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19 June 2024 Data-driven Raman imaging of single cells
Yun Gao, Hao He, Lei Wang
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Proceedings Volume PC13011, Data Science for Photonics and Biophotonics; PC1301106 (2024) https://doi.org/10.1117/12.3016852
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
Raman imaging has become a vital tool for studying biological processes thanks to its label-free and non-invasive nature. However, photodamage has been a long-term concern in Raman imaging which requires large number of photons to produce enough contrast due to its inherent weak scattering efficiency. In this paper, we proposed to optimize the instrument slit-width and leverage deep learning approach to accelerate the Raman imaging, and thereby reduce the photodamage. Experiment results have shown that the collaborative effort yields Raman image with high SNR, SR and SpR, whose quality is comparable to the image obtained using narrow slits, small scan steps, and long integration times, while an 80-fold improved imaging speed allows the photodamage to be reduced greatly. Subsequently, we have been successfully employed it to observe the dynamic changes of cytochrome c in a single cell during the apoptotic before reaching the photodamage limit. It is a new endeavor in the study of cell dynamics and provides a reliable tool for more observation of other biochemical processes.
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Yun Gao, Hao He, and Lei Wang "Data-driven Raman imaging of single cells", Proc. SPIE PC13011, Data Science for Photonics and Biophotonics, PC1301106 (19 June 2024); https://doi.org/10.1117/12.3016852
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KEYWORDS
Biological imaging
Raman spectroscopy
Cell death
Image quality
Signal to noise ratio
Singular value decomposition
Spatial resolution
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