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.

No abstract

AbstractLarge-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

Little is known about the heterogeneity of small-RNA expression as small-RNA profiling has so far required large numbers of cells. Here we present a single-cell method for small-RNA sequencing and apply it to naive and primed human embryonic stem cells and cancer cells. Analysis of microRNAs and fragments of tRNAs and small nucleolar RNAs (snoRNAs) reveals the potential of microRNAs as markers for different cell types and states.

The large tegument proteins of herpesviruses encode conserved cysteine proteases of unknown function. Here we show that BPLF1, the Epstein-Barr-virus-encoded member of this protease family, is a deneddylase that regulates virus production by modulating the activity of cullin-RING ligases (CRLs). BPLF1 hydrolyses NEDD8 conjugates in vitro, acts as a deneddylase in vivo, binds to cullins and stabilizes CRL substrates. Expression of BPLF1 alone or in the context of the productive virus cycle induces accumulation of the licensing factor CDT1 and deregulates S-phase DNA synthesis. Inhibition of BPLF1 during the productive virus cycle prevents cellular DNA re-replication and inhibits virus replication. Viral DNA synthesis is restored by overexpression of CDT1. Homologues encoded by other herpesviruses share the deneddylase activity. Thus, these enzymes are likely to have a key function in the virus life cycle by inducing a replication-permissive S-phase-like cellular environment.