Single-cell RNA sequencing (scRNA-seq) examines the gene expression of individual cells. Compared to traditional RNA sequencing it provides a higher resolution of cellular differences. scRN- seq can provide information about an individual cell in the context of its microenvironment. Bulk RNA sequencing groups together different cells and provides an average expression signal. scRNA-seq allows the resolution of rare cell populations, regulatory relationships between genes and the trajectories of distinct cell lineages during development. Research has shown that gene expression is heterogeneous even in cell types the are similar. scRNA-seq can identify stochastic biological processes. The technology can identify outlier cells within a population, which has applications in drug resistance and cancer relapse.
Technology for sequencing of all RNA transcripts, also known as the transcriptome, comes from techniques presented by two research groups in the early 1990s, in the labs of P. Coleman and Norman Iscove. The groups presented the technology of expanding the complementary DNA (cDNA) of an individual cell using in vitro transcription for linear amplification and PCR for exponential amplification. Initially the technologies were applied to commercially available, high-density DNA microarray chips and later adapted for single-cell RNA sequencing. In 2009, single-cell transcriptome analysis with a next-generation sequencing platform was published, describing cells from early developmental stages. The research was Led by M Azim Surani at the University of Cambridge.
The first step is single-cell isolation, by limiting dilution with a pipette. Micromanipulation is used for early embryos or uncultivated microorganisms. Flow-activated cell sorting (FACS) and microfluidics methods are also used to isolate individual cells. Laser capture microdissection is aided by a computer system to isolate cells from solid samples.
The next step is library preparation which involves cell lysis, reverse transcription into first-strand cDNA, second-strand synthesis and cDNA amplification. The raw data is handled using computational software such as those provided by 10X Genomics and Fluidigm. Quality control tests are performed using tools such as FastQC (Babraham Institute). Alignment of sequences, de-duplication and normalization with raw expression data is done using statistical methods.
