Name: Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing (Video) | JoVE Journal
Uploaded: 2025-08-01T13:30:00.000+00:00
Duration: 8 min 58 s
Description: Learn single-cell transcriptomics analysis for wound healing research using R, Seurat, and CellChat. This protocol guides researchers through dataset processing, cell clustering, and visualization of cellular dynamics in mouse skin.

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5 minutes

The laboratory of M.S. Wietecha received funding from the NIH/NIGMS grant R35-GM154921, the Wound Healing Society Research Grant, and the Department of Oral Biology at the UIC College of Dentistry.

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<li>Rinkevich, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Skin+fibrosis.+Identification+and+isolation+of+a+dermal+lineage+with+intrinsic+fibrogenic+potential%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Skin fibrosis. Identification and isolation of a dermal lineage with intrinsic fibrogenic potential.</a> Science. 348 (6232), aaa2151(2015).</li>
<li>Guerrero-Juarez, C. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-cell+analysis+reveals+fibroblast+heterogeneity+and+myeloid-derived+adipocyte+progenitors+in+murine+skin+wounds%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds.</a> Nat Commun. 10 (1), 650(2019).</li>
<li>Gay, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Phagocytosis+of+Wnt+inhibitor+SFRP4+by+late+wound+macrophages+drives+chronic+Wnt+activity+for+fibrotic+skin+healing%5BTitle%5D)+AND+%22Sci+Adv%22%5BJournal%5D)">Phagocytosis of Wnt inhibitor SFRP4 by late wound macrophages drives chronic Wnt activity for fibrotic skin healing.</a> Sci Adv. 6 (12), eaay3704(2020).</li>
<li>Haensel, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Defining+epidermal+basal+cell+states+during+skin+homeostasis+and+wound+healing+using+single-cell+transcriptomics%5BTitle%5D)+AND+%22Cell+Rep%22%5BJournal%5D)">Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics.</a> Cell Rep. 30 (11), 3932-3947.e6 (2020).</li>
<li>Phan, Q. M., Sinha, S., Biernaskie, J., Driskell, R. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-cell+transcriptomic+analysis+of+small+and+large+wounds+reveals+the+distinct+spatial+organization+of+regenerative+fibroblasts%5BTitle%5D)+AND+%22Exp+Dermatol%22%5BJournal%5D)">Single-cell transcriptomic analysis of small and large wounds reveals the distinct spatial organization of regenerative fibroblasts.</a> Exp Dermatol. 30 (1), 92-101 (2021).</li>
<li>Foster, D. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrated+spatial+multiomics+reveals+fibroblast+fate+during+tissue+repair%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Integrated spatial multiomics reveals fibroblast fate during tissue repair.</a> Proc Natl Acad Sci U S A. 118 (41), e2110025118(2021).</li>
<li>Hu, K. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcriptional+space-time+mapping+identifies+concerted+immune+and+stromal+cell+patterns+and+gene+programs+in+wound+healing+and+cancer%5BTitle%5D)+AND+%22Cell+Stem+Cell%22%5BJournal%5D)">Transcriptional space-time mapping identifies concerted immune and stromal cell patterns and gene programs in wound healing and cancer.</a> Cell Stem Cell. 30 (6), 885-903.e10 (2023).</li>
<li>Hao, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Dictionary+learning+for+integrative%2C+multimodal+and+scalable+single-cell+analysis%5BTitle%5D)+AND+%22Nat+Biotechnol%22%5BJournal%5D)">Dictionary learning for integrative, multimodal and scalable single-cell analysis.</a> Nat Biotechnol. 42 (2), 293-304 (2024).</li>
<li>Jin, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Inference+and+analysis+of+cell-cell+communication+using+CellChat%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Inference and analysis of cell-cell communication using CellChat.</a> Nat Commun. 12 (1), 1088(2021).</li>
<li>Germain, P. -L., Lun, A., Garcia Meixide, C., Macnair, W., Robinson, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Doublet+identification+in+single-cell+sequencing+data+using+scDblFinder%5BTitle%5D)+AND+%22F1000Research%22%5BJournal%5D)">Doublet identification in single-cell sequencing data using scDblFinder.</a> F1000Research. 10, 979(2021).</li>
<li>Jin, S., Plikus, M. V., Nie, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CellChat+for+systematic+analysis+of+cell-cell+communication+from+single-cell+transcriptomics%5BTitle%5D)+AND+%22Nat+Protoc%22%5BJournal%5D)">CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.</a> Nat Protoc. 20 (1), 180-219 (2024).</li>
<li>Luecken, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Benchmarking+atlas-level+data+integration+in+single-cell+genomics%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Benchmarking atlas-level data integration in single-cell genomics.</a> Nat Methods. 19 (1), 41-50 (2022).</li>
<li>Hao, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrated+analysis+of+multimodal+single-cell+data%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Integrated analysis of multimodal single-cell data.</a> Cell. 184 (13), 3573-3587.e29 (2021).</li>
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<li>Aran, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Reference-based+analysis+of+lung+single-cell+sequencing+reveals+a+transitional+profibrotic+macrophage%5BTitle%5D)+AND+%22Nat+Immunol%22%5BJournal%5D)">Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.</a> Nat Immunol. 20 (2), 163-172 (2019).</li>
<li>Andreatta, M., Berenstein, A. J., Carmona, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((scGate%3A+marker-based+purification+of+cell+types+from+heterogeneous+single-cell+RNA-seq+datasets%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.</a> Bioinformatics. 38 (9), 2642-2644 (2022).</li>
<li>Efremova, M., Vento-Tormo, M., Teichmann, S. A., Vento-Tormo, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CellPhoneDB%3A+inferring+cell-cell+communication+from+combined+expression+of+multi-subunit+ligand-receptor+complexes%5BTitle%5D)+AND+%22Nat+Protoc%22%5BJournal%5D)">CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes.</a> Nat Protoc. 15 (4), 1484-1506 (2020).</li>
<li>Hu, Y., Peng, T., Gao, L., Tan, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CytoTalk%3A+de+novo+construction+of+signal+transduction+networks+using+single-cell+transcriptomic+data%5BTitle%5D)+AND+%22Sci+Adv%22%5BJournal%5D)">CytoTalk: de novo construction of signal transduction networks using single-cell transcriptomic data.</a> Sci Adv. 7 (16), eabf1356(2021).</li>
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</ol>

The authors have no conflicts of interest to disclose.

In this protocol, RStudio is used to run prescribed lines of code that enable a basic analysis of a complex single-cell dataset using Seurat. Several methods are presented that are relevant for wound healing research, including installation of the R coding environment, downloading a previously published single-cell wound healing dataset, performing critical quality control steps and standard single-cell analysis workflows including visualizations, major cell type annotations, cell subtype analyses, and integrative analyses using Seurat, and performing cell-cell interaction analyses using CellChat.
The methods introduced herein are simplified vignettes of typical workflows for single-cell analysis using R and its popular open-source scientific packages, Seurat21 and CellChat22. Indeed, the workflow is only one example of the type of analysis one may accomplish with a complex single-cell wound healing dataset. The possible modifications to this method are nearly infinite, with the only restraint being the user&#39;s particular scientific inquiry. For example, the user may change some of the key parameters, such as cell types and time-points, according to the research questions that they may wish to ask of this dataset. The authors also hope that the user feels comfortable enough to adapt this workflow to their own single-cell dataset of interest; however, care must be taken when using this workflow to analyze other datasets, since each experiment may propagate technical and sample preparation issues to the data itself. Therefore, it is imperative that the user reads and understands all of the experimental details before interpreting the results of any previously published and re-analyzed single-cell datasets. It is important to remember that bioinformatics tools are a powerful method for the exploration of biological process and hypothesis generation, and that any critical biological interpretations of results must be validated in follow-up experiments.
Throughout the protocol, note that major modifications may be made to particular areas of the workflow to accomplish other tasks. However, the details of all possible combinations of modifications to the workflow are beyond the scope of this manuscript. For example, the resolution used for cell clustering and the dimensions used for UMAP analysis are necessarily subjective, and the tools presented here allow for both large-scale analyses (as was demonstrated here for broadly-defined major cell types) and very specific analyses that could entail the sub-clustering of cells into more rare subpopulations within the larger dataset. For more information on this aspect of the single-cell analysis method, and for details about all the other parameters that may be changed in the single-cell analysis pipeline, the authors refer the user to the Seurat publications21,26 and website (<a href="https://satijalab.org/seurat/">https://satijalab.org/seurat/</a>), where the authors of this evolving tool provide in-depth explanations, vignettes, and tutorials.
This manuscript introduced some of the most cited and used tools in the single-cell transcriptomics literature, namely Seurat21 and CellChat22, for single-cell and cell-cell interaction analyses, respectively. However, other tools exist that perform similar functions in slightly different ways. For single-cell dataset analysis, there are Scran27, Scater 28, and the Python-based ScanPy29, which use various methods for dataset integration25. In this protocol, manual annotation of cell types was demonstrated, which relies on the user&#39;s judgement for interpreting cluster cell marker enrichments, but there now exist various tools enabling automated classification of cell types, such as SingleR30 and scGate31, among others. For cell-cell communication analyses, CellChat was demonstrated in this protocol, but there exist other tools for estimating cell-cell communications, including CellPhoneDB32, Cytotalk33, and other ligand-receptor databases that are implemented within the LIANA (LIgand-receptor ANalysis framework) consensus framework34. All bioinformatics tools are unique and come with their own peculiarities and modifiable parameters. Therefore, it is important for the user to carefully read each tool&#39;s associated documentation in order to understand its nuances before interpreting any output generated from their use. Finally, whatever bioinformatics tools one uses, it is critical to remember that such tools are continually evolving and that different versions of the packages may yield different outputs.
In R, syntax is critical, and a misplaced punctuation, quotation mark, bracket, or even a wrongly capitalized letter will result in an error. Therefore, it is vital for the user to pay attention to details when typing code and to be especially mindful when copying lines of code in order to adapt it to new scientific questions and datasets. For troubleshooting the specific errors one may encounter, the authors recommend simply copy-pasting the error message into the user&#39;s favorite web search engine and browsing through the results from bioinformatics forums such as GitHub and Stack Overflow, as the most commonly encountered errors have already likely been answered by a knowledgeable power user. In some forums, the most successful answers are &#39;upvoted&#39; by other users who have found the solution to be best for the problem. The user must be careful not to simply copy-paste lines of code they found on the internet into their own computer (especially if a solution calls for changing one&#39;s system settings outside of the R programming language), since there is a chance that such programs may be malicious. An exciting emerging method of troubleshooting coding errors is using the powerful generative large language AI models such as OpenAI&#39;s ChatGPT, Microsoft&#39;s Copilot, or Google&#39;s Gemini. These models have proven to be especially useful for software engineering in general and troubleshooting in particular. For this, the user may copy-paste entire lines of their code after providing a simple prompt to the chatbot about the user&#39;s intention for the code. The usual caveat exists that these models are not foolproof, and the user may have to try multiple prompts in order to generate an answer that is appropriate for solving the problem.

Wound healing is one of the most complex processes in mammalian biology, and it involves a spectrum of three phases of healing: Inflammatory, Proliferative, and Resolution1,2. These phases of healing broadly classify the coordinated actions of dozens of cell types and hundreds of their molecular products across space and time of wound repair3. Several decades of histological and molecular studies based on wound tissue sampling across the time-course of healing have elucidated the overarching cellular patterns of tissue repair3, particularly in reproducible mouse models of excisional skin wound healing4,5,6. Only in the last two decades has it become possible to more fully appreciate the complexity of wound healing, beginning with the advent of high-throughput transcriptomic analyses of wounds at the scales of bulk tissues7,8,9 and cells10,11,12,13,14. Most recently, several studies have transcriptionally profiled skin wounds at the single-cell level, identifying new wound cell subtypes and showing how they may interact with one another during healing15,16,17,18,19,20. Hu et al. used an innovative spatial single-cell RNA sequencing approach to profile skin wounds throughout the time-course of healing at several radial distances from the wound center, which revealed new inter-cellular and molecular &#39;movements&#39; across space and time20. Such studies are unraveling the complexity of wound healing in unprecedented detail, and they are beginning to paint a picture of tremendous cellular and molecular heterogeneity.
Major recent advances in bioinformatics analysis methods are making it possible to make biological sense of the complex multi-omic datasets being generated in the field of wound healing research. Single-cell analysis packages like Seurat provide tools for robust analysis and integration of datasets, including the classification of cell types in complex tissues such as wounds21. For downstream interpretation of single-cell data, tools like CellChat are used to identify putative cell-cell interaction programs that may explain how cells coordinate to repair wounds22. While these tools are well-documented and well-cited, they must be run within a computer coding environment such as R, a statistical and graphical programming language that is most commonly used in the bioinformatics fields of genomics and transcriptomics. While biologists and clinicians in the wound healing field are increasingly using single-cell approaches to study tissue repair, few have the bioinformatics training required to use tools such as Seurat and CellChat directly in their own labs. Such a barrier in the use of these bioinformatics tools not only prevents scientists from more deeply analyzing their own datasets without the help of bioinformaticians, but it also prevents scientists from reliably re-analyzing the wealth of single-cell data already published by other groups.
Therefore, a step-by-step workflow is presented here to enable scientists with no bioinformatics background to analyze a previously published and publicly available single-cell wound healing dataset20. The protocol uses the prevalent and free graphical R coding environment called RStudio, and it demonstrates how to navigate this environment to run prescribed lines of code that enable a basic analysis of a complex single-cell dataset using Seurat and CellChat. Within this protocol, the seven major methods relevant for wound healing research are presented, including: 1) coding environment installation, 2) downloading the dataset and critical quality control steps, 3) single-cell analysis workflows including visualizations and cell type annotations, 4) cell subtype analyses, 5) module scoring analyses, 6) cell-cell interaction analyses, and 7) integrative analyses of multiple datasets. Within each method, actual code is provided for the user to run side-by-side with the protocol, and actual graphical results from every line of code are shown to guide the user through the workflow. The primary goal of this guided and visual introduction to RStudio and a fundamental single-cell analysis workflow is to enable more wound healing scientists to use these powerful tools directly in order to enable faster progress in the field of research.

<table><tbody><tr><td>Laptop or desktop computer</td><td>N/A</td><td>N/A</td><td>Running Windows or MacOS&amp;nbsp;</td></tr><tr><td>R</td><td>N/A</td><td>Version 4.4.1</td><td>Free to download from https://cran.rstudio.com/</td></tr><tr><td>Rstudio</td><td>Posit Software, PBC</td><td>Version 2024.09.0</td><td>Free to download from https://posit.co/download/rstudio-desktop/</td></tr><tr><td>Office Excel</td><td>Microsoft</td><td>Any version</td><td>For analysis of table data</td></tr><tr><td>Internet browser</td><td>N/A</td><td>N/A</td><td>For navigating to websites</td></tr><tr><td>&lt;strong&gt;R Packages&lt;/strong&gt;</td><td>&lt;strong&gt;Repository&lt;/strong&gt;</td><td>&lt;strong&gt;Version&lt;/strong&gt;</td><td></td></tr><tr><td>devtools</td><td>CRAN</td><td>2.4.5</td><td></td></tr><tr><td>readxl</td><td>CRAN</td><td>1.4.3</td><td></td></tr><tr><td>openxlsx</td><td>CRAN</td><td>4.2.7.1</td><td></td></tr><tr><td>tidyverse</td><td>CRAN</td><td>2.0.0</td><td></td></tr><tr><td>scCustomize</td><td>CRAN</td><td>2.1.2</td><td></td></tr><tr><td>BiocManager</td><td>Bioconductor</td><td>1.30.25</td><td></td></tr><tr><td>NMF</td><td>Bioconductor</td><td>0.28</td><td></td></tr><tr><td>ComplexHeatmap</td><td>Bioconductor</td><td>2.20.0</td><td></td></tr><tr><td>BiocNeighbors</td><td>Bioconductor</td><td>1.22.0</td><td></td></tr><tr><td>SingleCellExperiment</td><td>Bioconductor</td><td>1.26.0</td><td></td></tr><tr><td>circlize</td><td>Bioconductor</td><td>0.4.16</td><td></td></tr><tr><td>edgeR</td><td>Bioconductor</td><td>4.2.1</td><td></td></tr><tr><td>scDblFinder</td><td>Bioconductor</td><td>1.18.0</td><td></td></tr><tr><td>Seurat</td><td>CRAN</td><td>5.1.0</td><td></td></tr><tr><td>CellChat</td><td>Github</td><td>2.1.2</td><td></td></tr></tbody></table>

using r, seurat, and cellchat to analyze a single-cell transcriptomics dataset of mouse skin wound healing

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

The process of wound healing is regulated by complex interactions between different cell types across space and time. Through the profiling of individual cells within their complex environment, single-cell transcriptomics methods enable the investigation of cellular heterogeneity, cell communication networks, and cell-cell interactions involved in the wound healing process. However, many single-cell analysis tools are run within a computer coding environment, and their more widespread use by wound healing scientists is thwarted by the apparent lack of bioinformatics expertise. Therefore, a step-by-step workflow is presented showing how to use a graphical coding environment called RStudio to perform a basic single-cell analysis of a temporal mouse excisional skin wound healing dataset. This visual and guided protocol will enable scientists with no bioinformatics background to download a previously published wound healing dataset, perform critical quality control steps, run a standard single-cell analysis workflow including dataset visualizations and cell type annotations using Seurat, run cell subtype analyses, run module scoring analyses, run cell-cell interaction analyses using CellChat, and perform integrative analyses of multiple datasets using Seurat. Narrative explanations are provided for each step in the protocol and graphical results from every line of code are presented to safely guide the user through the workflow. The goal of this visual introduction to a single-cell analysis pipeline is to enable more wound healing scientists to use bioinformatics tools directly in their own laboratories in order to facilitate deeper analyses of their own single-cell datasets as well as more widespread re-analyses of previously published single-cell datasets.

Here, we present a step-by-step, visual workflow for analyzing a single-cell time-course transcriptomics dataset of mouse skin wound healing using R. The protocol includes a standard pipeline for dataset download, quality control, visualizations, and cell type annotations using Seurat, and cell-cell interaction analysis using CellChat.

The process of wound healing is regulated by complex interactions between different cell types across space and time. Through the profiling of individual ...

using-r-seurat-cellchat-to-analyze-single-cell-transcriptomics

Research

JoVE Journal

Genetics

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Learn single-cell transcriptomics analysis for wound healing research using R, Seurat, and CellChat. This protocol guides researchers through dataset processing, cell clustering, and visualization of cellular dynamics in mouse skin.

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

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