{"title":"高尔基染色脑组织的高分辨率单神经元重建分析。","authors":"Qiaowei Tang, Binfu Fan, Xiaoqing Cai, Zhiming Shen, Jichao Zhang, Jun Hu, Jiang Li, Ying Zhu","doi":"10.1111/cpr.70092","DOIUrl":null,"url":null,"abstract":"<p><p>Understanding the structural and functional organisation of brain networks is a fundamental objective in neuroscience, with three-dimensional (3D) reconstruction of single-neuron morphology serving as a critical foundation. The Golgi staining method, which enables random neuronal labeling and provides high-contrast signals in both optical and X-ray microscopy, remains a valuable tool for morphological analysis. However, its widespread application in large-scale neuronal reconstructions is hindered by signal discontinuities in neuronal branches, high-density labeling, and complex background interference. While automated reconstruction methods perform well in sparsely labelled and morphologically simple neuronal populations, their effectiveness is limited in Golgi-stained samples. Here we develop a semi-automated single-neuron reconstruction method for Golgi-stained mouse brain neurons (SNR-Golgi). By integrating three key technical modules-background denoising, single-neuron extraction, and branch repair-SNR-Golgi significantly enhances the accuracy and completeness of neuronal reconstruction. In fluorescence micro-optical sectioning tomography (fMOST) datasets, SNR-Golgi demonstrated superior performance in neuronal reconstruction within the mouse somatosensory cortex, achieving a 30% increase in reconstructed branch count, a 76% improvement in total branch length, and a 3.7-fold increase in axonal length. Additionally, in synchrotron-based X-ray imaging datasets, SNR-Golgi enabled submicron-resolution 3D reconstruction of single neurons. These results demonstrate that SNR-Golgi effectively addresses the complexity of Golgi-stained samples and provides robust technical support for the structural analysis of brain neurons across various imaging modalities.</p>","PeriodicalId":9760,"journal":{"name":"Cell Proliferation","volume":" ","pages":"e70092"},"PeriodicalIF":5.6000,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-Resolution Single-Neuron Reconstruction Analysis in Golgi-Stained Brain Tissues.\",\"authors\":\"Qiaowei Tang, Binfu Fan, Xiaoqing Cai, Zhiming Shen, Jichao Zhang, Jun Hu, Jiang Li, Ying Zhu\",\"doi\":\"10.1111/cpr.70092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Understanding the structural and functional organisation of brain networks is a fundamental objective in neuroscience, with three-dimensional (3D) reconstruction of single-neuron morphology serving as a critical foundation. The Golgi staining method, which enables random neuronal labeling and provides high-contrast signals in both optical and X-ray microscopy, remains a valuable tool for morphological analysis. However, its widespread application in large-scale neuronal reconstructions is hindered by signal discontinuities in neuronal branches, high-density labeling, and complex background interference. While automated reconstruction methods perform well in sparsely labelled and morphologically simple neuronal populations, their effectiveness is limited in Golgi-stained samples. Here we develop a semi-automated single-neuron reconstruction method for Golgi-stained mouse brain neurons (SNR-Golgi). By integrating three key technical modules-background denoising, single-neuron extraction, and branch repair-SNR-Golgi significantly enhances the accuracy and completeness of neuronal reconstruction. In fluorescence micro-optical sectioning tomography (fMOST) datasets, SNR-Golgi demonstrated superior performance in neuronal reconstruction within the mouse somatosensory cortex, achieving a 30% increase in reconstructed branch count, a 76% improvement in total branch length, and a 3.7-fold increase in axonal length. Additionally, in synchrotron-based X-ray imaging datasets, SNR-Golgi enabled submicron-resolution 3D reconstruction of single neurons. These results demonstrate that SNR-Golgi effectively addresses the complexity of Golgi-stained samples and provides robust technical support for the structural analysis of brain neurons across various imaging modalities.</p>\",\"PeriodicalId\":9760,\"journal\":{\"name\":\"Cell Proliferation\",\"volume\":\" \",\"pages\":\"e70092\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Proliferation\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1111/cpr.70092\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Proliferation","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1111/cpr.70092","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
High-Resolution Single-Neuron Reconstruction Analysis in Golgi-Stained Brain Tissues.
Understanding the structural and functional organisation of brain networks is a fundamental objective in neuroscience, with three-dimensional (3D) reconstruction of single-neuron morphology serving as a critical foundation. The Golgi staining method, which enables random neuronal labeling and provides high-contrast signals in both optical and X-ray microscopy, remains a valuable tool for morphological analysis. However, its widespread application in large-scale neuronal reconstructions is hindered by signal discontinuities in neuronal branches, high-density labeling, and complex background interference. While automated reconstruction methods perform well in sparsely labelled and morphologically simple neuronal populations, their effectiveness is limited in Golgi-stained samples. Here we develop a semi-automated single-neuron reconstruction method for Golgi-stained mouse brain neurons (SNR-Golgi). By integrating three key technical modules-background denoising, single-neuron extraction, and branch repair-SNR-Golgi significantly enhances the accuracy and completeness of neuronal reconstruction. In fluorescence micro-optical sectioning tomography (fMOST) datasets, SNR-Golgi demonstrated superior performance in neuronal reconstruction within the mouse somatosensory cortex, achieving a 30% increase in reconstructed branch count, a 76% improvement in total branch length, and a 3.7-fold increase in axonal length. Additionally, in synchrotron-based X-ray imaging datasets, SNR-Golgi enabled submicron-resolution 3D reconstruction of single neurons. These results demonstrate that SNR-Golgi effectively addresses the complexity of Golgi-stained samples and provides robust technical support for the structural analysis of brain neurons across various imaging modalities.
期刊介绍:
Cell Proliferation
Focus:
Devoted to studies into all aspects of cell proliferation and differentiation.
Covers normal and abnormal states.
Explores control systems and mechanisms at various levels: inter- and intracellular, molecular, and genetic.
Investigates modification by and interactions with chemical and physical agents.
Includes mathematical modeling and the development of new techniques.
Publication Content:
Original research papers
Invited review articles
Book reviews
Letters commenting on previously published papers and/or topics of general interest
By organizing the information in this manner, readers can quickly grasp the scope, focus, and publication content of Cell Proliferation.