scholarly journals Untangling the effects of cellular composition on coexpression analysis

2019 ◽  
Author(s):  
Marjan Farahbod ◽  
Paul Pavlidis
Keyword(s):  
Regulatory Networks ◽  
Large Fraction ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Composition ◽  
Cell Type ◽  
Coexpression Analysis ◽  
The Impact ◽  
Bulk Tissue ◽  
Rna Levels

AbstractBackgroundCoexpression analysis is one of the most widely used methods in genomics, with applications to inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies. Most studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis have not been studied in detail. Here we examine this issue for the case of human brain RNA analysis.ResultsWe found that for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels in brain (median R2 = 0.64). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression in the bulk tissue can be attributed to this effect. We further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. Attempt to correct for composition yielded mixed results.ConclusionsThe dominant coexpression signal in brain can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships, and this is likely to be true for other tissues. These results have important implications for the relevance and interpretation of coexpression in many applications.

scholarly journals High-Throughput Sequencing is a Crucial Tool to Investigate the Contribution of Human Endogenous Retroviruses (HERVs) to Human Biology and Development

Viruses ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 633 ◽  
Author(s):  
Maria Paola Pisano ◽  
Nicole Grandi ◽  
Enzo Tramontano
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Large Fraction ◽  
Expression Patterns ◽  
Cell Types ◽  
Endogenous Retroviruses ◽  
Human Endogenous Retroviruses ◽  
Retroviral Infections ◽  
The Impact

Human Endogenous retroviruses (HERVs) are remnants of ancient retroviral infections that represent a large fraction of our genome. Their transcriptional activity is finely regulated in early developmental stages and their expression is modulated in different cell types and tissues. Such activity has an impact on human physiology and pathology that is only partially understood up to date. Novel high-throughput sequencing tools have recently allowed for a great advancement in elucidating the various HERV expression patterns in different tissues as well as the mechanisms controlling their transcription, and overall, have helped in gaining better insights in an all-inclusive understanding of the impact of HERVs in biology of the host.

scholarly journals Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Jong Kyoung Kim ◽  
Aleksandra A. Kolodziejczyk ◽  
Tomislav Ilicic ◽  
Sarah A. Teichmann ◽  
John C. Marioni
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Large Fraction ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Technical Noise ◽  
Allele Specific ◽  
High Level

Abstract Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

scholarly journals Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254194
Author(s):  
Hong-Tae Park ◽  
Woo Bin Park ◽  
Suji Kim ◽  
Jong-Sung Lim ◽  
Gyoungju Nah ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Single Cell ◽  
Mycobacterium Avium ◽  
Expression Patterns ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals Differences in DNA methylation of white blood cell types at birth and in adulthood reflect postnatal immune maturation and influence accuracy of cell type prediction

2018 ◽  
Author(s):  
Meaghan J Jones ◽  
Louie Dinh ◽  
Hamid Reza Razzaghian ◽  
Olivia de Goede ◽  
Julia L MacIsaac ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Immune System ◽  
Blood Cell ◽  
Cord Blood ◽  
White Blood Cell ◽  
Blood Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Specific ◽  
The Impact

AbstractBackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

scholarly journals Identification of Split-GAL4 Drivers and Enhancers That Allow Regional Cell Type Manipulations of the Drosophila melanogaster Intestine

Genetics ◽  
2020 ◽  
Vol 216 (4) ◽  
pp. 891-903
Author(s):  
Ishara S. Ariyapala ◽  
Jessica M. Holsopple ◽  
Ellen M. Popodi ◽  
Dalton G. Hartwick ◽  
Lily Kahsai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Enteroendocrine Cells ◽  
Epithelial Tissue ◽  
Cell Type ◽  
Cell Functions ◽  
Distinct Subset ◽  
Targeted Gene Expression

The Drosophila adult midgut is a model epithelial tissue composed of a few major cell types with distinct regional identities. One of the limitations to its analysis is the lack of tools to manipulate gene expression based on these regional identities. To overcome this obstacle, we applied the intersectional split-GAL4 system to the adult midgut and report 653 driver combinations that label cells by region and cell type. We first identified 424 split-GAL4 drivers with midgut expression from ∼7300 drivers screened, and then evaluated the expression patterns of each of these 424 when paired with three reference drivers that report activity specifically in progenitor cells, enteroendocrine cells, or enterocytes. We also evaluated a subset of the drivers expressed in progenitor cells for expression in enteroblasts using another reference driver. We show that driver combinations can define novel cell populations by identifying a driver that marks a distinct subset of enteroendocrine cells expressing genes usually associated with progenitor cells. The regional cell type patterns associated with the entire set of driver combinations are documented in a freely available website, providing information for the design of thousands of additional driver combinations to experimentally manipulate small subsets of intestinal cells. In addition, we show that intestinal enhancers identified with the split-GAL4 system can confer equivalent expression patterns on other transgenic reporters. Altogether, the resource reported here will enable more precisely targeted gene expression for studying intestinal processes, epithelial cell functions, and diseases affecting self-renewing tissues.

scholarly journals Retroviral Transcriptional Regulation and Embryonic Stem Cells: War and Peace

2014 ◽  
Vol 35 (5) ◽  
pp. 770-777 ◽  
Author(s):  
Sharon Schlesinger ◽  
Stephen P. Goff
Keyword(s):  
Dna Sequences ◽  
Regulatory Networks ◽  
Es Cells ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Cell Types ◽  
Endogenous Retroviruses ◽  
Regulatory Sequences ◽  
Cellular Genes ◽  
Wide Range

Retroviruses have evolved complex transcriptional enhancers and promoters that allow their replication in a wide range of tissue and cell types. Embryonic stem (ES) cells, however, characteristically suppress transcription of proviruses formed after infection by exogenous retroviruses and also of most members of the vast array of endogenous retroviruses in the genome. These cells have unusual profiles of transcribed genes and are poised to make rapid changes in those profiles upon induction of differentiation. Many of the transcription factors in ES cells control both host and retroviral genes coordinately, such that retroviral expression patterns can serve as markers of ES cell pluripotency. This overlap is not coincidental; retrovirus-derived regulatory sequences are often used to control cellular genes important for pluripotency. These sequences specify the temporal control and perhaps “noisy” control of cellular genes that direct proper cell gene expression in primitive cells and their differentiating progeny. The evidence suggests that the viral elements have been domesticated for host needs, reflecting the wide-ranging exploitation of any and all available DNA sequences in assembling regulatory networks.

scholarly journals Cell Specific Dopamine Modulation of the Transient Potassium Current in the Pyloric Network by the Canonical D1 Receptor Signal Transduction Cascade

2010 ◽  
Vol 104 (2) ◽  
pp. 873-884 ◽  
Author(s):  
Hongmei Zhang ◽  
Edmund W. Rodgers ◽  
Wulf-Dieter C. Krenz ◽  
Merry C. Clark ◽  
Deborah J. Baro
Keyword(s):  
Potassium Current ◽  
Expression System ◽  
Expression Patterns ◽  
Motor Pattern ◽  
Cell Types ◽  
Receptor Expression ◽  
Intracellular Camp ◽  
Cell Type ◽  
Pyloric Network ◽  
Cell Type Specific

Dopamine (DA) modifies the motor pattern generated by the pyloric network in the stomatogastric ganglion (STG) of the spiny lobster, Panulirus interruptus , by directly acting on each of the circuit neurons. The 14 pyloric neurons fall into six cell types, and DA actions are cell type specific. The transient potassium current mediated by shal channels ( IA) is a common target of DA modulation in most cell types. DA shifts the voltage dependence of IA in opposing directions in pyloric dilator (PD) versus lateral pyloric (LP) neurons. The mechanism(s) underpinning cell-type specific DA modulation of IA is unknown. DA receptors (DARs) can be classified as type 1 (D1R) or type 2 (D2R). D1Rs and D2Rs are known to increase and decrease intracellular cAMP concentrations, respectively. We hypothesized that the opposing DA effects on PD and LP IA were due to differences in DAR expression patterns. In the present study, we found that LP expressed somatodendritic D1Rs that were concentrated near synapses but did not express D2Rs. Consistently, DA modulation of LP IA was mediated by a Gs-adenylyl cyclase-cAMP-protein kinase A pathway. Additionally, we defined antagonists for lobster D1Rs (flupenthixol) and D2Rs (metoclopramide) in a heterologous expression system and showed that DA modulation of LP IA was blocked by flupenthixol but not by metoclopramide. We previously showed that PD neurons express D2Rs, but not D1Rs, thus supporting the idea that cell specific effects of DA on IA are due to differences in receptor expression.

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

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