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Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes

A Publisher Correction to this article was published on 26 March 2021

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Abstract

CD4+ effector lymphocytes (Teff) are traditionally classified by the cytokines they produce. To determine the states that Teff cells actually adopt in frontline tissues in vivo, we applied single-cell transcriptome and chromatin analyses to colonic Teff cells in germ-free or conventional mice or in mice after challenge with a range of phenotypically biasing microbes. Unexpected subsets were marked by the expression of the interferon (IFN) signature or myeloid-specific transcripts, but transcriptome or chromatin structure could not resolve discrete clusters fitting classic helper T cell (TH) subsets. At baseline or at different times of infection, transcripts encoding cytokines or proteins commonly used as TH markers were distributed in a polarized continuum, which was functionally validated. Clones derived from single progenitors gave rise to both IFN-γ- and interleukin (IL)-17-producing cells. Most of the transcriptional variance was tied to the infecting agent, independent of the cytokines produced, and chromatin variance primarily reflected activities of activator protein (AP)-1 and IFN-regulatory factor (IRF) transcription factor (TF) families, not the canonical subset master regulators T-bet, GATA3 or RORγ.

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Fig. 1: The transcriptional landscape of CD4+ T cells in the colon.
Fig. 2: Variation in Teff transcriptomes shows continuous distribution that is not dictated by ‘TH subsets’.
Fig. 3: Teff phenotypes are distinguishable by infection rather than by TH type.
Fig. 4: Repeated clonotypes can adopt different phenotypes and do not diverge over time.
Fig. 5: The chromatin states of Teff cells are found on a continuum.
Fig. 6: Transcriptional and functional validation of Teff continuity.
Fig. 7: New Teff populations.

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Data availability

The data reported in this paper were deposited in the Gene Expression Omnibus (GEO) database under accession no. GSE160055).

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Acknowledgements

We thank K. Murphy, N. Yosef, V. Kuchroo, R. Ramirez, D. Ramanan, M. Sassone-Corsi, E. Pamer, A. Anderson and J. Huh for insightful discussions, data and mouse lines and K. Hattori, C. Araneo, K. Seddu, D. Dionne and the Klarman Cell Observatory team and N. El-Ali and the Bauer Core Facility for help with mice, cell sorting and single-cell profiling. This work was supported by grants from the NIH to C.B. and D.M. (AI125603) and to the ImmGen consortium (AI072073). E.K. was supported by a PhD fellowship from Boehringer Ingelheim Fonds. K.C. was supported by NIGMS-T32GM007753 and a Harvard Stem Cell Institute MD/PhD Training Fellowship.

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E.K. and E.W. performed experiments. E.K., B.V., K.C., H.S., S.M. and C.B. analyzed and interpreted data. A.S., P.I.T., J.C. and G.L. provided data or reagents. E.K., S.M., D.M. and C.B. designed the study and wrote the manuscript.

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Correspondence to Diane Mathis or Christophe Benoist.

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Peer review information Nature Immunology thanks Thomas Korn, Evan Newell and Masahiro Ono for their contribution to the peer review of this work. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 scRNAseq of Teff under normal conditions.

a, Quality control plots (per-cell number of unique reads vs number of transcripts detected) for the scRNAseq data from total colonic CD4+ T cells (data from Fig. 1a). b, Same plots as (a), for CD4+ QC of scRNAseq data from total colonic CD4+ T cells of germ-free and SPF mice. c, SMART-SEQ2 single-cell data from colon T memory cells (from ref. 16). Aggregate expression of Th-specific genesets (defined as for Fig. 1) are overlayed on the tSNE.

Extended Data Fig. 2 scRNAseq of Teff under infectious conditions.

a, tSNE representation of all CD4+ T cells in the scRNAseq data from the parallel infection experiment of Fig. 2. Left panel: each color represents cells from a different infection condition. Tregs, naive Tconvs, cycling cells and Teffs are circled; right panel: expression of key genes. b, UMAP representation of Teff cells from the same experiment, colored by condition; Right panels: Overlay of TH genesets (per Fig. 2). c, Data from the same parallel-infection experiment as Fig. 2c and displayed using the same tSNE coordinates, highlighted with aggregate expression of TH signature genes from ref. 19. d, Expression of key cytokines and transcription factors in the same scRNAseq data as Fig. 2c. e, Independent parallel infection experiment. Samples were not hash-tagged, and processed in parallel encapsulations, and cell data were aligned by canonical correlation analysis (CCA) for tSNE representation, color-coded by sample. Right: expression of Th-specific genesets, defined as for Fig. 2c.

Extended Data Fig. 3 Different clustering approaches and signatures do not parse out the data into TH subsets.

a, KNN clusters shown on hash-tagged tSNE. Percentages of cells corresponding to each signature in each KNN cluster are shown in the table. b, Biscuit clusters shown on hash-tagged tSNE. Percentages of cells corresponding to each signature in each Biscuit cluster are shown in the table. c, Backspin clusters shown on hash-tagged tSNE. Percentages of cells corresponding to each signature in each Backspin cluster are shown in the table. d, Overlay of pathogenic TH17 signatures from refs. 22,23. Left panel: all Teff; right panel: only Il17a+ Teff. e, Overlay of Citrobacter TH17 signature from ref. 24 on the tSNE plot.

Extended Data Fig. 4 Neural Network prediction of IFN-γ and Il17-producing phenotypes.

a, A Keras neural network was trained to use as input the expression of 500 most variable genes in Teff single-cell RNAseq data to predict Ifng or Il17a expression in each cell. Loss as a function of training epochs plotted here. Note the overfitting beyond 10 epochs (representative of >50 independent training runs with random 80/20 training/test). b, Accuracy of DNN-predicted cytokine expression by individual Teff cells, relative to their actual expression in the test scRNAseq data (non-expressing cells were not included as input, since there is uncertainty as to their real nature given drop-out frequencies in scRNAseq data). Numbers shown represent the range observed in 10 independent training runs (with different training/test sets). c, Contribution of each transcript to the prediction of Il17a or Ifng expression, as score in the Integrated Gradients, comparing the model learned in two independent runs. A positive score indicates influence on predicting Il17a expression, a negative score influence in predicting Ifng expression.

Extended Data Fig. 5 Th-associated genes are not the main drivers of Teff heterogeneity.

a, Distribution of Top 6 PCs of Teffs from all hash-tagged samples, with cell cycle genes regressed out. Genes that are Th-associated are highlighted. b, Co‐expression of key cytokines across all samples. Mean Pearson gene:gene correlation of cytokine genes across all samples. Only significantly correlated cytokines are colored (p < 0.05, χ² test). Significant P values: Il4/Il13 6.3 × 10−3, Il4/Il5 1.8 × 10−98, Il5/Il13 5.5 × 10−129, Il17a/Il17f 1.3 × 10−4. c, Coregulated gene modules in Teff single-cells. Gene:gene correlation between 588 most variable genes was calculated independently within each condition/infection of the single-cell datasets, then averaged between conditions. 16 gene modules were determined by Affinity Propagation within this matrix, annotated at right. d, Overlay of average expression of these gene modules on Teff tSNE (per 2c) with barplots showing genes with highest mean correlation (full list in Supplementary Table 3).

Extended Data Fig. 6 Unique clonotypes are not restricted to a TH type and do not diversify over time.

a, Quantification of flow cytometry data on cells from mouse LP at different timepoints of infection; Left: Proportion of CD4+ T cells within total CD45+; Middle: Proportion of Teff (CD44hi Foxp3) within total CD4+ T; Right: Proportion of IFN-γ+ cells within total CD4 T. b, Cell numbers per scRNAseq clustering by day post infection. Treg clusters were identified as Foxp3+, naive cluster as Foxp3 Ccr7+ and Teff clusters as Foxp3 Cd44+. c, Left: UMAP as in 5a, showing two groups of cell clusters: cells taken from mice after day 10 are colored in red, and cells taken prior to day 7 are colored in blue. Right: DEG analysis on top 20 differentially expressed genes between the two cluster groups. Asterisks represent genes that overlap with genes that are higher in Teff after Salmonella infection in Fig. 3a. d, Bar graph representing proportions of cells belonging to singlet clones (clones that appear only once) or expanded clones (clones that appear more than once) in each of the clusters defined in S6b, grouped by day post infection. e, Median Euclidean distances between cells within the same clonotype across the top 10 clonotypes for each timepoint. Euclidean distance was calculated based on the top 1000 variable genes. Each color dot represents a unique clonotype, and the size of the dot signifies the number of cells within each clonotype.

Extended Data Fig. 7 The unexpected MyT subset.

a, Flow cytometric analysis (gated CD4+TCRβ+FOXP3 Teff) cells from colonic LP of Salmonella infected mice. b, Volcano plot of bulk RNAseq from colonic Teff sorted as in C (LP of Salmonella infected mice). Genes highlighted in red belong to the myeloid genes listed in B.

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Kiner, E., Willie, E., Vijaykumar, B. et al. Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes. Nat Immunol 22, 216–228 (2021). https://doi.org/10.1038/s41590-020-00836-7

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