Leveraging Optimized Transcriptomic and Personalized Stem Cell Technologies to Better Understand Syncytialization Defects in Preeclampsia

Journal Article (Review;Journal)

Understanding the process of human placentation is important to the development of strategies for treatment of pregnancy complications. Several animal and in vitro human model systems for the general study human placentation have been used. The field has expanded rapidly over the past decades to include stem cell-derived approaches that mimic preclinical placental development, and these stem cell-based models have allowed us to better address the physiology and pathophysiology of normal and compromised trophoblast (TB) sublineage development. The application of transcriptomic approaches to these models has uncovered limitations that arise when studying the distinctive characteristics of the large and fragile multinucleated syncytiotrophoblast (STB), which plays a key role in fetal-maternal communication during pregnancy. The extension of these technologies to induced pluripotent stem cells (iPSCs) is just now being reported and will allow, for the first time, a reproducible and robust approach to the study of the developmental underpinnings of late-manifesting diseases such as preeclampsia (PE) and intrauterine growth retardation in a manner that is patient- and disease-specific. Here, we will first focus on the application of various RNA-seq technologies to TB, prior limitations in fully accessing the STB transcriptome, and recent leveraging of single nuclei RNA sequencing (snRNA-seq) technology to improve our understanding of the STB transcriptome. Next, we will discuss new stem-cell derived models that allow for disease- and patient-specific study of pregnancy disorders, with a focus on the study of STB developmental abnormalities in PE that combine snRNA-seq approaches and these new in vitro models.

Full Text

Duke Authors

Cited Authors

  • Choi, S; Khan, T; Roberts, RM; Schust, DJ

Published Date

  • March 30, 2022

Published In

Volume / Issue

  • 13 /

Electronic International Standard Serial Number (EISSN)

  • 1664-8021

Digital Object Identifier (DOI)

  • 10.3389/fgene.2022.872818

Citation Source

  • Scopus