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Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)

Nature Biotechnology volume 40, pages 54–63 (2022)Cite this article

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Abstract

Links between T cell clonotypes, as defined by T cell receptor (TCR) sequences, and phenotype, as reflected in gene expression (GEX) profiles, surface protein expression and peptide:major histocompatibility complex binding, can reveal functional relationships beyond the features shared by clonally related cells. Here we present clonotype neighbor graph analysis (CoNGA), a graph theoretic approach that identifies correlations between GEX profile and TCR sequence through statistical analysis of GEX and TCR similarity graphs. Using CoNGA, we uncovered associations between TCR sequence and GEX profiles that include a previously undescribed ‘natural lymphocyte’ population of human circulating CD8+ T cells and a set of TCR sequence determinants of differentiation in thymocytes. These examples show that CoNGA might help elucidate complex relationships between TCR sequence and T cell phenotype in large, heterogeneous, single-cell datasets.

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Fig. 1: CoNGA graph-versus-graph analysis.
Fig. 2: CoNGA identifies unconventional HOBIT+ CD8 T cells in blood.
Fig. 3: CoNGA plots and cluster logos for a large dataset of thymic T cells (thymus_atlas).
Fig. 4: Graph-versus-feature analysis highlights TCR:GEX covariation.
Fig. 5: EPHB6 co-expression is a feature of TRBV30+ T cells.
Fig. 6: CoNGA identifies convergence of TCR sequence and GEX profile within pMHC-positive clonotype subsets.

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Data availability

All datasets analyzed here are openly available and accessible at https://www.10xgenomics.com/resources/datasets/ and https://developmentcellatlas.ncl.ac.uk/ (human thymic T cell data) (see Supplementary Table 1 for details). Source data are provided with this paper.

Code availability

The CoNGA software repository is available on GitHub (https://github.com/phbradley/conga).

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Acknowledgements

The authors would like to thank J. Park and S. Teichmann for assistance with the thymus atlas T cell dataset, E. Matsen for comments and suggestions on an earlier version of this manuscript, E. Newell and T. Bi for helpful discussions and N. Bradley for suggesting the use of kernel principal components analysis. We would also like to thank the developers of the scanpy single-cell analysis package, which provides the framework on which the CoNGA software is built. This research was supported by National Institutes of Health (NIH) grant R01 AI136514 to P.T., NIH ORIP S10OD028685 to support high-performance computing at the Fred Hutchinson Cancer Research Center, the St. Jude Neoma Boadway Postdoctoral Fellowship to S.S. and the American Lebanese Syrian Associated Charities to P.T.

Author information

Authors and Affiliations

  1. Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Stefan A. Schattgen, Jeremy Chase Crawford, Aisha Souquette & Paul G. Thomas

  2. Herbold Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Kate Guion & Philip Bradley

  3. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA

    Kate Guion

  4. Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, USA

    Aisha Souquette & Paul G. Thomas

  5. 10x Genomics, Pleasanton, CA, USA

    Alvaro Martinez Barrio & Michael J. T. Stubbington

  6. Institute for Protein Design, University of Washington, Seattle, WA, USA

    Philip Bradley

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Contributions

S.S. designed, conducted and interpreted experiments, analyzed data and helped prepare the manuscript. K.G. and J.C.C. analyzed data and helped prepare the manuscript. A.S. conducted experiments. A.M.B. and M.J.T.S. provided technical expertise and advice. P.T. designed and interpreted experiments and helped prepare the manuscript. P.B. conceptualized and coded the software, analyzed and interpreted data and prepared the manuscript.

Corresponding authors

Correspondence to Paul G. Thomas or Philip Bradley.

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Competing interests

M.J.T.S. is employed by 10x Genomics. M.J.T.S., A.M.B. and J.C.C. are option or shareholders of 10x Genomics. P.B., P.G.T. and J.C.C. served as unpaid consultants for 10x Genomics on the initial data analysis of the 10x_200k dataset. P.G.T. has filed patents related to the cloning, expression and characterization of T cell receptors. P.G.T. has received travel or speaking expenses from 10x Genomics, Illumina and PACT Pharma.

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Peer review information Nature Biotechnology thanks Benny Chain, Dmitriy Chudakov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 T cells belonging to the same clonotype have similar gene expression profiles.

Gene expression UMAP projections of the 10x_200k_donor2a dataset before condensing to a single cell per clonotype, with the 16 largest clonotypes shown in blue (one per panel) and the remainder of the dataset in gray.

Extended Data Fig. 2 CoNGA graph-vs-graph analysis of human and mouse peripheral blood T cells.

CoNGA graph-vs-graph results for PBMC T cell datasets: (a-c) human CD4 and CD8 T cells (human_pbmc1); (d-f) human CD4 and CD8 T cells (human_pbmc2); (g-i) mouse CD4 and CD8 T cells (mouse_pbmc). Same arrangement of plots as in main text Fig. 3.

Source data

Extended Data Fig. 3 Matching of CoNGA cluster TCR sequences to bulk repertoires.

TCRβ sequences from human CoNGA clusters were matched to bulk TCRβ repertoires using TCRdist. To score the overlap between the set of TCR sequences in a CoNGA cluster and the set of sequences in a bulk repertoire, we developed a variant of the Morisita-Horn (MH) overlap index that accounts for sequence similarity in addition to exact identity (see Methods for further details). (a) The MH overlaps (y-axis) are plotted against subject age (x-axis) for the two CoNGA clusters indicated in the panel titles. The first cluster (a MAIT cluster) appears to decline with subject age, while the second one (a HOBIT cluster) appears to increase (R value and 2-sided P value in legend). (b) The distribution of MH overlaps for a set of CD4+ repertoires is compared with the distribution of MH overlaps for a set of CD8+ repertoires for two different clusters from the thymus_atlas dataset. (c) The distribution of MH overlaps for a set of memory repertoires is compared with the distribution of MH overlaps for a set of naive repertoires for the two clusters indicated in the panel titles. Boxes in panels b and c show quartiles with whiskers extending to 1.5*IQR. (d) All-vs-all scatter plots (with kernel density estimates along the diagonal) for the following CoNGA cluster features (see Methods for feature calculation details): log10_Pgen, the average log10 generation probability of the cluster TCRβ chains; log10_publicity, the average log10 rate of occurrence in a large (N = 666) dataset of PBMC repertoires; age_correlation, the linear correlation coefficient between MH overlap and subject age (see panel (a)); CD8_vs_CD4, t-statistic comparing MH overlaps for CD8 and CD4 repertoires (higher indicates greater preference for CD8 repertoires; see panel (b)); memory_vs_naive, t-statistic comparing MH overlaps for memory and naive repertoires (higher indicates greater preference for memory repertoires; see panel (c)). The CoNGA clusters are grouped according to the discussion in the main text; ‘pre_hobit’ refers to the two clusters in the thymus_atlas dataset that may be precursors of the HOBIT+ population, (CD8αα(I):2) and (CD8αα(II):2).

Source data

Extended Data Fig. 4 Specific versus non-specific binding in the 10x_200k dataset.

Comparison of binding data for four ‘specific’ pMHC multimers (A02_GIL, A02_ELA, B08_RAK, A02_GLC) and four ‘sticky’ pMHC multimers (A03_KLG, A03_RLR, A03_RIA, A11_AVF) in the 10x_200k_donor2 dataset. (a) GEX landscapes colored by pMHC binding signal (log(1+ UMI read count)). (b) TCR landscapes colored by pMHC binding signal. The ‘specific’ pMHCs show binding that is focused in certain areas of the landscapes, whereas the binding of the putative ‘sticky’ pMHCs is dispersed across the landscapes. (c) The Pearson correlation between binding profiles for different pMHCs is shown in matrix form according to the indicated color mapping. The specific pMHCs show little correlation whereas the sticky pMHCs are significantly correlated in their binding, suggesting that a shared cellular property (TCR or CD8 surface expression, expression of other HLA-interacting molecules, general level of activation) is jointly influencing their binding. Note that A11_AVF (and A11_IVT) show additional specific binding in donor 1, who is A*11:01 positive; the A*03:01 pMHC multimers appear non-specific regardless of donor HLA type.

Extended Data Fig. 5 Flow cytometry gating strategies for HOBIT/HELIOS CD8 T cells in Fig. 2.

(a) Gating strategy for KLRC2+ KIR2Dmix and KLRC2-KIR2D+ CD8 T cells in panels (b+ c). After gating on single lymphocytes the gating is Ghost510-CD14-CD19-CD3+ CD8B+ CCR7-CD45RA+. (b) Representative example of CD1d:PBS-57 and MR1:5-OP-RU tetramer labeling of KLRC2+ KIR2Dmix, KLRC2-KIR2D+, and CCR7-CD45RO+ CD8 T cells. (c) Frequency of CD1d and MR1-labelled KLRC2+ KIR2Dmix, KLRC2-KIR2D+, and CCR7-CD45RO+ CD8 T cells (n = 12; Supplementary Note 3). P values calculated by 1-sided t-test. The lower limit of the box corresponds to the 1st quartile, center line the median, and upper limit the 3rd quartile (d) Gating strategy for HELIOS intracellular staining of KLRC2+ KIR2Dmix and KLRC2-KIR2D+ CD8 T cells in panels. Single lymphocytes were gated on Ghost510-CD14-CD19-CD3+ CD8B+ CD248-CCR7-CD45RO-CD45RA+.

Source data

Extended Data Fig. 6 Detection of GEX neighborhoods with elevated iMHC scores across multiple donors.

2D GEX projection of the 10x_200k_donor1 (a), 10x_200k_donor2 (b), 10x_200k_donor3 (c), and 10x_200k_donor4 (d) datasets colored by P values for iMHC enrichment in each clonotype’s graph neighborhood (the set of iMHC scores in each clonotype’s neighborhood are compared to the remainder of the iMHC scores using an unpaired, 1-sided Mann-Whitney-Wilcoxon test). (e) Top 10 DEGs for the clonotypes with significant iMHC enrichment in the 10x_200k_donor1 dataset. (f) Top 10 DEGs for the clonotypes with significant iMHC enrichment in the 10x_200k_donor3 dataset. (g) Top 10 DEGs for the clonotypes with significant iMHC enrichment in the 10x_200k_donor4 dataset. (There were too few clonotypes with significant iMHC enrichment in the 10x_200k_donor2 dataset to identify differentially expressed genes). (h) Graph-vs-feature correlation between a TCR feature, iMHC score (left panel), and 2 scores derived from the GEX profile (right panels, ZNF683 and KLRC3 expression) is illustrated by mapping the scores onto the 2D UMAP GEX landscape for the 10x_200k_donor1 dataset (after Z-score normalization and averaging over graph neighborhoods).

Extended Data Fig. 7

Gating strategy for assessment of EPHB6 protein levels in TRBV30 ± CD4+ and CD8+ T cells in Fig. 5f.

Extended Data Fig. 8 Matching of pMHC-positive TCR sequences to bulk repertoires and epitope-specific TCR sequences from the literature.

(a) TCRβ sequences from the pMHC-positive clonotypes in the 10x_200k dataset were matched to bulk TCRβ repertoires using TCRdist. To score the overlap between the set of TCR sequences in a pMHC-positive repertoire and the set of sequences in a bulk repertoire, we developed a variant of the Morisita-Horn (MH) overlap index that accounts for sequence similarity in addition to exact identity (see Methods for further details). All-vs-all scatter plots (with kernel density estimates along the diagonal) are shown for the following pMHC-positive repertoire features (see Methods for feature calculation details): log10_Pgen, the average log10 generation probability of the repertoire TCRβ chains; log10_publicity, the average log10 rate of occurrence in a large (N = 666) dataset of PBMC repertoires; age_correlation, the linear correlation coefficient between MH overlap and subject age in the N = 666 PBMC repertoire dataset (see Extended Data Fig. 3a); CD8_vs_CD4, t-statistic comparing MH overlaps for CD8 and CD4 repertoires (higher indicates greater preference for CD8 repertoires; see Extended Data Fig. 3b); memory_vs_naive, t-statistic comparing MH overlaps for memory and naive repertoires (higher indicates greater preference for memory repertoires; see Extended Data Fig. 3c). (b) The pMHC-positive repertoires were matched against one another and against a set of literature-derived TCR sequences taken primarily from the VDJdb55 and McPAS56 databases (excluding those TCRs in the VDJdb that were themselves derived from the 10x_200k dataset). The heatmap shows MH overlaps calculated using paired-chain TCRdist distances. Reasonable concordance between repertoires positive for the same pMHC from different donors and between pMHC-positive and literature-derived repertoires can be seen.

Source data

Extended Data Fig. 9 Epitope-specific T cell populations differ in activation status.

(a) Log-transformed read counts for DNA-barcoded anti-CD45RA (x-axis) and anti-CD45RO (y-axis) antibodies, averaged over pMHC+ clonotypes, are plotted for the pMHCs shown in Fig. 6. In the panel on the left, clonotypes are weighted equally, while in the panel on the right, larger clonotypes are given more weight (proportional to the logarithm of the clone size) to better reflect the underlying distribution of cells (particularly for the d1_A11 pMHCs, both of which have a relatively large number of positive cells distributed unevenly among a small number of clonotypes). (b) Heatmap of gene set variation analysis (GSVA) scores for pMHC-specific clonotypes by donor. Significant hits (P values < 0.05 after multiple hypothesis correction using the Benjamini-Hochberg method) from the MSigDB (https://www.gsea-msigdb.org/gsea/msigdb) C7 collection57,58 are shown. Analysis performed using Seurat59, GSVA60, and Cerebro61 R packages.

Source data

Extended Data Fig. 10 CoNGA’s ability to recover invariant T cell subsets depends on their frequency in the dataset.

To assess the sensitivity of CoNGA’s graph-vs-graph algorithm in detecting a known GEX/TCR correlation, we created artificial datasets by subsampling the MAIT cell clonotypes (iNKT cell clonotypes in mouse) down to specified levels within the context of five datasets in which those clonotypes could be clearly identified both as a distinct GEX cluster and by virtue of their invariant TCR sequences. (a) The fraction of MAIT or iNKT clonotypes recovered as CoNGA hits (y-axis) is plotted against the frequency to which these clonotypes were downsampled in the dataset. (b) The fraction of recovered clonotypes is plotted against the absolute number of downsampled clonotypes present in the dataset. Recovery rate appears to depend more strongly on the number of downsampled clonotypes than their fraction in the total dataset.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9, Notes 1–3, Tables 1–8 and References.

Reporting Summary

Supplementary Data 1

TCR sequence information on all CoNGA clusters for 10x_200k_donors.

Supplementary Data 2

TCR sequence information for pMHC-specific CD8 T cells from 10x_200k_donors used for analysis

Supplementary Data 3

Source Data for Supplementary Fig. 3

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Source Data for Supplementary Fig. 5

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Source Data Fig. 1

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Source Data Extended Data Fig. 9

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Schattgen, S.A., Guion, K., Crawford, J.C. et al. Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA). Nat Biotechnol 40, 54–63 (2022). https://doi.org/10.1038/s41587-021-00989-2

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