UniCell: Unifying Cell Image Understanding and Generation

Biological discovery increasingly relies on two complementary capabilities from microscopy images: understanding cellular phenotypes and generating images that simulate morphological responses to perturbations. Existing models treat these tasks in isolation, leaving their potential synergy underexplored. We introduce UniCell, a unified mixture-of-transformers model that accommodates microscopy understanding and perturbation-conditioned generation within a single architecture. This unified structure enables two synergistic designs: a two-stage training strategy, where the model first learns visual question answering and object localization and then transfers these representations to image generation; and text phenotype conditioning, where natural-language descriptions of expected morphological changes further improve generation quality. UniCell achieves state-of-the-art perturbation-conditioned generation and competitive performance on microscopy understanding benchmarks. We further show that understanding-first training directly improves generation quality, and that phenotype descriptions inferred from related perturbations further improve generation on unseen perturbations. Together, these findings establish joint understanding and generation as a viable paradigm for data-driven biological discovery.