Autoregressive forecasting of future single-cell state transitions

Luo, E.; Gao, H.; BIAN, H.; Li, Y.; Li, C.; Hao, M.; Chen, M.; She, Y.; Wei, L.; Liu, K.; Zhang, X.

doi:10.64898/2026.02.08.704720

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bioRxiv · 10.64898/2026.02.08.704720

Autoregressive forecasting of future single-cell state transitions

Luo, E.·Gao, H.·BIAN, H.·Li, Y.·Li, C.·Hao, M.·Chen, M.·She, Y.·Wei, L.·Liu, K.·Zhang, X.

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Abstract

Predicting future cell state transitions from static transcriptomic snapshots is vital for understanding and steering cellular dynamics. Although existing trajectory and RNA velocity methods can infer local transitions within observed data, they are fundamentally limited in extrapolating long-range cellular dynamics. Here, we introduce CellTempo, an autoregressive generative framework that enables long-range forecasting of cell state trajectories directly from static transcriptomic snapshots. CellTempo is pretrained on scBaseTraj, a large-scale collection of multi-step cellular transition sequences constructed by integrating diverse experimental technologies and data sources. Across diverse biological systems, CellTempo accurately predicts long-range lineage progression and reconstructs unseen cellular potential landscapes. It further distinguishes transient perturbation responses from long-term cell-fate changes, enabling prioritization of candidate compounds for cell-fate engineering. These results demonstrate that long-range cellular dynamics are inferable from static observations, establishing a foundation for predictive and controllable modeling of cell fate.

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Luo, E., Gao, H., BIAN, H., Li, Y., Li, C., Hao, M., Chen, M., She, Y., Wei, L., Liu, K., Zhang, X.. 2026-02-10. Autoregressive forecasting of future single-cell state transitions. https://doi.org/10.64898/2026.02.08.704720

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