Division-resolved inference of flow and trajectories in proliferating cell populations
High-throughput single-cell assays are widely used to quantify distributions of cell size, morphology, and molecular content across thousands of cells. However, such population distributions do not reveal how the measured cellular states change within individual cells over time. We introduce division-resolved inference of flow and trajectories (DRIFT), a computational framework that infers the…
Researchers have developed a computational method called division-resolved inference of flow and trajectories (DRIFT) to uncover how cellular states evolve within individual cells over time. This technique analyzes population distributions of key cellular characteristics, such as size, morphology, and molecular content, collected at different time points.
By solving a population-balance equation, DRIFT separates the progression of a measured cellular state from the redistribution caused by cell division in proliferating populations. In simulations, DRIFT successfully recovered ground-truth mean volume trajectories across simulated single-cell lineages.
When applied to live L1210 leukemia cells, DRIFT inferred perturbation-specific volume trajectories that aligned with longitudinal single-cell measurements. Moreover, DRIFT was able to deduce DNA-content dynamics from fixed-cell flow cytometry in L1210 cells, findings that corroborated independent DNA-synthesis assays.
In live HeLa cells, DRIFT inferred cell area dynamics, which were validated by continuous imaging. By converting endpoint measurements of cell populations into division-resolved cellular dynamics, DRIFT offers a scalable approach for high-throughput drug-response screening and mechanistic investigations.
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