DOI: 10.5061/dryad.b8gtht78n
title: Micro-anatomic alterations of the placenta in a non-human primate model of gestational protein-restriction

description: Objectives: Maternal protein malnutrition is associated with
 impaired fetal growth, and lifetime consequences for the offspring. Our
 group has previously developed a model of protein-restriction in the
 non-human primate, which was associated with fetal growth restriction,
 stillbirth, decreased placental perfusion, and evidence of fetal hypoxia,
 suggesting perturbed vascular development. Previous assessments
 of placental vasculature have relied upon stereological or
 vascular casting methods, but these methods have limitations. Our
 objective was to histologically characterize the micro-anatomic
 alterations associated with adverse pregnancy outcomes using a newer
 method that permits investigation of the 3D vascular structure and
 surrounding histology. Methods: Rhesus macaques were assigned in
 the pre-gestational period to a control diet that contained 26% protein,
 or study diet containing 13% protein (50% PR diet). Placental tissue was
 collected at delivery and processed using a clarification,
 immunohistochemistry, and confocal microscopy protocol published
 previously by our group. 3-dimensional reconstructions and quantitative
 analysis of the vascular micro-anatomy was performed using analysis
 software (Imaris®) and statistical analysis incorporated maternal,
 pregnancy, and perinatal outcomes. Results: In unadjusted
 analysis, when comparing those pregnancies on a 50% PR diet (n=4) with
 those on a control diet (n=4), protein-restriction diet was associated
 with decreased maternal pre-pregnancy weight (difference of -1.975kg, 95%
 CI -3.267 to -0.6826).  When controlling for maternal
 pre-pregnancy weight, fetal sex, and latency from tissue collection to
 imaging, a gestational protein-restriction diet was associated with
 decreases in total vascular length, total vascular surface area, total
 vascular volume, and vascular density. Conclusion: In this pilot
 study, a gestational protein-restriction diet was associated with changes
 in the placental micro-vasculature, which may be related to the observed
 adverse pregnancy outcomes and perturbed placental perfusion demonstrated
 in this model.
description: Files were uploaded to Imaris® 9.2.1 software (Bitplane AG,
 Zurich, Switzerland) and converted to .ims format. All data acquisition
 occurred on a custom-built Dell Precision Tower 7910 workstation with an
 Intel Xeon CPU E5-2623 v4 2.60GHz dual processor with 192GB RAM. Once
 uploaded into Imaris®, z-stacks were cropped to isolate the area of
 interest. Pre-processing steps were minimized to prevent loss or
 alteration of data: a linear stretch was performed to utilize the entire
 voxel intensity histogram (0 to 65,535), and a background subtraction with
 a large filter width. Using the filament module, a
 pipeline was created to minimize processing parameters between tissues. A
 single starting point was selected subjectively for each tissue, and a
 seed point minimum of 5.00µm was set based upon the size of the smallest
 structure of interest (i.e. capillary diameter). Within the filamentation
 module of Imaris®, the “no loop” algorithm was used, and the variables of
 interest (total vascular length, total vascular surface area, total
 vascular volume, total number of termini, median branching angle, sholl
 Intersections) were collected and exported for analysis. A full
 description of the measured and calculated variables of interest are
 included in the Supplemental. Maternal and pregnancy data was compiled
 including: maternal age at delivery, maternal pre-pregnancy weight,
 maternal gestational weight gain, maternal weight at delivery, fetal sex,
 fetal weight, placenta weight, fetal:placental weight ratio, gestational
 age at delivery, and latency from tissue collection to imaging.
 Statistical Analysis
 Statistical analysis was completed using GraphPad Prism version
 8.2.0. Descriptive statistics (compared using student’s t-test, with
 significance threshold of p &lt; 0.01), histograms, correlations
 (compared using Pearson’s correlation with a threshold p&lt;0.01), and
 univariate linear regression methods were utilized to assess relationships
 between variables of interest. A multiple linear regression model was
 generated to isolate the effect of group assignment on when controlling
 for multiple covariates chosen based on either a detected significance
 with the univariate analysis or based on previous studies.
description: None.