{
  "abstract": "Recent advances have improved understanding of infection; however many detailed datasets are based on single timepoints post-infection and cannot generate longitudinal insights needed to predict infection outcomes. For example, pulmonary infection with Mycobacterium tuberculosis (Mtb) initiates a complex system of immune and bacterial interactions that leads to granuloma formation, yet single snapshots in time need elaboration long-term. We develop a new approach, the tissue time machine, applied to predict outcomes of non-human primate granulomas from initiation to endpoint using single timepoint tissue datasets including high-dimensional spatial features. This involves identifying granuloma digital partners, simulations most closely resembling real granulomas from a virtual library of over 50,000. By analyzing digital partners, we predict infection outcomes of 22/28 granulomas, and that Mtb-specific, IFN-{gamma}-producing T cell recruitment rates influence uncertainty for the 6/28. This method is applicable to any system that allows for mechanistic predictions over time using heterogeneous, incomplete, and high-dimensional datasets.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Microbiology and Immunology, University of Michigan-Michigan Medicine, Ann Arbor, MI, USA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "P. Nanda"
    },
    {
      "name": "E. F. McCaffrey"
    },
    {
      "name": "C. T. Michael"
    },
    {
      "name": "A. C. Delmastro"
    },
    {
      "name": "G. Michael"
    },
    {
      "name": "J. T. Mattila"
    },
    {
      "name": "J. L. Flynn"
    },
    {
      "name": "R. M. Angelo"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Kirschner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cn877f3fws2wv.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.758083",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758083v1.full",
  "id": "cn877f3fws2wv",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758083v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "openalex",
      "2725|Tuberculosis Research and Epidemiology"
    ]
  ],
  "openalex": "W7221576169",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758083v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cn877f3fws2wv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:24Z",
      "native_id": "10.64898/2026.10.09.758083",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758083v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "A tissue time machine to forward-predict single-timepoint data applied to tuberculosis granulomas",
  "title_text": "A tissue time machine to forward-predict single-timepoint data applied to tuberculosis granulomas",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:24Z",
  "url": "https://research.draconitic.com/paper/cn877f3fws2wv",
  "venue": "bioRxiv",
  "versions": []
}