Dara L. Dickstein, Daniel R. Dickstein, William G. M. Janssen, Patrick R. Hof, Jacob R. Glaser, Alfredo Rodriguez, Nate O'Connor, Paul Angstman, Susan J. Tappan
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{"title":"Automatic Dendritic Spine Quantification from Confocal Data with Neurolucida 360","authors":"Dara L. Dickstein, Daniel R. Dickstein, William G. M. Janssen, Patrick R. Hof, Jacob R. Glaser, Alfredo Rodriguez, Nate O'Connor, Paul Angstman, Susan J. Tappan","doi":"10.1002/cpns.16","DOIUrl":null,"url":null,"abstract":"<p>Determining the density and morphology of dendritic spines is of high biological significance given the role of spines in synaptic plasticity and in neurodegenerative and neuropsychiatric disorders. Precise quantification of spines in three dimensions (3D) is essential for understanding the structural determinants of normal and pathological neuronal function. However, this quantification has been restricted to time- and labor-intensive methods such as electron microscopy and manual counting, which have limited throughput and are impractical for studies of large samples. While there have been some automated software packages that quantify spine number, they are limited in terms of their characterization of spine structure. This unit presents methods for objective dendritic spine morphometric analysis by providing image acquisition parameters needed to ensure optimal data series for proper spine detection, characterization, and quantification with Neurolucida 360. These protocols will be a valuable reference for scientists working towards quantifying and characterizing spines. © 2016 by John Wiley & Sons, Inc.</p>","PeriodicalId":40016,"journal":{"name":"Current Protocols in Neuroscience","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpns.16","citationCount":"51","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Protocols in Neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cpns.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Neuroscience","Score":null,"Total":0}
引用次数: 51
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Abstract
Determining the density and morphology of dendritic spines is of high biological significance given the role of spines in synaptic plasticity and in neurodegenerative and neuropsychiatric disorders. Precise quantification of spines in three dimensions (3D) is essential for understanding the structural determinants of normal and pathological neuronal function. However, this quantification has been restricted to time- and labor-intensive methods such as electron microscopy and manual counting, which have limited throughput and are impractical for studies of large samples. While there have been some automated software packages that quantify spine number, they are limited in terms of their characterization of spine structure. This unit presents methods for objective dendritic spine morphometric analysis by providing image acquisition parameters needed to ensure optimal data series for proper spine detection, characterization, and quantification with Neurolucida 360. These protocols will be a valuable reference for scientists working towards quantifying and characterizing spines. © 2016 by John Wiley & Sons, Inc.
利用Neurolucida 360从共焦数据自动定量树突棘
鉴于树突棘在突触可塑性以及神经退行性和神经精神疾病中的作用,确定树突棘的密度和形态具有高度的生物学意义。在三维(3D)中精确量化棘对于理解正常和病理神经元功能的结构决定因素至关重要。然而,这种定量仅限于时间和劳动密集型方法,如电子显微镜和手动计数,这些方法的吞吐量有限,对于大样本的研究是不切实际的。虽然已经有一些自动化软件包可以量化脊椎数量,但它们在脊椎结构表征方面受到限制。本单元介绍了通过提供所需的图像采集参数进行客观树突棘形态计量分析的方法,以确保使用Neurolucida 360进行正确的棘检测、表征和定量所需的最佳数据系列。这些协议将为致力于量化和表征脊椎的科学家提供有价值的参考。©2016,作者:John Wiley&;股份有限公司。
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