Nuclear segmentation and quantification
Stardist 3D employs a neuronal network to separate densely packed nuclei
in 3D image stacks33. The model was trained with 5
manually annotated image stacks, and thereafter used to segment
placental nuclei.
Quantitative analysis of predicted label maps was performed in FIJI with
3D ROI manager28. Nuclear density was defined as the
number of nuclei relative to the villous volume (#nuclei/villous volume
in mm3).
Segmentation of syncytial knots was performed with KnotMiner ,
available at https://github.com/stegmaierj/KnotMiner.KnotMiner was built as an extension package for the MATLAB
toolbox SciXMiner34. The volume of knots was
normalised to the volume of the corresponding villus to determine knot
fraction. Knot shape was described by elongation index (EI=intermediate
principal axis/ longest principal axis) and flatness index (FI=Short
principal axis/intermediate principal axis).
Statistical
analysis
Data are given as mean ±SD. GraphPad prism 5.0 was used for statistical
analysis and graph representation. ANOVA with Bonferroni post-test was
used to assess significant differences in parameters that are normally
distributed. In case data was assumed not normally distributed Kruskal
Wallis with Dunn’s post-test was employed. If normality could not be
statistically determined (by Shapiro-Wilk test) because of too small
sample size, the assumption of normality was made. P values of
<0.05 were considered statistically significant.