A Novel Framework for Quantitative Analysis of Neuronal Primary Cilia in Brain Tissue

BackgroundAccurate analysis of neuronal primary cilia is essential for understanding developmental processing of neurons. But existing image segmentation methods struggle with staining variability and background noise. To address this, we developed a more robust segmentation and statistical analysis pipeline using an animal model small sample size and with known neuronal microstructure alterations. MethodsMaternal obesity was induced in mice via a high-fat/high-sucrose diet. Hippocampal tissue from 6-month-old offspring of obese and control dams was analyzed. We developed a MATLAB-based pipeline to segment neuronal cilia from z-stack images, applying mathematical transformations and using the Weibull distribution and Bayesian Information Criterion (BIC) to assess group differences ResultsThe technique segmented cilia despite artifacts, revealing group-specific patterns. Statistical analysis confirmed significant differences, highlighting the methods robustness over traditional tests, especially with small samples. ConclusionOur method reliably segments neuronal primary cilia in immune-stained sections with thionin-counter staining and offers a sensitive, assumption-free alternative to traditional statistical tests, ideal for small-sample neurobiological studies