An Overview of Spatial Transcriptomics Methodologies in Traversing the Biological System

Transcriptomics covers the in-depth analysis of RNA molecules in cells or tissues and plays an essential role in understanding cellular functions and disease mechanisms. Advances in spatial transcriptomics (ST) in recent times have revolutionized the field by combining gene expression data with spatial information, enabling the analysis of RNA molecules within their tissue context. The evolution of spatial transcriptomics, particularly the integration of artificial intelligence (AI) in data analysis, and its diverse applications have been found to be superior methods in developmental research. Spatial transcriptomics technologies, along with single-cell RNA sequencing (scRNA-seq), offer unprecedented possibilities to unravel intricate cellular interactions within tissues. It emphasizes the importance of accurate cell localization for in-depth discoveries and developments via highthroughput spatial transcriptome profiling. The integration of artificial intelligence in spatial transcriptomics analysis is a key focus, showcasing its role in detecting spatially variable genes, clustering cell populations, communication analysis, and enhancing data interpretation. The evolution of AI methods tailored for spatial transcriptomics is highlighted, addressing the unique challenges posed by spatially resolved transcriptomic data. Applications of spatial transcriptomics integrated with other omics data, such as genomics, proteomics, and metabolomics, provide a detailed view of molecular processes within tissues and emerge in diverse applications. Integrating spatial transcriptomics with AI represents a transformative approach to understanding tissue architecture and cellular interactions. This innovative synergy not only enhances our understanding of gene expression patterns but also offers a holistic view of molecular processes within tissues, with profound implications for disease mechanisms and therapeutic development.