scholarly journals Adipose tissue in health and disease through the lens of its building blocks

2018 ◽  
Author(s):  
Michael Lenz ◽  
Ilja C.W. Arts ◽  
Ralf L.M. Peeters ◽  
Theo M. de Kok ◽  
Gökhan Ertaylan
Keyword(s):  
Adipose Tissue ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Cell Type ◽  
Tissue Samples ◽  
Cell Type Composition ◽  
Type Composition ◽  
Adipose Tissue Cell ◽  
Cellular Markers

AbstractBackgroundHighly specialized cells work in synergy forming tissues to perform functions required for the survival of organisms. Understanding this tissue-specific cellular heterogeneity and homeostasis is essential to comprehend the development of diseases within the tissue and also for developing regenerative therapies. Cellular subpopulations in the adipose tissue have been related to disease development, but efforts towards characterizing the adipose tissue cell type composition are limited due to lack of robust cell surface markers, limited access to tissue samples, and the labor-intensive process required to identify them.ResultsWe propose a framework, identifying cellular heterogeneity while providing state-of-the-art cellular markers for each cell type present in tissues using transcriptomics level analysis. We validate our approach with an independent dataset and present the most comprehensive study of adipose tissue cell type composition to date, determining the relative amounts of 21 different cell types in 779 adipose tissue samples detailing differences across four adipose tissue depots, between genders, across ranges of BMI and in different stages of type-2 diabetes. We also highlight the heterogeneity in reported marker-based studies of adipose tissue cell type composition and provide novel cellular markers to distinguish different cell types within the adipose tissue.ConclusionsOur study provides a systematic framework for studying cell type composition in a given tissue and valuable insights into adipose tissue cell type heterogeneity in health and disease.

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 Transit-amplifying cells coordinate changes in intestinal epithelial cell-type composition

2019 ◽  
Author(s):  
Laura E. Sanman ◽  
Ina W. Chen ◽  
Jake M. Bieber ◽  
Veronica Steri ◽  
Byron Hann ◽  
...  
Keyword(s):  
Quantitative Imaging ◽  
Cell Types ◽  
Culture Conditions ◽  
Tissue Cell ◽  
Specific Cell ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Coordinate Changes

AbstractRenewing tissues have the remarkable ability to continually produce both proliferative progenitor and specialized differentiated cell-types. How are complex milieus of microenvironmental signals interpreted to coordinate tissue cell-type composition? Here, we develop a high-throughput approach that combines organoid technology and quantitative imaging to address this question in the context of the intestinal epithelium. Using this approach, we comprehensively survey enteroid responses to individual and paired perturbations to eight epithelial signaling pathways. We uncover culture conditions that enrich for specific cell-types, including Lgr5+ stem and enteroendocrine cells. We analyze interactions between perturbations and dissect mechanisms underlying an unexpected mutual antagonism between EGFR and IL-4 signals. Finally, we show that, across diverse perturbations, modulating proliferation of transit-amplifying cells also consistently changes the composition of differentiated secretory and absorptive cell-types. This property is conserved in vivo and can arise from differential amplification of secretory and absorptive progenitor cells. Taken together, the observations highlight an underappreciated role for transit-amplifying cells in which proliferation of these short-lived progenitors provides a lineage-based mechanism for tuning differentiated cell-type composition.

scholarly journals The effect of tissue composition on gene co-expression

2019 ◽  
Author(s):  
Yun Zhang ◽  
Jonavelle Cuerdo ◽  
Marc K Halushka ◽  
Matthew N McCall
Keyword(s):  
Expression Patterns ◽  
Real Data ◽  
Cell Types ◽  
Tissue Level ◽  
Tissue Composition ◽  
Cell Type ◽  
Tissue Samples ◽  
Cell Type Composition ◽  
Type Composition ◽  
Component Cell

Abstract Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.

scholarly journals Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs

2018 ◽  
Author(s):  
Craig A. Glastonbury ◽  
Alexessander Couto Alves ◽  
Julia S. El-Sayed Moustafa ◽  
Kerrin S. Small
Keyword(s):  
Adipose Tissue ◽  
Complex Traits ◽  
Positive Association ◽  
Population Level ◽  
Fat Distribution ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Cell Type ◽  
The Impact

AbstractAdipose tissue is comprised of a heterogeneous collection of cell-types which can differentially impact disease phenotypes. We investigated cell-type heterogeneity in two population-level subcutaneous adipose tissue RNAseq datasets (TwinsUK, N =766 and GTEx, N=326). We find that adipose cell-type composition is heritable and confirm the positive association between macrophage proportion and obesity (BMI), but find a stronger BMI-independent association with DXA-derived body-fat distribution traits. Cellular heterogeneity can confound ‘omic analyses, but is rarely taken into account in analysis of solid-tissue transcriptomes. We benchmark the impact of adipose tissue cell-composition on a range of standard analyses, including phenotypegene expression association, co-expression networks and cis-eQTL discovery. We applied G x Cell Type Proportion interaction models to identify 26 cell-type specific eQTLs in 20 genes, including 4 autoimmune disease GWAS loci, demonstrating the potential of in silico deconvolution of bulk tissue to identify cell-type restricted regulatory variants.

scholarly journals SpatialDWLS: accurate deconvolution of spatial transcriptomic data

2021 ◽  
Author(s):  
Rui Dong ◽  
Guo-Cheng Yuan
Keyword(s):  
Spatial Information ◽  
Cell Types ◽  
Computational Method ◽  
The Other ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Cell Type Composition ◽  
Type Composition

AbstractRecent development of spatial transcriptomic technologies has made it possible to systematically characterize cellular heterogeneity while preserving spatial information, which greatly enables the investigation of structural organization of a tissue and its impact on modulating cellular behavior. On the other hand, the technology often does not have sufficient resolution to distinguish neighboring cells which may belong to different cell types, therefore it is difficult to identify cell-type distribution directly from the data. To overcome this challenge, we have developed a computational method, called spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmarked the performance of spatialDWLS by comparing with a number of existing deconvolution methods using both real and simulated datasets, and we found that spatialDWLS outperformed the other methods in terms of accuracy and speed. By applying spatialDWLS to analyze a human developmental heart dataset, we observed striking spatial-temporal changes of cell-type composition which becomes increasing spatially coherent during development. As such, spatialDWLS provides a valuable computational tool for faithfully extracting biological information from spatial transcriptomic data.

scholarly journals Cell Type Aware analysis of RNA-seq data (CARseq) reveals difference and similarities of the molecular mechanisms of Schizophrenia and Autism

2020 ◽  
Author(s):  
Chong Jin ◽  
Mengjie Chen ◽  
Danyu Lin ◽  
Wei Sun
Keyword(s):  
Differential Expression ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Tissue Samples ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific Expression

AbstractMost tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a new framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). After evaluating its performance in simulations, we apply CARseq to compare gene expression of schizophrenia/autism subjects versus controls. Our results show that these two neurodevelopmental disorders differ from each other in terms of cell type composition changes and differential expression associated with different types of neurotransmitter receptors. We also discover overlapping signals of differential expression in microglia, supporting the two diseases’ similarity through immune regulation.

scholarly journals SpatialDWLS: accurate deconvolution of spatial transcriptomic data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Dong ◽  
Guo-Cheng Yuan
Keyword(s):  
Spatial Information ◽  
Spatial Location ◽  
Cell Types ◽  
The Other ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Neighboring Cell ◽  
Transcriptomic Data ◽  
Cell Type Composition ◽  
Type Composition

AbstractRecent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development.

scholarly journals The effect of tissue composition on gene co-expression

2018 ◽  
Author(s):  
Yun Zhang ◽  
Jonavelle Cuerdo ◽  
Marc K Halushka ◽  
Matthew N McCall
Keyword(s):  
Expression Patterns ◽  
Real Data ◽  
Cell Types ◽  
Tissue Level ◽  
Tissue Composition ◽  
Cell Type ◽  
Tissue Samples ◽  
Cell Type Composition ◽  
Type Composition ◽  
Component Cell

Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell type specific markers are ideally suited to deconvolute both the expression and co expression patterns of an individual cell type. Finally, we provide a Shiny application for users to interactively explore the effect of cell type composition on correlation-based co-expression estimation for any cell types of interest.

scholarly journals Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

2021 ◽  
Author(s):  
Yunhee Jeong ◽  
Reka Toth ◽  
Marlene Ganslmeier ◽  
Kersten Breuer ◽  
Christoph Plass ◽  
...  
Keyword(s):  
Cell Types ◽  
Systematic Evaluation ◽  
Cell Type ◽  
Factors Affecting ◽  
Genome Wide ◽  
Cell Type Composition ◽  
Type Composition ◽  
Level Information ◽  
Genomic Regions ◽  
The Impact

DNA methylation sequencing is becoming increasingly popular, yielding genome-wide methylome data at single-base pair resolution through the novel cost- and labor-optimized protocols. It has tremendous potential for cell-type heterogeneity analysis, particularly in tumors, due to intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, their systematic evaluation has not been performed so far. Here, we thoroughly review and evaluate five previously published deconvolution methods: Bayesian epiallele detection (BED), PRISM, csmFinder + coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation. Accordingly, we individually assessed the performance of each step and demonstrated the impact of the former step upon the performance of the following one. In conclusion, we demonstrate the best method showing the highest accuracy in different samples, and infer factors affecting cell-type deconvolution performance according to the number of cell types in the mixture. We found that cell-type deconvolution performance is influenced by different factors according to the number of components in the mixture. Whereas selecting similar genomic regions to DMRs generally contributed to increasing the performance in bi-component mixtures, the uniformity of cell-type distribution showed a high correlation with the performance in five cell-type bulk analyses.

scholarly journals Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

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