Rxivist logo

Deep learning-enabled phenotyping reveals distinct patterns of neurodegeneration induced by aging and cold-shock

By Sahand Saberi-Bosari, Kevin B Flores, Adriana San-Miguel

Posted 09 Mar 2020
bioRxiv DOI: 10.1101/2020.03.08.982074

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. 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 sub-cellular 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.

Download data

  • Downloaded 162 times
  • Download rankings, all-time:
    • Site-wide: 74,949 out of 88,456
    • In systems biology: 2,085 out of 2,299
  • Year to date:
    • Site-wide: 20,786 out of 88,456
  • Since beginning of last month:
    • Site-wide: 26,444 out of 88,456

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)