Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications

Spatial transcriptomics (ST) integrates spatial information with gene expression data to quantify mRNA levels across diverse genes within the structural context of tissues and cells. This field enables simultaneous capture of cellular gene expression at the transcriptomic level while preserving spatial localization information. This approach enhances our understanding of cellular interactions and their immediate microenvironments. Utilizing this technique, researchers can gain deep insights into biological development and disease mechanisms across different tissue regions. Recently, ST has witnessed substantial advancements; however, it faces challenges such as reliance on specific sample types, the resolution of visualized genes, commercial feasibility, and the capability to collect comprehensive single-cell data. This article summarizes four primary ST techniques, comparing and analyzing diverse research methodologies to improve experimental design and analytical evaluation. It highlights the essential role of ST in integrated multi-omics analyses and the development of disease models while contemplating its future advancements.