Ask AI: discuss what single cell RNA-sequencing is. Include what controls are used, what you will quantify and why and how you will interpret the data

Single cell RNA-sequencing (scRNA-seq) is a method used to quantify individual transcriptomes at the single-cell level. It is a powerful tool used to study gene expression in different cell types and has been used to gain insight into the complexity and heterogeneity of tissues. The main control in scRNA-seq is the choice of library preparation method which involves the selection of a sequence capture technique and target regions and selection of a cDNA synthesis method. Typically, scRNA-seq relies on the use of random priming, which is the most commonly used and accepted method in the field. Using scRNA-seq, it is possible to quantify the amount of RNA transcripts expressed in a single cell. This includes both protein-coding and non-coding transcripts. The data generated from scRNA-seq can be used to identify cell types, measure cell-to-cell variability, and identify rare sub populations in a given sample. To interpret the data, bioinformatics and computational techniques are used. Data analysis begins with quality control and data filtering. Signatures from each cell are then clustered and compared to known or previously identified cell types. Differential expression analysis can be used to identify genes or gene sets which are differentially expressed between two or more groups of cells.