Palma Alessandro, Theis Fabian J, Lotfollahi Mohammad
Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
School of Computing, Information and Technology, Technical University of Munich, Munich, Germany.
Nat Commun. 2025 Jan 8;16(1):505. doi: 10.1038/s41467-024-55707-8.
Advancements in high-throughput screenings enable the exploration of rich phenotypic readouts through high-content microscopy, expediting the development of phenotype-based drug discovery. However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions. We show that IMPA accurately captures morphological and population-level changes of both seen and unseen perturbations on breast cancer and osteosarcoma cells. Additionally, IMPA accounts for batch effects and can model perturbations across various sources of technical variation, further enhancing its robustness in diverse experimental conditions. With the increasing availability of large-scale high-content imaging screens generated by academic and industrial consortia, we envision that IMPA will facilitate the analysis of microscopy data and enable efficient experimental design via in-silico perturbation prediction.
高通量筛选技术的进步使得通过高内涵显微镜探索丰富的表型读数成为可能，加速了基于表型的药物发现进程。然而，由于对干扰因素的采样不完整以及实验之间存在技术差异，分析大规模复杂的高内涵成像筛选仍然具有挑战性。为了解决这些缺点，我们提出了图像扰动自动编码器（IMPA），这是一种生成式风格迁移模型，可预测基因和化学干预中扰动的形态变化。我们表明，IMPA能够准确捕捉乳腺癌和骨肉瘤细胞上可见和不可见扰动的形态及群体水平变化。此外，IMPA考虑了批次效应，并且可以对各种技术变异来源的扰动进行建模，进一步增强了其在不同实验条件下的稳健性。随着学术和工业联盟生成的大规模高内涵成像筛选数据越来越多，我们设想IMPA将有助于显微镜数据分析，并通过计算机模拟扰动预测实现高效的实验设计。
