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

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: GSE255057 | 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 [RNA-Seq]

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 | GSE255053 | GEO

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2024-01-12 | PXD044157 | Pride

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.

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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.

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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

Immune and HCT116 RNA mixtures

Project description:These experiments were designed as a benchmark tool for deconvolution methods. 5 immune cell populations were sorted from 3 healthy donors' peripheral bloods. Peripheral Blood Mononuclear Cells (PBCMs) and PolymorphoNuclear Cells (PMN) were separated using gradient centrifugation. T cells (DAPI-/CD3+/CD14-/CD19-/CD56-), monocytes (DAPI-/CD3-/CD14+/CD19-/CD56-), B cells (DAPI-/CD3-/CD14-/CD19+/CD56-) and NK cells (DAPI-/CD3-/CD14-/CD19-/CD56+) were FACS-sorted from PBMCs and neutrophils (DAPI-/CD66b+/CD19-/CD3-/CD56-/CD14-) were sorted from PMNs. RNA was extracted from the purified cell population, as well as from the HCT116 colon cancer cell line. RNAs from pure populations were then mixed in various proportions. RNA from HCT116 cells and FACS-sorted T cells (DAPI-/CD3+/CD14-/CD19-/CD56-), monocytes (DAPI-/CD3-/CD14+/CD19-/CD56-), B cells (DAPI-/CD3-/CD14-/CD19+/CD56-), NK cells (DAPI-/CD3-/CD14-/CD19-/CD56+), and neutrophils (DAPI-/CD66b+/CD19-/CD3-/CD56-/CD14-) were mixed in various proportions.

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