Inferring division-associated stochasticity from time-series single-cell transcriptomes

Okochi, Y.; Sawazaki, Y.; Kondo, Y.; Naoki, H.

doi:10.64898/2026.04.14.718485

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bioRxiv · 10.64898/2026.04.14.718485

Inferring division-associated stochasticity from time-series single-cell transcriptomes

Okochi, Y.·Sawazaki, Y.·Kondo, Y.·Naoki, H.

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Abstract

Cell division is fundamental to multicellular organisms and stochastic partitioning of cellular components can strongly affect genome-wide gene expression states. However, how cell division-associated partitioning noise shapes the dynamics of proliferating cells is poorly understood. Here, we propose scDIVIDE, a neural stochastic differential equation framework to infer continuous cellular dynamics and division rates while accounting for partitioning noise. We combined birth-death-mutation processes from population genetics with dynamical optimal transport and revealed that the birth rate is embedded in the diffusion coefficient, enabling its inference from time-series scRNA-seq data. scDIVIDE accurately inferred birth rates in synthetic data and the inferred birth rates recapitulated turnover-related programs in mouse hematopoiesis data. By exploiting the birth-diffusion coupling, scDIVIDE provides a biologically-informed constraint on growth rate estimation, outperforming existing methods in predicting future cell distributions. scDIVIDE provides a conceptual avenue for quantitatively dissecting how partitioning noise shapes fate decisions in multicellular systems.

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Okochi, Y., Sawazaki, Y., Kondo, Y., Naoki, H.. 2026-04-16. Inferring division-associated stochasticity from time-series single-cell transcriptomes. https://doi.org/10.64898/2026.04.14.718485

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