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ABSTRACT Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR

profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the

effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over

existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in

antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is

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SIMILAR CONTENT BEING VIEWED BY OTHERS HIGH-THROUGHPUT AND SINGLE-CELL T CELL RECEPTOR SEQUENCING TECHNOLOGIES Article 19 July 2021 BENCHMARKING OF T CELL RECEPTOR REPERTOIRE PROFILING

METHODS REVEALS LARGE SYSTEMATIC BIASES Article 07 September 2020 SINGLE-CELL IMMUNE REPERTOIRE ANALYSIS Article 18 April 2024 DATA AVAILABILITY The bulk RNA-seq datasets used for deriving

TCRs and then for the auto-encoder training are publicly available at https://gdc.cancer.gov/about-data/publications/panimmune (TCGA23), https://www.iedb.org/database_export_v3.php (IEDB)

and http://friedmanlab.weizmann.ac.il/McPAS-TCR/ (McPAS25). We made the Kidney-bulkRNA24 dataset available in csv format at

https://github.com/jcao89757/TESSA/tree/master/Tessa_released_data. All scRNA-seq/TCR-seq datasets are publicly available. The NSCLC-1 and healthy PBMC-1 datasets are available on the 10x

website https://support.10xgenomics.com/single-cell-vdj/datasets/2.2.0. The healthy-CD8 1–4 datasets are available on

https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/. The healthy PBMC-2

dataset is also available on the 10x Genomics website https://support.10xgenomics.com/single-cell-vdj/datasets/3.0.0. The NSCLC-2 (ref. 26), CRC27 and HCC28 datasets are downloaded from the

European Genome-Phenome Archive (EGA) under accession numbers EGAS00001002430, EGAS00001002791 and EGAS00001002072, respectively. The Breast-1–5 (ref. 29) datasets are available on the Gene

Expression Omnibus (GEO) under accession numbers GSE114727 and GSE114724. The Melanoma30, BCC31 and ECCITE-Seq16 datasets are also on the GEO database under study numbers GSE123139,

GSE113590 and GSE126310. The Glanville10 dataset is downloaded from https://doi.org/10.1038/nature22976. The Dash11 dataset is available in the National Center for Biotechnology Information

Sequence Read Archive under accession number SRP101659. The details of the data used, including sample size, role in the analysis and references, are shown in Supplementary Table 1. All

scRNA-seq data were involved in Fig. 2 (directly or indirectly mentioned), the BCC scRNA-seq data were used in Fig. 3 and all scRNA-seq data were used in Fig. 4. Source data are provided

with this paper. CODE AVAILABILITY The tessa model is available at https://github.com/jcao89757/tessa (https://doi.org/10.5281/zenodo.4161819)46. The SCINA model is available at

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cell transcriptomics. _Zenodo_ https://doi.org/10.5281/zenodo.4161819 (2020). Download references ACKNOWLEDGEMENTS We thank L.H.R. Xu for his valuable input on the manuscript writing. This

study was supported by the National Institutes of Health (NIH) (grant nos. CCSG 5P30CA142543 to T.W. and R15GM131390 to X.W.) and Cancer Prevention Research Institute of Texas (grant no.

CPRIT RP190208 to T.W.). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern

Medical Center, Dallas, TX, USA Ze Zhang, Hongyu Liu & Tao Wang * Department of Statistical Science, Southern Methodist University, Dallas, TX, USA Danyi Xiong & Xinlei Wang * Center

for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA Tao Wang Authors * Ze Zhang View author publications You can also search for this author

inPubMed Google Scholar * Danyi Xiong View author publications You can also search for this author inPubMed Google Scholar * Xinlei Wang View author publications You can also search for this

author inPubMed Google Scholar * Hongyu Liu View author publications You can also search for this author inPubMed Google Scholar * Tao Wang View author publications You can also search for

this author inPubMed Google Scholar CONTRIBUTIONS Z.Z. contributed to the computational analyses and manuscript writing. D.X. and X.W. contributed to the design and write-up of the

statistical methodologies. H.L. provided valuable suggestions on the direction of the project, and contributed to manuscript writing. T.W. contributed to the overall supervision of the

project, study design and manuscript writing. CORRESPONDING AUTHOR Correspondence to Tao Wang. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL

INFORMATION PEER REVIEW INFORMATION Madhura Mukhopadhyay 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. EXTENDED DATA EXTENDED DATA FIG. 1

DETAILS OF THE STACKED AUTO-ENCODER FOR TCR EMBEDDING. A, The structure of the auto-encoder, with the configurations of each layer shown. B, Typical examples of TCR CDR3b sequences, heatmaps

of the initially embedded ‘Atchley’ matrices of TCRs, and heatmaps of the auto-encoder-reconstructed ‘Athley’ matrices. The TCR sequence examples were not used in the training step of the

auto-encoder. C, Scatterplots showing the consistency between the ‘Atchley factor’ values of the original and re-constructed TCRs. Green points represent tiles in the heatmaps in (B). Source

data EXTENDED DATA FIG. 2 SCATTERPLOTS SHOWING THE RELATIONSHIPS BETWEEN THE DISTANCES OF TCRS AND THE DISTANCES OF RNA EXPRESSION LEVELS FOR SEVERAL MORE DATASETS. Both distances are

calculated in a pair-wise manner between all the T cell clonotypes of each dataset. Four example datasets are shown: Healthy-CD8-3 (A), Healthy-CD8-4 (B), Breast-1 (C), and Breast-2 (D)

(Supplementary Table 1). The P values indicate the significance of the Pearson correlation coefficients. The shaded areas denote the 95% confidence intervals for linear regressions. Source

data EXTENDED DATA FIG. 3 THE WEIGHTS OF THE TCR EMBEDDINGS LEARNED FROM TESSA. The X axis shows the digits of the 30-dimensional embeddings, and the Y axis shows the weights learned for all

datasets. Each bar represents one digit of the weights and shows the values of that digit obtained from all the 19 scRNA datasets in the Supplementary Table 1. Source data EXTENDED DATA

FIG. 4 BENCHMARKING RESULTS USING GLIPH. A, Clustering rates of the four Healthy-CD8 datasets from 10x Genomics, the Glanville dataset, and the Dash dataset under different global

convergence distance cutoff (‘_gccutoff_’) values (Supplementary Table 1). The dashed lines represented the tessa clustering rates of the corresponding datasets. B, Clustering purities of

GLIPH when the ‘_gccutoff_’ equals to 3. The cutoff value was selected so that the GLIPH clusters achieved clustering rates that are most similar to the tessa networks. The clustering

purities were calculated with the same method as in Fig. 2. C, D, The GLIPH network purities (C) and number of networks (D) with different _‘gccutoff’_ values, compared with the tessa

network purities and the number of networks. Source data EXTENDED DATA FIG. 5 CLUSTERING OF TCR CLONOTYPES INFORMED BY TESSA IS REFLECTIVE OF ANTIGEN BINDING SPECIFICITY. The antigen binding

specificity of 207 Human TCRβ chains from 704 T cells were profiled against two epitopes in the Dash dataset, and 276 TCRs from 415 T cells against three epitopes in the Glanville dataset.

A, B, T-SNE plots showing the TCR clonotypes in the space of the TCR embeddings, with the embeddings adjusted by the tessa-inferred weights. The hierarchical clustering tree cutoff used in

the two plots was represented with green dashed lines in c-f. Each point in the plots represents one TCR clonotype, and the size of the point refers to the clone size. Points are colored by

the true antigens that the corresponding TCRs target according to the original report. Points are connected if they are clustered into the same network based on hierarchical clustering of

the TCR embeddings. T cell clones with only one cell were deemed as having low confidence and unclustered clones, which does not affect the calculation of the purities, were excluded from

visualization. C, D, The numbers of TCR networks and the clustering rates with different hierarchical tree cutoffs in the Dash dataset (C) and in the Glanville dataset (D). Cluster rates

were calculated as the number of TCR clonotypes that are clustered with at least another TCR clonotype, divided by the total number of TCR clonotypes. E, F, The network purities and p-values

testing the significance of the purities with different hierarchical tree cutoffs in the Dash dataset (C) and the Glanville dataset (D). The network purity and P value calculations were

described in the Methods section. Source data EXTENDED DATA FIG. 6 T CELL PATHWAY ACTIVITY SCORES OF THE DIFFERENT T CELL SUBSETS IN THE BCC DATASET. The naive and activated pathways are

shown, to be compared against the inhibition, memory and exhausted pathways shown in Fig. 3. The T cell subsets were the same as those in Fig. 3e-g. Source data EXTENDED DATA FIG. 7

PSEUDOTIME ANALYSIS OF THE DIFFERENT T CELL SUBSETS IN THE BCC DATASET. The T cell subsets were the same as those in Fig. 3e–g. Source data EXTENDED DATA FIG. 8 A CARTOON SKETCH SHOWS HOW

THE UNEXPLAINED VARIANCE IN GENE EXPRESSION OF THE TCR NETWORKS WERE DETERMINED. Details were described in the MATERIALS AND METHODS section. Source data SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary Tables 1 and 2 and Notes 1 and 2. REPORTING SUMMARY SOURCE DATA SOURCE DATA FIG. 1 Statistical source data. SOURCE DATA FIG. 2 Statistical source

data. SOURCE DATA FIG. 3 Statistical source data. SOURCE DATA FIG. 4 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 1 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 2

Statistical source data. SOURCE DATA EXTENDED DATA FIG. 3 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 4 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 5 Statistical

source data. SOURCE DATA EXTENDED DATA FIG. 6 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 7 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 8 Statistical source data.

RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Zhang, Z., Xiong, D., Wang, X. _et al._ Mapping the functional landscape of T cell receptor repertoires

by single-T cell transcriptomics. _Nat Methods_ 18, 92–99 (2021). https://doi.org/10.1038/s41592-020-01020-3 Download citation * Received: 08 April 2020 * Accepted: 12 November 2020 *

Published: 06 January 2021 * Issue Date: January 2021 * DOI: https://doi.org/10.1038/s41592-020-01020-3 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

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