Moyle Mark W.

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Moyle
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Mark W.
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  • Article
    Differential adhesion regulates neurite placement via a retrograde zippering mechanism
    (eLife Sciences Publications, 2021-11-16) Sengupta, Titas ; Koonce, Noelle L. ; Vázquez-Martínez, Nabor ; Moyle, Mark W. ; Duncan, Leighton H. ; Emerson, Sarah E. ; Han, Xiaofei ; Shao, Lin ; Wu, Yicong ; Santella, Anthony ; Fan, Li ; Bao, Zhirong ; Mohler, William A. ; Shroff, Hari ; Colón-Ramos, Daniel
    During development, neurites and synapses segregate into specific neighborhoods or layers within nerve bundles. The developmental programs guiding placement of neurites in specific layers, and hence their incorporation into specific circuits, are not well understood. We implement novel imaging methods and quantitative models to document the embryonic development of the C. elegans brain neuropil, and discover that differential adhesion mechanisms control precise placement of single neurites onto specific layers. Differential adhesion is orchestrated via developmentally regulated expression of the IgCAM SYG-1, and its partner ligand SYG-2. Changes in SYG-1 expression across neuropil layers result in changes in adhesive forces, which sort SYG-2-expressing neurons. Sorting to layers occurs, not via outgrowth from the neurite tip, but via an alternate mechanism of retrograde zippering, involving interactions between neurite shafts. Our study indicates that biophysical principles from differential adhesion govern neurite placement and synaptic specificity in vivo in developing neuropil bundles.
  • 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.