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Physical interaction of regulatory elements in three-dimensional space poses a challenge for studies of disease because non-coding risk variants may be great distances from the genes they regulate. Experimental methods to capture these interactions, such as chromosome conformation capture, usually cannot assign causal direction of effect between regulatory elements, an important component of fine-mapping studies. We developed a Bayesian hierarchical approach that uses two-stage least squares and applied it to an ATAC-seq (assay for transposase-accessible chromatin using sequencing) data set from 100 individuals, to identify over 15,000 high-confidence causal interactions. Most (60%) interactions occurred over <20 kb, where chromosome conformation capture-based methods perform poorly. For a fraction of loci, we identified a single variant that alters accessibility across multiple regions, and experimentally validated the BLK locus, which is associated with multiple autoimmune diseases, using CRISPR genome editing. Our study highlights how association genetics of chromatin state is a powerful approach for identifying interactions between regulatory elements. A Bayesian hierarchical approach identifies over 15,000 causal regulatory interactions in the human genome using ATAC-seq data from 100 individuals. The majority of detected interactions were over distances of <20 kb, a range where 3C methods perform poorly.

Natsuhiko Kumasaka, Andrew Knights and Daniel Gaffney develop a new statistical approach for association mapping that models genetic effects and accounts for biases in sequencing data in a single probabilistic framework. They apply this method to generate a map of chromatin accessibility QTLs and show how it can be used to fine-map regulatory variants and link distal regulatory elements with genes. When cellular traits are measured using high-throughput DNA sequencing, quantitative trait loci (QTLs) manifest as fragment count differences between individuals and allelic differences within individuals. We present RASQUAL (Robust Allele-Specific Quantitation and Quality Control), a new statistical approach for association mapping that models genetic effects and accounts for biases in sequencing data using a single, probabilistic framework. RASQUAL substantially improves fine-mapping accuracy and sensitivity relative to existing methods in RNA-seq, DNase-seq and ChIP-seq data. We illustrate how RASQUAL can be used to maximize association detection by generating the first map of chromatin accessibility QTLs (caQTLs) in a European population using ATAC-seq. Despite a modest sample size, we identified 2,707 independent caQTLs (at a false discovery rate of 10%) and demonstrated how RASQUAL and ATAC-seq can provide powerful information for fine-mapping gene-regulatory variants and for linking distal regulatory elements with gene promoters. Our results highlight how combining between-individual and allele-specific genetic signals improves the functional interpretation of noncoding variation.

Induced pluripotent stem cells (iPSCs), and cells derived from them, have become key tools for modeling biological processes, particularly in cell types that are difficult to obtain from living donors. Here we present a map of regulatory variants in iPSC-derived neurons, based on 123 differentiations of iPSCs to a sensory neuronal fate. Gene expression was more variable across cultures than in primary dorsal root ganglion, particularly for genes related to nervous system development. Using single-cell RNA-sequencing, we found that the number of neuronal versus contaminating cells was influenced by iPSC culture conditions before differentiation. Despite high differentiation-induced variability, our allele-specific method detected thousands of quantitative trait loci (QTLs) that influenced gene expression, chromatin accessibility, and RNA splicing. On the basis of these detected QTLs, we estimate that recall-by-genotype studies that use iPSC-derived cells will require cells from at least 20–80 individuals to detect the effects of regulatory variants with moderately large effect sizes. This study identifies regulatory variants in sensory neurons derived from induced pluripotent stem cells. Despite differentiation-induced variability, an allele-specific method allowed detection of loci influencing gene expression, chromatin accessibility and RNA splicing.

Regulatory variants are often context specific, modulating gene expression in a subset of possible cellular states. Although these genetic effects can play important roles in disease, the molecular mechanisms underlying context specificity are poorly understood. Here, we identified shared quantitative trait loci (QTLs) for chromatin accessibility and gene expression in human macrophages exposed to IFNγ, Salmonella and IFNγ plus Salmonella . We observed that ~60% of stimulus-specific expression QTLs with a detectable effect on chromatin altered the chromatin accessibility in naive cells, thus suggesting that they perturb enhancer priming. Such variants probably influence binding of cell-type-specific transcription factors, such as PU.1, which can then indirectly alter the binding of stimulus-specific transcription factors, such as NF-κB or STAT2. Thus, although chromatin accessibility assays are powerful for fine-mapping causal regulatory variants, detecting their downstream effects on gene expression will be challenging, requiring profiling of large numbers of stimulated cellular states and time points. Analysis of chromatin accessibility and expression quantitative trait loci in stimulated or naïve macrophages identifies loci that constitutively alter chromatin but affect expression only after stimulation, thus indicating an effect on enhancer priming.

Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios. Souporcell clusters single-cell RNA-seq data using genotype information without the use of a genotype reference.

Remyelination following CNS demyelination restores rapid signal propagation and protects axons; however, its efficiency declines with increasing age. Both intrinsic changes in the oligodendrocyte progenitor cell population and extrinsic factors in the lesion microenvironment of older subjects contribute to this decline. Microglia and monocyte-derived macrophages are critical for successful remyelination, releasing growth factors and clearing inhibitory myelin debris. Several studies have implicated delayed recruitment of macrophages/microglia into lesions as a key contributor to the decline in remyelination observed in older subjects. Here we show that the decreased expression of the scavenger receptor CD36 of aging mouse microglia and human microglia in culture underlies their reduced phagocytic activity. Overexpression of CD36 in cultured microglia rescues the deficit in phagocytosis of myelin debris. By screening for clinically approved agents that stimulate macrophages/microglia, we have found that niacin (vitamin B3) upregulates CD36 expression and enhances myelin phagocytosis by microglia in culture. This increase in myelin phagocytosis is mediated through the niacin receptor (hydroxycarboxylic acid receptor 2). Genetic fate mapping and multiphoton live imaging show that systemic treatment of 9–12-month-old demyelinated mice with therapeutically relevant doses of niacin promotes myelin debris clearance in lesions by both peripherally derived macrophages and microglia. This is accompanied by enhancement of oligodendrocyte progenitor cell numbers and by improved remyelination in the treated mice. Niacin represents a safe and translationally amenable regenerative therapy for chronic demyelinating diseases such as multiple sclerosis.

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts. Studying the genetic effects on early stages of human development is challenging due to a scarcity of biological material. Here, the authors utilise induced pluripotent stem cells from 125 donors to track gene expression changes and expression quantitative trait loci at single cell resolution during in vitro endoderm differentiation.

A silicon detector that is capable of long-wavelength photodetection at multi-gigabit per second data rates could prove useful for unlocking a new wavelength window for optical communications. With continued steep growth in the volume of data transmitted over optical networks there is a widely recognized need for more sophisticated photonics technologies to forestall a ‘capacity crunch’ 1 . A promising solution is to open new spectral regions at wavelengths near 2 μm and to exploit the long-wavelength transmission and amplification capabilities of hollow-core photonic-bandgap fibres 2 , 3 and the recently available thulium-doped fibre amplifiers 4 . To date, photodetector devices for this window have largely relied on III–V materials 5 or, where the benefits of integration with silicon photonics are sought, GeSn alloys, which have been demonstrated thus far with only limited utility 6 , 7 , 8 , 9 . Here, we describe a silicon photodiode operating at 20 Gbit s –1 in this wavelength region. The detector is compatible with standard silicon processing and is integrated directly with silicon-on-insulator waveguides, which suggests future utility in silicon-based mid-infrared integrated optics for applications in communications.

Microglia, the tissue-resident macrophages of the central nervous system (CNS), play critical roles in immune defense, development and homeostasis. However, isolating microglia from humans in large numbers is challenging. Here, we profiled gene expression variation in primary human microglia isolated from 141 patients undergoing neurosurgery. Using single-cell and bulk RNA sequencing, we identify how age, sex and clinical pathology influence microglia gene expression and which genetic variants have microglia-specific functions using expression quantitative trait loci (eQTL) mapping. We follow up one of our findings using a human induced pluripotent stem cell-based macrophage model to fine-map a candidate causal variant for Alzheimer’s disease at the BIN1 locus. Our study provides a population-scale transcriptional map of a critically important cell for human CNS development and disease. A population-scale map of gene expression in primary human microglia provides a systematic exploration of microglia diversity and how age, sex, pathology, cortical anatomy and common germline genetic variation influence the microglia transcriptome.

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.