Single-cell RNA sequencing (scRNA-seq), or single-cell transcriptomics, is a method for measuring the expression levels of genes in individual cells within a population of input cells. Traditional bulk transcriptomic analysis methods require input material from thousands of cells and generate only an average of the genetic expression levels of the input cells. However, gene expression has proved to be heterogeneous, even among cells from a common tissue source, making traditional methods ill adapted for the identification and analysis of rare cell types or comparative transcriptomics between similar cell types. By investigating transcriptomes at the single-cell level, scRNA-seq can provide deeper insight into tissue heterogeneity, cancer and disease, cellular response, immunobiology, and many other areas of interest. The method has rapidly gained acceptance in life science and applied research, and is gaining momentum in clinical settings, particularly for liquid biopsy applications. In the pharmaceutical and biotechnology sector, scRNA-seq has found applications in the development of biologics-based therapeutics.

As scRNA-seq and microfluidics technologies have matured, the ability to perform transcription analysis at a single-cell level has become much more accessible and practical. scRNA-seq methods have evolved from a set of very labor intensive, multistage workflows to kits, and now to automated instruments. Today, high-throughput instruments capable of analyzing tens of thousands of single cells in parallel are available, as a handful of companies have brought dedicated scRNA-seq instruments to the market. While each company has taken a different approach to the technique, general workflow steps include droplet encapsulation of single cells, lysis, reverse transcription of mRNA, amplification of cDNA and library generation. From there, sequencing is performed separately using high-throughput sequencing instruments, followed by computational analysis. The approaches differ in which strategies are used for reverse transcription and cDNA synthesis, amplification and the inclusion or exclusion of sequence-specific barcoding.

In addition to dedicated scRNA-seq platforms, the technique can also be adapted by single-cell analysis systems, which are capable of performing several types of single-cell analysis, including genomic, epigenomic and proteomic. However, it should be noted that single-cell analysis systems cannot yet perform multimodal single-cell analysis; that is, performing one type of analysis consumes the input cell, so that no further types of analysis can be performed on that cell.

Despite many advances in method development, challenges remain in performing scRNA-seq. As with other experimental methods that involves amplification, there is a risk that lower-abundance RNA transcripts can fail to be detected. Computational analysis challenges also remain, as many labs do not possess the expertise to perform the analysis. Bioinformatics software tools for scRNA-seq analysis are still in their early stages.

scRNA-seq at a Glance:

Leading vendors

• Becton, Dickinson
• 1CellBio
• Dolomite Bio

Largest Markets

• Academia
• Pharmaceutical and biotechnology
• Hospital and clinical