scChronos: Hierarchical Temporal Context Learning for Missing Snapshot Prediction in Single-Cell Time Series.

scChronos achieves superior missing-snapshot prediction in single-cell time series by explicitly modeling population distributions, outperforming existing methods in both interpolation and extrapolation.

• Why it matters: Reconstructing continuous developmental trajectories from discrete, unpaired single-cell snapshots is crucial for understanding dynamic biological processes, but current methods often ignore population-level statistical constraints and are sensitive to noise.
• What they did: The authors developed scChronos, a hierarchical framework that predicts target population distributions by compressing cell populations into cluster representations and integrating temporal context from multiple snapshots using a dual-branch module.
• The result: scChronos improves distributional recovery, better preserves developmental topology, and maintains cell-type-specific programs compared to state-of-the-art approaches, enabling more accurate trajectory reconstruction.