scholarly journals Divergent evolution of developmental timing in the neocortex revealed by marsupial and eutherian transcriptomes

Development ◽  
2022 ◽  
Author(s):  
Peter Kozulin ◽  
Rodrigo Suárez ◽  
Qiong-Yi Zhao ◽  
Annalisa Paolino ◽  
Linda J. Richards ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Early Stage ◽  
Projection Neuron ◽  
Divergent Evolution ◽  
Cortical Projection ◽  
Neocortical Development ◽  
Cell Type Composition ◽  
Type Composition ◽  
Layer Neuron

Only mammals evolved a neocortex, which integrates sensory-motor and cognitive functions. Significant diversifications in the cellular composition and connectivity of the neocortex occurred between the two main Therian groups: marsupials and eutherians. However, the developmental mechanisms underlying these diversifications are largely unknown. Here, we compared the neocortical transcriptomes of Sminthopsis crassicaudata, a mouse-sized marsupial, with those of eutherian mice at two developmentally equivalent timepoints corresponding to deeper and upper layer neuron generation. Enrichment analyses revealed more mature gene networks in marsupials at the early stage, which reverted at the later stage, suggesting a more precocious but protracted neuronal maturation program relative to birth timing of cortical layers. We ranked genes expressed in different species and identified important differences in gene expression rankings between species. For example, genes known to be enriched in upper-layer cortical projection neuron subtypes, such as Cux1, Lhx2 and Satb2, likely relating to corpus callosum emergence in eutherians. These results show molecular heterochronies of neocortical development in Theria, and highlight changes in gene expression and cell type composition that may underlie neocortical evolution and diversification.

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 Basic Helix-Loop-Helix Transcription Factors Cooperate To Specify a Cortical Projection Neuron Identity

2007 ◽  
Vol 28 (5) ◽  
pp. 1456-1469 ◽  
Author(s):  
Pierre Mattar ◽  
Lisa Marie Langevin ◽  
Kathryn Markham ◽  
Natalia Klenin ◽  
Salma Shivji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Projection Neuron ◽  
Luciferase Assay ◽  
Bhlh Gene ◽  
Cortical Projection ◽  
Basic Helix Loop Helix ◽  
Helix Loop Helix ◽  
Bhlh Genes ◽  
Cortical Projection Neuron

ABSTRACT Several transcription factors are essential determinants of a cortical projection neuron identity, but their mode of action (instructive versus permissive) and downstream genetic cascades remain poorly defined. Here, we demonstrate that the proneural basic helix-loop-helix (bHLH) gene Ngn2 instructs a partial cortical identity when misexpressed in ventral telencephalic progenitors, inducing ectopic marker expression in a defined temporal sequence, including early (24 h; Nscl2), intermediate (48 h; BhlhB5), and late (72 h; NeuroD, NeuroD2, Math2, and Tbr1) target genes. Strikingly, cortical gene expression was much more rapidly induced by Ngn2 in the dorsal telencephalon (within 12 to 24 h). We identify the bHLH gene Math3 as a dorsally restricted Ngn2 transcriptional target and cofactor, which synergizes with Ngn2 to accelerate target gene transcription in the cortex. Using a novel in vivo luciferase assay, we show that Ngn2 generates only ∼60% of the transcriptional drive in ventral versus dorsal telencephalic domains, an activity that is augmented by Math3, providing a mechanistic basis for regional differences in Ngn2 function. Cortical bHLH genes thus cooperate to control transcriptional strength, thereby temporally coordinating downstream gene expression.

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.

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 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.

scholarly journals Regenerated crustacean limbs are precise replicas

2021 ◽  
Author(s):  
Alba Almazan ◽  
Cagri Cevrim ◽  
Jacob M Musser ◽  
Michalis Averof ◽  
Mathilde Paris
Keyword(s):  
Gene Expression ◽  
Spatial Distribution ◽  
Sensory Function ◽  
Original Structure ◽  
Cellular Composition ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Gene Expression Dynamics ◽  
Molecular Profiles

Animals can regenerate complex organs, yet this frequently results in imprecise replicas of the original structure. In the crustacean Parhyale, embryonic and regenerating legs differ in gene expression dynamics but produce apparently similar mature structures. We examine the fidelity of Parhyale leg regeneration using complementary approaches to investigate microanatomy, sensory function, cellular composition and cell molecular profiles. We find that regeneration precisely replicates the complex microanatomy and spatial distribution of external sensory organs, and restores their sensory function. Single-nuclei sequencing shows that regenerated and uninjured legs are indistinguishable in terms of cell type composition and transcriptional profiles. This remarkable fidelity highlights the ability of organisms to achieve identical outcomes via distinct processes.

scholarly journals CytoPro: A computational platform for accurate and robust assessment of cell contributions in bulk expression from diverse tissues and conditions

2021 ◽  
Author(s):  
Adir Katz ◽  
Renaud Gaujoux ◽  
Hadas Orly ◽  
Elina Starosvetsky ◽  
Roye Rozov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Biology ◽  
Simulated Data ◽  
Ground Truth ◽  
Multiple Datasets ◽  
Cell Type Composition ◽  
Type Composition ◽  
Robustness To Noise ◽  
Multiple Cell ◽  
Computational Platform

AbstractCells are the quanta unit of biology and their relative composition in a tissue is the prime driver of bulk tissue gene expression variation. When there is no cell information, deconvolution is an effective tool to achieve cell resolution, which provides important information for learning disease complexity and its interactions with treatments, drugs and/or the environment in a wide variety of contexts. Here we present CytoPro, a production-level tissue and condition-specific deconvolution platform, based on a large collection of human tissue-specific signatures derived from single and sorted cells. CytoPro infer per-sample multiple cell-type composition, given input bulk gene expression. CytoPro includes a rigorous QC pipeline for learning, generating and selecting signatures and performs internal automated validation using multiple QC test criteria including: Comparison to ground truth cytometry and pure sorted cells data, performance evaluation using simulated data including robustness to noise as well as agreement with biological expectations in validation datasets regarding genes and cells. We demonstrate that CytoPro outperforms existing deconvolution tools, in both accuracy and robustness. By exploring multiple datasets with predefined disease phenotypes, and analyzing a use-case of biological treatment response, we show the ability of CytoPro to flush out relevant cell biology in real pathological conditions.

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