10.1177/1056789514562152

Vollständiger Abstract

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Cellular diversity in multicellular organisms arises from the functional specialization of individual cells and the influence of both the local tissue microenvironment and external stimuli. Understanding this heterogeneity requires accurate characterization of cell types and the molecular dynamics that define them. In this context, transcriptomic technologies at the single-cell level have become central tools, as they provide a comprehensive view of gene expression and reveal functional molecular patterns. Recent advances have dramatically expanded the number of detectable transcripts and improved data resolution, shifting from bulk measurements that averaged signals across tissues to single‑cell approaches capable of quantifying gene expression at cellular resolution. This finer resolution enables detailed investigation of cellular functions, interactions, and transitions, and supports the development of multiscale computational models. Within this landscape, biological network-based approaches, particularly gene regulatory networks, have emerged as powerful tools for interpreting the functional organization of gene circuits. These methods facilitate the identification of biomarkers, regulatory factors, and key pathways, deepening our understanding of gene regulation and cellular identity through high‑resolution transcriptomic data. Transferring this knowledge to clinical practice is what we here refer to as precision health. This manuscript explores the current landscape of single-cell RNA sequencing (scRNA-seq), highlighting key studies that have leveraged this technology to advance biological understanding for clinical purposes through the construction of gene regulatory networks (GRNs) from single-cell transcriptomic data. Furthermore, it examines how these insights could contribute to clinical applications and, ultimately, the advancement of precision health. Finally, it discusses the key challenges in data analysis and practical applications within this rapidly evolving field.

Abstract: PubMed · Datensatz