Efficient super‐resolution volumetric imaging by radial fluctuation Bayesian analysis light‐sheet microscopy. Issue 8 (4th June 2020)

• Record Type: Journal Article
• Authors: Chen, Rong; Zhao, Yuxuan; Li, Mengna; Wang, Yarong; Zhang, Luoying; Fei, Peng
• Is Part Of: Journal of biophotonics. Volume 13:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-04
• Subjects: Bayesian analysis -- Bessel light‐sheet -- SRRF -- three‐dimensional super resolution; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201960242
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Various computational super‐resolution methods are available based on the analysis of fluorescence fluctuation behind acquired frames. However, dilemmas often exist in the balance of fluorophore characteristics, computation cost, and achievable resolution. Here we present an approach that uses a super‐resolution radial fluctuations (SRRF) image to guide the Bayesian analysis of fluorophore blinking and bleaching (3B) events, allowing greatly accelerated localization of overlapping fluorophores with high accuracy. This radial fluctuation Bayesian analysis (RFBA) approach is also extended to three dimensions for the first time and combined with light‐sheet fluorescence microscopy, to achieve super‐resolution volumetric imaging of thick samples densely labeled with common fluorophores. For example, a 700‐nm thin Bessel plane illumination is developed to optically section the Drosophila brain, providing a high‐contrast 3D image of rhythmic neurons. RFBA analyzes 30 serial volumes to reconstruct a super‐resolved 3D image at 4‐times higher resolutions (~70 and 170 nm), and precisely resolve the axon terminals. The computation is over 2‐orders faster than conventional 3B analysis microscopy. The capability of RFBA is also verified through dual‐color imaging of cell nucleus in live Drosophila brain. The spatial co‐localization patterns of the nuclear envelope and DNA in a neuron deep inside the brain can be precisely extracted by our approach. Abstract : A new 3D super‐resolution method that includes SRRF guide‐star into Bayesian localization analysis is developed for significantly reducing the super‐resolution computation time while keeping a high localization accuracy. The further combination of this radial fluctuation Bayesian analysis (RFBA) method with Bessel light‐sheet microscopy allows super‐resolution volumetric imaging of thick samples densely labeled with common fluorophores. The dual‐color co‐localization patterns of the nuclear envelope and DNA in single neurons of Drosophila brain can be precisely extracted using this method.