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Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model
Wang, Xuesong Fan, Yimin Guo, Yucheng Fu, Chenghao Lee, Kinhei Dallakyan, Khachatur Li, Yaxuan Yin, Qijin Li, Yu Song, Le
Wang, Xuesong
Fan, Yimin
Guo, Yucheng
Fu, Chenghao
Lee, Kinhei
Dallakyan, Khachatur
Li, Yaxuan
Yin, Qijin
Li, Yu
Song, Le
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s41467-025-63478-z.pdf
Adobe PDF, 5.22 MB
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Department
Machine Learning
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Journal article
Date
2025
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Language
English
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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 perturbations. Additionally, MorphDiff enhances MOA retrieval, achieving an accuracy comparable to ground-truth morphology and outperforming baseline methods by 16.9% and 8.0%, respectively. This work highlights MorphDiff’s potential to accelerate phenotypic screening and improve MOA identification, making it a powerful tool in drug discovery.
Citation
X. Wang et al., “Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model,” Nature Communications , vol. 16, no. 1, pp. 1–18, Dec. 2025, doi: 10.1038/S41467-025-63478-Z
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Nature Communications
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Keywords
Cell Component, Drug, Morphology, Rna
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Springer Nature