Linus J. Schumacher

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

This study develops a computational pipeline for analyzing spatial proteomics data to identify tissue states associated with chemotherapy response in TNBC, revealing distinct spatial patterns and predictive markers.

The paper investigates how mutation timing, fitness, and clonal structure influence the clinical progression of clonal hematopoiesis using longitudinal cohort data.

The paper develops stochastic models to show that licensing and competition for niche access together regulate stem cell homeostasis and prevent extinction.

The paper harmonizes single-cell perturbation datasets and introduces E-statistics for analyzing perturbation effects across diverse data sources.

This study models how senescence spreads between cells and finds that differences in secreted signalling molecules and timedependent paracrine signalling limit its spread.

The paper shows how cell populations can appear heterogeneous in migration despite identical individual behavior, highlighting statistical pitfalls in analyzing heterogeneity.