Metasurface enhanced and machine learning aided spectrochemical liquid biopsy

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PROJECT SUMMARY Liquid biopsy modalities that can non-invasively detect disease-associated biomarkers from biofluids can enable early cancer detection and patient monitoring with implications for improved survival rates. However, current methods have not achieved critical sensitivity and accuracy to be approved for population screening programs. New spectrochemical liquid biopsy methods, such as Raman and infrared spectroscopy, coupled with machine learning models are emerging as next-generation diagnostic modalities. Yet, fundamental physical limitations of light-matter interactions using conventional optical setups hinder the analytical performance of molecular spectroscopy techniques. Here, we propose to employ novel electromagnetic metasurfaces that can advance the analytical sensitivity and chemical selectivity of infrared absorption spectroscopy enabling its real-world applications in the biomedical field. Moreover, our innovative laser-based spectral imaging approach can achieve on-chip spectrometer-less chemical fingerprint retrieval eliminating clinically incompatible, complex, and bulky instrumentation requirements. The long-term goal of this project is to develop a rapid, label-free, portable, and non-invasive cancer detection platform based on sensitive and accurate chemometric liquid biopsy and machine learning-aided discrimination modalities. The overall objectives in this application are to (i) determine a potent metasurface design that can robustly extract chemical fingerprint information from a complex biosample matrix, (ii) identify optimized design parameters for spectral imaging-based on-chip fingerprint retrieval (iii) establish measurement protocols and data processing pipeline (iv) identify a machine learning model by which sensitive and accurate sample discrimination can be achieved. In the short term, we will pursue two specific aims: 1) develop novel engineered metasurfaces for sensitive and specific spectrochemical biofluid analysis and demonstrate spectrometer-less on-chip chemical fingerprinting 2) Test and validate the platform using biofluids from an ovarian cancer patient cohort and non-cancer controls. Our proposed approach is innovative because it catalyzes the state-of-the-art laser-based infrared spectral imaging technology with powerful nanophotonic tools to enable its impact in biomedical diagnostics and address an unmet medical need. In addition, the proposed interdisciplinary project is significant because it is expected to develop a non-invasive and accessible health screening platform that can ultimately impact the clinical management of cancer and the survival outcomes equitably among diverse populations.