Imaging-Based Spatial Transcriptomics: Data Interpretation Methods and Biomedical Applications

Abstract

Title: Simple Summary Spatial transcriptomics allows researchers to measure gene expression while preserving the positions of cells and molecules within tissues. Imaging-based spatial transcriptomics methods do this by directly visualizing RNA molecules or amplified signals in tissue samples, often at single-cell or subcellular resolution. However, these assays generate complex microscopy images, and reliable biological interpretation depends on careful computational processing. This review aims to help readers understand how these data can be interpreted reliably. It summarizes the development of imaging-based spatial transcriptomics and explains how raw image signals are transformed into molecular maps, cell-level measurements, tissue region annotations, and biological insights. It highlights key computational analytical steps and major challenges such as signal crowding, barriers in thick tissues, data integration, and error propagation issues. Biomedical applications ranging from subcellular RNA localization to tissue atlases and pathology-compatible disease studies are also discussed.