Definition and Quantification of Three-Dimensional Imaging Targets to Phenotype Pre-Eclampsia Subtypes: An Exploratory Study

International Journal of Molecular Sciences, vol. 24, pp. 3240

Abstract

Pre-eclampsia is a severe placenta-related complication of pregnancy with limited early diagnostic and therapeutic options. Aetiological knowledge is controversial, and there is no universal consensus on what constitutes the early and late phenotypes of pre-eclampsia. Phenotyping of native placental three-dimensional (3D) morphology offers a novel approach to improve our understanding of the structural placental abnormalities in pre-eclampsia. Healthy and pre-eclamptic placental tissues were imaged with multiphoton microscopy (MPM). Imaging based on inherent signal (collagen, and cytoplasm) and fluorescent staining (nuclei, and blood vessels) enabled the visualization of placental villous tissue with subcellular resolution. Images were analysed with a combination of open source (FIJI, VMTK, Stardist, MATLAB, DBSCAN), and commercially (MATLAB) available software. Trophoblast organization, 3D-villous tree structure, syncytial knots, fibrosis, and 3D-vascular networks were identified as quantifiable imaging targets. Preliminary data indicate increased syncytial knot density with characteristic elongated shape, higher occurrence of paddle-like villous sprouts, abnormal villous volume-to-surface ratio, and decreased vascular density in pre-eclampsia compared to control placentas. The preliminary data presented indicate the potential of quantifying 3D microscopic images for identifying different morphological features and phenotyping pre-eclampsia in placental villous tissue.

Authors 8

1. Sammy Hermans

   Maastricht University

   Affiliation as printed

   Department of Genetics and Cell Biology, Maastricht University, 6200 MD Maastricht, The Netherlands

2. Jacob Pilon

   Maastricht University

   Affiliation as printed

   Department of Genetics and Cell Biology, Maastricht University, 6200 MD Maastricht, The Netherlands

3. Dennis Eschweiler Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, 52074 Aachen, Germany

4. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, 52074 Aachen, Germany

5. C. Severens‐Rijvers

   Maastricht University Medical Centre

   Affiliation as printed

   Pathology, GROW, Maastricht University Medical Centre (MUMC), 6229 HX Maastricht, The Netherlands

6. Salwan Al‐Nasiry

   Maastricht University Medical Centre

   Affiliation as printed

   Obstetrics and Gynaecology, GROW, Maastricht University Medical Centre (MUMC), 6229 HX Maastricht, The Netherlands

7. Marc A.M.J. van Zandvoort Aachen Institute for Molecular Cardiovascular Research IMCAR

   Maastricht University · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Genetics and Cell Biology, GROW, CARIM, MHeNS, Maastricht University, 6200 MD Maastricht, The Netherlands

   Institute for Molecular Cardiovascular Research IMCAR, University Hospital RWTH Aachen, 52074 Aachen, Germany

8. Dimitrios Kapsokalyvas corresponding Aachen Interdisciplinary Centre for Clinical Research IZKF

   Maastricht University · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Genetics and Cell Biology, Maastricht University, 6200 MD Maastricht, The Netherlands

   Interdisciplinary Centre for Clinical Research IZKF, University Hospital RWTH Aachen, 52074 Aachen, Germany