scholarly journals Single nuclei chromatin profiles of ventral midbrain reveal cell identity transcription factors and cell type-specific gene regulatory variation

2020 ◽  
Author(s):  
Yujuan Gui ◽  
Kamil Grzyb ◽  
Mélanie H. Thomas ◽  
Jochen Ohnmacht ◽  
Pierre Garcia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Types ◽  
Mouse Strains ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Identity ◽  
Regulatory Variation ◽  
Ventral Midbrain ◽  
Cell Type Specific

SUMMARYCell types in ventral midbrain are involved in diseases with variable genetic susceptibility such as Parkinson’s disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression. We report 20 658 single nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons. Our dataset provides an extensive resource to study gene regulation in mesencephalon.

scholarly journals Single-nuclei chromatin profiling of ventral midbrain reveals cell identity transcription factors and cell-type-specific gene regulatory variation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yujuan Gui ◽  
Kamil Grzyb ◽  
Mélanie H. Thomas ◽  
Jochen Ohnmacht ◽  
Pierre Garcia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Types ◽  
Mouse Strains ◽  
Cell Type ◽  
Data Set ◽  
Cell Identity ◽  
Regulatory Variation ◽  
Ventral Midbrain ◽  
Cell Type Specific

Abstract Background Cell types in ventral midbrain are involved in diseases with variable genetic susceptibility, such as Parkinson’s disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression in a cell-type-specific manner depending on the chromatin structure and accessibility. Results We report 20,658 single-nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell-type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell-type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons. Conclusions Our data set provides an extensive resource to study gene regulation in mesencephalon and provides insights into control of cell identity in the midbrain and identifies cell-type-specific regulatory variation possibly underlying phenotypic and behavioural differences between mouse strains.

scholarly journals Whole-body integration of gene expression and single-cell morphology

2020 ◽  
Author(s):  
Hernando M. Vergara ◽  
Constantin Pape ◽  
Kimberly I. Meechan ◽  
Valentyna Zinchenko ◽  
Christel Genoud ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
High Resolution Electron Microscopy ◽  
Cell Types ◽  
Whole Body ◽  
Specific Gene ◽  
Entire Body ◽  
Cell Type ◽  
Cellular Expression ◽  
Cell Type Specific

SummaryAnimal bodies are composed of hundreds of cell types that differ in location, morphology, cytoarchitecture, and physiology. This is reflected by cell type-specific transcription factors and downstream effector genes implementing functional specialisation. Here, we establish and explore the link between cell type-specific gene expression and subcellular morphology for the entire body of the marine annelid Platynereis dumerilii. For this, we registered a whole-body cellular expression atlas to a high-resolution electron microscopy dataset, automatically segmented all cell somata and nuclei, and clustered the cells according to gene expression or morphological parameters. We show that collective gene expression most efficiently identifies spatially coherent groups of cells that match anatomical boundaries, which indicates that combinations of regionally expressed transcription factors specify tissue identity. We provide an integrated browser as a Fiji plugin to readily explore, analyse and visualise multimodal datasets with remote on-demand access to all available datasets.

scholarly journals Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ana J. Chucair-Elliott ◽  
Sarah R. Ocañas ◽  
David R. Stanford ◽  
Victor A. Ansere ◽  
Kyla B. Buettner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Tissue Level ◽  
Cell Phenotype ◽  
Specific Gene ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific central nervous system cell types. Analysis of gene expression and DNA modifications in astrocytes or microglia from the same animal demonstrates differential usage of DNA methylation and hydroxymethylation in CpG and non-CpG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any brain cell type for which a cell type-specific cre is available.

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 Genetic, parental and lifestyle factors influence telomere length

2021 ◽  
Author(s):  
Sergio Andreu-Sanchez ◽  
Geraldine Aubert ◽  
Aida Ripoll-Cladellas ◽  
Sandra Henkelman ◽  
Daria V. Zhernakova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Average Length ◽  
Cell Types ◽  
Paternal Age ◽  
Specific Gene ◽  
Cell Type ◽  
Sequencing Data ◽  
Nonsense Mediated Decay ◽  
Cell Type Specific ◽  
Telomere Biology

The average length of telomere repeats (TL) declines with age and is considered to be a marker of biological ageing. Here, we measured TL in six blood cell types from 1,046 individuals using the clinically validated Flow-FISH method. We identified remarkable cell-type-specific variations in TL. Host genetics, environmental, parental and intrinsic factors such as sex, parental age, and smoking are associated to variations in TL. By analysing the genome-wide methylation patterns, we identified that the association of maternal, but not paternal, age to TL is mediated by epigenetics. Coupling these measurements to single-cell RNA-sequencing data for 62 participants revealed differential gene expression in T-cells. Genes negatively associated with TL were enriched for pathways related to translation and nonsense-mediated decay. Altogether, this study addresses cell-type-specific differences in telomere biology and its relation to cell-type-specific gene expression and highlights how perinatal factors play a role in determining TL, on top of genetics and lifestyle.

Cell type-specific gene expression underpins remodelling of cell wall pectin in exocarp and cortex during apple fruit development

2019 ◽  
Vol 70 (21) ◽  
pp. 6085-6099
Author(s):  
Patrick P Collins ◽  
Erin M O’donoghue ◽  
Ria Rebstock ◽  
Heather R Tiffin ◽  
Paul W Sutherland ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Wall ◽  
Fruit Development ◽  
Cell Types ◽  
Apple Fruit ◽  
Epidermal Cells ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific

Young apple epidermal cells process cell wall pectic arabinan and galactan side chains different from other cell types, resulting in debranched linear arabinans and the absence of galactans.

scholarly journals CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kai Kang ◽  
Caizhi Huang ◽  
Yuanyuan Li ◽  
David M. Umbach ◽  
Leping Li
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Biological Tissues ◽  
Specific Gene ◽  
Specific Cell ◽  
Specific Information ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific

Abstract Background Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. Result We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Conclusions The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell–cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

scholarly journals Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

2019 ◽  
Author(s):  
Ana J. Chucair-Elliott ◽  
Sarah R. Ocañas ◽  
David R. Stanford ◽  
Victor A. Ansere ◽  
Kyla B. Buettner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Tissue Level ◽  
Cell Phenotype ◽  
Specific Gene ◽  
Cell Type ◽  
Current Cell ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific CNS cell types. Paired analysis of the transcriptome and DNA modifications in astrocytes and microglia demonstrates differential usage of DNA methylation and hydroxymethylation in CG and non-CG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any CNS cell type for which a cell type-specific cre is available.

scholarly journals Structural Changes in Transcriptional Regulatory Networks for Cell-type-specific Gene Expression During Hematopoiesis

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Structural Changes ◽  
Specific Gene ◽  
Cell Type ◽  
Hematopoietic Stem ◽  
Specific Gene Expression ◽  
Transcriptional Regulatory ◽  
Cell Type Specific

Hematopoiesis is an extensively studied model system for cell differentiation. Cell-type-specific gene expression patterns are observed during hematopoiesis. Gene expression is governed by regulatory networks composed of cell-type-specific transcription factors. Resolving the transcriptional regulatory network for cell-type-specific gene expression provides a promising means of understanding the mechanisms underlying cell fate decisions. In this study, transcriptional regulatory networks in hematopoietic stem and progenitor cells were predicted based on gene expression profiles and distributions of transcription factor binding motifs in the promoter regions of cell-type-specific transcription factors. In particular, structural changes that occur when pluripotent stem cells progress to lineage-committed progenitors were evaluated. Marked changes in the regulatory circuit of transcription throughout the differentiation process could be elucidated by network analysis. Modular structures were a frequently described feature of biological networks observed in estimated networks. Within a module, most transcription factors were found to be regulated by a small number of regulators acting as downstream targets. Certain regulators within these modules coincide with known key regulators of hematopoietic cell differentiation. In addition to the modular structure, a twolayered structure was clearly observed in progenitor regulatory networks. Transcription factors could be distinctly divided into regulators within the regulatory layer and into targets in the output layer according to their degree of distribution. The restriction of mutual regulation between transcription factors was remarkable in that it allowed for alterations in network structures between hematopoietic stem cells and progenitors. Thus, using this approach, the relationships among transcription factors could be revealed by a reduction in mutual regulation to form a modular structure within the regulatory network

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