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Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses

Nature Immunology volume 15, pages 365–372 (2014)Cite this article

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Abstract

T lymphocytes responding to microbial infection give rise to effector cells that mediate acute host defense and memory cells that provide long-lived immunity, but the fundamental question of when and how these cells arise remains unresolved. Here we combined single-cell gene-expression analyses with 'machine-learning' approaches to trace the transcriptional 'roadmap' of individual CD8+ T lymphocytes throughout the course of an immune response in vivo. Gene-expression signatures predictive of eventual fates could be discerned as early as the first T lymphocyte division and may have been influenced by asymmetric partitioning of the receptor for interleukin 2 (IL-2Rα) during mitosis. Our findings emphasize the importance of single-cell analyses in understanding fate determination and provide new insights into the specification of divergent lymphocyte fates early during an immune response to microbial infection.

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Figure 1: Gating strategy and experimental approach.
Figure 2: Effector and memory CD8+ T lymphocyte subsets are molecularly distinct on a single-cell level.
Figure 3: Early heterogeneity of gene expression in individual CD8+ T lymphocytes during an immune response.
Figure 4: Classifier analysis allows prediction of eventual fates of individual CD8+ T lymphocytes.
Figure 5: Temporal model for predicting the differentiation paths of individual CD8+ T lymphocytes.
Figure 6: Asymmetric segregation of IL-2Rα during the division of T lymphocytes influences the eventual fate of daughter cells.

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Acknowledgements

We thank S. Hedrick, J. Bui, A. Goldrath, S. Schoenberger and members of the Chang and Yeo laboratories for discussions and critical reading of the manuscript. Supported by the US National Institutes of Health (DK080949, OD008469 and AI095277 to J.T.C., and HG004659 and NS075449 to G.W.Y.), the UCSD Digestive Diseases Research Development Center (DK80506), the California Institute for Regenerative Medicine (RB1-01413 and RB3-05009 to G.W.Y.), the National Science Foundation (B.K.), the Alfred P. Sloan Foundation (G.W.Y.) and the Howard Hughes Medical Institute (J.T.C.).

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Author notes

  1. Janilyn Arsenio and Boyko Kakaradov: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medicine, University of California San Diego, La Jolla, California, USA

    Janilyn Arsenio, Patrick J Metz, Stephanie H Kim & John T Chang

  2. Department of Cellular and Molecular Medicine, UCSD Stem Cell and Bioinformatics Programs, and Institute for Genomic Medicine, University of California San Diego, La Jolla, California, USA

    Boyko Kakaradov & Gene W Yeo

  3. Department of Physiology, National University of Singapore and Genome Institute of Singapore and Molecular Engineering Laboratory, A*STAR, Singapore

    Gene W Yeo

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Contributions

J.A. and J.T.C. designed experiments; J.A., P.J.M. and S.H.K. did experiments. B.K. and G.W.Y. analyzed data; and J.A., B.K., G.W.Y. and J.T.C. wrote the manuscript.

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Correspondence to Gene W Yeo or John T Chang.

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Integrated supplementary information

Supplementary Figure 1 Single-cell gene-expression data acquisition.

(a) Fluidigm 96.96 IFC array heatmap representing individual qPCR reactions of 94 Taqman gene expression assays in 94 single CD8+ T cells. Heatmap is representative of at least three independent experiments per cell population. Negative (no template) and positive controls (cDNA from an in vitro stimulated CD8+ T cell bulk population sample) were included in every array. (b) Data matrix containing raw Ct values of all single-cell qRT-PCR reactions used in the analyses. Individual lines along the vertical axis represent single cells. Expression data from 1,300 single cells representing naïve (149 cells), division 1 (144), distal daughter (68), proximal daughter (83), day 3 (143), day 5 (154), day 7 (134), Tmp (62), Tsle (89), Tcm (138), and Tem (136) cells were used for analyses.

Supplementary Figure 2 t-distributed stochastic neighbor embedding (tSNE) analysis.

tSNE reduces the dimensions of a multivariate dataset (94 dimensions for each of the 94 genes in our analysis). Each data point (a single cell) is assigned a location in a two- or three-dimensional map to illustrate potential clusters (populations) of neighboring cells, which contain similar gene expression patterns.

Supplementary Figure 3 Single CD8+ T lymphocytes responding to microbe exhibit the greatest divergence in gene expression early after infection.

(a) The top five genes that drive variance in mean gene expression within and between the indicated CD8+ T lymphocyte populations were identified by JSD analysis. Red bars represent the divergence of each gene. The top five genes that drive intra-population divergence for a single population are listed in the box located at the intersection of that population with itself. For example, the top five genes driving intra-population divergence for naïve cells are Ifngr1, Ptprc, Ccr5, Psmb7, and Sell. The top five genes that drive inter-population divergence between any two cell populations are listed in the box located at the intersection of those two populations. For example, the top five genes driving inter-population divergence for naïve and division 1 cells are Klf2, Lgals1, Irf4, Il2ra, and Myc. (b) The JSD analysis shown in Figure 3b was repeated by sub-sampling the populations so that each pair was compared with equal sizes. This analysis confirmed that the inter-population divergence measurement was not affected by unequal group sizes.

Supplementary Figure 4 Supervised analysis approaches.

(a) Decision tree built from the data consisting of several predictive rules comparing expression of Ptprc, Ccl5, and Sell to decide whether a cell is more Tcm- or Tsle-like; two terminal nodes labeled "…" depict a continuation of the decision tree. The full decision tree is available at: http://sauron.ucsd.edu/public_data/AlternatingDecisionTree_Tcm_Tsle.pdf (b) We evaluated the Tcm vs. Tsle misclassification error as a function of the classifier complexity (number of trees in the ensemble). The training error (blue curve) for a single instance of the classifier was calculated on the entire gene expression dataset of 138 Tcm and 89 Tsle cells. The generalization error (red dots) was estimated by the leave-one-out cross-validation procedure. Briefly, each cell in the dataset was set aside and a separate instance of the classifier was trained on the remaining cells, which was used to predict the class of the set-aside cell. The number of held-out cells that were misclassified is reported as the cross-validation error as an approximation to the generalization error of the classifier on future gene expression data from similar cell populations. It is clear that an ensemble of 10 – 20 trees is sufficient to discriminate between Tcm and Tsle cells without overfitting. (c) The significance and robustness of each proposed differentiation path for the HMM model was measured by performing 10 random initializations of the HMM parameters and 100 random shuffles of the data. Each panel compares the cumulative distribution functions (CDFs) of log-likelihoods for the proposed model on the real data (green for linear, blue for bifurcating, and pink for the best performing model) to the CDF of log-likelihoods for the same model on randomly reshuffled data. These CDFs were compared by the 2-sample Kolmogorov-Smirnov test, whose p-value is shown above each panel. The last panel (bottom right) shows the reproducibility of the best model by comparing the log-likelihood CDFs of the model on the original data (pink) versus a bootstrap resampled version of the same data (red). Insets include each proposed differentiation path. Transition states include pre-memory (p-Mem), pre-short-lived-effector (p-Tsle), and common progenitor (Pre-X). (d) Cells in early states of differentiation (division 1, day 3, day 5) were ranked by their Tsle- or memory-like expression profiles. Cells were then linked to sorted naïve and sorted Tsle, Tem and Tcm cells by bootstrap resampling, forming hypothetical differentiation paths that were analyzed with a Hidden Markov Model. Shown is the matrix of probabilities that a CD8+ T cell will transition from one state (vertical axis) to another (horizontal axis).

Supplementary Figure 5 Change in log gene expression associated with each transition phase during specification of CD8+ T lymphocyte fates.

The absolute change in expression of each of the 94 genes during each unique transition is shown: naïve to pre-memory, naïve to pre-Tsle, pre-Tsle to Tsle, pre-memory to Tcm, and pre-memory to Tem.

Supplementary Figure 6 Frequencies of the progeny of adoptively transferred IL-2RαhiCD62Llo and IL-2RαloCD62Lhi cells.

IL-2RαhiCD62Llo or IL-2RαloCD62Lhi cells that had undergone their first division were sorted and adoptively transferred into infection-matched recipients (n=13). The frequencies of transferred CD45.1+ cells were measured in the blood following adoptive transfer and their numbers are shown as a percentage of total CD8+ T cells (a) at day 7 and (b) at multiple timepoints following the primary infection. Data are representative of two independent experiments. Error bars indicate s.e.m. (Kolmogorov-Smirnov test)

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Arsenio, J., Kakaradov, B., Metz, P. et al. Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses. Nat Immunol 15, 365–372 (2014). https://doi.org/10.1038/ni.2842

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