Spatial Transcriptomics

Spatial transcriptomics maps gene expression within tissue samples, aiming to reveal the spatial organization of cellular processes and their relationship to tissue morphology. Current research heavily utilizes deep learning, employing architectures like transformers, graph neural networks, and diffusion models to predict gene expression from histology images, integrate multi-modal omics data, and address challenges like data sparsity and noise. This technology significantly advances our understanding of tissue biology and disease, enabling more precise diagnostics and potentially personalized medicine by providing spatially resolved insights into gene regulation and cellular interactions.