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.

Nanomaterials for Natural Killer Cell-Based Immunoimaging and Immunotherapies in Cancer.

Natural killer (NK) cells are an important component of the tumor immunosurveillance; activated NK cells can recognize and directly lyse tumor cells eliciting a potent antitumor immune response. Due to their intrinsic ability to unleash cytotoxicity against tumor cells, NK cell-based adoptive cell therapies have gained rapid clinical significance, and many clinical trials are ongoing. However, priming and activating NK cells, infiltration of activated NK cells in the immunosuppressive tumor microenvironment, and tracking the infiltrated NK cells in the tumors remain a critical challenge. To address these challenges, NK cells have been successfully interfaced with nanomaterials where the morphology, composition, and surface characteristics of nanoparticles (NPs) were leveraged to enable longitudinal tracking of NK cells in tumors or deliver therapeutics to prime NK cells. Distinct from other published reviews, in this tutorial review, we summarize the recent findings in the past decade where NPs were used to label NK cells for immunoimaging or deliver treatment to activate NK cells and induce long-term immunity against tumors. We discuss the NP properties that are key to surmounting the current challenges in NK cells and the different strategies employed to advance NK cells-based diagnostics and therapeutics. We conclude the review with an outlook on future directions in NP-NK cell hybrid interfaces, and overall clinical impact and patient response to such interfaces that need to be addressed to enable their clinical translation.