Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids - Kegeles, Evgenii; Naumov, Anton; Karpulevich, Evgeny A.; Volchkov, Pavel; Baranov, Petr (Frontiers in Cellular Neuroscience, 2020)
We have developed a deep learning-based computer algorithm to recognize and predict retinal differentiation in stem cell-derived organoids based on the brightfield imaging. The three-dimensional, “organoid” approach for the differentiation of pluripotent stem cells into retinal and other neural tissues has become a major in-vitro strategy to recapitulate development. We decided to develop a universal, robust and non-invasive method to assess retinal differentiation that would not require chemical probes or reporter gene expression. We hypothesized that basic contrast brightfield images contain sufficient information on the tissue specification and it is possible to extract this data using convolutional neural networks (CNN). Retina-specific Rx-GFP mouse embryonic reporter stem cells have been used for all of the differentiation experiments in this work. The brightfield (BF) images of organoids have been taken on day 6 and fluorescent on day 9. To train the CNN we utilized a transfer learning approach: ImageNet pre-trained ResNet50v2, VGG19, Xception and DenseNet121 CNNs had been trained on labeled BF images of the organoids, divided into two categories (retina and non-retina), based on the fluorescent reporter gene expression. The best performing classifier with ResNet50v2 architecture showed a ROC-AUC score of 0.91 on a test dataset. A comparison of the best performing CNN with the human-based classifier showed that the CNN algorithm performs better than the expert in predicting organoid fate: 84% vs 67  6% of correct predictions respectively, confirming our original hypothesis. Overall, we have demonstrated that the computer algorithm can successfully recognize and predict retinal differentiation in organoids before the onset of reporter gene expression. This is the first demonstration of CNN ability to classify stem cell-derived tissue in-vitro.