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Moyle, M.

Publications and source records attributed to Moyle, M..

3 recordsLinked to original sources

Transient morphogenetic constraints organize self-assembling axon neighborhoods for robust yet flexible wiring

Nervous systems form wiring patterns that are reproducible across individuals. This reproducibility is thought to emerge from molecular encoding and developmental events, but their relative contributions remain unclear. We address this question in the C. elegans neuropil, where embryonic developmental dynamics and adult anatomy are resolved at single-cell resolution. We find that transient morphogenetic structures -- rosettes, corridor cells, pioneer axon scaffold -- restrict which axons make contact, shaping the neuropil into overlapping neighborhoods. This demonstrates how early events constrain wiring choices, but not whether they explain the resulting reproducibility. To explain, we use an agent-based model of stochastic innervation that recapitulates macro- and micro-level reproducibility, revealing a trade-off between physical constraint and molecular specificity that limits neighborhood size. Counterintuitively, less selective axons produce more reproducible wiring when constrained within neighborhoods. This trade-off lets nervous systems maximize reproducibility without having to molecularly encode every axon-contact, a strategy for robust yet flexible wiring.

neuroscience↗

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained de-aberration networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

bioengineering↗

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

We present Richardson-Lucy Network (RLN), a fast and lightweight deep learning method for 3D fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, improving network interpretability and robustness. Containing only [~]16 thousand parameters, RLN enables 4- 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 Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides 4- to 6-fold faster reconstructions of large, cleared tissue datasets than classic multi-view pipelines. We demonstrate RLNs performance on cells, tissues, and embryos imaged with widefield-, light sheet-, and structured illumination microscopy.

biophysics↗