scholarly journals Postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary gland

2022 ◽  
Author(s):  
Huayun Hou ◽  
Cadia Chan ◽  
Kyoko E Yuki ◽  
Dustin Sokolowski ◽  
Anna Roy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Pituitary Gland ◽  
Developmental Trajectory ◽  
Small Rna Sequencing ◽  
Sexually Dimorphic ◽  
Cell Type ◽  
Type Composition ◽  
Mouse Pituitary ◽  
Sex Biases

The pituitary gland controls sexually dimorphic processes such as growth, pubertal onset, and lactation. However, the mechanisms underlying sex biases in pituitary gene regulation are not fully understood. To capture pituitary gene regulation dynamics during postnatal development, we ascertained gene and miRNA expression across five postnatal days that span the pubertal transition in mice. Using three prime untranslated region and small RNA sequencing, we observed over 900 instances of sex-biased gene expression, including 18 genes that were putative targets of 5 sex-biased miRNAs. In addition, by combining bulk RNA-seq with scRNA-seq pituitary data, we obtained evidence that cell-type proportion sex differences exist prior to puberty and contribute substantially to the observed sex-biased gene expression post-puberty. This work provides a resource for postnatal mouse pituitary gene regulation and highlights the importance of sex-biases in both cell-type composition and gene regulation when understanding the sexually dimorphic processes regulated by the pituitary gland.

scholarly journals Seasonal changes in gene expression represent cell-type composition in whole blood

2014 ◽  
Vol 23 (10) ◽  
pp. 2721-2728 ◽  
Author(s):  
S. De Jong ◽  
M. Neeleman ◽  
J. J. Luykx ◽  
M. J. Ten Berg ◽  
E. Strengman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Whole Blood ◽  
Seasonal Changes ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition

scholarly journals Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue

2021 ◽  
Vol 8 ◽  
Author(s):  
Marianthi Kalafati ◽  
Michael Lenz ◽  
Gökhan Ertaylan ◽  
Ilja C. W. Arts ◽  
Chris T. Evelo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Subcutaneous Adipose Tissue ◽  
Tissue Cell ◽  
Cell Type ◽  
Expression Of Genes ◽  
Cell Type Composition ◽  
Type Composition ◽  
Adipose Tissue Cell ◽  
Subcutaneous Adipose

Background: Macrophages play an important role in regulating adipose tissue function, while their frequencies in adipose tissue vary between individuals. Adipose tissue infiltration by high frequencies of macrophages has been linked to changes in adipokine levels and low-grade inflammation, frequently associated with the progression of obesity. The objective of this project was to assess the contribution of relative macrophage frequencies to the overall subcutaneous adipose tissue gene expression using publicly available datasets.Methods: Seven publicly available microarray gene expression datasets from human subcutaneous adipose tissue biopsies (n = 519) were used together with TissueDecoder to determine the adipose tissue cell-type composition of each sample. We divided the subjects in four groups based on their relative macrophage frequencies. Differential gene expression analysis between the high and low relative macrophage frequencies groups was performed, adjusting for sex and study. Finally, biological processes were identified using pathway enrichment and network analysis.Results: We observed lower frequencies of adipocytes and higher frequencies of adipose stem cells in individuals characterized by high macrophage frequencies. We additionally studied whether, within subcutaneous adipose tissue, interindividual differences in the relative frequencies of macrophages were reflected in transcriptional differences in metabolic and inflammatory pathways. Adipose tissue of individuals with high macrophage frequencies had a higher expression of genes involved in complement activation, chemotaxis, focal adhesion, and oxidative stress. Similarly, we observed a lower expression of genes involved in lipid metabolism, fatty acid synthesis, and oxidation and mitochondrial respiration.Conclusion: We present an approach that combines publicly available subcutaneous adipose tissue gene expression datasets with a deconvolution algorithm to calculate subcutaneous adipose tissue cell-type composition. The results showed the expected increased inflammation gene expression profile accompanied by decreased gene expression in pathways related to lipid metabolism and mitochondrial respiration in subcutaneous adipose tissue in individuals characterized by high macrophage frequencies. This approach demonstrates the hidden strength of reusing publicly available data to gain cell-type-specific insights into adipose tissue function.

scholarly journals The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Brian B Nadel ◽  
David Lopez ◽  
Dennis J Montoya ◽  
Feiyang Ma ◽  
Hannah Waddel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Data ◽  
Expression Data ◽  
Cell Type ◽  
Tissue Samples ◽  
Interactive Tool ◽  
Cell Type Composition ◽  
Type Composition ◽  
Human And Mouse

Abstract Background The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Computational approaches that use expression data to infer cell type abundance offer an alternative solution. While these methods have gained popularity, most fail to produce accurate predictions for the full range of platforms currently used by researchers or for the wide variety of tissue types often studied. Results We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a flexible tool that utilizes gene expression data to accurately predict cell type abundances. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include multiple platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from 8 sources spanning a broad range of stromal and hematopoietic types in both human and mouse. GEDIT also accepts user-submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data are available. Conclusions GEDIT is a powerful method for evaluating the cell type composition of tissue samples and provides excellent accuracy and versatility compared to similar tools. The reference database provided here also allows users to obtain estimates for a wide variety of tissue samples without having to provide their own data.

Sexually dimorphic gene expression in the hypothalamus, pituitary gland, and cortex

Genomics ◽  
2005 ◽  
Vol 85 (6) ◽  
pp. 679-687 ◽  
Author(s):  
Yuichiro Nishida ◽  
Mayumi Yoshioka ◽  
Jonny St-Amand
Keyword(s):  
Gene Expression ◽  
Pituitary Gland ◽  
Sexually Dimorphic ◽  
Dimorphic Gene Expression

scholarly journals Gene expression variability in human and chimpanzee populations share common determinants

2020 ◽  
Author(s):  
Benjamin J. Fair ◽  
Lauren E. Blake ◽  
Abhishek Sarkar ◽  
Bryan J. Pavlovic ◽  
Claudia Cuevas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Comparative Analysis ◽  
Individual Variation ◽  
Disease Susceptibility ◽  
Expression Data ◽  
Cell Type ◽  
Orthologous Genes ◽  
Expression Variability ◽  
Gene Regulatory

AbstractInter-individual variation in gene expression has been shown to be heritable and it is often associated with differences in disease susceptibility between individuals. Many studies focused on mapping associations between genetic and gene regulatory variation, yet much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative analysis of gene expression variability and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples. We found that expression variability in both species is often determined by non-genetic sources, such as cell-type heterogeneity. However, we also provide evidence that inter-individual variation in gene regulation can be genetically controlled, and that the degree of such variability is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of orthologous genes associated with eQTLs in both species. We conclude that gene expression variability in humans and chimpanzees often evolves under similar evolutionary pressures.

scholarly journals The Role of Scale in the Estimation of Cell-type Proportions

2019 ◽  
Author(s):  
Gregory J. Hunt ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Hybrid Approach ◽  
Expression Data ◽  
Cell Type ◽  
Statistical Efficiency ◽  
Gene Expressions ◽  
New Approach ◽  
Cell Type Composition ◽  
Type Composition

ABSTRACTComplex tissues are composed of a large number of different types of cells, each involved in a multitude of biological processes. Consequently, an important component to understanding such processes is understanding the cell-type composition of the tissues. Estimating cell type composition using high-throughput gene expression data is known as cell-type deconvolution. In this paper, we first summarize the extensive deconvolution literature by identifying a common regression-like approach to deconvolution. We call this approach the Unified Deconvolution-as-Regression (UDAR) framework. While methods that fall under this framework all use a similar model, they fit using data on different scales. Two popular scales for gene expression data are logarithmic and linear. Unfortunately, each of these scales has problems in the UDAR framework. Using log-scale gene expressions proposes a biologically implausible model and using linear-scale gene expressions will lead to statistically inefficient estimators. To overcome these problems, we propose a new approach for cell-type deconvolution that works on a hybrid of the two scales. This new approach is biologically plausible and improves statistical efficiency. We compare the hybrid approach to other methods on simulations as well as a collection of eleven real benchmark datasets. Here, we find the hybrid approach to be accurate and robust.deconvolution, gene expression, microarray, RNA-seq

scholarly journals MON-725 Transcriptome Profiling in Postnatal Pituitary Gland Identifies Cell Type-Driven Sex-Specific Changes

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Huayun Hou ◽  
Cadia Chan ◽  
Liis Uusküla-Reimand ◽  
Kyoko E Yuki ◽  
Dustin Sokolowski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Pituitary Gland ◽  
Single Cell ◽  
Postnatal Development ◽  
Mirna Expression ◽  
Expression Patterns ◽  
Cellular Composition ◽  
Mouse Pituitary ◽  
Pubertal Transition

Abstract The pituitary gland is integral to the regulation of growth, metabolism, puberty, reproduction, and stress responses. Previously, we found that many genes associated with age-at-menarche in genome-wide association studies (GWAS) displayed increasingly sex-biased expression across the pubertal transition in the mouse pituitary. However, whether this trend exists beyond puberty-related genes was not known. In addition, the regulatory mechanisms underlying these gene expression changes remained to be explored. To answer these questions, we profiled the transcriptome, including microRNAs, of mouse pituitary in both sexes across pubertal transition in an unbiased manner and leveraged a recently published pituitary single cell transcriptome to explore cellular composition changes. We found that the most dynamic temporal changes in both mRNA and miRNA expression occur prior to puberty, underscoring a role for regulation of early pituitary postnatal development. We also observed ~900 genes displaying sex-biased expression patterns, arising during early development and becoming increasingly biased across puberty, including known sex-biased genes such as Fshb and Lhb. However, sex differences in miRNA expression are less pronounced, only 13 miRNAs were found to be sex-biased, suggesting lesser contribution of miRNAs to sex-biased gene expression relative to other forms of regulation. To assess whether pituitary cellular composition could underlie changes in gene expression across pubertal transition, we performed single cell deconvolution of our bulk pituitary gland gene expression. Interestingly, we found that sex differences in cell proportions were estimated to emerge across puberty: a greater proportion of lactotropes was found among females, and greater proportions of gonadotropes and somatotropes were found among males. We observed sex-biased expression patterns of marker genes for these cell types, including Prl, Fshb, and Gh. This finding suggests that cell proportion differences between sexes likely contribute to whole pituitary transcriptome changes we observed, however, to what extent remains to be studied. Together our study indicates that miRNAs play a substantial role in regulation of pituitary postnatal development but that differences in cellular composition may contribute more robustly to sex-biased gene expression.

scholarly journals Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition

2019 ◽  
Author(s):  
Gonzalo S. Nido ◽  
Fiona Dick ◽  
Lilah Toker ◽  
Kjell Petersen ◽  
Guido Alves ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Ribosomal Rna ◽  
Post Mortem ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Gene Expression Signatures ◽  
Differential Gene ◽  
Rna Depletion

AbstractBackgroundThe etiology of Parkinson’s disease (PD) is largely unknown. Genome-wide transcriptomic studies in bulk brain tissue have identified several molecular signatures associated with the disease. While these studies have the potential to shed light into the pathogenesis of PD, they are also limited by two major confounders: RNA post mortem degradation and heterogeneous cell type composition of bulk tissue samples. We performed RNA sequencing following ribosomal RNA depletion in the prefrontal cortex of 49 individuals from two independent case-control cohorts. Using cell-type specific markers, we estimated the cell-type composition for each sample and included this in our analysis models to compensate for the variation in cell-type proportions.ResultsRibosomal RNA depletion results in substantially more even transcript coverage, compared to poly(A) capture, in post mortem tissue. Moreover, we show that cell-type composition is a major confounder of differential gene expression analysis in the PD brain. Correcting for cell-type proportions attenuates numerous transcriptomic signatures that have been previously associated with PD, including vesicle trafficking, synaptic transmission, immune and mitochondrial function. Conversely, pathways related to endoplasmic reticulum, lipid oxidation and unfolded protein response are strengthened and surface as the top differential gene expression signatures in the PD prefrontal cortex.ConclusionsDifferential gene expression signatures in PD bulk brain tissue are significantly confounded by underlying differences in cell-type composition. Modeling cell-type heterogeneity is crucial in order to unveil transcriptomic signatures that represent regulatory changes in the PD brain and are, therefore, more likely to be associated with underlying disease mechanisms.

scholarly journals Single Cell Transcriptional Signatures of the Human Placenta in Term and Preterm Parturition

2019 ◽  
Author(s):  
Roger Pique-Regi ◽  
Roberto Romero ◽  
Adi L.Tarca ◽  
Edward D. Sendler ◽  
Yi Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Human Placenta ◽  
Preterm Labor ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Activated T Cell

AbstractMore than 135 million births occur each year; yet, the molecular underpinnings of human parturition in gestational tissues, and in particular the placenta, are still poorly understood. The placenta is a complex heterogeneous organ including cells of both maternal and fetal origin, and insults that disrupt the maternal-fetal dialogue could result in adverse pregnancy outcomes such as preterm birth. There is limited knowledge of the cell type composition and transcriptional activity of the placenta and its compartments during physiologic and pathologic parturition. To fill this knowledge gap, we used scRNA-seq to profile the placental villous tree, basal plate, and chorioamniotic membranes of women with or without labor at term and those with preterm labor. Significant differences in cell type composition and transcriptional profiles were found among placental compartments and across study groups. For the first time, two cell types were identified: 1) lymphatic endothelial decidual cells in the chorioamniotic membranes, and 2) non-proliferative interstitial cytotrophoblasts in the placental villi. Maternal macrophages from the chorioamniotic membranes displayed the largest differences in gene expression (e.g. NFKB1) in both processes of labor; yet, specific gene expression changes were also detected in preterm labor. Importantly, several placental scRNA-seq transcriptional signatures were modulated with advancing gestation in the maternal circulation, and specific immune cell type signatures were increased with labor at term (NK-cell and activated T-cell) and with preterm labor (macrophage, monocyte, and activated T-cell). Herein, we provide a catalogue of cell types and transcriptional profiles in the human placenta, shedding light on the molecular underpinnings and non-invasive prediction of the physiologic and pathologic parturition.One sentence summaryThe common molecular pathway of parturition for both term and preterm spontaneous labor is characterized using single cell gene expression analysis of the human placenta.

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