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Transcriptome and DNA methylation cell-type deconvolutions produce similar estimates of bulk differential gene expression and differential methylation [RNA-Seq]

ABSTRACT: Motivation: Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods: Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modelling with Cibersortx or IDOL, respectively. Results: Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60-0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion: Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.

ORGANISM(S): Homo sapiens

PROVIDER: GSE255053 | GEO | 2024/02/09

REPOSITORIES: GEO

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Transcriptome and DNA methylation cell-type deconvolutions produce similar estimates of bulk differential gene expression and differential methylation [bead array]

Project description:Motivation: Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods: Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modelling with Cibersortx or IDOL, respectively. Results: Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60-0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion: Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.

2024-02-09 | GSE255057 | GEO

ProteoMixture: A Cell Deconvolution Tool for Bulk Tissue Proteomics Data

Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

2024-01-12 | PXD044157 | Pride

Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures

Project description:Difference in RNA content of different cell types introduces bias to gene expression deconvolution methods. If ERCC spike-ins are introduced into samples, predicted proportions of deconvolution methods can be corrected

2019-04-03 | GSE129240 | GEO

Nasal DNA methylation differentiates severe from non-severe asthma in African-American (AA) children

Project description:Background: Asthma is highly heterogeneous and severity evaluation is key to asthma management. DNA methylation (DNAm) contributes to asthma pathogenesis. This study aimed to identify nasal epithelial DNAm differences between severe and non-severe asthmatic children and evaluate the impact of environmental exposures. Methods: Thirty-three non-severe and 22 severe asthmatic African-American children were included in an epigenome-wide association study. Genome-wide nasal epithelial DNAm and gene expression were measured. CpG sites associated with asthma severity and environmental exposures and predictive of severe asthma were identified. DNAm was correlated with gene expression. Enrichment for transcription factor (TF) binding sites or histone modifications surrounding DNAm differences were determined. Results: We identified 816 differentially methylated CpG positions (DMPs) and 10 differentially methylated regions (DMRs) associated with asthma severity. Three DMPs exhibited discriminatory ability for severe asthma. Intriguingly, six DMPs were simultaneously associated with asthma, allergic asthma, total IgE, environmental IgE, and FeNO in an independent cohort of children. 27 DMPs were associated with traffic-related air pollution or secondhand smoke. DNAm at 22 DMPs were altered by diesel particles or allergen in human bronchial epithelial cells. DNAm levels at 39 DMPs were correlated with mRNA expression. Proximal to 816 DMPs, three histone marks and several TFs involved in asthma pathogenesis were enriched. Conclusions: Significant differences in nasal epithelial DNAm were observed between non-severe and severe asthma in African-American children, a subset of which may be useful to predict disease severity. These CpG sites are subject to the influences of environmental exposures and may regulate gene expression.

2022-04-15 | GSE161485 | GEO

Rat deconvolution as knowledge miner for immune cell trafficking from toxicogenomics databases

Project description:Toxicogenomics databases are useful for understanding biological responses in individuals because they are derived from well-controlled experiments and include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could add information regarding immune cell trafficking to the database. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats and established a methodology to obtain information regarding immune cells from toxicogenomics databases. The contributions of this work are three-fold. First, we obtained the gene expression profiles of various rat immune cells necessary for deconvolution and constructed a dataset; second, we compared the accuracy of models based on human and mouse datasets and showed the impact of species differences on deconvolution; third, we showed that rat deconvolution could retrieve information regarding immune cell trafficking from toxicogenomics databases. Correspondence: Tadahaya Mizuno

2023-08-15 | GSE239996 | GEO

A novel computational complete deconvolution method using RNA-seq data

Project description:The cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell-type composition and compared its performance to the performance of two state-of-the-art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-type-specific gene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue .

2018-12-12 | GSE123604 | GEO

Multi-cell type deconvolution using a probabilistic model of single-molecule DNA methylation haplotypes

Project description:Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.

2023-08-02 | GSE239605 | GEO

Deconvolution of whole blood DNA methylomes reveals immune cell type-specific differential methylation in Multiple Sclerosis

Project description:Whole blood is a highly convenient and informative tissue from which to sample DNA and RNA in epigenomic and functional genomic studies, but it is comprised of multiple distinct cell types and this complexity significantly impairs our ability to interpret downstream differential methylation and/or differential expression results. In this multiple sclerosis (MS)-focused study we utilised an application of current statistical deconvolution methods to interrogate whole blood DNA methylation data thereby enabling the methylome of several immune cell types to be analysed independently. Methylome profiling on cell type-purified blood samples revealed optimal CpG sets for use as robust immune cell markers in the statistical deconvolution process. We show that it is possible to identify differentially methylated (DM) loci in a cell type specific manner using statistical deconvolution. Finally, we demonstrate that deconvolution improved the biological relevance and interpretability of our DM results, significantly enhancing concordance of the identified DM loci with loci previously shown to be genetically or epigenetically associated with MS.

2017-06-30 | GSE88824 | GEO

HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor‑type‑specific DNA methylation data

Project description:The solid tumor microenvironment is heterogeneous and varies in composition by tumor type. High-resolution profiling of the cell composition in tumor microenvironments is crucial to understanding its biological role and function in clinical outcomes. Previously, gene expression and DNA methylation-based deconvolution approaches for tumor microenvironment have succeeded in deconvolving major cell types. However, existing methods lack accuracy and specificity to tumor type and include a limited amount of cell types. We developed a novel DNA methylation-based algorithm, HiTIMED, to estimate cell proportions in tumor microenvironments with a high resolution. HiTIMED employs a tumor-site-specific hierarchical model to enhance the accuracy of the predictions. Seventeen cell types, three major tumor microenvironment components (tumor, immune, angiogenic), and twenty carcinoma types can be profiled by HiTIMED. We demonstrated prognostic significance of HiTIMED cell types that other methods in the tumor microenvironment deconvolution were not capturing. The high resolution of HiTIMED deconvolution provides additional opportunities to study the impact of the tumor microenvironment on clinically significant outcomes like immunotherapy response.

2022-11-16 | GSE193297 | GEO

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

Project description:People with neurodegenerative disorders have diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) is a widely studied chemical modification to DNA, and increasing evidence suggests that DNAm alterations play an important role in neurodegenerative disorders. In this study we aimed to identify shared blood DNAm differences between 471-1,522 controls and people with Alzheimer’s disease (N = 161), amyotrophic lateral sclerosis (N = 3,032) and Parkinson’s disease (N = 1,132). We used a mixed-linear model method (MOMENT) that has been shown to account for the effect of (un)known confounders, to test for association of each DNAm site with each disorder. A fixed-effects meta-analysis of Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease results identified 12 differentially methylated positions passing a genome-wide significant threshold. Predicted immune cell-type proportions were disrupted across all neurodegenerative disorders, as well as schizophrenia and rheumatoid arthritis. Protein inflammatory markers were correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy ageing cohort. In contrast, they were not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. We have identified shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

2020-11-23 | GSE153712 | GEO

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