Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning

Haonan Lin, Hyeon Jeong Lee, Nathan Tague, Jean-Baptiste Lugagne, Cheng Zong, Fengyuan Deng, Jonghyeon Shin, Lei Tian, Wilson Wong, Mary J. Dunlop and Ji-Xin Cheng ()
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Haonan Lin: Boston University
Hyeon Jeong Lee: Boston University
Nathan Tague: Boston University
Jean-Baptiste Lugagne: Boston University
Cheng Zong: Boston University
Fengyuan Deng: Boston University
Jonghyeon Shin: Boston University
Lei Tian: Boston University
Wilson Wong: Boston University
Mary J. Dunlop: Boston University
Ji-Xin Cheng: Boston University

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400–1800 cm−1) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm−1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23202-z

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DOI: 10.1038/s41467-021-23202-z

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