Predicting Gene Expression from RPE Cell Images

A two-stage learning pipeline was implemented, in which multiple deep learning backbones were first pretrained using an autoencoder-based strategy to extract histology-relevant features in an unsupervised manner, followed by fine-tuning for supervised gene expression regression and treatment 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.

DenseNet121 demonstrated superior performance, achieving the highest Pearson correlation coefficients for four genes: α-SMA (0.79), ZEB1(0.84), TGF-β (0.83), and Snail (0.86). ResNet34 outperformed other models for CD90 (0.87) and β-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 (α-SMA) to 0.74 (TGF-β), 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.

These findings demonstrate the feasibility of RPEGENE-Net to predict selected molecular markers and distinguish treatment conditions in RPE cultures. Following validation in larger and biologically independent datasets, this approach may support experimental studies and non-destructive quality assessment of cultured RPE cells.