Multiview confocal super-resolution microscopy

Yicong Wu (), Xiaofei Han, Yijun Su, Melissa Glidewell, Jonathan S. Daniels, Jiamin Liu, Titas Sengupta, Ivan Rey-Suarez, Robert Fischer, Akshay Patel, Christian Combs, Junhui Sun, Xufeng Wu, Ryan Christensen, Corey Smith, Lingyu Bao, Yilun Sun, Leighton H. Duncan, Jiji Chen, Yves Pommier, Yun-Bo Shi, Elizabeth Murphy, Sougata Roy, Arpita Upadhyaya, Daniel Colón-Ramos, Patrick La Riviere and Hari Shroff
Additional contact information
Yicong Wu: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Xiaofei Han: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Yijun Su: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Melissa Glidewell: Applied Scientific Instrumentation
Jonathan S. Daniels: Applied Scientific Instrumentation
Jiamin Liu: National Institutes of Health
Titas Sengupta: Yale University School of Medicine
Ivan Rey-Suarez: University of Maryland
Robert Fischer: National Heart, Lung, and Blood Institute, National Institutes of Health
Akshay Patel: University of Maryland
Christian Combs: National Institutes of Health
Junhui Sun: National Heart, Lung, and Blood Institute, National Institutes of Health
Xufeng Wu: National Institutes of Health
Ryan Christensen: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
Corey Smith: University of Chicago
Lingyu Bao: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)
Yilun Sun: National Institutes of Health
Leighton H. Duncan: Yale University School of Medicine
Jiji Chen: National Institutes of Health
Yves Pommier: National Institutes of Health
Yun-Bo Shi: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)
Elizabeth Murphy: National Heart, Lung, and Blood Institute, National Institutes of Health
Sougata Roy: University of Maryland
Arpita Upadhyaya: University of Maryland
Daniel Colón-Ramos: Yale University School of Medicine
Patrick La Riviere: University of Chicago
Hari Shroff: National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health

Nature, 2021, vol. 600, issue 7888, 279-284

Abstract: Abstract Confocal microscopy1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching2. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than 20 distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae and adults; myoblasts in imaginal disks of Drosophila wings; and mouse renal, oesophageal, cardiac and brain tissues.

Date: 2021
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DOI: 10.1038/s41586-021-04110-0

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