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 significantly 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, in order to estimate the effect that TCRs confer upon 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 post-immunotherapy treatment, and is weakened in tumor contexts.

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Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. We reversed this paradigm and developed SCINA, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation-maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

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