{
  "abstract": "We present immgenT-GP, a gene-program framework for resolving mouse T cell heterogeneity across the immgenT atlas. On ~ 800,000 T cells spanning lineages, organs, and immune challenges, we defined 200 reproducible gene programs, discovered by empirical Bayes matrix factorization approach and validated through a new deep learning approach, that capture major axes of T cell variation, including lineage identity, activation states and tissue location. Gene-program analysis complemented cluster-based annotation by decomposing T cell states into molecular modules, revealing quantitative, shared, modules not represented with discrete labels alone. Across tissues, gene programs reflected both tissue-imposed programs and changes in cluster composition. Integrating GP activity with cell-surface marker expression from the CITE-seq data, revealed that markers can report different programs depending on lineage and context. Together, immgenT-GP extends the atlas from a map of T cell states to a molecular reference of the programs that underlie them.",
  "abstract_shown": true,
  "affiliations": [
    "Departments of Pathology, University of Chicago, Chicago, IL, USA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Z. Zhang"
    },
    {
      "name": "T. Wang"
    },
    {
      "name": "S. S. Panigrahi"
    },
    {
      "name": "P. Carbonetto"
    },
    {
      "name": "M. Stephens"
    },
    {
      "name": "C. Benoist"
    },
    {
      "name": "S. Mostafavi"
    },
    {
      "name": "M. Brbic"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Zemmour"
    },
    {
      "name": "the immgenT Project"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5bzgj5btdtqrq.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.15.751918",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751918v1.full",
  "id": "5bzgj5btdtqrq",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751918v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7213553987",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751918v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5bzgj5btdtqrq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:58Z",
      "native_id": "10.64898/2026.09.15.751918",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751918v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "A Gene-Program Architecture of Mouse T cells",
  "title_text": "A Gene-Program Architecture of Mouse T cells",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:58Z",
  "url": "https://research.draconitic.com/paper/5bzgj5btdtqrq",
  "venue": "bioRxiv",
  "versions": []
}