Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Abstract

Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

“…Therefore, an FCM data set, despite its smaller size, may have a higher information context than traditional untargeted assay. An example of this is demonstrated in a recent study of normal pregnancy in which a mass cytometry data set, despite its relatively small number of cell types and signaling pathways measured, required a higher number of principal components to account for 90% variance than large microbiome and transcriptomics datasets with tens of thousands of measurements . Therefore, computationally accounting for not only the number of measurements but also the redundancy of the measurements is of critical importance when integrating FCM data with other omics platforms .…”

Multiomic immune clockworks of pregnancy

“…It has heretofore been difficult to synthesize what we know about these diverse inputs in order to gain a holistic understanding of the factors governing the progression of both healthy and pathologic pregnancies. In the past decade, the exponential development of high-content -omic technologies has allowed the simultaneous assessment of the cellular (cytomic), transcriptomic (encompassing the assessment of RNA as well as changes in the microbiome), proteomic, and metabolomic components of regulatory networks in a variety of medical conditions ranging from diabetes to pregnancy [39,55,152,179]. A strength of these multiomic integrative approaches is the potential to incorporate large and complex bodies of information into a universal view of the biological states being studied, and they carry substantial clinical potential.…”

“…In a recent publication by Ghaemi et al, the authors have utilized these machine learning techniques to address human pregnancy. The approach is based on stacked generalization, a technique developed to combine multiple sets of predictions and the use of elastic net (EN) analysis [55]. EN models extend standard linear regression to highdimensional data, where there are many more features than observations (or samples) with complex inter-correlations.…”

“…The recent advent of high-content transcriptomic, epigenomic, proteomic, and cytomic technologies has provided powerful means to capture the complexity of multiomic adaptations during pregnancy [2,3,18,50,54,55,179]. Specifically, a network of interrelated immune features that are chronologically regulated over the course of gestation has recently been demonstrated [2].…”

How Clinical Flow Cytometry Rebooted Sepsis Immunology

“…These high dimensional analysis methods will allow not only the integration of multiple classes of single‐cell mass cytometry measurements but also the integration of mass cytometry data with other high‐throughput biological assays (such as metabolomics, transcriptomics, an proteomics assays) . Such multi‐omic modeling of inflammation in humans may reveal novel biological connections between functional readouts from discrete cell types and circulating molecular factors, which hold promise for the development of integrative bioassays with high predictive performance .…”