Single-cell epigenomics reveals mechanisms of human cortical development

AbstractDuring mammalian development, differences in chromatin state coincide with cellular differentiation and reflect changes in the gene regulatory landscape1. In the developing brain, cell fate specification and topographic identity are important for defining cell identity2 and confer selective vulnerabilities to neurodevelopmental disorders3. Here, to identify cell-type-specific chromatin accessibility patterns in the developing human brain, we used a single-cell assay for transposase accessibility by sequencing (scATAC-seq) in primary tissue samples from the human forebrain. We applied unbiased analyses to identify genomic loci that undergo extensive cell-type- and brain-region-specific changes in accessibility during neurogenesis, and an integrative analysis to predict cell-type-specific candidate regulatory elements. We found that cerebral organoids recapitulate most putative cell-type-specific enhancer accessibility patterns but lack many cell-type-specific open chromatin regions that are found in vivo. Systematic comparison of chromatin accessibility across brain regions revealed unexpected diversity among neural progenitor cells in the cerebral cortex and implicated retinoic acid signalling in the specification of neuronal lineage identity in the prefrontal cortex. 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.

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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.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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A single cell brain atlas in human Alzheimer’s disease

10.1101/628347 ◽

2019 ◽

Cited By ~ 4

Author(s):

Alexandra Grubman ◽

Gabriel Chew ◽

John F. Ouyang ◽

Guizhi Sun ◽

Xin Yi Choo ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Cell Fate ◽

Expression Patterns ◽

Cell Types ◽

Gene Expression Patterns ◽

Cell Type ◽

Web Resource ◽

Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

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Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility

10.1101/2021.04.01.438068 ◽

2021 ◽

Author(s):

Risa Karakida Kawaguchi ◽

Ziqi Tang ◽

Stephan Fischer ◽

Rohit Tripathy ◽

Peter K. Koo ◽

...

Keyword(s):

Single Cell ◽

Marker Gene ◽

Cell Types ◽

Chromatin Accessibility ◽

Marker Genes ◽

Cell Type ◽

Gene Sets ◽

Typing Methods ◽

Cell Type Specific ◽

Cell Typing

Background: Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) measures genome-wide chromatin accessibility for the discovery of cell-type specific regulatory networks. ScATAC-seq combined with single-cell RNA sequencing (scRNA-seq) offers important avenues for ongoing research, such as novel cell-type specific activation of enhancer and transcription factor binding sites as well as chromatin changes specific to cell states. On the other hand, scATAC-seq data is known to be challenging to interpret due to its high number of zeros as well as the heterogeneity derived from different protocols. Because of the stochastic lack of marker gene activities, cell type identification by scATAC-seq remains difficult even at a cluster level. Results: In this study, we exploit reference knowledge obtained from external scATAC-seq or scRNA-seq datasets to define existing cell types and uncover the genomic regions which drive cell-type specific gene regulation. To investigate the robustness of existing cell-typing methods, we collected 7 scATAC-seq datasets targeting mouse brain for a meta-analytic comparison of neuronal cell-type annotation, including a reference atlas generated by the BRAIN Initiative Cell Census Network (BICCN). By comparing the area under the receiver operating characteristics curves (AUROCs) for the three major cell types (inhibitory, excitatory, and non-neuronal cells), cell-typing performance by single markers is found to be highly variable even for known marker genes due to study-specific biases. However, the signal aggregation of a large and redundant marker gene set, optimized via multiple scRNA-seq data, achieves the highest cell-typing performances among 5 existing marker gene sets, from the individual cell to cluster level. That gene set also shows a high consistency with the cluster-specific genes from inhibitory subtypes in two well-annotated datasets, suggesting applicability to rare cell types. Next, we demonstrate a comprehensive assessment of scATAC-seq cell typing using exhaustive combinations of the marker gene sets with supervised learning methods including machine learning classifiers and joint clustering methods. Our results show that the combinations using robust marker gene sets systematically ranked at the top, not only with model based prediction using a large reference data but also with a simple summation of expression strengths across markers. To demonstrate the utility of this robust cell typing approach, we trained a deep neural network to predict chromatin accessibility in each subtype using only DNA sequence. Through model interpretation methods, we identify key motifs enriched about robust gene sets for each neuronal subtype. Conclusions: Through the meta-analytic evaluation of scATAC-seq cell-typing methods, we develop a novel method set to exploit the BICCN reference atlas. Our study strongly supports the value of robust marker gene selection as a feature selection tool and cross-dataset comparison between scATAC-seq datasets to improve alignment of scATAC-seq to known biology. With this novel, high quality epigenetic data, genomic analysis of regulatory regions can reveal sequence motifs that drive cell type-specific regulatory programs.

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A giant cell enhancer achieves cell-type specificity through activation via TCP and repression by Dof transcription factors

10.1101/2020.08.28.270454 ◽

2020 ◽

Author(s):

Lilan Hong ◽

Clint S. Ko ◽

S. Earl Kang ◽

Jose L. Pruneda-Paz ◽

Adrienne H. K. Roeder

Keyword(s):

Transcription Factors ◽

Giant Cell ◽

Cell Fate ◽

Giant Cells ◽

Cell Fate Specification ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Cell Type Specific ◽

Dof Transcription Factors

AbstractProper pattern formation relies on the tight coordination of cell fate specification and cell cycle regulation in growing tissues. How this can be organized at enhancers that activate gene expression necessary for differentiation is not well understood. One such example is the patterning of the Arabidopsis thaliana sepal epidermis where giant cell fate specification is associated with the endoreduplication cell cycle. Previously, we identified an enhancer region capable of driving giant cell-specific expression. In this study, we use the giant cell enhancer as a model to understand the regulatory logic that promotes cell-type specific expression. Our dissection of the enhancer revealed that giant cell specificity is achieved primarily through the combination of two elements: an activator and a repressor. TCP transcription factors are involved in activation of non-specific expression throughout the epidermis with higher expression in endoreduplicated giant cells than small cells. Dof transcription factors act via the second element to repress activity of the enhancer and limit expression to giant cells. Thus, we find that cell-type specific expression emerges from the combined activities of two broadly acting enhancer elements.

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Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics

10.1101/2019.12.19.882381 ◽

2019 ◽

Author(s):

Carmen Bravo González-Blas ◽

Xiao-Jiang Quan ◽

Ramon Duran-Romaña ◽

Ibrahim Ihsan Taskiran ◽

Duygu Koldere ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Chromatin Accessibility ◽

Virtual Space ◽

Spatial Integration ◽

Cell Type ◽

Enhancer Activity ◽

Specific Effects ◽

Bona Fide ◽

Cell Type Specific

AbstractSingle-cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single-cell RNA-seq and single-cell ATAC-seq atlases of the Drosophila eye-antennal disc and spatially integrate the data using a virtual latent space that mimics the organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ∼85% of enhancers in which chromatin accessibility and enhancer activity are coupled. Next, we infer enhancer-to-gene relationships in the virtual space, finding that genes are regulated by multiple redundant enhancers. Exploiting cell-type specific enhancers, we deconvolute cell-type specific effects of bulk-derived chromatin accessibility QTLs. Finally, we discover that Prospero drives neuronal differentiation through the binding of a GGG motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue.

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SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

10.1101/508085 ◽

2018 ◽

Cited By ~ 8

Author(s):

Yuqi Tan ◽

Patrick Cahan

Keyword(s):

Single Cell ◽

Cell Fate ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

Computational Tool ◽

Cell Type Composition ◽

Type Composition ◽

Cell Type Specific

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.

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CellWalkR: An R Package for integrating single-cell and bulk data to resolve regulatory elements

10.1101/2021.02.23.432593 ◽

2021 ◽

Author(s):

Pawel F. Przytycki ◽

Katherine S. Pollard

Keyword(s):

Random Walk ◽

Single Cell ◽

R Package ◽

Regulatory Elements ◽

Random Walk Model ◽

Open Chromatin ◽

Cell Type ◽

Regulatory Regions ◽

Bulk Data ◽

Cell Type Specific

AbstractWhile single-cell open chromatin (scATAC-seq) data allows for the identification of cell type-specific regulatory regions, it is much sparser than bulk data. CellWalkR is an R package that performs an integration of external labeling and bulk epigenetic data with scATAC-seq using a network-based random walk model to help overcome this sparsity. Outputs include cell type labels for individual cells and regulatory regions.Availability and implementationCellWalkR is freely available as an R package under a GNU GPL-2.0 License, and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette for analyzing example data.

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Impact of regulatory variation across human iPSCs and differentiated cells

10.1101/091660 ◽

2016 ◽

Cited By ~ 6

Author(s):

Nicholas E. Banovich ◽

Yang I. Li ◽

Anil Raj ◽

Michelle C. Ward ◽

Peyton Greenside ◽

...

Keyword(s):

Complex Disease ◽

Cell Types ◽

Chromatin Accessibility ◽

Open Chromatin ◽

Cell Type ◽

Regulatory Variation ◽

Differentiated Cells ◽

Human Ipscs ◽

Cell Type Specific ◽

The Impact

AbstractInduced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation across different cell types and as models for studies of complex disease. We established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression, chromatin accessibility and DNA methylation. Regulatory variation between individuals is lower in iPSCs than in the differentiated cell types, consistent with the intuition that developmental processes are generally canalized. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific QTLs are in shared open chromatin. Finally, we developed a deep neural network to predict open chromatin regions from DNA sequence alone and were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.

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Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

Frontiers in Plant Science ◽

10.3389/fpls.2021.661361 ◽

2021 ◽

Vol 12 ◽

Author(s):

Laura Serrano-Ron ◽

Javier Cabrera ◽

Pablo Perez-Garcia ◽

Miguel A. Moreno-Risueno

Keyword(s):

Single Cell ◽

Cell Fate ◽

Gene Regulatory Networks ◽

Root Development ◽

Regulatory Networks ◽

Primary Root ◽

Cell Fate Specification ◽

Rna Seq ◽

Fate Specification ◽

Gene Regulatory

Over the last decades, research on postembryonic root development has been facilitated by “omics” technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

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