Single-Cell Transcriptomics in Developmental Biology: Bridging Cellular Heterogeneity and Disease Mechanisms

Abstract

ScRNA-seq is a promise to discover cellular heterogeneity and dynamism during development by being a disruptive technology. Being able to capture gene expression on a single-cell scale, it prevents the weaknesses of bulk transcriptomics and has the benefits of identifying rare subpopulations, lineage dynamics, and control networks. Other recent technological and computational solutions have enabled the use of scRNA-seq to profile and integrate with spatial and multi-omics data in large scales to increase its application in the mapping of embryogenesis, organogenesis and stem cell differentiation. Experiences of these researches are being used more and more to learn about disease mechanisms such as congenital disease, cancer, neurodegradation, and immune dysregulation. Although there are obstacles to artificial intelligence clinical relevance, including sparsity of data, technical variability and interpretability, the continued advancement of artificial intelligence, integrative modeling and translational pipelines is increasing clinical relevance. Single-cell transcriptomics is proving to be a foundation of precise medicine and tissue regenerative biology by connecting fundamental developmental events to the pathological states.