SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation

Integrating transcriptome-wide single-cell gene expression data with spatial context significantly enhances our understanding of tissue biology, cellular interactions, and disease progression. Although single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data, it lacks crucial spatial context, whereas spatial transcriptomics techniques offer spatial resolution but are limited in the transcriptomic coverage. To address these limitations, integrating scRNA-seq and spatial transcriptomics data is essential. We introduce SpaGene, a novel deep learning framework designed to integrate scRNA-seq data and spatial transcriptomics data. SpaGene consists of two encoder-decoder pairs combined with two translators and two discriminators to effectively impute missing gene expressions within spatial transcriptomics datasets. We benchmarked SpaGene against existing state-of-the-art methods across diverse datasets. Across the datasets, SpaGene achieved an average 33% higher Pearson correlation coefficient (PCC), 21% higher Structural similarity index (SSIM), and 6.6% lower Root mean squared error (RMSE) compared to the existing approaches, highlighting its capability to reliably impute missing genes and provide comprehensive transcriptomics profiles. Application of our model to lung tumor tissue revealed immune cell enrichment at tumor boundaries, restricted myeloid cell trafficking in adjacent normal regions, and microenvironmental-driven pathways linked to immune neighborhoods. These results provide novel insight into immune exclusion and tumor-immune interactions that drive tumor progression, highlighting potential avenues for therapeutic development. Thus, SpaGene extends the power of spatial transcriptomics by delivering spatially resolved, enhanced transcriptome data that enable deeper biological understanding.