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Tissue-resident memory CD8+ T cells possess unique transcriptional, epigenetic and functional adaptations to different tissue environments

Abstract

Tissue-resident memory T cells (TRM cells) provide protective immunity, but the contributions of specific tissue environments to TRM cell differentiation and homeostasis are not well understood. In the present study, the diversity of gene expression and genome accessibility by mouse CD8+ TRM cells from distinct organs that responded to viral infection revealed both shared and tissue-specific transcriptional and epigenetic signatures. TRM cells in the intestine and salivary glands expressed transforming growth factor (TGF)-β-induced genes and were maintained by ongoing TGF-β signaling, whereas those in the fat, kidney and liver were not. Constructing transcriptional–regulatory networks identified the transcriptional repressor Hic1 as a critical regulator of TRM cell differentiation in the small intestine and showed that Hic1 overexpression enhanced TRM cell differentiation and protection from infection. Provision of a framework for understanding how CD8+ TRM cells adapt to distinct tissue environments, and identification of tissue-specific transcriptional regulators mediating these adaptations, inform strategies to boost protective memory responses at sites most vulnerable to infection.

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Fig. 1: TRM cells in distinct tissue microenvironments possess unique transcriptional programs.
Fig. 2: ScRNA-seq identifies distinct tissue and function-specific transcriptional programs in TRM cells.
Fig. 3: Sustained TGF-β signaling is required for the maintenance of TRM cells in IEL and SG.
Fig. 4: TRM cells in distinct tissue microenvironments possess unique epigenetic programs.
Fig. 5: Loss of Hic1 prevents the formation of SI TRM cells.
Fig. 6: Hic1 overexpression enhances the formation of SI TRM. cells.

Data availability

All bulk RNA-seq, ATAC-seq and scRNA-seq datasets have been uploaded to the Gene Expression Omnibus repository (accession no. GSE182276). The following published datasets were used in addition: accession nos. GSE125527 (ref. 45), GSE70813 (ref. 10), GSE131847 (ref. 7), PRJNA414132 (ref. 20), GSE117568 (ref. 42), GSE63340 (ref. 17) and GSE128197 (ref. 41). The mouse reference genome mm10 has been used for RNA-seq, ATAC-seq and scRNA-seq analysis.

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Acknowledgements

This work was funded by the National Institutes of Health (grant no. AI067545 to A.W.G. and no. AI132122 to A.W.G. and J.T.Chang) and the American Cancer Society Postdoctoral Fellowship (grant no. PF-20-048-01-LIB to J.T.Crowl). ATAC-seq and scRNA-seq using the 10× Genomics platform was conducted at the IGM Genomics Center, UCSD and supported by grant nos. P30KC063491 and P30CA023100. A.W.G. is a UCSD Tata Chancellor’s Professor. M.H. was supported by the German Research Foundation fellowship (no. HE 8656/1-1). We thank H. Nguyen for assistance with measuring LCMV titers, the Goldrath laboratory members for technical advice, helpful discussion and critical reading of the manuscript and the Immunological Genome Project for reagents and sample/data processing.

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Authors

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J.T. Crowl, M.H., J.T. Chang and A.W.G. conceived the project and performed the methodology. J.T. Crowl, M.H., A.F., C.T., K.D.O., J.J.M. and Z.E. did the investigations. J.T. Crowl, M.H., A.F. and J.J.M. carried out the formal analysis. J.T. Crowl, M.H. and A.W.G. wrote the original draft of the paper. J.T. Crowl, M.H., J.T. Chang, K.D.O. and A.W.G. wrote the paper. J.T. Chang. and A.W.G. supervised the project. A.W.G. and J.T. Chang acquired the funds.

Corresponding author

Correspondence to Ananda W. Goldrath.

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A.W.G. is a member of the ArsenalBio scientific advisory board. J.T. Crowl is a current employee of Outpace Bio. The remaining authors declare no competing interests.

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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Ioana Visan in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Gating strategy.

a, Gating strategy used to identify indicated IV TRM populations.

Extended Data Fig. 2 Phenotypic characterization of TRM after LM-gp33 infection and expression of select genes in TRM from other published datasets.

a-b, CD69 and CD103 expression by CD8+ TRM isolated from tissues 30-40 days after infection with LM-GP33. Representative flow cytometry plots (a) and quantification (b). c-d, Percent of GZMA+ (c) and GZMB+ (d) P14 cells isolated from the indicated tissues 30-40 days after infection with LM-GP33 as assessed by flow cytometry. Datasets are from (e) Mackay et al, Science 2016. (f) Han et al, Immunity 2017. (g) ex vivo PD1 and Lag3 expression in P14 cells isolated from the indicated tissues. Quantification of flow cytometry data in b, c and d displays the mean ± SD for 10 mice from 3 experimental replicates. Data in g shows a representative experiment with 3 mice from a total of 3 experiments with 10 mice. Significance was calculated using a one-way ANOVA and corrected for multiple comparisons using Tukey’s test. ****p < 0.0001.

Extended Data Fig. 3 Collagenase digestion induces upregulation of a subset of genes also associated with tissue residency.

a-d, P14 cells were adoptively transferred into CD45 congenic hosts one day prior to infection with LCMV. 30-40 days after initial infection, P14 cells were isolated from tissues using no additional treatment (NoTx), collagenase (Coll), or a cold active protease (CAP). a, Quantification of the number of P14 cells recovered from each tissue using the indicated digestion methods. b, Percent of P14 cells expressing CD69 (top left), CD103 (top center left), IL-18R1 (top center right), CD8a (top right), KLRG1 (bottom left), CD127 (bottom center), or CD62L (bottom right) assessed by flow cytometry. c-e, RNA-sequencing of P14 cells isolated from the spleen or kidney using NoTx, Coll, dithioerythritol (DTE), or CAP. c, Differentially expressed genes (348) were clustered with k-means = 3. Select genes in each cluster displayed on the right. Genes that were upregulated in CAP-treated tissues compared to CAP-treated spleens indicated with an asterisk. d, Principal Component Analysis. e, Cd69 expression by P14 cells isolated from the spleen or kidney with CAP. f,g, Genes included in the TRM signatures from this paper (left), Milner et al, Nature 2017 (center) and Mackay et al, Science 2016 (right) were selected. f, Corresponding expression values for collagenase-digested kidney, CAP-digested kidney, and CAP-digested spleen samples were plotted. Each gene in the corresponding TRM signature is represented by a single point and colored by influence of digestion on expression. g, Venn diagram of the preceding data. h, Principal component analysis of RNA-sequencing data from Fig. 1 with all digestion-associated genes removed. Genes were considered digestion-associated if they were expressed above a minimum threshold and at >1.5 fold in collagenase-digested kidney compared to CAP-digested kidney samples. Graphs in a and b display the mean ± SD for 10 mice from 3 experimental replicates. RNA-seq data displayed in c-f contains 2-3 experimental replicates for each sample, and tissues from multiple mice were pooled. Graph in e displays the mean ± SD. Significance calculated using a two-way ANOVA and correcting for multiple comparisons using Dunnett’s test. *p < 0.05, ***p < 0.001, ****p < 0.0001.

Extended Data Fig. 4 Top enriched genes identified in bulk RNA-sequencing of TRM are also found in scRNA-sequencing.

a, The top 5 genes enriched in bulk RNA-sequening samples for TRM isolated from the blood, IEL, SG, fat, and liver are shown on a UMAP dimensional reduction plot.

Extended Data Fig. 5 Removal of digestion-associated gene signature from the TRM gene signature does not alter the enrichment of tissue signature.

a,b, scRNA-sequencing data described in Fig. 2. Each cell was scored based on the enrichment of genes included in the indicated signatures. Cells were colored by score on a UMAP dimensional reduction (a) and separated by cluster and ordered based on score (b).

Extended Data Fig. 6 TRM differentiation programs are a source of intra-tissue heterogeneity.

a, UMAP dimensional reduction of scRNA-sequencing of TRM separated by tissue. Cells were colored by the expression of the indicated genes. Scales are consistent across tissues to allow for comparison within and among tissues. b-c, Expression of CD69, Ly6C, IL18R1 on P14 cells harvested 30-40 days after initial infection with LCMV. Representative flow cytometry plots (b) and quantification (c). d, Quantification of IL18R1 expression on P14 cells harvested from the indicated tissues 30-40 days after initial infection with LM-GP33. Quantification of flow cytometry data in c and d displays the mean ± SD for 6 (c) 10 (d) mice from 2 experimental replicates.

Extended Data Fig. 7 TRM in distinct tissue microenvironments possess unique epigenetic programs.

a-d, ATAC-seq of P14 CD8+ T cells in the spleen and IV P14 CD8+ T cells isolated from the IEL, kidney, SG, fat, and liver. a, Pearson correlation for all peaks across all samples. b, Annotation of the genomic region type for all identified accessible regions (left) and DAR (right). c,d, Shared and unique upregulated DAR (c) and downregulated DAR (d) in each tissue compared to the spleen for all DAR with a p-value <0.05 and a fold change >4 using a Wald statistics.

Extended Data Fig. 8 Blimp1 deletion impairs TRM formation in the IEL and SG more than the kidney.

a-c, Gzmb-Cre/Prdm1fl/fl (WT) and Gzmb-Cre+/Prdm1fl/fl (KO) were transferred at a 1:1 ratio into congenically distinct recipients one day prior to infection with LCMV. Tissues were harvested 60 days after initial infection. a, Ratio of KO to WT P14 cells in the indicated tissues. b-c, % of CD69+ (b) and CD103+ (c) P14 cells for WT and KO populations. Graphs display mean ± SD for a combined 2 experimental replicates, each with m = 4 mice. Significance in (a) calculated with a one-way ANOVA using Tukey’s multiple comparison test. Significance in (b-c) calculated with a two-way ANOVA using with Sidak’s multiple comparison test. ****p < 0.0001.

Extended Data Fig. 9 Hic1 is critical for the differentiation of small intestine TRM.

a, Hic1 expression by resident immune cell populations isolated from the indicated tissues. b-g, 1:1 mixed transfer of P14 cells transduced with a control shRNA or a Hic1-targeting shRNA. b-c, Percentage of P14 cells that are CD69+CD103 (left) or CD69+CD103+ (right) on day 7-8 (b) or day 20-21 post-infection with LCMV (c). d, Percentage of P14 cells that are CD69+CD103 (left) or CD69+CD103+ (right) on day 20 post-infection with LM-GP33. e, Percentage of P14 cells that were terminal effectors (TE, KLRG1+CD127) or memory precursors (MP, KLRG1CD127+) on day 7-8 post-infection with LCMV. f, Percentage of P14 cells that are terminal effector memory (tTEM, CD127-CD62L-), effector memory (TEM, CD127+CD62L-), or central memory (TCM, CD127+CD62L+) on day 20-21 post-infection with LCMV. g-h, Percentage of P14 cells that were TE or MP on day 7 (g) or day 20 (h) after infection with LM-GP33. i-l, 1:1 mixed transfer of P14 cells transduced with a control vector or a Hic1-overexpression vector. i-j, Percentage of P14 cells that are CD69+CD103 (left) or CD69+CD103+ (right) on day 7-8 (i) or day 20-21 (j) post-infection with LCMV. k, Percentage of P14 cells that were TE or MP on day 7-8 post-infection with LCMV l, Percentage of P14 cells that were tTEM, TEM, or TCM on day 20-21 post-infection with LCMV. m, P2xr7 expression by resident immune cell populations isolated from the indicated tissues. Graphs in a and m display mean ± SD for the expression values from RNA-Seq samples (22 samples for CD8+, 17 samples for CD4+, 18 samples for Macrophages, 26 samples for ILC2). Graphs in b, c, e, f, il display mean ± SD for 11 mice from 3 experimental replicates. Graphs in d, g, and h display mean ± SD for 8 mice from 2 individual experiments. Significance calculated with a two-way ANOVA using with Sidak’s multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Extended Data Fig. 10 Human TRM recapitulate phenotypes observed in murine TRM.

a-c, Single-cell RNA-sequencing of healthy human tissue in Boland et al, Science Immunology 2020. a, Hic1 expression after MAGIC imputation. b,c, Individual cells are scored based on enrichment for genes included in the TGFβ signature (b) and TRM signature (c). Single cell data was pooled from 13 different healthy donors for PBMC and rectum biopsies and 10 healthy donors for intestinal samples. Boxplot shows median. The lower and upper hinges correspond to the first and third quartiles. The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge. Statistics were calculated by aggregating the scRNA data to pseudo-bulk samples for each patient and cell type. A T statistics test as implemented in the R package limma was then used to calculate the P values.

Supplementary information

Reporting summary

Supplementary Table 1

RNA-seq analysis of memory CD8+ T cells isolated from distinct tissues.

Supplementary Table 2

RNA-seq analysis of memory CD8+ T cells isolated using distinct methods.

Supplementary Table 3

ScRNA-seq analysis of memory CD8+ T cells isolated from individual tissues.

Supplementary Table 4

ATAC-seq analysis of memory CD8+ T cells isolated from the indicated tissues.

Supplementary Table 5

PageRank analysis of memory CD8+ T cells isolated from distinct tissues.

Supplementary Table 6

RNA-seq analysis of control and Hic1-overexpressing CD8+ T cells from the spleen.

Supplementary Table 7

Antibodies used in the present paper.

Supplementary Table 8

Gene signatures used in the present paper.

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Crowl, J.T., Heeg, M., Ferry, A. et al. Tissue-resident memory CD8+ T cells possess unique transcriptional, epigenetic and functional adaptations to different tissue environments. Nat Immunol 23, 1121–1131 (2022). https://doi.org/10.1038/s41590-022-01229-8

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