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.

Characterizing 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.

Differentiation of naïve CD4⁺ T cells into functionally distinct T helper (T_H) subsets is crucial for the orchestration of immune responses. Because of extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis with a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of T_H1 and T_FH (T follicular helper) cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous T cell receptor sequences, we first demonstrated that T_H1/T_FH bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with T_H1 or T_FH fates and demonstrated a T cell–intrinsic role for Galectin-1 in supporting T_H1 differentiation. We also revealed the close molecular relationship between T_H1 and interleukin-10–producing Tr1 cells in this infection. T_H1 and T_FH fates emerged from a highly proliferative precursor that up-regulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell interaction in driving T_H1/T_FH fates. In particular, we found that precursor T_H cells were coached toward a T_H1 but not a T_FH 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 T_H1/T_FH fate commitment, and, more generally, GPfates, a modeling framework for characterizing cell differentiation toward 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.