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. 2020 Apr 14;11(1):1801.
doi: 10.1038/s41467-020-15543-y.

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines (V体育2025版)

Eddie Cano-Gamez #  1   2 , Blagoje Soskic #  3   4 , Theodoros I Roumeliotis  5 , Ernest So  5 , Deborah J Smyth  1   2 , Marta Baldrighi  1   2 , David Willé  6 , Nikolina Nakic  7 , Jorge Esparza-Gordillo  8 , Christopher G C Larminie  8 , Paola G Bronson  9 , David F Tough  10 , Wendy C Rowan  11 , Jyoti S Choudhary  5 , Gosia Trynka  12   13
Affiliations

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Eddie Cano-Gamez et al. Nat Commun. .

Abstract

Naïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines VSports手机版. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation. .

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Conflict of interest statement

All authors declare no competing interests.

V体育2025版 - Figures

Fig. 1
Fig. 1. TCR/CD28-activation induces cell type specific gene expression programs in CD4+ T cells.
a Overview of the experimental design. b List of cytokine conditions. c PCA plots from the whole transcriptome (upper panel) and proteome (lower panel) of TN and TM cells. Different colors correspond to cell types and different shades to stimulation time points. PCA plots were derived using 47 naive and 47 memory T cell samples for RNAseq and 21 naive and 19 memory T cell samples for proteomics. d Gene expression changes at the RNA and protein levels by comparing TCR/CD28-activated (Th0) cells to resting cells. Up-regulated genes are in red and down-regulated genes are in blue. Different shades indicate different fold-change thresholds. e A selection of significantly enriched pathways (with enrichment scores > 0.7) from genes and proteins differentially expressed after 5 days of activation using the 1D enrichment method. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Cytokines induce cell type specific gene expression programs in CD4+ T cells.
a PCA plot from the full transcriptome and proteome of TN and TM cells following five days of cytokine stimulations. Only stimulated cells were included in this analysis. PCA plots were derived using 20 naive and 21 memory T cells samples RNAseq and 18 naive and 17 memory T cells for proteomics. b Gene expression changes at the RNA and protein levels from pairwise comparisons between cytokine-stimulated cells and Th0-stimulated cells. Up-regulated genes are in red and down-regulated genes are in blue. Different shades indicate different fold-change thresholds. c Selection of significantly enriched pathways (with enrichment scores > 0.7 in at least one cytokine condition) estimated using differentially expressed genes and proteins with the 1D enrichment method. Colors correspond to the enrichment score estimated for each pathway. For the pathways and conditions indicated in light gray, not enough genes were detected to reliably estimate enrichment scores (NAs). d Volcano plots highlighting significant differences in gene and protein expression between Th17 and iTreg-stimulated TN and TM cells. Red indicates expression upregulation in iTreg with respect to Th17-stimulation, blue indicates expression upregulation in Th17 with respect to iTreg-stimulation. Labels were added to IL17, FOXP3 and the top 20 most differentially expressed genes. e Cell state-specific gene signatures defined using jointly RNA and protein expression. Colours encode normalized (Z-scored) gene and protein expression levels. Example genes for each signature are labelled. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Effectorness gradient in resting CD4+ T cells.
a UMAP of single-cell RNA-seq data from resting T cells. Colors represent cells in the five clusters defined using top variable genes and unsupervised clustering. Bar plots represent the proportion of cells assigned to different clusters in each biological replicate. b Gene markers of each cell cluster (Wilcoxon rank sum test) combined with well known markers from the literature. Colors encode the mean expression of each gene in each cluster. c Branched pseudotime trajectory, each cell is colored by its pseudotime value (left panel) or its cluster label (right panel), as determined in a. d Heatmap of genes variable along the pseudotime trajectory (from Monocle). The X axis represents cells ordered by pseudotime (from left to right) and different colors correspond to the scaled (Z-scored) expression of each gene in each cell. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. The effectorness gradient is preserved after T cell activation.
a Th0-stimulated TN and TM cells ordered in a branched pseudotime trajectory. Cells are colored by cell type (top panel) or effectorness (bottom panel). b Heatmap of the most variable genes along the Th0 pseudotime trajectory (Methods). The x axis represents cells ordered by effectorness (from left to right) and colors correspond to the scaled (Z-scored) expression of each gene in each cell. c Mapping of Th0-stimulated cells to their corresponding resting T cell populations. Integration of resting and Th0-stimulated cells using canonical correlation analysis (Methods) followed by UMAP embedding. Cells are colored by activation status (resting vs Th0, top panel) or cell type (bottom panel). d Density plots highlighting the location of cell clusters as defined in resting state. e UMAP embedding color-coded by the effectorness values of resting and stimulated cells. Source data are provided as a Source Data file.
Fig. 5
Fig. 5. The effectorness gradient persists after cytokine-induced T cell polarization.
a UMAP embedding of stimulated T cells into a two-dimensional space. Cells are colored by cell type (top panel) or effectorness value (bottom panel). b Density plots highlighting cells based on the cytokine stimulations. c Expression of selected cytokine-specific markers obtained from our bulk RNA-seq analysis. Cells are colored by their expression level of each marker gene. d UniFrac distances between TN and TM cells exposed to different cytokines summarized in a correlation plot. e Annotation of 17 cell clusters identified from unsupervised clustering using the top variable genes. Each cluster is annotated based on either the genes with highest expression or the effectorness and cytokine condition of the cells contained in it. b Heatmap of the top 10 markers of each cluster (Wilcoxon rank sum test). Colors encode the mean expression of each gene in each cluster. Labels were added to a number of example genes for each cluster. Source data are provided as a Source Data file.
Fig. 6
Fig. 6. Interactions between effectorness and cytokine condition regulate gene expression.
a Schematic representation of the gene expression interaction model. Effectorness and cytokine conditions were incorporated into a linear model with an interaction term (Methods). Genes were assigned to four groups: genes induced by cytokine-stimulation regardless of effectorness (left panel), genes which correlate with effectorness regardless of cytokine-stimulation (central left panel), and genes which correlate with both effectorness and cytokine-stimulation either independently (central right panel) or through an interaction (right panel). b Plots of gene expression (y axis) as a function of effectorness (x axis), with cells stratified by cytokine condition. Two example genes significantly associated with effectorness regardless of cytokine conditions (top panels) and two example genes with a strong interaction between effectorness and Th17 or iTreg-stimulation (bottom panels). Each dot represents a single-cell. c Protein expression levels for 18 genes associated with effectorness in activated TN, TCM, and TEM and TEMRA cells regardless of cytokines condition (genes used in the analysis: ACTB, CCL3, CCL5, CTSW, GNLY, GZMA, GZMB, HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1, HOPX, IFNG, LGMN, LMNA, NFKBIA, TMEM173, TNFRSF18)). Expression values were obtained from a publicly available quantitative proteomics data set (Methods). Significance was calculated using one-way ANOVA (p = 3.42 × 10−12) and group means were compared using Tukey’s Honest Significant Difference test. Only significant p-values are shown (p < 0.05). Each dot represents an individual gene (n = 18). d Levels of IFNγ and IL-9 protein in Th0 and Th17-stimulated TN, TCM, and TEM cells as assessed by flow cytometry. Representative cytometry histograms of IFNγ and IL-9 expression (left panels) and mean fluorescence intensity (MFI) of cytokine-expressing cells (right panels). P-values were calculated using one-way ANOVA. Each dot represents a biologically independent sample. Number of samples: 6 (IFNγ) and 4 (IL-9). Boxplot centers represent median values, while the boxplot bounds represent the 25% and 75% quantiles. Boxplot whiskers represent the 25% quantile − 1.5 × interquartile range (IQR) and the 75% quantile + 1.5 × IQR, respectively. Source data are provided as a Source Data file.
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