DOI: 10.5061/dryad.vq83bk40f
title: Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells

description: Skin-resident CD8+ T cells comprise distinct IFN-γ- (TRM1) and
 IL-17-producing (TRM17) subsets that differentially contribute to immune
 responses. However, whether these populations employ common mechanisms to
 establish tissue residence is unknown. Here, we show that TRM1 and TRM17
 cells navigate divergent trajectories to acquire tissue residency in skin.
 While TRM1 cells depend on a T-bet-Hobit-IL-15 axis, TRM17 cells develop
 independently of these factors. Instead, c-Maf commands a tissue-resident
 program in TRM17 cells parallel to that induced by Hobit in TRM1 cells,
 with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment.
 Accordingly, targeting this pathway enables ablation of skin TRM17 cells
 without compromising their TRM1 counterparts. Thus, skin-resident T cells
 rely on distinct molecular circuitries, which can be exploited to
 strategically modulate local immunity.
description: Mice C57BL/6,
 B6.SJL‐PtprcaPep3b/BoyJ (CD45.1), B6.SJL‐PtprcaPep3b/BoyJ×C57BL/6
 (CD45.1×CD45.2), OT-I×B6.SJL-PtprcaPep3b/BoyJ (OT-I.CD45.1), T-bet-ZsGreen×Rorgt-E2Crimson,
 Tgfbr2<sup>flox/flox</sup>×dLck-cre
 (<em>Tgfbr2<sup>–/–</sup></em>),
 <em>Tbx21<sup>–/–</sup>,
 Tbx21<sup>–/–</sup>×Eomes</em>×<em>Cd4</em><sup>cre</sup>,<em>  Nfil3<sup>–/–</sup>, Hobit<sup>Tomato</sup>, Blimp1<sup>Tomato</sup>, Il15<sup>–/–</sup>, </em>lox-stop-lox-<em>Tgfbr1</em><sup>CA</sup> (<em>Tgfbr1</em><sup>CA</sup>)×<em>Cd8</em>(E8i)<sup>cre</sup>, <em>Trac</em><sup>flox/flox</sup>×Rosa26<sup>creERT2/+</sup>, <em>Zfp683<sup>–/–</sup></em><sup> </sup>(Hobit-KO), <em>Maf<sup>flox/flox</sup>×Cd4</em><sup>cre</sup> (c-Maf<em>-</em>cKO) and <em>Icos</em><sup>–/–</sup> mice were bred and maintained in the Department of Microbiology and Immunology at the The University of Melbourne, Australia. <em>Hobit<sup>Tomato</sup></em> and<em> Blimp1<sup>Tomato</sup></em> were generated by the Kallies laboratory by inserting tandem tomato expression cassettes into the endogenous <em>Zfp683</em> and <em>Blimp1</em> loci, respectively. For parabiosis experiments, C57BL/6 and B6.SJL‐PtprcaPep3b/BoyJ (CD45.1) mice were obtained from either the NIAID Taconic exchange program or purchased from The Jackson Laboratory and maintained at the National Institutes of Health, Bethesda, USA. Bowie×Rag1<sup>−/−</sup> transgenic mice (<em>6</em>) were bred and maintained at the National Institutes of Health, Bethesda, USA. <em>Trac</em><sup>flox/flox </sup>mice were kindly provided by D. Mucida (The Rockefeller University, New York, USA). <em>Tcf7</em><em><sup>f</sup></em><sup>lox/flox</sup>×<em>dLck</em><sup>cre</sup> and <em>Tcf7</em><sup>flox/flox</sup>×<em>Cd4</em><sup>cre</sup> (<em>Tcf7 </em>cKO) mice were kindly provided by L. Mielke (Olivia Newton-John Cancer Research Institute) and were bred and maintained at La Trobe University, Bundoora, Victoria, Australia. For wound-healing assays, male <em>Maf<sup>flox/flox</sup>×Cd4</em><sup>cre</sup> mice were bred and maintained at the National Institutes of Health, Bethesda, USA. All other mice used were female and all mice were aged between 6–18 weeks at the beginning of experiments. All animal experiments were approved by the University of Melbourne Animal Ethics Committee or performed in an American Association for the Accreditation of Laboratory Animal Care (AAALAC)-accredited animal facility at the NIAID and housed in accordance with the procedures outlined in the Guide for the Care and Use of Laboratory Animals. Mouse and human tissue processing Lymphocytes were isolated from mouse spleens by dissociating tissues through a metal mesh to create single-cell suspensions and then treating them with 1X Red Blood Cell Lysis Buffer (eBioscience) for 2–3 min. Except where indicated, T cells were isolated from mouse flank skin by incubating skin (3 cm<sup>2</sup>) in Liberase TL Research Grade solution (Sigma, 0.25 mg/ml) in Hank’s balanced saline solution (HBSS) at 37°C for 25 min. The epidermis was then separated from the dermis before finely chopping and incubating for a further 60 min at 37°C. In certain experiments, T cells were isolated from both ear pinnae of mice by separating the dorsal and ventral sides of the ear, incubating in Liberase TL Research Grade solution (Sigma, 0.25 mg/ml in HBSS) for 90 min at 37°C and 6.5% CO<sub>2</sub> before chopping and mixing in RP-10 solution. Skin T<sub>RM</sub> cell enumeration is combined from two ear pinnae and 3 cm<sup>2</sup> of associated flank skin. T cells were isolated from human PBMC by separation on a Ficoll (Sigma) gradient and from human skin by incubating skin for 90 min in Dispase solution (Roche, 2.5 mg/ml) at 37°C and, peeling the epidermis from the dermis and then incubating the skin for a further 90 mins in Collagenase Type III (Worthington, 3 mg/ml) at 37°C. Suspensions were filtered through 70-μm meshes prior to stimulation. Flow cytometric analysis For single-cell sequencing experiments, cells were sorted from the skin or spleen using a BD FACS Aria III (BD Biosciences) with the following gating strategies: DAPI<sup>–</sup>→TCRβ<sup>+</sup>TCRγδ<sup>–</sup>→CD44<sup>hi</sup>→CD8β<sup>+</sup>CD4<sup>–→</sup>CD45.1<sup>+</sup>CD45.2<sup>+</sup> or CD45.2<sup>+</sup> (spleen), DAPI<sup>–</sup>→TCRβ<sup>+</sup>TCRγδ<sup>–</sup>→CD44<sup>hi</sup>→CD8β<sup>+</sup>CD4<sup>–</sup>→CD69<sup>+</sup>CD103<sup>+</sup>→CD45.1<sup>+</sup>CD45.2<sup>+</sup> or CD45.2<sup>+</sup> (skin), or DAPI<sup>–</sup>→TCRβ<sup>+</sup>TCRγδ<sup>–</sup>→CD44<sup>lo</sup>→Vα2<sup>+</sup>CD8β<sup>+</sup> (naïve gBT-I cells). Bone marrow (BM) chimeras Mixed BM chimeras were generated by irradiating recipient mice twice with 550 rads before injecting 0.25-1×10<sup>7</sup> congenically marked bone marrow cells from WT or KO mice at a 1:1 ratio to CD45.1<sup>+ </sup>recipients i.v. For single-cell-sequencing experiments, irradiated mice were singly transferred <em>Maf<sup>flox/flox</sup>×Cd4</em><sup>cre</sup> (c-Maf-cKO) or Hobit<sup>–/–</sup> (Hobit-KO) or WT cells. Mice were rested for at least 8 weeks prior to <em>S. epidermidis</em> association. For tamoxifen treatment, mice were administered 2 mg of tamoxifen diluted in sunflower seed oil (both from Sigma) i.p. daily for a total of five injections. Bulk RNA-sequencing analysis RNA sequencing datasets were obtained as either summarized raw counts or raw sequence read data files from NCBI Gene Expression Omnibus (GEO) or Bioproject datasets with accession numbers PRJNA41936 (T<sub>RM</sub>1 versus T<sub>RM</sub>17 cells) (Ref. 1), GSE70813 (<em>Hobit</em> or<em> Blimp1</em> knockout (KO) versus WT and T<sub>RM</sub> cell core signature) (Ref. 2) and GSE120427 (<em>Maf</em>-KO versus WT) (Ref. 3). To generate T<sub>RM</sub>1 and T<sub>RM</sub>17 gene expression profiles, <em>S. epidermidis</em> Tc17 and Tc1 samples were selected for analysis. To generate <em>Hobit </em>or<em> Blimp1 </em>KO versus WT (i) or T<sub>RM</sub> core signatures (ii) (Ref. 2), either WT, <em>Hobit</em>-KO, and <em>Blimp1</em>-KO bone marrow NKT cell samples (i) or HSV skin T<sub>RM</sub> cell, LCMV gut/liver T<sub>RM</sub> cell, and HSV/LCMV T<sub>EM</sub> and T<sub>CM</sub> cell (combined and designated T<sub>CIRC</sub>) samples (ii) were selected for analysis. For T<sub>RM</sub>1 versus T<sub>RM</sub>17 comparisons, raw sequence read data was processed by mapping reads to the <em>Mus musculus </em>(GRCm38/mm10) genome using <em>Rsubread </em>(Ref 4.) (v2.2.6)<em> </em>and reads mapping to NCBI RefSeq annotated genes were counted and summarized with <em>featureCounts </em>(Ref 5). For all data sets, genes were annotated according to the NCBI database and genes with retired Entrez IDs or that failed to achieve a count above 10 in all samples in at least one biological group were removed. To generate <em>Hobit </em>or<em> Blimp1 </em>KO versus WT and T<sub>RM</sub> cell core signatures, genes designated rRNA were also excluded. T<sub>RM</sub> cell core signatures (Ref 2.) were processed further using the imputation strategy previously described (Ref. 6, 7). Each data set was upper-quartile normalized using <em>EDAseq</em> (Ref. 8) and log<sub>2</sub>-transformed with an offset of 1, before applying <em>RUVIII</em> (Ref. 9) with biological replicates nominated as technical replicates, mouse housekeeping genes (Ref. 4) nominated as ‘negative control’ genes, and <em>k</em> factors of unwanted variation where: <em>k</em> = 1 (for c-Maf versus WT), <em>k</em> = 5 (for Hobit/Blimp1versus WT) or <em>k</em> = 3 (for T<sub>RM</sub> core signature and T<sub>RM</sub>1 versus T<sub>RM</sub>17). <em>edgeR </em>(v 3.30.3) (Ref. 10) was used to calculate upper quartile normalization factors and fit gene-wise negative binomial generalized linear models with the output from <em>RUVIII</em> as additional covariates where a prior count of 1 was used for <em>Hobit </em>or <em>Blimp1</em> KO versus WT or T<sub>RM</sub> cell core signature generation or default values applied elsewhere. Differentially expressed (DE) genes were called when likelihood ratio tests returned a Benjamini and Hochberg (Ref. 11) adjusted <em>P</em>-value (false discovery rate) &lt;0.05. Standardized log<sub>2</sub>-fold-change (logFC) values (empirical Bayes moderated t-statistics) were calculated by computing log<sub>2</sub> counts-per-million values with <em>edgeR</em> and using <em>limma </em>(Ref. 12) (v3.44.3) to fit gene-wise linear models with the output from <em>RUVIII </em>as additional covariates, where the ‘trend’ and ‘robust’ (Ref. 12) options were specified. To generate <em>Hobit </em>or<em> Blimp1</em>KO versus WT signatures, the corresponding main effects contrast for a 2×2 factorial design was used. To generate the T<sub>RM</sub> cell core signature, skin T<sub>RM</sub> versus T<sub>CIRC</sub> cell (HSV) and liver or gut T<sub>RM</sub> versus T<sub>CIRC</sub> cell (LCMV) samples were compared and genes that were DE up or DE down in all three comparisons defined the core up and down T<sub>RM</sub> cell signatures, respectively. Tests of association of expression changes, as displayed in scatter plots, were performed by constructing a two-way contingency table of the number of genes with concordant/discordant logFCs, where genes with small logFCs (between –0.25 and 0.25) in either comparison were ignored and Fisher’s exact test was applied to the resulting table. Gene sets were selected from lists ranked by adjusted <em>P</em>-value. PCA plots and heatmaps were produced from <em>RUVIII</em> normalized data, where heatmaps used gene-wise standardization to produce a Z-score and genes clustered by Pearson correlation. Barcode enrichment plots were generated using <em>limma</em> by producing separate plots for top up genes and top down genes and then stacked into a single plot and <em>P</em>-values were calculated using the <em>camera </em>method (Ref. 14) applied to the top up and down genes separately. Venn diagram visualization was performed in R using the <em>venneuler</em> package (v1.1.3). Where specified, RNA sequencing data sets were processed to impute missing values. For each gene, when a biological group had (post-filtered) non-zero counts for some replicates but zero counts for other replicates, the zeros were regarded as inconsistent, and thus inferred to be missing data. Network analysis Protein–protein network interaction analysis was performed by selecting for differentially expressed genes upregulated in either T<sub>RM</sub>1 or T<sub>RM</sub>17 cells and annotated with either of the Gene Ontology (GO) terms GO:0003700 (DNA-binding TF activity) or GO:0043565 (sequence-specific DNA binding), as well TFs as per Fig. 1D. Interaction networks were generated in string-db (Ref. 15) with a minimum required interaction score of 0.400 then visualized in Cytoscape v3.2.0. TF with less than 2 interactions in the network or log Fold-Change &lt;0.5 and &gt;–0.5 were excluded from the network. PageRank scores for TF in the network were calculated in <em>igraph</em> with edges weighted by STRING network score. Expression score rankings were generated by assigning each TF a ranking based on adjusted <em>P</em>-value (FDR) and a ranking based on logFC then combining both to produce an average ranking. Single-cell RNA sequencing library preparation CD69<sup>+</sup>CD103<sup>+</sup> CD8<sup>+</sup> T<sub>RM</sub> cells and CD8<sup>+</sup>CD44<sup>hi</sup> splenic T cells were sorted from bone marrow chimeras (CD45.1<sup>+</sup>) reconstituted with either (i) Hobit<sup>–/–</sup> (Hobit-KO, CD45.2<sup>+</sup>) or B6 (WT, CD45.2<sup>+</sup>) cells or (ii) <em>c-Maf</em><sup>flox/flox</sup>×CD4<sup>cre+/−</sup> (c-Maf-cKO, CD45.2<sup>+</sup>) or <em>c-Maf</em><sup>+/+</sup>×CD4<sup>cre+/–</sup> (WT, CD45.2<sup>+</sup>) cells 14 days after <em>S. epidermidis</em> association. Sorted cells were stained with TotalSeq-C antibodies against CD49a, CCR6 and CD62L and barcoded with individual Hashtags then pooled. A total of 2–3.6×10<sup>4</sup> cells per experiment were filtered through a Flowmi cell strainer and loaded onto a 10x chromium controller then prepared for sequencing using a Chromium Next GEM Single Cell 5′ kit with feature barcoding and immune receptor mapping (v2, Dual Index) from 10x and VDJ enrichment kit for mouse T cells (v1.1) from 10x. Libraries were generated according to the manufacturer’s instructions. Libraries were profiled on an Agilent Tapestation and quantified using a KAPA Library Quantification Kit (KK4824) before sequencing on an Illumina NextSeq 6000. Single-cell RNA sequencing analysis Sequencing data were generated using the cellranger multi and cellranger vdj pipelines in 10x CellRanger (v7.0.0) with reads aligned to the mm10 genome. Data were processed and analysed in <em>R</em> (version 4.2.1) using the <em>Seurat </em>package (v4.1) with the default parameters used unless otherwise noted. Cells were demultiplexed using HTODemux with margin set to 1 and custom positive.quantile values assigned to each experiment (the minimum value for which RidgePlots were bimodal) and negative droplets and doublets removed. Cells with &lt;1000 or &gt;20,000 counts, &lt;250 features and &gt;5% mitochondrial reads were excluded from analysis. Remaining cells were processed using SCTransform v2 with 4000 variable features and regression of mitochondrial read percentage. Integration was performed using reciprocal PCA and excluded TCR genes, ribosomal protein genes, Hsp and Dnaj family genes and a set of genes upregulated during heat treatment (Ref. 16) from the integration anchor features to minimize effects of digestion-induced stress on clustering. Standard processing and normalization steps were then run on the integrated assay (RunPCA, RunUMAP, FindNeighbors) and FindClusters run with a final resolution of 0.3 (for spleen and skin samples) or 0.4 (for skin samples alone). Clusters with low quality (low RNA reads and high mitochondrial reads) that resolved at high resolution and contaminant clusters comprising γδT cells were removed from the object. Gene expression was visualized using SCTransform normalized counts generated prior to integration. Marker genes for each cluster and differentially expressed genes were determined using PrepSCTFindMarkers, FindAllMarkers or FindMarkers on the unintegrated SCT assay with an adjusted p-value cutoff of 0.05, avg_log2FC cutoff of 0.125, min pct=0.02 and no umi recorrection. Volcano plots were generated in <em>ggplot2</em> or <em>EnhancedVolcano </em>(v1.14.0). For heatmap visualization, average expression was calculated using the function AverageExpression on normalized counts and heatmaps generated using <em>pheatmap</em> (v1.0.12). Venn diagram visualization was performed using the <em>venneuler</em> package (v1.1.3). Gene module score enrichment was performed using AddModuleScore in <em>Seurat.</em> TCR Clonotype analysis was performed using <em>scRepertoire</em> (v1.6.0) using combineTCR to generate clonotypes and remove cells with multiple TCRα or multiple TCRβ chains (removeMulti = TRUE). Individual clones were called using CTstrict (identical CDR3 nucleotide sequences and the same TCRα and TCRβ chains). Frequency barplots were generated using <em>dittoSeq </em>(v1.8.1). Gene ontology analysis was performed in Metascape (Ref. 17) for GO and Reactome pathways using differentially expressed genes with a <em>P</em>-value&lt;0.01 (with ribosomal protein genes excluded) and extracting the top Summary pathways from the Metascape output ranked by <em>P</em>-value. Statistics and reproducibility All experiments were performed at least twice with either similar results obtained, and representative plots shown, or data pooled from multiple experiments. Mice of the same group were randomly allocated to groups, without the need for blinding and sample sizes were selected based on prior research to provide informative results and perform statistical testing. References 1.              J. L. Linehan<em> et al.</em>, Non-classical Immunity Controls Microbiota Impact on Skin Immunity and Tissue Repair. <em>Cell</em> <strong>172</strong>, 784-796.e718 (2018). 2.              L. K. Mackay<em>et al.</em>, Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes.<em>Science</em><strong>352</strong>, 459-463 (2016). 3.              M. K. Zuberbuehler et al., The transcription factor c-Maf is essential for the commitment of IL-17-producing γδ T cells. Nat Immunol 20, 73-85 (2019). 4.              Y. Liao, G. K. Smyth, W. Shi, The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. <em>Nucleic Acids Research</em> <strong>41</strong>, e108-e108 (2013). 5.              Y. Liao, G. K. Smyth, W. Shi, featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. <em>Bioinformatics</em> <strong>30</strong>, 923-930 (2014). 6.              S. N. Christo<em> et al.</em>, Discrete tissue microenvironments instruct diversity in resident memory T cell function and plasticity. <em>Nat Immunol</em> <strong>22</strong>, 1140-1151 (2021). 7.              R. Fonseca<em> et al.</em>, Runx3 drives a CD8(+) T cell tissue residency program that is absent in CD4(+) T cells. <em>Nat Immunol</em> <strong>23</strong>, 1236-1245 (2022). 8.              D. Risso, K. Schwartz, G. Sherlock, S. Dudoit, GC-content normalization for RNA-Seq data. <em>BMC Bioinformatics</em> <strong>12</strong>, 480 (2011). 9.              R. Molania, J. A. Gagnon-Bartsch, A. Dobrovic, T. P. Speed, A new normalization for Nanostring nCounter gene expression data. <em>Nucleic Acids Res</em> <strong>47</strong>, 6073-6083 (2019). 10.           M. D. Robinson, D. J. McCarthy, G. K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. <em>Bioinformatics</em> <strong>26</strong>, 139-140 (2010). 11.           Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. <em>J R Stat Soc Series B Stat Methodol</em> <strong>57</strong>, 459-463 (1995). 12.           M. E. Ritchie<em> et al.</em>, limma powers differential expression analyses for RNA-sequencing and microarray studies. <em>Nucleic Acids Res</em> <strong>43</strong>, e47 (2015). 13.           B. Phipson, S. Lee, I. J. Majewski, W. S. Alexander, G. K. Smyth, Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. <em>Ann Appl Stat</em> <strong>10</strong>, 946-963 (2016). 14.           D. Wu, G. K. Smyth, Camera: a competitive gene set test accounting for inter-gene correlation. <em>Nucleic Acids Res</em> <strong>40</strong>, e133 (2012). 15.           D. Szklarczyk<em> et al.</em>, STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. <em>Nucleic Acids Res</em> <strong>47</strong>, D607-d613 (2019). 16.           C. H. O'Flanagan<em> et al.</em>, Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. <em>Genome Biol</em> <strong>20</strong>, 210 (2019). 17.           Y. Zhou<em> et al.</em>, Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. <em>Nat Commun</em> <strong>10</strong>, 1523 (2019).
description: # Divergent molecular networks program functionally distinct CD8+
 skin-resident memory T cells
 [https://doi.org/10.5061/dryad.vq83bk40f](https://doi.org/10.5061/dryad.vq83bk40f) This dataset contains 1) code used to compare the following cell populations and determine differential gene expression patterns and gene signatures and/or 2) associated gene expression comparison outputs (i.e. differential gene expression (DEG) lists) used to generate Figures in our manuscript listed below. For each Figure, the dataset contains code, resources or analysis outputs related to performing the following comparisons in the manners outlined in our Methodology section: **Figure 1F and Figure 3A-F** 1. Skin Tc1 vs. Tc17 cells from Linehan *et al.* Cell 2018 (PMID 29358051) **Figure 3A-F** 2) Trm vs. circulating T cells (i.e. generation of the Trm core signature) from Mackay *et al.* Science 2016 (PMID 27102484) 3) Hobit-KO vs. WT immune cells from Mackay *et al.* Science 2016 (PMID 27102484) 4) Blimp1-KO vs. WT immune cells from Mackay *et al.* Science 2016 (PMID 27102484) 5) Maf-KO vs. WT immune cells from Zuberbuehler *et al.* Nature Immunology 2019 (PMID 30538336) **Figure 3G-O** 6) Hobit-KO vs. WT or c-Maf cKO vs WT skin CD8+ Trm cells sequenced in the present manuscript. ## Description of the data and file structure Files included in this repository are described in the following table. Files prefaced 'bulk' are related to bulk RNA sequencing analysis and files prefaced with 'sc' are related to single-cell RNA sequencing analysis. | File number | File name | File category | File type | Experiment or Analysis | Related to Figure(s) | Description | | ----------- | --------- | ------------- | --------- | ---------------------- | -------------------- | ----------- | | 1 | bulk\_DE\_Blimp\_WT-vs-KO.txt | DEG list | .txt | Bulk RNA sequencing of Blimp WT and KO cells | S8E, S8G | Differential expression results table for WT vs KO | | 2 | bulk\_DE\_Hobit\_WT-vs-KO.txt | DEG list | .txt | Bulk RNA sequencing of Hobit WT and KO cells | 3E, 3F, S8F, S8G | Differential expression results table for WT vs KO | | 3 | bulk\_DE\_Mackay2016-core-sig.txt | Gene list | .txt | Bulk RNA sequencing of Liver, Gut, and Skin TRM cells and Circulating T cells | 3F, S8G | List of genes in the core TRM signature, as defined in the text | | 4 | bulk\_DE\_MafWT-vs-MafKO.txt | DEG list | .txt | Bulk RNA sequencing of Maf WT and KO cells | 3E, 3F, S8F, S8G | Differential expression results table for WT vs KO | | 5 | bulk\_DE\_skinTRM-vs-Circ.txt | DEG list | .txt | Bulk RNA sequencing of Skin TRM and Circulating T cells | S8F | Differential expression results table for TRM vs Circ | | 6 | bulk\_DE\_Tc1-vs-Circ.txt | DEG list | .txt | Bulk RNA sequencing of Th 1 T cells and Circulating T cells | 3B | Differential expression results table for Th1 vs Circ | | 7 | bulk\_DE\_Tc1-vs-Tc17.txt | DEG list | .txt | Bulk RNA sequencing of Th 1 T cells and Th 17 T cells | 1F, 3E, S8A, S8E | Differential expression results table for Th1 vs Th17 | | 8 | bulk\_DE\_Tc17-vs-Circ.txt | DEG list | .txt | Bulk RNA sequencing of Th 17 T cells and Circulating T cells | 3B | Differential expression results table for Th17 vs Circ | | 9 | sc\_fine\_skin\_Trm\_cluster\_gene\_expression.csv | DEG list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | Gene expression comparison between 10 unbiased skin Trm clusters resolved by single-cell RNA sequencing | | 10 | sc\_WT\_Trm1\_vs\_WT\_Trm17\_gene\_expression.csv | DEG list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | Gene expression comparison between WT Trm1 and Trm17 cells | | 11 | sc\_WT\_Trm17\_versus\_WT\_Tcirc\_gene\_expression.csv | DEG list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | Gene expression comparison between WT Trm17 and circulating T cells | | 12 | sc\_WT\_Trm1\_versus\_WT\_Tcirc\_gene\_expression.csv | DEG list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | Gene expression comparison between WT Trm1 and circulating T cells | | 13 | sc\_WT\_Trm17\_versus\_cMafcKO\_Trm17\_gene\_expression.csv | DEG list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | Gene expression comparison between WT and Maf-KO skin Trm17 cells | | 14 | sc\_WT\_Trm1\_versus\_HobitKO\_Trm1\_gene\_expression.csv | DEG list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | Gene expression comparison between WT and Hobit-KO skin Trm1 cells | | 15 | sc\_list\_of\_genes\_excluded\_from\_integration.csv | Gene list | .csv | Single-cell RNA-sequencing of Hobit-KO, c-Maf-KO and WT skin CD8+ Trm cells | 3G-O | List of heatshock, ribosomal and TCR genes excluded from integration anchors. | **Plain text (.txt) files can be interpreted as follows:** Stand-alone plain text files, each containing a table of differential expression results, for all genes, ordered by most differentially expressed genes (top) to least differentially expressed genes (bottom). These tables can be used to search for results of genes not discussed in the text. Tables contain the following information (in columns) for each gene (in rows): \- GeneID = entrez gene ID \- Symbol = gene symbol \- Synonyms = gene synonyms \- Chromosome = chromosome where the gene is located \- Description = description of gene \- type\_of\_gene = type of gene \- Length = gene length \(measured in base\-pairs\) \- logFC = log2 fold\-change \- logCPM = log2 counts\-per\-million \- LR = value of likelihood ratio statistic \- PValue = unadjusted p\-value for the statistical test of differential expression \- FDR = false discovery rate; adjusted p\-value \(using the Benjamini\-Hochberg method\) \- moderated\.t \(if present\) = moderated t\-statistic \(referred to as "standardised logFC" in the text\) **Comma separated values (.csv) files can be interpreted as described:** Each .csv file contains a table of differential expression results for all genes. These tables can be used to search for results of genes not discussed in the text. Tables contain the following information (in columns) for each gene (in rows): \- Gene \- gene symbol \(name of the gene\) \- p\_val \- P value calculated with Seurat FindAllMarkers\(\) or FindMarkers\(\) functions \- avg\_log2FC \- average log2 Fold\-Change value \- pct \- percentage \- p\_val\_adj \- adjusted P value \(using the Benjamini\-Hochberg method\) \- cluster \- name annotated of unbiased cluster \- Category \(if present\) \- classification of gene excluded from integration ## Sharing/Access information Links to other publicly accessible locations of the data: * https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA419368 * https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70813 * https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120427 * [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233389](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233389 "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233389") Data was derived from the following sources: * Tc1 vs Tc17 gene expression derived from PRJNA419368: 'Non classical immunity controls microbiota impact on skin immunity and tissue repair' * Core Trm vs. Tcirc signature and Hobit KO or Blimp1 KO vs. WT gene expression derived from GSE70813: 'Hobit and Blimp1 instruct a universal transcriptional program of tissue-residency in lymphocytes' * Maf KO vs. WT gene expression derived from GSE120427: 'c-Maf is an essential commitment factor for IL-17-producing gd T cells' * Single-cell sequencing analysis of Hobit KO, c-Maf cKO and WT skin Trm cells derived from GSE233389: 'Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells' ## Code/Software Code used to generate Figure 1F and Figures 3A-F (bulk sequencing analysis) is included in the repository as an R markdown (.Rmd) file which is a stand-alone plain text file of R markdown notebook for performing all bulk RNA-seq analyses discussed in the text, titled "Code\_bulk-RNA-seq-Park-et-al.Rmd". If the code in the notebook is run from start to finish it will produce (in the notebook output) all figures and results that relate to the bulk RNA-seq data. To run this code, the following is needed: versions of R, R-studio, and the software libraries listed in the sessionInfo .Rds file titled "sessionInfo\_Code\_bulk-RNA-seq-Park-et-al.Rds". | File number | File name | File Category | File Type | Description | | ----------- | --------- | ------------- | --------- | ----------- | | 1 | Code\_bulk-RNA-seq-Park-et-al.Rmd | Code | .Rmd | Code outlined in an R markdown file which can be used to generate the Figures pictured in 1F, 3A-F and SF8 | | 2 | sessionInfo\_Code\_bulk-RNA-seq-Park-et-al.Rds | sessionInfo object | .Rds | sessionInfo archiving package versions used to run the code above |