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

Authors

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

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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 13 Oct 2026.

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