Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock
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RESEARCH ARTICLE
Open Access
Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock Sahand Saberi-Bosari1, Kevin B. Flores2 and Adriana San-Miguel1*
Abstract Background: Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative assessment of phenotypes, particularly for complex subcellular morphologies. The PVD neuron in Caenorhabditis elegans, which is responsible for harsh touch and thermosensation, undergoes structural degeneration as nematodes age characterized by the appearance of dendritic protrusions. Analysis of these neurodegenerative patterns is labor-intensive and limited to qualitative assessment. Results: In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans. We apply a convolutional neural network algorithm (Mask R-CNN) to identify neurodegenerative subcellular protrusions that appear after cold-shock or as a result of aging. A multiparametric phenotypic profile captures the unique morphological changes induced by each perturbation. We identify that acute cold-shock-induced neurodegeneration is reversible and depends on rearing temperature and, importantly, that aging and cold-shock induce distinct neuronal beading patterns. Conclusion: The results of this work indicate that implementing deep learning for challenging image segmentation of PVD neurodegeneration enables quantitatively tracking subtle morphological changes in an unbiased manner. This analysis revealed that distinct patterns of morphological alteration are induced by aging and cold-shock, suggesting different mechanisms at play. This approach can be used to identify the molecular components involved in orchestrating neurodegeneration and to characterize the effect of other stressors on PVD degeneration. Keywords: Deep learning, Convolutional neural networks, Neurodegeneration, Neuronal beading, Aging, Machine learning, Phenotyping, C. elegans
Background Aging, environmental stressors, and injury can induce reversible or irreversible changes at the subcellular, cellular, and tissue levels of an organism [1–11]. The Caenorhabditis elegans nervous system is not an exception and undergoes morphological and functional deterioration under these conditions. Morphological phenotypes * Correspondence: [email protected] 1 Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA Full list of author information is available at the end of the article
indicative of neurodegeneration in this roundworm include somatic outgrowth, distorted soma, branched and wavy dendrites, and dendritic beading [2, 8, 12–18]. For instance, degenerative axonal beading has been observed and identified previously in various neurons such as ALM, PLM, and HSN [8, 19–22], and in dopaminergic
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