Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.

No abstract

In the last decade, genome-wide transcriptome analyses have been routinely used to monitor tissue-, disease- and cell type-specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a novel and robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which significantly enhances detailed analyses of alternative transcript isoforms and identification of SNPs. We have determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including new candidate biomarkers for melanoma circulating tumor cells. Importantly, our protocol can easily be utilized for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

Summary Mammalian gene expression is inherently stochastic 1 , 2 resulting in discrete bursts of RNA molecules synthesised from each allele 3 – 7 . Although known to be regulated by promoters and enhancers, it is unclear how cis -regulatory sequences encode transcriptional burst kinetics. Characterization of transcriptional bursting, including the burst size and frequency, have mainly relied on live-cell 4 , 6 , 8 or single-molecule RNA-FISH 3 , 5 , 8 , 9 recordings of selected loci. Here, we inferred transcriptome-wide burst frequencies and sizes for endogenous genes using allele-sensitive single-cell RNA-sequencing (scRNA-seq). We show that core promoter elements affect burst size and uncover synergistic effects between TATA and Initiator elements, which were masked at mean expression levels. Importantly, we provide transcriptome-wide support for enhancers controlling burst frequencies and we additionally demonstrate that cell-type-specific gene expression is primarily shaped by changes in burst frequencies. Altogether, our data show that burst frequency is primarily encoded in enhancers and burst size in core promoters, and that allelic scRNA-seq is a powerful paradigm for investigating transcriptional kinetics.