Functional genomics assays based on high-throughput sequencing greatly expand our ability to understand the genome. Here, we define the ENCODE blacklist- a comprehensive set of regions in the human, ...mouse, worm, and fly genomes that have anomalous, unstructured, or high signal in next-generation sequencing experiments independent of cell line or experiment. The removal of the ENCODE blacklist is an essential quality measure when analyzing functional genomics data.
Trimethylation of histone H3 at lysine 4 (H3K4me3) is a chromatin modification known to mark the transcription start sites of active genes. Here, we show that H3K4me3 domains that spread more broadly ...over genes in a given cell type preferentially mark genes that are essential for the identity and function of that cell type. Using the broadest H3K4me3 domains as a discovery tool in neural progenitor cells, we identify novel regulators of these cells. Machine learning models reveal that the broadest H3K4me3 domains represent a distinct entity, characterized by increased marks of elongation. The broadest H3K4me3 domains also have more paused polymerase at their promoters, suggesting a unique transcriptional output. Indeed, genes marked by the broadest H3K4me3 domains exhibit enhanced transcriptional consistency rather than increased transcriptional levels, and perturbation of H3K4me3 breadth leads to changes in transcriptional consistency. Thus, H3K4me3 breadth contains information that could ensure transcriptional precision at key cell identity/function genes.
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•Broad H3K4me3 domains mark cell identity genes and can be used as a discovery tool•Broad H3K4me3 domains are a distinct entity defined by specific Pol II regulation•Genes marked by broad H3K4me3 domains have increased transcriptional consistency•Perturbation of H3K4me3 breadth leads to changes in transcriptional consistency
Genes marked by broad H3K4me3 domains have increased transcriptional consistency.
Alzheimer's disease (AD) is a severe age-related neurodegenerative disorder characterized by accumulation of amyloid-β plaques and neurofibrillary tangles, synaptic and neuronal loss, and cognitive ...decline. Several genes have been implicated in AD, but chromatin state alterations during neurodegeneration remain uncharacterized. Here we profile transcriptional and chromatin state dynamics across early and late pathology in the hippocampus of an inducible mouse model of AD-like neurodegeneration. We find a coordinated downregulation of synaptic plasticity genes and regulatory regions, and upregulation of immune response genes and regulatory regions, which are targeted by factors that belong to the ETS family of transcriptional regulators, including PU.1. Human regions orthologous to increasing-level enhancers show immune-cell-specific enhancer signatures as well as immune cell expression quantitative trait loci, while decreasing-level enhancer orthologues show fetal-brain-specific enhancer activity. Notably, AD-associated genetic variants are specifically enriched in increasing-level enhancer orthologues, implicating immune processes in AD predisposition. Indeed, increasing enhancers overlap known AD loci lacking protein-altering variants, and implicate additional loci that do not reach genome-wide significance. Our results reveal new insights into the mechanisms of neurodegeneration and establish the mouse as a useful model for functional studies of AD regulatory regions.
Genetic perturbations of cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the ...activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Base-pair-resolution neural network models identified strong cell-type-specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD individuals and identified frequently disrupted TF binding sites. This approach illustrates how cell-type-specific mapping can provide insights into the programs governing human development and disease.
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•Single-cell RNA and chromatin profiling charts human corticogenesis•Distinct TFs underlie neurogenesis and gliogenesis regulatory programs•Lineage-determining TFs adopt an active chromatin state early in differentiation•Neural networks prioritize noncoding de novo mutations in autism spectrum disorder
A single-cell atlas of gene expression and chromatin accessibility of human developing cortex during mid-gestation reveals lineage-determining transcription factors for human corticogenesis and identifies prioritized mutations for autism spectrum disorder.
We define the chromatin accessibility and transcriptional landscapes in 13 human primary blood cell types that span the hematopoietic hierarchy. Exploiting the finding that the enhancer landscape ...better reflects cell identity than mRNA levels, we enable 'enhancer cytometry' for enumeration of pure cell types from complex populations. We identify regulators governing hematopoietic differentiation and further show the lineage ontogeny of genetic elements linked to diverse human diseases. In acute myeloid leukemia (AML), chromatin accessibility uncovers unique regulatory evolution in cancer cells with a progressively increasing mutation burden. Single AML cells exhibit distinctive mixed regulome profiles corresponding to disparate developmental stages. A method to account for this regulatory heterogeneity identified cancer-specific deviations and implicated HOX factors as key regulators of preleukemic hematopoietic stem cell characteristics. Thus, regulome dynamics can provide diverse insights into hematopoietic development and disease.
We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility profiling that works across multiple applications with substantial improvement of signal-to-background ratio and ...information content. The Omni-ATAC protocol generates chromatin accessibility profiles from archival frozen tissue samples and 50-μm sections, revealing the activities of disease-associated DNA elements in distinct human brain structures. The Omni-ATAC protocol enables the interrogation of personal regulomes in tissue context and translational studies.
Mapping open chromatin regions has emerged as a widely used tool for identifying active regulatory elements in eukaryotes. However, existing approaches, limited by reliance on DNA fragmentation and ...short-read sequencing, cannot provide information about large-scale chromatin states or reveal coordination between the states of distal regulatory elements. We have developed a method for profiling the accessibility of individual chromatin fibers, a single-molecule long-read accessible chromatin mapping sequencing assay (SMAC-seq), enabling the simultaneous, high-resolution, single-molecule assessment of chromatin states at multikilobase length scales. Our strategy is based on combining the preferential methylation of open chromatin regions by DNA methyltransferases with low sequence specificity, in this case EcoGII, an N
-methyladenosine (m
A) methyltransferase, and the ability of nanopore sequencing to directly read DNA modifications. We demonstrate that aggregate SMAC-seq signals match bulk-level accessibility measurements, observe single-molecule nucleosome and transcription factor protection footprints, and quantify the correlation between chromatin states of distal genomic elements.
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results ...across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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