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

2018 ◽  
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.

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 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 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 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 LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

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