Music
• Computers & Technology
• Cultural & Academic Films
• Ephemeral Films
• Sports Videos
• Videogame Videos
• Youth Media

Search the history of over 866 billion web pages on the Internet.

Mobile Apps

• Wayback Machine (iOS)
• Wayback Machine (Android)

Browser Extensions

Archive-it subscription.

• Explore the Collections
• Build Collections

Save Page Now

Capture a web page as it appears now for use as a trusted citation in the future.

Please enter a valid web address

• Donate Donate icon An illustration of a heart shape

Qualitative research from start to finish

Bookreader item preview, share or embed this item, flag this item for.

• Graphic Violence
• Explicit Sexual Content
• Hate Speech
• Misinformation/Disinformation
• Marketing/Phishing/Advertising
• Misleading/Inaccurate/Missing Metadata

plus-circle Add Review comment Reviews

41 Previews

2 Favorites

DOWNLOAD OPTIONS

No suitable files to display here.

PDF access not available for this item.

IN COLLECTIONS

Uploaded by station07.cebu on December 19, 2023

SIMILAR ITEMS (based on metadata)

2nd Edition

Qualitative Research from Start to Finish, Second Edition

Description

This book will help readers understand the practice of qualitative research--whether they want to do it, teach it, or just learn about it. All the major research phases are encompassed (startup, design, data collection, analysis, and composing), including newly emerging trends. Numerous easy-to-read vignettes show how other scholars have successfully implemented specific procedures. Equally distinctive, the book presents qualitative research as an adaptive craft. The array of choices among different procedures and methods enables readers to customize their own studies and to accommodate different worldviews and genres. New to This Edition: *Stronger discussion of different worldviews (e.g., constructivism, postpositivism, and pragmatism) and how they relate to different methodological choices. *Clearer emphasis on doing a generalized qualitative study, while acknowledging 12 specialized genres (e.g., action-based research, arts-based research, autoethnography, grounded theory, phenomenology, and others). *Expanded discussions of different kinds of qualitative study samples and of mixed methods. *New ideas on how to avoid getting stalled when analyzing qualitative data. *Consideration of an additional way of concluding a qualitative study: by taking action. Pedagogical Features *Chapters start with an abstract and end with a suggested exercise. *Key terms and concepts appear in boldface throughout the text and are listed in end-of-chapter recaps as well as in the book’s glossary. *Sections within each chapter start with a preview box: “What you should learn from this section." *An appendix presents a semester- or yearlong field-based project.

Table of Contents

Robert K. Yin, PhD, is Chairman of the Board and CEO of COSMOS Corporation, an applied research and social science firm that has been in operation since 1980. COSMOS has successfully completed hundreds of projects for government agencies, private foundations, and other entrepreneurial and nonprofit organizations. Dr. Yin has authored five books--including Case Study Research , now in its fifth edition--and over 100 peer-reviewed articles. His research has covered a wide array of fields, such as primary, secondary, and postsecondary education; health promotion, HIV/AIDS prevention, and substance abuse prevention; organizational development and program evaluation; neighborhood, community, and urban development; and technological innovation and communications.

Critics' Reviews

"With remarkable breadth of coverage, this is an excellent guide for both beginning and advanced students working their way through field-based projects. Practical in approach and documented with countless examples, the book covers everything from dealing with institutional review boards and preparing a pilot study to producing analytical generalizations, assessing rival explanations, and managing reviewer feedback. Few books cover so many aspects of the qualitative research process with such clarity, insight, and common sense. Yin has produced an impressive handbook that students and instructors will return to again and again."--Mario Luis Small, PhD, Grafstein Family Professor of Sociology, Harvard University "Written from a practical, inductive, and adaptive perspective, this text offers the best blend of scholarly examples and commonsense advice that I've seen. It is highly readable and the definitions and explanations are very easy to follow. The preview boxes, bullet points, and quick study boxes make it easy to find what you need. I really like the research vignettes and the extended sample study. The end-of-chapter exercises are excellent; I would assign many of them."--Michelle C. Bligh, PhD, Division of Behavioral and Organizational Sciences, Claremont Graduate University "With impressive content and coverage, this book offers 'nuts-and-bolts' guidance for conducting qualitative inquiry. The book emphasizes that qualitative researchers need to be practical, inductive, and adaptive, and offers compelling illustrations of these three principles in action. Yin is particularly adroit at showing the value of qualitative approaches for conducting evaluation research. The book is amply illustrated, with plenty of exercises to familiarize students with the practices described. This text will be useful for a wide variety of researchers, from advanced undergraduates to graduate students in such disciplines as nursing, public health, and social policy."--James A. Holstein, PhD, Department of Social and Cultural Sciences, Marquette University "Yin presents the basics of qualitative research in a clear, methodical manner that provides a basis for discussion and exploration in a course. Vignettes throughout the book give students a deeper understanding of the topics discussed. I appreciate the interdisciplinary nature of the vignettes, which are not wed to any one field. An additional strength is the discussion of ethics and the role of the institutional review board."--Penny A. Pasque, PhD, Department of Educational Leadership and Policy Studies, University of Oklahoma "This book's inductive, 'learn-by-doing' approach is the best way to learn how to do qualitative research. It is chock full of practical tips and suggestions, presented in an understandable, jargon-free format. Our introductory qualitative research methods class requires pilot studies, which makes the text a perfect fit. The sections on analyzing, interpreting, and writing up data are superb, and detailed enough for use in an advanced qualitative data analysis class."--Lance D. Fusarelli, PhD, Department of Leadership, Policy, and Adult and Higher Education, North Carolina State University "The pedagogical features are well constructed, and the examples bring a real-world focus to qualitative research. This is the approach that I try to use in my courses, as most of my students are going to be administrators or policy analysts, not academics."--Miriam Levitt, PhD, Faculty of Social Sciences, University of Ottawa, Canada "This text is very practical and gives a solid overview of the qualitative process. The book's organization enables students to easily follow along as they conduct their own research projects. The descriptions of different types of data collection are especially strong, and the level of detail regarding interviewing and field observations will be beneficial for students who are new to field research. Yin encourages students to remain self-reflective throughout the research process--something that other texts keep on the periphery."--Brandi Lawless, PhD, Department of Communication Studies, University of San Francisco -I recommend this book for teaching and conducting qualitative research. It would be possible to follow Dr. Yin's suggestions using the end-of-chapter exercises to develop a course for one or two semesters….This book would be a great help for busy professors.--Family and Consumer Sciences Research Journal, 3/1/2016ƒƒAn exciting addition to the list of sources available for the new researcher....Within [his] linear, clear sequencing, Yin offered multiple options and reasons for choosing certain options (or choosing other options), encouraging researchers to do the same. The writing style was clear, concise and not jargon-loaded, which also might appeal to the novice researcher….I connected with Yin's work as a researcher and as a teacher.(on the first edition)--The Qualitative Report, 12/31/2014

About VitalSource eBooks

VitalSource is a leading provider of eBooks.

• Access your materials anywhere, at anytime.
• Customer preferences like text size, font type, page color and more.
• Take annotations in line as you read.

Multiple eBook Copies

This eBook is already in your shopping cart. If you would like to replace it with a different purchasing option please remove the current eBook option from your cart.

Book Preview

The country you have selected will result in the following:

• Product pricing will be adjusted to match the corresponding currency.
• The title Perception will be removed from your cart because it is not available in this region.

Qualitative Research from Start to Finish

Second edition, robert k. yin.

• An appendix presents a semester- or yearlong field-based project. -->

• sample chapter
• about the author Robert K. Yin , PhD, is Chairman of the Board and CEO of COSMOS Corporation, an applied research and social science firm that has been in operation since 1980. COSMOS has successfully completed hundreds of projects for government agencies, private foundations, and other entrepreneurial and nonprofit organizations. Dr. Yin has authored five books—including Case Study Research , now in its fifth edition—and over 100 peer-reviewed articles. His research has covered a wide array of fields, such as primary, secondary, and postsecondary education; health promotion, HIV/AIDS prevention, and substance abuse prevention; organizational development and program evaluation; neighborhood, community, and urban development; and technological innovation and communications. -->
• prior editions First Edition, © 2011 ISBN: 9781606237014 -->
• Consideration of an additional way of concluding a qualitative study: by taking action. -->

Qualitative Research from Start to Finish

Second edition, robert k. yin.

• An appendix presents a semester- or yearlong field-based project. -->

• sample chapter
• about the author Robert K. Yin , PhD, is Chairman of the Board and CEO of COSMOS Corporation, an applied research and social science firm that has been in operation since 1980. COSMOS has successfully completed hundreds of projects for government agencies, private foundations, and other entrepreneurial and nonprofit organizations. Dr. Yin has authored five books—including Case Study Research , now in its fifth edition—and over 100 peer-reviewed articles. His research has covered a wide array of fields, such as primary, secondary, and postsecondary education; health promotion, HIV/AIDS prevention, and substance abuse prevention; organizational development and program evaluation; neighborhood, community, and urban development; and technological innovation and communications. -->
• prior editions First Edition, © 2011 ISBN: 9781606237014 -->
• Consideration of an additional way of concluding a qualitative study: by taking action. -->

• Kindle Store
• Kindle eBooks
• Business & Money

Promotions apply when you purchase

These promotions will be applied to this item:

Some promotions may be combined; others are not eligible to be combined with other offers. For details, please see the Terms & Conditions associated with these promotions.

• Highlight, take notes, and search in the book
• Create digital flashcards instantly

Buy for others

Buying and sending ebooks to others.

• Select quantity
• Buy and send eBooks
• Recipients can read on any device

These ebooks can only be redeemed by recipients in the US. Redemption links and eBooks cannot be resold.

Sorry, there was a problem.

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required .

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

Image Unavailable

• To view this video download Flash Player

Follow the author

Qualitative Research from Start to Finish 2nd Edition, Kindle Edition

• ISBN-13 978-1462517978
• Edition 2nd
• Sticky notes Not Enabled
• Publisher The Guilford Press
• Publication date August 14, 2015
• Language English
• File size 3315 KB
• See all details

Kindle E-Readers

• Kindle Paperwhite
• Kindle Paperwhite (5th Generation)
• Kindle Touch
• Kindle Voyage
• Kindle Oasis
• All new Kindle paperwhite
• All New Kindle E-reader
• Kindle Oasis (9th Generation)
• Kindle Paperwhite (10th Generation)
• Kindle Paperwhite (11th Generation)
• All New Kindle E-reader (11th Generation)
• Kindle Scribe (1st Generation)
• Kindle (10th Generation)
• Kindle Oasis (10th Generation)

Fire Tablets

• Fire HD 8 (8th Generation)
• Fire 7 (9th Generation)
• Fire HD 10 (9th Generation)
• Fire HD 8 (10th Generation)
• Fire HD 10 (11th Generation)
• Fire HD 10 Plus
• Fire 7 (12th Generation)
• Fire HD 8 (12th Generation)
• Fire HD 8 Plus

Free Kindle Reading Apps

• Kindle for Android Phones
• Kindle for Android Tablets
• Kindle for iPhone
• Kindle for iPad
• Kindle for Mac
• Kindle for PC
• Kindle for Web

Customers who bought this item also bought

Editorial Reviews

About the author, product details.

• ASIN ‏ : ‎ B00Z7B109M
• Publisher ‏ : ‎ The Guilford Press; 2nd edition (August 14, 2015)
• Publication date ‏ : ‎ August 14, 2015
• Language ‏ : ‎ English
• File size ‏ : ‎ 3315 KB
• Text-to-Speech ‏ : ‎ Enabled
• Enhanced typesetting ‏ : ‎ Enabled
• X-Ray ‏ : ‎ Not Enabled
• Word Wise ‏ : ‎ Enabled
• Sticky notes ‏ : ‎ Not Enabled
• Print length ‏ : ‎ 386 pages
• #188 in Psychology Research
• #250 in Education Theory Research
• #283 in Social Science Research

About the author

Robert k. yin.

Robert K. Yin, Ph.D., serves as Chairman of the Board and CEO of COSMOS Corporation, an applied research and social science firm that has been in operation since 1980. Over the years, COSMOS has successfully completed hundreds of projects for government agencies, private foundations, and other entrepreneurial and non-profit organizations. At COSMOS, Dr. Yin actively leads various research projects, including those in which the case study method is used. He has authored numerous books and peer-reviewed articles, including Case Study Research and Applications of Case Study Research. In 1998 he founded the “Robert K. Yin Fund” at M.I.T., which supports seminars on brain sciences, as well as other activities related to the advancement of pre-doctoral students in the Department of Brain and Cognitive Sciences.

Customer reviews

• 5 star 4 star 3 star 2 star 1 star 5 star 85% 10% 3% 1% 1% 85%
• 5 star 4 star 3 star 2 star 1 star 4 star 85% 10% 3% 1% 1% 10%
• 5 star 4 star 3 star 2 star 1 star 3 star 85% 10% 3% 1% 1% 3%
• 5 star 4 star 3 star 2 star 1 star 2 star 85% 10% 3% 1% 1% 1%
• 5 star 4 star 3 star 2 star 1 star 1 star 85% 10% 3% 1% 1% 1%

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.

• Sort reviews by Top reviews Most recent Top reviews

Top reviews from the United States

There was a problem filtering reviews right now. please try again later..

Top reviews from other countries

Report an issue

• About Amazon
• Investor Relations
• Amazon Devices
• Amazon Science
• Sell products on Amazon
• Sell on Amazon Business
• Sell apps on Amazon
• Become an Affiliate
• Advertise Your Products
• Self-Publish with Us
• Host an Amazon Hub
• › See More Make Money with Us
• Amazon Business Card
• Shop with Points
• Reload Your Balance
• Amazon Currency Converter
• Amazon and COVID-19
• Your Account
• Your Orders
• Shipping Rates & Policies
• Returns & Replacements
• Manage Your Content and Devices

• Conditions of Use
• Privacy Notice
• Consumer Health Data Privacy Disclosure
• Your Ads Privacy Choices

• Social Science
• Quantitative Social Research
• Research Design

Robert K. Yin. (2014). Case Study Research Design and Methods (5th ed.). Thousand Oaks, CA: Sage. 282 pages.

• Canadian Journal of Program Evaluation 30(1)

• University of Ottawa

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Tamara Klicek

• Child Adolesc Soc Work J
• Maynette Heyns

• Sifiso Shabangu

• Wallace Chigona
• Amir Syarifudin
• S. Ali Jadid Al Idrus
• Subki Subki
• Abdul Haris Rasyidi
• Hadia Alhaddad

• Huy Quang Truong

• Lucy A. LePeau
• Ting-Han Chang

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Peeling Off the Layers in Qualitative Research: A Book Review of Robert K. Yin’s Qualitative Research from Start to Finish

2019, The Qualitative Report

The title of Robert Yin’s (2016) book, Qualitative Research from Start to Finish, second edition, aptly summarizes what he intended to accomplish and did. The author’s writing and organizational style made for an enjoyable and engaging read given the subject matter. For the student, teacher, or researcher seeking an in-depth understanding of the entire research process, this book covers it all, including an exhaustive reference list on qualitative studies in varied disciplines. In keeping with his training as a social scientist, Yin incorporated his global research experience and understanding of human behavior to produce a book with something to offer researchers at various levels. I integrated my perspective as a doctorate student with intermediate experience in qualitative and quantitative research, highlighting what I found most useful.

Related Papers

Book Review

Tesfaye E R E S S O Gofe , Kenenisa Debela

In this article, we examine Sarah J. Tracy's (2013) book Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact, and First Edition. Typically, we carefully read the texts we are reviewing to make sure we don't misunderstand them and underline any portions we feel are particularly important. Next, we begin by describing the subject matter and target audience of the book (since having this information first may allow readers who are not interested to skip the rest of the review, and readers who are interested to raise their attention). Next, we describe the topic's development in terms of its depth of treatment and various content-related aspects. Then we highlight the aspects of the book that, in our opinion, have strengths and shortcomings. We attempt to provide a broad assessment of the book's value and potential use. In the end, we aim to make the form as long as necessary and polish it. We discovered that the Sarah J. Tracy book was beneficial for both beginners and seasoned scholars. Since the author has extensive experience with qualitative research, their publication may present a chance to advance our comprehension of qualitative research design. Furthermore, the book contains a complete, step-by-step explanation of the methodology for evaluating qualitative quality as well as how to gather, examine, and write qualitative data. It is a complete resource for the theoretical foundations and practical application of technique and it is meant to provide graduate students and advanced academics with enough methodological material to be relevant.

Mohammed Worku

Sumaya Usama

Khalid Arar

Katy Pearce

The Qualitative Report

biniam getnet

We review Sharan B. Merriam and Elizabeth J. Tisdell’s 2016 book, Qualitative Research: A Guide to Design and Implementation and then compare it to three other introductory to qualitative research texts. We found the Merriam and Tisdell book to be useful for both novice and more experienced researchers. The two authors are highly experienced in qualitative research and their book may provide an opportunity to improve our understanding of qualitative research design and implementation when compared to other different qualitative books.

Seda Khadimally

Crafting a research design is a daunting task no matter what research method the researcher chooses to work with. Qualitative research study stands as one of the most rigorous and demanding—yet rewarding—research paradigms when the researchers have a narrative, a story to portray in the literature-specific both for their readers and the scientific community. With the intent to describe not only their own personal journey, but also that of those who help unravel previously unexplained phenomena, qualitative researchers undertake a central theme and refer to the lived experiences of their study participants, starting from the data collection stage to analysis and interpretation of their findings. They ask the very fundamental question words (i.e., how, why, and what) with which the study builds up to a rather more profound level. With certain philosophical underpinnings, dimensions, and approaches, qualitative researchers find themselves engaged in a plethora of words, texts, and images (Creswell, 2007). Researchers’ ontological and epistemological positioning additionally informs their choices of methods and methodology, the latter which is a theoretical perspective acting as a bridge between the two (Creswell, 2007). Regarding this and other characteristics, it is also important to note that an impeccable qualitative study design cannot be achieved without researchers’ investing a long time and finances, the resources without which a seamless research study would not be produced. With the purpose of discussing such and several more distinctive characteristics, metaphors, various research paradigms, and considerations throughout, the purpose of this paper is to shed light to what a qualitative research study essentially is, how researchers deal with it throughout the research process, when it is appropriate to conduct one and when not, its advantages, disadvantages, and others. Each component that goes into the design of qualitative research study will be handled under close lens.

This seminar focuses on qualitative methods in the social sciences. It is structured as a survey course, exposing students to a range of issues, rather than intensive training in a single approach. The purpose of the seminar is twofold: First, to provide participants with a broad sense of qualitative research strategies, a better understanding of how to design and carry out research, an awareness of the different logics and trade-offs that distinguish methodologies and methods and an improved capacity to read and evaluate diverse qualitative social science research. Second, to write a dissertation proposal that will be competitive for various external dissertation fellowship funders—such as NSF, Fulbright, SSRC, etc.—and defensible before one’s dissertation committee.

BHAHARI A B D U L GANI

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

ngoc diep nguyen

Qualitative Market Research

Ahir Gopaldas

Jurij Fikfak

Dr. Awais H. Gillani

Athenkosi Mpemba

Nurse Researcher

Sharon Rallis

Seng Li Kareng

Sandra Mathison

Washington Santos

Introduction to Qualitative Research Methods (Masters/Doctoral)

Sarah Riforgiate

International Journal of Therapy and Rehabilitation

David Nicholls

Qualitative Research: Practices and Challenges

Elizabeth M Pope

Victoria Clarke

Qualitative Research Study Report

Evelyn Mayes

Olga Castiblanco

Martin J Packer

Brahmdev Paswan

Clemencia Navarro

John Schostak

Tanu Tandon

Judith Green

Advances in single-cell transcriptomics in animal research

• Yunan Yan 1 ,
• Senlin Zhu 1 ,
• Minghui Jia 1 ,
• Xinyi Chen 1 ,
• Wenlingli Qi 1 ,
• Fengfei Gu 1 , 2 ,
• Teresa G. Valencak 1 , 3 ,
• Jian-Xin Liu 1 &
• Hui-Zeng Sun   ORCID: orcid.org/0000-0001-5380-6030 1 , 2  

Journal of Animal Science and Biotechnology volume  15 , Article number:  102 ( 2024 ) Cite this article

71 Accesses

Metrics details

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health.

Introduction

Cells, the basic unit of life, vary widely in shape, size, and gene expression. It is vital to explore the different biological properties of individual cells in complex tissues to understand the process of life activities. The invention of flow cytometry at the end of the 1960s was a major breakthrough in both qualitative and quantitative measurements of cellular characteristics, as well as cell sorting [ 1 ]. It remains a widely applied strategy for single-cell analysis and isolation to date. In 1990, polymerase chain reaction was introduced to amplify DNA or RNA in individual cells, providing even more functional information [ 2 ], which further demonstrated that transcriptomic studies on individual cells were feasible. However, this method is limited by amplification bias and low throughput. The development of first-generation sequencing techniques made significant progress in molecular sequencing technology, but the costs remained high, and the sequencing throughput was low [ 3 , 4 ]. Next-generation sequencing technologies, such as “sequencing by synthesis” and accelerated parallel sequencing, have successfully overcome the above limitations, and RNA sequencing (RNA-Seq) is the best-known and most commonly used approach to date [ 5 ]. RNA-Seq involves total RNA extracted from organs, tissues, or a group of cells, obtaining average transcriptomic data while often masking specific information of individual cells in the population. To systematically study complex biological processes at higher resolution and reveal functional heterogeneity within tissues, researchers have developed a series of technologies, including multidimensional studies at the single-cell level. In 2009, Tang’s team developed the single-cell RNA sequencing (scRNA-Seq) method, enabling large-scale access to gene expression information from individual cells [ 6 ]. Since then, the use of scRNA-Seq has undergone rapid growth and evolution. In 2018, the journal “Science” ranked scRNA-Seq at the top of its list of the year’s most noteworthy technologies. Using scRNA-seq, international research projects, such as the Human Cell Atlas ( https://www.humancellatlas.org/ ), have been launched to identify all cell types involved in human development, health, and disease. Notably, a collaborative project consortium focusing on livestock, FarmGTEx ( http://farmgtex.org/ ), joined forces in the single-cell transcriptomic research of different livestock species by bringing together researchers from around the world to discover regulatory variants, molecular targets, and phenotype predictions at the single-cell level. Single-cell technologies are now emerging at multiple molecular levels, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics, at continuously enhanced resolution, accuracy, and efficiency, setting the stage for the practical application of single-cell technologies.

Single-cell RNA sequencing is a technology that can be used to comprehensively reveal the gene expression profiles of cells by sequencing transcripts in individual cells one by one [ 7 ]. It is currently the most mature technology for high-throughput functional resolution at the single-cell level. As shown in Fig.  1 , the process begins with the dissociation of fresh tissue into a single-cell suspension, followed by the selection of different single-cell capture strategies depending on the cell numbers. Manual operations (e.g., limited dilution, laser cutting, micromanipulation) are generally chosen for small cell numbers, but microfluidic and microwell techniques are used when the cell throughput is on the order of tens of thousands [ 7 ]. After single-cell capture, cDNA is obtained through reverse transcription, amplified, and then sequenced. Downstream analysis can be conducted after completion of the single-cell sequencing run. The analysis process typically involves 3 stages: primary analysis (base detection), secondary analysis (multiple isolation, alignment, and genetic identification), and tertiary analysis (data visualization and interpretation) [ 8 ]. As scRNA-Seq involves a sufficient number of active cells from fresh samples, it is impossible to perform it efficiently from frozen or indigestible samples. The emergence of modern single-nucleus RNA sequencing has solved this problem by inserting an extraction procedure to extract the nuclei of single cells before isolating and labelling the nuclei, making it possible to detect nuclear gene expression at the single-cell level [ 9 , 10 ]. However, this method is currently not applicable for immune cells, and sequencing sensitivity may be low due to the low abundance of mRNA in the nuclei of some cells. Overall, single-cell transcriptomics has allowed us to fully decipher cell types/subtypes and functions in almost all species by constructing a reference catalogue of gene expression encompassing cells throughout the body. In addition, single-cell transcriptomic data can be visualized in multiple dimensions via bioinformatics analyses, such as differential enrichment and proposed time-series analyses. These strategies enable the reconstruction and simulation of biological system operations.

The workflow of single-cell/nucleus RNA sequencing

Livestock produces more than 15% of the world’s high-quality protein. The Food and Agriculture Organization of the United Nations predicts that by 2050, up to 50% of animal food will be required to feed 10 billion people. Rapidly emerging bioscience biotechnologies have assisted animal science experts in producing even more animal products with limited resources. However, when facing new challenges, such as studying the biological factors determining economic traits, product quality, and animal health, it is crucial to use high-throughput methods, especially single-cell transcriptomics, to better investigate cellular functions and interactions. Relevant research has already been conducted in animal studies worldwide using scRNA-Seq (see Additional file 1 for details). For example, identifying and annotating various cell types in livestock can reveal their specific functions and interactions, which is crucial for obtaining an even better understanding of the cellular composition, differentiation status, and intercellular regulatory networks in animal tissues and organs. This technology provides high-resolution information on gene expression in individual cells, facilitating the discovery and comparison of expression differences between cells and allowing for the identification of key genes related to economically important characteristics, nutrient metabolism, and disease resistance. In addition, scRNA-Seq can enable the discovery of new genes and regulatory elements, particularly those that are poorly expressed or cell-specific. Thus, our understanding of livestock animal genomes is enhanced, and a new approach to functional studies is provided. The transcriptomic changes in cells can be tracked at different time points via scRNA-Seq, revealing the dynamic processes of cell development, differentiation, and functional transformation. This information is important for understanding tissue growth, development, regeneration, and immune responses in livestock.

Our paper provides an overview of the use of scRNA-Seq in livestock research in recent years. It also offers insights and references on the single-cell data analysis process, which can serve to make scRNA-Seq a more robust tool for research on animal husbandry and breed optimization.

Applications of single-cell transcriptomics for livestock husbandry

Exploration of nutrient metabolism and immune responses.

The gastrointestinal tract (GIT) serves as a vital organ for nutrient absorption, and the mucosal immune system in animals largely affects production performance, animal welfare, and the safety of livestock products. Through single-cell sequencing of the livestock GIT, we will be able to investigate the composition and distinct metabolic patterns of various cell types, and ultimately, we may provide precise targets for nutritional manipulation and enhance nutrient absorption in these animals. Among the GIT in ruminants, the rumen holds undeniable significance for nutrient digestion and absorption, particularly for short-chain fatty acids (SCFAs), which are absorbed by the rumen epithelium and can meet 70%–80% of the body’s energy requirements [ 11 ]. However, the rumen epithelium is a complex structure consisting of four layers of cells of different types and functions. This complexity has hindered mechanistic explorations of rumen epithelial absorption and turnover of specific nutrients. Earlier scRNA-Seq work has shown that spiny cells of the rumen epithelium play a crucial role in SCFAs [ 12 ], but more detailed cell subtypes were not identified due to factors such as the lack of identified marker genes. The application of the newly developed rumen single-cell suspension preparation method for scRNA-Seq has given rise to even more comprehensive insights into rumen epithelial cell types. Wu et al. [ 13 ] integrated and expanded specific marker genes for bovine cell lineages based on previous studies; 20,728 rumen epithelial cells were clustered into 18 rumen epithelial cell types, and the specific metabolic characteristics of each cell subtype were characterized. The ability of different epithelial cells to absorb SCFAs was explored by analysing the expression of genes encoding transporter proteins and scoring the related functional pathways. The findings revealed that channel-gap-like (Cg-like) spinous cell regulated by IL-17 was the preferred subtype for SCFA absorption in dairy cows [ 13 ]. The microbiota is essential for nutrient absorption in the GIT, and further joint mining of rumen metagenomic data characterized the interactions between Cg-like cells and fibre-degrading bacteria via structural domains of the secreted proteins (Fig.  2 ) [ 14 ]. Unfortunately, the study did not address ruminal nutrient uptake during different developmental stages. Studies on intestinal nutrient absorption in monogastric animals based on single-cell transcriptomics have primarily concentrated on cross-species comparisons of distinct cell types and functional analyses. Specifically, studies have examined significant interspecies differences and regional characteristics in hormone-secreting enteroendocrine cell [ 15 , 16 ]. For example, there is a gradual decrease in the number of these cells after the birth of piglets [ 17 ].

Functional mechanism of rumen short-chain fatty acids uptake

The immune system is vital for animal health and plays a pivotal role in defending against external hazards by maintaining physiological equilibrium. The application of scRNA-Seq for investigating livestock immunity has generated valuable insights. The advent of scRNA-Seq has contributed to an in-depth understanding of functional heterogeneity, molecular regulatory mechanisms, and infection responses among cellular subsets within central immune organs (e.g., the bursa and thymus) and peripheral immune organs (e.g., Pyle’s collecting lymph node) [ 18 , 19 , 20 ]. However, investigations on the bone marrow of domestic animals remain a major issue. Single-cell transcriptional profiling of various immune cells, such as peripheral blood monocytes and lymphocytes of domestic animals, underlines the homology and species specificity of immune gene expression, providing a molecular basis for understanding the immune response process in livestock [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. For example, single-cell transcriptomic data of peripheral blood mononuclear cells (PBMCs) following disease in domestic animals have been used to identify pathogenic immune cell subpopulations and transcriptional modules driving pathogenesis [ 32 ]. This approach has helped with identifying subpopulations and molecular markers of PBMCs in chickens infected with avian influenza and avian leukemia virus [ 33 , 34 , 35 ], unravelling key signalling factors associated with lipopolysaccharide-induced glycolipid metabolism abnormalities in cattle [ 22 ], and uncovering cell types and genes linked to periparturient immunosuppression in dairy cows [ 36 ]. In recent years, single-cell sequencing research has shifted towards porcine immune cells during piglet weaning stress, weaning diarrhoea, and intestinal inflammation, as it enables the tracking of immune cell heterogeneity during pathogenic microbial infections and host responses during diseases. For example, scRNA-Seq analysis revealed that tumor necrosis factor-α secreted by different immune cells could contribute to disease, as observed in studies investigating African swine fever and porcine intestinal inflammation [ 37 , 38 ]. These observations have opened new avenues for the development of novel vaccines and targeted therapies. In summary, scRNA-Seq studies allow for the precise identification and analysis of cell types and their interactions involved in the immune processes of domestic animals. They also allow for the examination of regulated genes, signalling pathways, transcription factors, and immune cell subpopulations with potential pathogenic functions. Current studies primarily focus on exploring immune mechanisms, however the potential of scRNA-Seq could be harnessed to evaluate the efficacy and response to immunotherapy. This involves concentrating on T cells, which are important players in the immune response and play key roles in animal health.

Investigation of cell factors for animal reproductive performance

The level of reproductive performance in livestock, which can directly affect productivity, depends on the ability to produce high-quality male and female gametes. The use of scRNA-Seq combined with proposed time-series analysis has allowed for the careful investigation of the dynamic mapping of cell fate transitions and gene expression changes during spermatogenesis in dairy goats, sheep, yaks, and premature piglets [ 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Through comprehensive analyses, researchers have identified specific marker genes and key signalling pathways related to germ cells within the testes of farm animals. Furthermore, studies have explored the homology and differences in male germ lines across different species, expanding our understanding of testicular development and spermatogenesis [ 40 , 46 , 47 ]. It has become evident that spermatogenesis relies on an ecological niche composed of testicular somatic cells. Consequently, studies have increasingly focused even more on single-cell sequencing analyses of these cells over the past few decades. However, due to variations in study samples and resolution, there are significant knowledge gaps related to the classification of testicular somatic cells, necessitating further investigation [ 40 , 42 , 43 , 44 , 46 ]. The growth and development of ovarian follicles are primarily regulated by granulosa cells. Through single-cell transcriptomics, the heterogeneity and differentiation pathways of follicular cells and granulosa cells in the ovaries of livestock animals have been characterized [ 48 , 49 , 50 , 51 , 52 ]. However, intercellular interactions within the ovary remain poorly understood. Recent studies in yaks, goats, and domesticated pigs have attempted to fill this gap [ 53 , 54 , 55 , 56 ]. For instance, Chen et al. discovered that porcine ovarian mural granulosa cells primarily engage in intercellular communication with cells of the same type, whereas ovarian theca granulosa cells predominantly emit signalling cues to different cell types [ 53 ]. Nevertheless, these studies are based on relatively small sample sizes, and more in-depth work is needed to corroborate their findings. High-precision mapping of germ cell genesis in livestock animals has enabled researchers to conduct in-depth studies on the screening, diagnosis, and treatment of reproduction-related disorders, such as sperm damage, abnormal oocyte development, and male infertility, in the progeny of interspecifically crossbred individuals [ 57 , 58 , 59 ]. The regulatory characteristics and interspecific differences of various types of cells in the gonads of livestock animals obtained at specific time points represent another research focus that can be assessed by single-cell transcriptomics [ 60 , 61 , 62 ]. To date, continuous developmental differentiation in livestock gonads is poorly understood. Only the gonadal differentiation of the chicken embryo has been analysed, and the discovery that the supporting cells during gonadal differentiation in the chicken embryo are derived from mesenchymal stem cells, in contrast to other vertebrates, has revolutionized our previous understanding of gonadal cell types [ 63 ]. Notably, noncoding RNAs significantly affect the regulation of germ cell proliferation and differentiation in livestock [ 40 , 64 ]. However, capturing these noncoding RNAs remains challenging, necessitating advancements in the application of this technology to enhance our understanding of livestock reproductive performance [ 65 ].

Single-cell transcriptomics has also been widely accepted as an efficient tool for investigating cell fate and transcriptional regulation during embryonic development. Studies employing scRNA-Seq have examined embryos at various life stages in livestock, such as cattle, pigs, chickens, sheep, and rabbits, thus making dynamic transcriptional profiles and cellular differentiation trajectories accessible within each germ layer of early-stage embryos [ 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]. For example, these analyses have uncovered species-specific features of early embryonic development (E5-E13) in pigs, revealing the differentiation of bovine trophoblast mononuclear cells into binucleated cells and the changes in differential gene expression and associated signalling pathways from the 8-cell to the mulberry embryo stage in sheep [ 67 , 68 , 71 ]. By leveraging scRNA-Seq, researchers can also analyse embryos with developmental abnormalities or those transferred from in vitro cultures. These investigations contribute to identifying potential causes of developmental abnormalities, offering valuable guidance for improving reproductive techniques such as in vitro fertilization and embryo transfer. It has been observed that incomplete activation of certain metabolic pathways leads to metabolic abnormalities. Epigenetic modification may be responsible for the significant effects on subsequent pregnancy and calving rates in females, and comparing the developmental transcriptional profiles of embryos transferred in vitro from different states [ 75 ] may provide new ideas for the treatment of embryonic developmental abnormalities.

Elucidation of genetics and developmental biology in livestock

Throughout the ontogeny of an organism, the transcriptome of certain cells undergoes substantial transformation. scRNA-Seq represents a novel approach for elucidating the dynamic patterns of gene expression during livestock genetic development and for revealing the regulatory networks governing developmental and evolutionary processes. The ultimate goal is to depict the trajectory of cellular fate transformations. Developmental mapping has been performed on the nervous systems and skeletal limbs of monogastric animals [ 74 , 76 , 77 , 78 ] while focusing on the nodes and key cell types involved in the developmental differentiation of organs or tissues. It is worth mentioning that aided by single-cell transcriptomics of embryonic limbs in poultry, studies have pinpointed pivotal regulators and signalling pathways driving limb differentiation formation along with their regionalization for the first time [ 79 , 80 ]. Among ruminants, rumen development has received significant attention. Scientists have constructed a comprehensive developmental landscape of thirteen metabolic tissues in ruminants [ 81 , 82 ], which encompasses a holistic comparison of heterogeneity within rumen epithelial cell types, cell functions and interacting microbiota between calves and adults. These findings suggested that calf epithelial progenitor cells exhibit greater differentiation potential and display greater activity in cell proliferation, differentiation, and innate immune responses [ 81 , 82 ]. Conversely, adult bovine cells show prominence in immune cells and have increased activity during antioxidant, adaptive immune, and fatty acid metabolism processes [ 81 , 82 ]. Similarly, studies have shown that the keratinization process during rumen epithelial cell development is associated with the cessation of keratinocyte differentiation at specific stages [ 12 , 83 ]. Previous studies have successfully identified key genes associated with rumen growth and development by scRNA-Seq of the rumen epithelium [ 84 , 85 ]. Overall, scRNA-Seq is assisting in the construction of a developmental evolutionary tree of livestock from the embryo to the mature individual. In single-cell transcriptional studies on animal genetic development, previously unidentified cell types are discovered, such as a specific class of endothelial cell clusters found in yak lungs, were revealed as potential factors in plateau acclimatization, and it was shown that calf-specific STOML3 +  cells have the potential to maintain the internal environment of the liver [ 82 , 86 ]. In addition, the exploration of unexpected functionalities harboured by common cell types has become possible, e.g., luminal epithelial cells could be involved in both lactation and immune responses [ 13 ]. It is worth mentioning that whole-body single-cell atlases have been generated for several mammalian species [ 87 , 88 , 89 , 90 , 91 ], with those in humans and mice covering the entirety of life stages [ 92 ]. However, to date, comprehensive whole-body single-cell atlases are still lacking for livestock animals.

The comparison of single-cell transcriptomic data from high- and low-productivity livestock has become an approach to identify genes and key cell types related to economic traits and has led to the selection and breeding of highly productive livestock breeds. Taking meat production traits as an example, the quantity and quality of meat production largely depend on the development of skeletal muscle. Analyses of the single-cell transcriptomics of skeletal muscle from fat and lean pigs could effectively identify key genes for adipocyte differentiation and epigenetic modifications [ 93 , 94 , 95 ]. By analysing the developmental trajectories of myogenic progenitor cells, it was observed that the skeletal muscles of lean livestock were more closely related to myogenic progenitor cells and more responsible for muscle development than the skeletal muscles of fat pigs, suggesting that mechanistic explorations of myogenesis can lead to the study of differences in genealogical cell differentiation [ 93 , 96 ]. The identification of specific liver cell clusters between laying and non-egg-laying populations in egg-laying birds may also provide new insights for improving egg production in the future [ 97 ]. More information can also be acquired from single-cell transcriptomic studies of niche economic traits such as fleece- and silk-producing traits. The former is limited by the asynchronous development of the wool bursa [ 98 ], and the latter is limited by the understudied organ of the silk gland; neither the wool bursa nor silk glands have been accurately analysed previously for specific cell types. The emergence of scRNA-Seq has enabled in-depth knowledge of the distribution of the composition of each cell type and the trajectories of key cells during the different developmental periods of the wool bursa and silk glands [ 99 , 100 , 101 , 102 ]. Additionally, new marker genes involved in the synthesis of velvet and silk proteins have been identified [ 100 ], which can improve the quality of cashmere and silk. For instance, the ACTA2 , COL1A1 , and CLCL6 genes may regulate cashmere fineness [ 103 ]. Although single-cell transcriptomic analyses have shown promise for improving genetic breeding, current research results are difficult to apply in animal production or have limited impact. Future research concentrating on diseases that are commonly associated with production should follow. For example, particular cell subpopulations associated with mastitis in dairy cows can be identified.

Emergence of disease models

The utilization of scRNA-Seq in model animals is highly important, especially in pigs, an animal commonly used as a medical/disease model [ 104 , 105 , 106 ]. By employing scRNA-Seq, researchers have gained profound insights into the composition of organelle types between pigs and men, thereby revealing the heterogeneity and conservatism of gene expression and regulatory mechanisms within the biological processes of interest and promoting in-depth biomedical research and widespread use of the domestic pig as an animal model. Previous studies have focused on individual tissues or organs (e.g., immune cells [ 15 , 18 , 23 , 24 , 27 , 60 ], lungs [ 107 ], liver, embryos [ 108 ], the reproductive system [ 46 , 47 , 109 ], and the digestive tract [ 16 , 110 ]) in pigs. These studies utilized cross-species analyses to identify conserved or specific gene modules in tissues or screened for cellular subpopulations and risk genes associated with disease. Moreover, they have revealed gene–trait associations through modelling or induction. Pig lung tissue data were characterized for both similarities and differences in cellular communication and expression patterns of respiratory virus receptors in each cell type of the lung compared with human lung tissue data [ 107 ]. A comprehensive porcine brain atlas has facilitated the identification of cell types and risk genes linked to eight neurological disorders, e.g., attention deficiency [ 111 ]. Data from the pancreas have suggested that TXNIP , a stress gene in acinar cells, could become a potential target for the treatment of diabetes [ 112 ]. The investigation of the gallbladder in neonatal piglets has shed light on the mechanisms of cystic fibrosis-related hepatobiliary disease [ 113 ]. For the first time, a pioneering study constructed a porcine single-cell atlas database that comprehensively describes the heterogeneity of cells among 20 tissues/organs in pigs [ 114 ], providing a global view of tissue differences between domestic pigs and humans. Rather than focusing on specific cell clusters, Wang et al. [ 114 ] emphasized distinct functions and typical markers of endothelial cells commonly involved in different tissues and suggested that the endothelium may interact with cells through the VEGF, PDGF, TGF-β, and BMP pathways. Microglia have also been noted to be highly conserved across species during evolution [ 114 ], strongly supporting the view of pigs as an invaluable data resource for research on human diseases. In the context of xenotransplantation, pigs are considered the most suitable donors for human organ transplantation. However, the occurrence of rejection has restricted the application of this technique [ 115 ]. The expression patterns of ten genes associated with human immunobiological incompatibility and dysregulation of coagulation have been obtained across different cell types in pigs [ 107 ]. This discovery holds the potential to enhance the immunocompatibility of porcine xenotransplantation in the future through targeted genetic engineering, thereby improving survival after organ transplantation [ 116 ]. However, is important to note that the current study does not adequately address the influence of physiological states and manipulation on the samples. These factors may affect the results by leading to differing numbers of captured cells and altered cell typing. Future research should address this crucial aspect.

In addition to pigs, chickens and rabbits serve as valuable disease models for constructing single-cell reference maps. Differential cellular components can be identified by comparing “disease maps” with “normal maps”, which may in turn predict molecular disease mechanisms. For example, rabbits have been used to study mammalian cardiac contraction, proto-gut embryonic development, proliferative vitreoretinopathy, and hyperlipidaemia-induced spongiosis [ 72 , 117 , 118 , 119 ]. Similarly, chickens have proven valuable for investigating retinal development, hearing damage, and the mechanism underlying melatonin-related weight loss [ 120 , 121 , 122 ]. Among wild animals, antlers, a unique mammalian appendage capable of complete natural regeneration, have been demonstrated to grow similarly to long bones in humans. Leveraging the potential of scRNA-Seq, scientists have revealed key cell types and differentiation trajectories involved in antler regeneration [ 123 , 124 , 125 ], opening new avenues for exploring mammalian organ regeneration and organ damage repair. Future research should address the current limitations in genome annotations for reindeer [ 123 , 124 , 125 ].

Experience in single-cell transcriptomic data analysis

The optimal sample size for single-cell transcriptomic research.

Increases in sample size and sequencing depth could result in the discovery of new and rare cell types, encompassing both previously unrecognized entities and those present in tissues where they have not been detected before [ 126 ]. For instance, among 42,182 cells from three forestomach samples, T helper 17 and epithelial stem and progenitor cells were first identified in dairy cows [ 13 ]. A total of 29,231 individual cells were obtained from three samples of porcine adipose tissue, from which subtypes of cells transformed from endothelial to mesenchymal cells were distinguished [ 114 ]. Compared to studies in livestock, there are more instances of new cell type identification in humans and model organisms, such as a new specialized uroepithelial cell type discovered among 25,307 cells across three bladder samples in humans and mice [ 127 ] and four clusters of nonsensory epithelial cells of the ampulla identified from four stages of mouse crista ampulla samples [ 128 ]. Based on the aforementioned examples of novel cell discoveries, it is evident that employing three or more biological replicates and analysing over 20,000 cells significantly enhances the likelihood of uncovering new cell types. Considering the spatial positioning of tissue sampling and individual differences in cell dissociation, a larger number of biological samples further provides advantages in elucidating the comprehensive distribution of cell types. However, due to the high cost of reagents and sequencing (a total of 10–15 K RMB per sample), excessive sampling may cause more significant input and divert focus from validation experiments. As an illustration, in cell atlas studies, larger biological sample numbers are preferable for identifying cell types at higher resolution [ 21 , 44 , 102 ]. However, if the focus is solely on identifying which cell type is responsive to an experimental treatment, one biological replicate may be effective, as that one cell type typically consists of more than 20 cells/transcriptomes [ 129 , 130 ]. In addition, time-series designs generally involve fewer biological replicates than two-group comparisons [ 36 , 129 ].

With the development of single-cell sequencing technologies, an eight-channel microfluidic system that can capture up to 10,000 cells per channel has been developed, allowing for simultaneous detection of sample cell counts ranging from 50,000 to 800,000 cells in a single run [ 131 ], making a single sample cover a large number of cell/transcriptomic replicates. Bioinformatics tools allow us to enhance and refine the power of single-cell data analysis through the interpretation of transcriptome data. Deconvolution algorithms are now being employed to dissect bulk transcriptomic data to the single-cell level [ 132 ], increasing the statistical power of bulk transcriptomics in single-cell experiments. In addition to deconvolution algorithms, a framework for integrating single-cell RNA sequencing, epigenomic SNP-to-gene maps and genome-wide data enables the identification of target cell types based on the strong statistical power of GWAS data. Currently, emerging large-scale single-cell pre-trained models with tens of millions of cells, such as scGPT, can empower the tasks of our small datasets, including cell classification, network inference, and transcription factor perturbation analysis [ 133 ]. Taken together, researchers need to determine the amount of biological replication that is sufficient to capture biological variability and provide statistically significant results while considering the cost of experiments and the complexity of data analysis. Bulk sequence data and large-scale pre-trained models could be used to enhance the statistical power of single cells.

The number of cell clusters that optimally match the real situation

In brief, the number of cell types is determined by clustering algorithms; specifically, after obtaining single-cell datasets, the cells can be categorized into 2–5 major clusters based on automatic annotation and positional variance in the dimensionality reduction results. With major biological classifications, multiple higher resolutions could be used to identify the more specific cell types corresponding to the basic biological knowledge of a given tissue (Fig.  3 ). As an example, the large intestine comprises diverse epithelial cell types arranged in distinct configurations. Absorptive enterocytes predominate, lining the villi and crypts and specializing in the uptake of water, electrolytes, and nutrients from the luminal contents [ 134 ]. Goblet cells interspersed throughout secrete protective mucus, creating a mucosal barrier against pathogens and toxins [ 135 ]. Enteroendocrine cells release hormones to regulate digestive functions, while Paneth cells, predominantly found in the small intestine but also present in smaller numbers in the large intestine, contribute to innate immunity through the secretion of antimicrobial peptides [ 136 , 137 ]. Mesenchymal cells, immune cells, endothelial cells and neural cells are present in the large intestine. However, the epithelium of the intestine is usually composed of a single layer of columnar epithelial cells, such as those with 1 or 2 clusters, and some cuboidal cells, such as goblet cells, with 2 or 3 clusters [ 21 ]. Different resolution values should be tested to provide the finest demonstration of the known cell types within each tissue. Furthermore, based on the objectives and experimental design, the resolution and number of cell clusters may vary while maintaining biological features. A greater number of clusters is essential for key subcluster identification. For instance, specific macrophage and conventional dendritic cell subsets were identified as key mediators of cellular cross-talk in the colon tumour microenvironment from 54,285 cells divided into 40 clusters [ 138 ].

A summary of the common cell types identified in the gastrointestinal tract of livestock

Moreover, clustering algorithms should also be essential for generating the number of cell clusters. First, suitable algorithms should be chosen based on the cell numbers. The K-nearest neighbours algorithm is typically applied to datasets with small cell numbers, with the number of clusters predetermined before clustering. Moreover, the Louvain and Leiden algorithms are normally used for single-cell datasets with more than 100,000 cells, with the number of clusters determined by the resolution set by the analyser [ 139 , 140 ]. The number of clusters can be determined by intra- and intercluster similarity, community detection, eigenvector-based metrics, and stability [ 141 ]. However, these four predominant clustering methods lack systematic evaluation strategies for estimating the number of cell types. Therefore, stability-based approaches for estimating the number of cell types, such as scCCESS, have been proposed [ 141 ], enabling the estimation of the number of cell clusters without the need for researcher observation. Some bioinformatic visualization tools also help us estimate the number of clusters. The Clustree algorithm [ 142 ] employs a bottom-up hierarchical clustering approach for cluster information at different resolutions, which facilitates our selection of a suitable resolution by incorporating biological insights.

A well-formulated strategy for cell type identification

The identification of cell types is a fundamental and essential step in the analysis of single-cell transcriptome data and is accompanied by dimension reduction and clustering [ 143 ]. Generally, the number of cell types and the annotation results should be consistent with the corresponding physiological characteristics.

The common cell types identified in the GIT of livestock are summarized in Fig.  3 . The clusters can be classified into five main cell groups: epithelial cells, endothelial cells, stem cells, immune cells and others. We noticed that the number of clusters for immune cell types in the forestomach is generally lower than that in the intestine, while fibroblasts and muscle cells were more diverse. In the intestine, specifically in the small intestine, B cells and macrophages are more abundant. The cluster numbers correspond to substantial peristalsis and mechanical wear in the foregut, as well as the clear ability of the small intestine to eliminate exogenous invading microorganisms [ 144 ]. Collectively, these cells form a complex biological system responsible for digestion, nutrient absorption, protection against pathogens, and tissue repair and regeneration. Based on the previous cell group summary, we propose three approaches that are beneficial for identifying cell types: (1) collecting biological background information in a given situation; (2) referencing standard cell type names and databases; and (3) assisting in cell type identification with the help of bioinformatic software. It is necessary to summarize basic knowledge in the process of annotating cell types, including biological knowledge and standards/universal naming rules. Regarding how many types of cells should be classified, an effective method is to summarize the physiological characteristics of animal tissues through the accumulation of relevant experimental data and scientific research results [ 143 ]. For example, according to basic knowledge, the cell types present in the epithelial tissue of the rumen of dairy cows consist of four layers of epithelial cells (basal, spiny, granular, and stratum corneum; stratum corneum cells are dead cells and will not be captured), immune cells residing in the tissue, endothelial cells in the connective tissue, smooth muscle cells, and fibroblasts [ 13 ]. Therefore, the presence of these cell types can be used as a reference when determining whether the number of cell types is accurate and whether the current clustering parameters should be adjusted. In animal science research, genes related to animal production or health traits may have corresponding biological significance to cell type. For example, Wu et al. [ 13 ] found a new subcluster of rumen epithelial cells called channel gap-like spinous cells in dairy cows via an in-depth analysis of solute carrier gene expression. Additionally, Wang et al. identified EndMT cells as critical for endothelial-to-mesenchymal transition (EndMT) based on gene expression in stromal and endothelial cells [ 114 ].

To make the cell type identification results more biologically meaningful, the integration of public databases and bioinformatics analysis software should be considered. Public databases of single-cell transcriptome data, such as the Human Cell Atlas, incorporate marker genes for cell type identification and standard or universal cell type nomenclature rules [ 145 ]. In animal science research, the cattle cell atlas constructed by Wu et al. [ 82 ] and the pig cell atlas constructed by Wang et al. [ 114 ] are also of high reference value, although they do not cover all tissue types. A unified system will improve efficiency and applicability for other studies [ 146 ]. Clustree software can be used for determining whether the number of cell types obtained from clustering is appropriate [ 142 ]. Software that automatically annotates cell types can also assist in identifying cell types, e.g., Cellhint [ 146 ], CellTypist [ 147 ], and SingleR [ 148 ]. Software programs for the automated annotation of cell types have been developed based on mathematical models and are specific for annotating certain cell types. For example, Cellhint is used to annotate human cells, and CellTypist is only used to annotate human immune cells. Notably, bioinformatics analysis should be performed manually to ensure the biological significance of the annotated results.

Currently, single-cell transcriptome sequencing technology is cumbersome and costly in terms of experimental steps. Professional experimentation and specialized software support are required for all aspects, from sample preparation to data analysis. Moreover, the bias of cell capture during the sequencing process may lead to inaccurate results, which is mainly attributed to the possibility of missing or detecting specific types of cells at low frequencies. Moreover, bioinformatics tools and algorithms are still suboptimal, making it challenging to extract meaningful information from the massive amount of data. In the future, it will be necessary to simplify and optimize the process of single-cell technology to reduce its cost and operational difficulty and to make it easier to apply in different fields. The accuracy and efficiency of cell capture techniques should be improved to reflect the diversity of cellular communities. In the meantime, techniques for cell capture and mRNA enrichment in prokaryotes are still under development. The implementation of microbiome single-cell transcriptome technology will substantially broaden the application landscape of single-cell technology [ 149 ].

One of the greatest limitations of scRNA-Seq is the loss of spatial information due to tissue dissociation. Spatial transcriptomics is a more recently developed methodology that allows for the localization and construction of a cellular expression map with a spatial dimension, which is not achievable with scRNA-Seq. Coupled with scRNA-Seq, this approach can stereoscopically demonstrate the heterogeneous distribution and functional localization of individual cells and reveal spatial differences in cells during evolutionary development or disease onset. However, the application of this approach to domestic animals is still relatively rare. Only studies on the spatiotemporal transcriptional profiling of chicken heart development have been reported, but have revealed the pathways through which cardiac cell differentiation and morphological changes occur at the same time as spatially restricted regulatory programmes [ 150 ]. The complexity of life activities is difficult to determine by single-modality omics methods; therefore, single-cell multi-omics technologies, including single-cell transcriptome as the core analysis combined with genome, proteome and metabolome analyses, are inevitable [ 151 ] and will be able to deepen the understanding of cell type and state in greater dimension. For example, the dynamic changes of single-cell RNA/ATAC sequencing in porcine embryonic skeletal muscle were consistent with the activity of different cell type-specific transcription factors, which helped identify key regulators of muscle formation after integrative analysis [ 152 ].

Sequencing of certain animals or organs can be challenging due to their limited availability, difficulty in manipulation, or ethical concerns. Organoids, 3D cultures developed from stem cells that closely resemble the source tissue [ 153 ], are valuable in vitro tools for organoid single-cell transcriptomics in such cases. Integrating organoids with scRNA-Seq can allow more precise comparisons between organoids and source tissues in terms of cell types and gene expression patterns; moreover, through real-time monitoring of transcriptome changes in single cells of organoids cultured under different treatments, it is possible to provide potential cellular and molecular phenotypic information on complex traits, such as feed efficiency and disease resistance. Organoid models have been constructed for various livestock species [ 154 , 155 , 156 ] and are mainly used to study organ development, host–microbe interactions, cellular nutrient metabolism mechanisms, and drug toxicity. Zhang et al. [ 83 ] utilized a significant quantity of butyric acid to stimulate rumen organoids, and the organoids showed noticeable keratinization and significant increases in the expression levels of related keratinization genes. These findings were confirmed by single-cell sequencing, demonstrating the potential for combining single-cell transcriptomes and organoids for the study of biological mechanisms.

Single-cell transcriptomics reveals the role of different cells in organisms. However, it is important to recognize that in multicellular organisms, the functionality of the organism is dependent on synergistic interactions between different cells. Thus, single-cell transcriptome-based research should consider the relationships between cell lineages and the interactions between cells. This might also prevent an overemphasis on the functions of individual cells and might rather ensure that the influence of the hostile environment on cell development and function is not ignored. Using a multidimensional approach to single-cell transcriptome studies, such as cell communication analysis, the interaction network between cells can be reconstructed and predicted, revealing signalling and regulatory mechanisms and providing a more accurate reference for studying overall organism and tissue function. It is also possible to validate the accuracy of single-cell sequencing data through various biological experimental means, ensuring a more reliable assessment of research results.

Conclusions

Single-cell transcriptomics has enabled the analysis of heterogeneity in gene expression in tissues and organs at the single-cell level. This advancement has provided a robust framework for identifying cell types, discovering rare cell populations, screening marker genes, exploring cellular developmental trajectories, and analysing cellular functions. ScRNA-Seq has also been extensively applied in animal science, particularly in studies involving domestic species of high economic importance. Using scRNA-Seq, animal researchers have investigated topics such as nutritional regulation, metabolic mechanisms, spermatogenesis, embryonic development, genetic breeding, and disease mechanisms in livestock. Through the construction of single-cell atlases for different animal organs, researchers have revealed the intricate cellular heterogeneity and gene expression variability present at the individual, organ, and tissue levels. This approach has facilitated deeper mechanistic investigations at the molecular level, shedding light on the underlying genetic basis of important traits and disease pathogenesis. Consequently, scRNA-Seq has made substantial and significant contributions to advancing our understanding of animal biology and is ultimately poised to enhance the quality of livestock products to meet increasing consumer demands. When analysing single-cell transcriptomic data, the optimal sample size should be determined based on the biological variation, statistical significance, cost and complexity of the data analysis. Accurate cell clustering and cell type annotation should consider background knowledge, as well as appropriate and stable algorithms. Currently, scRNA-Seq still faces several great challenges, ranging from sample processing to data analysis, and the integration of novel experimental methodologies and sequencing technologies is needed to probe and elucidate the intricate regulatory networks and causal relationships among diverse biomolecules in a multidimensional manner. Through continued innovation and interdisciplinary collaboration, single-cell transcriptomics holds the promise of unlocking new frontiers in animal research and fostering sustainable advancements in agricultural productivity and animal health.

Availability of data and materials

Not applicable.

Abbreviations

Channel-gap-like

Endothelial-to-mesenchymal transition

Gastrointestinal tract

Peripheral blood mononuclear cells

RNA-Sequencing

Short-chain fatty acid

• Single-cell RNA sequencing

Otto O, Rosendahl P, Mietke A, Golfier S, Herold C, Klaue D, et al. Real-time deformability cytometry: On-the-fly cell mechanical phenotyping. Nat Methods. 2015;12(3):199–202. https://doi.org/10.1038/nmeth.3281 .

Article   CAS   PubMed   Google Scholar  

Brady G, Barbara M, Iscove NN. Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol Cell Biol. 1990;2:17–25. https://wwwlabs.uhnresearch.ca/labs/iscove/MMCB90.pdf .

Maxam AM, Gilbert WA. A new method for sequencing DNA. Proc Natl Acad Sci. 1977;74(2):560–4. https://doi.org/10.1073/pnas.74.2.560 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sanger F, Coulson AR. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J Mol Biol. 1975;94(3):441–8. https://doi.org/10.1016/0022-2836(75)90213-2 .

Stark R, Grzelak M, Hadfield J. RNA sequencing: The teenage years. Nat Rev Genet. 2019;20(11):631–56. https://doi.org/10.1038/s41576-019-0150-2 .

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–82. https://doi.org/10.1038/nmeth.1315 .

Potter SS. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol. 2018;14(8):479–92. https://doi.org/10.1038/s41581-018-0021-7 .

Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. https://doi.org/10.15252/msb.20188746 .

Article   PubMed   PubMed Central   Google Scholar  

Bakken TE, Hodge RD, Miller JA, Yao Z, Nguyen TN, Aevermann B, et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE. 2018;13(12):e209648. https://doi.org/10.1371/journal.pone.0209648 .

Article   Google Scholar  

Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol. 2019;30(1):23–32. https://doi.org/10.1681/ASN.2018090912 .

Flint HJ, Bayer EA. Plant cell wall breakdown by anaerobic microorganisms from the mammalian digestive tract. Ann N Y Acad Sci. 2008;1125:280–8. https://doi.org/10.1196/annals.1419.022 .

Yuan Y, Sun DM, Qin T, Mao SY, Zhu WY, Yin YY, et al. Single-cell transcriptomic landscape of the sheep rumen provides insights into physiological programming development and adaptation of digestive strategies. Zool Res. 2022;43(4):634–47. https://doi.org/10.24272/j.issn.2095-8137.2022.086 .

Wu JJ, Zhu S, Gu F, Valencak TG, Liu JX, Sun HZ. Cross-tissue single-cell transcriptomic landscape reveals the key cell subtypes and their potential roles in the nutrient absorption and metabolism in dairy cattle. J Adv Res. 2021;37:1–18. https://doi.org/10.1016/j.jare.2021.11.009 .

Xue MY, Wu JJ, Xie YY, Zhu SL, Zhong YF, Liu JX, et al. Investigation of fiber utilization in the rumen of dairy cows based on metagenome-assembled genomes and single-cell RNA sequencing. Microbiome. 2022;10:11. https://doi.org/10.1186/s40168-021-01211-w .

Li H, Wang X, Wang Y, Zhang M, Hong F, Wang H, et al. Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium. Cell Regen. 2022;11:19. https://doi.org/10.1186/s13619-022-00118-7 .

Wiarda JE, Becker SR, Sivasankaran SK, Loving CL. Regional epithelial cell diversity in the small intestine of pigs. J Anim Sci. 2023;101:skac318. https://doi.org/10.1093/jas/skac318 .

Article   PubMed   Google Scholar  

Meng Q, Chen L, Xiong B, Kang B, Zhang P, Tang S, et al. Single-cell transcriptome sequencing and proteomics reveal neonatal ileum dynamic developmental potentials. mSystems. 2021;6(5):e0072521. https://doi.org/10.1128/mSystems.00725-21 .

Gu W, Madrid DMC, Joyce S, Driver JP. A single-cell analysis of thymopoiesis and thymic iNKT cell development in pigs. Cell Rep. 2022;40:111050. https://doi.org/10.1016/j.celrep.2022.111050 .

Shah AU, Li Y, Ouyang W, Wang Z, Zuo J, Shi S, et al. From nasal to basal: Single-cell sequencing of the bursa of Fabricius highlights the IBDV infection mechanism in chickens. Cell Biosci. 2021;11:212. https://doi.org/10.1186/s13578-021-00728-9 .

Wiarda JE, Trachsel JM, Sivasankaran SK, Tuggle CK, Loving CL. Intestinal single-cell atlas reveals novel lymphocytes in pigs with similarities to human cells. Life Sci Alliance. 2022;5(10):e202201442. https://doi.org/10.26508/lsa.202201442 .

Chen D, Sun J, Zhu J, Ding X, Lan T, Wang X, et al. Single cell atlas for 11 non-model mammals, reptiles and birds. Nat Commun. 2021;12:7083. https://doi.org/10.1038/s41467-021-27162-2 .

Gao Y, Li J, Cai G, Wang Y, Yang W, Li Y, et al. Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide. BMC Genomics. 2022;23:338. https://doi.org/10.1186/s12864-022-08562-0 .

Herrera-Uribe J, Wiarda JE, Sivasankaran SK, Daharsh L, Liu H, Byrne KA, et al. Reference transcriptomes of porcine peripheral immune cells created through bulk and single-cell RNA sequencing. Front Genet. 2021;12:689406. https://doi.org/10.3389/fgene.2021.689406 .

Jiao A, Zhang C, Wang X, Sun L, Liu H, Su Y, et al. Single-cell sequencing reveals the evolution of immune molecules across multiple vertebrate species. J Adv Res. 2024;55:73–87. https://doi.org/10.1016/j.jare.2023.02.017 .

Kotliar D, Lin AE, Logue J, Hughes TK, Khoury NM, Raju SS, et al. Single-Cell profiling of ebola virus disease in vivo reveals viral and host dynamics. Cell. 2020;183(5):1383–401. https://doi.org/10.1016/j.cell.2020.10.002 .

Li Z, Sun C, Wang F, Wang X, Zhu J, Luo L, et al. Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single-cell sequencing. Clin Transl Med. 2022;12:e689. https://doi.org/10.1002/ctm2.689 .

Li J, Xu Y, Zhang J, Zhang Z, Guo H, Wei D, et al. Single-cell transcriptomic analysis reveals transcriptional and cell subpopulation differences between human and pig immune cells. Genes Genomics. 2024;46(3):303–22. https://doi.org/10.1007/s13258-023-01456-9 .

Patel RS, Tomlinson JE, Divers TJ, Van de Walle GR, Rosenberg BR. Single-cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet + B cells. BMC Biol. 2021;19:13. https://doi.org/10.1186/s12915-020-00947-5 .

Tang Q, Iyer S, Lobbardi R, Moore JC, Chen H, Lareau C, et al. Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single-cell resolution using RNA sequencing. J Exp Med. 2017;214(10):2875–87. https://doi.org/10.1084/jem.20170976 .

Winkler ES, Shrihari S, Hykes BJ, Handley SA, Andhey PS, Huang YS, et al. The intestinal microbiome restricts alphavirus infection and dissemination through a bile acid-type I IFN signaling axis. Cell. 2020;182(4):901–18. https://doi.org/10.1016/j.cell.2020.06.029 .

Zhu L, Yang P, Zhao Y, Zhuang Z, Wang Z, Song R, et al. Single-cell sequencing of peripheral mononuclear cells reveals distinct immune response landscapes of COVID-19 and influenza patients. Immunity. 2020;53(3):685–96. https://doi.org/10.1016/j.immuni.2020.07.009 .

Stubbington M, Rozenblatt-Rosen O, Regev A, Teichmann SA. Single-cell transcriptomics to explore the immune system in health and disease. Science. 2017;358(6359):58–63. https://doi.org/10.1126/science.aan6828 .

Dai M, Zhu S, An Z, You B, Li Z, Yao Y, et al. Dissection of key factors correlating with H5N1 avian influenza virus driven inflammatory lung injury of chicken identified by single-cell analysis. PLoS Pathog. 2023;19(10):e1011685. https://doi.org/10.1371/journal.ppat.1011685 .

Dai M, Zhao L, Li Z, Li X, You B, Zhu S, et al. The transcriptional differences of avian CD4 + CD8 + double-positive T cells and CD8 + T cells from peripheral blood of ALV-J infected chickens revealed by Smart-Seq2. Front Cell Infect Microbiol. 2021;11:747094. https://doi.org/10.3389/fcimb.2021.747094 .

Qu X, Li X, Li Z, Liao M, Dai M. Chicken peripheral blood mononuclear cells response to avian leukosis virus subgroup j infection assessed by single-cell RNA sequencing. Front Microbiol. 2022;13:800618. https://doi.org/10.3389/fmicb.2022.800618 .

Gu F, Zhu S, Tang Y, Liu X, Jia M, Malmuthuge N, et al. Gut microbiome is linked to functions of peripheral immune cells in transition cows during excessive lipolysis. Microbiome. 2023;11:40. https://doi.org/10.1186/s40168-023-01492-3 .

Tang W, Zhong Y, Wei Y, Deng Z, Mao J, Liu J, et al. Ileum tissue single-cell mRNA sequencing elucidates the cellular architecture of pathophysiological changes associated with weaning in piglets. BMC Biol. 2022;20:123. https://doi.org/10.1186/s12915-022-01321-3 .

Zheng Y, Li S, Li SH, Yu S, Wang Q, Zhang K, et al. Transcriptome profiling in swine macrophages infected with African swine fever virus at single-cell resolution. Proc Natl Acad Sci U S A. 2022;119(19):e2201288119. https://doi.org/10.1073/pnas.2201288119 .

Huang L, Zhang J, Zhang P, Huang X, Yang W, Liu R, et al. Single-cell RNA sequencing uncovers dynamic roadmap and cell-cell communication during buffalo spermatogenesis. iScience. 2022;26(1):105733. https://doi.org/10.1016/j.isci.2022.105733 .

Tian Y, Sun P, Liu WX, Shan LY, Hu YT, Fan HT, et al. Single-cell RNA sequencing of the Mongolia sheep testis reveals a conserved and divergent transcriptome landscape of mammalian spermatogenesis. FASEB J. 2022;36(6):e22348. https://doi.org/10.1096/fj.202200152R .

Lingkai Z, Ming G, Zidong L, Ruifang L, Yi Z, Taiyong Y, et al. Single-cell RNA-seq analysis of testicular somatic cell development in pigs. J Genet Genomics. 2022;49(11):1016–28. https://doi.org/10.1016/j.jgg.2022.03.014 .

Article   CAS   Google Scholar  

Wang X, Pei J, Xiong L, Guo S, Cao M, Kang Y, et al. Single-cell RNA sequencing reveals atlas of yak testis cells. Int J Mol Sci. 2023;24(9):7982. https://doi.org/10.3390/ijms24097982 .

Wu Y, Guo T, Li J, Niu C, Sun W, Zhu S, et al. The transcriptional cell atlas of testis development in sheep at pre-sexual maturity. Curr Issues Mol Biol. 2022;44(2):483–97. https://doi.org/10.3390/cimb44020033 .

Yang H, Ma J, Wan Z, Wang Q, Wang Z, Zhao J, et al. Characterization of sheep spermatogenesis through single-cell RNA sequencing. FASEB J. 2021;35(2):e21187. https://doi.org/10.1096/fj.202001035RRR .

Yu XW, Li TT, Du XM, Shen QY, Zhang MF, Wei YD, et al. Single-cell RNA sequencing reveals atlas of dairy goat testis cells. Zool Res. 2021;42(4):401–5. https://doi.org/10.24272/j.issn.2095-8137.2020.373 .

Zhao Y, Yang C, Du Z. Integrated single cell transcriptome sequencing analysis reveals species-specific genes and molecular pathways for pig spermiogenesis. Reprod Domest Anim. 2023;58(12):1745–55. https://doi.org/10.1111/rda.14493 .

Zhang LK, Ma HD, Guo M, Wang L, Zheng Y, Wu XD, et al. Dynamic transcriptional atlas of male germ cells during porcine puberty. Zool Res. 2022;43(4):600–3. https://doi.org/10.24272/j.issn.2095-8137.2022.037 .

Li D, Ning C, Zhang J, Wang Y, Tang Q, Kui H, et al. Dynamic transcriptome and chromatin architecture in granulosa cells during chicken folliculogenesis. Nat Commun. 2022;13:131. https://doi.org/10.1038/s41467-021-27800-9 .

Ge T, Wen Y, Li B, Huang X, Jiang S, Zhang E. Single-cell sequencing reveals the reproductive variations between primiparous and multiparous Hu ewes. J Anim Sci Biotechnol. 2023;14:144. https://doi.org/10.1186/s40104-023-00941-1 .

Li Z, Wang J, Zhao Y, Ma D, Zhao M, Li N, et al. ScRNA-seq of ovarian follicle granulosa cells from different fertility goats reveals distinct expression patterns. Reprod Domest Anim. 2021;56(5):801–11. https://doi.org/10.1111/rda.13920 .

Li Z, Song X, Yin S, Yan J, Lv P, Shan H, et al. Single-cell RNA-Seq revealed the gene expression pattern during the in vitro maturation of donkey oocytes. Genes (Basel). 2021;12(10):1640. https://doi.org/10.3390/genes12101640 .

Liu Z, Zhang G, Deng M, Yang H, Pang J, Cai Y, et al. Inhibition of lysine-specific histone demethylase 1A results in meiotic aberration during oocyte maturation in vitro in goats. Theriogenology. 2020;143:168–78. https://doi.org/10.1016/j.theriogenology.2019.12.011 .

Chen N, Zhang Y, Tian Y, Wu S, Gao F, Yuan X. Deciphering cellular heterogeneity and communication patterns in porcine antral follicles by single-cell RNA sequencing. Animals (Basel). 2023;13(19):3019. https://doi.org/10.3390/ani13193019 .

Ghafouri F, Sadeghi M, Bahrami A, Naserkheil M, Dehghanian RV, Javanmard A, et al. Construction of a circRNA- lincRNA-lncRNA-miRNA-mRNA ceRNA regulatory network identifies genes and pathways linked to goat fertility. Front Genet. 2023;14:1195480. https://doi.org/10.3389/fgene.2023.1195480 .

Pei J, Xiong L, Guo S, Wang X, La Y, Chu M, et al. Single-cell transcriptomics analysis reveals a cell atlas and cell communication in yak ovary. Int J Mol Sci. 2023;24(3):1839. https://doi.org/10.3390/ijms24031839 .

Pei J, Xiong L, Guo S, Wang X, Bao P, Wu X, et al. A single-cell transcriptomic atlas characterizes cell types and their molecular features in yak ovarian cortex. FASEB J. 2023;37(1):e22718. https://doi.org/10.1096/fj.202201176RR .

Yang CX, Song ZQ, Pei S, Yu XX, Miao JK, Liang H, et al. Single cell RNA-seq reveals molecular pathways altered by 7, 12-dimethylbenz[a]anthracene treatment on pig oocytes. Theriogenology. 2020;157:449–57. https://doi.org/10.1016/j.theriogenology.2020.08.020 .

Mipam T, Chen X, Zhao W, Zhang P, Chai Z, Yue B, et al. Single-cell transcriptome analysis and in vitro differentiation of testicular cells reveal novel insights into male sterility of the interspecific hybrid cattle-yak. BMC Genomics. 2023;24:149. https://doi.org/10.1186/s12864-023-09251-2 .

Zhang T, Sun P, Geng Q, Fan H, Gong Y, Hu Y, et al. Disrupted spermatogenesis in a metabolic syndrome model: The role of vitamin A metabolism in the gut-testis axis. Gut. 2022;71(1):78–87. https://doi.org/10.1136/gutjnl-2020-323347 .

Chen M, Long X, Hao F, Kang J, Wang N, Wang Y, et al. Integration of single-cell transcriptome and chromatin accessibility of early gonads development among goats, pigs, macaques, and humans. Cell Rep. 2022;41(5):111587. https://doi.org/10.1016/j.celrep.2022.111587 .

Soto DA, Ross PJ. Similarities between bovine and human germline development revealed by single-cell RNA sequencing. Reproduction. 2021;161(3):239–53. https://doi.org/10.1530/REP-20-0313 .

Jung KM, Seo M, Han JY. Comparative single-cell transcriptomic analysis reveals differences in signaling pathways in gonadal primordial germ cells between chicken ( Gallus gallus ) and zebra finch ( Taeniopygia guttata ). FASEB J. 2023;37(1):e22706. https://doi.org/10.1096/fj.202201569R .

Estermann MA, Williams S, Hirst CE, Roly ZY, Serralbo O, Adhikari D, et al. Insights into gonadal sex differentiation provided by Single-Cell transcriptomics in the chicken embryo. Cell Rep. 2020;31:107491. https://doi.org/10.1016/j.celrep.2020.03.055 .

Tesfaye D, Gebremedhn S, Salilew-Wondim D, Hailay T, Hoelker M, Grosse-Brinkhaus C, et al. MicroRNAs: tiny molecules with a significant role in mammalian follicular and oocyte development. Reproduction. 2018;155(3):R121–35. https://doi.org/10.1530/REP-17-0428 .

Gong X, Zhang Y, Ai J, Li K. Application of Single-Cell RNA sequencing in ovarian development. Biomolecules. 2022;13(1):47. https://doi.org/10.3390/biom13010047 .

Davenport KM, O’Neil EV, Ortega MS, Patterson A, Kelleher AM, Warren WC, et al. Single cell insights into development of the bovine placenta. Biol Reprod. 2024;110(1):169–84. https://doi.org/10.1093/biolre/ioad123 .

Davenport KM, Ortega MS, Liu H, O’Neil EV, Kelleher AM, Warren WC, et al. Single-nuclei RNA sequencing (snRNA-seq) uncovers trophoblast cell types and lineages in the mature bovine placenta. Proc Natl Acad Sci U S A. 2023;120(12):e2221526120. https://doi.org/10.1073/pnas.2221526120 .

Di Fang, Tao W, Wang J, Huang F, Gao Q. Transcriptome analysis of sheep embryos in vivo based on single cell rna-seq. Acta Medica Mediterr. 2022;38(2):1263–72. https://doi.org/10.19193/0393-6384_2022_2_192 .

Guillot C, Djeffal Y, Michaut A, Rabe B, Pourquie O. Dynamics of primitive streak regression controls the fate of neuromesodermal progenitors in the chicken embryo. Elife. 2021;10:e64819. https://doi.org/10.7554/eLife.64819 .

Jia GX, Ma WJ, Wu ZB, Li S, Zhang XQ, He Z, et al. Single-cell transcriptomic characterization of sheep conceptus elongation and implantation. Cell Rep. 2023;42(8):112860. https://doi.org/10.1016/j.celrep.2023.112860 .

Liu T, Li J, Yu L, Sun HX, Li J, Dong G, et al. Cross-species single-cell transcriptomic analysis reveals pre-gastrulation developmental differences among pigs, monkeys, and humans. Cell Discov. 2021;7:8. https://doi.org/10.1038/s41421-020-00238-x .

Ton MN, Keitley D, Theeuwes B, Guibentif C, Ahnfelt-Ronne J, Andreassen TK, et al. An atlas of rabbit development as a model for single-cell comparative genomics. Nat Cell Biol. 2023;25(7):1061–72. https://doi.org/10.1038/s41556-023-01174-0 .

Vermillion KL, Bacher R, Tannenbaum AP, Swanson S, Jiang P, Chu LF, et al. Spatial patterns of gene expression are unveiled in the chick primitive streak by ordering single-cell transcriptomes. Dev Biol. 2018;439(1):30–41. https://doi.org/10.1016/j.ydbio.2018.04.007 .

Williams RM, Lukoseviciute M, Sauka-Spengler T, Bronner ME. Single-cell atlas of early chick development reveals gradual segregation of neural crest lineage from the neural plate border during neurulation. Elife. 2022;11:e74464. https://doi.org/10.7554/eLife.74464 .

Zhao L, Long C, Zhao G, Su J, Ren J, Sun W, et al. Reprogramming barriers in bovine cells nuclear transfer revealed by single-cell RNA-seq analysis. J Cell Mol Med. 2022;26(18):4792–804. https://doi.org/10.1111/jcmm.17505 .

Li J, Wang L, Yu D, Hao J, Zhang L, Adeola AC, et al. Single-cell RNA sequencing reveals thoracolumbar vertebra heterogeneity and rib-genesis in pigs. Genomics Proteomics Bioinformatics. 2021;19(3):423–36. https://doi.org/10.1016/j.gpb.2021.09.008 .

Kim DW, Place E, Chinnaiya K, Manning E, Sun C, Dai W, et al. Single-cell analysis of early chick hypothalamic development reveals that hypothalamic cells are induced from prethalamic-like progenitors. Cell Rep. 2022;38(3):110251. https://doi.org/10.1016/j.celrep.2021.110251 .

Zeller R, Lopez-Rios J, Zuniga A. Vertebrate limb bud development: Moving towards integrative analysis of organogenesis. Nat Rev Genet. 2009;10(12):845–58. https://doi.org/10.1038/nrg2681 .

Esteves DLJ, Blavet C, Bonnin MA, Hirsinger E, Havis E, Relaix F, et al. TMEM8C-mediated fusion is regionalized and regulated by NOTCH signalling during foetal myogenesis. Development. 2022;149(2):dev199928. https://doi.org/10.1242/dev.199928 .

Esteves DLJ, Blavet C, Bonnin MA, Hirsinger E, Comai G, Yvernogeau L, et al. Unexpected contribution of fibroblasts to muscle lineage as a mechanism for limb muscle patterning. Nat Commun. 2021;12(1):3851. https://doi.org/10.1038/s41467-021-24157-x .

Wu JJ, Zhu S, Tang YF, Gu F, Liu JX, Sun HZ. Microbiota-host crosstalk in the newborn and adult rumen at single-cell resolution. BMC Biol. 2022;20:280. https://doi.org/10.1186/s12915-022-01490-1 .

Wu J, Zhu S, Tang Y, Gu F, Valencak TG, Liu J, et al. Age- and microbiota-dependent cell stemness plasticity revealed by cattle cell landscape. Research (Washington). 2023;6:0025. https://doi.org/10.34133/research.0025 .

Zhang K, Zhang Y, Qin J, Zhu H, Liu N, Sun D, et al. Early concentrate starter introduction induces rumen epithelial parakeratosis by blocking keratinocyte differentiation with excessive ruminal butyrate accumulation. J Adv Res. 2023;S2090–1232(23):00401. https://doi.org/10.1016/j.jare.2023.12.016 .

Lin S, Fang L, Kang X, Liu S, Liu M, Connor EE, et al. Establishment and transcriptomic analyses of a cattle rumen epithelial primary cells (REPC) culture by bulk and single-cell RNA sequencing to elucidate interactions of butyrate and rumen development. Heliyon. 2020;6(6):e04112. https://doi.org/10.1016/j.heliyon.2020.e04112 .

Gao Y, Fang L, Baldwin RT, Connor EE, Cole JB, Van Tassell CP, et al. Single-cell transcriptomic analyses of dairy cattle ruminal epithelial cells during weaning. Genomics. 2021;113(4):2045–55. https://doi.org/10.1016/j.ygeno.2021.04.039 .

Gao X, Wang S, Wang Y, Li S, Wu S, Yan R, et al. Long read genome assemblies complemented by single cell RNA-sequencing reveal genetic and cellular mechanisms underlying the adaptive evolution of yak. Nat Commun. 2022;13:4887. https://doi.org/10.1038/s41467-022-32164-9 .

Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, et al. Construction of a human cell landscape at single-cell level. Nature. 2020;581(7808):303–9. https://doi.org/10.1038/s41586-020-2157-4 .

Han L, Wei X, Liu C, Volpe G, Zhuang Z, Zou X, et al. Cell transcriptomic atlas of the non-human primate Macaca fascicularis. Nature. 2022;604(7907):723–31. https://doi.org/10.1038/s41586-022-04587-3 .

Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, et al. Mapping the mouse cell atlas by Microwell-seq. Cell. 2018;172(5):1307. https://doi.org/10.1016/j.cell.2018.05.012 .

Liao Y, Ma L, Guo Q, Weigao E, Fang X, Yang L, et al. Cell landscape of larval and adult Xenopus laevis at single-cell resolution. Nat Commun. 2022;13:4306. https://doi.org/10.1038/s41467-022-31949-2 .

Li H, Janssens J, De Waegeneer M, Kolluru SS, Davie K, Gardeux V, et al. Fly Cell Atlas : a single-nucleus transcriptomic atlas of the adult fruit fly. Science. 2022;375(6584):eabk2432. https://doi.org/10.1126/science.abk2432 .

Ye F, Wang J, Li J, Mei Y, Guo G. Mapping cell atlases at the single-cell level. Adv Sci (Weinh). 2024;11(8):e2305449. https://doi.org/10.1002/advs.202305449 .

Lyu P, Qi Y, Tu ZJ, Jiang H. Single-cell RNA sequencing reveals heterogeneity of cultured bovine satellite cells. Front Genet. 2021;12:742077. https://doi.org/10.3389/fgene.2021.742077 .

Li J, Xing S, Zhao G, Zheng M, Yang X, Sun J, et al. Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing. BMC Genomics. 2020;21:752. https://doi.org/10.1186/s12864-020-07136-2 .

Zhang X, Yao Y, Han J, Yang Y, Chen Y, Tang Z, et al. Longitudinal epitranscriptome profiling reveals the crucial role of N 6 -methyladenosine methylation in porcine prenatal skeletal muscle development. J Genet Genomics. 2020;47(8):466–76. https://doi.org/10.1016/j.jgg.2020.07.003 .

Qiu K, Xu D, Wang L, Zhang X, Jiao N, Gong L, et al. Association analysis of single-cell RNA sequencing and proteomics reveals a vital role of Ca 2+ signaling in the determination of skeletal muscle development potential. Cells. 2020;9(4):1045. https://doi.org/10.3390/cells9041045 .

Du X, Lai S, Zhao W, Xu X, Xu W, Zeng T, et al. Single-cell RNA sequencing revealed the liver heterogeneity between egg-laying duck and ceased-laying duck. BMC Genomics. 2022;23:857. https://doi.org/10.1186/s12864-022-09089-0 .

Schlake T. Determination of hair structure and shape. Semin Cell Dev Biol. 2007;18(2):267–73. https://doi.org/10.1016/j.semcdb.2007.01.005 .

Ge W, Zhang W, Zhang Y, Zheng Y, Li F, Wang S, et al. A single-cell transcriptome atlas of cashmere goat hair follicle morphogenesis. Genomics Proteomics Bioinformatics. 2021;19(3):437–51. https://doi.org/10.1016/j.gpb.2021.07.003 .

Ma Y, Zeng W, Ba Y, Luo Q, Ou Y, Liu R, et al. A single-cell transcriptomic atlas characterizes the silk-producing organ in the silkworm. Nat Commun. 2022;13:3316. https://doi.org/10.1038/s41467-022-31003-1 .

Zheng Q, Ye N, Bao P, Zhang X, Wang F, Ma L, et al. Construction of transcriptome atlas of white yak hair follicle during anagen and catagen using single-cell RNA sequencing. BMC Genomics. 2022;23:813. https://doi.org/10.1186/s12864-022-09003-8 .

Yang F, Li R, Zhao C, Che T, Guo J, Xie Y, et al. Single-cell sequencing reveals the new existence form of dermal papilla cells in the hair follicle regeneration of cashmere goats. Genomics. 2022;114(2):110316. https://doi.org/10.1016/j.ygeno.2022.110316 .

Wang Z, Wang Y, Hui T, Chen R, Xu Y, Zhang Y, et al. Single-cell sequencing reveals differential cell types in skin tissues of liaoning cashmere goats and key genes related potentially to the fineness of cashmere fiber. Front Genet. 2021;12:726670. https://doi.org/10.3389/fgene.2021.726670 .

Chiappalupi S, Salvadori L, Luca G, Riuzzi F, Calafiore R, Donato R, et al. Do porcine Sertoli cells represent an opportunity for Duchenne muscular dystrophy? Cell Prolif. 2019;52(3):e12599. https://doi.org/10.1111/cpr.12599 .

Lunney JK, Van Goor A, Walker KE, Hailstock T, Franklin J, Dai C. Importance of the pig as a human biomedical model. Sci Transl Med. 2021;13(621):eabd5758. https://doi.org/10.1126/scitranslmed.abd5758 .

Ekser B, Ezzelarab M, Hara H, van der Windt DJ, Wijkstrom M, Bottino R, et al. Clinical xenotransplantation: the next medical revolution? Lancet. 2012;379(9816):672–83. https://doi.org/10.1016/S0140-6736(11)61091-X .

Zhang L, Zhu J, Wang H, Xia J, Liu P, Chen F, et al. A high-resolution cell atlas of the domestic pig lung and an online platform for exploring lung single-cell data. J Genet Genomics. 2021;48(5):411–25. https://doi.org/10.1016/j.jgg.2021.03.012 .

Shetty A, Lim S, Strell P, Steer CJ, Rivera-Mulia JC, Low WC. In Silico stage-matching of human, marmoset, mouse, and pig embryos to enhance organ development through interspecies chimerism. Cell Transplant. 2023;32:9636897231158728. https://doi.org/10.1177/09636897231158728 .

Voigt AL, Dardari R, Su L, Lara NLM, Sinha S, Jaffer A, et al. Metabolic transitions define spermatogonial stem cell maturation. Hum Reprod. 2022;37(9):2095–112. https://doi.org/10.1093/humrep/deac157 .

Wang X, Hong F, Li H, Wang Y, Zhang M, Lin S, et al. Cross-species single-cell transcriptomic analysis of animal gastric antrum reveals intense porcine mucosal immunity. Cell Regen. 2023;12:27. https://doi.org/10.1186/s13619-023-00171-w .

Zhu J, Chen F, Luo L, Wu W, Dai J, Zhong J, et al. Single-cell atlas of domestic pig cerebral cortex and hypothalamus. Sci Bull (Beijing). 2021;66(14):1448–61. https://doi.org/10.1016/j.scib.2021.04.002 .

Peng H, Zhang K, Miao J, Yang Y, Xu S, Wu T, et al. SnRNA-Seq of pancreas revealed the dysfunction of endocrine and exocrine cells in transgenic pigs with prediabetes. Int J Mol Sci. 2023;24(9):7701. https://doi.org/10.3390/ijms24097701 .

Zarei K, Stroik MR, Gansemer ND, Thurman AL, Ostedgaard LS, Ernst SE, et al. Early pathogenesis of cystic fibrosis gallbladder disease in a porcine model. Lab Invest. 2020;100(11):1388–99. https://doi.org/10.1038/s41374-020-0474-8 .

Wang F, Ding P, Liang X, Ding X, Brandt CB, Sjostedt E, et al. Endothelial cell heterogeneity and microglia regulons revealed by a pig cell landscape at single-cell level. Nat Commun. 2022;13:3620. https://doi.org/10.1038/s41467-022-31388-z .

Shin JS, Kim JS, Kim JM, Jang JY, Kim YH, Kim HJ, et al. Minimizing immunosuppression in islet xenotransplantation. Immunotherapy. 2014;6(4):419–30. https://doi.org/10.2217/imt.14.14 .

Adams AB, Kim SC, Martens GR, Ladowski JM, Estrada JL, Reyes LM, et al. Xenoantigen deletion and chemical immunosuppression can prolong renal xenograft survival. Ann Surg. 2018;268(4):564–73. https://doi.org/10.1097/SLA.0000000000002977 .

Santiago CP, Gimmen MY, Lu Y, McNally MM, Duncan LH, Creamer TJ, et al. Comparative analysis of single-cell and single-nucleus RNA-sequencing in a rabbit model of retinal detachment-related proliferative vitreoretinopathy. Ophthalmol Sci. 2023;3(4):100335. https://doi.org/10.1016/j.xops.2023.100335 .

Rao WT, Jiang S, Shen YH, Wang YH, Liu SN, Tang JD, et al. Myofibroblasts: A new factor affecting the Hyperlipidemia-Induced elastic abnormality of corpus cavernosum in rabbits detected by 2-D shear wave elastography. Ultrasound Med Biol. 2023;49(11):2336–45. https://doi.org/10.1016/j.ultrasmedbio.2023.07.010 .

Liang D, Xue J, Geng L, Zhou L, Lv B, Zeng Q, et al. Cellular and molecular landscape of mammalian sinoatrial node revealed by single-cell RNA sequencing. Nat Commun. 2021;12:287. https://doi.org/10.1038/s41467-020-20448-x .

El-Hodiri HM, Campbell WA, Kelly LE, Hawthorn EC, Schwartz M, Jalligampala A, et al. Nuclear Factor I in neurons, glia and during the formation of Muller glia-derived progenitor cells in avian, porcine and primate retinas. J Comp Neurol. 2022;530(8):1213–30. https://doi.org/10.1002/cne.25270 .

Li Z, Zheng M, Mo J, Li K, Yang X, Guo L, et al. Single-cell RNA sequencing of preadipocytes reveals the cell fate heterogeneity induced by melatonin. J Pineal Res. 2021;70(3):e12725. https://doi.org/10.1111/jpi.12725 .

Ghinia TM, Buenaventura DF, Kim DY, Thakurdin C, Gonzalez KC, Emerson MM. OTX2 represses sister cell fate choices in the developing retina to promote photoreceptor specification. Elife. 2020;9:e54279. https://doi.org/10.7554/eLife.54279 .

Ba H, Wang X, Wang D, Ren J, Wang Z, Sun H, et al. Single-cell transcriptome reveals core cell populations and androgen - RXFP2 axis involved in deer antler full regeneration. Cell Regen. 2022;11:43. https://doi.org/10.1186/s13619-022-00153-4 .

Qin T, Zhang G, Zheng Y, Li S, Yuan Y, Li Q, et al. A population of stem cells with strong regenerative potential discovered in deer antlers. Science. 2023;379(6634):840–7. https://doi.org/10.1126/science.add0488 .

Sinha S, Sparks HD, Labit E, Robbins HN, Gowing K, Jaffer A, et al. Fibroblast inflammatory priming determines regenerative versus fibrotic skin repair in reindeer. Cell. 2022;185(25):4717–36. https://doi.org/10.1016/j.cell.2022.11.004 .

Kashima Y, Sakamoto Y, Kaneko K, Seki M, Suzuki Y, Suzuki A. Single-cell sequencing techniques from individual to multiomics analyses. Exp Mol Med. 2020;52(9):1419–27. https://doi.org/10.1038/s12276-020-00499-2 .

Yu Z, Liao J, Chen Y, Zou C, Zhang H, Cheng J, et al. Single-Cell transcriptomic map of the human and mouse bladders. J Am Soc Nephrol. 2019;30(11):2159–76. https://doi.org/10.1681/ASN.2019040335 .

Wilkerson BA, Zebroski HL, Finkbeiner CR, Chitsazan AD, Beach KE, Sen N, et al. Novel cell types and developmental lineages revealed by single-cell RNA-seq analysis of the mouse crista ampullaris. Elife. 2021;10:e60108. https://doi.org/10.7554/eLife.60108 .

Harman RM, Patel RS, Fan JC, Park JE, Rosenberg BR, Van de Walle GR. Single-cell RNA sequencing of equine mesenchymal stromal cells from primary donor-matched tissue sources reveals functional heterogeneity in immune modulation and cell motility. Stem Cell Res Ther. 2020;11:524. https://doi.org/10.1186/s13287-020-02043-5 .

Xie L, Zhang S, Huang L, Peng Z, Lu H, He Q, et al. Single-cell RNA sequencing of peripheral blood reveals that monocytes with high cathepsin S expression aggravate cerebral ischemia-reperfusion injury. Brain Behav Immun. 2023;107:330–44. https://doi.org/10.1016/j.bbi.2022.11.001 .

Zolfaghari A, Djakiew D. Inhibition of chemomigration of a human prostatic carcinoma cell (TSU-pr1) line by inhibition of epidermal growth factor receptor function. Prostate. 1996;28(4):232–8. https://doi.org/10.1002/(SICI)1097-0045(199604)28:4%3c232::AID-PROS4%3e3.0.CO;2-F .

Chu T, Wang Z, Pe’Er D, Danko CG. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer. 2022;3(4):505–17. https://doi.org/10.1038/s43018-022-00356-3 .

Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, et al. ScGPT: Toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods. 2024. https://doi.org/10.1038/s41592-024-02201-0 . Epub ahead of print.

Goulet O, Pigneur B, Charbit-Henrion F. Congenital enteropathies involving defects in enterocyte structure or differentiation. Best Pract Res Clin Gastroenterol. 2022;56–57:101784. https://doi.org/10.1016/j.bpg.2021.101784 .

Gustafsson JK, Johansson M. The role of goblet cells and mucus in intestinal homeostasis. Nat Rev Gastroenterol Hepatol. 2022;19(12):785–803. https://doi.org/10.1038/s41575-022-00675-x .

He GW, Lin L, DeMartino J, Zheng X, Staliarova N, Dayton T, et al. Optimized human intestinal organoid model reveals interleukin-22-dependency of paneth cell formation. Cell Stem Cell. 2022;29(9):1333–45. https://doi.org/10.1016/j.stem.2022.08.002 .

Sanchez JG, Enriquez JR, Wells JM. Enteroendocrine cell differentiation and function in the intestine. Curr Opin Endocrinol Diabetes Obes. 2022;29(2):169–76. https://doi.org/10.1097/MED.0000000000000709 .

Zhang L, Li Z, Skrzypczynska KM, Fang Q, Zhang W, O’Brien SA, et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell. 2020;181(2):442-59 e29. https://doi.org/10.1016/j.cell.2020.03.048 .

Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: Guaranteeing well-connected communities. Sci Rep. 2019;9:5233. https://doi.org/10.1038/s41598-019-41695-z .

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech-Theory E. 2008;2008:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 .

Yu L, Cao Y, Yang J, Yang P. Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data. Genome Biol. 2022;23:49. https://doi.org/10.1186/s13059-022-02622-0 .

Zappia L, Oshlack A. Clustering trees : A visualization for evaluating clusterings at multiple resolutions. Gigascience. 2018;7(7):giy083. https://doi.org/10.1093/gigascience/giy083 .

Zhang S, Li X, Lin J, Lin Q, Wong KC. Review of single-cell RNA-seq data clustering for cell-type identification and characterization. RNA. 2023;29(5):517–30. https://doi.org/10.1261/rna.078965.121 .

Steele MA, Penner GB, Chaucheyras-Durand F, Guan LL. Development and physiology of the rumen and the lower gut: Targets for improving gut health. J Dairy Sci. 2016;99(6):4955–66. https://doi.org/10.3168/jds.2015-10351 .

Jones RC, Karkanias J, Krasnow MA, Pisco AO, Quake SR, Salzman J, et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science. 2022;376(6594):eabl4896. https://doi.org/10.1126/science.abl4896 .

Xu C, Prete M, Webb S, Jardine L, Stewart BJ, Hoo R, et al. Automatic cell-type harmonization and integration across Human Cell Atlas datasets. Cell. 2023;186(26):5876–91. https://doi.org/10.1016/j.cell.2023.11.026 .

Dominguez CC, Xu C, Jarvis LB, Rainbow DB, Wells SB, Gomes T, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science. 2022;376(6594):eabl5197. https://doi.org/10.1126/science.abl5197 .

Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–72. https://doi.org/10.1038/s41590-018-0276-y .

Jia M, Zhu S, Xue M, Chen H, Xu J, Song M, et al. Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome. Nat Microbiol. 2024.  https://doi.org/10.1038/s41564-024-01723-9 .

Mantri M, Scuderi GJ, Abedini-Nassab R, Wang M, McKellar D, Shi H, et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat Commun. 2021;12:1771. https://doi.org/10.1038/s41467-021-21892-z .

Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol. 2023;24(10):695–713. https://doi.org/10.1038/s41580-023-00615-w .

Cai S, Hu B, Wang X, Liu T, Lin Z, Tong X, et al. Integrative single-cell RNA-seq and ATAC-seq analysis of myogenic differentiation in pig. BMC Biol. 2023;21:19. https://doi.org/10.1186/s12915-023-01519-z .

Lancaster MA, Knoblich JA. Organogenesis in a dish: Modeling development and disease using organoid technologies. Science. 2014;345(6194):1247125. https://doi.org/10.1126/science.1247125 .

Beaumont M, Blanc F, Cherbuy C, Egidy G, Giuffra E, Lacroix-Lamande S, et al. Intestinal organoids in farm animals. Vet Res. 2021;52:33. https://doi.org/10.1186/s13567-021-00909-x .

Kar SK, Wells JM, Ellen ED, Te PM, Madsen O, Groenen M, et al. Organoids: a promising new in vitro platform in livestock and veterinary research. Vet Res. 2021;52:43. https://doi.org/10.1186/s13567-021-00904-2 .

Xu Z, Xu X, Yang B, Mi Y, Wang J. 3D sheep rumen epithelial structures driven from single cells in vitro. Vet Res. 2023;54:104. https://doi.org/10.1186/s13567-023-01234-1 .

Download references

Acknowledgements

We thank all the members in the Institute of Dairy Science, College of Animal Sciences, Zhejiang University.

This study was supported by the Natural Science Foundation of Zhejiang Province Outstanding Youth Fund Project (LR23C170001).

Author information

Authors and affiliations.

Institute of Dairy Science, Ministry of Education Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China

Yunan Yan, Senlin Zhu, Minghui Jia, Xinyi Chen, Wenlingli Qi, Fengfei Gu, Teresa G. Valencak, Jian-Xin Liu & Hui-Zeng Sun

Key Laboratory of Dairy Cow Genetic Improvement and Milk Quality Research of Zhejiang Province, Zhejiang University, Hangzhou, 310058, China

Fengfei Gu & Hui-Zeng Sun

Agency for Health and Food Safety Austria, 1220, Vienna, Austria

Teresa G. Valencak

You can also search for this author in PubMed   Google Scholar

Contributions

YNY: Conceptualization, Writing–Original Draft, Visualization. SLZ: Conceptualization, Visualization. MHJ: Writing–Original Draft. XYC: Visualization. WLLQ: Visualization. FFG: Conceptualization, Writing–Review & Editing, Supervision. TGV: writing, reviewing, revising. JXL: Conceptualization, Writing–Review & Editing, Supervision. HZS: Conceptualization, Writing–Original Draft, Writing–Review & Editing, Supervision.

Corresponding author

Correspondence to Hui-Zeng Sun .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Supplementary Information

Additional file 1.

. Applications of single-cell transcriptomics in animal science.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Yan, Y., Zhu, S., Jia, M. et al. Advances in single-cell transcriptomics in animal research. J Animal Sci Biotechnol 15 , 102 (2024). https://doi.org/10.1186/s40104-024-01063-y

Download citation

Received : 30 March 2024

Accepted : 12 June 2024

Published : 02 August 2024

DOI : https://doi.org/10.1186/s40104-024-01063-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Disease modelling
• Reproductive performance

Journal of Animal Science and Biotechnology

ISSN: 2049-1891

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]