Comprehensive analysis of human dendritic spine morphology and density
Schünemann KD, Hattingh RM, Verhoog MB, Yang D, Bak AV, Peter S, van Loo KM, Wolking S, Kronenberg-Versteeg D, Weber Y, Schwarz N, Raimondo JV, Melvill R, Tromp SA, Butler JT, Höllig A, Delev D, Wuttke TV, Kampa BM, Koch H (2025)
Publication Type: Journal article
Publication year: 2025
Journal
Book Volume: 133
Pages Range: 1086-1102
Journal Issue: 4
Abstract
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.
Involved external institutions
Universitätsklinikum Aachen (UKA)
Germany (DE)
University of Cape Town (UCT)
South Africa (ZA)
Forschungszentrum Jülich / Research Centre Jülich (FZJ)
Germany (DE)
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
Germany (DE)
Hertie-Institut für klinische Hirnforschung
Germany (DE)
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Germany (DE)
University of Cape Town (UCT)
South Africa (ZA)
Forschungszentrum Jülich / Research Centre Jülich (FZJ)
Germany (DE)
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
Germany (DE)
Hertie-Institut für klinische Hirnforschung
Germany (DE)
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
How to cite
APA:
Schünemann, K.D., Hattingh, R.M., Verhoog, M.B., Yang, D., Bak, A.V., Peter, S.,... Koch, H. (2025). Comprehensive analysis of human dendritic spine morphology and density. Journal of Neurophysiology, 133(4), 1086-1102. https://doi.org/10.1152/jn.00622.2024
MLA:
Schünemann, Kerstin D., et al. "Comprehensive analysis of human dendritic spine morphology and density." Journal of Neurophysiology 133.4 (2025): 1086-1102.
BibTeX: Download