Name: large-scale reconstructions and independent, unbiased clustering based on morphological metrics to classify neurons in selective populations
Uploaded: 2017-02-15T12:30:00.000+00:00
Duration: 12 min 27 s
Description: Watch this Scientific Journal Video about Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations at JoVE.com

我们想感谢博士。埃德卡拉威和马蒂Usrey允许我们使用的预先研究和利比Fairless和柿园刘与神经重建帮助的一部分准备的组织。和白厅基金:这项工作是由美国国立卫生研究院（NEI EY018683）资助。

注意:在此研究中检查的组织制备为单独的研究5的一部分。因此，所有的涉及使用动物的实验方法已经详细在Briggs 等人的实验方法部分中所述。 （2016年）。所有涉及这在现有研究的一部分进行的动物程序进行的机构动物护理和使用委员会的批准。 2 - 注射病毒进入dLGN和脑组织的组织学处理的步骤中的部分1简要地描述如下。 1.立体定位注射<ol><li>使用无菌技术无菌环境中执行所有的外科手术。蒸汽杀菌釜中所有的金属手术器械，纱布和悬垂性材料。消毒可能通过使用化学灭菌（ 例如 ，环氧乙烷）的蒸汽被损坏的所有材料。 </li><li>诱导并根据动物 - 和对维持麻醉具体rotocol-要求。 <ol><li>对于在猴子病毒注射，诱发用氯胺酮（10毫克/千克）麻醉并保持在用异氟烷充分外科麻醉动物-过期的 CO 2监测（1氧气吸入3％），心电图，呼吸率，和温度连续地和周期性地检查颚音，以确保适当的麻醉深度。 </li></ol></li><li>放置动物在立体定位框架，以稳定磁头位置，并允许使用立体定位坐标来定位感兴趣的注入结构（ 例如 dLGN）。如果测量的视觉反应，地点滴眼剂（如果瞳孔扩张需要1％阿托品滴眼剂，否则盐水滴眼液），然后在双眼隐形眼镜，以防止干燥。如果没有测量的视觉反应，将眼药膏眼睛。 </li><li>做一个中线头皮切口，并收回皮肤和肌肉。 </li><li>根据立体一个hemisph坐标的利息（dLGN）的结构ERE，做一个小的开颅手术。 </li><li>逐步走低记录电极（ 例如钨或铂/铱电极（ 具体见材料/设备 ， 第 3行表 ）钳位到手动定位在显微）通过开颅进入大脑。低阻抗（&lt;1兆欧）和稍微厚（〜直径250微米）的电极可以用于穿透硬脑膜，如果需要的话。 </li><li>记录在皮质表面，并在那里视觉响应测量的点处的机械手的位置。视觉反应是听得见的，时间锁定神经元的反应要轻眼中闪过用眼底镜或小的LED /手电筒。 </li><li>通过在开始，中间，以及视觉反应结束注意到操纵位置和减去这些值皮质表面位置确定dLGN的位置和厚度。纪录为dLGN内的每个设计注射部位深处。 注:类似procedu资源可用于通过使用适当的感官刺激定位非可视结构。此外，再加上记录/注射系统也可用于靶向小大脑结构。 </li><li>一旦最佳喷射位置指出的那样，在相同的立体定位位置和下部取出记录电极和放置一个注射针（玻璃吸管或注射器），以适当的深度为每次注射，从最深注射部位并进行到最浅的，改变电极的位置之前注入后等待至少1分钟。用〜50微米外直径的玻璃移液管可能会降低病毒的倒流相比注射器提示（〜直径为200微米）。 </li><li>使用注射系统（参见具体材料的表/ Equipmen T，第4行），picospritzer，或注射泵，注射少量（1 - 5 L;参照制造商的说明注射程序）在多个（3改性狂犬病毒- 10）深度在〜100升/分钟的目标结构（dLGN）范围内的速率。注:单独的注射液渗透可在更大的目标结构进行（ 如猴子dLGN）。根据目标的结构尺寸，调整总的注射量。 </li><li>缩回注射针之前暂停至少5分钟。当注射针被删除，填充保护材料开颅手术（胶骨片的地方，应用骨蜡或明胶海绵），然后缝合肌肉和皮肤重新走到一起。 </li><li>辖镇痛药（ 如酮洛芬），并在手术结束抗生素和连续监测动物，直到动物卧床。 </li><li>至少3天，长达10天手术后，每日监测动物以确保缝线是干净，干燥，和完好动物没有显示出疼痛或不适的迹象。根据需要施用每日止痛药和抗生素。开始手术后的动物社会住房动物后已完全康复手术。 </li> &lt;/ OL&gt; 2.组织收获，切片，染色和<ol><li>允许7 - 14天为病毒的逆行运输，然后通过安乐死溶液（ 例如 Euthasol）过量transcardially用0.1M磷酸盐缓冲盐水，4％多聚甲醛，然后4％低聚甲醛以10％的蔗糖安乐死动物和灌注动物。 </li><li>在0.1M磷酸盐缓冲液的4％多聚甲醛和冷藏1 30％蔗糖- - 12天除去在20脑（见12为类似的步骤，在啮齿动物模型）和地点。 </li><li>当大脑已经下沉到容器的底部，将其取出并切割组织成含有用逆标神经元的大脑区域块（ 例如视觉皮层）和注射靶结构（ 例如 dLGN）。切断从非注射的半球的组织的相同的块 - 这些将作为控制部分。为了达到最佳效果，开始组织程序，立即出现Ÿ在刚切开组织。 </li><li>在厚度为每用冷冻切片部分50μm的部分组织冠状（ 具体见材料/设备 ，第5行的表 ）。 </li><li>染色的细胞色素氧化酶的活性的所有部分（ 具体见材料/设备 ，5行表 - 8）可视化皮质层和皮质下结构13。注:部分可以存储在一夜之间后的细胞色素氧化酶染色在最后冲洗冰箱。 </li><li>标记所有部分与针对GFP的初级抗体（1:1000稀释，〜12小时的温育;见具体材料/设备的表 ，9行），然后通过匹配主主机的第二抗体和标记的与生物素5（1:500稀释，2 - 4小时培养;看到具体的材料/设备的表 ，行10）。建议孵育切片在二次抗体过夜。 </li><li>用抗生物素蛋白/生物素复合物的标签部分，然后用DAB和过氧化反应，永久染色所有标记的神经元5。 </li><li>安装在涂胶的载玻片部分，让干通宵。 </li><li>使用一系列酒精和二甲苯淋洗然后部分脱脂盖玻片14。 </li></ol> 3.神经重构注:使用由一个显微镜的神经元重建系统作了原始实验全部重建（ 具体见材料/设备的表 ，行14），连接的摄像头（ 具体见材料/设备的表 ，行13），以及重建软件包（ 具体见材料/设备 ，11排表 - 12）。软件辅助神经重建能够与神经元过程的基于计算机的附图覆盖组织切片的可视化。重要的是，软件数字化形态reconstruc在三维化数据的特定位置形态学信息，使提取。相关的数据提取程序（ 具体见材料/设备的表 ，行12）支持从每个保存重建了一套丰富的形态数据的提取。 <ol><li>放置在显微镜载玻片架幻灯片，并专注于使用低倍物镜（ 例如 2倍或10倍），感兴趣的单身节。确保相机图像是软件视图中可见通过正确定位相机快门。 </li><li>选择的被孤立合理，从而明确重建尽可能多的树突分支尽可能的利益（ 例如 TRN）的大脑结构中标记的神经元。优先选择的神经元，如果细胞体是完全单一的段内，以捕捉每个单元体的最大范围和准确地估计其面积和圆度。使用10X显微镜物镜来确定小区BODY家庭部分。 </li><li>一旦一个良好的染色，以及隔离神经被识别，加上通过点击"新科"按钮，选择包含细胞体到部门经理的"家"一节。输入部分的数量，包括（建议2 - 3节开始）。提示时指定为50微米的切片厚度（见步骤2.3）。 </li><li>跟踪有关的大脑结构的轮廓在含有细胞体归属部分。使用轮廓跟踪的目标2X /放大倍率。 <ol><li>首先，从顶部工具栏上的下拉菜单中选择"轮廓"，然后选择从下拉菜单（ 如 "LGN"或"TRN"）轮廓类型。 </li><li>跟踪目标结构（ 例如 TRN）以及相邻结构的轮廓，如果需要的话，通过点击沿使用鼠标作为拉伸工具的轮廓点。 </li><li>选择"关闭轮廓"通过右击鼠标，选择该选项从菜单来完成，并附每个跟踪轮廓。 </li></ol></li><li>通过点击合适的工具栏上的所需的标记符号，然后点击所需位置在重建放置标记在该位置放置在靶结构的中心的标记。使用同一类型的标记来标记所述靶结构中的所有重建的中心。 </li><li>一旦轮廓完整，微量使用40细胞体的轮廓 - 60X目标/放大。要做到这一点，首先从顶部工具栏上的下拉菜单中选择"神经元"，然后选择神经元结构追查，在这种情况下，"细胞体"。下一个跟踪细胞体在3.4。 </li><li>放置在细胞体的中心的不同风格的标记，以下在3.5概述的步骤。 </li><li>在跟踪胞体开头的树突。 <ol><li>首先，从下拉菜单中选择"枝蔓"。然后追踪每枝晶开始在小区BODy和使用鼠标作为一个平局工具。一定要调整整个跟踪的z深度准确地捕捉到枝晶的角度和方向。 </li><li>将一个节点通过右键单击鼠标，然后从下拉菜单中选择"分叉节点"或"Trifurcating节点"沿枝晶每个分支点。使用40 - 60X目标/放大倍率为所有枝蔓重建。 </li><li>在每枝晶结束后，右击鼠标，然后从下拉菜单中选择"结束"。 </li></ol></li><li>确定树突的结局有可能持续到相邻的部分，并把这些成焦点在显微镜图像的相应Z深度。包括附近的主要地标，如血管或容易识别的枝蔓图案或束的显微图像。 <ol><li>降低放大倍数20X或10倍的显微镜（选择允许的最大标志性建筑可视化的放大倍率），并采取使用数码相机一个显微镜图像的图像（ 例如手机，平板电脑）手持电脑屏幕。 </li></ol></li><li>移动到幻灯片上的相邻部分，排队先前跟踪部分的轮廓与dLGN和TRN在新部分的界限。 </li><li>返回的视场的细胞体（基于轮廓和主要地标）的一般区域，并把放大回升至10 - 20X。 </li><li>使用树突结尾的照片从先前跟踪的部分对准新节树突的开端的结局，以帮助。 <ol><li>要旋转的跟踪和移动对准树突，使用箭头工具选择重建，右键单击，然后从下拉菜单中选择"移动"。 </li><li>或者，使用"匹配"的工具，以树突结局匹配开端。从顶部工具栏中选择匹配工具和提示时，单击结束在重建和图像中的相应的连续点。重复3个或更多的结局。 </li></ol></li><li>一旦从上一节的跟踪一字排开在新的节枝，确保相应的新的部分在科长通过点击当前部分选择。或者，添加一个新的部分到部门经理，如3.3如上所述，相应地调整每个新的相对到户节一节的z深度和位置，每个部分的厚度设定为50毫米（见步骤2.3）。 </li><li>增加放大倍数为40 - 60X和通过右键单击从重建的枝蔓末端的鼠标，并从下拉菜单中选择"添加到结束"，然后跟踪使用鼠标作为一个枝蔓跟踪的枝蔓延续绘制工具。按照提示指定是否延续，是在新的一节。 </li><li>通过至少3个相邻部分遵循树突（一个上的各侧3.15 - 以下步骤3.8家里一节）。添加到重建直到至少3总段被跟踪为神经元或直至树突不再可以遵循或找到。跟踪按照上述步骤，如果需要，在邻近的部分轮廓。 </li></ol> 4.独立集群注:独立聚类分析启用大，多维数据集，否则可能很难想象，更重要的偏见的分析，提供了形态多样的定量评估。基于矩阵的编程平台是多维数据集的分析非常有用，使复杂的数据操作和统计分析。在步骤4中列出的功能- 6在具体材料/设备的表中列出的编程平台定义，行15。 <ol><li>使用分机从每个神经元的重建形态提取数据与神经元重建系统相关raction方案（见特定的材料/设备 ，11排表 - 12）。 <ol><li>首先，选择所需的每个重建，包括形态学数据:轮廓信息，标记信息，胞体和树突分支等形态学数据。 </li><li>从顶部工具栏中的编辑下拉菜单中选择"选择所有对象"。然后在顶部工具栏中选择从分析下拉菜单"分支结构分析"，然后点击每个选项卡上，选择在每个选项卡所需的分析选项。 </li><li>通过单击分析窗口中的"确定"按钮，提取所有需要的数据，并通过右键点击电子表格格式保存在输出窗口，选择"导出到Excel"。 </li></ol></li><li>编译Master表（ 具体见材料/设备的表 ，行16），其中每个变量形态/ METRIC是由神经元每一个单一的数字观察表示。在这种Master表，为保持每个神经元的行标识符的记录。 </li><li>验证包括在群集分析所有变量（ 例如形态指标）是彼此独立的。例如，节点的数量将与分支的树枝状或轴突树枝状的数量相关。因此，只有这两个变量中的一个应包括在一个聚类分析。 </li><li>确保每个测量包含每个变量的观察， 即每一神经元（测量），必须有相应于每个形态度量（变量）一个数据点（观察）。如果缺乏对神经元的一个子集为一个特定变量（例如，轴突长度）的观察，这个变量（轴突长度）不能被包括在聚类分析。 </li><li>组织对Master表数据到其中的每一行代表一个神经元和EAC一个矩阵H柱包含每个形态度量（ 图1）的数值数据。例如，包括100元数据集为其中有将由100×5（行逐列）矩阵表示为5个独立变量的观测。它没有必要包括对矩阵内的每个神经元的任何标识 - 行索引将作为每一个神经元的唯一标识符。此外，还应该在基质空白或"南"S。保存矩阵作为单个变量（ 如数据= 100×5矩阵]，见补充文件的代码示例代码）。 </li><li>选择一种算法来计算表示在其中，n是由变量的数目限定的n维空间的单个神经元点之间的距离。在&#39;pdist"功能，可以灵活地计算神经元间的距离。默认距离计算是欧几里德距离和神经元morphologica的类似分析先前已经使用升数据5,6。 </li><li>随着神经元间的距离的&#39;pdist"功能的输出，用"联动"功能，形成集群。此外，多个集群选项。 Ward的方法和形心的距离都在形态数据集5,6的分析产生了类似的结果。 </li><li>如果需要的话，可以使用&#39;k均值"功能作为替代的聚类方法"pdist&#39;和&#39;联动&#39;的功能。 "K均值"采用平方欧氏距离来计算神经元间的距离，然后质心的距离来分配集群。 </li><li>以可视化聚类树，请使用"联动"操作的输出的"树状图"功能。此函数具有限制可视30测量一缺省设置，但它可以通过指定（测量次数&lt;重写EM&gt;如神经元）显示。树形图说明了在y轴上的每个神经元的，由在x轴的行索引对应的联动距离。 注:"联动"和"树形图"的输出没有定义的簇的最佳数目。 &#39;k均值&#39;的输出不分配测量群集，但它不测试这些簇是否是最优的。单独的统计比较和最优聚类的验证可以进行（见下文）。 </li></ol> 5.集群的验证注意:如上所述，所述聚类分析本身并不直接提供在聚类图中示出的簇是否是唯一的，并代表了样品的统计评估。用于验证从树形簇方法被提出15，但是这些并不能提供最佳聚类的统计验证。还有多PLE方法来验证最优聚类。 <ol><li>方法1:评估集群: <ol><li>使用&#39;evalclusters&#39;功能指定用于计算集群，如&#39;k均值"或"连锁"的方法。高斯混合模型输出还可以评估（见步骤5.2以下;见补充文件的代码示例代码）。 </li><li>从多个选项指定的评价标准（ 如 &#39;CalinskiHarabasz" -为标准选项的说明，请参阅帮助菜单，搜索"evalclusters"）。 </li><li>指定的范围内的最佳簇编号的检测（ 例如 ，[1:6]测试是否1，2，3，4，5，或6簇是最佳的）。 注:&#39;evalclusters&#39;的输出是给定的指定的聚类算法和标准簇的最佳数目。 </li></ol></li><li>方法2:高斯混合模型的聚类: 注:高斯混合模型的聚类ALGORithms（的GMM）假设对于每个变量观测来自高斯分布限定每个推定的簇，每个都有自己的均值和协方差的混合物。的GMM然后使用期望最大化算法来验概率分配给每个测量，指示属于特定群16的概率。 <ol><li>实施使用通过输入簇中的数据可观察到的推定数目的&#39;fitgmdist&#39;功能的GMM。另外，为了避免簇的数目的先验分配，使用具有一系列使用此处描述的步骤不同的最优簇编号的主成分分析（PCA）（参见补充编码文件样本代码）。 </li><li>使用上的原始数据矩阵的&#39;PCA&#39;功能和主成分得分保存作为输出。 </li><li>在一个循环从1开始，并通过一些许多假定最佳集群将使用日E&#39;fitgmdist"的主成分得分函数生成的GMM推定的最佳集群的每个数字。 </li><li>通过检查阴性数似然，赤池信息标准，和贝叶斯信息标准评价每个GMM。具有最低标准GMM是最佳的。 </li><li>如果需要的话，测试每个群集的平均值是否显著不同当簇的数目是最佳的（每个簇的装置被存储在每个GMM的"μ"输出）。 </li></ol></li></ol> 6.集群数据的统计分析<ol><li>一旦使用等级聚类测定，评价簇的最佳数目，返回到原来的数据，独立的神经元成群集，并确定哪些形态指标有助于神经元的聚类成独特的类。 </li><li>排序的原始形态度量数据，使得神经元根据簇分配分组。 </l我&gt; <li>要检查形态学数据跨神经元（所有神经元或分隔成集群神经元）之间的关系，进行线性回归拟合度形态学观察与2-和3路比较。这些拟合可以使用"适合"功能进行估计，并使用"fitlm"功能，其中合适的输出善良包括回归拟合R 2和p值评估。 </li><li>检查每个簇神经元之间的统计关系中，使用非参数双样本（Wilcoxon秩和检验）或多样品比较测试（ANOVA），这取决于簇的数目。重要的是，使用多个样本方差分析的确保是p值多重比较校正。 </li></ol>

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神经解剖学研究已经保持神经和连接组学和结构与功能的关系近期备受关注的支柱已经更新了选择性神经元群的详细的形态学特征热情。传统上，神经解剖学研究依赖于神经元的定性分类成形态不同的类专家neuroanatomists定义的神经元。在所述技术用于重建神经元和提取形态学数据的进步，现在可以利用更复杂的和定量的数据分析方法，形态不同的神经元类无偏方式进行分类。在这项研究中，一步?...

神经解剖学处于"连接组学"神经科学1和近期利益的基础之一续签了解神经元群体的形态多样性和特殊的神经元2之间的连接热情。用于标记的方法和重建神经元与最近的创新，包括遗传性和病毒介导的电路跟踪有很大的提高接近3，4，使神经元群体5的更全面的形态的调查。除了在标记单个神经元的改进，定量数据分析技术也已出现，使神经元的独立的和无偏分类成基于形态学数据5，6个不同亚群。这些偏见的技术是在更多的繁体版的改进升定性分类方法已经在该领域的标准了一个多世纪。这项研究的目的是概述，一步一步，选择性群体内神经元的病毒介导的标记的组合，这些神经元的综合样本，和定量数据分析的大规模重建基于与独立聚类统计评估。通过结合这些方法，我们概述朝向收集和神经解剖学数据的分析的新方法，以促进选择性的神经元群体中全面采样和形态学独特神经类型的无偏分类。 由于这些方法的一个例子，我们描述了猕猴的丘脑网状核（TRN）的单个扇区内我国人口众多的神经元进行分析。这些数据是从以前的研究7。用于选择性地标记TRN神经元投射到背latera方法采用手术注射编码EGFP 4,8修改狂犬病毒的丘脑（dLGN）l的膝状体（ 具体见材料/设备 ，第2行的表 ）进行了概述。本变形狂犬病毒缺乏基因编码的基本外壳蛋白，消除了病毒的跨突触运动。一旦病毒在注射部位进入轴突终端，它的作用就像与驱动整个感染神经元5，9，10的全树突分支EGFP表达的重要的益处传统逆行示踪剂。因此，该G-删除狂犬病毒可被用于选择性地感染和标签以下注射和逆行运输任何神经元群体。 为了执行特定的神经元群的综合分析，它从采样是重要神经元的人口中的广泛分布。由于病毒介导的标记技术产生完整的细胞内，"高尔基样"与在病毒注射部位轴突许多神经元的填充，是可能的一个大脑结构的全部范围内重建神经元的一个非常大的样品。此外，由于改性狂犬病毒是在感染和标签大量的神经元，以便有效的，但是可能重建数百每只动物的神经元。为为了生成dLGN投射TRN神经元的全面样品取样160的神经元在整个TRN 11的视觉扇区程序进行了概述。描述重建使用神经重建系统包括显微镜，照相机，和重建软件单个神经元的过程。还描述了方法来确定大脑结构中（TRN在中在这种情况下）单个神经元的位置，并核实病毒注射坐使用体积轮廓重建一个结构（在dLGN内这种情况下）内È体积和位置。步骤导出形态学数据和执行独立的聚类分析的基础上对每个神经元的形态测量指标进行了概述。有对聚类方法的限制，也有可用的多种不同的聚类算法。因此，这些选项和的一些更常用算法的优点进行说明。聚类分析不提供集群的唯一性的统计验证。因此，额外的步骤概述，以验证最佳聚类以及内和跨集群形态学数据之间的关系。用于评价集群的TRN数据集，以确认TRN神经元的统计方法被分组为基于被描述10独立形态度量三个独特簇。 因此，通过概述用于选择性标记步骤，重构，并从一个特定的神经元群体分析形态学数据，我们描述用于定量群体内的神经元之间的形态差异的方法。猕猴TRN视觉部门不同的神经元类型的此前的研究结果证实，独立的统计评价方法。总之，我们希望这些技术将广泛适用于神经解剖数据集，并有助于通过建立大脑的神经元群体的多样性量化分级。

<table><tbody><tr><td>SAD&amp;Delta;G-EGFP</td><td>E.M. Callaway Laboratory, Salk Institute</td><td></td><td>Prepared by Dr. F. Osakada. G-deleted rabies virus available through the Salk Institute Viral Core</td></tr><tr><td>Recording electrode: platinum/iridium or tungsten</td><td>FHC</td><td>UEPSGGSE1N2M</td><td>Visit website (www.fh-co.com) for alternative order specifications</td></tr><tr><td>Nanoject II</td><td>Drummod Scientific</td><td>3-000-204, 110V</td><td>Alternatives: picospritzer, Hamilton syringe</td></tr><tr><td>Freezing microtome</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>DAB</td><td>Sigma Aldrich</td><td>D5905-50TAB</td><td>3,3&#039;-Diaminobenzidine tetrahydrochloride, tablet, 10 mg substrate per tablet. Caution: carcinogen - must be bleached before discarding</td></tr><tr><td>Cytochrome C</td><td>Sigma Aldrich</td><td>C2037-100MG</td><td></td></tr><tr><td>Catalase</td><td>Sigma-Aldich</td><td>C9322-5G</td><td></td></tr><tr><td>Rabbit anti-GFP</td><td>Life Technologies/Thermo Fisher</td><td>#A-11122&amp;nbsp;</td><td>Primary antibody</td></tr><tr><td>Biotinylated goat anti-rabbit</td><td>Vector Laboratories</td><td>#BA-1000</td><td>Secondary antibody</td></tr><tr><td>Neurolucida System&amp;nbsp;</td><td>MicroBrightField</td><td></td><td>Software for neuron tracing and analysis. http://www.mbfbioscience.com/neurolucida</td></tr><tr><td>Neurolucida Explorer</td><td>MicroBrightField</td><td></td><td>Data export software</td></tr><tr><td>Microfire Camera&amp;nbsp;</td><td>Optronics</td><td></td><td>2-Megapixel true color microscope camera. http://www.simicroscopes.com/pdfs/microfire.pdf</td></tr><tr><td>Nikon E800 Microscope</td><td>Nikon Instruments Inc.</td><td></td><td>Biological research microscope. http://www.microscopyu.com/museum/eclipseE800.html</td></tr><tr><td>Matlab</td><td>The MathWorks Inc.&amp;nbsp;</td><td></td><td>Matrix-based computational mathematics software. http://www.mathworks.com</td></tr><tr><td>Microsoft Office Excel</td><td>Microsoft</td><td></td><td>Spreadsheet program</td></tr></tbody></table>

large-scale reconstructions and independent, unbiased clustering based on morphological metrics to classify neurons in selective populations

该协议概述了神经元与使用独立，公正的集群相结合的大规模重建分析创建的选择性神经人口中观察到的形态特征进行全面的调查。这些技术的组合构成用于神经解剖学数据的收集和分析的新方法。总之，这些技术使大规模，和选择性的神经元群体的因此更全面，取样和建立用于说明人口内形态学独特神经类偏定量方法。 该协议概述了采用改性狂犬病毒的选择性标记神经元。 G-删除以下立体定位注射到感兴趣的目标大脑结构的狂犬病毒的作用就像一个逆行示踪剂，并作为EGFP的神经元递送和表达的载体。神经元大量地使用这个感染技术，并表示在他们的树突GFP，生产"高尔基样"单个神经元的完整罢了。因此，病毒介导的逆行追踪方法，通过生产完整的细胞内填充在传统的基于染料逆行追踪技术改进。 个体以及隔离神经元跨越所研究的大脑区域的所有区域，以获得的神经元的一个代表性的样品被选择用于重建。该协议概述了程序，以重建胞体和完成标记细胞跨越多个组织切片的树突分支模式。形态数据，包括脑结构中的每个神经元的位置，提取用于进一步分析。标准编程功能被用于执行独立的聚类分析和基于形态学指标群的评价。为了验证这些分析的聚类分析PERFO的效用，统计评估Rmed指对在猕猴丘脑（TRN）的丘脑网状核重建神经元160制成。无论是原聚类分析和这里执行的统计评价表明TRN神经元被分为三个亚群，每个具有独特形态特征。

我们以前曾表明，选择性群体内神经元的大规模重建是可行的下面喷射改性狂犬病病毒的进入dLGN 5。最近，相同组织被利用来重建160中的神经元TRN的视觉扇区（布拉格等人 ，在审查; 图2A-B），按照上述的详细的方法步骤。在TRN的研究中，TRN神经元的三种独特簇鉴定根据10形态度量独立聚类分析:细胞体区域中，单元主体的圆度，相对于TRN?...

Watch this Scientific Journal Video about Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations at JoVE.com

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

这个协议描述以下具有修饰的狂犬病毒表达荧光标记物，以及独立的，公正的聚类分析，使不同的神经元亚类之间的形态度量的全面表征逆行感染选择性神经元群体的大规模重建，进行标记。

基于形态学度量大规模重建和独立，公正聚类分类神经元选择性人群

This protocol outlines large-scale reconstructions of neurons combined with the use of independent and unbiased clustering analyses to create a ...

large-scale-reconstructions-independent-unbiased-clustering-based-on

Research

JoVE Journal

Neuroscience

6.9K 次观看.  Geisel School of Medicine at Dartmouth. 这个协议描述以下具有修饰的狂犬病毒表达荧光标记物，以及独立的，公正的聚类分析，使不同的神经元亚类之间的形态度量的全面表征逆行感染选择性神经元群体的大规模重建，进行标记。 

视频: Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Here, we provide a workflow that allows the identification of healthy and pathological cells based on their 3-dimensional shape. We describe the process of using 2D projection outlines based on the 3D surfaces to train a Self-Organizing Map that will provide objective clustering of the investigated cell populations.

利用傅里叶变换和自组织映射的形态学区分健康和病理细胞

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to ...

癌症研究

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 ...

Analyzing Dendritic Morphology in Columns and Layers

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 columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

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 ...

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

How cortical network activity processes information is of importance to a large number of basic and clinical scientific questions. The protocol described here identifies the basic building blocks of this circuitry. The in-depth studies of cortical regions will ultimately provide other scientists with the circuit components needed for an understanding of how the brain acquires, processes and stores information and what goes wrong in disease, while the electrophysiological and morphological data are widely used by computational neuroscientists in the construction of model networks that explore information processing. The protocol outlined here describes how biocytin-filled cells recorded in the CA2 region of the hippocampus are recovered and then reconstructed in&#160;3D. Additionally, the protocol describes the demonstration of calcium binding protein or peptide content in recorded interneurons.

The protocol outlined here describes the immunofluorescence analysis, biocytin recovery and high-quality reconstructions of hippocampal CA2 interneurons following the intracellular electrophysiological recordings in vitro, allowing neuronal characterization and ultimately fine neuronal anatomy to be studied.

填充海马 ca2 间神经元的生物环素恢复与三维重建

How cortical network activity processes information is of importance to a large number of basic and clinical scientific questions. The protocol described ...

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

胶质细胞和神经细胞的三维重建与虚拟现实分析方法

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

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 ...

A Tissue Clearing Method for Neuronal Imaging from Mesoscopic to Microscopic Scales

A detailed protocol is provided here to visualize neuronal structures from mesoscopic to microscopic levels in brain tissues. Neuronal structures ranging from neural circuits to subcellular neuronal structures are visualized in mouse brain slices optically cleared with ScaleSF. This clearing method is a modified version of ScaleS and is a hydrophilic tissue clearing method for tissue slices that achieves potent clearing capability as well as a high-level of preservation of fluorescence signals and structural integrity. A customizable three dimensional (3D)-printed imaging chamber is designed for reliable mounting of cleared brain tissues. Mouse brains injected with an adeno-associated virus vector carrying enhanced green fluorescent protein gene were fixed with 4% paraformaldehyde and cut into slices of 1-mm thickness with a vibrating tissue slicer. The brain slices were cleared by following the clearing protocol, which include sequential incubations in three solutions, namely, ScaleS0 solution, phosphate buffer saline (&#8211;), and ScaleS4 solution, for a total of 10.5&#8211;14.5 h. The cleared brain slices were mounted on the imaging chamber and embedded in 1.5% agarose gel dissolved in ScaleS4D25(0) solution. The 3D image acquisition of the slices was carried out using a confocal laser scanning microscope equipped with a multi-immersion objective lens of a long working distance. Beginning with mesoscopic neuronal imaging, we succeeded in visualizing fine subcellular neuronal structures, such as dendritic spines and axonal boutons, in the optically cleared brain slices. This protocol would facilitate understanding of neuronal structures from circuit to subcellular component scales.

The protocol provides a detailed method of neuronal imaging in brain slice using a tissue clearing method, ScaleSF. The protocol includes brain tissue preparation, tissue clarification, handling of cleared slices and confocal laser scanning microscopy imaging of neuronal structures from mesoscopic to microscopic levels.

一种从介观尺度到微观尺度的神经元成像组织清除方法

A detailed protocol is provided here to visualize neuronal structures from mesoscopic to microscopic levels in brain tissues. Neuronal structures ranging ...

<meta charset="utf8"></head><body>使用 Neurolucida 360 进行树突状脊柱分析的方案

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software

Synaptic connections allow for the exchange and processing of information between neurons. The post-synaptic site of excitatory synapses is often formed on dendritic spines. Dendritic spines are structures of great interest in research centered around synaptic plasticity, neurodevelopment, and neurological and psychiatric disorders. Dendritic spines undergo structural modifications during their lifespan, with properties such as total spine number, dendritic spine size, and morphologically defined subtype altering in response to different processes. Delineating the molecular mechanisms regulating these structural alterations of dendritic spines relies on morphological measurement. This mandates accurate and reproducible dendritic spine analysis to provide experimental evidence. The present study outlines a detailed protocol for dendritic spine quantification and classification using Neurolucida 360 (automatic three-dimensional neuron reconstruction software). This protocol allows for the determination of key dendritic spine properties such as total spine density, spine head volume, and classification into spine subtypes thus enabling effective analysis of dendritic spine structural phenotypes.

<meta charset="utf8"></head><body>作者聚焦:优化树突状棘分析，实现海马 CA1 顶端树突的平衡手动和自动评估

Dendritic spines are post-synaptic compartments of most excitatory synapses. Alterations to dendritic spine morphology occur during neurodevelopment, aging, learning, and many neurological and psychiatric disorders, underscoring the importance of reliable dendritic spine analysis. This protocol describes quantifying dendritic spine morphology accurately and reproducibly using automatic three-dimensional neuron reconstruction software.

使用自动三维神经元重建软件进行树突状脊柱定量

Synaptic connections allow for the exchange and processing of information between neurons. The post-synaptic site of excitatory synapses is often formed ...