The CD8 immgenT framework as a universal reference of mouse CD8αβ T cell differentiation states

Mouse CD8 T cell differentiation has been studied extensively in models of infections and cancer, yet no unified framework spans the full spectrum of immunological contexts. We present the CD8 immgenT framework, integrating >200,000 single-cell transcriptomes and 128-plex surface proteomes from 734 samples spanning multiple perturbations, tissues, and timepoints. Unbiased analysis identifies 21 states encompassing naive, effector, circulating memory, tissue-resident memory, progenitor-exhausted, and terminally-exhausted compartments, among others. These states re-emerge with striking molecular convergence across acute/chronic infections, cancer, autoimmunity, aging, and homeostasis, showing that near-identical transcriptional programs support protective or dysfunctional outcomes depending on developmental history and microenvironment. Classic archetypes map to discrete clusters but exhibit unappreciated heterogeneity and overlap, cautioning against rigid nomenclature. We provide validated combinatorial markers, flow cytometry gating strategies, and immgenT reference-based integration for reproducible annotation of new datasets. This universal coordinate system harmonizes fragmented CD8 T cell literature and clarifies relationships across diverse immune challenges.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-02-04
DOI
https://doi.org/10.64898/2026.02.02.703365
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
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article

The CD8 immgenT framework as a universal reference of mouse CD8αβ T cell differentiation states

Nicole E. Scharping, Kennidy K. Takehara, Tomoyo Shinkawa, Samuel M. Behar et al.
bioRxiv (Cold Spring Harbor Laboratory)
Single-cell and spatial transcriptomics
article

The CD8 immgenT framework as a universal reference of mouse CD8αβ T cell differentiation states

Nicole E. Scharping, Kennidy K. Takehara, Tomoyo Shinkawa, Samuel M. Behar, Shuozhi Liu, Susan M. Kaech, Ananda W. Goldrath, Samantha Borys, Amir Ferry, Olga Barreiro, Anna-Maria Globig, Sara Quon, Kitty P. Cheung, Giovanni Galletti, Ellen Duong, David Zemmour, Odhran Casey, Taylor Heim, Alexander Troy Monell, Dhruv Patravali
article en

Abstract

Mouse CD8 T cell differentiation has been studied extensively in models of infections and cancer, yet no unified framework spans the full spectrum of immunological contexts. We present the CD8 immgenT framework, integrating >200,000 single-cell transcriptomes and 128-plex surface proteomes from 734 samples spanning multiple perturbations, tissues, and timepoints. Unbiased analysis identifies 21 states encompassing naive, effector, circulating memory, tissue-resident memory, progenitor-exhausted, and terminally-exhausted compartments, among others. These states re-emerge with striking molecular convergence across acute/chronic infections, cancer, autoimmunity, aging, and homeostasis, showing that near-identical transcriptional programs support protective or dysfunctional outcomes depending on developmental history and microenvironment. Classic archetypes map to discrete clusters but exhibit unappreciated heterogeneity and overlap, cautioning against rigid nomenclature. We provide validated combinatorial markers, flow cytometry gating strategies, and immgenT reference-based integration for reproducible annotation of new datasets. This universal coordinate system harmonizes fragmented CD8 T cell literature and clarifies relationships across diverse immune challenges.

bioRxiv (Cold Spring Harbor Laboratory)
Salk Institute for Biological Studies (US), Harvard University (US), University of Massachusetts Chan Medical School (US), Brown University (US), Ragon Institute of MGH, MIT and Harvard (US), University of California San Diego (US), University of Chicago (US), Allen Institute for Immunology (US), IIT@MIT (US), Allen Institute (US), Koch Institute for Integrative Cancer Research At MIT (US)
Good health and well-being
Openalex Percentile: Top 67%
Single-cell and spatial transcriptomics
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