Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.

ABSTRACT: Investigating cell morphology changes after perturbations using high-throughput image-based profiling is increasingly important for phenotypic drug discovery, including predicting mechanisms of action (MOA) and compound bioactivity. The vast space of chemical and genetic perturbations makes it impractical to explore all possibilities using conventional methods. Here we propose MorphDiff, a transcriptome-guided latent diffusion model that simulates high-fidelity cell morphological responses to perturbations. We demonstrate MorphDiff's effectiveness on three large-scale datasets, including two drug perturbation and one genetic perturbation dataset, covering thousands of perturbations. Extensive benchmarking shows MorphDiff accurately predicts cell morphological changes under unseen perturbat

SUBMITTER: Wang X 

PROVIDER: S-EPMC12405515 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature