Browsing by Subject "Regeneration and repair in the nervous system"

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    Magnetic separation of peripheral nerve-resident cells underscores key molecular features of human Schwann cells and fibroblasts: an immunochemical and transcriptomics approach
    (Nature Publishing Group, 2020-10-28) Peng, Kaiwen; Sant, David; Andersen, Natalia; Silvera, Risset; Camarena, Vladimir; Piñero, Gonzalo; Graham, Regina; Khan, Aisha; Xu, Xiao-Ming; Wang, Gaofeng; Monje, Paula V.; Neurological Surgery, School of Medicine
    Nerve-derived human Schwann cell (SC) cultures are irreplaceable models for basic and translational research but their use can be limited due to the risk of fibroblast overgrowth. Fibroblasts are an ill-defined population consisting of highly proliferative cells that, contrary to human SCs, do not undergo senescence in culture. We initiated this study by performing an exhaustive immunological and functional characterization of adult nerve-derived human SCs and fibroblasts to reveal their properties and optimize a protocol of magnetic-activated cell sorting (MACS) to separate them effectively both as viable and biologically competent cells. We next used immunofluorescence microscopy imaging, flow cytometry analysis and next generation RNA sequencing (RNA-seq) to unambiguously characterize the post-MACS cell products. High resolution transcriptome profiling revealed the identity of key lineage-specific transcripts and the clearly distinct neural crest and mesenchymal origin of human SCs and fibroblasts, respectively. Our analysis underscored a progenitor- or stem cell-like molecular phenotype in SCs and fibroblasts and the heterogeneity of the fibroblast populations. In addition, pathway analysis of RNA-seq data highlighted putative bidirectional networks of fibroblast-to-SC signaling that predict a complementary, yet seemingly independent contribution of SCs and fibroblasts to nerve regeneration. In sum, combining MACS with immunochemical and transcriptomics approaches provides an ideal workflow to exhaustively assess the identity, the stage of differentiation and functional features of highly purified cells from human peripheral nerve tissues.
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    Rapid, automated nerve histomorphometry through open-source artificial intelligence
    (Springer, 2022-04-08) Daeschler, Simeon Christian; Bourget, Marie-Hélène; Derakhshan, Dorsa; Sharma, Vasudev; Asenov, Stoyan Ivaylov; Gordon, Tessa; Cohen-Adad, Julien; Borschel , Gregory Howard; Surgery, School of Medicine
    We aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was trained for automated axon/myelin segmentation using a dataset of light-microscopic cross-sectional images of osmium tetroxide-stained rat nerves including various axonal regeneration stages. In a second dataset, accuracy of automated segmentation was determined against manual axon/myelin labels. Automated morphometry results, including axon diameter, myelin sheath thickness and g-ratio were compared against manual straight-line measurements and morphometrics extracted from manual labels with AxonDeepSeg as a reference standard. The neural network achieved high pixel-wise accuracy for nerve fiber segmentations with a mean (± standard deviation) ground truth overlap of 0.93 (± 0.03) for axons and 0.99 (± 0.01) for myelin sheaths, respectively. Nerve fibers were identified with a sensitivity of 0.99 and a precision of 0.97. For each nerve fiber, the myelin thickness, axon diameter, g-ratio, solidity, eccentricity, orientation, and individual x -and y-coordinates were determined automatically. Compared to manual morphometry, automated histomorphometry showed superior agreement with the reference standard while reducing the analysis time to below 2.5% of the time needed for manual morphometry. This open-source convolutional neural network provides rapid and accurate morphometry of entire peripheral nerve cross-sections. Given its easy applicability, it could contribute to significant time savings in biomedical research while extracting unprecedented amounts of objective morphologic information from large image datasets.