Qinyu Ge, Yuqi Sheng, Yuting Shan, Yuwei Yang, Haohan Jiang, Ruyue Wang
Spatial transcriptomics technology represents a groundbreaking advancement in the life sciences, enabling the analysis of gene expression patterns within their native spatial context. However, inefficiencies in RNA capture from tissue samples have historically limited its effective application. This article presents a systematic review of innovative technologies and strategies that have enhanced the efficiency of spatial transcriptome RNA capture in recent years. These strategies include nanomaterial-enhanced capture, optimization of microfluidic chips, advancements in molecular biology techniques, and computationally assisted prediction methods, among others. Through a comparative analysis of cutting-edge technologies such as Decoder-seq, Stereo-seq V2, MAGIC-seq, and MSN-seq, this article summarizes progress made in addressing challenges related to RNA diffusion, probe density, and tissue processing. Particular emphasis is placed on optimization approaches for formalin-fixed paraffin-embedded (FFPE) clinical samples and computational prediction methodologies that integrate artificial intelligence. These innovations provide valuable references for future technological development. The objective of this review is to provide researchers with a comprehensive understanding of how to enhance spatial transcriptome capture efficiency while promoting the utility of this technology in both basic research and clinical applications.