Precision in 3D: A Fast and Accurate Algorithm for Reproducible Motoneuron Structure and Protein Expression Analysis - Morgan Highlander; Shelby Ward; Bradley LeHoty; Teresa Garrett; Sherif Elbasiouny (Bioengineering)
Structural analysis of motoneuron somas and their associated proteins via immunohistochemistry (IHC) remains tedious and subjective, requiring costly software or adapted 2D manual methods that lack reproducibility and analytical rigor. Yet, neurodegenerative disease and aging research demands precise structural comparisons to elucidate mechanisms driving neuronal degeneration. To address this need, we developed a novel algorithm that automates repetitive and subjective IHC analysis tasks, enabling thorough, objective, blinded, order-agnostic, and reproducible 3D batch analysis. With no manual tracing, the algorithm produces 3D Cartesian reconstructions of motoneuron somas from 60× IHC images of mouse lumbar spinal tissue. From these reconstructions, it measures 3D soma volume and efficiently quantitates net somatic protein expression and macro-cluster size. In this validation study, we applied the algorithm to assess soma size and C-bouton expression in various healthy control mice, comparing its measurements against manual measurements and across multiple algorithm users to confirm its accuracy and reproducibility. This novel, customizable tool enables efficient and high-fidelity 3D motoneuron analysis, replacing tedious, qualitative, cell-by-cell manual tuning with automatic threshold adaptation and quantified batch settings. For the first time, we attain reproducible results with quantifiable accuracy, exhaustive sampling, and a high degree of objectivity.