scholarly journals Saliva cell type DNA methylation reference panel for epidemiology studies in children

2020 ◽  
Author(s):  
Lauren Y M Middleton ◽  
John F Dou ◽  
Jonah Fisher ◽  
Jonathan A Heiss ◽  
Vy Nguyen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Epithelial Cells ◽  
Immune Cell ◽  
R Package ◽  
Magnetic Bead ◽  
Reference Panel ◽  
Size Exclusion ◽  
Cell Type ◽  
Whole Saliva ◽  
Epidemiology Studies

Saliva is a widely used biological sample, especially in pediatric research, containing a heterogenous mixture of immune and epithelial cells. Associations of exposure or disease with saliva DNA methylation can be influenced by cell-type proportions. Here, we developed a saliva cell-type DNA methylation reference panel to estimate interindividual cell-type heterogeneity in whole saliva studies. Saliva was collected from 22 children (7-16 years) and sorted into immune and epithelial cells, using size exclusion filtration and magnetic bead sorting. DNA methylation was measured using the Illumina MethylationEPIC BeadChip. We assessed cell-type differences in DNA methylation profiles and tested for enriched biological pathways. Immune and epithelial cells differed at 164,793 (20.7%) DNA methylation sites (t-test p < 10-8). Immune cell hypomethylated sites mapped to genes enriched for immune pathways (p < 3.2 x 10-5). Epithelial cell hypomethylated sites were enriched for cornification (p = 5.2 x 10-4), a key process for hard palette formation. Saliva immune and epithelial cells have distinct DNA methylation profiles which can drive whole saliva DNA methylation measures. A primary saliva DNA methylation reference panel, easily implemented with an R package, will allow estimates of cell proportions from whole saliva samples and improve epigenetic epidemiology studies by accounting for measurement heterogeneity by cell-type proportions.

scholarly journals Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

2019 ◽  
Author(s):  
Clementine Decamps ◽  
Florian Privé ◽  
Raphael Bacher ◽  
Daniel Jost ◽  
Arthur Waguet ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cell Types ◽  
R Package ◽  
Lessons Learned ◽  
Future Research ◽  
Cell Type ◽  
Software Packages ◽  
Response To Chemotherapy ◽  
Key Factor ◽  
Pre Treatment

AbstractCell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking.Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell’s rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-treatment steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms’ performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

scholarly journals Differential DNA methylation patterns of homeobox genes in proximal and distal colon epithelial cells

2016 ◽  
Vol 48 (4) ◽  
pp. 257-273 ◽  
Author(s):  
Alan Barnicle ◽  
Cathal Seoighe ◽  
Aaron Golden ◽  
John M. Greally ◽  
Laurence J. Egan
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Epithelial Cells ◽  
Distal Colon ◽  
Human Colon ◽  
Differential Methylation ◽  
Cell Type ◽  
Cell Type Specific ◽  
Inflammatory Bowel ◽  
Methylation Patterns

Region and cell-type specific differences in the molecular make up of colon epithelial cells have been reported. Those differences may underlie the region-specific characteristics of common colon epithelial diseases such as colorectal cancer and inflammatory bowel disease. DNA methylation is a cell-type specific epigenetic mark, essential for transcriptional regulation, silencing of repetitive DNA and genomic imprinting. Little is known about any region-specific variations in methylation patterns in human colon epithelial cells. Using purified epithelial cells and whole biopsies ( n = 19) from human subjects, we generated epigenome-wide DNA methylation data (using the HELP-tagging assay), comparing the methylation signatures of the proximal and distal colon. We identified a total of 125 differentially methylated sites (DMS) mapping to transcription start sites of protein-coding genes, most notably several members of the homeobox ( HOX) family of genes. Patterns of differential methylation were validated with MassArray EpiTYPER. We also examined DNA methylation in whole biopsies, applying a computational technique to deconvolve variation in methylation within cell types and variation in cell-type composition across biopsies. Including inferred epithelial proportions as a covariate in differential methylation analysis applied to the whole biopsies resulted in greater overlap with the results obtained from purified epithelial cells compared with when the covariate was not included. Results obtained from both approaches highlight region-specific methylation patterns of HOX genes in colonic epithelium. Regional variation in methylation patterns has implications for the study of diseases that exhibit regional expression patterns in the human colon, such as inflammatory bowel disease and colorectal cancer.

scholarly journals Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Clémentine Decamps ◽  
◽  
Florian Privé ◽  
Raphael Bacher ◽  
Daniel Jost ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cell Types ◽  
R Package ◽  
Lessons Learned ◽  
Future Research ◽  
Cell Type ◽  
Software Packages ◽  
Response To Chemotherapy ◽  
Key Factor ◽  
Selection Of

Abstract Background Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. Results Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell’s rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms’ performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Conclusion Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

scholarly journals Genomic analysis and clinical implications of immune cell infiltration in gastric cancer

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Ming Wu ◽  
Yadong Wang ◽  
Hang Liu ◽  
Jukun Song ◽  
Jie Ding
Keyword(s):  
Gastric Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
R Package ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Cell Type ◽  
Clinical Prognosis ◽  
Immune Infiltration

Abstract The immune infiltration of patients with gastric cancer (GC) is closely associated with clinical prognosis. However, previous studies failed to explain the different subsets of immune cells involved in immune responses and diverse functions. The present study aimed to uncover the differences in immunophenotypes in a tumor microenvironment (TME) between adjacent and tumor tissues and to explore their therapeutic targets. In our study, the relative proportion of immune cells in 229 GC tumor samples and 22 paired matched tissues was evaluated with a Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithm. The correlation between immune cell infiltration and clinical information was analyzed. The proportion of 22 immune cell subsets was assessed to determine the correlation between each immune cell type and clinical features. Three molecular subtypes were identified with ‘CancerSubtypes’ R-package. Functional enrichment was analyzed in each subtype. The profiles of immune infiltration in the GC cohort from The Cancer Genome Atlas (TCGA) varied significantly between the 22 paired tissues. TNM stage was associated with M1 macrophages and eosinophils. Follicular helper T cells were activated at the late stage. Monocytes were associated with radiation therapy. Three clustering processes were obtained via the ‘CancerSubtypes’ R-package. Each cancer subtype had a specific molecular classification and subtype-specific characterization. These findings showed that the CIBERSOFT algorithm could be used to detect differences in the composition of immune-infiltrating cells in GC samples, and these differences might be an important driver of GC progression and treatment response.

scholarly journals Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types

Epigenetics ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. 941-958 ◽  
Author(s):  
Xinyi Lin ◽  
Jane Yi Lin Tan ◽  
Ai Ling Teh ◽  
Ives Yubin Lim ◽  
Samantha J Liew ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cord Blood ◽  
Cell Types ◽  
Reference Panel ◽  
Cell Type ◽  
Cell Type Specific

scholarly journals Longitudinal study of DNA methylation and epigenetic clocks prior to and following test-confirmed COVID-19 and mRNA vaccination

2021 ◽  
Author(s):  
Alina PS Pang ◽  
Albert T. Higgins-Chen ◽  
Florence Comite ◽  
Ioana Raica ◽  
Christopher Arboleda ◽  
...  
Keyword(s):  
Dna Methylation ◽  
T Cells ◽  
B Cells ◽  
Immune Cell ◽  
Principal Component ◽  
Epigenetic Clock ◽  
Cell Type ◽  
Immune Cell Type ◽  
Compositional Changes ◽  
The Impact

AbstractThe host epigenetic landscape is rapidly changed during SARS-CoV-2 infection and evidence suggests that severe COVID-19 is associated with durable scars to the epigenome. Specifically, aberrant DNA methylation changes in immune cells and alterations to epigenetic clocks in blood relate to severe COVID-19. However, a longitudinal assessment of DNA methylation states and epigenetic clocks in blood from healthy individuals prior to and following test-confirmed non-hospitalized COVID-19 has not been performed. Moreover, the impact of mRNA COVID-19 vaccines upon the host epigenome remains understudied. Here, we first examined DNA methylation states in blood of 21 participants prior to and following test confirmed COVID-19 diagnosis at a median timeframe of 8.35 weeks. 261 CpGs were identified as differentially methylated following COVID-19 diagnosis in blood at an FDR adjusted P value <0.05. These CpGs were enriched in gene body and northern and southern shelf regions of genes involved in metabolic pathways. Integrative analysis revealed overlap among genes identified in transcriptional SARS-CoV-2 infection datasets. Principal component-based epigenetic clock estimates of PhenoAge and GrimAge significantly increased in people over 50 following infection by an average of 2.1 and 0.84 years. In contrast, PCPhenoAge significantly decreased in people under 50 following infection by an average of 2.06 years. This observed divergence in epigenetic clocks following COVID-19 was related to age and immune cell-type compositional changes in CD4+ T cells, B cells, granulocytes, plasmablasts, exhausted T cells, and naïve T cells. Complementary longitudinal epigenetic clock analyses of 36 participants prior to and following Pfizer and Moderna mRNA-based COVID-19 vaccination revealed vaccination significantly reduced principal component-based Horvath epigenetic clock estimates in people over 50 by an average of 3.91 years for those that received Moderna. This reduction in epigenetic clock estimates was significantly related to chronological age and immune cell-type compositional changes in B cells and plasmablasts pre- and post-vaccination. These findings suggest the potential utility of epigenetic clocks as a biomarker of COVID-19 vaccine responses. Future research will need to unravel the significance and durability of short-term changes in epigenetic age related to COVID-19 exposure and mRNA vaccination.

scholarly journals Salivary Outer Membrane Vesicles and DNA Methylation of Small Extracellular Vesicles as Biomarkers for Periodontal Status: A Pilot Study

2021 ◽  
Vol 22 (5) ◽  
pp. 2423
Author(s):  
Pingping Han ◽  
Peter Mark Bartold ◽  
Carlos Salomon ◽  
Sašo Ivanovski
Keyword(s):  
Dna Methylation ◽  
Pilot Study ◽  
Outer Membrane ◽  
Extracellular Vesicles ◽  
Membrane Vesicles ◽  
High Sensitivity ◽  
Outer Membrane Vesicles ◽  
Size Exclusion ◽  
Periodontal Pathogens ◽  
Whole Saliva

Periodontitis is an inflammatory disease, associated with a microbial dysbiosis. Early detection using salivary small extracellular vesicles (sEVs) biomarkers may facilitate timely prevention. sEVs derived from different species (i.e., humans, bacteria) are expected to circulate in saliva. This pilot study recruited 22 participants (seven periodontal healthy, seven gingivitis and eight periodontitis) and salivary sEVs were isolated using the size-exclusion chromatography (SEC) method. The healthy, gingivitis and periodontitis groups were compared in terms of salivary sEVs in the CD9+ sEV subpopulation, Gram-negative bacteria-enriched lipopolysaccharide (LPS+) outer membrane vesicles (OMVs) and global DNA methylation pattern of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and N6-Methyladenosine (m6dA). It was found that LPS+ OMVs, global 5mC methylation and four periodontal pathogens (T. denticola, E. corrodens, P. gingivalis and F. nucleatum) that secreted OMVs were significantly increased in periodontitis sEVs compared to those from healthy groups. These differences were more pronounced in sEVs than the whole saliva and were more superior in distinguishing periodontitis than gingivitis, in comparison to healthy patients. Of note, global 5mC hypermethylation in salivary sEVs can distinguish periodontitis patients from both healthy controls and gingivitis patients with high sensitivity and specificity (AUC = 1). The research findings suggest that assessing global sEV methylation may be a useful biomarker for periodontitis.

scholarly journals Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology

2019 ◽  
Vol 35 (14) ◽  
pp. i436-i445 ◽  
Author(s):  
Gregor Sturm ◽  
Francesca Finotello ◽  
Florent Petitprez ◽  
Jitao David Zhang ◽  
Jan Baumbach ◽  
...  
Keyword(s):  
Single Cell ◽  
Computational Methods ◽  
Immune Cell ◽  
Comprehensive Evaluation ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Cell Type ◽  
Real World Datasets

Abstract Motivation The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. Results We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. Availability and implementation A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Immune cell deconvolution of bulk DNA methylation data reveals an association with methylation class, key somatic alterations, and cell state in glial/glioneuronal tumors

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Omkar Singh ◽  
Drew Pratt ◽  
Kenneth Aldape
Keyword(s):  
Dna Methylation ◽  
Immune Cell ◽  
Critical Role ◽  
Cns Tumors ◽  
Large Set ◽  
Cell Type ◽  
Genomic Aberration ◽  
Methylation Array ◽  
Specific Distribution ◽  
Glioneuronal Tumors

AbstractIt is recognized that the tumor microenvironment (TME) plays a critical role in the biology of cancer. To better understand the role of immune cell components in CNS tumors, we applied a deconvolution approach to bulk DNA methylation array data in a large set of newly profiled samples (n = 741) as well as samples from external data sources (n = 3311) of methylation-defined glial and glioneuronal tumors. Using the cell-type proportion data as input, we used dimensionality reduction to visualize sample-wise patterns that emerge from the cell type proportion estimations. In IDH-wildtype glioblastomas (n = 2,072), we identified distinct tumor clusters based on immune cell proportion and demonstrated an association with oncogenic alterations such as EGFR amplification and CDKN2A/B homozygous deletion. We also investigated the immune cluster-specific distribution of four malignant cellular states (AC-like, OPC-like, MES-like and NPC-like) in the IDH-wildtype cohort. We identified two major immune-based subgroups of IDH-mutant gliomas, which largely aligned with 1p/19q co-deletion status. Non-codeleted gliomas showed distinct proportions of a key genomic aberration (CDKN2A/B loss) among immune cell-based groups. We also observed significant positive correlations between monocyte proportion and expression of PD-L1 and PD-L2 (R = 0.54 and 0.68, respectively). Overall, the findings highlight specific roles of the TME in biology and classification of CNS tumors, where specific immune cell admixtures correlate with tumor types and genomic alterations.

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.

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