Biology subjects

Buenrostro, J. D.

Publications and source records attributed to Buenrostro, J. D..

28 records · Page 2Linked to original sources

bioRxiv2019

AtacWorks: A deep convolutional neural network toolkit for epigenomics

We introduce AtacWorks (https://github.com/clara-genomics/AtacWorks), a method to denoise and identify accessible chromatin regions from low-coverage or low-quality ATAC-seq data. AtacWorks uses a deep neural network to learn a mapping between noisy ATAC-seq data and corresponding higher-coverage or higher-quality data. To demonstrate the utility of AtacWorks, we train a model on data from four human blood cell types and show that this model accurately denoises chromatin accessibility at base-pair resolution and identifies peaks from low-coverage bulk sequencing of unseen cell types and experimental conditions. We use the same framework to obtain high-quality results from as few as 50 aggregate single-cell ATAC-seq profiles, and also from data with a low signal-to-noise ratio. We further show that AtacWorks can be adapted for cross-modality prediction of transcription factor footprints and ChIP-seq peaks from low input ATAC-seq. Finally, we demonstrate the applications of our approach to single-cell genomics by using AtacWorks to identify regulatory regions that are differentially-accessible between rare lineage-primed subpopulations of hematopoietic stem cells.

genomics↗

bioRxiv2019

Inference and effects of barcode multiplets in droplet-based single-cell assays

A widespread assumption for single-cell analyses specifies that one cells nucleic acids are predominantly captured by one oligonucleotide barcode. However, we show that [~]13-21% of cell barcodes from the 10x Chromium scATAC-seq assay may have been derived from a droplet with more than one oligonucleotide sequence, which we call \"barcode multiplets\". We demonstrate that barcode multiplets can be derived from at least two different sources. First, we confirm that [~]4% of droplets from the 10x platform may contain multiple beads. Additionally, we find that [~]5-7% of beads may contain multiple oligonucleotide barcodes. We show that this artifact can confound single-cell analyses, including the interpretation of clonal diversity and proliferation of intra-tumor lymphocytes. Overall, our work provides a conceptual and computational framework to identify and assess the impacts of barcode multiplets in single-cell data.

genomics↗

bioRxiv2019

Assessment of computational methods for the analysis of single-cell ATAC-seq data

BackgroundRecent 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 (20-50% 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.\n\nResultsWe present a benchmarking framework that was 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 evaluated 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.\n\nConclusionsThis 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 was the only method able to analyze a large dataset (> 80,000 cells).

bioinformatics↗

bioRxiv2019

Droplet-based combinatorial indexing for massive scale single-cell epigenomics

While recent technical advancements have facilitated the mapping of epigenomes at single-cell resolution, the throughput and quality of these methods have limited the widespread adoption of these technologies. Here, we describe a droplet microfluidics platform for single-cell assay for transposase accessible chromatin (scATAC-seq) for high-throughput single-cell profiling of chromatin accessibility. We use this approach for the unbiased discovery of cell types and regulatory elements within the mouse brain. Further, we extend the throughput of this approach by pairing combinatorial indexing with droplet microfluidics, enabling single-cell studies at a massive scale. With this approach, we measure chromatin accessibility across resting and stimulated human bone marrow derived cells to reveal changes in the cis- and trans- regulatory landscape across cell types and upon stimulation conditions at single-cell resolution. Altogether, we describe a total of 502,207 single-cell profiles, demonstrating the scalability and flexibility of this droplet-based platform.

genomics↗

bioRxiv2018

STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

genomics↗

bioRxiv2018

Interrogation of human hematopoiesis at single-cell and single-variant resolution

Incomplete annotation of cell-to-cell state variance and widespread linkage disequilibrium in the human genome represent significant challenges to elucidating mechanisms of trait-associated genetic variation. Here, using data from the UK Biobank, we perform genetic fine-mapping for 16 blood cell traits to quantify posterior probabilities of association while allowing for multiple independent signals per region. We observe an enrichment of fine-mapped variants in accessible chromatin of lineage-committed hematopoietic progenitor cells. Further, we develop a novel analytic framework that identifies \"core gene\" cell type enrichments and show that this approach uniquely resolves relevant cell types within closely related populations. Applying our approach to single cell chromatin accessibility data, we discover significant heterogeneity within classically defined multipotential progenitor populations. Finally, using several lines of empirical evidence, we identify relevant cell types, predict target genes, and propose putative causal mechanisms for fine-mapped variants. In total, our study provides an analytic framework for single-variant and single-cell analyses to elucidate putative causal variants and cell types from GWAS and high-resolution epigenomic assays.

genetics↗

bioRxiv2017

High-Resolution Dissection of Conducive Reprogramming Trajectory to Ground State Pluripotency

The ability to reprogram somatic cells into induced pluripotent stem cells (iPSCs) with four transcription factors Oct4, Sox2, Klf4 and cMyc (abbreviated as OSKM)1 has provoked interest to define the molecular characteristics of this process2-7. Despite important progress, the dynamics of epigenetic reprogramming at high resolution in correctly reprogrammed iPSCs and throughout the entire process remain largely undefined. This gap in understanding results from the inefficiency of conventional reprogramming methods coupled with the difficulty of prospectively isolating the rare cells that eventually correctly reprogram into iPSCs. Here we characterize cell fate conversion from fibroblast to iPSC using a highly efficient deterministic murine reprogramming system engineered through optimized inhibition of Gatad2a-Mbd3/NuRD repressive sub-complex. This comprehensive characterization provides single-day resolution of dynamic changes in levels of gene expression, chromatin modifications, TF binding, DNA accessibility and DNA methylation. The integrative analysis identified two transcriptional modules that dominate successful reprogramming. One consists of genes whose transcription is regulated by on/off epigenetic switching of modifications in their promoters (abbreviated as ESPGs), and the second consists of genes with promoters in a constitutively active chromatin state, but a dynamic expression pattern (abbreviated as CAPGs). ESPGs are mainly regulated by OSK, rather than Myc, and are enriched for cell fate determinants and pluripotency factors. CAPGs are predominantly regulated by Myc, and are enriched for cell biosynthetic regulatory functions. We used the ESPG module to study the identity and temporal occurrence of activating and repressing epigenetic switching during reprogramming. Removal of repressive chromatin modifications precedes chromatin opening and binding of RNA polymerase II at enhancers and promoters, and the opposite dynamics occur during repression of enhancers and promoters. Genome wide DNA methylation analysis demonstrated that de novo DNA methylation is not required for highly efficient conducive iPSC reprogramming, and identified a group of super-enhancers targeted by OSK, whose early demethylation marks commitment to a successful reprogramming trajectory also in inefficient conventional reprogramming systems. CAPGs are distinctively regulated by multiple synergystic ways: 1) Myc activity, delivered either endogenously or exogenously, dominates CAPG expression changes and is indispensable for induction of pluripotency in somatic cells; 2) A change in tRNA codon usage which is specific to CAPGs, but not ESPGs, and favors their translation. In summary, our unbiased high-resolution mapping of epigenetic changes on somatic cells that are committed to undergo successful reprogramming reveals interleaved epigenetic and biosynthetic reconfigurations that rapidly commission and propel conducive reprogramming toward naive pluripotency.

developmental biology↗

bioRxiv2017

Single-cell epigenomics maps the continuous regulatory landscape of human hematopoietic differentiation

AbstractNormal human hematopoiesis involves cellular differentiation of multipotent cells into progressively more lineage-restricted states. While epigenomic landscapes of this process have been explored in immunophenotypically-defined populations, the single-cell regulatory variation that defines hematopoietic differentiation has been hidden by ensemble averaging. We generated single-cell chromatin accessibility landscapes across 8 populations of immunophenotypically-defined human hematopoietic cell types. Using bulk chromatin accessibility profiles to scaffold our single-cell data analysis, we constructed an epigenomic landscape of human hematopoiesis and characterized epigenomic heterogeneity within phenotypically sorted populations to find epigenomic lineage-bias toward different developmental branches in multipotent stem cell states. We identify and isolate sub-populations within classically-defined granulocyte-macrophage progenitors (GMPs) and use ATAC-seq and RNA-seq to confirm that GMPs are epigenomically and transcriptomically heterogeneous. Furthermore, we identified transcription factors and cis-regulatory elements linked to changes in chromatin accessibility within cellular populations and across a continuous myeloid developmental trajectory, and observe relatively simple TF motif dynamics give rise to a broad diversity of accessibility dynamics at cis-regulatory elements. Overall, this work provides a template for exploration of complex regulatory dynamics in primary human tissues at the ultimate level of granular specificity - the single cell.\n\nOne Sentence SummarySingle cell chromatin accessibility reveals a high-resolution, continuous landscape of regulatory variation in human hematopoiesis.

genomics↗

bioRxiv2017

chromVAR: Inferring transcription factor variation from single-cell epigenomic data

Single cell ATAC-seq (scATAC) yields sparse data that makes application of conventional computational approaches for data analysis challenging or impossible. We developed chromVAR, an R package for analyzing sparse chromatin accessibility data by estimating the gain or loss of accessibility within sets of peaks sharing the same motif or annotation while controlling for known technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and enables characterization of known, or the de novo identification of novel, sequence motifs associated with variation in chromatin accessibility across single cells or other sparse epigenomic data sets.

bioinformatics↗

bioRxiv2016

Chromatin accessibility dynamics reveal novel functional enhancers in C. elegans

Chromatin accessibility, a crucial component of genome regulation, has primarily been studied in homogeneous and simple systems, such as isolated cell populations or early-development models. Whether chromatin accessibility can be assessed in complex, dynamic systems in vivo with high sensitivity remains largely unexplored. In this study, we use ATAC-seq to identify chromatin accessibility changes in a whole animal, the model organism C. elegans, from embryogenesis to adulthood. Chromatin accessibility changes between developmental stages are highly reproducible, recapitulate histone modification changes, and reveal key regulatory aspects of the epigenomic landscape throughout organismal development. We find that over 5,000 distal non-coding regions exhibit dynamic changes in chromatin accessibility between developmental stages, and could thereby represent putative enhancers. When tested in vivo, several of these putative enhancers indeed drive novel cell-type-and temporal-specific patterns of expression. Finally, by integrating transcription factor binding motifs in a machine learning framework, we identify EOR-1 as a unique transcription factor that may regulate chromatin dynamics during development. Our study provides a unique resource for C. elegans, a system in which the prevalence and importance of enhancers remains poorly characterized, and demonstrates the power of using whole organism chromatin accessibility to identify novel regulatory regions in complex systems.

genomics↗