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 Differences in DNA methylation of white blood cell types at birth and in adulthood reflect postnatal immune maturation and influence accuracy of cell type prediction

2018 ◽  
Author(s):  
Meaghan J Jones ◽  
Louie Dinh ◽  
Hamid Reza Razzaghian ◽  
Olivia de Goede ◽  
Julia L MacIsaac ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Immune System ◽  
Blood Cell ◽  
Cord Blood ◽  
White Blood Cell ◽  
Blood Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Specific ◽  
The Impact

AbstractBackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

scholarly journals Virtual methylome dissection facilitated by single-cell analyses

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Liduo Yin ◽  
Yanting Luo ◽  
Xiguang Xu ◽  
Shiyu Wen ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Mixed Cell ◽  
Numerous Cell ◽  
Cell Type Specific ◽  
Methylation Patterns

Abstract Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

scholarly journals Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

2020 ◽  
Author(s):  
Marco Schmidt ◽  
Tiago Maié ◽  
Edgar Dahl ◽  
Ivan G. Costa ◽  
Wolfgang Wagner
Keyword(s):  
Dna Methylation ◽  
Basic Research ◽  
Cell Types ◽  
Cell Type ◽  
Cpg Sites ◽  
Targeted Analysis ◽  
Cell Type Specific ◽  
Induced Pluripotent ◽  
Genomic Regions ◽  
Cg Dinucleotides

AbstractBackgroundThe complex composition of different cell types within a tissue can be estimated by deconvolution of omics datasets. For example, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. In this study, we investigated if deconvolution is also feasible with individual cell-type-specific CG dinucleotides (CpG sites), which can be addressed by targeted analysis, such as pyrosequencing.ResultsWe compiled and curated a dataset of 579 samples from Illumina 450k BeadChip technology that comprised 14 different purified and characterized human cell types. A training and validation strategy was applied to identify and test cell-type-specific CpGs. Initially, the amount of fibroblasts was estimated using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. This FibroScore correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts and induced pluripotent stem cells. Using previously published BeadChip datasets with cell mixtures the accuracy of this eight CpG signature was comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types.ConclusionThis proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.

scholarly journals Cell-type specific epigenetic links to schizophrenia risk in brain

2019 ◽  
Author(s):  
Isabel Mendizabal ◽  
Stefano Berto ◽  
Noriyoshi Usui ◽  
Kazuya Toriumi ◽  
Paramita Chatterjee ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cell Types ◽  
Epigenetic Variation ◽  
Transcriptome Data ◽  
Cell Type ◽  
Coding Regions ◽  
Methylation Difference ◽  
Distinct Cell ◽  
Cell Type Specific ◽  
Targeted Deep Sequencing

AbstractThe importance of cell-type specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generated cell-type specific whole-genome methylomes (N=95) and transcriptomes (N=89) from neurons and oligodendrocytes from brains of schizophrenia and matched controls. The methylomes of these two cell-types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation difference between control and schizophrenia brains is subtle compared to cell-type difference, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell-type differentially methylated sites, highlighting the significance of cell-type specific epigenetic dysregulation in a complex neuropsychiatric disorder. Our resource provides novel and comprehensive methylome and transcriptome data from distinct cell populations from schizophrenia brains, further revealing reduced cell-type epigenetic distinction in schizophrenia.

scholarly journals Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Marco Schmidt ◽  
Tiago Maié ◽  
Edgar Dahl ◽  
Ivan G. Costa ◽  
Wolfgang Wagner
Keyword(s):  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Basic Research ◽  
Cell Types ◽  
Cell Type ◽  
Cpg Sites ◽  
Targeted Analysis ◽  
Cell Type Specific ◽  
Induced Pluripotent

Abstract Background The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand—methylated or non-methylated—and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing. Results We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a “FibroScore” correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types. Conclusion This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.

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 Single-cell multi-omic profiling of chromatin conformation and DNA methylome

2018 ◽  
Author(s):  
Dong-Sung Lee ◽  
Chongyuan Luo ◽  
Jingtian Zhou ◽  
Sahaana Chandran ◽  
Angeline Rivkin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Cell Types ◽  
Mouse Cell ◽  
Regulatory Sequences ◽  
Chromatin Conformation ◽  
Cell Type ◽  
Dynamic Regulation ◽  
Single Nucleus ◽  
Cell Type Specific

AbstractRecent advances in the development of single cell epigenomic assays have facilitated the analysis of gene regulatory landscapes in complex biological systems. Methods for detection of single-cell epigenomic variation such as DNA methylation sequencing and ATAC-seq hold tremendous promise for delineating distinct cell types and identifying their critical cis-regulatory sequences. Emerging evidence has shown that in addition to cis-regulatory sequences, dynamic regulation of 3D chromatin conformation is a critical mechanism for the modulation of gene expression during development and disease. It remains unclear whether single-cell Chromatin Conformation Capture (3C) or Hi-C profiles are suitable for cell type identification and allow the reconstruction of cell-type specific chromatin conformation maps. To address these challenges, we have developed a multi-omic method single-nucleus methyl-3C sequencing (sn-m3C-seq) to profile chromatin conformation and DNA methylation from the same cell. We have shown that bulk m3C-seq and sn-m3C-seq accurately capture chromatin organization information and robustly separate mouse cell types. We have developed a fluorescent-activated nuclei sorting strategy based on DNA content that eliminates nuclei multiplets caused by crosslinking. The sn-m3C-seq method allows high-resolution cell-type classification using two orthogonal types of epigenomic information and the reconstruction of cell-type specific chromatin conformation maps.

scholarly journals Calling differential DNA methylation at cell-type resolution: an objective status-quo

2019 ◽  
Author(s):  
Han Jing ◽  
Shijie C. Zheng ◽  
Charles E. Breeze ◽  
Stephan Beck ◽  
Andrew E. Teschendorff
Keyword(s):  
Dna Methylation ◽  
Association Studies ◽  
Cell Types ◽  
Status Quo ◽  
Specific Cell ◽  
Cell Type ◽  
Causal Pathways ◽  
Key Issues ◽  
Cell Type Specific ◽  
Different Cell Types

AbstractDue to cost and logistical reasons, Epigenome-Wide-Association Studies (EWAS) are normally performed in complex tissues, resulting in average DNA methylation profiles over potentially many different cell-types, which can obscure important cell-type specific associations with disease. Identifying the specific cell-types that are altered is a key hurdle for elucidating causal pathways to disease, and consequently statistical algorithms have recently emerged that aim to address this challenge. Comparisons between these algorithms are of great interest, yet here we find that the main comparative study so far was substantially biased and potentially misleading. By using this study as an example, we highlight some of the key issues that need to be considered to ensure that future assessments between methods are more objective.

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