Division-resolved inference of flow and trajectories in proliferating cell populations
- 1Seoul National University
Abstract
Interpreting cellular responses requires understanding how cellular states change over time, yet many scalable assays provide only population snapshots. In proliferating populations, changes in population distributions reflect both state progression within cells and redistribution caused by division. We introduce division-resolved inference of flow and trajectories (DRIFT), a computational framework that infers the dynamics of a measured cellular state from population distributions collected over time, without synchronizing or tracking individual cells. DRIFT solves a population-balance equation to separate state progression from the redistribution caused by cell division. In simulations of growth and division perturbations, DRIFT recovered the ground-truth mean volume trajectories across simulated single-cell lineages. Applied to live L1210 leukemia cells, DRIFT inferred perturbation-specific volume trajectories consistent with longitudinal single-cell measurements. Beyond volume, the framework captured cell area dynamics in live HeLa cells, with validation by continuous imaging. Its application to an endpoint molecular assay further yielded DNA-content dynamics from fixed-cell flow cytometry in L1210 cells, consistent with complementary analyses of DNA synthesis. Overall, DRIFT converts population measurements into division-resolved cellular dynamics, extending the utility of routine population assays to kinetic studies of cellular progression when longitudinal tracking is impractical.
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BibTeX
@misc{kang2026divisionresolved,
title = {{Division-resolved inference of flow and trajectories in proliferating cell populations}},
author = {J. h. Kang and G. Shin and T. Miettinen},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.03.747958},
url = {https://www.biorxiv.org/content/10.64898/2026.09.03.747958v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Division-resolved inference of flow and trajectories in proliferating cell populations
AU - J. h. Kang
AU - G. Shin
AU - T. Miettinen
PY - 2026
DA - 2026/09/03
PB - bioRxiv
DO - 10.64898/2026.09.03.747958
UR - https://www.biorxiv.org/content/10.64898/2026.09.03.747958v1
AB - Interpreting cellular responses requires understanding how cellular states change over time, yet many scalable assays provide only population snapshots. In proliferating populations, changes in population distributions reflect both state progression within cells and redistribution caused by division. We introduce division-resolved inference of flow and trajectories (DRIFT), a computational framework that infers the dynamics of a measured cellular state from population distributions collected over time, without synchronizing or tracking individual cells. DRIFT solves a population-balance equation to separate state progression from the redistribution caused by cell division. In simulations of growth and division perturbations, DRIFT recovered the ground-truth mean volume trajectories across simulated single-cell lineages. Applied to live L1210 leukemia cells, DRIFT inferred perturbation-specific volume trajectories consistent with longitudinal single-cell measurements. Beyond volume, the framework captured cell area dynamics in live HeLa cells, with validation by continuous imaging. Its application to an endpoint molecular assay further yielded DNA-content dynamics from fixed-cell flow cytometry in L1210 cells, consistent with complementary analyses of DNA synthesis. Overall, DRIFT converts population measurements into division-resolved cellular dynamics, extending the utility of routine population assays to kinetic studies of cellular progression when longitudinal tracking is impractical.
ER -