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

Mapping Intimacies ◽

10.1101/612713 ◽

2019 ◽

Cited By ~ 1

Author(s):

Caleb A. Lareau ◽

Fabiana M. Duarte ◽

Jennifer G. Chew ◽

Vinay K. Kartha ◽

Zach D. Burkett ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Droplet Microfluidics ◽

Massive Scale ◽

Single Cell Profiling ◽

Bone Marrow Derived Cells ◽

Accessible Chromatin

AbstractWhile 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.

Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq

Frontiers in Genetics ◽

10.3389/fgene.2021.658352 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhe Cui ◽

Ya Cui ◽

Yan Gao ◽

Tao Jiang ◽

Tianyi Zang ◽

...

Keyword(s):

Single Cell ◽

Mononuclear Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Support Vector ◽

Cell Type ◽

Peripheral Blood Mononuclear ◽

Copy Numbers ◽

Genome Wide ◽

Accessible Chromatin

Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been widely used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, compared with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peak signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells, and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based markers and robust across datasets in different libraries and platforms. The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.

A cis-regulatory atlas in maize at single-cell resolution

10.1101/2020.09.27.315499 ◽

2020 ◽

Author(s):

Alexandre P. Marand ◽

Zongliang Chen ◽

Andrea Gallavotti ◽

Robert J. Schmitz

Keyword(s):

Single Cell ◽

Cell Types ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Cell Type ◽

Crop Species ◽

Cell Functions ◽

Cell Type Specific ◽

Type Specification ◽

Accessible Chromatin

ABSTRACTCis-regulatory elements (CREs) encode the genomic blueprints for coordinating spatiotemporal gene expression programs underlying highly specialized cell functions. To identify CREs underlying cell-type specification and developmental transitions, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin in an atlas of Zea mays organs. We describe 92 distinct states of chromatin accessibility across more than 165,913 putative CREs, 56,575 cells, and 52 known cell-types in maize using a novel implementation of regularized quasibinomial logistic regression. Cell states were largely determined by combinatorial accessibility of transcription factors (TFs) and their binding sites. A neural network revealed that cell identity could be accurately predicted (>0.94) solely based on TF binding site accessibility. Co-accessible chromatin recapitulated higher-order chromatin interactions, with distinct sets of TFs coordinating cell type-specific regulatory dynamics. Pseudotime reconstruction and alignment with Arabidopsis thaliana trajectories identified conserved TFs, associated motifs, and cis-regulatory regions specifying sequential developmental progressions. Cell-type specific accessible chromatin regions were enriched with phenotype-associated genetic variants and signatures of selection, revealing the major cell-types and putative CREs targeted by modern maize breeding. Collectively, our analysis affords a comprehensive framework for understanding cellular heterogeneity, evolution, and cis-regulatory grammar of cell-type specification in a major crop species.

Functional Inference of Gene Regulation using Single-Cell Multi-Omics

10.1101/2021.07.28.453784 ◽

2021 ◽

Author(s):

Vinay K Kartha ◽

Fabiana M Duarte ◽

Yan Hu ◽

Sai Ma ◽

Jennifer G Chew ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Regulatory Networks ◽

Immunological Response ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Human Blood Cells ◽

Functional Inference ◽

Omic Data

Cells require coordinated control over gene expression when responding to environmental stimuli. Here, we apply scATAC-seq and scRNA-seq in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis -regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multi-omic data, we develop FigR - a framework to computationally pair scATAC-seq with scRNA-seq cells, connect distal cis -regulatory elements to genes, and infer gene regulatory networks (GRNs) to identify candidate TF regulators. Utilizing these paired multi-omic data, we define Domains of Regulatory Chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility prior to production of gene expression at time scales of minutes. Further, the construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables the elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Single-cell regulatory landscape and disease vulnerability map of adult Macaque cortex

10.1101/2020.05.14.087601 ◽

2020 ◽

Author(s):

Ying Lei ◽

Mengnan Cheng ◽

Zihao Li ◽

Zhenkun Zhuang ◽

Liang Wu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Primary Motor Cortex ◽

Neurological Diseases ◽

Single Cells ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Nucleotide Polymorphisms ◽

Cell Type

Non-human primates (NHP) provide a unique opportunity to study human neurological diseases, yet detailed characterization of the cell types and transcriptional regulatory features in the NHP brain is lacking. We applied a combinatorial indexing assay, sci-ATAC-seq, as well as single-nuclei RNA-seq, to profile chromatin accessibility in 43,793 single cells and transcriptomics in 11,477 cells, respectively, from prefrontal cortex, primary motor cortex and the primary visual cortex of adult cynomolgus monkey Macaca fascularis. Integrative analysis of these two datasets, resolved regulatory elements and transcription factors that specify cell type distinctions, and discovered area-specific diversity in chromatin accessibility and gene expression within excitatory neurons. We also constructed the dynamic landscape of chromatin accessibility and gene expression of oligodendrocyte maturation to characterize adult remyelination. Furthermore, we identified cell type-specific enrichment of differentially spliced gene isoforms and disease-associated single nucleotide polymorphisms. Our datasets permit integrative exploration of complex regulatory dynamics in macaque brain tissue at single-cell resolution.

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

10.1101/109843 ◽

2017 ◽

Cited By ~ 8

Author(s):

Jason D Buenrostro ◽

M Ryan Corces ◽

Beijing Wu ◽

Alicia N Schep ◽

Caleb A Lareau ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Developmental Trajectory ◽

Hematopoietic Differentiation ◽

Ensemble Averaging ◽

Regulatory Variation ◽

Multipotent Cells ◽

Normal Human

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 andcis-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.One Sentence SummarySingle cell chromatin accessibility reveals a high-resolution, continuous landscape of regulatory variation in human hematopoiesis.

A combinatorial indexing strategy for epigenomic profiling of plant single cells

10.1101/2021.11.07.467612 ◽

2021 ◽

Author(s):

Xiaoyu Tu ◽

Alexandre P Marand ◽

Robert J. Schmitz ◽

Silin Zhong

Keyword(s):

Single Cell ◽

Single Cells ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Arabidopsis Root ◽

Indexing Method ◽

Specialized Equipment ◽

Cell Type Specific ◽

Accessible Chromatin ◽

Insight Into

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we profiled the Arabidopsis root single-cell epigenome using a combinatorial index and dual PCR barcode strategy without the need of any specialized equipment. We generated chromatin accessibility profiles for 13,576 Arabidopsis thaliana root nuclei with an average of 12,784 unique Tn5 integrations per cell and 85% of the Tn5 insertions localizing to discrete accessible chromatin regions. Comparison with data generated from a commercial microfluidic platform revealed that our method is capable of unbiased identification of cell type-specific chromatin accessibility with improved throughput, quality, and efficiency. We anticipate that by removing cost, instrument, and other technical obstacles, this combinatorial indexing method will be a valuable tool for routine investigation of single-cell epigenomes and usher new insight into plant growth, development and their interactions with the environment.

Unbiased integration of single cell multi-omics data

10.1101/2020.12.11.422014 ◽

2020 ◽

Author(s):

JINZHUANG DOU ◽

Shaoheng Liang ◽

Vakul Mohanty ◽

Xuesen Cheng ◽

Sangbae Kim ◽

...

Keyword(s):

Single Cell ◽

Immune Cell ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Mouse Retina ◽

Transcriptomics Data ◽

Mouse Brain Cortex ◽

Broad Variety ◽

Data Matrices

Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

Unbiased integration of single cell multi-omics data

10.21203/rs.3.rs-126986/v1 ◽

2020 ◽

Author(s):

Jinzhuang Dou ◽

Shaoheng Liang ◽

Vakul Mohanty ◽

Xuesen Cheng ◽

Sangbae Kim ◽

...

Keyword(s):

Single Cell ◽

Immune Cell ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Mouse Retina ◽

Transcriptomics Data ◽

Mouse Brain Cortex ◽

Broad Variety ◽

Data Matrices

Abstract Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

Single cell epigenomic atlas of the developing human brain and organoids

10.1101/2019.12.30.891549 ◽

2019 ◽

Cited By ~ 5

Author(s):

Ryan S. Ziffra ◽

Chang N. Kim ◽

Amy Wilfert ◽

Tychele N. Turner ◽

Maximilian Haeussler ◽

...

Keyword(s):

Human Brain ◽

Single Cell ◽

Cell Fate ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Chromatin State ◽

Cell Type ◽

Fate Specification ◽

Cell Type Specific

AbstractDynamic changes in chromatin accessibility coincide with important aspects of neuronal differentiation, such as fate specification and arealization and confer cell type-specific associations to neurodevelopmental disorders. However, studies of the epigenomic landscape of the developing human brain have yet to be performed at single-cell resolution. Here, we profiled chromatin accessibility of >75,000 cells from eight distinct areas of developing human forebrain using single cell ATAC-seq (scATACseq). We identified thousands of loci that undergo extensive cell type-specific changes in accessibility during corticogenesis. Chromatin state profiling also reveals novel distinctions between neural progenitor cells from different cortical areas not seen in transcriptomic profiles and suggests a role for retinoic acid signaling in cortical arealization. Comparison of the cell type-specific chromatin landscape of cerebral organoids to primary developing cortex found that organoids establish broad cell type-specific enhancer accessibility patterns similar to the developing cortex, but lack many putative regulatory elements identified in homologous primary cell types. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

The cis-regulatory dynamics of embryonic development at single cell resolution

10.1101/166066 ◽

2017 ◽

Cited By ~ 1

Author(s):

Darren A. Cusanovich ◽

James P. Reddington ◽

David A. Garfield ◽

Riza Daza ◽

Raquel Marco-Ferreres ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Cell Types ◽

Chromatin Accessibility ◽

Open Chromatin ◽

Germ Layer ◽

Regulatory Changes ◽

Single Cell Profiling ◽

Transgenic Embryos ◽

Cell Trajectories

ABSTRACTSingle cell measurements of gene expression are providing new insights into lineage commitment, yet the regulatory changes underlying individual cell trajectories remain elusive. Here, we profiled chromatin accessibility in over 20,000 single nuclei across multiple stages of Drosophila embryogenesis. Our data reveal heterogeneity in the regulatory landscape prior to gastrulation that reflects anatomical position, a feature that aligns with future cell fate. During mid embryogenesis, tissue granularity emerges such that cell types can be inferred by their chromatin accessibility, while maintaining a signature of their germ layer of origin. We identify over 30,000 distal elements with tissue-specific accessibility. Using transgenic embryos, we tested the germ layer specificity of a subset of predicted enhancers, achieving near-perfect accuracy. Overall, these data demonstrate the power of shotgun single cell profiling of embryos to resolve dynamic changes in open chromatin during development, and to uncover the cis-regulatory programs of germ layers and cell types.