MorphoDiff: Cellular Morphology Painting with Diffusion Models

Zeinab Navidi, Jun Ma, Esteban Miglietta, Le Liu, Anne E Carpenter, Beth A Cimini, Benjamin Haibe-Kains, BO WANG

Keywords: Generative Modelling, Latent Diffusion Model, Cell Painting, Morphology, Drug Response Prediction, Cellular Phenotype, Machine Learning

TL;DR: We present MorphoDiff as the first generative framework capable of producing guided, high-resolution predictions of cellular morphology that generalizes across both chemical and genetic interventions.

Abstract: Understanding cellular responses to external stimuli is critical for parsing biological mechanisms and advancing therapeutic development. High-content image-based assays provide a cost-effective approach to examine cellular phenotypes induced by diverse interventions, which offers valuable insights into biological processes and cellular states. We introduce MorphoDiff, a generative pipeline to predict high-resolution cell morphological responses under different conditions based on perturbation encoding. To the best of our knowledge, MorphoDiff is the first framework capable of producing guided, high-resolution predictions of cell morphology that generalize across both chemical and genetic interventions. The model integrates perturbation embeddings as guiding signals within a 2D latent diffusion model. The comprehensive computational, biological, and visual validations across three open-source Cell Painting datasets show that MorphoDiff can generate high-fidelity images and produce meaningful biology signals under various interventions. We envision the model will facilitate efficient in silico exploration of perturbational landscapes towards more effective drug discovery studies.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 7849