Single-cell RNA sequencing technologies and applications: A brief overview

Abstract

Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

Keywords: RNA sequencing; atlas; big data; precision medicine; regenerative medicine; single cell.

FIGURE 1

Development of single‐cell RNA sequencing technology. With the technological advances in single‐cell RNA sequencing (scRNA)‐seq, (A) the number of analyzed cells increased, (B) the cost (in US dollar) was exponentially reduced, (C) the number of published papers increased and (D) the history of technology evolution in the last decade using more sophisticated, accurate, high throughput analysis was achieved. Part (D) is created with icons from BioRender with license for publication

FIGURE 2

An overview of the single‐cell RNA‐sequencing procedures. (A) Isolation of the cells from tissue samples and capturing of the single cells, wrapping of each individual cell with a bead inside a nanoscale droplet (each bead contains unique molecular identifiers), (B) barcoding and amplification of complementary DNA (cDNA) and (C) library preparation procedure. After single‐cell RNA sequencing (D), the snapshot data would be analyzed to present and classify the landscape of gene expression in cells of a heterogeneous population (E). Illustrative figure in (E) is generated with BioRender with license for publication

FIGURE 3

Roadmap for typical single‐cell RNA sequencing data analysis. The classic roadmap for single‐cell RNA sequencing (scRNA‐seq) data analysis mainly consists of data preprocessing (blue panel), general analyses (green panel) and exploratory analyses (yellow panel). Data preprocessing includes quality control, alignment and quantification; general analyses include low‐quality cell filtering, normalization, HVG selection, dimension reduction, clustering and annotation of cell types; exploratory analyses include DEG analysis, function enrichment, GSVA, TF prediction, cell trajectory, cell‐cell interaction, cell cycle and spatial transcriptome analysis. The plot below each box gives a schematic of the visualized results in each analysis step. HVG, highly variable gene; DEG, differentially expressed gene; GSVA, gene set variation analysis; TF, transcription factor. Demo figures were generated with data set GSM4041174

FIGURE 4

Overview of the analysis modules for single‐cell RNA sequencing data analysis. The diagram shows a summary of analysis modules in the actual analysis of single‐cell RNA sequencing (scRNA‐seq) data, which can be divided into four analysis modules; they are (A) data preprocessing module, (B) general analysis module, (C) exploratory analysis module, and (D) optional analysis module, respectively. More details about each module can be found in the “Streamline scRNA‐seq Data Analysis” section

FIGURE 5

Application of single‐cell RNA sequencing technology. Single‐cell RNA sequencing has been employed in different species (humans, animals, plants) to improve understanding of normal and disease models. A special note is placed on human health, and many single‐cell RNA sequencing (scRNA‐seq) methods are focused on understanding (A) development, (B) immunology, (C) diabetes, (D) microbiology, (E) SARS‐CoV‐2, (F) cancer biology, (G) vascular biology (H) neurobiology and (I) clinical diagnostics. Figure was created with BioRender with license for publication

FIGURE 6

Cell atlases of model organisms. First cell atlases of model organism Caenorhabditis elegans (A), planarian (B), Drosophila melanogaster (C), zebrafish (D), mouse (E), monkey (F), and human (G). Year of published data, cell number and cell type analyzed by single‐cell RNA sequencing (scRNA‐seq) were indicated. Icons of model organisms are created with BioRender with license for publication