Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes.

BACKGROUND: Placental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes. METHODS: Using our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters. RESULTS: Pairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups. CONCLUSIONS: Our results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions

Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes

Abstract Background Placental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes. Methods Using our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters. Results Pairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups. Conclusions Our results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions

Additional file 1 of Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes

Additional file 1: Fig. S1. Workflow of the study. Fig. S2. Pairwise comparison of disease groups vs the term control, for all omics data. Fig. S3. Pairwise comparison of the FGR+HDP group vs the control-PT group across all omics datatypes. Fig. S4. Shared analytes between the FGR+HDP and the two control groups across all omics data. Fig. S5. Hierarchical clustering for key pairwise comparisons. Fig. S6. RNA canonical pathways and metabolomics enrichment pathway analysis, comparing the FGR+HDP and control groups. Fig. S7. Correlation of expression between clusters II and III, and clusters I and IV. Fig. S8. Deconvolution of cell type in placental bulk RNAseq. Fig. S9. Performance of the elastic net regression in cluster label prediction. Fig. S10. Causal models prediction of SNF cluster labels. Fig. S11. Gene expression in the placenta and maternal plasma. Table S1. Primers for PCR validation. Table S2. Clinical characteristics of the cohort. Table S3. The number of differentially expressed omics analytes across pairwise comparisons. Table S4. Distributions of clinical variables across the SNF clusters. Table S5. Distributions of maternal vascular malperfusion (MVM) lesions across the clinical syndromes and SNF clusters

Beyond team types and taxonomies: a dimensional scaling conceptualization for team description

Research on teams has prompted the development of many alternative taxonomies but little consensus on how to differentiate team types. We show that there is greater consensus on the underlying dimensions differentiating teams than there is on how to use those dimensions to generate categorical team types. We leverage this literature to create a conceptual framework for differentiating teams that relies on a dimensional scaling approach with three underlying constructs: skill differentiation, authority differentiation, and temporal stability