Sammy Hermans1, Jacob Pilon1, Dennis
Eschweiler2, Johannes Stegmaier2,
Carmen A. H. Severens – Rijvers3, Salwan
Al-Nasiry4, Marc van Zandvoort5,6,
Dimitrios Kapsokalyvas1,7
1Department of Genetics and Cell biology, Maastricht
University, Maastricht, The Netherlands.2Institute of Imaging and Computer Vision, RWTH Aachen
University, Aachen, Germany3Pathology, Maastricht University Medical Centre,
Maastricht (MUMC), The Netherlands.4Obstetrics and Gynaecology, GROW, Maastricht
University Medical Centre (MUMC), Maastricht, The Netherlands.
5Department of Genetics and Cell biology, GROW, CARIM,
MHeNS, Maastricht University, Maastricht, The Netherlands.
6Institute for Molecular Cardiovascular Research
IMCAR, University Hospital RWTH Aachen, Aachen, Germany
7Interdisciplinary Centre for Clinical Research IZKF,
University Hospital RWTH Aachen, Aachen, Germany
* Correspondence: Dimitrios Kapsokalyvas
Address: PO Box 616, 6200 MD Maastricht, The Netherlands
Telephone: N/A
email: d.kapsokalyvas@maastrichtuniversity.nl
Short running title: Phenotyping pre-eclampsia based on
3D-imaging
Abstract
Objective:Pre-eclampsia is a severe placenta related complication of pregnancy and
aetiological knowledge, with limited early diagnostic and therapeutic
options. Phenotyping of native placental three-dimensional (3D)
morphology offers a novel approach to improve our understanding of the
functional and structural placental abnormalities underlying this
clinical syndrome. The aim of this
project was to develop a 3D placental imaging protocol using multiphoton
microscopy (MPM) and demonstrate quantifiable imaging targets for
phenotyping 3D features of pre-eclampsia.
Design: Exploratory pilot
study.
Setting: Single centre, MUMC.
Sample: Formalin fixed placental biopsies from: term control
(n=3), pre-eclampsia (n=3), preterm birth (n=2), 2ndtrimester placenta (n=1), and intra-uterine growth restriction cases
without pre-eclampsia (n=2).
Methods: Placental slabs were visualised with MPM. Collagen and
cytoplasm (based on inherent signal), and fluorescently stained nuclei
and blood vessels, enabled the visualization of villous tissue with
subcellular resolution. Segmentation based on pixel classification, deep
learning, and clustering algorithms were used to generate quantifiable
features.
Main outcome measures: Trophoblast arrangement, 3D-villous tree
structure, syncytial knots, fibrosis, and 3D-vascular networks were
identified as imaging targets. Villous morphology, vascular fraction,
vascular network (i.e., branchpoint density and diameter), nuclear
density, and knot fraction were quantified to describe placental
phenotypes.
Results: Pre-eclamptic placentas had disorganized trophoblast
arrangement, decreased vascular fraction, and altered vessel diameters,
compared to control placentas. The developed 3D-methodology indicated
that placental vasculature, syncytial knotting, and villous growth are
altered in pre-eclampsia.
Conclusion: Our preliminary data demonstrate the potential of the
developed quantification method for phenotyping pre-eclampsia, to
improve future disease stratification.
Keywords: Placenta, multiphoton microscopy, pre-eclampsia,
placental syndromes, stratification.