RNA-Seq of single cells has been limited by biases in transcript coverage and unknown technical variability. Ramsköld et al. describe a protocol to reproducibly recover full-length transcripts and use it to quantitatively analyze splice isoforms in single cells. Genome-wide transcriptome analyses are 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 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 enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

Expressing One Allele at a Time: Although genetic traits are often dominant or recessive, the impact of the same heterozygous genotype can vary quite a bit between individuals. Deng et al. (p. 193 ), analyzed global gene expression in hundreds of individual mouse cells and found that a substantial fraction of the genes only expressed one of the alleles, chosen randomly, at any given point in time. Such stochasticity in transcription increases the heterogeneity among cells and likely contributes to the phenotypic variance among individuals of identical genotype.

The parts of the genome transcribed by a cell or tissue reflect the biological processes and functions it carries out. We characterized the features of mammalian tissue transcriptomes at the gene level through analysis of RNA deep sequencing (RNA-Seq) data across human and mouse tissues and cell lines. We observed that roughly 8,000 protein-coding genes were ubiquitously expressed, contributing to around 75% of all mRNAs by message copy number in most tissues. These mRNAs encoded proteins that were often intracellular, and tended to be involved in metabolism, transcription, RNA processing or translation. In contrast, genes for secreted or plasma membrane proteins were generally expressed in only a subset of tissues. The distribution of expression levels was broad but fairly continuous: no support was found for the concept of distinct expression classes of genes. Expression estimates that included reads mapping to coding exons only correlated better with qRT-PCR data than estimates which also included 3′ untranslated regions (UTRs). Muscle and liver had the least complex transcriptomes, in that they expressed predominantly ubiquitous genes and a large fraction of the transcripts came from a few highly expressed genes, whereas brain, kidney and testis expressed more complex transcriptomes with the vast majority of genes expressed and relatively small contributions from the most expressed genes. mRNAs expressed in brain had unusually long 3′UTRs, and mean 3′UTR length was higher for genes involved in development, morphogenesis and signal transduction, suggesting added complexity of UTR-based regulation for these genes. Our results support a model in which variable exterior components feed into a large, densely connected core composed of ubiquitously expressed intracellular proteins. A variety of genes are active within the nuclei of our cells. Some are needed for the day-to-day maintenance of cell functions, while others have roles that are more specific to certain tissues or particular cell types; for example, only the pancreas produces insulin. As a result, every tissue has its own profile of gene activity. Since active genes produce RNA, tissue differences in gene activity can be probed by characterizing the RNA they contain. Essentially the entire set of RNAs or ‘transcriptome’ has been sequenced from various tissues, and we used these data to compare the degree of specialization of different tissues and to investigate the set of ‘core’ genes active in every tissue. A central observation was that there are an abundance of such core genes, and that these genes account for the majority of the transcriptome in each tissue. These findings will aid in the understanding of what makes tissues, and cell types, different from each other and what each requires to function.

Pluripotent embryonic stem (ES) cells can generate all cell types, but how cell lineages are initially specified and maintained during development remains largely unknown. Different classes of Sox transcription factors are expressed during neurogenesis and have been assigned important roles from early lineage specification to neuronal differentiation. Here we characterize the genome-wide binding for Sox2, Sox3, and Sox11, which have vital functions in ES cells, neural precursor cells (NPCs), and maturing neurons, respectively. The data demonstrate that Sox factor binding depends on developmental stage-specific constraints and reveal a remarkable sequential binding of Sox proteins to a common set of neural genes. Interestingly, in ES cells, Sox2 preselects for neural lineage-specific genes destined to be bound and activated by Sox3 in NPCs. In NPCs, Sox3 binds genes that are later bound and activated by Sox11 in differentiating neurons. Genes prebound by Sox proteins are associated with a bivalent chromatin signature, which is resolved into a permissive monovalent state upon binding of activating Sox factors. These data indicate that a single key transcription factor family acts sequentially to coordinate neural gene expression from the early lineage specification in pluripotent cells to later stages of neuronal development.