Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Preeclampsia is a life-threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at-risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients.

First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics

Preterm birth (PTB) is the leading cause of infant deaths globally. Current clinical measures often fail to identify women who may deliver preterm. Therefore, accurate screening tools are imperative for early prediction of PTB. Here, we show that Raman spectroscopy is a promising tool for studying biological interfaces, and we examine differences in the maternal metabolome of the first trimester plasma of PTB patients and those that delivered at term (healthy). We identified fifteen statistically significant metabolites that are predictive of the onset of PTB. Mass spectrometry metabolomics validates the Raman findings identifying key metabolic pathways that are enriched in PTB. We also show that patient clinical information alone and protein quantification of standard inflammatory cytokines both fail to identify PTB patients. We show for the first time that synergistic integration of Raman and clinical data guided with machine learning results in an unprecedented 85.1% accuracy of risk stratification of PTB in the first trimester that is currently not possible clinically. Correlations between metabolites and clinical features highlight the body mass index and maternal age as contributors of metabolic rewiring. Our findings show that Raman spectral screening may complement current prenatal care for early prediction of PTB, and our approach can be translated to other patient-specific biological interfaces.

3D tissue-engineered lung models to study immune responses following viral infections of the small airways

Small airway infections caused by respiratory viruses are some of the most prevalent causes of illness and death. With the recent worldwide pandemic due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is currently a push in developing models to better understand respiratory diseases. Recent advancements have made it possible to create three-dimensional (3D) tissue-engineered models of different organs. The 3D environment is crucial to study physiological, pathophysiological, and immunomodulatory responses against different respiratory conditions. A 3D human tissue-engineered lung model that exhibits a normal immunological response against infectious agents could elucidate viral and host determinants. To create 3D small airway lung models in vitro, resident epithelial cells at the air-liquid interface are co-cultured with fibroblasts, myeloid cells, and endothelial cells. The air-liquid interface is a key culture condition to develop and differentiate airway epithelial cells in vitro. Primary human epithelial and myeloid cells are considered the best 3D model for studying viral immune responses including migration, differentiation, and the release of cytokines. Future studies may focus on utilizing bioreactors to scale up the production of 3D human tissue-engineered lung models. This review outlines the use of various cell types, scaffolds, and culture conditions for creating 3D human tissue-engineered lung models. Further, several models used to study immune responses against respiratory viruses, such as the respiratory syncytial virus, are analyzed, showing how the microenvironment aids in understanding immune responses elicited after viral infections.