Reflective imaging improves spatiotemporal resolution and collection efficiency in light sheet microscopy

Yicong Wu (), Abhishek Kumar, Corey Smith, Evan Ardiel, Panagiotis Chandris, Ryan Christensen, Ivan Rey-Suarez, Min Guo, Harshad D. Vishwasrao, Jiji Chen, Jianyong Tang, Arpita Upadhyaya, Patrick J. La Riviere and Hari Shroff
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Yicong Wu: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Abhishek Kumar: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Corey Smith: University of Chicago
Evan Ardiel: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Panagiotis Chandris: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Ryan Christensen: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Ivan Rey-Suarez: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Min Guo: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Harshad D. Vishwasrao: Advanced Imaging and Microscopy Resource, National Institutes of Health
Jiji Chen: Advanced Imaging and Microscopy Resource, National Institutes of Health
Jianyong Tang: JT Scientific Consulting LLC
Arpita Upadhyaya: University of Maryland
Patrick J. La Riviere: University of Chicago
Hari Shroff: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: Abstract Light-sheet fluorescence microscopy (LSFM) enables high-speed, high-resolution, and gentle imaging of live specimens over extended periods. Here we describe a technique that improves the spatiotemporal resolution and collection efficiency of LSFM without modifying the underlying microscope. By imaging samples on reflective coverslips, we enable simultaneous collection of four complementary views in 250 ms, doubling speed and improving information content relative to symmetric dual-view LSFM. We also report a modified deconvolution algorithm that removes associated epifluorescence contamination and fuses all views for resolution recovery. Furthermore, we enhance spatial resolution (to

Date: 2017
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DOI: 10.1038/s41467-017-01250-8

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