Development of Hyperspectral Raman Imaging for Biology and Medicine: Optical Platform and Data Mining Methods
Development of Hyperspectral Raman Imaging for Biology and Medicine: Optical Platform and Data Mining Methods
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Molecules vibrate with energies determined by molecular composition, structure, and vibrational mode type. Various optical methods exist to record these vibrational spectra, enabling access to the rich chemical landscape within materials, cells, cell systems, and tissues—without the addition of exogenous labels. Recently, we have developed a broadband coherent anti-Stokes Raman scattering (BCARS) microscope system with unprecedented speed, spectral clarity, and spectral breadth. This system has been applied to cells, tissues, and biomaterials.
Our interest in further development is two-fold: (1) Numerical processing and analysis methods for extraction of actionable information from the rich hyperspectral data. (2) A new microscopy platform with 100-fold improvement in speed and detection limit over even this newest instrument.
These efforts will facilitate high throughput imaging to attack pressing metrological needs in the biological and medical community. This interdisciplinary research opportunity involves theoretical, simulation, and applications work in collaboration with experts in government, industry, and academia. Additionally, the Associate will have access to a full cell culture facility (eukaryotic and prokaryotic), confocal and brightfield microscopies, and two BCARS microscopes developed in-house.
References
Camp CH Jr, et al: “High-speed coherent Raman fingerprint imaging of biological tissues.” Nature Photonics 8: 627-634, 2014
Camp CH Jr, Cicerone MT: “Chemically sensitive bioimaging with coherent Raman scattering.” Nature Photonics 9: 295-305, 2014
Hartshorn CM, Lee YJ, Camp CH Jr, et al: “Multicomponent chemical imaging of pharmaceutical solid dosage forms with broadband CARS microscopy.” Analytical Chemistry 85: 8102-8111, 2013
Raman spectroscopy; CARS; Spectroscopy; Chemical imaging; Bioimaging; Hyperspectral imaging; Data mining; Machine learning; Microspectroscopy;
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