1/10 image-based screening advanced drug discovery but scaling to massive perturbation space is hard! Given a cell image, we asked if we could predict the morphological effect induced by a perturbation! Led by Alessandro Palma & w Fabian Theis we propose IMPA

2/n: IMPA uses "style-transfer" to model phenotypic responses in high-content imaging. It decomposes images into perturbation styles and cell contents, generating counterfactual images of perturbed cells.

3/n: Our model employs an autoencoder with versatile perturbation embeddings derived from pre-trained molecules or gene embedding. IMPA trains on adversarial methods to learn perturbation-specific styles for precise image generation.

4/n: IMPA on BBBC021 dataset images of perturbed MCF7 cells. Using RDKit drug embeddings, our model predicts morphological changes while preserving content. IMPA accurately generates actin degradation and nuclear integrity loss with Vincristine and Cytochalasin B.

5/n: CellProfiler (@DrAnneCarpenter) reveals IMPA's ability to capture relevant morphological features, outperforming control cells. The model generates perturbation-specific images that closely match morphological shifts in actual perturbed images.

6/n: IMPA compares well with existing GAN models for style transfer, exhibiting promising performance in evaluation metrics such as FID, density, and coverage, while maintaining a competitive mode of action generation accuracy.

7/n: IMPA's smooth style space allows for drug response interpolation. This allows navigation of the style space and studying the associated morphological response and effect similarities between perturbations.

8/n: IMPA predicts responses to unseen drugs when they are structurally similar to training compounds with the same mode of action. However, IMPA’s accuracy drops when unseen treatments are not structurally related/functionally similar training drugs.

9/n: We compared IMPA to Mol2Image, showcasing distinct advantages. While Mol2Image generates images from pure noise, IMPA performs style transfer on existing images, enhancing the study of differential morphology and significantly improving overall performance.

10/n: We applied IMPA to predict gene KO effects on two datasets (BBBC025, RxRx1), using Gene2Vec for embeddings. We showed that the model captures morphological changes caused by active perturbations, correctly identifying subtle changes in less active phenotypes!

@ale__palmaa Happy that our „image perturbation autoencoder“ approach is out: Led by @ale__palmaa and @mo_lotfollahi, we learn the effect of drug/CRISPR perturbations on cell morphometry. This lets you style-transfer cell images to those under various perturbations.