Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.

28Recent developments in stem cell biology have enabled the study of cell fate decisions in early 29 human development that are impossible to study in vivo. However, understanding how 30 development varies across individuals and, in particular, the influence of common genetic 31 variants during this process has not been characterised. Here, we exploit human iPS cell lines 32 from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study 33 population variation of endoderm differentiation. We identify molecular markers that are 34 predictive of differentiation efficiency, and utilise heterogeneity in the genetic background 35 across individuals to map hundreds of expression quantitative trait loci that influence 36 expression dynamically during differentiation and across cellular contexts. 37Population-scale single-cell profiling of differentiating iPS cells 63 We considered a panel of well-characterized human iPSC lines derived from 125 unrelated 64 donors from the Human Induced Pluripotent Stem Cell initiative (HipSci) collection [1]. In order 65to increase throughput and mitigate the effects of batch variation, we exploited a novel pooled 66 differentiation assay, combining sets of four to six lines in one well prior to differentiation (28 67 differentiation experiments performed in total; hereon "experiments"; Fig. 1A, S1, S2). Cells 68 were collected at four differentiation time points (iPSC; one, two and three days post initiation 69 -hereon day0, day1, day2 and day3) and their transcriptomes were assayed using full-length 70RNA-sequencing (Smart-Seq2 [7]) alongside the expression of selected cell surface markers 71using FACS (TRA-1-60, CXCR4; Fig. S3, S4; Methods). Following quality control (QC), 72 36,044 cells were retained for downstream analysis, across which 11,231 genes were 73 expressed ( Fig. S5; Methods). Exploiting that each cell line's genotype acts as a unique 74 barcode, we demultiplexed the pooled cell populations, enabling identification of the cell line 75 of origin for each cell (similar to [8]; Methods). At each time point, cells from between 104 and 76 112 donors were captured, with each donor being represented by an average of 286 cells 77 (after QC, Fig. S2; Tables S1, S2; Methods). The success of the differentiation protocol was 78 validated using canonical cell-surface marker expression: consistent with previous studies [9], 79 an average of 72% cells were TRA-1-60(+) in the undifferentiated state (day0) and an average 80 of 49% of cells were CXCR4(+) three days post differentiation (day3; Fig. S3). 81 82Variance component analysis across all genes (using a linear mixed model; Methods) 83 revealed the time point of collection as the main source of variation, followed by the cell line 84 of origin and the experimental batch (Fig. 1B). Consistent with this, the first Principal 85 Component (PC) was strongly associated with differentiation time (Fig. 1C, S6; Methods), 86 motivating its use to order cells by their differentiation status (hereafter "pseudotime" ...

BackgroundThe IRE1a-XBP1 pathway is a conserved adaptive mediator of the unfolded protein response. The pathway is indispensable for the development of secretory cells by facilitating protein folding and enhancing secretory capacity. In the immune system, it is known to function in dendritic cells, plasma cells, and eosinophil development and differentiation, while its role in T helper cell is unexplored. Here, we investigated the role of the IRE1a-XBP1 pathway in regulating activation and differentiation of type-2 T helper cell (Th2), a major T helper cell type involved in allergy, asthma, helminth infection, pregnancy, and tumor immunosuppression.MethodsWe perturbed the IRE1a-XBP1 pathway and interrogated its role in Th2 cell differentiation. We performed genome-wide transcriptomic analysis of differential gene expression to reveal IRE1a-XBP1 pathway-regulated genes and predict their biological role. To identify direct target genes of XBP1 and define XBP1’s regulatory network, we performed XBP1 ChIPmentation (ChIP-seq). We validated our predictions by flow cytometry, ELISA, and qPCR. We also used a fluorescent ubiquitin cell cycle indicator mouse to demonstrate the role of XBP1 in the cell cycle.ResultsWe show that Th2 lymphocytes induce the IRE1a-XBP1 pathway during in vitro and in vivo activation. Genome-wide transcriptomic analysis of differential gene expression by perturbing the IRE1a-XBP1 pathway reveals XBP1-controlled genes and biological pathways. Performing XBP1 ChIPmentation (ChIP-seq) and integrating with transcriptomic data, we identify XBP1-controlled direct target genes and its transcriptional regulatory network. We observed that the IRE1a-XBP1 pathway controls cytokine secretion and the expression of two Th2 signature cytokines, IL13 and IL5. We also discovered that the IRE1a-XBP1 pathway facilitates activation-dependent Th2 cell proliferation by facilitating cell cycle progression through S and G2/M phase.ConclusionsWe confirm and detail the critical role of the IRE1a-XBP1 pathway during Th2 lymphocyte activation in regulating cytokine expression, secretion, and cell proliferation. Our high-quality genome-wide XBP1 ChIP and gene expression data provide a rich resource for investigating XBP1-regulated genes. We provide a browsable online database available at http://data.teichlab.org.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0589-3) contains supplementary material, which is available to authorized users.

Polycomb repressive complexes (PRCs) are important histone modifiers, which silence gene expression, yet there exists a subset of PRC-bound genes actively transcribed by RNA polymerase II (RNAPII). It is likely that the role of PRC is to dampen expression of these PRC-active genes. However, it is unclear how this flipping between chromatin states alters the kinetics of transcriptional burst size and frequency relative to genes with exclusively activating marks. To investigate this, we integrate histone modifications and RNAPII states derived from bulk ChIP-seq data with single-cell RNA-sequencing data. We find that PRC-active genes have a greater cell-to-cell variation in expression than active genes with the same mean expression levels, and validate these results by knockout experiments. We also show that PRCactive genes are clustered on chromosomes in both two and three dimensions, and interactions with active enhancers promote a stabilization of gene expression noise.These findings provide new insights into how chromatin regulation modulates stochastic gene expression and transcriptional bursting, with implications for regulation of pluripotency and development.