BRIDGING COTYLEDON PATHOLOGY AND PERFUSION IN HEALTHY PRIMATE PREGNANCY

Healthy and diseased placentae alike often display some degree of pathology. However, quantitative techniques to characterize common pathologies and their relationship to local maternal hemodynamics in healthy primate placentae are currently limited.

Placentae from seven rhesus macaques were imaged by MRI at three time points across mid- to late-gestation, to quantify placental blood volume, flow, and perfusion from maternal spiral arteries across pregnancy. Near term, we collected placental cotyledons, digitized hematoxylin/eosin-stained slides, then segmented and annotated sub-tissues and major pathologies (intervillous gaps, fibrin deposition, villous agglutination, inflammatory agglutination, and stromal mineralization) within each cotyledon. Individual pathologies were assessed in relation to each other and MRI perfusion metrics, in a cotyledon-specific manner. Parallel analyses were performed to investigate both basic (Spearman correlation) and animal variance-negated (dimensionality-reduction) relationships.

Cotyledons with increased stromal mineralization demonstrated low blood perfusion across pregnancy, alongside significant compensatory changes. Mineralization was further associated with decreased fetal weight, across all sub-tissues. Dimensionality reduction revealed maternal vascular malperfusion-associated pathologies as the largest contributor to dataset variance. Additionally, pathologies commonly associated with healthy placental function demonstrated low cotyledon blood flow and volume at all timepoints, with no evidence of compensatory changes across gestation.

Comprehensive digital annotation revealed several relationships connecting pathology and maternal blood perfusion in the healthy primate pregnancy, at the smallest functional unit of the placenta. This methodological framework embeds pathologist-refined morphological expertise into a quantitative, spatially resolved format that can ground, rather than be replaced by, unsupervised computational approaches to placental analysis.