Abstract Imaging-based spatial transcriptomics (ST) technologies offer unparalleled resolution for mapping gene expression within intact tissues but are fundamentally constrained by the limited size of their gene panels. This restriction hinders comprehensive biological discovery by omitting potentially crucial genes from analysis. To overcome this limitation, we introduce STGNET, a deep learning framework that extends gene panel coverage by integrating generative adversarial networks (GANs) with graph neural networks. STGNET employs a multi-stage GAN to learn the global transcriptomic distribution from single-cell RNA sequencing data, followed by a spatially aware graph convolutional network that refines imputations by modeling both physical cell proximity and transcriptional similarity. We rigorously benchmarked STGNET against seven state-of-the-art methods across nine diverse ST datasets. STGNET consistently achieved superior performance, demonstrating enhanced accuracy in gene imputation, and exceptional preservation of cellular topology. We further showcase its biological utility by accurately reconstructing developmental marker patterns in mouse embryogenesis, revealing a novel transitional cell state in breast cancer progression, and uncovering extensive, previously obscured cell–cell communication networks in the mouse brain. STGNET provides a powerful and robust solution for unlocking the full potential of targeted ST assays, thereby enabling deeper and more comprehensive spatial biology. STGNET is freely accessible at https://github.com/wuyuanwuhuii/STGNET.
Wang et al. (Sun,) studied this question.