Temporal tissue dynamics from a spatial snapshot

Abstract

Physiological and pathological processes such as inflammation or cancer emerge from the interactions between cells over time. However, methods to follow cell populations over time within the native context of a human tissue are lacking, because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR), an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighborhoods influences division rate, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data, and reconstruct two fixed points of fibroblasts and macrophage interactions. These fixed points correspond to hot and cold fibrosis, in agreement with co-culture experiments that measured these dynamics directly. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumor microenvironment, suggesting temporal flares of adaptive anti-cancer responses. Finally, we study longitudinal biopsies from a triple negative breast cancer clinical trial, where OSDR predicts the collapse of the tumor cell population in responders versus non-responders based on early treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.