A tissue time machine to forward-predict single-timepoint data applied to tuberculosis granulomas

Nanda, P.; McCaffrey, E. F.; Michael, C. T.; Delmastro, A. C.; Michael, G.; Mattila, J. T.; Flynn, J. L.; Angelo, R. M.; Kirschner, D.

doi:10.64898/2026.10.09.758083

bioRxiv · 10.64898/2026.10.09.758083

A tissue time machine to forward-predict single-timepoint data applied to tuberculosis granulomas

Nanda, P.·McCaffrey, E. F.·Michael, C. T.·Delmastro, A. C.·Michael, G.·Mattila, J. T.·Flynn, J. L.·Angelo, R. M.·Kirschner, D.

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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.

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Nanda, P., McCaffrey, E. F., Michael, C. T., Delmastro, A. C., Michael, G., Mattila, J. T., Flynn, J. L., Angelo, R. M., Kirschner, D.. 2026-10-12. A tissue time machine to forward-predict single-timepoint data applied to tuberculosis granulomas. https://doi.org/10.64898/2026.10.09.758083

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