SUMMARY Enhancers control the correct temporal and cell type-specific activation of gene expression in higher eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. We use the FANTOM5 panel of samples covering the majority of human tissues and cell types to produce an atlas of active, in vivo transcribed enhancers. We show that enhancers share properties with CpG-poor mRNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, identify disease-associated regulatory single nucleotide polymorphisms, and classify cell type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell type-specific enhancers and gene regulation.

SummaryCharacterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

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.

Differentiation of naïve CD4 + T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a * Correspondence to: st9@sanger.ac.uk, Ashraful.Haque@qimrberghofer.edu.au or stegle@ebi.ac.uk. # denotes equal contribution † denotes equal contribution Author contributions TL and KRJ performed the single-cell RNA-seq experiments. VS developed the GPfates model in collaboration with MZ, NDL, OS and SAT. DFR and WRH generated the PbTII mouse model. KRJ, RM, IS, MSFS, LGF, ASN, UL, FSFG, PTB and CRE performed the mouse experiments. TL, VS, KRJ, LHL and FOB analysed the data and interpreted the results MJTS performed the TCR clonality analysis. TL, KRJ, RM, OB, AH and SAT designed the experiments. OS, AH and SAT cosupervised the study. TL, VS, KRJ, OS, AH and SAT wrote the manuscript. All authors have read and approved the manuscript. Competing interestsThe authors declare no competing interests. Data and materials availabilityThe data presented in this paper is publically available in the ArrayExpress database with accession number E-MTAB-4388. Europe PMC Funders Group Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis using a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of Th1 and Tfh cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous TCR sequences, we first demonstrated that Th1/Tfh bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with Th1 or Tfh fates, and demonstrated a T-cell intrinsic role for Galectin-1 in supporting a Th1 differentiation. We also revealed the close molecular relationship between Th1 and IL-10-producing Tr1 cells in this infection. Th1 and Tfh fates emerged from a highly proliferative precursor that upregulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell in driving Th1/Tfh fates. In particular, we found that precursor Th cells were coached towards a Th1 but not a Tfh fate by inflammatory monocytes. Thus, by integrating genomic and computational approaches, our study has provided two unique resources, a database www.PlasmoTH.org, which facilitates discovery of novel factors controlling Th1/Tfh fate commitment, and more generally, GPfates, a modelling framework for characterizing cell differentiation towards multiple fates.

Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely inaccessible. Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival. To address these shortcomings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles from FACS sorted healthy and malignant haematopoietic cells. A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan–Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database. The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool. Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia. Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well-defined haematopoietic cellular space.

DNA methylation is tightly regulated throughout mammalian development, and altered DNA methylation patterns are a general hallmark of cancer. The methylcytosine dioxygenase TET2 is frequently mutated in hematological disorders, including acute myeloid leukemia (AML), and has been suggested to protect CG dinucleotide (CpG) islands and promoters from aberrant DNA methylation. In this study, we present a novel Tet2-dependent leukemia mouse model that closely recapitulates gene expression profiles and hallmarks of human AML1-ETO-induced AML. Using this model, we show that the primary effect of Tet2 loss in preleukemic hematopoietic cells is progressive and widespread DNA hypermethylation affecting up to 25% of active enhancer elements. In contrast, CpG island and promoter methylation does not change in a Tet2-dependent manner but increases relative to population doublings. We confirmed this specific enhancer hypermethylation phenotype in human AML patients with TET2 mutations. Analysis of immediate gene expression changes reveals rapid deregulation of a large number of genes implicated in tumorigenesis, including many down-regulated tumor suppressor genes. Hence, we propose that TET2 prevents leukemic transformation by protecting enhancers from aberrant DNA methylation and that it is the combined silencing of several tumor suppressor genes in TET2 mutated hematopoietic cells that contributes to increased stem cell proliferation and leukemogenesis.

BloodSpot is a gene-centric database of mRNA expression of haematopoietic cells. The web-based interface to the database includes three concomitant levels of visualization for a gene query; foremost is the expression across hematopoietic cell types, second is analysis of survival of Acute Myeloid Leukaemia patients based on gene expression, and lastly, the expression visualized in an interactive developmental tree. With the introduction of single cell data we have now also included an unbiased dimensionality reduction method to show gene expression over the continuum of haematopoiesis. The webserver includes a few select analysis functionalities, like Student's t-test, identification of correlating genes and lookup of whole genetic signatures, with the aim of making generation and testing of hypotheses quick and intuitive. The visualizations have been updated to accommodate new datatypes and the database has been largely expanded with RNA-sequencing datasets, both purified in bulk and at single cell resolution, increasing the number of single samples more than 10 fold, while keeping simplicity in presentation. The database should be of interest for any researcher within leukaemia, haematopoiesis, cellular development, or stem cells. The database is freely available at www.bloodspot.eu

Key Points This study describes a method for the comparison of gene expression data of any type of cancer cells with their corresponding normal cells. Our analyses reveal novel disease entities, identify common deregulated transcriptional networks, and predict survival.