Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

     In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. These protein aggregates are found in the majority of animal and cell models – such as the nematode Caenorhabditis Elegans (C. Elegans) – and are quite useful for the recreation of these diseases. 
     Until now, authors have been studying aggregation phenotypes by counting aggregates manually on a limited cell sample or neuronal subtypes. Manual quantification is time-consuming, user-dependent, error-prone, subjective, and requires expert knowledge to yield accurate and robust results. 
       
To overcome these limitations, we propose a 3D image processing application to automatically quantify several worm features, such as: total area, perimeter or volume; distance from head to tail; thickness of the worm; volume, area and perimeter of its protein aggregates. It might be a step forward to quantitatively evaluate the behavioural phenotypes of novel mutants, gene knockout lines and pharmacological treatments, by analysing the 3D distributions of all protein aggregates.

Additional Information

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