BioE PhD Proposal- Nischita Kaza
Francisco E. Robles, Ph.D. (Georgia Institute of Technology and Emory University)
Ahmet F. Coskun, Ph.D. (Georgia Institute of Technology and Emory University School of Medicine)
Peng Qiu, Ph.D. (Georgia Institute of Technology and Emory University)
Thomas K. Gaylord, Ph.D. (Georgia Institute of Technology)
Wilbur A. Lam, M.D., Ph.D. (Georgia Institute of Technology and Emory University School of Medicine)
Simple, high-resolution molecular imaging for biological and clinical applications via label-free Deep ultraviolet microscopy
Imaging with deep ultraviolet (~200 - 400 nm) light enables label-free molecular imaging due to the distinctive absorption and dispersion properties of several physiologically important, endogenous biomolecules in this spectral region. In addition, the shorter wavelength of UV light offers higher spatial resolution than conventional imaging systems that use visible light. Furthermore, recent advances in UV light sources and detectors have resulted in simple, low-cost setups that enable contiguous imaging of live cells over long durations without significant photodamage. Therefore, deep-UV microscopy yields quantitative molecular and structural information from biological samples that can aid in monitoring or diagnosing diseases.
This proposal focuses on hyperspectral, multispectral, and single-wavelength deep-UV microscopy techniques for cellular phenotyping and analysis. We first develop methods to extract quantitative absorption information from biological samples using non-interferometric hyperspectral imaging and multispectral deep-UV microscopy and validate our approach using red blood cells. We then leverage recent advances in deep learning to realize a fully automated pipeline for label-free hematology analysis using single-wavelength UV microscopy images. The results of this work can pave the way for low-cost, label-free imaging systems for use in clinical, at-home, and point-of-care settings. Finally, we propose a UV microscopy system capable of 3D live cell imaging with molecular specificity, which would not only provide unique biological insights but also enable robust cell phenotyping and disease diagnosis.