Single-cell RNA sequencing identifies CXADR as a fate determinant of the placental exchange surface.

The placenta is the critical interface between mother and fetus, and consequently, placental dysfunction underlies many pregnancy complications. Placental formation requires an adequate expansion of trophoblast stem and progenitor cells followed by finely tuned lineage specification events. Here, using single-cell RNA sequencing of mouse trophoblast stem cells during the earliest phases of differentiation, we identify gatekeepers of the stem cell state, notably Nicol1, and uncover unsuspected trajectories of cell lineage diversification as well as regulators of lineage entry points.

Genetic Variants Associated With Preeclampsia and Maternal Serum sFLT1 Levels.

Background: Elevated maternal serum sFLT1 (soluble fms-like tyrosine kinase 1) has a key role in the pathophysiology of preeclampsia. We sought to determine the relationship between the maternal and fetal genome and maternal levels of sFLT1 at 12, 20, 28, and 36 weeks of gestational age (wkGA).

Methods: We studied a prospective cohort of nulliparous women (3968 mother-child pairs).

GDF15 linked to maternal risk of nausea and vomiting during pregnancy.

GDF15, a hormone acting on the brainstem, has been implicated in the nausea and vomiting of pregnancy, including its most severe form, hyperemesis gravidarum (HG), but a full mechanistic understanding is lacking. Here we report that fetal production of GDF15 and maternal sensitivity to it both contribute substantially to the risk of HG. We confirmed that higher GDF15 levels in maternal blood are associated with vomiting in pregnancy and HG.

Placental Streptococcus agalactiae DNA is associated with neonatal unit admission and foetal pro-inflammatory cytokines in term infants.

Streptococcus agalactiae (Group B Streptococcus; GBS) is a common cause of sepsis in neonates. Previous work detected GBS DNA in the placenta in ~5% of women before the onset of labour, but the clinical significance of this finding is unknown. Here we re-analysed this dataset as a case control study of neonatal unit (NNU) admission.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

Objectives: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.

Design: Case-cohort design within a prospective cohort study.

Setting: Cambridge, UK.