Publications by authors named "Leighton H Duncan"

Purpose: Proliferative vitreoretinopathy (PVR) is the most common cause of failure of retinal reattachment surgery, and the molecular changes leading to this aberrant wound healing process are currently unknown. Our ultimate goal is to study PVR pathogenesis by employing single-cell transcriptomics to dissect cellular heterogeneity.

Design: Here we aimed to compare single-cell RNA sequencing (scRNA-seq)  and single-nucleus RNA-sequencing (snRNA-seq) of retinal PVR samples in the rabbit model.

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Precise and reliable cell-specific gene delivery remains technically challenging. Here we report a splicing-based approach for controlling gene expression whereby separate translational reading frames are coupled to the inclusion or exclusion of mutated, frameshifting cell-specific alternative exons. Candidate exons are identified by analyzing thousands of publicly available RNA sequencing datasets and filtering by cell specificity, conservation, and local intron length.

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Confocal microscopy remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration.

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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 brain neuropil and discover that differential adhesion mechanisms control precise placement of single neurites onto specific layers.

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Human organoids stand at the forefront of basic and translational research, providing experimentally tractable systems to study human development and disease. These stem cell-derived, in vitro cultures can generate a multitude of tissue and organ types, including distinct brain regions and sensory systems. Neural organoid systems have provided fundamental insights into molecular mechanisms governing cell fate specification and neural circuit assembly and serve as promising tools for drug discovery and understanding disease pathogenesis.

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Neuropil is a fundamental form of tissue organization within the brain, in which densely packed neurons synaptically interconnect into precise circuit architecture. However, the structural and developmental principles that govern this nanoscale precision remain largely unknown. Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation' to identify nested circuit structures within the Caenorhabditis elegans neuropil, which is known as the nerve ring.

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The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an 'unmatched back projector' accelerates deconvolution relative to the classic Richardson-Lucy algorithm by at least tenfold.

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Caenorhabditis elegans (C. elegans) stands out as the only organism in which the challenge of understanding the cellular origins of an entire nervous system can be observed, with single cell resolution, in vivo. Here, we present an integrated protocol for the examination of neurodevelopment in C.

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