Abstract 27: Unravelling Cell-specific Interactions At The Preeclamptic Maternal-foetal Interface From Early To Late Pregnancy

Autor: Naveed Ishaque, Anne Cathrine Staff, Martin Gauster, Kerim Secener, Olivia Debnath, Dominik N Mueller, Ralf Dechend, Florian Herse, Katja Sallinger, Olivia Nonn, Daniela Sofia Valdes, Sebastian Tiesmeyer, Amin El-Heliebi, Cornelius Fischer
Rok vydání: 2021
Předmět:
Zdroj: Hypertension. 78
ISSN: 1524-4563
0194-911X
DOI: 10.1161/hyp.78.suppl_1.27
Popis: Preeclamptic syndrome arrises in the fetal part of the placenta (villi). In this study, we analyse placental development by single nuclei RNA-sequencing in early and term pregnancy and draw conclusions about pathological processes in preeclampsia (PE) that originate early in gestation. We profiled the transcriptome of 101,067 nuclei obtained from a total of 12 pregnancies, spanning early, term and PE doners. Using unsupervised computational approaches, we identified 12 and 16 different cell types and states in decidua and villi, respectively. Our comprehensively identified catalogue of cell types and states aligns well with the previous single cell studies. We identified different subpopulations of syncytiotrophoblast and GATA3+/GREM2+ trophoblast stem cells (TSC) in villi. Through gestation, gene expression in cell populations from the matrisome or vascular environments show dynamic expression reflecting vascular development associated with spiral artery remodelling and concordant decidual stroma reorganisation. Global differential gene expression analysis shows that trophoblast cell types are most dysregulated in PE. Cell-cell communication analysis revealed important dysregulation between villi and decidual cell types. The secretory signalling characteristic of this trophoblastic disease may be used for early biomarker screening. Overall, this study paves the way to a deeper understanding of the early pathophysiology of PE. Figure 1: Villi (v) and decidua (d) cell clusters from early, late control and preeclampsia (PE) villi and decidua visualised as a UMAP. Datasets were integrated separately for each tissue and merged for embedding.
Databáze: OpenAIRE