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

• Published in: Nature communications Vol. 12; no. 1; p. 3052
• Main Authors: Lin, Haonan, Lee, Hyeon Jeong, Tague, Nathan, Lugagne, Jean-Baptiste, Zong, Cheng, Deng, Fengyuan, Shin, Jonghyeon, Tian, Lei, Wong, Wilson, Dunlop, Mary J., Cheng, Ji-Xin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.05.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 14/69; 140/133; 631/1647/245/2226; 631/326; 639/624/1107/328/2057; 639/624/1107/527/1821; Animals; Bandwidths; Biofuels; Biomedical engineering; Biomolecules; Brain - diagnostic imaging; Cell Line; Cell Line, Tumor; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Deep learning; Fingerprints; High speed; Humanities and Social Sciences; Imaging; Lipid Metabolism; Mice; Microbiological Techniques - methods; microbiology; multidisciplinary; multiphoton microscopy; Neural networks; optical imaging; Optical Imaging - methods; Polygons; Raman spectra; Raman spectroscopy; Scanners; Science; Science (multidisciplinary); Signal to noise ratio; Spatial discrimination learning; Spectral resolution; Spectroscopy; Spectrum allocation; Spectrum analysis; Spectrum Analysis, Raman - methods; Temporal resolution; Vibration
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-23202-z

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. The authors employ a polygon-based ultrafast delay scanner and a deep learning framework for acquiring stimulated Raman scattering spectrum with high spectral and temporal resolution. They demonstrate high-speed imaging and tracking of multiple biomolecules in the fingerprint region.