Cell metabolites are small molecules used or produced by cells and are direct indicators of the cellular state, function, and health. Metabolomic analyses is the characterization of cellular metabolites, and it is key to our understanding of cell function. Current metabolomic methods include mass spectrometry and nuclear magnetic resonance, techniques which require large, expensive instruments and long times for analysis. Here, an alternative approach is to use integrated optical sensing devices to detect the chemical fingerprints of metabolic changes within cells on the time scale of minutes. These devices will fingerprint bacterial metabolites involved in stress responses-a basic component of bacterial survival in dynamic conditions. As part of this work, scientific questions about the relationship between the molecular scale architecture of optical sensors and the ability to differentiate metabolite response in the resulting data will be answered. The demonstration and characterization of this sensing platform will also involve the rapid collection of large data sets and the application of automated big data analysis software to interpret complex sensor signals. Results from these studies will inform understanding of bacterial behavior in diverse habitats ranging from medical infections and industrial contamination to probiotics in human health and agriculture, and microbial interactions influencing nutrient cycling in the environment. The principal investigators and students will engage in an annual two-week summer outreach program for a diverse group of high school students - ASPIRE: Access Student Program to Inspire, Recruit, and Enrich. Graduate, undergraduate and high school students will learn how to define, analyze and solve research problems and communicate results that focus on the importance of metabolomics in health and the environment and the importance of technology innovation.
Practical nanophotonic sensing platforms will provide a new lens for examining dynamic biological function. Control of surface-enhanced Raman scattering sensor architectures and surface chemistry will be investigated to design sensitive receptors that both enable the rapid collection of large data sets and differentiation of metabolite fingerprints in complex biological media. Measurements of phonon-plasmon coupling will inform of chemical interactions between functionalized sensor surfaces and metabolites of interest. Machine learning algorithms will be further developed that are best suited to accurately analyze the large volume of vibrational spectra generated as part of these studies. Libraries of microbial metabolic fingerprints and methods for analysis of spectral information will be disseminated. The fundamental studies of nanophotonic device architecture, nanoparticle surface chemistry, and machine learning analysis of complex Raman spectra will then be utilized for the detection of metabolic fingerprints associated with stress response in polymicrobial bacterial communities. Metabolic changes associated with bacterial stress responses to antimicrobials and inter-species interactions will be measured to develop new methods to understand how to control, nurture, and mitigate various microbe-host interactions-a key application area of the investigated sensing technologies. High throughput detection of the bacterial stress response is important for screening antimicrobial treatments for bacterial contamination in medical and industrial settings. Similarly, metabolic markers of bacterial stress can be used as indicators of the presence of chemicals of interest in the environment, such as heavy metals or toxic organic compounds. In both cases, the ability to quickly and accurately detect stress-induced metabolic changes in bacteria is a critical technological challenge uniquely addressed by this nanophotonic sensing platform.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
