Exploring Additional Valuable Information From Single-Cell RNA-Seq Data.

Yunjin Li,Qiyue Xu,Geng Chen,Duojiao Wu

doi:10.3389/fcell.2020.593007

Abstract

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

Highlights

In recent years, single-cell RNA-seq technologies and related bioinformatics methods have been developing and innovating rapidly, which significantly revolutionized our understanding of the expression heterogeneity and transcriptome dynamics of individual cells for diverse species including human (Quadrato et al, 2017), mouse (Brown et al, 2016), zebrafish (Wagner et al, 2018), and Drosophila (Karaiskos et al, 2017)
ScRNA-seq is widely applied to diverse organisms to dissect a range of biological questions related to developmental biology, oncology, immunology, neurology, and microbiology at the single-cell resolution
Besides those routine analyses conducted in most studies, much more other valuable information can be mined from scRNA-seq data

Summary

Introduction

Single-cell RNA-seq (scRNA-seq) technologies and related bioinformatics methods have been developing and innovating rapidly, which significantly revolutionized our understanding of the expression heterogeneity and transcriptome dynamics of individual cells for diverse species including human (Quadrato et al, 2017), mouse (Brown et al, 2016), zebrafish (Wagner et al, 2018), and Drosophila (Karaiskos et al, 2017). Cellular differentiation and cell state transition processes are controlled by the underlying GRNs. An increasing number of approaches have been developed to infer the GRNs from scRNA-seq data generally based on the assumption that the genes with similar expression profiles could be regulated by a common transcription factor [such as SCENIC To identify the potential interactions within or between cell subpopulations from scRNA-seq data, an increasing number of computational methods have been developed based on the expression abundance of ligand and receptor pairs (Table 1).

Results

Conclusion