scholarly journals Chromatin accessibility changes betweenArabidopsisstem cells and mesophyll cells illuminate cell type-specific transcription factor networks

2017 ◽  
Author(s):  
Paja Sijacic ◽  
Marko Bajic ◽  
Elizabeth C. McKinney ◽  
Richard B. Meagher ◽  
Roger B. Deal
Keyword(s):  
Stem Cells ◽  
Transcription Factor ◽  
Regulatory Networks ◽  
Target Genes ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Mesophyll Cells ◽  
Leaf Mesophyll ◽  
Cell Type Specific

AbstractBackgroundCell differentiation is driven by changes in transcription factor (TF) activity and subsequent alterations in transcription. To study this process, differences in TF binding between cell types can be deduced by methods that probe chromatin accessibility. We used cell type-specific nuclei purification followed by the Assay for Transposase Accessible Chromatin (ATAC-seq) to delineate differences in chromatin accessibility and TF regulatory networks between stem cells of the shoot apical meristem (SAM) and differentiated leaf mesophyll cells ofArabidopsis thaliana.ResultsChromatin accessibility profiles of SAM stem cells and leaf mesophyll cells were highly similar at a qualitative level, yet thousands of regions of quantitatively different chromatin accessibility were also identified. We found that chromatin regions preferentially accessible in mesophyll cells tended to also be substantially accessible in the stem cells as compared to the genome-wide average, whereas the converse was not true. Analysis of genomic regions preferentially accessible in each cell type identified hundreds of overrepresented TF binding motifs, highlighting a set of TFs that are likely important for each cell type. Among these, we found evidence for extensive co-regulation of target genes by multiple TFs that are preferentially expressed in one cell type or the other. For example, a set of zinc-finger TFs appear to control a suite of growth-and development-related genes specifically in stem cells, while another TF set co-regulates genes involved in light responses and photosynthesis specifically in mesophyll cells. Interestingly, the TFs within both of these sets also show evidence of extensively co-regulating each other.ConclusionsQuantitative analysis of chromatin accessibility differences between stem cells and differentiated mesophyll cells allowed us to identify TF regulatory networks and downstream target genes that are likely to be functionally important in each cell type. Our findings that mesophyll cell-enriched accessible sites tend to already be substantially accessible in stem cells, but not vice versa, suggests that widespread regulatory element accessibility may be important for the developmental plasticity of stem cells. This work also demonstrates the utility of cell type-specific chromatin accessibility profiling in quickly developing testable models of regulatory control differences between cell types.

scholarly journals A single cell brain atlas in human Alzheimer’s disease

2019 ◽  
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

scholarly journals Accurate imputation of histone modifications using transcription

2020 ◽  
Author(s):  
Zhong Wang ◽  
Alexandra G. Chivu ◽  
Lauren A. Choate ◽  
Edward J. Rice ◽  
Donald C. Miller ◽  
...  
Keyword(s):  
Transcription Initiation ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Active Chromatin ◽  
Histone Marks ◽  
Repressive Mark ◽  
Cell Type Specific ◽  
The Relationship ◽  
Machine Learning Tool

AbstractWe trained a sensitive machine learning tool to infer the distribution of histone marks using maps of nascent transcription. Transcription captured the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. The relationship between active histone marks and transcription was conserved in all cell types examined, allowing individual labs to annotate active functional elements in mammals with similar richness as major consortia. Using imputation as an interpretative tool uncovered cell-type specific differences in how the PRC2-dependent repressive mark, H3K27me3, corresponds to transcription, and revealed that transcription initiation requires both chromatin accessibility and an active chromatin environment demonstrating that initiation is less promiscuous than previously thought.

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

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.

scholarly journals Dynamic regulatory module networks for inference of cell type specific transcriptional networks

2020 ◽  
Author(s):  
Alireza Fotuhi Siahpirani ◽  
Deborah Chasman ◽  
Morten Seirup ◽  
Sara Knaack ◽  
Rupa Sridharan ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Dynamics ◽  
Cell Types ◽  
Small Sample ◽  
Cell Type ◽  
Regulatory Module ◽  
A Cell ◽  
Cell Type Specific ◽  
Time Point

AbstractChanges in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled transcriptomes and epigenomes at different stages of a developmental process. However, integrating these data across multiple cell types to infer cell type specific regulatory networks is a major challenge because of the small sample size for each time point. We present a novel approach, Dynamic Regulatory Module Networks (DRMNs), to model regulatory network dynamics on a cell lineage. DRMNs represent a cell type specific network by a set of expression modules and associated regulatory programs, and probabilistically model the transitions between cell types. DRMNs learn a cell type’s regulatory network from input expression and epigenomic profiles using multi-task learning to exploit cell type relatedness. We applied DRMNs to study regulatory network dynamics in two different developmental dynamic processes including cellular reprogramming and liver dedifferentiation. For both systems, DRMN predicted relevant regulators driving the major patterns of expression in each time point as well as regulators for transitioning gene sets that change their expression over time.

scholarly journals EAGLE: an algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions

2019 ◽  
Author(s):  
Tianshun Gao ◽  
Jiang Qian
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Cross Validation ◽  
Cell Types ◽  
Gene Interactions ◽  
Tissue Cell ◽  
Cell Type ◽  
Genomic Features ◽  
Link Type ◽  
Cell Type Specific

AbstractLong-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.Author summaryEnhancers are DNA sequences that interact with promoters and activate target genes. Since enhancers often located far from the target genes and the nearest genes are not always the targets of the enhancers, the prediction of enhancer-target gene relationships is a big challenge. Although a few computational tools are designed for the prediction of enhancer-target genes, it’s difficult to apply them in most tissue/cell types due to a lack of enough genomic datasets. Here we proposed a new method, EAGLE, which utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Comparing with other existing tools, EAGLE displayed a better performance in the 10-fold cross-validation and cross-sample test. Moreover, the predictions by EAGLE were validated by other independent evidence such as the enrichment of relevant transcriptional factors, epigenetic modifications, and eQTLs.Finally, we integrated the enhancer-target relationships obtained from human and mouse genomes into an interactive database EnhancerAtlas, http://www.enhanceratlas.org/.

scholarly journals CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

2019 ◽  
Author(s):  
Tom Aharon Hait ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Type ◽  
Chromatin Interactions ◽  
Cell Type Specific ◽  
Novel Method ◽  
Validation Scheme

AbstractSpatiotemporal gene expression patterns are governed to a large extent by enhancer elements, typically located distally from their target genes. Identification of enhancer-promoter (EP) links that are specific and functional in individual cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a new statistical inference method that utilizes multiple replicates per cell type to infer cell type-specific EP links. Computationally predicted EP links are usually benchmarked against experimentally determined chromatin interactions measured by ChIA-PET and promoter-capture HiC techniques. We expand this validation scheme by using also loops that overlap in their anchor sites. In analyzing 1,366 samples from ENCODE, Roadmap epigenomics and FANTOM5, CT-FOCS inferred highly cell type-specific EP links more accurately than state-of-the-art methods. We illustrate how our inferred EP links drive cell type-specific gene expression and regulation.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals Probing transcription factor combinatorics in different promoter classes and in enhancers

2017 ◽  
Author(s):  
Jimmy Vandel ◽  
Océane Cassan ◽  
Sophie Lèbre ◽  
Charles-Henri Lecellier ◽  
Laurent Bréhélin
Keyword(s):  
Binding Sites ◽  
Target Genes ◽  
Cell Types ◽  
Model Parameters ◽  
Binding Motif ◽  
Cell Type ◽  
Regulatory Regions ◽  
New Approach ◽  
Cell Type Specific ◽  
Common Target

In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combina-tions among different gene classes, regulatory regions and cell types. We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a tar-get TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combina-tions involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for pre-dicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in pro-moters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and en-hancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the dif-ferent targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately dis-tinguish promoters associated with specific biological processes.

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