SINGLE-CELL TRANSCRIPTOME ANALYSIS FOR IDENTIFYING CELLULAR HETEROGENEITY IN BIOLOGICAL SYSTEMS

Abstract

The cellular heterogeneity is involved in the basic interpretation of complex biological systems, which shapes such processes as the development, development of disease, and a response to therapy. Conventional bulk RNA sequencing methods tend to obscure cell-to-cell differences and thus restrict the characterization of rare or functionally separate classes of cell. With a revolution in molecular biology, nomenclature, and analysis, single-cell transcriptomics, specifically single-cell RNA sequencing (scRNA-seq) now allows the examination of the state of individual cells in detail, including gene expression and state comparisons across cells in vivo. The review gives a general overview of the latest development in single-cell transcriptome studies and their applications in identifying cellular heterogeneity in a wide range of biological systems. The most important technological platforms, which include Smart-seq, Drop-seq, and 10x Genomics, are mentioned and computational methodologies, including data preprocessing, normalization, dimensionality reduction, clustering and differential gene expression analysis. The recent uses of scRNA-seq in cancer biology, immunology and plant sciences are analyzed critically with emphasis to the discoveries of new cell subpopulations and mechanisms of regulation. Besides, the latest issues, such as technical noise, drop out events, and batch effects are discussed, and possible solutions are examined. The possible avenues of emerging multi-omics integration and spatial transcriptomics are also investigated. This survey seeks to present perspectives on the potential and constraints of single-cell transcriptomics to inform future studies about more specific and scalable analytical models.