Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA and surface protein data from 500,089 memory T cells to define 31 cell states from 259 individuals in a Peruvian tuberculosis (TB) progression cohort. At immune steady state >4 years after infection and disease resolution, we found that, after accounting for significant effects of age, sex, season and genetic ancestry on T cell composition, a polyfunctional type 17 helper T (TH17) cell–like effector state was reduced in abundance and function in individuals who previously progressed from Mycobacterium tuberculosis (M.tb) infection to active TB disease. These cells are capable of responding to M.tb peptides. Deconvoluting this state—uniquely identifiable with multimodal analysis—from public data demonstrated that its depletion may precede and persist beyond active disease. Our study demonstrates the power of integrative multimodal single-cell profiling to define cell states relevant to disease and other traits.
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Single-cell sequencing data are available on dbGaP; count matrices and metadata are available on the GEO (GSE158769). Single-cell expression and cluster abundance data can also be explored in an interactive browser at https://immunogenomics.io/tbru/. Genotype data are available on dbGaP under accession code phs002025.v1.p1.
Custom analysis scripts are available on GitHub (https://github.com/immunogenomics/TB_Tcell_CITEseq).
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We thank K. Slowikowski for building an interactive data browser. This work is supported in part by funding from the National Institutes of Health (U19 AI111224 to S.R., M.B.M. and D.B.M.; UH2AR067677 to S.R.; T32 HG002295 to A.N.; T32 AR007530 to A.N.; U01 HG009379 to S.R.; U19 AI111211 to J.D.E.; and R01AI049313 to D.B.M.).
The authors declare no competing interests.
Peer review information Nature Immunology thanks Thomas Scriba and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. 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.
a, In silico memory T cell gating. Each cell is plotted based on its normalized surface expression of CD3 and CD45RO, measured through CITE-seq. Gates are demarcated with red dashed lines. Red cells were removed. Counts represent the number of cells in each quadrant. b, UMAP representation of gated cells. Red cells were gated out in (a) and cells clustering with them in the UMAP (shown in black in (a) and (b)) were also removed. c, Normalized CD3 and CD45RO surface protein expression. d, Number of cells per sample after QC, stratified by TB progression status. P value is from a two-sided t test comparing mean cell counts in each group. e, Pearson correlation coefficient (r) was calculated between normalized mRNA and surface protein expression for each marker across cells passing QC. r is plotted against average normalized mRNA expression for each protein. f, Each cell is plotted based on normalized expression of each marker in surface protein and mRNA, both measured through CITE-seq, with density contours. We fit a best-fit line (in blue) with a linear model.
Extended Data Fig. 2 Comparing proportions of eight major T cell states between flow cytometry and CITE-seq.
a, Average percents per population in CITE-seq vs. flow cytometry. Gates in (b). Dashed line indicates the identity line. b, For each population, proportions plotted across 259 donors. Flow cytometry gating occurred after gating T cells. CITE-seq gating occurred after isolation of memory T cells. The dashed line indicates the identity line, and we calculated Pearson correlation coefficients (r) for each state.
a, Correlations for the top 20 canonical dimensions used in downstream analysis. Bars represent the Pearson correlation between mRNA and protein projections for each dimension. b, Marker correlation with canonical variates (CVs). Each marker is plotted based on its mRNA and protein correlation with CV1 (left) or CV2 (right). c, Innateness scores. UMAP is colored based on a gene expression-derived cytotoxicity score defined in Gutierrez-Arcelus, et al. d, Correlation between innateness score and CV1. Each cell is plotted based on its innateness score from (c) and its CV1 projection, and we report the Pearson correlation coefficient.
Each cell is colored according to its expression of each protein and plotted in UMAP space. Colors are scaled independently for each marker from minimum (blue) to maximum (yellow) expression.
For each of the 31 multimodal clusters, we plotted its proportion in replicate 1 and in replicate 2 of each donor. We calculated the Pearson correlation coefficients (r) for each cluster.
Effects of age, sex, winter blood draw and proportion of European genetic ancestry in (a) model correcting for technical covariates (# UMIs/cell, % MT UMIs/cell), donor, batch and TB disease status, and (b) full model with TB disease status, age, sex, winter blood draw, proportion of European genetic ancestry, technical covariates, donor and batch. For all, n = 271 samples from 259 independent donors. For each cluster, data are presented as the MASC OR of a cell being in each cluster given the contrast covariate (95% CI error bars) and the -log(LRT p value) of the association. The dashed horizontal line corresponds to a Bonferroni p-value threshold of 0.05/31. Labeled clusters are significant at this threshold.
a–c, mRNA clusters. d–f, protein clusters. a and d, UMAPs colored by unimodal clusters. Clusters boxed in red are CD4+, purple are mixed CD4+ and CD8+, blue are CD8+, and green are CD4-CD8-. b and e, Expression of major lineage-defining surface markers measured through CITE-seq. The UMAPs are colored by the expression of five markers measured through CITE-seq. Colors are scaled independently for each marker from minimum (blue) to maximum (yellow) expression. c and f, Heatmap of overlap between unimodal and multimodal clusters. Colors indicate the proportion of the multimodal cluster (column) overlapping with the unimodal cluster (row). g, Associations between TB disease status and unimodal protein clusters. For each cluster, the data are presented as MASC ORs of a cell being in each cluster for cases vs. controls (95% CI error bars), and the -log(LRT p value) of the association (n = 271 samples from 259 independent donors). The dashed horizontal line corresponds to a Bonferroni p-value threshold of 0.05/40. Labeled clusters are significant at this threshold. h, Abundance of C-20 in 128 cases and 131 controls. P value is from an LRT with 1 d.f. Boxplots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of box, respectively), and 1.5xIQR (or minimum/maximum if they lie within that range; end of whiskers).
a, Correlation between actual and predicted C-12 proportion, per sample in memory T cell CITE-seq study (n = 271, black) and bulk PBMC RNA-seq (n = 15, blue). Line represents the identity line and we calculated the Pearson correlation coefficients (r) for T cell samples and bulk PBMC samples separately. Predicted (b) C-12 or (c) C-11 (TH2, negative control) proportion in 3 categories of donors from Berry, et al. d, Predicted C-12 proportion in active donors at 3 time points during anti-mycobacterial treatment (0, 2, 12 months) and uninfected controls. In b–d, we calculated p values with a two-sided t test. e, Histogram of two-sided t test p values from 1,000 trials of downsampling LIMAA cohort to 7 cases and 12 controls (as in Berry, et al.) and comparing the average C-12 proportion in cases vs controls. Dashed line is the significance threshold of p = 0.05 (power = 0.15). f, Predicted C-12 proportion in active cases and latent controls at 2 pre-disease-progression time points in Scriba, et al. g, Predicted C-12 proportion in active cases and latent controls in pre- and post-disease cohorts. Pre-disease data are aggregated across 2 time points. In f and g, p-values are from a one-sided t-test (Satterthwaite’s d.f. method) of the beta estimate for TB progression status in the linear mixed model. h, Concordance of Pearson correlations between each cluster’s proportion and the C-12 score or the C-12 cluster’s proportion. Pearson correlation coefficients were computed for each cluster across 271 memory T cell samples. Each point represents one of the 31 clusters, and the dashed line is the identity line. All boxplots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of box, respectively), and 1.5xIQR (or minimum/maximum if they lie within that range; end of whiskers).
Bars represent the mean and error bars show standard error of the mean across 5 Boston donors unascertained for TB. a, Per-donor percent of cells producing each cytokine in gated populations. b, Per-donor percent of total cytokine-producing memory CD4 + T cells in each gated population.
a, Biaxial plots showing representative gating of CD3+CD4+CD45RO+CD161+CCR6+CD26+ cells in a Boston donor. b, Intracellular staining for IL-17A and IFNγ in a Boston control donor and two Peruvian TB cohort donors after either no peptide stimulation (control) or stimulation with the MTB300 megapool. c, IL-17A or IFNγ response to MTB300 stimulation in all CD3 + T cells from either Boston control donors (n = 2) or Peruvian TB cohort donors (n = 6). Each point corresponds to the percent of cells producing IL-17A or IFNγ from one donor, measured with intracellular cytokine staining. Lines connect measurements from the same donor before and after stimulation with MTB300 peptide megapool. Boxplots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of box, respectively), and minimum/maximum (end of whiskers). P values are from a two-sided Wilcoxon signed-rank test comparing donors before and after antigen stimulation.
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Nathan, A., Beynor, J.I., Baglaenko, Y. et al. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. Nat Immunol 22, 781–793 (2021). https://doi.org/10.1038/s41590-021-00933-1
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