![]() It is no secret that full-length scRNAseq is more difficult and technically challenging.įirst, as a method that is created to take sorted individual cells and analyze them deeply for their transcriptional state, it must be extremely sensitive to very small quantities of RNA – often as little as 1-2 picograms – while still detecting as many RNA transcripts as possible – often 10,000 or more. Overcoming the Challenges of Full-Length scRNAseq The cDNA is then converted into a sequenceable library using methods for multiplexed NGS library prep. Once sorted into individual wells of a microtiter plate, the cells are lysed and their RNA is amplified as full-length copies of cDNA. ![]() The typical full-length scRNA-seq workflow begins with a population of cells – whether from an organism or cultured cells – that are then sorted using flow cytometry to select for individual cells that have distinguishable features which correspond to a cell type of interest, or a cell with certain binding sites or other characteristics. If scRNA-seq is the “telescope” to map the cellular landscape of organisms, then full-length scRNA is the “microscope.” So why do researchers use full-length scRNA-seq, if it is more difficult and lower-throughput? Because full-length RNA-seq provides a view of transcript information, allowing researchers to hone in and deeply characterize the internal transcriptional state of more focused populations of cells. These methods are invariably more difficult and therefore problematic to scale to higher cell numbers achieved by compartmentalized barcoding. ![]() Because short-read sequencing platforms are the most economical way to collect sequencing information, the process of generating full-length scRNA-seq data typically has to first capture and amplify whole transcripts (cDNA synthesis and PCR) and then convert them into an individual sequencing library for each cell (library prep). In contrast, full-length transcript sequencing represents a complementary group of methods (e.g., Smart-seq2) that sequence whole transcripts from single cells. Most approaches here use droplet-based or similar compartmentalization and barcoding technologies (e.g., the 10X Chromium platform) to individually capture and barcode transcripts from thousands of cells at a time. Transcript end sequencing (3’ end primarily, but also 5’ end scRNA-seq) comprises the most prolific and widely used group of methods. Transcript End Sequencing versus Full-Length Sequencing There are a number of different types of scRNA-seq methods for measuring expressed transcripts in cells, and one way that these methods can diverge is whether they measure transcript ends only or full-length transcripts from those cells. Among these methods, a broad class of single-cell RNA sequencing (scRNA-seq) techniques remains the most widely utilized, allowing researchers to simultaneously measure many thousands of individual cells and their internal gene expression programs. In less than a decade, single-cell sequencing has grown from being the relatively nascent “ method of the year” 1 to an innovative field of hundreds of powerful analytical methods for analyzing a vast array of cellular features.
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