VNC-Dist

VNC-Dist quantifies neuronal cell body positions in the C. elegans ventral nerve cord through a machine learning-based semi-automated pipeline that replaces manual measurements with computer-assisted analysis.

Key Features:

• Manual Neuron Localization: Users manually localize neuron cell bodies using Fiji's multipoint tool.
• Deep Learning Segmentation: Performs worm segmentation using a modified Segment Anything Model (SAM).
• Spline-Based Distance Measurement: Calculates accurate neuronal distances along the ventral nerve cord using spline-based methods.
• Statistical Analysis and Graphing: Includes built-in tools for statistical analysis and graphing of results.
• Batch Processing: Enables batch acquisition and analysis of large datasets across genotypes.

Scientific Applications:

• Neuronal Position Quantification: Quantifies motoneuron cell body positions within large microscopy datasets of the C. elegans ventral nerve cord.
• Genetic Mutant Analysis: Applied to genetic mutants known to disrupt neuronal positioning in the ventral nerve cord to assess robustness and versatility.
• Developmental Mechanism Investigation: Advances investigations into cellular and molecular mechanisms governing ventral nerve cord neuronal positioning and arrangement.

Methodology:

The pipeline integrates manual neuron cell body localization using Fiji's multipoint tool, deep learning-based worm segmentation with modified Segment Anything Model (SAM), and spline-based measurements of neuronal distances along the ventral nerve cord.