Characterization of drug effects on cell cultures from phase-contrast microscopy images. (December 2022)

• Record Type: Journal Article
• Authors: Baručić, Denis; Kaushik, Sumit; Kybic, Jan; Stanková, Jarmila; Džubák, Petr; Hajdúch, Marián
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: Deep learning -- Phase-contrast images -- Drug discovery -- Anti-cancer drugs -- Convolutional neural networks; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106171
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging. We use a convolutional neural network (CNN) trained end-to-end directly on the input images without requiring for manual segmentations or any other auxiliary data. Our method can distinguish between tested cytotoxic drugs with an accuracy of 98%, provided that their mechanism of action differs, outperforming previous work. The results are even better when substance-specific concentrations are used. We show the benefit of sharing the extracted features over all classes (drugs). Finally, a 2D visualization of these features reveals clusters, which correspond well to known class labels, suggesting the possible use of our methodology for drug discovery application in analyzing new, unseen drugs. Highlights: Three topoisomerase inhibitors can be distinguished from phase-contrast microscopy images. Phase-contrast images are as good for classification as fluorescence images. Classification accuracy is better on images taken 72 h after treatment than 24 h. Classification is better when substance-specific concentrations are used.