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Proceedings Paper

Parallel-multiplexed excitation light-sheet microscopy (Conference Presentation)
Author(s): Dongli Xu; Weibin Zhou; Leilei Peng

Paper Abstract

Laser scanning light-sheet imaging allows fast 3D image of live samples with minimal bleach and photo-toxicity. Existing light-sheet techniques have very limited capability in multi-label imaging. Hyper-spectral imaging is needed to unmix commonly used fluorescent proteins with large spectral overlaps. However, the challenge is how to perform hyper-spectral imaging without sacrificing the image speed, so that dynamic and complex events can be captured live. We report wavelength-encoded structured illumination light sheet imaging (λ-SIM light-sheet), a novel light-sheet technique that is capable of parallel multiplexing in multiple excitation-emission spectral channels. λ-SIM light-sheet captures images of all possible excitation-emission channels in true parallel. It does not require compromising the imaging speed and is capable of distinguish labels by both excitation and emission spectral properties, which facilitates unmixing fluorescent labels with overlapping spectral peaks and will allow more labels being used together. We build a hyper-spectral light-sheet microscope that combined λ-SIM with an extended field of view through Bessel beam illumination. The system has a 250-micron-wide field of view and confocal level resolution. The microscope, equipped with multiple laser lines and an unlimited number of spectral channels, can potentially image up to 6 commonly used fluorescent proteins from blue to red. Results from in vivo imaging of live zebrafish embryos expressing various genetic markers and sensors will be shown. Hyper-spectral images from λ-SIM light-sheet will allow multiplexed and dynamic functional imaging in live tissue and animals.

Paper Details

Date Published: 24 April 2017
PDF: 1 pages
Proc. SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II, 100760A (24 April 2017); doi: 10.1117/12.2253250
Show Author Affiliations
Dongli Xu, The Univ. of Arizona (United States)
Weibin Zhou, Univ. of Michigan (United States)
Leilei Peng, College of Optical Sciences, The Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 10076:
High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II
Kevin K. Tsia; Keisuke Goda, Editor(s)

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