Rey-Suarez Ivan

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Rey-Suarez
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Ivan
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  • Article
    Reflective imaging improves spatiotemporal resolution and collection efficiency in light sheet microscopy
    (Nature Publishing Group, 2017-11-13) Wu, Yicong ; Kumar, Abhishek ; Smith, Corey ; Ardiel, Evan L. ; Chandris, Panagiotis ; Christensen, Ryan ; Rey-Suarez, Ivan ; Guo, Min ; Vishwasrao, Harshad D. ; Chen, Jiji ; Tang, Jianyong ; Upadhyaya, Arpita ; La Riviere, Patrick J. ; Shroff, Hari
    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 <300 nm in all three dimensions) by applying our method to single-view LSFM, permitting simultaneous acquisition of two high-resolution views otherwise difficult to obtain due to steric constraints at high numerical aperture. We demonstrate the broad applicability of our method in a variety of samples, studying mitochondrial, membrane, Golgi, and microtubule dynamics in cells and calcium activity in nematode embryos.
  • Article
    Incorporating the image formation process into deep learning improves network performance
    (Nature Research, 2022-10-31) Li, Yue ; Su, Yijun ; Guo, Min ; Han, Xiaofei ; Liu, Jiamin ; Vishwasrao, Harshad D ; Li, Xuesong ; Christensen, Ryan ; Sengupta, Titas ; Moyle, Mark W ; Rey-Suarez, Ivan ; Chen, Jiji ; Upadhyaya, Arpita ; Usdin, Ted B ; Colón-Ramos, Daniel Alfonso ; Liu, Huafeng ; Wu, Yicong ; Shroff, Hari
    We present Richardson–Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson–Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson–Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN’s performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.
  • Article
    Three-dimensional structured illumination microscopy with enhanced axial resolution
    (Nature Research, 2023-01-26) Li, Xuesong ; Wu, Yicong ; Su, Yijun ; Rey-Suarez, Ivan ; Matthaeus, Claudia ; Updegrove, Taylor B. ; Wei, Zhuang ; Zhang, Lixia ; Sasaki, Hideki ; Li, Yue ; Guo, Min ; Giannini, John P. ; Vishwasrao, Harshad D. ; Chen, Jiji ; Lee, Shih-Jong J. ; Shao, Lin ; Liu, Huafeng ; Ramamurthi, Kumaran S. ; Taraska, Justin W. ; Upadhyaya, Arpita ; La Riviere, Patrick ; Shroff, Hari
    The axial resolution of three-dimensional structured illumination microscopy (3D SIM) is limited to ∼300 nm. Here we present two distinct, complementary methods to improve axial resolution in 3D SIM with minimal or no modification to the optical system. We show that placing a mirror directly opposite the sample enables four-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with ∼120-nm lateral and 160-nm axial resolution. We also developed a deep learning method achieving ∼120-nm isotropic resolution. This method can be combined with denoising to facilitate volumetric imaging spanning dozens of timepoints. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers and visualizing cytoskeletal dynamics within T cells in the early stages of immune synapse formation.