Single-cell RNA-sequencing

Background

Deep sequencing of DNA and RNA from a single cell is of great importance in the investigation of cellular functions. Generally, typical next-generation sequencing experiments often contain the following steps: isolation of single-cell, nucleic acid extraction and amplification, sequencing library preparation, sequencing, and bioinformatic data analysis. However, it is obviously more challenging to perform single-cell sequencing than sequencing from cells in bulk. As the development of technology, many novel methods are proposed. Recent technical improvements make single-cell sequencing a promising tool for approaching a set of seemingly inaccessible problems.

RNA sequencing has two main schools: bulk RNA seq & single-cell RNA seq (scRNA-seq). Before the appearance of scRNA-seq, transcriptomics analysis relies on the prior one. Bulk RNA-seq is a direct method to discover the averages of cellular expression. Although regarded as a good way to find disease biomarkers, it neglects the heterogeneity from the samples. It means that the difference at single-cell level is not discovered sufficiently. While scRNA-seq provides the expression profiles of individual cells and is considered the gold standard for defining cell states and phenotypes¹. In other words, scRNA-seq can obtain the genic expression in single-cell level. Although it is impossible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, gene expression patterns can be identified through gene clustering analyses². In this way, the rare cell types within a cell population that may never have been seen before can be uncovered.

Methods

Generally, current scRNA-seq protocols involve isolating single cells and their RNA, and then following the same steps as bulk RNA-seq: reverse transcription (RT) → amplification → library generation → sequencing.

Reverse transcription

RT is a process of creating double-standard DNA from an RNA template. It is a critical step as the efficiency of RT reaction determines how much of the cell's RNA population will be eventually analyzed by the sequencer. The processivity of RT and the priming strategies used may affect full-length complementary DNA (cDNA) production and the generation of libraries.

Amplification

Currently, either Polymerase Chain Reaction (PCR) or in vitro transcription (IVT) is used to amplify cDNA. However, different PCR efficiency on particular sequences (for instance, GC content and snapback structure) may also be exponentially amplified, producing libraries with uneven coverage. On the other hand, while libraries generated by IVT can avoid PCR-induced sequence bias, specific sequences may be transcribed inefficiently, thus causing sequence drop-out or generating incomplete sequences ³.

Applications

scRNA-seq has now been widely used across biological disciplines including Developmental biology, Neurology, Oncology, Immunology, Cardiovascular research and Infectious disease. Combined with machine learning methods, data collected from bulk RNA-seq has been used to increase the signal / noise ratio in scRNA-seq. Specifically, scientists have used gene expression profiles from pan-cancer datasets in order to build coexpression networks, and then have applied theses on single cell gene expression profiles, obtaining more robust method to detect the presence of mutations in individual cells using transcript levels.

At the same time, some scRNA-seq methods have also been applied to single cell microorganisms. For example SMART-seq2 has been used to analyze single cell eukaryotic microbes. Single-cell analysis of the several transcription factors by scRNA-seq revealed heterogeneity across the population.

What's more, scRNA-seq has provided considerable insight into the development of embryos and organisms, including the worm Caenorhabditis elegans, and the regenerative planarian Schmidtea mediterranea and axolotl Ambystoma mexicanum.

Limitations

Most RNA-seq methods depend on poly(A) tail capture to enrich mRNA and deplete abundant and uninformative rRNA. Thus, they are often restricted to sequencing polyadenylated mRNA molecules. Also, Bacteria and other prokaryotes are currently not amenable to single-cell RNA-seq due to the lack of polyadenylated mRNA. Thus, the development of single-cell RNA-seq methods that do not depend on poly(A) tail capture will also be instrumental in enabling single-cell resolution microbiome studies. Bulk bacterial studies typically apply general rRNA depletion to overcome the lack of polyadenylated mRNA on bacteria, but at the single-cell level, the total RNA found in one cell is too small. Lack of polyadenylated mRNA and scarcity of total RNA found in single bacteria cells are two important barriers limiting the deployment of scRNA-seq in bacteria.