How can you define a single cell transcriptional analysis?
Before the advent of single-cell transcriptional analysis or RNAseq, the transcriptional analysis was done on a population level. While bulk sequencing brought the developmental biology community quite a bit of vital information that has certainly advanced the field, there are some major drawbacks of population studies that are resolved via scRNAseq. Firstly, population studies cannot account for heterogeneity within cell populations. Secondly, bulk studies cannot access rare cell populations within a sample, or understand population substructure. They also require much more starting material. scRNAseq has been able to help to define cell types in a new way, and has helped our understanding of cellular decision making, particularly early in development. However, there are many limitations with scRNAseq currently, and while scRNAseq is a useful source to answer the question ‘what,’ it does not, as of yet, seem to have the answers for ‘how.’
scRNAseq is a relatively new technology. With that in mind, it is important to explain what exactly scRNAseq is and how it works. The first study using scRNAseq was published in 2009. The standard procedure includes single cell capture, single cell lysis, reverse transcription, preamplification, and finally library preparation and sequencing. There is variation in how to go about these different steps, as summarized in Figure 1. Single cell capture can be done via micropipetting/micromanipulation, laser capture microdissection, FACS, microdroplets, or microfluidics. Each of these has their own pros and cons, and depending on the limitations of the experiment, can be chosen per preference.