Single-cell sequencing, which provides a high-resolution of cellular differences by sequencing nucleic acid information namely DNA and RNA from individual cells, has become one of the key applications in the next-generation sequencing (NGS) to better understand the individual heterogeneity in physiological processes and the personal differences in responding of medical treatments, shedding light on discovering novel biological mechanisms and personalized therapies.

Currently, most single-cell RNA-sequencing (scRNA-seq) methods map a short part of the RNA molecules, from either the 5′ or 3′ end, together with a unique molecular identifier (UMI)(Mereu et al., Nat. Biotechnol, 2020). These RNA end-counting strategies have been efficient in estimating gene expression across large numbers of cells, while controlling for PCR amplification biases attributed to low quantity input of RNAs and compensating on the throughput capacity of sequencing, but the limited coverage of those methods heavily obscure the detection of those transcribed genetic variations and transcript isoform expression caused by, for instance, RNA alternative-splicing, which is a common mechanism of RNA processing and abnormalities in such a process have been shown to associate with the development of various diseases, such as cancers and neurodegenerative diseases (Zhang et al., Nature, 2021; Garcia-Blanco et al., Nat. Biotechnol, 2004; Mills et al., Neurobiol Aging, 2012).   Moreover, though those end-counting scRNA-seq enable higher throughput sequencing capacity in terms of cell number, their relatively low sensitivity and high cost are the major bottlenecks to be challenged. In the meanwhile, long-read sequencing technologies enable direct quantification of allele- and isoform-level expression, however their current read depths, biased-error rate and sample preparation procedures obstruct their broad applications across cells, tissues and organisms (Byrne et al., Nat. Commum, 2017; Gupta et al., Nat. Biotechnol, 2018)

To overcome those technical demerits, Hagemann-Jensen and Ziegenhain et al. at Karolinska Institute (Nat. Biotechnol, 2022; Nat. Biotechnol, 2020. https://www.nature.com/articles/s41587-022-01311-4) have developed and further optimized a scalable and sensitive short-read based sequencing method, Smart-seq3xpress to enable full-length RNA coverage and more importantly to provide an unprecedent high-resolution of individual RNAs to isoforms and their alleic origin in single cells with lower operating cost and shortened library preparation duration to a single workday. The well-established Smart-seq3xpress protocol implements UMI in the 5’-end of full-length RNA transcripts to establish a unique identity of each RNA molecule, thus inclusion of UMI empowers the detection of transcript copy number variation (CNV) and to overcome the biases caused by PCR amplification processes, further bringing up sequencing sensitivity and specificity. Moreover, Smart-seq3xpress is a plate-based method, which could be easily scaled up from 96- to 384-well plates possibly facilitated by automation processes. More practically, sorting of the cells of interest into the well-plates can be separated in spatial and temporal manner as the filled well-plates can be frozen anytime until further processing, showing a major advantage when compared to the method that viable cell suspension needs to be processed immediately. Moreover, this study also put substantial effort in miniaturizing the key factors in scRNA-seq, including lowering the reaction volume, decreasing the input of cDNA, reducing PCR cycles, and adjusting Tn5 and cDNA ratio for more cost-effective tagmentation without compromising the detection performance. Additionally, adjusting of the salt components and concentration required for the reactions, implementation of new enzymes and uniquely setting an extensive RNA count QC by utilizing of self-developed new UMIcountR R package to further improve the overall detection power of Smart-seq3xpress. Furthermore, this method provides a more environmental-friendly and cost-effective solution because the material and resources are ten-fold less when compared with other RNA-seq methods, and the usage plastics consumables is significantly reduced by new 3D-printed tools and by contact-less reagent dispensing as well as pre-dispensed desiccated index primers`.