Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm’s C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0938-8) contains supplementary material, which is available to authorized users.

Heteroplasmy, the mixture of mitochondrial genomes (mtDNA), varies among individuals and cells. Heteroplasmy levels alter the penetrance of pathological mtDNA mutations, and the susceptibility to age-related diseases such as Parkinson's disease. Although mitochondrial dysfunction occurs in age-related type 2 diabetes mellitus (T2DM), the involvement of heteroplasmy in diabetes is unclear. We hypothesized that the heteroplasmic mutational (HM) pattern may change in T2DM. To test this, we used next-generation sequencing, i.e. massive parallel sequencing (MPS), along with PCR-cloning-Sanger sequencing to analyze HM in blood and skeletal muscle DNA samples from monozygotic (MZ) twins either concordant or discordant for T2DM. Great variability was identified in the repertoires and amounts of HMs among individuals, with a tendency towards more mutations in skeletal muscle than in blood. Whereas many HMs were unique, many were either shared among twin pairs or among tissues of the same individual, regardless of their prevalence. This suggested a heritable influence on even low abundance HMs. We found no clear differences between T2DM and controls. However, we found ~5-fold increase of HMs in non-coding sequences implying the influence of negative selection (P < 0.001). This negative selection was evident both in moderate to highly abundant heteroplasmy (>5% of the molecules per sample) and in low abundance heteroplasmy (<5% of the molecules). Although our study found no evidence supporting the involvement of HMs in the etiology of T2DM, the twin study found clear evidence of a heritable influence on the accumulation of HMs as well as the signatures of selection in heteroplasmic mutations.

The interaction between a pathogen and a host is a highly dynamic process in which both agents activate complex programs. Here, we introduce a single-cell RNA-sequencing method, scDual-Seq, that simultaneously captures both host and pathogen transcriptomes. We use it to study the process of infection of individual mouse macrophages with the intracellular pathogen Salmonella typhimurium. Among the infected macrophages, we find three subpopulations and we show evidence for a linear progression through these subpopulations, supporting a model in which these three states correspond to consecutive stages of infection.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1340-x) contains supplementary material, which is available to authorized users.

RNA transcripts are generally identical to the underlying DNA sequences. Nevertheless, RNA–DNA differences (RDDs) were found in the nuclear human genome and in plants and animals but not in human mitochondria. Here, by deep sequencing of human mitochondrial DNA (mtDNA) and RNA, we identified three RDD sites at mtDNA positions 295 (C-to-U), 13710 (A-to-U, A-to-G), and 2617 (A-to-U, A-to-G). Position 2617, within the 16S rRNA, harbored the most prevalent RDDs (>30% A-to-U and ∼15% A-to-G of the reads in all tested samples). The 2617 RDDs appeared already at the precursor polycistrone mitochondrial transcript. By using traditional Sanger sequencing, we identified the A-to-U RDD in six different cell lines and representative primates (Gorilla gorilla, Pongo pigmaeus, and Macaca mulatta), suggesting conservation of the mechanism generating such RDD. Phylogenetic analysis of more than 1700 vertebrate mtDNA sequences supported a thymine as the primate ancestral allele at position 2617, suggesting that the 2617 RDD recapitulates the ancestral 16S rRNA. Modeling U or G (the RDDs) at position 2617 stabilized the large ribosomal subunit structure in contrast to destabilization by an A (the pre-RDDs). Hence, these mitochondrial RDDs are likely functional.