Monitoring Metabolic Changes in Response to Chemotherapies in Cancer with Raman Spectroscopy and Metabolomics

Resistance to clinical therapies remains a major barrier in cancer management. There is a critical need for rapid and highly sensitive diagnostic tools that enable early prediction of treatment response to allow accurate clinical decisions. Here, Raman spectroscopy was employed to monitor changes in key metabolites as early predictors of response in KRAS-mutant colorectal cancer (CRC) cells, HCT116, treated with chemotherapies. We show at the single cell level that HCT116 is resistant to cetuximab (CTX), the first-line treatment in CRC, but this resistance can be overcome with pre-sensitization of cells with oxaliplatin (OX). In combination treatment of CTX + OX, sequential delivery of OX followed by CTX rather than simultaneous administration of drugs was observed to be critical for effective therapy. Our results demonstrated that metabolic changes are well aligned to cellular mechanical changes where Young's modulus decreased after effective treatment, indicating that both changes in mechanical properties and metabolism in cells are likely responsible for cancer proliferation. Raman findings were verified with mass spectrometry (MS) metabolomics, and both platforms showed changes in lipids, nucleic acids, and amino acids as predictors of resistance/response. Finally, key metabolic pathways enriched were identified when cells are resistant to CTX but downregulated with effective treatment. This study highlights that drug-induced metabolic changes both at the single cell level (Raman) and ensemble level (MS) have the potential to identify mechanisms of response to clinical cancer therapies.

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.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Physicochemical Properties and Route of Systemic Delivery Control the In Vivo Dynamics and Breakdown of Radiolabeled Gold Nanostars

The in vivo dynamics of nanoparticles requires a mechanistic understanding of multiple factors. Here, for the first time, the surprising breakdown of functionalized gold nanostars (F-AuNSs) conjugated with antibodies and 64 Cu radiolabels in vivo and in artificial lysosomal fluid ex vivo, is shown. The short-term biodistribution of F-AuNSs is driven by the route of systemic delivery (intravenous vs intraperitoneal) and long-term fate is controlled by the tissue type in vivo. In vitro studies including endocytosis pathways, intracellular trafficking, and opsonization, are combined with in vivo studies integrating a milieu of spectroscopy and microcopy techniques that show F-AuNSs dynamics is driven by their physicochemical properties and route of delivery. F-AuNSs break down into sub-20 nm broken nanoparticles as early as 7 days postinjection. Martini coarse-grained simulations are performed to support the in vivo findings. Simulations suggest that shape, size, and charge of the broken nanoparticles, and composition of the lipid membrane depicting various tissues govern the interaction of the nanoparticles with the membrane, and the rate of translocation across the membrane to ultimately enable tissue clearance. The fundamental study addresses critical gaps in the knowledge regarding the fate of nanoparticles in vivo that remain a bottleneck in their clinical translation.

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.