OSDR2.0 infers microenvironment-driven cell-state transitions and population dynamics from a single spatial biopsy

Cell populations in human tissues change over time by cell division, death and transitions between functional states. In the tumor microenvironment (TME), such dynamics are central to immune evasion, stromal remodeling and therapeutic response. However, it is difficult to measure such dynamics in vivo because usually only a single biopsy is available providing a static snapshot. To obtain cell population dynamics from a snapshot we previously developed One Shot Dynamic Reconstruction (OSDR1.0), an algorithm that reconstructs cell population dynamics over weeks to months from a spatial biopsy using cell-type information and a proliferation marker. OSDR1.0 however does not include transitions between cell states. Here we present OSDR2.0, an extension that incorporates transitions between cell states inferred from the local cellular neighborhood. The algorithm OSDR2.0 models the probability of a cell being in a given state (e.g., PD1 ⁺ vs. PD1 ⁻ T cell, or cancer-associated vs. resting fibroblast) as a function of its surrounding cell types. These state-transition rules are then integrated into simulations of population dynamics. After the cell population is advanced by a timestep, the cell states are adjusted according to the new neighborhoods, using the fact that cell state transitions, which take hours, are typically much more rapid than cell population changes on the scale of weeks, and can thus be considered at quasi-steady-state. Applying OSDR2.0 to spatial proteomics data from triple-negative breast cancer (TNBC), we find that cell state is strongly associated with local microenvironment composition. Incorporating state transitions significantly improves the ability to predict treatment response - OSDR2.0 accurately separates responders from nonresponders to chemotherapy and immunotherapy based on early post-treatment biopsies, outperforming OSDR1.0 that does not include cell state transitions. This work highlights the importance of cell state plasticity in shaping tumor response and opens a way to infer both population dynamics and cell state transitions from static clinical samples.