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

Abstract

BackgroundThe 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.

Physical activity specifically evokes release of cell-free DNA from granulocytes thereby affecting liquid biopsy

“…As shown by Schmidt et al, the approach allows reliable estimation for the cellular composition by targeted analysis of the individual CpGs. 15 Granulocytes are the major source of cfDNA during exercise. Cell type specific proportions increase significantly from 54.1% (50.1; 61.8) to 90.2% (79.7; 94.4) after exercise (Figure C,D), which is not reflected by the correlation of the cell count and cfDNA concentrations (Figure E).…”

“…Deconvolution of cell types is based on CG dinucleotides (CpGs) that are specifically hypomethylated in different cell types. Pre-validated CpG methylation sites were selected to estimate the origin of cfDNA from lymphocytes (cg17587997, FYN protooncogene ( FYN )), 18 monocytes (cg10480329, centromere protein A ( CENPA )), 18 and granulocytes (cg05398700, WD repeat domain 20 ( WDR20 )), 18 and to differentiate leukocytes from other cells (cg10673833, myosin IG ( MYO1G )), 15 including endothelial cells, epithelial cells, fibroblasts, mesenchymal stem cells, hepatocytes, and muscle cells, described in Schmidt et al, 2020. 15 We then generated a reference-based non-negative least-squares (NNLS) algorithm for the four CpGs of these cellular categories.…”

“…Pre-validated CpG methylation sites were selected to estimate the origin of cfDNA from lymphocytes (cg17587997, FYN protooncogene ( FYN )), 18 monocytes (cg10480329, centromere protein A ( CENPA )), 18 and granulocytes (cg05398700, WD repeat domain 20 ( WDR20 )), 18 and to differentiate leukocytes from other cells (cg10673833, myosin IG ( MYO1G )), 15 including endothelial cells, epithelial cells, fibroblasts, mesenchymal stem cells, hepatocytes, and muscle cells, described in Schmidt et al, 2020. 15 We then generated a reference-based non-negative least-squares (NNLS) algorithm for the four CpGs of these cellular categories. 15,18 An Excel calculation tool for cell type deconvolution based on the pyrosequencing measurements will be provided as a supplemental file after final publication of the article.…”

“…15 We then generated a reference-based non-negative least-squares (NNLS) algorithm for the four CpGs of these cellular categories. 15,18 An Excel calculation tool for cell type deconvolution based on the pyrosequencing measurements will be provided as a supplemental file after final publication of the article.…”

“…Schmidt et al have shown that a targeted approach can be utilized to determine the cell types of origin reliably. 15 In the present study, we use targeted methylation analysis with pre-validated CpG sites to assess the origin of cfDNA in healthy participants during exercise and patients with hematological malignancies under resting conditions. We provide evidence that targeted analysis of individual CpGs facilitates reliable, and fast analysis with low amount of starting material, and that exercise can interfere with clinical accuracy of methylation-based analysis.…”