Static2Dynamic: Reconstructing videos of unobservable cellular, developmental, and disease processes

A fundamental limitation in biology is that many of its most important processes unfold as visual dynamics that cannot be directly observed. Development, tissue remodeling, and disease progression often occur deep in living organisms, over extended timescales, and at cellular resolution beyond the reach of current live imaging technologies. As a result, much of biology remains accessible only through static snapshots, while the underlying phenotypic trajectories and visual transformations remain hidden. Here, we introduce Static2Dynamic, a general framework to reconstruct unseen biological dynamics from sets of cross-sectional image data. Starting from time-unpaired static samples, Static2Dynamic first recovers a continuous pseudotime for individual images in a time-discriminative deep representation space, then learns a generative model of images conditionally to the underlying process, and finally reconstructs temporally coherent videos initialized from real samples. This makes it possible to infer past and future visual states of a static image and to simulate complete trajectories of cellular, developmental, and disease processes that were never directly recorded. We quantitatively validate Static2Dynamic on two large-scale experimental microscopy video datasets generated specifically for benchmarking, enabling direct comparison of inferred pseudotime trajectories and reconstructed videos against ground-truth biological dynamics. We further show that the framework generalizes across biological scales, organisms, and imaging modalities, including processes inaccessible to continuous live observation. More broadly, Static2Dynamic establishes the foundations of pseudotime microscopy, a new paradigm for reconstructing the visual and temporal dynamics of biological processes directly from static imaging data, thereby expanding the observable space of living systems beyond current experimental limits.