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

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

Abstract

Links in between T cell clonotypes, as specified by T cell receptor (TCR) series, and phenotype, as shown in gene expression (GEX) profiles, surface area protein expression and peptide: significant histocompatibility complex binding, can expose practical relationships beyond the functions shared by clonally associated cells. Here we provide clonotype next-door neighbor chart analysis (CoNGA), a chart logical technique that determines connections in between GEX profile and TCR series through analytical analysis of GEX and TCR resemblance charts. Utilizing CoNGA, we revealed associations in between TCR series and GEX profiles that consist of a formerly undescribed ‘natural lymphocyte’ population of human flowing CD8 T cells and a set of TCR series factors of distinction in thymocytes. These examples reveal that CoNGA may assist illuminate intricate relationships in between TCR series and T cell phenotype in big, heterogeneous, single-cell datasets.

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Data schedule

All datasets evaluated here are honestly offered and available at https://www.10 xgenomics.com/resources/datasets/ and https://developmentcellatlas.ncl.ac.uk/(human thymic T cell information) (see Supplementary Table 1 for information). Source information are offered with this paper.

Code schedule

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

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Acknowledgements

The authors want to thank J. Park and S. Teichmann for help with the thymus atlas T cell dataset, E. Matsen for remarks and ideas on an earlier variation of this manuscript, E. Newell and T. Bi for useful conversations and N. Bradley for recommending making use of kernel principal elements analysis. We would likewise like to thank the designers of the scanpy single-cell analysis bundle, which supplies the structure on which the CoNGA software application is constructed. This research study was supported by National Institutes of Health (NIH) grant R01 AI136514 to P.T., NIH ORIP S10 OD028685 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 details

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. 10 x Genomics, Pleasanton, CA, USA

    Alvaro Martinez Barrio & Michael J. T. Stubbington

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

    Philip Bradley

Contributions

S.S. created, performed and translated experiments, evaluated information and assisted prepare the manuscript. K.G. and J.C.C. examined information and assisted prepare the manuscript. A.S. carried out experiments. A.M.B. and M.J.T.S. supplied technical proficiency and suggestions. P.T. created and analyzed experiments and assisted prepare the manuscript. P.B. conceived and coded the software application, evaluated and translated information and prepared the manuscript.

Corresponding authors

Correspondence to.
Paul G. Thomas or Philip Bradley

Ethics statements

Competing interests

M.J.T.S. is utilized by 10 x Genomics. M.J.T.S., A.M.B. and J.C.C. are alternative or investors of 10 x Genomics. P.B., P.G.T. and J.C.C. worked as unsettled experts for 10 x Genomics on the preliminary information analysis of the 10 x _200 k dataset. P.G.T. has actually submitted patents associated to the cloning, expression and characterization of T cell receptors. P.G.T. has actually gotten travel or speaking costs from 10 x Genomics, Illumina and PACT Pharma.

Additional info

Peer evaluation details Nature Biotechnology thanks Benny Chain, Dmitriy Chudakov and the other, confidential, customer( s) for their contribution to the peer evaluation of this work.

Publisher’s note Springer Nature stays neutral with regard to jurisdictional claims in released maps and institutional associations.

Extended information

Extended Data Fig. 1 T cells coming from the very same clonotype have comparable gene expression profiles.

Gene expression UMAP forecasts of the 10 x _200 k_donor2a dataset prior to condensing to a single cell per clonotype, with the 16 biggest clonotypes displayed in blue (one per panel) and the rest 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 outcomes 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). Very same plan of plots as in primary text Fig. 3

Source information

Extended Data Fig. 3 Matching of CoNGA cluster TCR series to bulk collections.

TCRβ series from human CoNGA clusters were matched to bulk TCRβ collections utilizing TCRdist. To score the overlap in between the set of TCR series in a CoNGA cluster and the set of series in a bulk collection, we established a variation of the Morisita-Horn (MH) overlap index that represent series resemblance in addition to precise identity (see Methods for additional information). ( a) The MH overlaps (y-axis) are outlined versus subject age (x-axis) for the 2 CoNGA clusters showed in the panel titles. The very first cluster (a MAIT cluster) appears to decrease with subject age, while the 2nd one (a HOBIT cluster) appears to increase ( R worth and 2-sided P worth in legend). ( b) The circulation of MH overlaps for a set of CD4 collections is compared to the circulation of MH overlaps for a set of CD8 collections for 2 various clusters from the thymus_atlas dataset. ( c) The circulation of MH overlaps for a set of memory collections is compared to the circulation of MH overlaps for a set of ignorant collections for the 2 clusters suggested in the panel titles. Boxes in panels b and c reveal quartiles with hairs reaching 1.5 IQR. ( d) All-vs-all scatter plots (with kernel density approximates along the diagonal) for the following CoNGA cluster functions (see Methods for function computation information): log10 _ Pgen, the typical log10 generation likelihood of the cluster TCRβ chains; log10 _ promotion, the typical log10 rate of event in a big (N=666) dataset of PBMC collections; age_correlation, the direct connection coefficient in between MH overlap and subject age (see panel (a)); CD8_vs_CD4, t-statistic comparing MH overlaps for CD8 and CD4 collections (greater shows higher choice for CD8 collections; see panel (b)); memory_vs_naive, t-statistic comparing MH overlaps for memory and ignorant collections (greater shows higher choice for memory collections; see panel (c)). The CoNGA clusters are organized according to the conversation in the primary text; ‘pre_hobit’ describes the 2 clusters in the thymus_atlas dataset that might be precursors of the HOBIT population, ( CD8αα( I):2) and ( CD8αα( II):2)

Source information

Extended Data Fig. 4 Specific versus non-specific binding in the 10 x _200 k dataset.

Comparison of binding information for 4 ‘particular’ pMHC multimers (A02 _ GIL, A02 _ ELA, B08 _ RAK, A02 _ GLC) and 4 ‘sticky’ pMHC multimers (A03 _ KLG, A03 _ RLR, A03 _ RIA, A11 _ AVF) in the 10 x _200 k_donor2 dataset. ( a) GEX landscapes colored by pMHC binding signal (log( 1 UMI checked out count)). ( b) TCR landscapes colored by pMHC binding signal. The ‘particular’ pMHCs reveal binding that is focused in particular locations of the landscapes, whereas the binding of the putative ‘sticky’ pMHCs is distributed throughout the landscapes. ( c) The Pearson connection in between binding profiles for various pMHCs is displayed in matrix type according to the suggested color mapping. The particular pMHCs reveal little connection whereas the sticky pMHCs are considerably associated in their binding, recommending that a shared cellular home (TCR or CD8 surface area expression, expression of other HLA-interacting particles, basic level of activation) is collectively affecting their binding. Keep in mind that A11 _ AVF (and A11 _ IVT) reveal extra particular binding in donor 1, who is A *11: 01 favorable; the A *03: 01 pMHC multimers appear non-specific despite donor HLA type.

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

( a) Gating technique 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-CD45 RA . ( b) Representative example of CD1d: PBS-57 and MR1:5- OP-RU tetramer labeling of KLRC2 KIR2Dmix, KLRC2-KIR2D , and CCR7-CD45 RO CD8 T cells. ( c) Frequency of CD1d and MR1-labelled KLRC2 KIR2Dmix, KLRC2-KIR2D , and CCR7-CD45 RO CD8 T cells (n=12; Supplementary Note 3). P worths determined by 1-sided t-test. The lower limitation of package represents the 1st quartile, center line the mean, and ceiling the 3rd quartile ( d) Gating method 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-CD45 RO-CD45 RA .

Source information

Extended Data Fig. 6 Detection of GEX communities with raised iMHC ratings throughout several donors.

2D GEX forecast of the 10 x _200 k_donor1( a), 10 x _200 k_donor2( b), 10 x _200 k_donor3( c), and 10 x _200 k_donor4( d) datasets colored by P worths for iMHC enrichment in each clonotype’s chart area (the set of iMHC ratings in each clonotype’s area are compared to the rest of the iMHC ratings utilizing an unpaired, 1-sided Mann-Whitney-Wilcoxon test). ( e) Top 10 DEGs for the clonotypes with considerable iMHC enrichment in the 10 x _200 k_donor1 dataset. ( f) Top 10 DEGs for the clonotypes with substantial iMHC enrichment in the 10 x _200 k_donor3 dataset. ( g) Top 10 DEGs for the clonotypes with substantial iMHC enrichment in the 10 x _200 k_donor4 dataset. (There were too couple of clonotypes with substantial iMHC enrichment in the 10 x _200 k_donor2 dataset to determine differentially revealed genes). ( h) Graph-vs-feature connection in between a TCR function, iMHC rating (left panel), and 2 ratings originated from the GEX profile (best panels, ZNF683 and KLRC3 expression) is shown by mapping ball games onto the 2D UMAP GEX landscape for the 10 x _200 k_donor1 dataset(after Z-score normalization and averaging over chart areas).

Extended Data Fig. 7

Gating technique for evaluation of EPHB6 protein levels in TRBV30 ± CD4 and CD8 T cells in Fig. 5f

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

( a) TCRβ series from the pMHC-positive clonotypes in the 10 x _200 k dataset were matched to bulk TCRβ collections utilizing TCRdist. To score the overlap in between the set of TCR series in a pMHC-positive collection and the set of series in a bulk collection, we established a version of the Morisita-Horn (MH) overlap index that represent series resemblance in addition to specific identity (see Methods for additional information). All-vs-all scatter plots (with kernel density approximates along the diagonal) are revealed for the following pMHC-positive collection functions (see Methods for function computation information): log10 _ Pgen, the typical log10 generation likelihood of the collection TCRβ chains; log10 _ promotion, the typical log10 rate of event in a big (N=666) dataset of PBMC collections; age_correlation, the direct connection coefficient in between MH overlap and subject age in the N=666 PBMC collection dataset (see Extended Data Fig. 3a); CD8_vs_CD4, t-statistic comparing MH overlaps for CD8 and CD4 collections (greater suggests higher choice for CD8 collections; see Extended Data Fig. 3b); memory_vs_naive, t-statistic comparing MH overlaps for memory and ignorant collections (greater suggests higher choice for memory collections; see Extended Data Fig. 3c). ( b) The pMHC-positive collections were matched versus one another and versus a set of literature-derived TCR series taken mainly from the VDJdb55 and McPAS56 databases (leaving out those TCRs in the VDJdb that were themselves originated from the 10 x _200 k dataset). The heatmap reveals MH overlaps computed utilizing paired-chain TCRdist ranges. Affordable concurrence in between collections favorable for the very same pMHC from various donors and in between pMHC-positive and literature-derived collections can be seen.

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

( a) Log-transformed read counts for DNA-barcoded anti-CD45 RA (x-axis) and anti-CD45 RO (y-axis) antibodies, balanced over pMHC clonotypes, are outlined for the pMHCs displayed in Fig. 6 In the panel left wing, clonotypes are weighted similarly, while in the panel on the right, bigger clonotypes are provided more weight (proportional to the logarithm of the clone size) to much better show the hidden circulation of cells (especially for the d1_A11 pMHCs, both of which have a fairly a great deal of favorable cells dispersed unevenly amongst a little number of clonotypes). ( b) Heatmap of gene set variation analysis (GSVA) ratings for pMHC-specific clonotypes by donor. Considerable hits ( P worths < 0.05 after numerous hypothesis correction utilizing the Benjamini-Hochberg approach) from the MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb) C7 collection57,58 are revealed. Analysis carried out utilizing Seurat59, GSVA60, and Cerebro61 R plans.

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Extended Data Fig. 10 CoNGA’s capability to recuperate invariant T cell subsets depends upon their frequency in the dataset.

To examine the level of sensitivity of CoNGA’s graph-vs-graph algorithm in spotting a recognized GEX/TCR connection, we produced synthetic datasets by subsampling the MAIT cell clonotypes (iNKT cell clonotypes in mouse) down to defined levels within the context of 5 datasets in which those clonotypes might be plainly recognized both as an unique GEX cluster and by virtue of their invariant TCR series. ( a) The portion of MAIT or iNKT clonotypes recuperated as CoNGA hits (y-axis) is outlined versus the frequency to which these clonotypes were downsampled in the dataset. ( b) The portion of recuperated clonotypes is outlined versus the outright variety of downsampled clonotypes present in the dataset. Healing rate appears to depend more highly on the variety of downsampled clonotypes than their portion in the overall dataset.

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Schattgen, S.A., Guion, K., Crawford, J.C. et al. Integrating T cell receptor series and transcriptional profiles by clonotype next-door neighbor chart analysis (CoNGA).
Nat Biotechnol(2021). https://doi.org/101038/ s41587 -021-00989 -2

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