Global and targeted approaches to single-cell transcriptome characterization

Abstract

Analysing transcriptomes of cell populations is a standard molecular biology approach to understand how cells function. Recent methodological development has allowed performing similar experiments on single cells. This has opened up the possibility to examine samples with limited cell number, such as cells of the early embryo, and to obtain an understanding of heterogeneity within populations such as blood cell types or neurons. There are two major approaches for single-cell transcriptome analysis: quantitative reverse transcription PCR (RT-qPCR) on a limited number of genes of interest, or more global approaches targeting entire transcriptomes using RNA sequencing. RT-qPCR is sensitive, fast and arguably more straightforward, while whole-transcriptome approaches offer an unbiased perspective on a cell's expression status.

Figure 1.

Single-cell methods provide insight into the nature of a population, its subpopulation structure and heterogeneity. (A) A conceptual example is the switch of cells from State 1 to State 2 in this schematic diagram. This process could be either a binary or gradual switch in transcriptomic state. While population methods cannot distinguish between the two states, single-cell methods can discriminate between these two transitions. (B) Examples of biological questions addressed with single-cell RNA sequencing (scRNA-seq).

Figure 2.

Molecular counting with UMIs. UMIs are random n-mer oligonucleotide sequences included in the reverse transcription primers. As the number of different UMI sequences exceeds the number of copies for any single-transcript species, the UMI sequences can be used for quantifying the number of molecules that were successfully captured and amplified, and thus control for amplification biases associated with PCR-based sample preparation.