Laying the Foundations of Placentomics

The science known as “omics” seeks to characterize an entire set of molecular products carried by or made by a cell or organism. Genomics, for example, is the identification of genes, and proteomics is the study of proteins produced. Scientists hoping to improve birth outcomes for pregnant people and their offspring are looking to the latest in omics technology to better understand the development and function of the placenta throughout gestation. Making use of omics data for the future development of diagnostics and treatments of placenta-related disorders requires a solid foundational understanding of the data. A team of scientists funded by NICHD through the Human Placenta Project (HPP) is collecting and sharing those baseline data. The team is already finding useful applications for the data, including using them to estimate ethnicity, sample cell composition, and calculate gestational age.

Estimating Ethnicity

Diseases of the placenta can result in growth restriction, preterm birth, and stillbirth. These disorders are all thought to have a genetic component. When analyzing genetic data, researchers need to account for various factors, like race, ethnicity, and age, to ensure that any observed differences are caused by genes and no other factors. However, data on the ancestry of donors of fetal and placental tissues are not always readily available. To address this gap, an international team of scientists, led by Wendy Robinson, Ph.D., has figured out how to predict ethnicity by using omics data.

In the study, the team introduces the Placental DNAme Elastic Net Ethnicity Tool (PlaNET). It is based on the methylome, the genome-wide pattern of DNA methylation (DNAme), which is responsible for regulating gene expression. PlaNET can be used to predict ethnicity when self-reported ethnicity/ancestry or other predictive genetic markers are not available.

Determining Gestational Age

Gestational age is another important factor that is often missing in clinical and research settings. It is often estimated from a woman’s last menstrual cycle, but even these data can be absent. In another study, Robinson’s team in collaboration with other groups used DNAme data collected across gestation to construct a “clock” that can be used to determine gestational age. DNAme signatures change in predictable ways throughout the pregnancy, which enabled them to construct the tool.

Placental Cell-Specific DNAme Profiles

One of the most important sources of heterogeneity between samples and study cohorts is the cell composition of the samples taken. The placenta is made up of several cell types, including syncytiotrophoblast, cytotrophoblast, endothelial, fibroblast, and Hofbauer cells. To understand this more, the Robinson team isolated these different cell types and identified the cell-specific DNAme signatures. Cell composition of placental tissue changes with gestational age and may also vary depending on the sampling technique. The data collected in the study can help identify developmental differences between the various cell types and estimate the cell composition of each sample. Correcting for cell composition of samples can improve the detection of differences between study groups and reduce false positive results.

Establishing Baselines

The team of researchers continues to publish studies establishing baseline placenta omics profiles for early and term gestation. For example, the scientists are actively working with samples from women exposed to antidepressants and environmental stressors during pregnancy. The research will help move the field of placentomics forward and will give the team and others useful data for comparison and identifying placental abnormalities.

Learn more about the team

Principal Investigator(s):

Learn more about the HPP-funded project:
Using 'omics to build an atlas of placental development and function across pregnancy