Single-cell RNA sequencing technologies and bioinformatics pipelines - Hwang, Byungjin; Lee, Ji Hyun; Bang, Duhee (Experimental & molecular medicine, 2018)
Rapid progress in the development of next-generation sequencing (NGS) technologies in recent years has provided many valuable insights into complex biological systems, ranging from cancer genomics to diverse microbial communities. NGS-based technologies for genomics, transcriptomics, and epigenomics are now increasingly focused on the characterization of individual cells. These single-cell analyses will allow researchers to uncover new and potentially unexpected biological discoveries relative to traditional profiling methods that assess bulk populations. Single-cell RNA sequencing (scRNA-seq), for example, can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track the trajectories of distinct cell lineages in development. In this review, we will focus on technical challenges in single-cell isolation and library preparation and on computational analysis pipelines available for analyzing scRNA-seq data. Further technical improvements at the level of molecular and cell biology and in available bioinformatics tools will greatly facilitate both the basic science and medical applications of these sequencing technologies. Genetic data: Zooming in on single cells Showing which genes are expressed, or switched on, in individual cells may help to reveal the first signs of disease. Each cell in an organism contains the same genetic information, but cell type and behavior depend on which genes are expressed. Previously, researchers could only sequence cells in batches, averaging the results, but technological improvements now allow sequencing of the genes expressed in an individual cell, known as single-cell RNA sequencing (scRNA-seq). Ji Hyun Lee (Kyung Hee University, Seoul) and Duhee Bang and Byungjin Hwang (Yonsei University, Seoul) have reviewed the available scRNA-seq technologies and the strategies available to analyze the large quantities of data produced. They conclude that scRNA-seq will impact both basic and medical science, from illuminating drug resistance in cancer to revealing the complex pathways of cell differentiation during development.