scholarly journals The TMEM106B rs1990621 protective variant is also associated with increased neuronal proportion

2019 ◽  
Author(s):  
Zeran Li ◽  
Fabiana G. Farias ◽  
Umber Dube ◽  
Jorge L. Del-Aguila ◽  
Kathie A. Mihindukulasuriya ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Specific Effect ◽  
Disease Status ◽  
Brain Cell ◽  
Joint Analysis ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Elderly Cohort ◽  
Protective Variant

AbstractBackgroundIn previous studies, we observed decreased neuronal and increased astrocyte proportions in AD cases in parietal brain cortex by using a deconvolution method for bulk RNA-seq. These findings suggested that genetic risk factors associated with AD etiology have a specific effect in the cellular composition of AD brains. The goal of this study is to investigate if there are genetic determinants for brain cell compositions.MethodsUsing cell type composition inferred from transcriptome as a disease status proxy, we performed cell type association analysis to identify novel loci related to cellular population changes in disease cohort. We imputed and merged genotyping data from seven studies in total of 1,669 samples and derived major CNS cell type proportions from cortical RNAseq data. We also inferred RNA transcript integrity number (TIN) to account for RNA quality variances. The model we performed in the analysis was: normalized neuronal proportion ∼ SNP + Age + Gender + PC1 + PC2 + median TIN.ResultsA variant rs1990621 located in the TMEM106B gene region was significantly associated with neuronal proportion (p=6.40×10−07) and replicated in an independent dataset. The association became more significant as we combined both discovery and replication datasets in multi-tissue meta-analysis (p=9.42×10−09) and joint analysis (p=7.66×10−10). This variant is in high LD with rs1990622 (r2 = 0.98) which was previously identified as a protective variant in FTD cohorts. Further analyses indicated that this variant is associated with increased neuronal proportion in participants with neurodegenerative disorders, not only in AD cohort but also in cognitive normal elderly cohort. However, this effect was not observed in a younger schizophrenia cohort with a mean age of death < 65. The second most significant loci for neuron proportion was APOE, which suggested that using neuronal proportion as an informative endophenotype could help identify loci associated with neurodegeneration.ConclusionThis result suggested a common pathway involving TMEM106B shared by aging groups in the present or absence of neurodegenerative pathology may contribute to cognitive preservation and neuronal protection.

scholarly journals SpiceMix: Integrative single-cell spatial modeling for inferring cell identity

2020 ◽  
Author(s):  
Benjamin Chidester ◽  
Tianming Zhou ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Spatial Information ◽  
Single Cells ◽  
Joint Analysis ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Identity ◽  
Cell Type Composition ◽  
Type Composition

AbstractSpatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that capture the unique properties of single-cell spatial transcriptome data to unveil cell identities remains a challenge. Here, we report SpiceMix, a new probabilistic model that enables effective joint analysis of spatial information and gene expression of single cells based on spatial transcriptome data. Both simulation and real data evaluations demonstrate that SpiceMix consistently improves upon the inference of the intrinsic cell types compared with existing approaches. As a proof-of-principle, we use SpiceMix to analyze single-cell spatial transcriptome data of the mouse primary visual cortex acquired by seqFISH+ and STARmap. We find that SpiceMix can improve cell identity assignments and uncover potentially new cell subtypes. SpiceMix is a generalizable framework for analyzing spatial transcriptome data that may provide critical insights into the cell-type composition and spatial organization of cells in complex tissues.

scholarly journals dtangle: accurate and robust cell type deconvolution

2018 ◽  
Vol 35 (12) ◽  
pp. 2093-2099 ◽  
Author(s):  
Gregory J Hunt ◽  
Saskia Freytag ◽  
Melanie Bahlo ◽  
Johann A Gagnon-Bartsch
Keyword(s):  
Differential Expression Analysis ◽  
Disease Status ◽  
Supplementary Information ◽  
Cell Type ◽  
Tuning Parameters ◽  
Cell Type Composition ◽  
Type Composition ◽  
Benchmark Datasets ◽  
Human Immune Response ◽  
Cross Platform

Abstract Motivation Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA). Results We propose dtangle, a new cell type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell type proportions using publicly available, often cross-platform, reference data. We evaluate dtangle on 11 benchmark datasets showing that dtangle is competitive with published deconvolution methods, is robust to outliers and selection of tuning parameters, and is fast. As a case study, we investigate the human immune response to Lyme disease. dtangle’s estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status. Availability and implementation dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array

2016 ◽  
Author(s):  
Jean-Philippe Fortin ◽  
Timothy J. Triche ◽  
Kasper D. Hansen
Keyword(s):  
Cumulative Effect ◽  
Joint Analysis ◽  
Cell Type ◽  
Methylation Array ◽  
Array Data ◽  
Cell Type Composition ◽  
Type Composition ◽  
Dna Methylation Array ◽  
Epic Array ◽  
Individual Human

AbstractThe minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC (”EPIC”) array from Illumina. We discuss methods for the joint analysis and normalization of data from the HumanMethylation450 (”450k”) and EPIC platforms. We also introduce the single-sample Noob (ssNoob) method, a normalization procedure suitable for incremental preprocessing of individual Human-Methylation arrays. Our results recommend the ssNoob method when integrating data from multiple generations of Infinium methylation arrays. Finally, we show how to use reference 450k datasets to estimate cell type composition of samples on EPIC arrays. The cumulative effect of these updates is to ensure that minfi provides the tools to best integrate existing and forthcoming Illumina methylation array data.

scholarly journals dtangle: accurate and fast cell-type deconvolution

2018 ◽  
Author(s):  
Gregory J. Hunt ◽  
Saskia Freytag ◽  
Melanie Bahlo ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Disease Status ◽  
Data Sets ◽  
Cell Type ◽  
Link Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Human Immune Response ◽  
Gene Expression Studies

AbstractMotivationUnderstanding cell type composition is important to understanding many biological processes. Furthermore, in gene expression studies cell type composition can confound differential expression analysis (DEA). To aid understanding cell type composition, methods of estimating (deconvolving) cell type proportions from gene expression data have been developed.ResultsWe propose dtangle, a new cell-type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell-type proportions using publicly available, often cross-platform, reference data. To comprehensively evaluate dtangle, we assemble ten benchmark data sets. Here, dtangle is competitive with published deconvolution methods, is robust to selection of tuning parameters and is quicker than other methods. As a case study, we investigate the human immune response to Lyme disease. dtangle’s estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status.Availabilitydtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io)[email protected]

scholarly journals Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147519 ◽  
Author(s):  
Yuh Shiwa ◽  
Tsuyoshi Hachiya ◽  
Ryohei Furukawa ◽  
Hideki Ohmomo ◽  
Kanako Ono ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Systematic Bias ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Dna Collection

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 Single nuclei sequencing of entire mammalian hearts: strain-dependent cell-type composition and velocity

2020 ◽  
Vol 116 (7) ◽  
pp. 1249-1251
Author(s):  
Markus Wolfien ◽  
Anne-Marie Galow ◽  
Paula Müller ◽  
Madeleine Bartsch ◽  
Ronald M Brunner ◽  
...  
Keyword(s):  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Dependent Cell ◽  
Strain Dependent
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