scholarly journals Exceeding the limits of 3D fluorescence microscopy using a dual-stage-processing network

Optica ◽  
2020 ◽  
Vol 7 (11) ◽  
pp. 1627
Author(s):  
Hao Zhang ◽  
Yuxuan Zhao ◽  
Chunyu Fang ◽  
Guo Li ◽  
Meng Zhang ◽  
...  
2018 ◽  
Author(s):  
Hao Zhang ◽  
Yuxuan Zhao ◽  
Chunyu Fang ◽  
Guo Li ◽  
Meng Zhang ◽  
...  

AbstractAlthough three-dimensional (3D) fluorescence microscopy is an essential tool for life science research, the fundamentally-limited optical throughput, as reflected in the compromise between speed and resolution, so far prevents further movement towards faster, clearer, and higher-throughput applications. We herein report a dual-stage mutual-feedback deep-learning approach that allows gradual reversion of microscopy degradation from high-resolution targets to low-resolution images. Using a single blurred-and-pixelated 3D image as input, our trained network infers a 3D output with notably higher resolution and improved contrast. The performance is better than conventional 1-stage network approaches. It pushes the throughput limit of current 3D fluorescence microscopy in three ways: notably reducing the acquisition time for accurate mapping of large organs, breaking the diffraction limit for imaging subcellular events with faster lower-toxicity measurement, and improving temporal resolution for capturing instantaneous biological processes. Combining our network approach with light-sheet fluorescence microscopy, we demonstrate the imaging of vessels and neurons in the mouse brain at single-cell resolution and with a throughput of 6 minutes for a whole brain. We also image cell organelles beyond the diffraction limit at a 2-Hz volume rate, and map neuronal activities of freely-moving C. elegans at single-cell resolution and 30-Hz volume rate.


2017 ◽  
Vol 26 (10) ◽  
pp. 4856-4870 ◽  
Author(s):  
Martin Storath ◽  
Dennis Rickert ◽  
Michael Unser ◽  
Andreas Weinmann

PLoS Genetics ◽  
2020 ◽  
Vol 16 (12) ◽  
pp. e1009246
Author(s):  
Johana Luhur ◽  
Helena Chan ◽  
Benson Kachappilly ◽  
Ahmed Mohamed ◽  
Cécile Morlot ◽  
...  

How organisms develop into specific shapes is a central question in biology. The maintenance of bacterial shape is connected to the assembly and remodelling of the cell envelope. In endospore-forming bacteria, the pre-spore compartment (the forespore) undergoes morphological changes that result in a spore of defined shape, with a complex, multi-layered cell envelope. However, the mechanisms that govern spore shape remain poorly understood. Here, using a combination of fluorescence microscopy, quantitative image analysis, molecular genetics and transmission electron microscopy, we show that SsdC (formerly YdcC), a poorly-characterized new member of the MucB / RseB family of proteins that bind lipopolysaccharide in diderm bacteria, influences spore shape in the monoderm Bacillus subtilis. Sporulating cells lacking SsdC fail to adopt the typical oblong shape of wild-type forespores and are instead rounder. 2D and 3D-fluorescence microscopy suggest that SsdC forms a discontinuous, dynamic ring-like structure in the peripheral membrane of the mother cell, near the mother cell proximal pole of the forespore. A synthetic sporulation screen identified genetic relationships between ssdC and genes involved in the assembly of the spore coat. Phenotypic characterization of these mutants revealed that spore shape, and SsdC localization, depend on the coat basement layer proteins SpoVM and SpoIVA, the encasement protein SpoVID and the inner coat protein SafA. Importantly, we found that the ΔssdC mutant produces spores with an abnormal-looking cortex, and abolishing cortex synthesis in the mutant largely suppresses its shape defects. Thus, SsdC appears to play a role in the proper assembly of the spore cortex, through connections to the spore coat. Collectively, our data suggest functional diversification of the MucB / RseB protein domain between diderm and monoderm bacteria and identify SsdC as an important factor in spore shape development.


2001 ◽  
Author(s):  
Alain Dieterlen ◽  
Marie-Pierre Gramain ◽  
Chengqi Xu ◽  
Francois H. Guillemin ◽  
Serge Jacquey

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