scholarly journals A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference

2017 ◽  
Author(s):  
Elior Rahmani ◽  
Regev Schweiger ◽  
Liat Shenhav ◽  
Theodora Wingert ◽  
Ira Hofer ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Alternative Methods ◽  
Cell Type ◽  
Composition Distribution ◽  
Linear Combinations ◽  
Cell Counts ◽  
Cell Type Composition ◽  
Type Composition ◽  
Genomic Studies ◽  
Single Cell Type

AbstractWe introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer explicit cell counts without methylation reference can only capture linear combinations of cell counts rather than provide one component per cell type. Our approach, which allows the construction of a set of components such that each component corresponds to a single cell type, therefore provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

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 DNA Methylation Profiles of Purified Cell Types in Bronchoalveolar Lavage: Applications for Mixed Cell Paediatric Pulmonary Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Shivanthan Shanthikumar ◽  
Melanie R. Neeland ◽  
Richard Saffery ◽  
Sarath C. Ranganathan ◽  
Alicia Oshlack ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bronchoalveolar Lavage ◽  
Association Studies ◽  
Cell Types ◽  
Alveolar Epithelial Cells ◽  
Cell Type ◽  
Mixed Cell ◽  
Alveolar Epithelial ◽  
Cell Type Composition ◽  
Type Composition

In epigenome-wide association studies analysing DNA methylation from samples containing multiple cell types, it is essential to adjust the analysis for cell type composition. One well established strategy for achieving this is reference-based cell type deconvolution, which relies on knowledge of the DNA methylation profiles of purified constituent cell types. These are then used to estimate the cell type proportions of each sample, which can then be incorporated to adjust the association analysis. Bronchoalveolar lavage is commonly used to sample the lung in clinical practice and contains a mixture of different cell types that can vary in proportion across samples, affecting the overall methylation profile. A current barrier to the use of bronchoalveolar lavage in DNA methylation-based research is the lack of reference DNA methylation profiles for each of the constituent cell types, thus making reference-based cell composition estimation difficult. Herein, we use bronchoalveolar lavage samples collected from children with cystic fibrosis to define DNA methylation profiles for the four most common and clinically relevant cell types: alveolar macrophages, granulocytes, lymphocytes and alveolar epithelial cells. We then demonstrate the use of these methylation profiles in conjunction with an established reference-based methylation deconvolution method to estimate the cell type composition of two different tissue types; a publicly available dataset derived from artificial blood-based cell mixtures and further bronchoalveolar lavage samples. The reference DNA methylation profiles developed in this work can be used for future reference-based cell type composition estimation of bronchoalveolar lavage. This will facilitate the use of this tissue in studies examining the role of DNA methylation in lung health and disease.

scholarly journals Differential DNA methylation in peripheral blood mononuclear cells in adolescents exposed to significant early but not later childhood adversity

2016 ◽  
Vol 28 (4pt2) ◽  
pp. 1385-1399 ◽  
Author(s):  
Elisa A. Esposito ◽  
Meaghan J. Jones ◽  
Jenalee R. Doom ◽  
Julia L MacIsaac ◽  
Megan R. Gunnar ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Striking Difference ◽  
Cell Type ◽  
Peripheral Blood Mononuclear ◽  
Cell Type Composition ◽  
Type Composition ◽  
Blood Mononuclear Cells

AbstractInternationally adopted adolescents who are adopted as young children from conditions of poverty and deprivation have poorer physical and mental health outcomes than do adolescents conceived, born, and raised in the United States by families similar to those who adopt internationally. Using a sample of Russian and Eastern European adoptees to control for Caucasian race and US birth, and nonadopted offspring of well-educated and well-resourced parents to control for postadoption conditions, we hypothesized that the important differences in environments, conception to adoption, might be reflected in epigenetic patterns between groups, specifically in DNA methylation. Thus, we conducted an epigenome-wide association study to compare DNA methylation profiles at approximately 416,000 individual CpG loci from peripheral blood mononuclear cells of 50 adopted youth and 33 nonadopted youth. Adopted youth averaged 22 months at adoption, and both groups averaged 15 years at testing; thus, roughly 80% of their lives were lived in similar circumstances. Although concurrent physical health did not differ, cell-type composition predicted using the DNA methylation data revealed a striking difference in the white blood cell-type composition of the adopted and nonadopted youth. After correcting for cell type and removing invariant probes, 30 CpG sites in 19 genes were more methylated in the adopted group. We also used an exploratory functional analysis that revealed that 223 gene ontology terms, clustered in neural and developmental categories, were significantly enriched between groups.

scholarly journals Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling

2021 ◽  
Author(s):  
Lucas A Salas ◽  
Ze Zhang ◽  
Devin C Koestler ◽  
Rondi A Butler ◽  
Helen M Hansen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
T Regulatory Cells ◽  
Immune Cell ◽  
Regulatory Cells ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Health And Disease ◽  
Immune Profiling ◽  
Artificial Mixtures

AbstractDNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ naïve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.

scholarly journals methylCC: technology-independent estimation of cell type composition using differentially methylated regions

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Stephanie C. Hicks ◽  
Rafael A. Irizarry
Keyword(s):  
Dna Methylation ◽  
Whole Blood ◽  
False Positives ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Differentially Methylated Regions ◽  
Link Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Unique Cell

AbstractA major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.

scholarly journals Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition

Epigenetics ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. 690-698 ◽  
Author(s):  
Kristina Gervin ◽  
Christian Magnus Page ◽  
Hans Christian D. Aass ◽  
Michelle A. Jansen ◽  
Heidi Elisabeth Fjeldstad ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cord Blood ◽  
Cell Count ◽  
Reference Data ◽  
Cell Type ◽  
Data Set ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific

scholarly journals BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Elior Rahmani ◽  
Regev Schweiger ◽  
Liat Shenhav ◽  
Theodora Wingert ◽  
Ira Hofer ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bayesian Framework ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition

scholarly journals Intra-individual changes in DNA methylation not mediated by cell-type composition are correlated with aging during childhood

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Kristina Gervin ◽  
Bettina Kulle Andreassen ◽  
Hanne Sagsveen Hjorthaug ◽  
Karin C. Lødrup Carlsen ◽  
Kai-Håkon Carlsen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition
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