scholarly journals Integrated single-cell transcriptomics and chromatin accessibility analysis reveals novel regulators of mammary epithelial cell identity

2019 ◽  
Author(s):  
Nicholas Pervolarakis ◽  
Quy H. Nguyen ◽  
Guadalupe Gutierrez ◽  
Peng Sun ◽  
Darisha Jhutty ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Single Cell ◽  
Mammary Epithelial Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Secretory Cells ◽  
Basal Cells ◽  
Accessibility Analysis

ABSTRACTThe mammary epithelial cell (MEC) system is a bi-layered ductal epithelial network consisting of luminal and basal cells, which is maintained by a lineage of stem and progenitor cell populations. Here, we used integrated single-cell transcriptomics and chromatin accessibility analysis to reconstruct the cell types of the mouse MEC system and their underlying gene regulatory features in an unbiased manner. We define previously unrealized differentiation states within the secretory type of luminal cells, which can be divided into distinct clusters of progenitor and mature secretory cells. By integrating single-cell transcriptomics and chromatin accessibility landscapes, we identified novel cis- and trans-regulatory elements that are differentially activated in the specific epithelial cell types and our newly defined luminal differentiation states. Our work provides an unprecedented resource to reveal novel cis/trans regulatory elements associated with MEC identity and differentiation that will serve as a valuable reference to determine how the chromatin accessibility landscape changes during breast cancer.

scholarly journals Integrated Single-Cell Transcriptomics and Chromatin Accessibility Analysis Reveals Regulators of Mammary Epithelial Cell Identity

Cell Reports ◽  
2020 ◽  
Vol 33 (3) ◽  
pp. 108273
Author(s):  
Nicholas Pervolarakis ◽  
Quy H. Nguyen ◽  
Justice Williams ◽  
Yanwen Gong ◽  
Guadalupe Gutierrez ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Single Cell ◽  
Mammary Epithelial Cell ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Identity ◽  
Accessibility Analysis

scholarly journals Integrated Single-Cell Transcriptomics and Chromatin Accessibility Analysis Reveals Novel Regulators of Mammary Epithelial Cell Identity

2019 ◽  
Author(s):  
Nicholas Pervolarakis ◽  
Peng Sun ◽  
Guadalupe Gutierrez ◽  
Quy H. Nguyen ◽  
Darisha Jhutty ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Single Cell ◽  
Mammary Epithelial Cell ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Identity ◽  
Accessibility Analysis

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

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.

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

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.

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

2017 ◽  
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.

scholarly journals Integration of microRNA signatures of distinct mammary epithelial cell types with their gene expression and epigenetic portraits

2015 ◽  
Vol 17 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Andrew Bert ◽  
Yifang Hu ◽  
Julie M. Sheridan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epithelial Cell ◽  
Mammary Epithelial Cell ◽  
Cell Types ◽  
Mammary Epithelial

scholarly journals Unbiased integration of single cell multi-omics data

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.

scholarly journals Unbiased integration of single cell multi-omics data

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. 

Sign in / Sign up

Export Citation Format

Share Document