bioRxivImmunologyPreprint
immgenT: A Comprehensive Reference of Convergent T-cell States in the Mouse
Ian MagilliD, Odhran Casey, Dania Mallah, Siba Smarak Panigrahi, Leon Lee ZhouiD, Olga Barreiro del Río, Derek J. BangsiD, Gavyn Chern Wei BeeiD, Samantha M. BorysiD, Josh Choi, Can ErgeniD, Enxhi Ferraj, Maurizio Fiusco, Antoine FreuchetiD, Giovanni GallettiiD, Anna-Maria GlobigiD, Taylor A. HeimiD, Charlotte J. ImianowskiiD, Rocky LaiiD, Zhitao LiangiD, Andrea Lebron Figueroa, Erin D. LucasiD, Julia MerkenschlageriD, Kevin C. OsumiD, Shannelle Reilly, , the immgenT project
Abstract
The immgenT collaborative project generated a comprehensive molecular atlas of T cells spanning virtually all mouse organs and disease states, profiling ~800,000 cells from 750 samples with RNA, 128-plex surface protein, and {beta}TCR sequence. Applying a deep generative model to joint RNA and protein data defined the landscape of T-cell states organized into eight lineages and 107 robust clusters, integrating similar cells from different contexts, and resolving prior nomenclatures. Analysis of effector molecules, transcription factors and modules showed that both immunological functions and regulatory programs are shared across cell states. This framework provides a stable, reusable reference, demonstrated by computationally integrating 16 external datasets from diverse biological contexts. A set of public web tools supports browsing of these data and mapping of any dataset onto the immgenT framework. These results propose a molecular classification of T cells organized around a set of shared states reused across immunological contexts.
- organism: mouse
- assay: scRNA-seq
- assay: multiome
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- Citations
- 2
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