MorphGen: Controllable Cell-Image Generation with Biological Representation Alignment

Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene editing. To support this, we introduce MorphGen, a state-of-the-art diffusion-based generative model for fluorescent microscopy that enables controllable generation across multiple cell types and perturbations. MorphGen is trained with an alignment loss that matches its representations to phenotypic embeddings from a biology foundation model, encouraging meaningful morphological patterns consistent with real cell images. Unlike prior approaches that compress multichannel stains into RGB images, sacrificing organelle-specific detail and focusing on a single cell type, MorphGen generates the complete set of fluorescent channels jointly, preserving per-organelle structure and enabling post-generation interpretation. We demonstrate biological consistency with real images via CellProfiler features, and MorphGen attains an FID score over 35% lower than the prior state-of-the-art MorphoDiff. Finally, in a compositional generalization test that holds out cell type--perturbation combinations during training, MorphGen achieves in-distribution quality on 55% of unseen pairs under a seen-calibrated criterion.