Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.

SummaryEmbryonic stem cell (ESC) culture conditions are important for maintaining long-term self-renewal, and they influence cellular pluripotency state. Here, we report single cell RNA-sequencing of mESCs cultured in three different conditions: serum, 2i, and the alternative ground state a2i. We find that the cellular transcriptomes of cells grown in these conditions are distinct, with 2i being the most similar to blastocyst cells and including a subpopulation resembling the two-cell embryo state. Overall levels of intercellular gene expression heterogeneity are comparable across the three conditions. However, this masks variable expression of pluripotency genes in serum cells and homogeneous expression in 2i and a2i cells. Additionally, genes related to the cell cycle are more variably expressed in the 2i and a2i conditions. Mining of our dataset for correlations in gene expression allowed us to identify additional components of the pluripotency network, including Ptma and Zfp640, illustrating its value as a resource for future discovery.

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.

Heart disease is the leading cause of death in the world. Heart tissue engineering holds a great promise for future heart disease therapy by building personalized heart tissues. Here we create heart constructs by repopulating decellularized mouse hearts with human induced pluripotent stem cell-derived multipotential cardiovascular progenitor cells. We show that the seeded multipotential cardiovascular progenitor cells migrate, proliferate and differentiate in situ into cardiomyocytes, smooth muscle cells and endothelial cells to reconstruct the decellularized hearts. After 20 days of perfusion, the engineered heart tissues exhibit spontaneous contractions, generate mechanical force and are responsive to drugs. In addition, we observe that heart extracellular matrix promoted cardiomyocyte proliferation, differentiation and myofilament formation from the repopulated human multipotential cardiovascular progenitor cells. Our novel strategy to engineer personalized heart constructs could benefit the study of early heart formation or may find application in preclinical testing.

BackgroundGenetically identical populations of cells grown in the same environmental condition show substantial variability in gene expression profiles. Although single-cell RNA-seq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts.ResultsWe develop a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA-seq data. By applying our model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, we find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Our results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency. Furthermore, we show that our model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.ConclusionsWe conclude that the proposed statistical model provides a flexible and efficient way to investigate the kinetics of transcription.

SummarySingle-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for low and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

MicroRNAs (miRNAs) are short, non-coding RNAs that target and silence protein coding genes through 3-UTR elements. Evidence increasingly assigns an immunosuppressive role for miRNAs in immunity, but relatively few miRNAs have been studied, and an overall understanding of the importance of these regulatory transcripts in complex in vivo systems is lacking.Here we have applied multiple technologies to globally analyze miRNA expression and function in allergic lung disease, an experimental model of asthma. Deep sequencing and microarray analyses of the mouse lung short RNAome revealed numerous extant and novel miRNAs and other transcript classes. Similar to mRNAs, lung miRNA expression changed dynamically during the transition from the naive to the allergic state, suggesting numerous functional relationships. A possible role for miRNA editing in altering the lung mRNA target repertoire was also identified. Multiple members of the highly conserved let-7 miRNA family were the most abundant lung miRNAs, and we confirmed in vitro that interleukin 13 (IL-13), a cytokine essential for expression for allergic lung disease, is regulated by mmulet-7a. However, inhibition of let-7 miRNAs in vivo using a locked nucleic acid profoundly inhibited production of allergic cytokines and the disease phenotype. Our findings thus reveal unexpected complexity in the miRNAome underlying allergic lung disease and demonstrate a proinflammatory role for let-7 miRNAs. MicroRNAs (miRNAs)3 are short regulatory RNAs with the potential to target and suppress multiple genes across diverse signaling pathways comprising biologically meaningful networks. Consequently, miRNAs that are inappropriately expressed in the context of specific diseases are of particular interest as therapeutic targets if they can be shown to coordinate such networks. Initially transcribed as relatively long primary transcripts, pri-miRNAs are subsequently modified by the nuclear RNAses Drosha and Pasha to yield precursor miRNAs (pre-miRNAs) that are further processed by the cytoplasmic RNase III Dicer to form short double-stranded miR-miR* duplexes, one strand of which (miR) is then integrated into the RNA-induced silencing complex that includes the enzymes Dicer and Argonaute (Ago). The mature miRNAs (ϳ17-24 nt) direct the RNA-induced silencing complex to specific target sites located within the 3Ј-UTR of target genes. Once bound to target sites, miRNAs repress translation through mRNA decay, translational inhibition, and/or sequestration into processing bodies (1-3).Recent estimates indicate that over 60% of protein-coding genes carry 3Ј-UTR miRNA target sites, suggesting a role for miRNAs in the control of gene expression in diverse processes (4). Indeed, miRNAs have now been firmly linked to the regulation of early development (5), cell proliferation, and cell death (6); apoptosis and fat metabolism (7); and cell differentiation (8). In addition, studies of miRNA expression in chronic lymphocytic leukemia (9), colonic adenocarcinoma (10), Burkitt lymphoma, and cardiac ...