Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation

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ABSTRACT The objective of this proposal is to identify linkages between T cell receptor (TCR) sequences and transcriptional profiles across the human T cell landscape. This work is enabled by our recent development of the CoNGA algorithm, a graph theoretic approach that integrates TCR and gene expression (GEX) datasets, and by technological advances that have made it possible to profile both features in parallel at high throughput. Submitted in response to Notice of Special Interest NOT-AI-21-011 ("Secondary Analysis of Existing Datasets for Advancing Immune-mediated and Infectious Disease Research"), our proposal brings together a team of computational biologists and immunologists with a track record of successful collaboration. Our goal is to apply CoNGA on diverse T cell datasets to define the landmark TCR features and their correlated phenotypes in human T cells. In the first Aim, we will identify, acquire, pre-process, and standardize all large, publicly available single-cell datasets that feature linked gene expression and paired TCR sequence information. We will then run the CoNGA pipeline on these individual datasets, correlate the results with available study metadata, and make these results available for download. In the second Aim, we will perform a meta-analysis of the relationship between T cell receptor sequence and T cell transcriptional profile across the entire dataset (1,000+ donors and 1,000,000+ individual T cells). Completion of the work proposed here will lay the groundwork for a comprehensive atlas of the human T cell landscape and provide a valuable dataset for further development of analytical tools and methods. T cell features and sub-populations identified by CoNGA will provide new insight into the individual datasets while also illuminating the global landscape of GEX/TCR covariation.