Quantitative neuronal morphometry by supervised and unsupervised learning

STAR Protocols

Gema Valera¹  Kayvan Bijari²  Hernán López-Schier³  Giorgio A. Ascoli³ 

[1] Plasticity and Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA;;Center for Neural Informatics, Structures, &Sensory Biology and Organogenesis, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany;

关键词: Bioinformatics;    Cell Biology;    Microscopy;    Neuroscience;    Computer sciences;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: We present a protocol to characterize the morphological properties of individual neurons reconstructed from microscopic imaging. We first describe a simple procedure to extract relevant morphological features from digital tracings of neural arbors. Then, we provide detailed steps on classification, clustering, and statistical analysis of the traced cells based on morphological features. We illustrate the pipeline design using specific examples from zebrafish anatomy. Our approach can be readily applied and generalized to the characterization of axonal, dendritic, or glial geometry.For complete context and scientific motivation for the studies and datasets used here, refer to Valera et al. (2021).

【 授权许可】

Unknown