Single-cell RNA sequencing technologies and bioinformatics pipelines

By Byungjin Hwang, J. Lee, Duhee Bang. Experimental and Molecular Medicine, 2018

1,502 citations recorded by the source corpus.

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. 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.

Subfields: Biophysics, Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/219bee9fde1303f423fac52ff373f19552c60d53

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Citations in the corpus

• Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Currently, several different approaches are available for isolating single cells, including limiting dilution, micromanipulation, flow-activated cell sorting (FACS), laser capture microdissection (LCM), and microfluidics (Gross et al., 2015; Kolodziejczyk et al., 2015; Hwang et al., 2018).”
• Machine learning approaches to drug response prediction: challenges and recent progress (2020, background): “It works by isolating individual cells and performing whole genome ampli fi cation to increase the amount of DNA present in order to be detectable by a DNA sequencer 80 .”