Skip to main content

CAREER: Open-Access, Real-Time High-Throughput Metabolomics for High-Field and Benchtop NMR for Biological Inquiry

NSF

open

About This Grant

Metabolomics is an indispensable approach in systems biology that uses analytical techniques to measure metabolites in cells, tissues, and biofluids and provides direct information of the biological phenotype. Nuclear magnetic resonance spectroscopy (NMR) is a powerful tool for metabolomics due to its excellent analytical reproducibility and ability to detect numerous metabolites in a single measurement. NMR metabolomics is conventionally performed on a high-field (HF) spectrometer, but the recent development of benchtop spectrometers has led to a resurgence of interest in low-field (LF) NMR due to its accessibility, low cost and small footprint compared to HF. NMR metabolomics at both HF and benchtop LF, however, require time-consuming, user-dependent processing and expertise for metabolite identification and quantification. Due to these limitations, both HF and LF NMR are underexplored for metabolomics research in the biological community. This project will fill these critical gaps by developing, validating, and disseminating real-time, high-throughput NMR metabolomic techniques for both HF and benchtop LF NMR for advancing biological infrastructure and research. This interdisciplinary project will prepare the next generation of women and minorities to pursue bioengineering and bioinformatics career—currently an under-represented discipline. The project will also integrate research with educational objectives to target the broader community from K–12 to graduate students and the general public: (a) coursework to strengthen biosignal processing & analysis skills in undergraduate and graduate curricula; (b) internships, targeted to talented women, minority, and low-income college students; (c) hands-on STEM projects to motivate high-schoolers through collaboration with school teachers; (d) disseminate bioengineering research to support K–12 learning through the Society of Women Engineers, West TN STEM Hub, and Girls Experiencing Engineering programs; and (e) educational exhibits at local museums to enable public outreach and exposure to NMR applications. This early hands-on exposure will benefit students of all ages to understand fundamental concepts and realize NMR applications in a broad range of fields—including molecular biology, biomedical engineering and chemical engineering—and ultimately motivate them to pursue a STEM career. The project will develop, validate, and disseminate open-access metabolomic techniques that will automatically quantify the metabolites in complex biological spectra obtained from high-field (HF) and benchtop low-field (LF) NMR via the following objectives: 1) investigate high-throughput metabolomic methods for HF NMR using deep learning, 2) reconstruct high-resolution and high-throughput spectra from benchtop LF NMR using autoencoder, 3) investigate these techniques for inquiring biological questions, and 4) disseminate metabolomic libraries and techniques for biological research and education via an open-access software. This research will provide a breakthrough in the field of NMR metabolomics by eliminating a major processing barrier for both HF and benchtop NMR, thus making NMR an accessible and effective analytical tool to the biological community. The results of this project will be available at the institutional website: https://www.memphis.edu/mrisl/projects/index.php 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.

Focus Areas

biologyengineeringeducation

Eligibility

universitynonprofitsmall business

How to Apply

Funding Range

Up to $506K

Deadline

2028-04-30

Complexity
Medium
Start Application

One-time $749 fee · Includes AI drafting + templates + PDF export

AI Requirement Analysis

Detailed requirements not yet analyzed

Have the NOFO? Paste it below for AI-powered requirement analysis.

0 characters (min 50)