Full Electronic Multiplexed Nucleic Acid Detection via Machine Learning Enhanced Solid-State Nanopore Sensing

Accurate and affordable detection of respiratory infections remains difficult outside centralized laboratories. Many respiratory pathogens circulate at the same time and produce similar symptoms, which makes rapid diagnosis challenging. This project will develop a compact electronic approach for multiplex nucleic acid detection that avoids the optics, fluorescent probes, and gel-based analysis used in many current tests. The work addresses a fundamental problem in molecular diagnostics: how to convert complex biochemical reactions into simple and reliable electronic signals. If successful, the project will advance portable molecular testing, support broader access to timely infectious disease detection, and strengthen the scientific foundation for next-generation diagnostic technologies. The project will also contribute to education and workforce development through curriculum modules, mentored student research, and community-facing activities that introduce biosensing, nanotechnology, and data-driven health technologies to broad audiences. This project will develop the electronic Multiplexed Amplicon Profiling (eMAP) platform, a fully electronic and probe-free framework that integrates single-pot multiplex recombinase polymerase amplification (RPA), solid-state nanopore sensing, and machine-learning-based signal classification for automated, gel-free molecular readout. Using a respiratory pathogen panel as a model system, the work has three aims. Aim 1 will develop and optimize a multi-target RPA assay with balanced amplification and minimal cross-reactivity. Aim 2 will establish a nanopore-machine-learning analytical engine that extracts multidimensional event-level current features and classifies amplicons across pores, voltages, and experimental variability. Aim 3 will integrate the assay and analytical engine into the complete eMAP workflow and evaluate performance through blinded zero-shot testing and benchmarking against conventional gel-based analysis for qualitative calls, sensitivity, and reproducibility. The expected outcome is a generalizable electronic sensing architecture that directly converts biochemical amplification into digital signals and enables scalable multiplex nucleic acid testing for respiratory pathogens and other diagnostic panels. 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. NSF Award ID: 2603909 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Weihua Guan | Institution: Indiana University, BLOOMINGTON, IN | Award Amount: $494,396 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2603909 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2603909.html