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 GEO (GSE159526), 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.

The human pan-tissue epigenetic clock is widely used for estimating age across the entire lifespan, but it does not lend itself well to estimating gestational age (GA) based on placental DNAm methylation (DNAm) data. We replicate previous findings demonstrating a strong correlation between GA and genome-wide DNAm changes. Using substantially more DNAm arrays (n=1,102 in the training set) than a previous study, we present three new placental epigenetic clocks: 1) a robust placental clock (RPC) which is unaffected by common pregnancy complications (e.g., gestational diabetes, preeclampsia), and 2) a control placental clock (CPC) constructed using placental samples from pregnancies without known placental pathology, and 3) a refined RPC for uncomplicated term pregnancies. These placental clocks are highly accurate estimators of GA based on placental tissue; e.g., predicted GA based on RPC is highly correlated with actual GA (r>0.95 in test data, median error less than one week). We show that epigenetic clocks derived from cord blood or other tissues do not accurately estimate GA in placental samples. While fundamentally different from Horvath’s pan-tissue epigenetic clock, placental clocks closely track fetal age during development and may have interesting applications.

Background The influence of genetics on variation in DNA methylation (DNAme) is well documented. Yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches to address confounding by population stratification using DNAme data may not generalize to populations or tissues outside those in which they were developed. To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PlaNET (Placental DNAme Elastic Net Ethnicity Tool), using five cohorts with Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples that is also compatible with the newer EPIC platform. Results Data from 509 placental samples were used to develop PlaNET and show that it accurately predicts (accuracy = 0.938, kappa = 0.823) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), and produces ethnicity probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP arrays (> 2.5 million SNP) and ancestry informative markers ( n = 50 SNPs). PlaNET’s ethnicity classification relies on 1860 HM450K microarray sites, and over half of these were linked to nearby genetic polymorphisms ( n = 955). Our placental-optimized method outperforms existing approaches in assessing population stratification in placental samples from individuals of Asian, African, and Caucasian ethnicities. Conclusion PlaNET provides an improved approach to address population stratification in placental DNAme association studies. The method can be applied to predict ethnicity as a discrete or continuous variable and will be especially useful when self-reported ethnicity information is missing and genotyping markers are unavailable. Electronic supplementary material The online version of this article (10.1186/s13072-019-0296-3) contains supplementary material, which is available to authorized users.