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. 

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 Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19

2020 ◽  
Author(s):  
Lei Han ◽  
Xiaoyu Wei ◽  
Chuanyu Liu ◽  
Giacomo Volpe ◽  
Zhifeng Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Viral Entry ◽  
Immune Cell ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
List Type ◽  
Innate Immune Responses ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Substantial Heterogeneity

ABSTRACTStopping COVID-19 is a priority worldwide. Understanding which cell types are targeted by SARS-CoV-2 virus, whether interspecies differences exist, and how variations in cell state influence viral entry is fundamental for accelerating therapeutic and preventative approaches. In this endeavor, we profiled the transcriptome of nine tissues from a Macaca fascicularis monkey at single-cell resolution. The distribution of SARS-CoV-2 facilitators, ACE2 and TMRPSS2, in different cell subtypes showed substantial heterogeneity across lung, kidney, and liver. Through co-expression analysis, we identified immunomodulatory proteins such as IDO2 and ANPEP as potential SARS-CoV-2 targets responsible for immune cell exhaustion. Furthermore, single-cell chromatin accessibility analysis of the kidney unveiled a plausible link between IL6-mediated innate immune responses aiming to protect tissue and enhanced ACE2 expression that could promote viral entry. Our work constitutes a unique resource for understanding the physiology and pathophysiology of two phylogenetically close species, which might guide in the development of therapeutic approaches in humans.Bullet pointsWe generated a single-cell transcriptome atlas of 9 monkey tissues to study COVID-19.ACE2+TMPRSS2+ epithelial cells of lung, kidney and liver are targets for SARS-CoV-2.ACE2 correlation analysis shows IDO2 and ANPEP as potential therapeutic opportunities.We unveil a link between IL6, STAT transcription factors and boosted SARS-CoV-2 entry.

scholarly journals Deep learning of immune cell differentiation

2020 ◽  
Vol 117 (41) ◽  
pp. 25655-25666 ◽  
Author(s):  
Alexandra Maslova ◽  
Ricardo N. Ramirez ◽  
Ke Ma ◽  
Hugo Schmutz ◽  
Chendi Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cell Differentiation ◽  
Dna Sequences ◽  
Immune Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Regulatory Sequence ◽  
B Lineage ◽  
Additional Support

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network (“AI-TAC”) does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

scholarly journals Single cell epigenomic atlas of the developing human brain and organoids

2019 ◽  
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.

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

2019 ◽  
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.

scholarly journals A rapid and robust method for single cell chromatin accessibility profiling

2018 ◽  
Author(s):  
Xi Chen ◽  
Ricardo J Miragaia ◽  
Kedar Nath Natarajan ◽  
Sarah A Teichmann
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Regulatory Regions ◽  
Cell Type Specific ◽  
Accessible Chromatin

AbstractThe assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrated that our method worked robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3,000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

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.

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