Regenerated crustacean limbs are precise replicas

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.

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

Human Molecular Genetics ◽

10.1093/hmg/ddt665 ◽

2014 ◽

Vol 23 (10) ◽

pp. 2721-2728 ◽

Cited By ~ 27

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

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Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue

Frontiers in Nutrition ◽

10.3389/fnut.2021.675935 ◽

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.

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Comprehensive investigation of temporal and autism-associated cell type composition-dependent and independent gene expression changes in human brains

Scientific Reports ◽

10.1038/s41598-017-04356-7 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 15

Author(s):

Qianhui Yu ◽

Zhisong He

Keyword(s):

Gene Expression ◽

Cell Type ◽

Comprehensive Investigation ◽

Independent Gene ◽

Cell Type Composition ◽

Type Composition ◽

Human Brains

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

GigaScience ◽

10.1093/gigascience/giab002 ◽

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.

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

10.1101/857805 ◽

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

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

10.1101/778910 ◽

2019 ◽

Cited By ~ 2

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.

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

10.1101/738658 ◽

2019 ◽

Cited By ~ 2

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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Deconvolving the contributions of cell-type heterogeneity on cortical gene expression

10.1101/566307 ◽

2019 ◽

Cited By ~ 5

Author(s):

Ellis Patrick ◽

Mariko Taga ◽

Ayla Ergun ◽

Bernard Ng ◽

William Casazza ◽

...

Keyword(s):

Gene Expression ◽

Marker Gene ◽

Cellular Heterogeneity ◽

Cell Type ◽

Gene Sets ◽

Genome Wide ◽

Cell Type Composition ◽

Type Composition ◽

Bulk Tissue ◽

Genome Wide Gene Expression

AbstractComplexity of cell-type composition has created much skepticism surrounding the interpretation of brain bulk-tissue transcriptomic studies. We generated paired tissue genome-wide gene expression data and immunohistochemistry data, enabling us to assess statistical methods for modeling and estimating cellular heterogeneity in the brain. We demonstrate that several algorithms that rely on single-cell and cell-sorted data to define cell marker gene sets yield accuraterelativeandabsoluteestimates of constituent cell-type proportions.

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scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data

10.1101/504944 ◽

2018 ◽

Cited By ~ 3

Author(s):

Nelson Johansen ◽

Gerald Quon

Keyword(s):

Gene Expression ◽

State Of The Art ◽

Cell Type ◽

Specific Expression ◽

Cell Type Composition ◽

Type Composition ◽

Cell Type Specific Expression ◽

Unsupervised Deep Learning ◽

Cell Type Specific ◽

Complete Set

AbstractscRNA-seq dataset integration occurs in different contexts, such as the identification of cell type-specific differences in gene expression across conditions or species, or batch effect correction. We present scAlign, an unsupervised deep learning method for data integration that can incorporate partial, overlapping or a complete set of cell labels, and estimate per-cell differences in gene expression across datasets. scAlign performance is state-of-the-art and robust to cross-dataset variation in cell type-specific expression and cell type composition. We demonstrate that scAlign identifies a rare cell population likely to drive malaria transmission. Our framework is widely applicable to integration challenges in other domains.

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