scholarly journals Ranking Reprogramming Factors for Directed Differentiation

2021 ◽  
Author(s):  
Jennifer Hammelman ◽  
Tulsi Patel ◽  
Michael Closser ◽  
Hynek Wichterle ◽  
David K. Gifford
Keyword(s):  
Transcription Factor ◽  
Biological Networks ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Directed Differentiation ◽  
Cell Type ◽  
Test Parameter ◽  
Over Expression ◽  
Reprogramming Factors ◽  
Proven Method

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. We examine the success rate of methods and data for directed differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBSeq, AME, DREME, HOMER, KMAC, diffTF, and DeepAccess) to correctly discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that utilize gene expression, biological networks, and chromatin accessibility data to identify eight sets of known reprogramming factors and comprehensively test parameter and pre-processing of input data to optimize performance of these methods. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top 10 candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF are more likely to consistently correctly rank the significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and DeepAccess are optimal methods for transcription factor recovery and ranking which will allow for systematic prioritization of transcription factor candidates to aid in the design of novel reprogramming protocols.

scholarly journals Tree Based Co-Clustering Identifies Variation in Chromatin Accessibility Across Hematopoietic Cell Types

2021 ◽  
Author(s):  
Thomas B George ◽  
Nate K Strawn ◽  
Sivan Leviyang
Keyword(s):  
Transcription Factor ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Hematopoietic Cell ◽  
Hematopoietic Differentiation ◽  
Cell Type ◽  
Genomic Locus ◽  
State Variation ◽  
A Cell ◽  
Clustering Approach

Chromatin accessibility, as measured by ATACseq, varies between hematopoietic cell types in different branches of the hematopoietic differentiation tree, e.g. T cells vs B cells, but methods that relate variation in chromatin accessibility to the placement of a cell type on the differentiation tree are lacking. Using an ATACseq dataset recently published by the ImmGen consortium, we construct associations between chromatin accessibility and hematopoietic cell types using a novel co-clustering approach that accounts for the structure of the hematopoietic, differentiation tree. Under a model in which all loci and cell types within a co-cluster have a shared accessibility state, we show that roughly 80\% of cell type associated accessibility variation can be captured through 12 cell type clusters and 20 genomic locus clusters. Using publicly available ChIPseq datasets, we show that our clustering reflects transcription factor binding patterns with implications for regulation across cell types. Our results provide a framework for analysis of chromatin state variation across cell types related by a tree or network.

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 SAT-298 Integrative Single-Cell Transcriptomic and Epigenomic Landscape of Mouse Anterior Pituitary Cell Types

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Frederique Murielle Ruf-Zamojski ◽  
Michel A Zamojski ◽  
German Nudelman ◽  
Yongchao Ge ◽  
Natalia Mendelev ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Anterior Pituitary ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Pituitary Cell ◽  
Integrated Analysis ◽  
Pituitary Cells ◽  
Rna Seq ◽  
Cell Type

Abstract The pituitary gland is a critical regulator of the neuroendocrine system. To further our understanding of the classification, cellular heterogeneity, and regulatory landscape of pituitary cell types, we performed and computationally integrated single cell (SC)/single nucleus (SN) resolution experiments capturing RNA expression, chromatin accessibility, and DNA methylation state from mouse dissociated whole pituitaries. Both SC and SN transcriptome analysis and promoter accessibility identified the five classical hormone-producing cell types (somatotropes, gonadotropes (GT), lactotropes, thyrotropes, and corticotropes). GT cells distinctively expressed transcripts for Cga, Fshb, Lhb, Nr5a1, and Gnrhr in SC RNA-seq and SN RNA-seq. This was matched in SN ATAC-seq with GTs specifically showing open chromatin at the promoter regions for the same genes. Similarly, the other classically defined anterior pituitary cells displayed transcript expression and chromatin accessibility patterns characteristic of their own cell type. This integrated analysis identified additional cell-types, such as a stem cell cluster expressing transcripts for Sox2, Sox9, Mia, and Rbpms, and a broadly accessible chromatin state. In addition, we performed bulk ATAC-seq in the LβT2b gonadotrope-like cell line. While the FSHB promoter region was closed in the cell line, we identified a region upstream of Fshb that became accessible by the synergistic actions of GnRH and activin A, and that corresponded to a conserved region identified by a polycystic ovary syndrome (PCOS) single nucleotide polymorphism (SNP). Although this locus appears closed in deep sequencing bulk ATAC-seq of dissociated mouse pituitary cells, SN ATAC-seq of the same preparation showed that this site was specifically open in mouse GT, but closed in 14 other pituitary cell type clusters. This discrepancy highlighted the detection limit of a bulk ATAC-seq experiment in a subpopulation, as GT represented ~5% of this dissociated anterior pituitary sample. These results identified this locus as a candidate for explaining the dual dependence of Fshb expression on GnRH and activin/TGFβ signaling, and potential new evidence for upstream regulation of Fshb. The pituitary epigenetic landscape provides a resource for improved cell type identification and for the investigation of the regulatory mechanisms driving cell-to-cell heterogeneity. Additional authors not listed due to abstract submission restrictions: N. Seenarine, M. Amper, N. Jain (ISMMS).

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

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.

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 Single-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS Loci

2021 ◽  
Author(s):  
Tiit Örd ◽  
Kadri Õunap ◽  
Lindsey Stolze ◽  
Rédouane Aherrahrou ◽  
Valtteri Nurminen ◽  
...  
Keyword(s):  
Smooth Muscle ◽  
Smooth Muscle Cells ◽  
Muscle Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Atherosclerotic Lesions ◽  
Genome Wide ◽  
Single Nucleus

Rationale: Genome-wide association studies (GWAS) have identified hundreds of loci associated with coronary artery disease (CAD). Many of these loci are enriched in cis-regulatory elements (CREs) but not linked to cardiometabolic risk factors nor to candidate causal genes, complicating their functional interpretation. Objective: Single nucleus chromatin accessibility profiling of the human atherosclerotic lesions was used to investigate cell type-specific patterns of CREs, to understand transcription factors establishing cell identity and to interpret CAD-relevant, non-coding genetic variation. Methods and Results: We used single nucleus ATAC-seq to generate DNA accessibility maps in > 7,000 cells derived from human atherosclerotic lesions. We identified five major lesional cell types including endothelial cells, smooth muscle cells, monocyte/macrophages, NK/T-cells and B-cells and further investigated subtype characteristics of macrophages and smooth muscle cells transitioning into fibromyocytes. We demonstrated that CAD associated genetic variants are particularly enriched in endothelial and smooth muscle cell-specific open chromatin. Using single cell co-accessibility and cis-eQTL information, we prioritized putative target genes and candidate regulatory elements for ~30% of all known CAD loci. Finally, we performed genome-wide experimental fine-mapping of the CAD GWAS variants using epigenetic QTL analysis in primary human aortic endothelial cells and STARR-Seq massively parallel reporter assay in smooth muscle cells. This analysis identified potential causal SNP(s) and the associated target gene for over 30 CAD loci. We present several examples where the chromatin accessibility and gene expression could be assigned to one cell type predicting the cell type of action for CAD loci. Conclusions: These findings highlight the potential of applying snATAC-seq to human tissues in revealing relative contributions of distinct cell types to diseases and in identifying genes likely to be influenced by non-coding GWAS variants.

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

scholarly journals Proximity labeling of protein complexes and cell-type-specific organellar proteomes in Arabidopsis enabled by TurboID

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Andrea Mair ◽  
Shou-Ling Xu ◽  
Tess C Branon ◽  
Alice Y Ting ◽  
Dominique C Bergmann
Keyword(s):  
Transcription Factor ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Cell Types ◽  
Specific Protein ◽  
Cell Type ◽  
Subcellular Compartment ◽  
Cellular Processes ◽  
Highly Active

Defining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurbo), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurbo in Arabidopsis and Nicotiana benthamiana and versatile vectors enable customization by plant researchers.

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