Single-cell ATAC-seq (scATAC) yields sparse data that make conventional analysis challenging. We developed chromVAR (http://www.github.com/GreenleafLab/chromVAR), an R package for analyzing sparse ...chromatin-accessibility data by estimating gain or loss of accessibility within peaks sharing the same motif or annotation while controlling for technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and characterization of known and de novo sequence motifs associated with variation in chromatin accessibility.
This unit describes Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq), a method for mapping chromatin accessibility genome-wide. This method probes DNA ...accessibility with hyperactive Tn5 transposase, which inserts sequencing adapters into accessible regions of chromatin. Sequencing reads can then be used to infer regions of increased accessibility, as well as to map regions of transcription-factor binding and nucleosome position. The method is a fast and sensitive alternative to DNase-seq for assaying chromatin accessibility genome-wide, or to MNase-seq for assaying nucleosome positions in accessible regions of the genome.
We describe an assay for transposase-accessible chromatin using sequencing (ATAC-seq), based on direct in vitro transposition of sequencing adaptors into native chromatin, as a rapid and sensitive ...method for integrative epigenomic analysis. ATAC-seq captures open chromatin sites using a simple two-step protocol with 500-50,000 cells and reveals the interplay between genomic locations of open chromatin, DNA-binding proteins, individual nucleosomes and chromatin compaction at nucleotide resolution. We discovered classes of DNA-binding factors that strictly avoided, could tolerate or tended to overlap with nucleosomes. Using ATAC-seq maps of human CD4(+) T cells from a proband obtained on consecutive days, we demonstrated the feasibility of analyzing an individual's epigenome on a timescale compatible with clinical decision-making.
Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct ...spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei. Using these data, we characterized parent-specific changes in genome structure across embryonic stages, revealed single-cell chromatin domains in zygotes, and uncovered epigenetic memory of global chromosome positioning within individual embryos. These results demonstrate how IGS can directly connect sequence and structure across length scales from single base pairs to whole organisms.
Recent advances in single-cell and single-molecule epigenomic technologies now enable the study of genome regulation and dynamics at unprecedented resolution. In this Perspective, we highlight some ...of these transformative technologies and discuss how they have been used to identify new modes of gene regulation. We also contrast these assays with recent advances in single-cell transcriptomics and argue for the essential role of epigenomic technologies in both understanding cellular diversity and discovering gene regulatory mechanisms. In addition, we provide our view on the next generation of biological tools that we expect will open new avenues for elucidating the fundamental principles of gene regulation. Overall, this Perspective motivates the use of these high-resolution epigenomic technologies for mapping cell states and understanding regulatory diversity at single-molecule resolution within single cells.
Human hematopoiesis involves cellular differentiation of multipotent cells into progressively more lineage-restricted states. While the chromatin accessibility landscape of this process has been ...explored in defined populations, single-cell regulatory variation has been hidden by ensemble averaging. We collected single-cell chromatin accessibility profiles across 10 populations of immunophenotypically defined human hematopoietic cell types and constructed a chromatin accessibility landscape of human hematopoiesis to characterize differentiation trajectories. We find variation consistent with lineage bias toward different developmental branches in multipotent cell types. We observe heterogeneity within common myeloid progenitors (CMPs) and granulocyte-macrophage progenitors (GMPs) and develop a strategy to partition GMPs along their differentiation trajectory. Furthermore, we integrated single-cell RNA sequencing (scRNA-seq) data to associate transcription factors to chromatin accessibility changes and regulatory elements to target genes through correlations of expression and regulatory element accessibility. Overall, this work provides a framework for integrative exploration of complex regulatory dynamics in a primary human tissue at single-cell resolution.
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•Single-cell chromatin accessibility reveals a heterogeneous hematopoietic landscape•TF motif-associated chromatin variability in HSCs follows erythroid/lymphoid paths•Characterization of two GMP subsets with chromatin and transcriptome differences•Integrative analysis enables regulatory insights into cis- and trans-acting factors
Integrative analysis of single-cell transcriptomics and chromatin accessibility provides insights into regulatory features and dynamics in human hematopoiesis.
Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq ...data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10-45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level.
We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed.
This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).
Recent technical advancements have facilitated the mapping of epigenomes at single-cell resolution; however, the throughput and quality of these methods have limited their widespread adoption. Here ...we describe a high-quality (10
nuclear fragments per cell) droplet-microfluidics-based method for single-cell profiling of chromatin accessibility. We use this approach, named 'droplet single-cell assay for transposase-accessible chromatin using sequencing' (dscATAC-seq), to assay 46,653 cells for the unbiased discovery of cell types and regulatory elements in adult mouse brain. We further increase the throughput of this platform by combining it with combinatorial indexing (dsciATAC-seq), enabling single-cell studies at a massive scale. We demonstrate the utility of this approach by measuring chromatin accessibility across 136,463 resting and stimulated human bone marrow-derived cells to reveal changes in the cis- and trans-regulatory landscape across cell types and under stimulatory conditions at single-cell resolution. Altogether, we describe a total of 510,123 single-cell profiles, demonstrating the scalability and flexibility of this droplet-based platform.
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 introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses ...stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.
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