Advances in rolling circle amplification-based <italic>in situ</italic> sequencing for spatial transcriptomics technologies

Spatial transcriptomics has emerged as a rapidly evolving technology in gene expression research that addresses the limitations of traditional single-cell sequencing, which cannot provide a spatial context for cellular distribution. This technique offers novel insights into the spatial organization of gene activity and cellular composition by precisely mapping the spatial localization of gene expression, thus enabling a deeper understanding of the intricate intercellular interactions and gene regulatory networks that govern biological organisms.Spatial transcriptomics is evolving in diverse directions, with in situ sequencing (ISS)-based methods becoming a prominent approach. ISS enables direct analysis of gene sequences at the cellular or tissue level by hybridizing specific probes to target genes, followed by rolling circle amplification (RCA) and multiple cycles of in situ imaging. The ISS not only provides quantitative gene expression data but also uncovers the spatial heterogeneity of gene expression at the molecular level in single cells, which is essential for understanding tissue structure and function.A key characteristic of ISS-based technologies is their reliance on RCA for signal amplification and repeated cycles of imaging and decoding to obtain spatial gene expression information for subsequent bioinformatics analysis. Although these technologies share a similar underlying principle, they differ in technical aspects, such as probe design, sequencing chemistry, and data processing strategies, leading to variations in sensitivity and specificity. For instance, owing to differences in probe design, some technologies are limited to target detection, whereas others enable non-targeted analysis. Furthermore, various sequencing chemistries have been employed, including sequencing-by-ligation, sequencing-by-hybridization, and sequencing-by-synthesis. In addition, distinct data processing approaches and algorithms are used, which influence the overall performance of these technologies. Despite these variations, all the methods aim to generate a high-resolution spatial gene expression matrix that is tailored to different research requirements.ISS-based spatial transcriptomics is a powerful tool for application in neuroscience and disease research. In neuroscience, this facilitates the investigation of cell types and gene expression patterns across different brain regions, which is crucial for understanding the mechanisms underlying neurodegenerative diseases and neurodevelopmental disorders. In disease research, these techniques help to elucidate the complex tumor microenvironment, including the interactions between the tumor and immune cells, offering valuable insights for the development of novel cancer therapies. ISS has also been applied to developmental biology, as well as other research fields, where generating a spatial gene expression atlas has helped to better understand the functions of genes and cells in their biological niches. The rapid advancement of spatial transcriptomics has driven the field toward higher resolution and broader dynamic ranges. However, single-omics approaches are insufficient for deeper biological understanding, and spatial transcriptomics is increasingly integrated with genomics, proteomics, and metabolomics to provide comprehensive biological data. Techniques such as RIBOmap, NIS-Seq, and MiP-seq have advanced our ability to study gene translation, nuclear sequencing, and multi-omics at the subcellular level. Additionally, efforts are being made to develop 3D spatial transcriptomics using methods such as STARmap and ExSeq, which enhance tissue transparency and resolution for more detailed spatial gene expression analyses, enabling deeper insights into biology and disease mechanisms.In conclusion, the development and application of ISS-based spatial transcriptomics technologies hold great promise, not only for advancing fundamental scientific research but also for their potential impact on clinical diagnosis and therapeutic interventions.