The article provides a comprehensive overview of single-cell RNA sequencing (scRNA-seq) technologies and bioinformatics pipelines. It highlights the rapid advancements in next-generation sequencing (NGS) that have enabled the characterization of individual cells, offering new insights into complex biological systems. ScRNA-seq can reveal rare cell populations, uncover regulatory relationships between genes, and track cell lineage development. The review discusses technical challenges in single-cell isolation and library preparation, as well as computational analysis pipelines for scRNA-seq data. It emphasizes the importance of improving molecular and cell biology techniques and bioinformatics tools to facilitate both basic science and medical applications. The article also covers various single-cell isolation techniques, such as limiting dilution, micromanipulation, flow-activated cell sorting (FACS), laser capture microdissection, microfluidic technology, and microdroplet-based microfluidics. It details the steps in scRNA-seq library preparation, including cell lysis, reverse transcription, second-strand synthesis, and cDNA amplification. The computational challenges in scRNA-seq data analysis are addressed, including data preprocessing, normalization, confounding factor estimation, and cell type identification. The article further explores the inference of regulatory networks and the reconstruction of cell hierarchy, highlighting the potential of scRNA-seq in drug resistance clone identification, non-invasive biopsy diagnosis, and single-cell lineage and stem cell regulatory network studies. Finally, it discusses the future prospects of scRNA-seq, including its role in mapping the human cell atlas and its applications in understanding physiological structure-function relationships.The article provides a comprehensive overview of single-cell RNA sequencing (scRNA-seq) technologies and bioinformatics pipelines. It highlights the rapid advancements in next-generation sequencing (NGS) that have enabled the characterization of individual cells, offering new insights into complex biological systems. ScRNA-seq can reveal rare cell populations, uncover regulatory relationships between genes, and track cell lineage development. The review discusses technical challenges in single-cell isolation and library preparation, as well as computational analysis pipelines for scRNA-seq data. It emphasizes the importance of improving molecular and cell biology techniques and bioinformatics tools to facilitate both basic science and medical applications. The article also covers various single-cell isolation techniques, such as limiting dilution, micromanipulation, flow-activated cell sorting (FACS), laser capture microdissection, microfluidic technology, and microdroplet-based microfluidics. It details the steps in scRNA-seq library preparation, including cell lysis, reverse transcription, second-strand synthesis, and cDNA amplification. The computational challenges in scRNA-seq data analysis are addressed, including data preprocessing, normalization, confounding factor estimation, and cell type identification. The article further explores the inference of regulatory networks and the reconstruction of cell hierarchy, highlighting the potential of scRNA-seq in drug resistance clone identification, non-invasive biopsy diagnosis, and single-cell lineage and stem cell regulatory network studies. Finally, it discusses the future prospects of scRNA-seq, including its role in mapping the human cell atlas and its applications in understanding physiological structure-function relationships.