Abstract
T cells are key elements of cancer immunotherapy1 but certain fundamental properties, such as the development and migration of T cells within tumours, remain unknown. The enormous T cell receptor (TCR) repertoire, which is required for the recognition of foreign and self-antigens2, could serve as lineage tags to track these T cells in tumours3. Here we obtained transcriptomes of 11,138 single T cells from 12 patients with colorectal cancer, and developed single T cell analysis by RNA sequencing and TCR tracking (STARTRAC) indices to quantitatively analyse the dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. Although both CD8+ effector and ‘exhausted’ T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8+ effector memory cells, implicating a TCR-based fate decision. Of the CD4+ T cells, most tumour-infiltrating T regulatory (Treg) cells showed clonal exclusivity, whereas certain Treg cell clones were developmentally linked to several T helper (TH) cell clones. Notably, we identified two IFNG+ TH1-like cell clusters in tumours that were associated with distinct IFNγ-regulating transcription factors —the GZMK+ effector memory T cells, which were associated with EOMES and RUNX3, and CXCL13+BHLHE40+ TH1-like cell clusters, which were associated with BHLHE40. Only CXCL13+BHLHE40+ TH1-like cells were preferentially enriched in patients with microsatellite-instable tumours, and this might explain their favourable responses to immune-checkpoint blockade. Furthermore, IGFLR1 was highly expressed in both CXCL13+BHLHE40+ TH1-like cells and CD8+ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provide a powerful approach to dissect the T cell properties in colorectal cancer comprehensively, and could provide insights into the dynamic relationships of T cells in other cancers.
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Data availability
The data that support the findings of this study are available from the corresponding author upon request. Sequencing data are available at EGA (accession number EGAS00001002791), and processed gene expression data can be obtained from Gene Expression Omnibus (GEO) (accession number GSE108989).
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Acknowledgements
We thank C. X. Ye for sample preparation and F. Wang, X. Zhang and J. S. Li for assistance with FACS. We thank the Computing Platform of the CLS (Peking University). This project was supported by Bei**g Advanced Innovation Centre for Genomics at Peking University, Key Technologies R&D Program (2016YFC0900100), National Natural Science Foundation of China (81573022, 31530036, 91742203 and 81672375) and Amgen Corporation (USA). L.Z. was supported by the Postdoctoral Foundation of CLS.
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Nature thanks N. Haining, M. Suva and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Z.Z., W.O. and L.Z. designed experiments. L.Z., X.Y., Q.F., R.G., Q.Z., J.Y.H., H.K. and X.G. performed the experiments. L.Z., L.T.Z., Y.Z., X.R. and X.H. analysed sequencing data. Y.L., J.Y., S.W., Y.G. and Z.S. collected clinical samples. B.K. constructed the website. L.Z., W.O. and Z.Z. wrote the manuscript with input from all authors.
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W.O., X.Y., H.K. and J.Y.H. are employees of Amgen Inc.
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Extended data figures and tables
Extended Data Fig. 1 Study design and tracking T cell dynamics of patients with CRC by STARTRAC.
a, The experimental flowchart of this study. b, A cartoon illustrating four indices defined by STARTRAC to characterize T cell dynamics. STARTRAC-dist, tissue preference of a cluster estimated by ratios of observed cell numbers to random expectations (Ro/e); STARTRAC-expa, degree of clonal expansion of a cluster defined as ‘1 − evenness’, with evenness as the normalized Shannon entropy of its TCR distribution; STARTRAC-migr, migratory potential of a cluster estimated by the average entropy of its clonotypes across tissues; STARTRAC-tran, potentials of developmental transitions of a cluster, estimated by the average entropy of its clonotypes across two different functional clusters. The detailed definitions of STARTRAC indices are in Methods. c, Opal multi-colour IHC staining with anti-CD3, -CD4, -CD8 and -FOXP3 antibodies to validate the existence of T cells in CRC tumours (exemplified by patient P0215). Original magnification, ×20. TC, CD8+ cytotoxic T cells; TH, CD4+ T helper cells. d, Gating strategy for single T cell sorting in this study (exemplified by patient P0215). TC, TH and Treg cells were enriched by sorting 7AAD−CD3+CD8+, 7AAD−CD3+CD4+ CD25-/int and 7AAD−CD3+CD4+ CD25hi T cells, respectively.
Extended Data Fig. 2 Pathological and genomic characteristics of CRC tumours in the study.
a, Deficiency of mismatch repair proteins including MLH1, MSH2, MSH6 and PMS2 in all MSI patients (P0413, P0825, P0909 and P0123) measured by IHC (n = 12 patients). +, proficiency; −, deficiency. Original magnification, ×200. b, Profiles of DNA copy numbers of two representative patients (MSI patient, top; MSS patient, bottom). The copy number information was obtained by ADTex and depicted in bin count plots across chromosomes. The read count ratios (‘1’ in y axis means baseline copy number) and B allele frequencies (BAF) are shown. Various coloured dots in the ratio graph represent different copy number status of each segment. ASCNA, allele-specific copy number alteration; HET, heterozygous; LOH, loss of heterozygosity.
Extended Data Fig. 3 Basic information of the single T cell RNA-seq data.
a, Saturation curves of the number of detected genes against sequencing depth (exemplified by cell NTC-53 from patient P1228). Each point on the curve is derived from calculations based on the random selection of a fraction of raw reads from each sample, representing the average of 100 replicate sub-samplings. Error bars denote s.d. Each line with a different colour shows how fast a gene can reach detection saturation at different expression levels, represented by a particular TPM value. b, Unbiased coverage of gene body from 5′ to 3′ between blood, tumours and adjacent normal tissues. c, Frequencies of the V and J segments of the TCR α chains. d, Frequencies of the V and J segments of the TCR β chains. e, Bar plots showing the number of clonotypes and clonal cells in each CD8+ and CD4+ T cell cluster. The clonotypes are categorized as unique (n = 1) and clonal (n = 2 and n ≥ 3) based on their cell numbers. Clonal cells are defined as those clonotypes containing at least two cells. f, t-SNE projection of 3,557 CD8+ T cells (CD8_C01-LEF1, n = 174; CD8_C02-GPR183, n = 169; CD8_C03-CX3CR1, n = 743; CD8_C04-GZMK, n = 773; CD8_C05-CD6, n = 487; CD8_C06-CD160, n = 351; CD8_C07-LAYN, n = 860) based on different clustering methods including SC3, Seurat and sscClust. Each point represents one single cell coloured by cluster label. g. Box plots showing the down-sampling analysis of clustering performed on CD8+ and CD4+ T cell dataset. Each dot represents an individual clustering of a given number of T cells. The down-sampling and clustering were performed iteratively for each cell number (n = 10 times). Each down-sampled clustering was compared to the clustering performed on the entire dataset, using the NMI index. Higher NMI values indicate more accurate cluster assignment.
Extended Data Fig. 4 Expression levels of signature genes in each T cell cluster.
a, Gene expression heat map of 8 CD8+ T cell (n = 3,628) clusters. Rows represent signature genes and columns represent different clusters. b, Gene expression heat map of 12 CD4+ T cell clusters (n = 4,902). c, t-SNE plot of expression levels of selected genes in different clusters indicated by the coloured oval corresponding to Fig. 1a. Number of cells contained in each cluster: CD8_C01-LEF1, n = 174; CD8_C02-GPR183, n = 169; CD8_C03-CX3CR1, n = 743; CD8_C04-GZMK, n = 773; CD8_C05-CD6, n = 487; CD8_C06-CD160, n = 351; CD8_C07-LAYN, n = 860; CD8_C08-SLC4A40, n = 71; CD4_C01-CCR7, n = 462; CD4_C02-ANXA1, n = 472; CD4_C03-GNLY, n = 190; CD4_C04-TCF7, n = 388; CD4_C05-CXCR6, n = 568; CD4_C06-CXCR5, n = 262; CD4_C07-GZMK, n = 185; CD4_C08-IL23R, n = 244; CD4_C09-CXCL13, n = 319; CD4_C10-FOXP3, n = 389; CD4_C11-IL10, n = 103; CD4_C12-CTLA4, n = 1,320.
Extended Data Fig. 5 Summary of functional properties of various T cell clusters.
a, Functional subsets of CD4+ T cell (n = 4,902) clusters defined by a set of known marker genes. Number of cells contained in each CD4+ cluster: TN, n = 462; P.TCM, n = 472; TEMRA, n = 190; N.TCM, n = 388; TRM, n = 568; follicular T helper (TFH), n = 262; TEM, n = 185; TH17, n = 244; TH1-like cells, n = 319; P.Treg, n = 389; N.Treg, n = 103; T.Treg, n = 1,320. N, normal tissue; P, peripheral blood; T, tumour. b, Characteristics of the CD8+ IEL T cells as defined by the expression properties of a panel of functionally relevant genes in CD8+ T cells (n = 3,628). Number of cells contained in each CD8+ cluster: TN, n = 174; TCM, n = 169; TEMRA, n = 743; TEM, n = 773; TRM, n = 487; IEL, n = 351; TEX, n = 860; MAIT, n = 71. For violin plots in a and b, colours denote average expression levels; widths denote cell densities. c, t-SNE plot showing the presence of different T cell clusters in peripheral blood (n = 2,449; CD8+ T cells, n = 1,021; CD4+ T cells, n = 1,428), adjacent normal tissues (n = 1,962; CD8+ T cells, n = 961; CD4+ T cells, n = 1,001) and tumours (n = 4,119; CD8+ T cells, n = 1,646; CD4+ T cells, n = 2,473). d, Overview of T cell cluster characteristics. STARTRAC-dist: +++ indicates Ro/e > 1; ++, 0.8 < Ro/e ≤ 1; +, 0.2 < Ro/e ≤ 0.8; +/−, 0 < Ro/e ≤ 0.2; −, Ro/e = 0. STARTRAC-expa: +++ indicates \({I}_{{\rm{expa}}}^{{\rm{STARTRAC}}}\) > 0.10; ++, 0.06 < \({I}_{{\rm{expa}}}^{{\rm{STARTRAC}}}\) ≤ 0.10; +, 0.005 < \({I}_{{\rm{expa}}}^{{\rm{STARTRAC}}}\) ≤ 0.05; -, \({I}_{{\rm{expa}}}^{{\rm{STARTRAC}}}\) ≤ 0.005. STARTRAC-migr: +++ indicates \({I}_{{\rm{migr}}}^{{\rm{STARTRAC}}}\) > 0.50; ++, 0.21 < \({I}_{{\rm{migr}}}^{{\rm{STARTRAC}}}\) ≤ 0.50; +, 0.1 < \({I}_{{\rm{migr}}}^{{\rm{STARTRAC}}}\) ≤ 0.20; −, \({I}_{{\rm{migr}}}^{{\rm{STARTRAC}}}\) ≤ 0.1. STARTRAC-tran, +++ indicates \({I}_{{\rm{tran}}}^{{\rm{STARTRAC}}}\) > 0.20; ++, 0.10 < \({I}_{{\rm{tran}}}^{{\rm{STARTRAC}}}\) ≤ 0.20; +, 0.05 < \({I}_{{\rm{tran}}}^{{\rm{STARTRAC}}}\) ≤ 0.10; −, \({I}_{{\rm{tran}}}^{{\rm{STARTRAC}}}\) ≤ 0.05.
Extended Data Fig. 6 CD8+ TEX cells are characterized by high proliferation property and production of effector molecules.
a, A subpopulation of CD8+ TEX shows high expression of MKI67 among 8,530 T cells. b, Gene set enrichment analysis (GSEA) showing the enrichment of proliferation-related pathways in CD8+ TEX cells (n = 3,628; false discovery rate < 0.01; labelled in red). c, Representative example of a CRC tumour stained by multi-coloured IHC showing co-expression of Ki67, CD8, PD-1 and HAVCR2 in CD8+ TEX cells (exemplified by P0413; n = 2 patients). Original magnification, ×20. d, Volcano plot showing the differentially expressed genes between high-proliferative (n = 140) and low-proliferative (n = 720) TEX cells. Most of the highly expressed genes in high-proliferative TEX cells are related to cell proliferation. Adjusted P < 0.01; fold change ≥ 2; two-sided unpaired limma-moderated t-test; Benjamini–Hochberg adjusted P value e, Violin plot showing the expression of TBX21, EOMES and PDCD1 in each CD8+ T cell (n = 3,628) cluster and the low-proliferative (n = 720) or high-proliferative (n = 140) TEX cell subsets. f, Most of the clonotypes of high-proliferative TEX cells were also found in low-proliferative TEX cells (top). Each row represents an individual clonotype from one patient. Venn diagram showing overlapped clonal clonotypes (≥2 cells) of high- and low-proliferative TEX cells (bottom). g. Characteristics of CD8+ TEX cells (n = 3,628) as defined by the gene expression of a series of transcription factors, checkpoint receptors, and effector molecules. For violin plots in e and g, colours denote average expression levels; widths denote cell densities.
Extended Data Fig. 7 Distinct migration capabilities of different CD8+ T cell clusters.
a, Top, chord diagram showing the distribution of clonotypes in blood, normal mucosa and tumours for different CD8+ T cell clusters. CD8+ TEMRA cells show remarkable TCR sharing among different tissues. The shadows coloured in transparent yellow, green and orange represent blood, normal and tumour-specific clonotypes, respectively. The bridges coloured in dark green, dark red and dark blue represent clonotypes shared by blood–normal, blood–tumour and normal–tumour, respectively. Bottom, Venn diagram showing the distribution of expanded clonotypes in blood, tumour and normal TEMRA cells. b, Relative average expression patterns of migration-related genes across CD8+ T cell clusters (total n = 3,557 cells, excluding MAIT cells). Number of cells contained in each cluster: TN, n = 174; TCM, n = 169; TEMRA, n = 743; TEM, n = 773; TRM, n = 487; IEL, n = 351; TEX, n = 860.
Extended Data Fig. 8 TCR sharing and state transitions of CD8+ T cell clusters implicated by STARTRAC-tran indices.
a, Pie charts showing the fraction of shared clonotypes with CD8+ TEM cells within the other indicated clusters (left). P12 represents merged data of 12 patients with CRC. Bar plots showing the fraction of shared clonotypes of CD8+ TEM with other clusters within the CD8+ TEM. b, pSTARTRAC-tran indices of CD8+ TCM, TEMRA, TRM, IEL and TEX cells for each patient (depicted by dots). *P < 0.05, **P < 0.01, ***P < 0.001, Kruskal–Wallis test. c, Potential developmental trajectory of CD8+ T cells (n = 3,557, excluding MAIT cells) inferred by Monocle2 based on gene expressions. d, Frequency of shared clonotypes in CD8+ TEMRA cells with various TEM cell subsets in each patient (n = 12). e, Statistical analysis of tumour TEM shared TCRs with blood TEMRA and tumour TEX cells based on the number of clonotypes and clonal cells (related to Fig. 1h). ***P < 0.001, two-sided Fisher’s exact test. f, Clonotypes of tumour TEM cells crossing different clusters showing mutually exclusive TCR sharing of tumour TEM cells with blood TEMRA and tumour TEX cells. Each row represents an individual clonotype from one patient. *P < 0.05, **P < 0.01, ***P < 0.001, two-sided Fisher’s exact test (based on the number of clonal cells in each patient). Number of clonal cells analysed in each patient: P1212, n = 30; P1228, n = 27; P0411, n = 11; P0825, n = 10; P1012, n = 7; P0701, n = 9; P0123, n = 9; P0215, n = 17; P0309, n = 9; P0413, n = 2; P1207, n = 7; P0909, n = 2.
Extended Data Fig. 9 Characterization of CD4+ TEMRA and tumour Treg cells by STARTRAC analysis.
a, Violin plots showing normalized expression of cytotoxic related molecules in 12 CD4+ (n = 4,902 cells) and 8 CD8+ (n = 3,628) T cell clusters. Colours denote mean values; width denotes cell densities. b, Venn diagram highlighting common clonotypes (ncell ≥ 2) shared between tumour Treg and other CD4+ T cell clusters. c, Developmental transition of tumour Treg cells, TH17 cells and TH1-like cells with other CD4+ cells quantified by pSTARTRAC-tran indices for each patient (n = 11). d, Representative example of a CRC tumour stained by IHC, with white arrow showing co-expression of CD3, FOXP3 and RORγ (n = 2 patients). Original magnification, ×20.
Extended Data Fig. 10 Comparative analysis of T cells from different cancer indications based on integrated analyses.
a, t-SNE plot of 8,874 single CD8+ T cells from this CRC study (n = 3,632), and previous HCC9 (n = 1,467) and NSCLC10 (n = 3,775) studies. Nine CD8+ clusters were generated by sscClust based on the integrated dataset. The CRC-specific IEL cells (CD8_C06-CD160) are highlighted. b, t-SNE plot of 12,635 single CD4+ T cells from this CRC study (n = 4,929), and previous HCC9 (n = 2,472) and NSCLC10 (n = 5,234) studies. The CRC-enriched TH17 cells (CD4_C10-IL23R) are highlighted. Each dot represents one single cell coloured by clusters and shaped by tumour types in a and b. c, Composition of different CD8+ T cells in each tumour type by different tissue origins. CD8+ T cell clusters with frequencies below 3% are not labelled. d, Composition of different CD4+ T cells in each tumour type by different tissue origins. CD4+ T cell clusters with frequencies below 3% are not labelled. e, Comparison of the fractions of CD8+ IEL (CD8_C08-CD160) and MAIT (CD8_C09-SLC4A10) cells in tumours from patients with CRC (n = 12), HCC (n = 5) and NSCLC (n = 14). f, Comparison of the fractions of different CD8+ T cells and CD4+ T cells in control tissues from patients with CRC (n = 12), HCC (n = 5) and NSCLC (n = 14). g, Validation of the enrichments of CXCL13+BHLHE40+ TH1-like cells in patients with MSI-H CRC (n = 62) and TH17 cells in patients with MSS CRC (n = 286) in the TCGA COAD and READ cohorts by comparison of the indicated signature gene expression. Centre lines denote the median, top and bottom lines denote the 25th and 75th percentiles. *P < 0.05, **P < 0.01, ***P < 0.001; two-sided Wilcoxon test (e–g).
Extended Data Fig. 11 IGFLR1 expression in activated CD4+ T cells and exhausted CD8+ T cells.
a, Volcano plot showing differentially expressed genes between tumour CXCL13+BHLHE40+ TH1-like T cells (n = 203) and other TH cells in tumours (n = 723; Supplementary Table 10). Adjusted P < 0.01 (two-sided unpaired limma-moderated t-test; Benjamini–Hochberg adjusted P value) and fold change ≥ 2. b, Venn diagram showing the overlap of tumour CD8+ exhaustion-related genes identified in this study (n = 68, Supplementary Table 11) with those from previous melanoma8 (n = 349), HCC9 (n = 82) and NSCLC10 (n = 90) studies. The detailed overlaps of CD8+ exhaustion-related genes in different cancer types are in Supplementary Table 11. P < 2.2 × 10−16, hypergeometric test. c, CD4+ naive (TN) and memory (TMEM) T cells were gated as CD45RA+CCR7+ and CD45RA−CCR7+/− cells by FACS. d, FACS plots of IGFLR1 expression in activated CD4+ T cells (n = 6 donors, n = 3 independent experiments). e, Quantification of IGFLR1 expression levels from d as a percentage of IGFLR1+ TN or TMEM CD4+ subsets under suboptimal activation conditions (n = 7). Each symbol represents a donor with mean ± s.e.m. shown (e, l). f, Representative FACS plots for HAVCR2 and IFNγ expression levels in CD8+ Tconv (activated by anti-CD3 plus anti-CD28) and TCS cells (in vitro chronically stimulated exhausted CD8+ T cells from corresponding individuals). Numbers in quadrants indicate the percentage of positive cells (n = 5 donors, n = 2 independent experiments). g, Representative histograms of PD-1, HAVCR2 (n = 8 donors, n = 3 independent experiments), CD39 and LAG3 (n = 4 donors, n = 2 independent experiments) expression levels in CD8+ Tconv and TCS cells. h, Quantification of IFNγ levels produced by CD8+ Tconv and TEX cells from g of three donors. i, Representative histogram of IGFLR1 expression levels in CD8+ Tconv and TCS cells. j, Expression levels of IGFLR1 in activated CD8+ Tconv and TCS cells determined by FACS (MFI, mean fluorescent intensity; n = 6 donors, n = 4 independent experiments). k, Representative histograms of HAVCR2 expression in TCS cells subjected to re-stimulation with anti-CD3 alone (control) or together with recombinant human IGFL3 as well as indicated antibodies for 2 days (n = 5 donors, n = 3 independent experiments). l, Quantification of HAVCR2 levels from k. Two-sided paired Student’s t-test (e, j and l).
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Zhang, L., Yu, X., Zheng, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018). https://doi.org/10.1038/s41586-018-0694-x
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DOI: https://doi.org/10.1038/s41586-018-0694-x
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