Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.

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 PV

SUBMITTER: Saberi-Bosari S 

PROVIDER: S-EPMC7510121 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature