A novel image segmentation method for the evaluation of inflammation-induced cortical and hippocampal white matter...

Area of Science:

• Neuroscience
• Developmental Biology
• Immunology

Background:

• Neonatal brain white matter injuries are linked to infection and inflammation, leading to long-term neurological issues.
• Quantifying white matter injury and myelinated axons in animal models is challenging due to limitations in current measurement methods.

Purpose of the Study:

• To develop and validate an automated segmentation method, MyelinQ, for quantifying myelinated white matter in neonatal mouse brain sections.
• To assess the impact of a viral infection mimic on white matter myelination in the neonatal mouse brain.

Main Methods:

• Development of MyelinQ, an automated segmentation software for analyzing immunohistochemical DAB-stained brain sections.
• Application of MyelinQ to neonatal mouse brains exposed to Poly I:C, a Toll-like receptor 3 ligand, to evaluate white matter hypomyelination.
• Comparison of MyelinQ results with a previously established quantification method.

Main Results:

• MyelinQ accurately quantifies myelinated white matter, yielding results comparable to existing methods.
• Poly I:C exposure resulted in significant hypomyelination in the cortical and hippocampal fimbria regions.
• No significant hypomyelination was observed in the striatal caudoputamen region following Poly I:C exposure.

Conclusions:

• MyelinQ offers an automated, objective, and accurate platform for quantifying myelin and evaluating white matter injuries in animal models.
• The study demonstrates MyelinQ's utility in identifying region-specific white matter alterations caused by infection mimics.
• MyelinQ provides a valuable tool for researchers studying neonatal brain development and injury.