Abstract
BackgroundAccess 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.ResultsIn 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.ConclusionThe 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.
Highlights
Access to quantitative information is crucial to obtain a deeper understanding of biological systems
The input to Mask R-convolutional neural network (CNN) is a 1channel grayscale microscopy image (1024 × 1024 × 1), and the output is a set of predicted bead regions consisting of one binary instance mask (1024 × 1024 × 1) per bead, i.e., a pixel has a value of 1 in the mask when it is part of a bead and 0 otherwise
Region of interest (ROI) are processed with region proposal and ROI align neural network layers to produce an instance segmentation mask for each detected object
Summary
Access to quantitative information is crucial to obtain a deeper understanding of biological systems. 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. Lezi et al identified the formation of protrusions (or beading) along the dendrites of PVD during aging, through a process driven by the expression of an antimicrobial peptide [39] They identified a decrease in nematode’s responsiveness to harsh touch as nematodes age coinciding with the increase in number of bubble-like protrusions throughout the dendrite. While these correlations suggest beading accompanies aging, their functional outcomes are still to be determined
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