Comprehensive analysis of human dendritic spine morphology and density - Schünemann, Kerstin D.; Hattingh, Roxanne M.; Verhoog, Matthijs B.; Yang, Danqing; Bak, Aniella V.; Peter, Sabrina; van Loo, Karen M.J.; Wolking, Stefan; Kronenberg-Versteeg, Deborah; Weber, Yvonne; Schwarz, Niklas; Raimondo, Joseph V.; et al. (Journal of Neurophysiology, 2025)
Dendritic spines, small protrusions on neuronal dendrites, play a crucial role in brain function by changing shape and size in response to neural activity. So far, in-depth analysis of dendritic spines in human brain tissue is lacking. This study presents a comprehensive analysis of human dendritic spine morphology and density using a unique dataset from human brain tissue from 27 patients (8 females, 19 males, aged 18-71 yr) undergoing tumor or epilepsy surgery at three neurosurgery sites. We used acute slices and organotypic brain slice cultures to examine dendritic spines, classifying them into the three main morphological subtypes: mushroom, thin, and stubby, via three-dimensional (3-D) reconstruction using ZEISS arivis Pro software. A deep learning model, trained on 39 diverse datasets, automated spine segmentation and 3-D reconstruction, achieving a 74% F1-score and reducing processing time by over 50%. We show significant differences in spine density by sex, dendrite type, and tissue condition. Females had higher spine densities than males, and apical dendrites were denser in spines than basal ones. Acute tissue showed higher spine densities compared with cultured human brain tissue. With time in culture, mushroom spines decreased, whereas stubby and thin spine percentages increased, particularly from 7-9 to 14 days in vitro, reflecting potential synaptic plasticity changes. Our study underscores the importance of using human brain tissue to understand unique synaptic properties and shows that integrating deep learning with traditional methods enables efficient large-scale analysis, revealing key insights into sex- and tissue-specific dendritic spine dynamics relevant to neurological diseases.NEW & NOTEWORTHY This study presents a dataset of nearly 4,000 morphologically reconstructed human dendritic spines across different ages, gender, and tissue conditions. The dataset was further used to evaluate a deep learning algorithm for three-dimensional spine reconstruction, offering a scalable method for semiautomated spine analysis across various tissues and microscopy setups. The findings enhance understanding of human neurology, indicating potential connections between spine morphology, brain function, and the mechanisms of neurological and psychiatric diseases.
CRIS-Team Scopus Importer:2025-04-11
Keywords: deep learning; dendritic spines; human tissue; morphology