Reproducible single-cell annotation of programs underlying T cell subsets, activation states and functions.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40903640.
- Also identified by DOI 10.1038/s41592-025-02793-1 and PMC identifier 12446064.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
T cells recognize antigens and induce specialized gene expression programs (GEPs), enabling functions like proliferation, cytotoxicity and cytokine production. Traditionally, different T cell classes are thought to exhibit mutually exclusive responses, including T<sub>H</sub>1, T<sub>H</sub>2 and T<sub>H</sub>17 programs. However, single-cell RNA sequencing has revealed a continuum of T cell states without clearly distinct subsets, necessitating new analytical frameworks. Here, we introduce T-CellAnnoTator (TCAT), a pipeline that improves T cell characterization by simultaneously quantifying predefined GEPs capturing activation states and cellular subsets. Analyzing 1,700,000 T cells from 700 individuals spanning 38 tissues and five disease contexts, we identify 46 reproducible GEPs reflecting core T cell functions including proliferation, cytotoxicity, exhaustion and effector states. We experimentally demonstrate new activation programs and apply TCAT to characterize activation GEPs that predict immune checkpoint inhibitor response across multiple tumor types. Our software package starCAT generalizes this framework, enabling reproducible annotation in other cell types and tissues.
Medical subject headings
- Single-Cell Analysis
- Lymphocyte Activation
- T-Lymphocyte Subsets