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.

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.

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 Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

2021 ◽  
Author(s):  
Kun Wang ◽  
Sushant Patkar ◽  
Joo Sang Lee ◽  
E. Michael Gertz ◽  
Welles Robinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Mixture ◽  
Cell Types ◽  
Cell Interactions ◽  
Specific Gene ◽  
Cell Type ◽  
Patient Response ◽  
Receptor Interactions ◽  
Cell Type Specific ◽  
Cell Cell

AbstractThe tumor microenvironment (TME) is a complex mixture of cell-types that interact with each other to affect tumor growth and clinical outcomes. To accelerate the discovery of such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a deconvolution tool inferring cell-type-specific gene expression in each sample from bulk expression measurements, and LIRICS (LIgand Receptor Interactions between Cell Subsets), a supporting pipeline that analyzes the deconvolved gene expression from CODEFACS to identify clinically relevant ligand-receptor interactions between cell-types. Using 15 benchmark test datasets, we first demonstrate that CODEFACS substantially improves the ability to reconstruct cell-type-specific transcriptomes from individual bulk samples, compared to the state-of-the-art method, CIBERSORTx. Second, analyzing the TCGA, we uncover cell-cell interactions that specifically occur in TME of mismatch-repair-deficient tumors and are associated with their high response rates to anti-PD1 treatment. These results point to specific T-cell co-stimulating interactions that enhance immunotherapy responses in tumors independently of their mutation burden levels. Finally, using machine learning, we identify a subset of cell-cell interactions that predict patient response to anti-PD1 therapy in melanoma better than recently published bulk transcriptomics-based signatures. CODEFACS offers a way to study bulk cancer and normal transcriptomes at a cell type-specific resolution, complementing single-cell transcriptomics.

scholarly journals A Comprehensive Tool Set for Inducible, Cell Type-Specific Gene Expression in Arabidopsis

2018 ◽  
Author(s):  
Ann-Kathrin Schürholz ◽  
Vadir Lopez-Salmeron ◽  
Zhenni Li ◽  
Joachim Forner ◽  
Christian Wenzl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rapid Assessment ◽  
Cell Types ◽  
Specific Gene ◽  
High Temporal Resolution ◽  
Cell Type ◽  
Compensatory Mechanisms ◽  
Modern Biology ◽  
Control Of Gene Expression ◽  
Cell Type Specific

AbstractUnderstanding the context-specific role of gene function is a key objective of modern biology. To this end, we generated a resource for inducible cell-type specific trans-activation based on the well-established combination of the chimeric GR-LhG4 transcription factor and the synthetic pOp promoter. Harnessing the flexibility of the GreenGate cloning system, we produced a comprehensive set of GR-LhG4 driver lines targeting most tissues in the Arabidopsis shoot and root with a strong focus on the indeterminate meristems. We show that, when combined with effectors under control of the pOp promoter, tight temporal and spatial control of gene expression is achieved. In particular, inducible expression in F1 plants obtained from crosses of driver and effector lines allows rapid assessment of the cell type-specific impact of an effector with high temporal resolution. Thus, our comprehensive and flexible toolbox is suited to overcome the limitations of ubiquitous genetic approaches, the outputs of which are often difficult to interpret due to widespread existence of compensatory mechanisms and the integration of diverging effects in different cell types.One sentence summary: A set of lines enabling spatio-temporal control of gene expression in Arabidopsis.

scholarly journals Integrative inference of brain cell similarities and differences from single-cell genomics

2018 ◽  
Author(s):  
Joshua Welch ◽  
Velina Kozareva ◽  
Ashley Ferreira ◽  
Charles Vanderburg ◽  
Carly Martin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Human Subjects ◽  
Cell Types ◽  
Joint Analysis ◽  
Specific Gene ◽  
Cell Type ◽  
Type Definition ◽  
Cell Type Specific

SummaryDefining cell types requires integrating diverse measurements from multiple experiments and biological contexts. Recent technological developments in single-cell analysis have enabled high-throughput profiling of gene expression, epigenetic regulation, and spatial relationships amongst cells in complex tissues, but computational approaches that deliver a sensitive and specific joint analysis of these datasets are lacking. We developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity, allowing flexible modeling of highly heterogeneous single-cell datasets. We demonstrated its broad utility by applying it to four diverse and challenging analyses of human and mouse brain cells. First, we defined both cell-type-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis, an anatomically complex brain region that plays important roles in sex-specific behaviors. Second, we analyzed gene expression in the substantia nigra of seven postmortem human subjects, comparing cell states in specific donors, and relating cell types to those in the mouse. Third, we jointly leveraged in situ gene expression and scRNA-seq data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical scRNA-seq profiles with single-cell DNA methylation signatures, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using the LIGER algorithm promise to accelerate single-cell investigations of cell-type definition, gene regulation, and disease states.

scholarly journals In search of a core cellular network in single cell transcriptome data

2021 ◽  
Author(s):  
Ming Yang ◽  
Benjamin R. Harrison ◽  
Daniel E.L. Promislow
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cellular Network ◽  
Cell Types ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractBackgroundAlong with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network.ResultsIn this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures.ConclusionsOverall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

scholarly journals An atlas of lamina-associated chromatin across thirteen human cell types reveals cell-type-specific and multiple subtypes of peripheral heterochromatin

2020 ◽  
Author(s):  
Kathleen C. Keough ◽  
Parisha P. Shah ◽  
Nadeera M. Wickramasinghe ◽  
Carolyn E. Dundes ◽  
Angela Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cell ◽  
Regulation Of Gene Expression ◽  
Nuclear Periphery ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cellular Identity ◽  
Peripheral Heterochromatin

AbstractThree-dimensional genome organization, specifically organization of heterochromatin at the nuclear periphery, coordinates cell type-specific gene regulation. While defining various histone modifications and chromatin-associated proteins in multiple cell types has provided important insights into epigenetic regulation of gene expression and cellular identity, peripheral heterochromatin has not been mapped comprehensively and relatively few examples have emerged detailing the role of peripheral heterochromatin in cellular identity, cell fate choices, and/or organogenesis. In this study, we define nuclear peripheral heterochromatin organization signatures based on association with LAMIN B1 and/or dimethylation of lysine 9 on H3 (H3K9me2) across thirteen human cell types encompassing pluripotent stem cells, intermediate progenitors and differentiated cells from all three germ layers. Genomic analyses across this atlas reveal that lamin-associated chromatin is organized into at least two different compartments, defined by differences in genome coverage, chromatin accessibility, residence of transposable elements, replication timing domains, and gene complements. Our datasets reveal that only a small subset of lamin-associated chromatin domains are cell type invariant, underscoring the complexity of peripheral heterochromatin organization. Moreover, by integrating peripheral chromatin maps with transcriptional data, we find evidence of cooperative shifts between chromatin structure and gene expression associated with each cell type. This atlas of peripheral chromatin provides the largest resource to date for peripheral chromatin organization and a deeper appreciation for how this organization may impact the establishment and maintenance of cellular identity.

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