A multiomic approach to prediction, causation, and prevention of preterm birth and preeclampsia

A multiomic approach to prediction, causation, and prevention of preterm birth and preeclampsia

Recent advances in both genomics, transcriptomics, and proteomics have allowed for the development of novel technologies with greater capabilities for biomarker profiling and discovery. When coupled with high-content data analysis, these multiomic approaches have allowed researchers to generate high-efficiency data to enable new correlations and insights between the genome, transcriptome, and proteome at a molecular level in health and disease. For example, an area of unmet need where multiomics is poised to impact significantly is pregnancy-related complications, such as preterm birth and preeclampsia. A better understanding of the molecular mechanisms of feto-maternal changes during pregnancy could help save lives and improve birthing processes.

Our distinguished presenters — members of the Gaudillière lab at Stanford University — describe how they employed a translational approach that combines single-cell mass cytometry with high-plex proteomic and metabolomic analyses to study the role of the human immune system in the pathobiology of fundamental clinical problems, including pregnancy pathologies. The presenters also explain how they used novel machine-learning methods to train multiomic models and identify biologically relevant predictive biomarkers to develop robust diagnostics rooted in a precise understanding of underlying pathobiological mechanisms.