Single-Cell RNA Sequencing and Its Combination with Protein and DNA Analyses

Abstract

Heterogeneity in cell populations poses a significant challenge for understanding complex cell biological processes. The analysis of cells at the single-cell level, especially single-cell RNA sequencing (scRNA-seq), has made it possible to comprehensively dissect cellular heterogeneity and access unobtainable biological information from bulk analysis. Recent efforts have combined scRNA-seq profiles with genomic or proteomic data, and show added value in describing complex cellular heterogeneity than transcriptome measurements alone. With the rising demand for scRNA-seq for biomedical and clinical applications, there is a strong need for a timely and comprehensive review on the scRNA-seq technologies and their potential biomedical applications. In this review, we first discuss the latest state of development by detailing each scRNA-seq technology, including both conventional and microfluidic technologies. We then summarize their advantages and limitations along with their biomedical applications. The efforts of integrating the transcriptome profile with highly multiplexed proteomic and genomic data are thoroughly reviewed with results showing the integrated data being more informative than transcriptome data alone. Lastly, the latest progress toward commercialization, the remaining challenges, and future perspectives on the development of scRNA-seq technologies are briefly discussed.

A Palette of Cytokines to Measure Anti-Tumor Efficacy of T Cell-Based Therapeutics

“…With the advent of next generation sequencing (NGS) technologies like ribonucleic acid (RNA) sequencing (RNA-seq), unbiased, multi-target analysis can be performed and rare cytokine transcripts can be detected. [ 77 , 78 ]. Bulk RNA analysis holds shortcomings such as an inability to identify the source of the gene, and to reveal whether actual protein is made and secreted, and lacks delivery of a holistic analysis to the TME or cytokine secretion [ 79 ].…”

Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

“…The application of machine learning should help further our understanding of the relationships among the regulation of gene expression by non-coding RNA, the level of coding mRNA, and their involvement in the development of pathologies. One can also expect that the field will progress beyond its present limitations and allow scRNA-seq and single-cell proteome analyses [122] in situ and in vivo, as described in recent developments of DNA microscopy [123]. In this technique, the use of DNA "bar codes" can pinpoint a molecule's relative spatial positions within a sample and offers an entirely new way to build a picture of the cell [29,123].…”

Applying Single-Cell Analysis to Gonadogenesis and DSDs (Disorders/Differences of Sex Development)

“…Cell isolation can be achieved using diverse methods, including micro-pipetting, magnetic activated cell sorting (MACS) and FACS ( Figure 3 ). With the incorporation of microfluidic technologies, this process has become semi-automated, allowing more reproducible results and avoiding DNA contamination [ 96 , 97 ]. Two main technologies are generally used for single-cell RNA sequencing (scRNA-seq), the droplet based 10× Genomics Chromium platform and the plate based Smart-seq ( Figure 3 ) [ 98 , 99 , 100 ].…”