Methylation profiling

Dataset Information

0

Cell-specific Characterization of the Placental Methylome


ABSTRACT: Background: DNA methylation (DNAm) profiling has emerged as a powerful tool for characterizing the placental methylome. However, previous studies have focused primarily on whole placental tissue, which is a mixture of epigenetically distinct cell populations. Here, we present the first methylome-wide analysis of first trimester (n=9) and term (n=19) human placental samples of four cell populations: trophoblasts, Hofbauer cells, endothelial cells, and stromal cells, using the Illumina EPIC methylation array, which quantifies DNAm at >850,000 CpGs. Results: The most distinct DNAm profiles were those of placental trophoblasts, which are central to many pregnancy-essential functions, and Hofbauer cells, which are a rare fetal-derived macrophage population. Cell-specific DNAm occurs at functionally-relevant genes, including genes associated with placental development and preeclampsia. Known placental-specific methylation marks, such as those associated with genomic imprinting, repetitive element hypomethylation, and placental partially methylated domains, were found to be more pronounced in trophoblasts and often absent in Hofbauer cells. Lastly, we characterize the cell composition and cell-specific DNAm dynamics across gestation. Conclusions: Our results provide a comprehensive analysis of DNAm in human placental cell types from first trimester and term pregnancies. This data will serve as a useful DNAm reference for future placental studies, and we provide access to this data via download from dbGAP (phs002013.v1.p1), through interactive exploration from the web browser (https://robinsonlab.shinyapps.io/Placental_Methylome_Browser/), and through the R package planet, which allows estimation of cell composition directly from placental DNAm data.

ORGANISM(S): Homo sapiens

PROVIDER: GSE159526 | GEO | 2020/10/21

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2015-05-20 | E-GEOD-59126 | biostudies-arrayexpress
2020-08-20 | GSE131696 | GEO
2020-06-08 | ST001398 | MetabolomicsWorkbench
2018-12-25 | GSE109682 | GEO
2011-11-15 | E-GEOD-31680 | biostudies-arrayexpress
2015-04-29 | E-MTAB-3309 | biostudies-arrayexpress
2019-11-05 | GSE132421 | GEO
2024-03-01 | E-MTAB-12795 | biostudies-arrayexpress
2015-05-20 | GSE59126 | GEO
| PRJNA707075 | ENA