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bioRxiv · 10.1101/2020.06.23.166546

Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses

Andreatta, M.·Corria Osorio, J.·Muller, S.·Cubas, R.·Coukos, G.·Carmona, S. J.
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Abstract

Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary technology for characterizing the heterogeneity of cell populations. However, robust reference atlases that can be used to systematically interpret cellular states across studies and diseases are currently lacking. Here, we generated the first cross-study T cell atlases for cancer and viral infection and developed a novel algorithm, ProjecTILs, that enables the projection of new scRNA-seq data onto these reference atlases. ProjecTILs accurately predicted the effects of multiple perturbations, including the ablation of immunoregulatory targets controlling T cell differentiation, such as Tox, Ptpn2, miR-155 and Regnase-1, and suggested novel gene programs that were altered in these cells. Moving beyond mouse models, we used ProjecTILs to conduct a meta-analysis of human tumor-infiltrating T lymphocytes (TILs), revealing a remarkable conservation of TIL subtypes between human and mouse and across cancer types. Clonotype analysis supported a model in which rare human tumor-specific effector-memory (EM)-like CD8 TILs that resemble blood-circulating EM cells, differentiate into proliferative terminal exhausted/dysfunctional effector TILs through a progenitor subtype that upregulates the exhaustion master regulator TOX. Our novel computational method allows exploring the effect of human and murine T cell perturbations (e.g. as the result of therapy or genetic engineering) in terms of reference cellular states, altered genetic programs and clonotype structure, revealing mechanisms of action behind immunotherapies and opening opportunities for their improvement.

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Andreatta, M., Corria Osorio, J., Muller, S., Cubas, R., Coukos, G., Carmona, S. J.. 2020-06-24. Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses. https://doi.org/10.1101/2020.06.23.166546

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