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MorphoNeuroNet: an automated method for dense neurite network analysis.
Giuseppe Pani1, Winnok H De Vos, Nada Samari
1Radiobiology Unit, Molecular and Cellular Biology Expert Group, Belgian Nuclear Research Centre, SCK•CEN, Mol, Belgium; Cell Systems and Imaging Research Group (CSI), Department of Molecular Biotechnology, Ghent University, Ghent, Belgium.
Summary
A new automated method, MorphoNeuroNet, accurately quantifies neuronal morphology and neurite network density in complex, dense neuronal cultures. This tool enhances the analysis of neuronal regeneration and disease models.
Area of Science:
- Neuroscience
- Cell Biology
- Biotechnology
Background:
- High-content cell-based screens are crucial for neuronal regeneration research.
- Existing automated image analysis methods struggle with dense, well-connected neuronal networks.
- Dense neuronal cultures are valuable for studying synaptogenesis and neuronal development.
Purpose of the Study:
- To develop a fully automated method for quantifying neuronal morphology and neurite network density in dense neuronal cultures.
- To overcome limitations of current tools in analyzing complex neuronal structures.
- To provide a robust analysis method for neuronal cultures grown for over 10 days.
Main Methods:
- Developed MorphoNeuroNet, an ImageJ script utilizing adaptive region growing for soma segmentation and a combination of intensity/edge detection for neurite delineation.
- Employed a multi-tier image analysis pipeline.
- Validated the method on dense neuronal cultures.
Main Results:
- MorphoNeuroNet accurately quantifies morphological parameters of soma and neurites (size, shape, starting points, fractional occupation).
- Demonstrated superior performance compared to existing analysis tools, particularly for subtle changes in thin neurites.
- Successfully revealed changes in neurites with weak fluorescence intensity.
Conclusions:
- MorphoNeuroNet offers a superior solution for analyzing dense neuronal cultures.
- The method facilitates the determination of compound effects on neuronal networks.
- Enhances the physiological relevance of cell-based assays for neuronal diseases and regeneration studies.
