Unknown

Dataset Information

0

Integrated Placental Modelling of Histology with Gene Expression to Identify Functional Impact on Fetal Growth.


ABSTRACT: Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Altered placental formation and functional capacity are major contributors to FGR pathogenesis. Relating placental structure to function across the placenta in healthy and FGR pregnancies remains largely unexplored but could improve understanding of placental diseases. We investigated integration of these parameters spatially in the term human placenta using predictive modelling. Systematic sampling was able to overcome heterogeneity in placental morphological and molecular features. Defects in villous development, elevated fibrosis, and reduced expression of growth and functional marker genes (IGF2, VEGA, SLC38A1, and SLC2A3) were seen in age-matched term FGR versus healthy control placentas. Characteristic histopathological changes with specific accompanying molecular signatures could be integrated through computational modelling to predict if the placenta came from a healthy or FGR pregnancy. Our findings yield new insights into the spatial relationship between placental structure and function and the etiology of FGR.

SUBMITTER: Yong HEJ 

PROVIDER: S-EPMC10093760 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

altmetric image

Publications

Integrated Placental Modelling of Histology with Gene Expression to Identify Functional Impact on Fetal Growth.

Yong Hannah Ee Juen HEJ   Maksym Katarzyna K   Yusoff Muhammad Ashraf Bin MAB   Salazar-Petres Esteban E   Nazarenko Tatiana T   Zaikin Alexey A   David Anna L AL   Hillman Sara L SL   Sferruzzi-Perri Amanda N AN  

Cells 20230406 7


Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Altered placental formation and functional capacity are major contributors to FGR pathogenesis. Relating placental structure to function across the placenta in healthy and FGR pregnancies remains largely unexplored but could improve understanding of placental diseases. We investigated integration of these parameters spatially in the term human placenta using predictive modelling. Systematic sampling was able  ...[more]

Similar Datasets

| S-EPMC5562325 | biostudies-literature
| S-EPMC6170611 | biostudies-literature
| S-EPMC6124516 | biostudies-literature
| S-EPMC7616564 | biostudies-literature
| S-EPMC5357827 | biostudies-literature
| S-EPMC7188669 | biostudies-literature
| S-EPMC5526045 | biostudies-other
| S-EPMC10947591 | biostudies-literature
| S-EPMC9608316 | biostudies-literature
| S-EPMC4884669 | biostudies-literature