Name: analyzing dendritic morphology in columns and layers
Uploaded: 2017-03-23T15:00:00.000+00:00
Duration: 8 min 41 s
Description: Watch this Scientific Journal Video about Analyzing Dendritic Morphology in Columns and Layers at JoVE.com

这项工作是由美国国立卫生研究院院内研究计划的支持，儿童健康和人类发展（授予HD008913到C-HL）的尤尼斯·肯尼迪·施莱佛国立研究所和信息技术中心（PGM，NP，ESM ，和MM）。

注意:该协议包含三个部分:双视图成像（部分1 - 3），树突跟踪和登记（第4 - 6），和树突分析（第7 - 9）（ 图1）。在材料/设备的表中提供的代码和示例文件。 1.双图像采集注意:此步骤是为了获得所关注的神经元的两个图像栈在两个正交（水平和额叶）取向。 <ol><li>制备该含有疏标记髓质神经元飞大脑（〜10个细胞/脑叶）用膜GFP标记（mCD8GFP），如前所述12。染色用兔抗GFP（为髓质神经元树突）和小鼠mAb24B10（对感光体的轴突），初级抗体和荧光二抗（Alexa的488抗兔和Alexa 568抗小鼠抗体）的脑，如前所述13。清除在1×PBS中的70％甘油的大脑。 </li><li>到安装在水平方向脑（ 图2A，B）中 ，甘油清除飞脑转移到在滑动的中心的抗褪色封固剂20μL的下降。 </li><li>在4个角盖玻片附上粘土小补丁，以防止盖玻片从安装过程中破碎的大脑样本。 注:粘土补丁提供缓冲，以防止盖玻片从破碎的样本。每个粘土贴片应在直径约为1毫米。 </li><li>在解剖显微镜下，在腹侧上位置定位大脑，并放置盖玻片放在上面，以确保大脑。用脑的凸背表面作为地标来识别脑样品（ 图2A）的方向。 </li><li>获得具有共焦显微镜的第一图像堆栈（水平视图）。使用高NA物镜（如63X 1.3 NA甘油或浸油OBJE莫如透镜）和2.5X数字变焦（像素尺寸是0.105微米每像素;平均数目为2）。获得超过180的光学部分（512×512象素），以覆盖0.2μm的步长髓质神经纤维。 </li><li>重新安装脑如步骤1.3中所述 - 1.4，但对准大脑中的前上位置（前视图）。 </li><li>获得相同的神经元的第二图像叠层（前视图），如在步骤1.5中描述。 注意:当有标记的视叶神经元大量发现相同的神经元可能是具有挑战性的。从两个方向标识相同的神经元，从两个视图低倍率图像堆栈可能需要（例如，使用0.7的变焦，和0.45微米的步长大小，以获得低分辨率图像栈）。如果超过512×512的影像越大，图像应该被裁剪为512×512像素的图像组合之前，像素大小应保持在0.105微米每像素成像时的大视场。信号丢失为深部组织的潜在问题。如果信号较弱，图像脑的腹侧一半。为了减少漂白扫描过程中，用尽可能低的激光功率成为可能。使用范围指示灯获取图像栈之前检查曝光过度。如果可能的话，使用配的GaAsP探测器共聚焦显微镜。 </li><li>确定并相对于所述髓质神经毡（右/左[R / L]和背/腹[D / V]）记录所关注的神经元的位置。检查样品通过检查图像堆栈图像采集期间移动。 注:采样运动往往是由于安装不当造成的。如果发生样品的运动，所述图像堆栈不能用于登记和应该被丢弃。重新装入样品和来自同一神经元获得的图像栈。 </li></ol> 2.图像反卷积注:卷积步骤使用图像解卷积软件，以便恢复由模糊降解获得的图像和噪声。虽然此步骤是可选的，它显著提高了图像质量。它建议使用用于图像配准和结合反褶积图像堆栈中部分3。 <ol><li>开始在交互模式下的反褶积程序。负载通过选择菜单中的图像堆栈（LSM中或特定的微观格式）:在主窗口中的文件/打开（或Ctrl-O）。 </li><li>单击以选中加载图像堆栈和选择菜单:OPS打开图像操作窗口。使用默认的经典最大似然估计（CMLE）算法。 </li><li>在图像操作窗口中，单击"参数"选项卡。输入透镜浸入介质中的适当的参数，包埋剂，和数值孔径（ 例如，油，甘油等 ）（ 例如，浸油， 等 ）（NA;在这里，1.3被使用）。检查剩余参数，以确保它们正确地反映成像条件。点击"设置所有验证"选项卡鳍ALIZE参数设置。 </li><li>在图像操作窗口中，单击"操作"选项卡。指定输出目标（ 例如，C）。在"每信道信号/噪音"（如 "12 12 12 12"是一个很好的起点，而默认设置为"20 20 20 20"）中输入相应的数字。使用默认设置为剩余的参数。 </li><li>点击"运行命令"选项卡，开始去卷积的图像堆栈;这个过程可能需要长达几十分钟才能完成，具体在计算机上。 </li><li>在主窗口中，单击并选择反褶积图像堆栈。选择菜单:另存为的反褶积图像保存在ICS图像文件格式（的.ics和.ids）。 注意:每个图像堆叠具有两个文件:ICS文件中包含的标题信息和IDS文件包含的原始图像信息。 <ol><li>根据成像方向重命名图像堆栈文件（如姓名，水平视角图像S大头针H.ids和H.ics和正面视图图像堆栈F.ids和F.ics）。 </li></ol></li></ol> 3.双视角图像组合注意:此步骤将两个图像叠加产生使用MIPAV软件的高分辨率3D图像。 <ol><li>生成图像组合矩阵。 <ol><li>启动MIPAV程序。通过选择菜单加载H和F图像的堆栈:从磁盘（或Ctrl-F）/H.ids和F.ids文件/打开图像（A）;窗口将显示两个图像。 </li><li>通过单击图像选择轰图片，然后选择菜单:工具/转换工具/ RGB /灰色;该窗口将显示GrayG，GrayB和GrayR图像。 </li><li>靠近GrayR和GrayB，只保留在窗口上GrayG。 </li><li>通过单击图像选择F图片，然后选择菜单:工具/转换工具/ RGB /灰色。该窗口将显示GrayG1，GrayB1和GrayR1图像。 </li><li>关闭GrayR1和GrayB1，只保留GrayG1在窗口上。在此步骤中，只的GrAYG和GrayG1是在窗口上。 </li><li>选择GrayG（高亮显示），选择菜单:算法/报名/自动优化图像配准;在"自动优化图像配准3D"对话框会弹出。 <ol><li>在输入选项，从默认的"仿射-12"改变"自由度"到"具体的重新调整-9"。在"旋转，"键-105到105，在"旋转角度采样范围"（默认值:-30〜30度），10中的"粗角度增量"（默认为15度），和3"精角度增量"（默认值:6度）。 </li></ol></li><li>点击确定;第一矩阵"GrayG_To_GrayG1.mtx"将被生成并保存在图像文件夹中。关闭所有图像窗口并继续下一步;这一步将至少需要15分钟。 </li><li>通过选择菜单加载H和F图像的堆栈:从磁盘（或Ctrl-F）/H.ids和F.ids，文件/打开图像（A），如步骤3.1.1。 </li><li>通过单击图像选择轰图片，然后选择菜单:工具/转换工具/ RGB /灰色;在"RGB-&gt;灰色"对话框会弹出。点击确定;该窗口上会显示"HGray"的图像。保持HGray并关闭轰形象。 </li><li>为F图像重复步骤3.1.9;在"FGray"图像将出现在窗口上。保持FGray并关闭F图像的;仅HGray和FGray将留在窗口上。 </li><li>选择HGray图像（高亮显示），然后转到菜单:算法/转换工具/变换。 "转换/重定图像像素"对话框会弹出。点击"重新取样"选项卡并更改重采样为"HGray"到"FGray"的大小。接下来，通过选择点击"转换"选项卡，并加载"GrayG_To_GrayG1.mtx"，"从文件中读取矩阵"。点击确定;窗口将显示HGray_transform图像。关闭HGray图像所以只有HGray_transform和FGray留在窗口上。 </li><li>选择"HGray_transform＆＃34;图像（高亮显示），并进入菜单:算法/报名/自动优化图像配准。在"自动优化图像配准3D"对话框会弹出。在"旋转，"键-5中的"旋转角度采样范围"5（默认值:-30〜30度），3中的"粗角度增量"（默认为15度），以及1中的"精角度增量"（默认值:6度）。点击OK。 注:仿射矩阵（HGray_transform_To_FGray.mtx）会生成并保存在图像文件夹和"HGray_Transform_register"的图像将在窗口上显示。这一步将至少需要40分钟。 </li><li>关闭"HGray_transform"的形象;只有"HGray_Transform_register"和"FGray"应留在窗口上。 </li><li>选择"HGray_Transform_register"图像（高亮显示），然后转到菜单:算法/注册/ B样条曲线自动配准的2D / 3D。在"B样条自动Registrati在-3D亮度"对话框会弹出，选择"在成本函数最小二乘"（默认为相关比）。 <ol><li>点击"执行两次通过注册"。中通1节，键2到"梯度下降最小化步长（样本单位）"（默认为1），并输入10为"最大迭代次数"（默认为10）。中通2部分，重点在进入1"梯度下降最小化步长（样本单位）"（默认为0.5），并键入2成"最大迭代次数"（默认为10）。 注:NLT矩阵，"HGray_transform_register.nlt"将被保存在图像文件夹和"HGray_transform_register_registered"的形象将在窗口中显示。这个步骤将至少需要5分钟。 </li></ol></li><li>在窗口关闭所有图像。 </li></ol></li><li>产生用于图像组合的基准图像。 注意:此步骤是为了GENER吃了组合的注册水平图像。 <ol><li>加载H和F图像的堆栈选择菜单:从磁盘（或Ctrl-F）/H.ids和F.ids文件/打开图像（A）;两个图像将出现在窗口上。 </li><li>选择轰图像（高亮显示），然后转到菜单:算法/转换工具/变换; "转换/重定图像像素"对话框会弹出。点击"重新取样"选项卡，并从大小"H"到"F"的改变重采样接下来，通过选择点击"转换"选项卡，并加载"GrayG_To_GrayG1.mtx"，"从文件中读取矩阵"。点击确定;该H_transform图像会出现在窗口上。关闭轰图像，但保持H_transform和F在窗口中。 </li><li>选择"H_transform"图像（高亮显示），然后转到菜单:算法/转换工具/变换; "转换/重定图像像素"对话框会弹出。点击"重新取样"选项卡，并从大小"H_transform"到"F＆＃改变重采样34;接下来，通过选择点击"转换"选项卡，并加载"HGray_transform_To_FGray.mtx"，"从文件中读取矩阵"。点击确定;在"H_transform_transform"的形象将出现在窗口上。关闭H_transform形象;在这一点上，仅H_transform_transform和F将被留在窗口上。 </li><li>选择"H_transform_transform"图像（高亮显示），然后转到菜单:算法/转换工具/变换非线性;在"非线性B样条转型"对话框会弹出。接下来，负荷"HGray_transform_register.nlt"，然后点击确定;在"H_transform_transform_registered"的形象将出现在窗口上。图像另存为ICS文件。在窗口关闭所有图像。 </li></ol></li><li>结合图像堆栈 注意:此步骤是在正交方向（水平和额叶）获取的两个图像堆栈组合成一个高分辨率栈。 <ol><li>进入菜单:插件/通用/果蝇视网膜注册istrationl;在"果蝇视网膜登记V2.9"对话框会弹出。从步骤3.2.4在图像H-注册转型1 - 格林在图像f"F.ics"，"GrayG_To_GrayG1.mtx"从步骤3.1.7上传图像H"，H_transform_transform_registered""H.ics"（可选），"HGray_transform_To_FGray.mtx"在转换步骤3.1.12转型2仿射和"HGray_transform_register.nlt"步骤3.1.14 3非线性（可选）。 <ol><li>选择的sqrt（强度，高x强度-F），并在"重缩放H键F."不重新调整保留其余参数的默认选项。点击确定;这一步将需要大约3分钟。 注:在处理之后，将产生3套图片数量:combinedImage_sqrRt_trilinear_norescale_ignoreBG.ids（最终重组图像），greenChannelsImage-Gxreg-GY-Gcomp.IDS（为H，F绿色通道，并最终重新组合图像），和redChannelsImage- RXREG-RY-Rcomp.ids（红色通道FOR H，F和最后的重组图像）;所有输出文件将被调整为512×512×512。 </li></ol></li><li>打开"combinedImage_sqrRt_trilinear_norescale_ignoreBG.ids"图像可视化软件下。保存在IMS格式的图像堆栈和重命名文件;此重组的图像文件将被用于神经突的跟踪和注册。 </li></ol></li></ol> 4.轴突跟踪和参考点分配注:此步骤是使用图像可视化软件来跟踪突起（4.1）和指定的参考点报名（4.2）。 <ol><li>跟踪突起<ol><li>启动图像可视化软件。打开重组图像文件。进入菜单:编辑/显示显示调整和关闭感光通道（红色）。 </li><li>在"超越"模式下的可视化图像。打开"立体"，并使用了"四缓冲区"模式，以可视化的三维图像如果计算机是EQuipped具有立体画系统。 </li><li>进入菜单:超越/花丝增加新的细丝。点击"跳过自动创建，手动编辑"选项卡。 </li><li>点击"绘图"选项卡，选择"AutoDepth"。 </li><li>选择"设置"，勾选"线"，并输入一个合适的像素数为更好的可视化（4像素线在本协议中使用）。勾选"显示树突"，"起点"和"分支点"。将"渲染质量"为100％。 </li><li>选择"绘图"选项卡，并开始跟踪突起。先从轴突，然后移动到树突（ 图2D）。轴突和transmedulla神经元树突很容易区分。 注:A TM神经细胞从细胞体延伸，其轴突和项目一路较高的视觉处理中心，小叶。系统会自动确定第一长丝作为一个轴突和剩余的短长丝树突。保持在灯丝（轴突）开始的起点追踪期间和确保跟踪突起相连。检查分支点和开始点。如果树突未连接，新的开始点将在非连接的灯丝来限定。 </li><li>追查后，返回到"设置"，取消选中"起点"和"分支点"，并转到菜单:超越/导出选定的对象../。保存长丝作为一个发明家文件（* .IV）。 </li></ol></li><li>分配基准点<ol><li>选择"显示画面调整"，把两个成像通道。在这个例子中，信道1是感光体染色和通道2是TM20神经元（GFP）。 </li><li>进入菜单:超越/测量。选择"编辑"选项卡，并选中"特定声道:"（选择感光通道[红色]）。 </li><li>分配的参考点为顶层。进入菜单:/超越/测量浦二NTS以创建新的测量点。标记M1层作为顶层的开始。点的顺序如下:赤道，前-赤道，前壁，前腹，腹侧，后腹，后侧，后部赤道和中心（ 图3F）;定义中心感光体作为具有最树枝状突起相关联的一个。 </li><li>分配R8和R7层按步骤4.2.3（ 图3G）。三个独立的测量点应在步骤4.2.3和4.2.4进行创建。 </li><li>导出点的坐标为每个层。点击"统计信息"选项卡，选择"详细"，"特定值"和"位置;"并单击"导出统计上的标签显示为文件"。另存为"逗号分隔值"（* .CSV）。 </li><li> （从步骤4.2.3 - 4.2.4）打开三个CSV文件，并复制和粘贴27基准点的坐标合并成一个新的csv文件（THË依次为上，R8和R7）。请参阅材料/设备表 ，并按照示例文件的格式。 </li></ol></li></ol> 5.刚体和TPS非线性注册注意:此步骤是注册轴突迹线（在四格式）参考列阵列和使用MIPAV程序生成的注册SWC文件。此部分需要以下文件:从3.3步重组图像堆栈（.ids），步骤4.2参考点文件（.csv）和神经纤维跟踪文件从步骤4.1（.IV）。 <ol><li>进入菜单:插件/通用/ DrosophilaStandardColumnRegistration。在"DrosophilaStandardColumnRegistration v6.6.1"窗口会弹出。 </li><li>加载图像文件（.ids），参考点档（.csv），与灯丝文件（.IV）。 </li><li>选择每层9分。 </li><li>选择成像神经元（LV / RD或RV / LD）的位置。 </li><li>选择"刚性Registrati并TPS"和"刚性登记"分别为非线性刚体登记。 </li><li>选中"创建SWC文件"生成以下输出文件:在SWC格式的注册轴突跟踪文件（参见定义特定的材料/设备），注册IV文件（.IV），配合标准化突起（.TXT）的，变换后的坐标（.txt）的，合并的图像（.ids）。 </li><li>更改SWC文件的名称。适用的位置的缩写来的文件名（步骤1.7）的端部。例如，使用"Tm20_3_RV.swc"为TM20神经元＃3位于右髓质腹侧一半。 </li></ol> 6.标准化为右腹配置注:此步骤是使用自定义脚本转换的突起痕迹（以SWC格式）标准RV（右腹）配置"RV_standardization.m"。在这里，剧本写于矩阵实验室语言。该输入SWC文件的名称应该采用以下格式:"NeuronName _ _ 数名为.swc 配置 "（ 例如，Tm20_3_LV.swc）。 <ol><li>打开"RV_standardization.m"脚本。 </li><li>编辑"用户输入"部分中的以下参数: <ol><li>键入神经元的名称，而不在编号（ 例如，TM2，TM20， 等等 ）"neuron_names"。 注:"file_end_in"默认为"_ *深港西部通道，"这看起来的文件包含名称（"*"是匹配任意数目的字符一个通配符）"_ *深港西部通道。" "swc_file_end_out"的默认值是"_f.swc"，这将"_f"添加到标准化后的文件名的末尾。 </li><li>指定的SWC文件中的"directory_in"的目录。指定目录，标准化的文件将在"directory_swcout"走。 </li></ol></li><li>运行脚本。 </li><li> Øptional:使用Vaa3D 14以可视化的SWC文件和验证的转换。 </li></ol> 7.计算树突分枝和终止频率注:此步骤使用刚体注册SWC文件来计算，一个树突段将达到给定的长度而不终止的概率的Kaplan-Meier估计。该脚本使用两个Dendritic_Tree_Toolbox功能:extractDendriticSegmentLengthDistribution和estimateDendriticSegmentLengthProbability。 <ol><li>打开"Branch_term_P.m"脚本。 </li><li>编辑"用户输入"部分中的以下参数: <ol><li>指定"pathToSWCFiles"的路径刚体注册SWC文件（ 例如，/ Rigid_Registered_swc /）。指定将持有的图形输出路径"pathToOutput"。指定的神经元或神经类型"neuron_names"的名称（ 例如，TM2，Tm为20， 等等 ）。 </li></ol></li><li>运行该脚本;输出是树突分枝和终止的Kaplan-Meier估计曲线。 注:可选的:应用函数拟合方法来提取的Kaplan-Meier估计器的局部概率树枝状段将跳转或终止。 </li></ol> 8.情节树突状乔木层特定终端的分布注意:此步骤图表树突终端在不同髓质层中的分布的柱状图。这可以应用到一个神经元，一组神经元，或神经元的组。该脚本使用从Dendritic_Tree_Toolbox的extractDistributionAlongAxis功能。 <ol><li>打开"Layer_term.m"脚本。 </li><li>编辑"用户输入"部分中的以下参数: <ol><li>指定包含在"pathToSWCFiles"非线性注册SWC文件的目录（例如 / Non_linear_Registered_swc /）。指定图形输出目录"pathToOutput"。在"neuron_names"（ 例如，TM2，TM20， 等等 ）指定的神经元或神经类型的名称。 </li></ol></li><li>运行教程脚本。输出是终端节点的特定髓质层的比例的柱状图。 </li></ol> 9.情节树突的平面投影方向注:此步骤树突绘制的平面投影方向为极坐标图。该脚本使用从Dendritic_Tree_Toolbox的extractAngularDistribution功能。 <ol><li>打开脚本"Planar_proj.m"。 </li><li>编辑"用户输入"部分中的下列参数。 <ol><li>指定包含非线性的"pathToSWCFiles"（ 例如，/ Non_linear_Registered_swc /）注册SWC文件的目录。指定在图形输出目录"pathToOutput"。指定的名称神经元或神经类型"neuron_names"（ 例如，TM2，TM20， 等等 ）。 </li></ol></li><li>运行该脚本;输出为平面投影方向的极坐标图。 </li></ol>

<ol>
	<li>Kaas, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Topographic+maps+are+fundamental+to+sensory+processing.">Topographic maps are fundamental to sensory processing.</a> Brain Res Bull. 44 (2), 107-112 (1997).</li><li>Sanes, J. R., Zipursky, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+principles+of+insect+and+vertebrate+visual+systems.">Design principles of insect and vertebrate visual systems.</a> Neuron. 66 (1), 15-36 (2010).</li><li>Huberman, A. D., Clandinin, T. R., Baier, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+and+cellular+mechanisms+of+lamina-specific+axon+targeting.">Molecular and cellular mechanisms of lamina-specific axon targeting.</a> CSH Perspect Biol. 2 (3), a001743 (2010).</li><li>Clandinin, T. R., Feldheim, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Making+a+visual+map:+mechanisms+and+molecules.">Making a visual map: mechanisms and molecules.</a> Curr Opin Neurobiol. 19 (2), 174-180 (2009).</li><li>Luo, J., McQueen, P. G., Shi, B., Lee, C. H., Ting, C. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wiring+dendrites+in+layers+and+columns.">Wiring dendrites in layers and columns.</a> J Neurogenet. 30 (2), 69-79 (2016).</li><li>Meinertzhagen, I. A., Hanson, T. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The development of the optic lobe. In The Development of Drosophila melanogaster. , 1363-1491 (1993).</li><li>Takemura, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+visual+motion+detection+circuit+suggested+by+Drosophila+connectomics.">A visual motion detection circuit suggested by Drosophila connectomics.</a> Nature. 500 (7461), 175-181 (2013).</li><li>Takemura, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synaptic+circuits+and+their+variations+within+different+columns+in+the+visual+system+of+Drosophila.">Synaptic circuits and their variations within different columns in the visual system of Drosophila.</a> Proc Natl Acad Sci U S A. 112 (44), 13711-13716 (2015).</li><li>Gao, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neural+substrate+of+spectral+preference+in+Drosophila.">The neural substrate of spectral preference in Drosophila.</a> Neuron. 60 (2), 328-342 (2008).</li><li>Karuppudurai, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+hard-wired+glutamatergic+circuit+pools+and+relays+UV+signals+to+mediate+spectral+preference+in+Drosophila.">A hard-wired glutamatergic circuit pools and relays UV signals to mediate spectral preference in Drosophila.</a> Neuron. 81 (3), 603-615 (2014).</li><li>Strother, J. A., Nern, A., Reiser, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+observation+of+ON+and+OFF+pathways+in+the+Drosophila+visual+system.">Direct observation of ON and OFF pathways in the Drosophila visual system.</a> Curr Biol. 24 (9), 976-983 (2014).</li><li>Ting, C. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Photoreceptor-derived+activin+promotes+dendritic+termination+and+restricts+the+receptive+fields+of+first-order+interneurons+in+Drosophila.">Photoreceptor-derived activin promotes dendritic termination and restricts the receptive fields of first-order interneurons in Drosophila.</a> Neuron. 81 (4), 830-846 (2014).</li><li>Ting, C. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tiling+of+R7+axons+in+the+Drosophila+visual+system+is+mediated+both+by+transduction+of+an+activin+signal+to+the+nucleus+and+by+mutual+repulsion.">Tiling of R7 axons in the Drosophila visual system is mediated both by transduction of an activin signal to the nucleus and by mutual repulsion.</a> Neuron. 56 (5), 793-806 (2007).</li><li>Peng, H., Ruan, Z., Long, F., Simpson, J. H., Myers, E. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=V3D+enables+real-time+3D+visualization+and+quantitative+analysis+of+large-scale+biological+image+data+sets.">V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets.</a> Nat Biotechnol. 28 (4), 348-353 (2010).</li><li>Takemura, S. Y., Lu, Z., Meinertzhagen, I. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synaptic+circuits+of+the+Drosophila+optic+lobe:+the+input+terminals+to+the+medulla.">Synaptic circuits of the Drosophila optic lobe: the input terminals to the medulla.</a> J Comp Neurol. 509 (5), 493-513 (2008).</li><li>Takemura, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cholinergic+circuits+integrate+neighboring+visual+signals+in+a+Drosophila+motion+detection+pathway.">Cholinergic circuits integrate neighboring visual signals in a Drosophila motion detection pathway.</a> Curr Biol. 21 (24), 2077-2084 (2011).</li><li>Keller, P. J., Schmidt, A. D., Wittbrodt, J., Stelzer, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+of+zebrafish+early+embryonic+development+by+scanned+light+sheet+microscopy.">Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy.</a> Science. 322 (5904), 1065-1069 (2008).</li><li>Wu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatially+isotropic+four-dimensional+imaging+with+dual-view+plane+illumination+microscopy.">Spatially isotropic four-dimensional imaging with dual-view plane illumination microscopy.</a> Nat Biotechnol. 31 (11), 1032-1038 (2013).</li><li>Popko, J., Fernandes, A., Brites, D., Lanier, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+analysis+of+NeuronJ+tracing+data.">Automated analysis of NeuronJ tracing data.</a> Cytometry A. 75 (4), 371-376 (2009).</li><li>Meijering, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+validation+of+a+tool+for+neurite+tracing+and+analysis+in+fluorescence+microscopy+images.">Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images.</a> Cytometry A. 58 (2), 167-176 (2004).</li><li>Pool, M., Thiemann, J., Bar-Or, A., Fournier, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NeuriteTracer:+a+novel+ImageJ+plugin+for+automated+quantification+of+neurite+outgrowth.">NeuriteTracer: a novel ImageJ plugin for automated quantification of neurite outgrowth.</a> J Neurosci Methods. 168 (1), 134-139 (2008).</li><li>Scorcioni, R., Polavaram, S., Ascoli, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=L-Measure:+a+web-accessible+tool+for+the+analysis,+comparison+and+search+of+digital+reconstructions+of+neuronal+morphologies.">L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies.</a> Nat Protoc. 3 (5), 866-876 (2008).</li><li>Kaplan, E. L., Meier, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonparametric+Estimation+from+Incomplete+Observations.">Nonparametric Estimation from Incomplete Observations.</a> JASA. 53, 457-481 (1958).</li></ol>

作者什么都没有透露。

在这里，我们将展示如何图像和分析果蝇神经髓质树突乔木。第一部分，双视图成像，描述了去卷积和两个图像栈的组合成一个高分辨率的图像栈。第二部分，枝晶追踪和登记，介绍髓质神经元的参考列数组的树突的追查​​和登记。第三部分，树突状分析，介绍了使用自定义脚本来分析树突状图案。总之这些协议提供了一个完整的工作流中提取树突状模式的信息相对于图层和列，并确定?...

在开发过程中，神经元在复杂的，但千篇一律的分支图案精致的树突形成与突触前伙伴突触。树突分枝模式相关与神经元的身份和功能。树突乔木的位置确定的，他们收到的突触前输入的类型，而树枝状分枝的复杂性和字段大小支配输入号码。因此，树突形态属性是突触连接和神经元计算的关键因素。在复杂的大脑，诸如苍蝇视叶和脊椎动物视网膜许多地区，突触电路中的列和层组织，以促进信息处理1,2。在这样的列和层的组织，一个独特的形态项目轴突突触前神经元在一个特定层（所谓的特定层的定位）终止，并形成一个有序的两维阵列（所谓的卡勒ð地形图），而突触后神经元延长特定层适当大小的树突接收到正确的类型和数量的突触前输入。而轴突靶向层和列已经得到很好的研究3，4，较少被知道关于树突如何路由到特定层和扩大适当大小感受域，以形成具有正确的突触前伙伴5突触连接。成像和量化树突靶向层和列的困难阻碍了柱状和夹层的大脑结构树突发育的研究。 果蝇神经髓质是研究在列和树突状层路由和电路组件的理想模型。苍蝇髓质神经纤维组织为10层的3D格与约750列。每列由一组传入，的p支配hotoreceptors R7 / R 8和薄层神经元L1 - L5，其终末形成层特定的方式6的地形图。约38种髓质的神经元存在于每髓质列和特定层和具有适当字段大小精细树突从这些传入7接收输入。在延髓的突触电路，在电子显微镜水平得到重建;因此，突触的伙伴关系已经非常成熟7,8。此外，用于标记各种类型髓质神经元的遗传工具可用9，10，11。通过检查三种transmedulla（Tm）为神经元（TM2，TM9和TM20），我们先前已经确定两种细胞类型特异性树突属性:（一）以旧换新神经元投射无论是在向前或向后方向树突（平面PROJ挠度方向），这取决于细胞类型和（ii）延髓神经元的树突终止于特定髓质层中的细胞类型特异性的方式（层特定的终端）12。平面投影方向和特定层的终止足以区分这三种类型的Tm的神经元，而扰乱的Tm反应层和列线索突变影响这些属性的不同的方面。 这里，我们提出用于检查果蝇髓质神经元的列和层（ 图1）的树枝状图案形成一个完整的工作流程。首先，我们展示了双景成像方法，它采用定制的软件合并两个共焦图像叠加产生高品质的图像各向同性。此方法只需要常规共焦显微镜，以产生高质量的图像，其允许树枝状分枝的可靠跟踪，而不诉诸超分辨率显微镜，这样的小号STED（受激发射损耗）或结构照明。第二，我们提出了跟踪树突乔木和用于登记所得突起痕迹到参考列阵列的方法。第三，我们显示了用于推导估计树枝状分支和终止频率提取的平面投影方向和树突的特定层的终端信息，以及作为计算方法。总之，这些方法允许在3D，基于树枝状形态的细胞类型的分类树枝状型态的表征，和潜在缺陷的突变体的鉴定。

<table><tbody><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Huygens Professional</td><td>Scientific Volume Imaging</td><td>version 16.05</td><td>for image deconvolution (https://svi.nl).&amp;nbsp; commercial software</td></tr><tr><td>MIPAV</td><td></td><td>version 7.3.0</td><td>for image recombination and registration (http://mipav.cit.nih.gov/); freeware</td></tr><tr><td>MIPAV plugin: PlugInDrosophila&lt;br/&gt;RetinalRegistration.class</td><td></td><td></td><td>freeware</td></tr><tr><td>MIPAV plugin: PlugInDrosophilaStandard&lt;br/&gt;ColumnRegistration.class</td><td></td><td></td><td>freeware</td></tr><tr><td>Imaris</td><td>Bitplane</td><td></td><td>for tracing neurites and assigning reference points for image registration (http://www.bitplane.com); commercial software</td></tr><tr><td>Vaa3D</td><td></td><td></td><td>for visualizing swc files (https://github.com/Vaa3D/release/releases/); freeware</td></tr><tr><td>Matlab</td><td>Mathworks</td><td>R2014b</td><td>for morphometric analysis of dendrites (http://www.mathworks.com); commercial software</td></tr><tr><td>Matlab toolbox: TREES1.14</td><td></td><td>v1.14</td><td>for analyzing dendritic morphometric parameters (http://www.treestoolbox.org/download.html); freeware</td></tr><tr><td>Matlab toolbox: Dendritic_Tree_Toolbox</td><td></td><td>v1.0</td><td>For calculating morphometric parameters (https://science.nichd.nih.gov/confluence/display/snc/Data+collections+for+imagines+combination+and+standardize+column+registration). Freeware</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Sample files&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>SWC file definition</td><td></td><td></td><td>http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html</td></tr><tr><td>The codes and sample files for image combination and registration</td><td></td><td></td><td>https://science.nichd.nih.gov/confluence/display/snc/Data+collections+for+imagines+combination+and+standardize+column+registration</td></tr><tr><td>Reference point example&amp;nbsp;</td><td></td><td></td><td>https://science.nichd.nih.gov/confluence/download/attachments/117216914/points.csv?version=1&amp;amp;modificationDate=&lt;br/&gt;1471880596000&amp;amp;api=v2</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Computer system&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>MS Windows Windows 7 x64 or Macintosh OS X 10.7 or later</td><td></td><td></td><td>3GHz 64-bit quad-core processor, 16G RAM (minimal)</td></tr><tr><td>Optional: Quadro4000&amp;nbsp; (or above) graphic card</td><td>Nvidia</td><td></td><td>for stereographic visualization of dendrites.</td></tr><tr><td>Optional: NVIDIA 3D vision2</td><td>Nvidia</td><td></td><td>http://www.nvidia.com/object/3d-vision-main.html</td></tr><tr><td>Optional: 120 Hz LCD display for NVIDIA 3D vision2</td><td></td><td></td><td>http://www.nvidia.com/object/3d-vision-system-requirements.html</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Reagents for imaging&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>24B10 antibody</td><td>The Developmental Studies Hybridoma Bank</td><td>24B10</td><td></td></tr><tr><td>GFP Tag Antibody</td><td>Thermofisher Scientific</td><td>G10362</td><td></td></tr><tr><td>Goat anti-Rabbit (H+L), Alexa Fluor 488</td><td>Thermofisher Scientific</td><td>A11034</td><td></td></tr><tr><td>Goat anti-Mouse (H+L), Alexa Fluor 568</td><td>Thermofisher Scientific</td><td>A21124</td><td></td></tr><tr><td>VECTASHIELD Antifade Mounting Medium</td><td>Vector Laboratories</td><td>H-1000</td><td></td></tr><tr><td>Mounting&amp;nbsp;Clay&amp;nbsp;</td><td>Fisher</td><td>S04179</td><td></td></tr><tr><td>70% glycerol in 1x PBS</td><td></td><td></td><td></td></tr><tr><td>Cover glasses, high performance, D = 0.17&amp;nbsp;mm</td><td>Zeiss</td><td>474030-9000-000</td><td></td></tr></tbody></table>

analyzing dendritic morphology in columns and layers

在中枢神经系统中，如苍蝇视叶和脊椎动物皮质许多地区，突触电路中的层和列组织发达的动物发展和信息处理中，以促进脑布线。突触后神经元精心树突在特定层的型特异性模式与适当的突触前末梢到突触。飞髓质神经毡是由10层到约750列;每一列是通过在38种髓质的神经元，配合部分7种类型的传入的在一个特定类型的方式终末其中树突支配。该报告详细介绍了程序，图像和分析延髓神经元树突。该工作流包括三个部分:（i）该双视图成像部分结合在正交取向成树突的高分辨率三维图像收集整理二共焦图像堆栈; （二）树突追踪和登记部分树突状痕迹在3D乔木和树突注册痕迹，参考列数组; （ⅲ）该树枝状分析部分分析的树突状图案相对于列和层，包括树突乔木层特定的终止和平面投影的方向，并导出树枝状分支和终止频率的估计。该协议利用建立在开源MIPAV（医学影像处理，分析和可视化）平台和自定义工具箱中的矩阵实验室语言自定义插件。总之，这些协议提供一个完整的工作流来分析层和列果蝇髓质神经元的树突的路由，以确定细胞类型，并确定在突变体的缺陷。

利用这里提出的双视图成像过程，含有疏标记TM20神经元蝇脑在两个正交的方向被成像。成像之前，将脑与用于可视化膜 - 拴系的GFP和感光体轴突适当初级和次级抗体染色。用于成像，大脑首次安装在水平方向（ 图2A，B）。一个GFP标记TM20神经和周围感光体轴突使用共聚焦显微镜（ 图3A）成像。大脑，然后重新对准（ 图2A，B）和在正面...

Watch this Scientific Journal Video about Analyzing Dendritic Morphology in Columns and Layers at JoVE.com

Analyzing Dendritic Morphology in Columns and Layers

在这里，我们将展示如何分析列和层果蝇髓质神经元树突路由。该工作流包括一个双视图成像技术，以改善跟踪，登记树突乔木于基准列数组以及用于分析在三维空间中的树枝状结构的图像质量和计算工具。

分析树突状形态的列和图层

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

analyzing-dendritic-morphology-in-columns-and-layers

Research

JoVE Journal

Neuroscience

9.3K 次观看.  National Institutes of Health (NIH). 在这里，我们将展示如何分析列和层果蝇髓质神经元树突路由。该工作流包括一个双视图成像技术，以改善跟踪，登记树突乔木于基准列数组以及用于分析在三维空间中的树枝状结构的图像质量和计算工具。 

视频: Analyzing Dendritic Morphology in Columns and Layers

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3. Specifically, it affects the ability of neurons to receive inputs from other cells2 and contributes to the propagation of action potentials4,5. The morphology of the neurites also affects how information is processed. The diversity of dendrite morphologies facilitate local and long range signaling and allow individual neurons or groups of neurons to carry out specialized functions within the neuronal network6,7. Alterations in dendrite morphology, including fragmentation of dendrites and changes in branching patterns, have been observed in a number of disease states, including Alzheimer's disease8, schizophrenia9,10, and mental retardation11. The ability to both understand the factors that shape dendrite morphologies and to identify changes in dendrite morphologies is essential in the understanding of nervous system function and dysfunction.
Neurite morphology is often analyzed by Sholl analysis and by counting the number of neurites and the number of branch tips. This analysis is generally applied to dendrites, but it can also be applied to axons. Performing this analysis by hand is both time consuming and inevitably introduces variability due to experimenter bias and inconsistency. The Bonfire program is a semi-automated approach to the analysis of dendrite and axon morphology that builds upon available open-source morphological analysis tools. Our program enables the detection of local changes in dendrite and axon branching behaviors by performing Sholl analysis on subregions of the neuritic arbor. For example, Sholl analysis is performed on both the neuron as a whole as well as on each subset of processes (primary, secondary, terminal, root, etc.) Dendrite and axon patterning is influenced by a number of intracellular and extracellular factors, many acting locally. Thus, the resulting arbor morphology is a result of specific processes acting on specific neurites, making it necessary to perform morphological analysis on a smaller scale in order to observe these local variations12.
The Bonfire program requires the use of two open-source analysis tools, the NeuronJ plugin to ImageJ and NeuronStudio. Neurons are traced in ImageJ, and NeuronStudio is used to define the connectivity between neurites. Bonfire contains a number of custom scripts written in MATLAB (MathWorks) that are used to convert the data into the appropriate format for further analysis, check for user errors, and ultimately perform Sholl analysis. Finally, data are exported into Excel for statistical analysis. A flow chart of the Bonfire program is shown in Figure 1.

We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

肖尔自动数字化的神经元形态分析，多尺度

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3.  Specifically, it affects the ability of neurons to ...

科研

神经科学

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

Dendritic spines are the sites of the majority of excitatory connections within the brain, and form the post-synaptic 
compartment of synapses. These structures are rich in actin and have been shown to be highly dynamic. In response to classical Hebbian plasticity 
as well as neuromodulatory signals, dendritic spines can change shape and number, which is thought to be critical for the refinement of neural 
circuits and the processing and storage of information within the brain. Within dendritic spines, a complex network of proteins link extracellular 
signals with the actin cyctoskeleton allowing for control of dendritic spine morphology and number. Neuropathological studies have demonstrated that 
a number of disease states, ranging from schizophrenia to autism spectrum disorders, display abnormal dendritic spine morphology or numbers. 
Moreover, recent genetic studies have identified mutations in numerous genes that encode synaptic proteins, leading to suggestions that these 
proteins may contribute to aberrant spine plasticity that, in part, underlie the pathophysiology of these disorders. In order to study the potential 
role of these proteins in controlling dendritic spine morphologies/number, the use of cultured cortical neurons offers several advantages. Firstly, 
this system allows for high-resolution imaging of dendritic spines in fixed cells as well as time-lapse imaging of live cells. Secondly, this in 
vitro system allows for easy manipulation of protein function by expression of mutant proteins, knockdown by shRNA constructs, or pharmacological 
treatments. These techniques allow researchers to begin to dissect the role of disease-associated proteins and to predict how mutations of these 
proteins may function in vivo.

Numerous recent studies have identified mutations in synaptic proteins associated with brain pathologies. Primary cultured cortical neurons offer great flexibility in examining the effects of these disease-associated proteins on dendritic spine morphology and motility.

在培养的中枢神经系统的神经元树突棘形态分析

Dendritic spines are the sites of the majority of excitatory connections within the brain, and form the post-synaptic 
compartment of synapses. These ...

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic development and axonal wiring. Recently the dendritic arborization (DA) sensory neurons of the Drosophila larval peripheral nervous system (PNS) have become powerful genetic models in which to elucidate both general and class-specific mechanisms of neuron differentiation.
There are four main DA neuron classes (I-IV)1. They are named in order of increasing dendrite arbor complexity, and have class-specific differences in the genetic control of their differentiation2-10. The DA sensory system is a practical model to investigate the molecular mechanisms behind the control of dendritic morphology11-13 because: 1) it can take advantage of the powerful genetic tools available in the fruit fly, 2) the DA neuron dendrite arbor spreads out in only 2 dimensions beneath an optically clear larval cuticle making it easy to visualize with high resolution in vivo, 3) the class-specific diversity in dendritic morphology facilitates a comparative analysis to find key elements controlling the formation of simple vs. highly branched dendritic trees, and 4) dendritic arbor stereotypical shapes of different DA neurons facilitate morphometric statistical analyses.
DA neuron activity modifies the output of a larval locomotion central pattern generator14-16. The different DA neuron classes have distinct sensory modalities, and their activation elicits different behavioral responses14,16-20. Furthermore different classes send axonal projections stereotypically into the Drosophila larval central nervous system in the ventral nerve cord (VNC)21. These projections terminate with topographic representations of both DA neuron sensory modality and the position in the body wall of the dendritic field7,22,23. Hence examination of DA axonal projections can be used to elucidate mechanisms underlying topographic mapping7,22,23, as well as the wiring of a simple circuit modulating larval locomotion14-17.
We present here a practical guide to generate and analyze genetic mosaics24 marking DA neurons via MARCM (Mosaic Analysis with a Repressible Cell Marker)1,10,25 and Flp-out22,26,27 techniques (summarized in Fig. 1).

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

The dendritic arborization sensory neurons of the Drosophila larval peripheral nervous system are useful models to elucidate both general and neuron class-specific mechanisms of neuron differentiation. We present a practical guide to generate and analyze dendritic arborization neuron genetic mosaics.

形态分析果蝇幼虫外周感觉神经元树突和轴突使用遗传马赛克

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic ...

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures

Purkinje cells are an attractive model system for studying dendritic development, because they have an impressive dendritic tree which is strictly oriented in the sagittal plane and develops mostly in the postnatal period in small rodents 3. Furthermore, several antibodies are available which selectively and intensively label Purkinje cells including all processes, with anti-Calbindin D28K being the most widely used. For viewing of dendrites in living cells, mice expressing EGFP selectively in Purkinje cells 11 are available through Jackson labs. Organotypic cerebellar slice cultures cells allow easy experimental manipulation of Purkinje cell dendritic development because most of the dendritic expansion of the Purkinje cell dendritic tree is actually taking place during the culture period 4. We present here a short, reliable and easy protocol for viewing and analyzing the dendritic morphology of Purkinje cells grown in organotypic cerebellar slice cultures. For many purposes, a quantitative evaluation of the Purkinje cell dendritic tree is desirable. We focus here on two parameters, dendritic tree size and branch point numbers, which can be rapidly and easily determined from anti-calbindin stained cerebellar slice cultures. These two parameters yield a reliable and sensitive measure of changes of the Purkinje cell dendritic tree. Using the example of treatments with the protein kinase C (PKC) activator PMA and the metabotropic glutamate receptor 1 (mGluR1) we demonstrate how differences in the dendritic development are visualized and quantitatively assessed. The combination of the presence of an extensive dendritic tree, selective and intense immunostaining methods, organotypic slice cultures which cover the period of dendritic growth and a mouse model with Purkinje cell specific EGFP expression make Purkinje cells a powerful model system for revealing the mechanisms of dendritic development.

We present a protocol that permits to view and to quantitatively asses the morphology of the dendritic tree of individual Purkinje cells grown in organotypic cerebellar slice cultures. This protocol is intended to promote studies on the mechanisms of Purkinje cell dendritic development.

浦肯野细胞树突形态的器官切片培养分析

Purkinje cells are an attractive model system for studying dendritic development, because they have an impressive dendritic tree which is strictly ...

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with simultaneous intracellular biocytin filling allows a correlated analysis of their structural and functional properties. With this method it is possible to identify and characterize both pre- and postsynaptic neurons by their morphology and electrophysiological response pattern. Paired recordings allow studying the connectivity patterns between these neurons as well as the properties of both chemical and electrical synaptic transmission. Here, we give a step-by-step description of the procedures required to obtain reliable paired recordings together with an optimal recovery of the neuron morphology. We will describe how pairs of neurons connected via chemical synapses or gap junctions are identified in brain slice preparations. We will outline how neurons are reconstructed to obtain their 3D morphology of the dendritic and axonal domain and how synaptic contacts are identified and localized. We will also discuss the caveats and limitations of the paired recording technique, in particular those associated with dendritic and axonal truncations during the preparation of brain slices because these strongly affect connectivity estimates. However, because of the versatility of the paired recording approach it will remain a valuable tool in characterizing different aspects of synaptic transmission at identified neuronal microcircuits in the brain.

Patch-clamp recordings and simultaneous intracellular biocytin filling of synaptically coupled neurons in acute brain slices allow a correlated analysis of their structural and functional properties. The aim of this protocol is to describe the essential technical steps of electrophysiological recording from neuronal microcircuits and their subsequent morphological analysis.

神经微电路急性脑片电生理和形态特征采用配对膜片钳记录

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with ...

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity and extent of dendritic arborization correlates well with the synaptic plasticity in these cells. A reliable method for assessment of dendritic arborization is needed to characterize the deleterious effects of neurological disorders on these structures and to determine the effects of therapeutic interventions. However, quantification of these structures has proven to be a formidable task given their complex and dynamic nature. Fortunately, sophisticated imaging techniques can be paired with conventional staining methods to assess the state of dendritic arborization, providing a more reliable and expeditious means of assessment. Below is an example of how these imaging techniques were paired with staining methods to characterize the dendritic arborization in wild type mice. These complementary imaging methods can be used to qualitatively and quantitatively assess dendritic arborization that span a rather wide area within the hippocampal region.

We describe two methods for visualization and quantification of dendritic arborization in the hippocampus of mouse models: real-time and extended depth of field imaging. While the former method allows sophisticated topographical tracing and quantification of the extent of branching, the latter allows speedy visualization of the dendritic tree.

树突分支中的海马区对小鼠的齿状回评

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity ...

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching variations of the neuronal dendrites within the hippocampus are implicated in cognition and memory formation. There are several approaches to Golgi staining, all of which have been useful for determining the morphological characteristics of dendritic arbors and produce a clear background. The present Golgi-Cox method, (a slight variation of the protocol that is provided with a commercial Golgi staining kit), was designed to assess how a relatively low dose of the chemotherapeutic drug 5-flurouracil (5-Fu) would affect dendritic morphology, the number of spines, and the complexity of arborization within the hippocampus. The 5-Fu significantly modulated the dendritic complexity and decreased the spine density throughout the hippocampus in a region-specific manner. The data presented show that the Golgi staining method effectively stained the mature neurons in the CA1, the CA3, and the dentate gyrus (DG) of the hippocampus. This protocol reports the details for each step so that other researchers can reliably stain tissue throughout the brain with high quality results and minimal troubleshooting.

Here we present a Golgi-Cox protocol in extensive detail. This reliable tissue stain method allows for a high-quality assessment of the cytoarchitecture in the hippocampus, and throughout the entire brain, with minimal troubleshooting.

使用高尔基Cox方法评估老年小鼠海马树突状复杂性

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching ...

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical properties, synaptic connections, and in vivo functions, but their basic functional and anatomical organization from cellular to network scale is poorly understood. Here we describe a method for the three-dimensional imaging of fluorescently-labeled neurons across large areas of the brain for the investigation of the cortical cellular organization. Specific types of neurons are labeled by the injection of fluorescent retrograde neuronal tracers or expression of fluorescent proteins in transgenic mice. Block brain samples, e.g., a hemisphere, are prepared after fixation, made transparent with tissue clearing methods, and subjected to fluorescent immunolabeling of the specific cell types. Large areas are scanned using confocal or two-photon microscopes equipped with large working distance objectives and motorized stages. This method can resolve the periodic organization of the cell type-specific microcolumn functional modules in the mouse neocortex. The procedure can be useful for the study of three-dimensional cellular architecture in the diverse brain areas and other complex tissues.

Here we describe a procedure for tissue clearing, fluorescent labeling, and large-scale imaging of mouse brain tissue which, thereby, enables visualization of the three-dimensional organization of cell types in the neocortex.

小鼠大脑皮层细胞组织的大尺度三维成像

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical ...

Ballistic Labeling of Pyramidal Neurons in Brain Slices and in Primary Cell Culture

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of pyramidal neurons and dendritic spines, a ballistic labeling technique can be utilized. In the present protocol, pyramidal neurons are labeled with DilC18(3) dye and analyzed using neuronal reconstruction software to assess neuronal morphology and dendritic spines. To investigate neuronal structure, dendritic branching analysis and Sholl analysis are performed, allowing researchers to draw inferences about dendritic branching complexity and neuronal arbor complexity, respectively. The evaluation of dendritic spines is conducted using an automatic assisted classification algorithm integral to the reconstruction software, which classifies spines into four categories (i.e., thin, mushroom, stubby, filopodia). Furthermore, an additional three parameters (i.e., length, head diameter, and volume) are also chosen to assess alterations in dendritic spine morphology. To validate the potential of wide application of the ballistic labeling technique, pyramidal neurons from in vitro cell culture were successfully labeled. Overall, the ballistic labeling method is unique and useful for visualizing neurons in different brain regions in rats, which in combination with sophisticated reconstruction software, allows researchers to elucidate the possible mechanisms underlying neurocognitive dysfunction.

We present a protocol to label and analyze pyramidal neurons, which is critical for evaluating potential morphological alterations in neurons and dendritic spines that may underlie neurochemical and behavioral abnormalities.

脑切片和原细胞培养中金字塔神经元的弹道标记

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of ...

Automatic Identification of Dendritic Branches and their Orientation

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology of dendrites is essential for a better understanding of neuronal function. However, the complexity of dendritic trees, both when isolated and especially when located within neuronal networks, has not been completely understood. We developed a new computational tool, SOA (Segmentation and Orientation Analysis), which allows automatic measurement of the orientation of dendritic branches from fluorescence images of 2D neuronal cultures. SOA, written in Python, uses segmentation to distinguish dendritic branches from the image background and accumulates a database on the spatial direction of each branch. The database is then used to calculate morphological parameters such as the directional distribution of dendritic branches in a network and the prevalence of parallel dendritic branch growth. The data obtained can be used to detect structural changes in dendrites in response to neuronal activity and to biological and pharmacological stimuli.

Presented is a computational tool that allows simple and direct automatic measurement of orientations of neuronal dendritic branches from 2D fluorescence images.

树枝状分支及其方向的自动识别

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology ...