RPEGENE-Net: A Multi-Resolution Deep Learning Framework for Predicting Gene Expression from Microscopy Images of Retinal Pigment Epithelium (RPE) Cells

PurposeTo develop a deep learning framework, RPEGENE-Net, capable of predicting gene expression profiles of retinal pigment epithelium (RPE) cells using live-cell microscopy images. MethodsA dataset of live-cell images of RPE cells, treated with various drug regimens and captured at magnifications of 40x, 100x, 200x, and 400x, was used. Gene expression of six key genes involved in epithelial-mesenchymal transition or EMT (including -SMA, ZEB1, TGF-{beta}, CD90, {beta}-catenin, Snail) and treatment classes (Aflibercept, Bevacizumab, Dexamethasone, Aflibercept + Dexamethasone, and untreated control) were analyzed. After preprocessing the image data and gene expression values, we trained and evaluated twelve state-of-the-art deep learning architectures, including three variants of DenseNet, five variants of ResNet, EfficientNet_b5, Inception_v3, RegNet_y_400mf, and a vision transformer model (Swin_b). A two-stage pipeline was implemented, combining autoencoder-based pretraining to extract meaningful features with fine-tuning specifically optimized for gene expression regression and treatment type classification tasks. Features extracted from the second stage across four magnifications were concatenated to generate the final prediction, leveraging multi-scale morphological information for improved accuracy. ResultsDenseNet121 demonstrated superior performance, achieving the highest Pearson correlation coefficients for four genes: -SMA (0.79), ZEB1(0.84), TGF-{beta} (0.83), and Snail (0.86). ResNet34 outperformed other models for CD90 (0.87) and {beta}-catenin (0.85) predictions. The average mean absolute error (MAE) and average root mean square error (RMSE) on test dataset were 0.0244 and 0.1228, respectively. The R2 scores ranged from 0.50 (-SMA) to 0.74 (TGF-{beta}), indicating strong alignment between predicted and actual gene expression values. A multi-level approach, combining data from 40x, 100x, 200x, and 400x magnifications yielded higher R2 scores for almost all genes compared to single-magnification models. For the classification task, DenseNet121 achieved F1 score, precision, recall, and accuracy of 0.98, with a specificity of 0.99. ConclusionsRPEGENE-Net provides a simple, cost-effective method to predict gene expression from live-cell images, with potential applications in experimental studies, RPE transplantation quality control, and broader cell-based research. Multi-magnification imaging enhances model performance, supporting its utility as a scalable tool for diverse gene expression studies.