Introduction

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. 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.

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.

Transcript End Sequencing versus Full-Length Sequencing

Transcript end sequencing (3’ end primarily, but also 5’ end scRNA-seq) comprises the most prolific and widely used group of methods. 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.

In contrast, full-length transcript sequencing represents a complementary group of methods (e.g., Smart-seq2) that sequence whole transcripts from single cells. 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). These methods are invariably more difficult and therefore problematic to scale to higher cell numbers achieved by compartmentalized barcoding.

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. If scRNA-seq is the “telescope” to map the cellular landscape of organisms, then full-length scRNA is the “microscope.”

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. 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 cDNA is then converted into a sequenceable library using methods for multiplexed NGS library prep.

Overcoming the Challenges of Full-Length scRNAseq

It is no secret that full-length scRNAseq is more difficult and technically challenging.

First, 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.

Second, the process of converting amplified cDNA into an NGS library is often a difficult process to perform at large scale, without performing time-consuming QC, purification and normalization steps to ensure that a balanced multiplexed NGS library from 100s or 1,000s of cells can be made robustly.

To overcome these challenges, the Smart-seq2 (SS2) method 2 represents a gold-standard for full-length scRNA-seq due to its ability to achieve high sensitivity at a scale and – at the time – reasonable economics. However, as the field of scRNA-seq has progressed with compartmentalized barcoding technologies, the relative cost to perform SS2 and related methods has become vastly more expensive on a per-cell basis, and so a significant amount of attention has been placed on how to reduce and improve the economics of these approaches. Newer methods, such as FLASH-seq and Smart-seq3 represent the evolution of such methods.

End-to-End Library Preparation for Scalable, Cost-Effective Single-Cell Transcriptome Analysis

At seqWell, we have embraced a number of workflow challenges that involve how to improve the scale and efficiency of different NGS methods. In 2021, we launched our first product into the field of scRNA-seq – the plexWell Rapid Single-Cell kit – that couples our expertise in multiplexed NGS library prep with a best-in-class single-cell synthesis and amplification workflow.

While our Rapid Single-Cell kit offers compelling performance in terms of transcript sensitivity and uniformity per cell for plate-based scRNAseq projects, we recognize that the ability to perform this type of analysis on as many cells as possible will require further innovation to improve the related workflows even more.

The need to extract more information from single cells has become a foundational direction in life science research, and full-length transcript information will continue to be a valuable piece of that puzzle.

References

  1. “Method of the year 2013″. Nature Methods. 11 (1): 1. January 2014. doi:10.1038/nmeth.2801. PMID 24524124.
  2. “Full-length RNA-seq from single cells using Smart-seq2” Nat Protocols 2014 Jan;9(1):171-81 doi: 10.1038/nprot.2014.006 PMID 24385147