作者感谢 Keali hm、路易莎 Frieß和海克 Menzel 的专家技术援助;海尔 Brünner 的动物护理;和 Chistopher s 病房为 ImageJ 软件的光 disector 网格插件的世代和发行。

对动物的照顾和使用的所有适用的国际、国家和/或机构准则都得到遵守。该议定书由德国 Wuerzburg Regierung Unterfranken 的地方当局批准. 
 1. 组织处理和免疫组化 <ol> <li> 安乐与 CO 2 或任何其他已批准方法的小鼠. </li>
	<li> 灌注六12周老 C56Bl/6N 雄性小鼠 transcardially 10 毫升0.1 米的磷酸盐缓冲盐水 (pbs) 使用25克针和输液泵, 其次70毫升4% 甲醛 (粉煤灰) 在 0.1 M PBS. 
	 警告: 煤灰是有毒、过敏和致癌的. </li>
	<li> 在 +0.74 mm Bregma (图 25, Paxinos 和富兰克林鼠脑图谱 14 ) 的区域内, 用大脑矩阵切片器解剖在日冕平面上的小鼠大脑. </li>
	<li> 将大脑的背侧部分, 包括 SN, 在0.1 米 PBS 中的4% 粉煤灰中, 1 d 在4和 #176; 浸泡固定的 C. </li>
	<li> 将30% 蔗糖/0.1 摩尔 PBS 溶液交换为低温保护, 并在4和 #176 中孵育另 2 d; C. </li>
	<li> 将大脑的背部部分放入一个 cryomold 填充最佳切削温度 (OCT) 化合物, 并在液态干冰冷却戊中缓慢冻结组织. 
	 警告: 干冰非常冷 (-78 和 #176; C);使用个人防护设备 (PPE), 包括手套, 同时处理干冰。干冰在 CO 2 中蒸发;因此, 在通风罩下工作. </li>
	<li> 连续剪切 30 #181; m 在冠状面上的冰冻切片, 并收集剖面, 从 Bregma (图 51, Paxinos 和富兰克林鼠脑图谱 14 ) 起2.46 毫米开始, 在4.04 毫米从 Bregma (图 64, Paxinos 和富兰克林鼠脑图谱 14 ). </li>
	<li> 存储四系列的管中充满剂 (30% 甘油, 30% 乙二醇, 40% PBS) 在-20 和 #176;在切片过程中, 通过在脑干的上部区域穿刺一个小孔来标记右半球. </li>
	<li> 对于免疫组织化学染色, 每只老鼠选择一组切片。Preincubate 自由漂浮部分1小时在10% 正常山羊血清 (NGS)/2% BSA/0.5% 洗涤剂在0.1 摩尔 PBS 在一个振动筛上。用兔 anti-mouse 酪氨酸羟化酶 (TH; 1:1, 000 稀释) 在0.1 摩尔 PBS 中稀释 2% NGS/2%bsa/0.5% 洗涤剂, 在4和 #176; C. </li>
	<li> 在室温下应用二次抗体对抗家兔 (1:100 稀释) 2 小时, 其次是素/生物素试剂 (1:100 稀释)。孵育和染色 33-diaminobenzidine-tetrahydrochloride (民建联) 和 H 2 O 2 。在涂层对象幻灯片上着色后, 将其装入这些部分. 
	注: TH + 多巴胺能 SN 神经元在 图 1a 中显示。通过染色一个系列, 平均 13 sn 部分, 从侧延伸到尾部部分的 sn 致密 (SNpc) 和福橘, 染色每动物 ( 图 1b )。各节以120和 #181 分隔; m (1/4 系列) ( 图 1c ). 
	 警告: 民建联是一种可疑的致癌物。它是有毒的接触和吸入。在与民建联合作时使用 PPE. </li>
</ol> 2。获取图像 <ol> <li> 使用与显微镜耦合的图像软件捕获 TH 免疫着色的 SN 剖面。分别分析一个系列的每个部分. </li>
	<li> 使用图像处理软件的扫描幻灯片选项。以 TIFF 格式保存高质量图像 ( 图 2 ). 
	注意: 图像分辨率是每 cm 312 像素. </li>
	<li> 按 #34; 获取和 #34; 打开获取窗口 ( 图 2a ). </li>
	<li> 将分设置为和 #34; 2 和 #34; 用于灰度图像. 
	注意: 获取灰度图像而不是彩色图片会减小最终堆栈图像文件的大小 ( 图 2a ). </li>
	<li> 单击并 #34; 显示实况和 #34; 在获取窗口中打开一个新窗口, 其中显示该节的实时图像 ( 图 2 a 和 b ). </li>
	<li> 单击并 #34; 舞台和 #34; 在获取窗口中。选择和 #34; 扫描幻灯片和 #34; 在下拉菜单中检查该框 ( 图 2c ). 
	注意: 这使得在 x、y 平面上的高度放大的 SN 图像 (630X 放大) 的获取和对准, 以及在1和 #181 的连续图像之间的距离的堆栈图像的获取; m 在 z 平面中, 覆盖总范围为13和 #181;m. </li> <li> 选择2.5X 目标以搜索 SN. </li>
	<li> 将目标更改为 63x. </li>
	通过单击和 #34, <li> 定义该节的左上角 (TopLeft) ( 图 2c ) 和右下角 (LowRight) ( 图 2d ); 设置. 和 #34; </li>
	<li> 单击并 #34; z 系列和 #34; 检查 #34; z 平面系列和 #34; 框 ( 图 2e ). </li>
	<li> 使用成像软件确定每节三随机选取的计数点的实际安装厚度。为此, 请单击 #34; 查看顶部偏移量和 #34; 若要定义节的顶部 ( 图 2e ), 然后单击并 #34; 停止查看顶部和 #34; ( 图 2f )。然后, 点击和 #34; 查看底部偏移量和 #34; ( 图 2f ), 然后单击并 #34; 停止查看底部和 #34; ( 图 2g )。记下剖面的粗细. </li>
	<li> 从顶部偏移量中减去3和 #181; m (保护区域), 并将结果键入顶部偏移槽 ( 图 2h )。从顶部偏移数减去13和 #181; m (光学 disector 的高度), 并将结果键入底部偏移槽 ( 图 2i )。选择以下参数: 步骤, 14;大小, 1.00 ( 图 2i ). 
	注: 这对应于13和 #181 的 z 平面上的厚度; m, 具有1和 #181; 连续图像之间的距离. </li>
	<li> 选择要保存文件的目录 ( 图 2j ). </li>
	<li> 单击并 #34; 序列和 #34; 启动获取 ( 图 2j ). </li>
	<li> 单击 #34;P 加工和 #34, 然后选择以下参数: #34; 所有飞机、#34、#34、序列、#34、#34、蒙太奇、#34; 和 #34; 快速和 #34; 和 #34; 拼接和 #34; 框, 图像重叠为 10% ( 图 2k )。开始通过点击和 #34 来缝合图像; 开始和 #34;. 
	注意: 这将生成用于分析的堆栈图像 ( 图 2 l, 图 3a e )。
</ol> 3。视学评估序列 <ol> <li> 使用 NIH ImageJ 软件版本4.7 执行对 SN 堆栈图像的分析. </li>
	<li> 下载 ImageJ.nih.gov 网站所需的两个插件: #34; 网格叠加和 #34; 插件 15 和 #34; 单元计数器和 #34; 插件 16 。按所述安装两个插件. </li>
	<li> 对于无偏视学评估, 请按如下所示定义计数参数: 网格大小、130 x 130 和 #181; 计数帧、50 x 50 和 #181; m; 和光学 disector 高度, 13 和 #181; m. </li>
	<li> 通过单击 #34 打开图像; 文件和 #34; #160; #8594; #34; 打开和 #34; ( 图 4a ) 通过使用和 #34; 分析与 #34; #160; #8594; #34; 设置缩放和 #34; 命令 ( 图 4b )。对于此步骤, 请将定义的大小从像素更改为和 #181; m ( 图 4c ). 
	注意: 对于分析的图像, 425 像素对应于100和 #181; m. </li>
	<li> 选择 #34;P lugins 和 #34; #160; #8594; #34; 网格和 #34; #160; #8594; #34; 网格类型: 线和 #34; 命令随机插入网格。检查 #34; 随机偏移和 #34; 框。定义网格中一个正方形的大小 (网格正方形) 为130和 #181; m x 130 和 #181; m = 16900 和 #181; m 2 ( 图 3b 和 图 4d 和 e ). </li>
	<li> 将图像类型从16位更改为 RGB 颜色 (单击和 #34; 图像和 #34; #160; #8594; #34; 类型和 #34; #160; #8594; #34; rgb 颜色和 #34;) ( 图 4f )。找到 SNpc, 如 Baquet et al. 所述 17 . 将 SNpc 与 #34;P aintbrush 工具和 #34 包围; (画笔宽度:11) ( 图 3c 和 图 4g i ). </li>
	<li> 撤消和 #34; 设置缩放和 #34; 命令通过单击和 #34; 分析和 #34; #8594; #34; 设置规模和 #34; #8594; #34; 单击以删除缩放和 #34; ( 图 4j -l ) 以像素为单位启用计算的性能, 而比和 #181; m. </li>
	<li> 制作截图 ( 图 4m ), 保存图像文件, 然后用 ImageJ ( 图 4n ) 打开新文件。选择和 #34;P 点和 #34; ( 图 4o ) 和画笔宽度 25 ( 图 4p )。标记与 SN 联系的每个网格正方形, 以放置一个光学 disector ( 图 4q )。对在所述 SN 中完全或部分局部化的所有光学 disectors 中的单元格数进行分析. </li>
	<li> 选择一个包含 SN 部分的网格正方形。用 ImageJ 中的光标测量此正方形的左上角, 以定义要开始的 x 和 y 坐标 ( 图 4r ). 
	注: 使用 #34; 光学 disector 位置和 #34; ( 图 4s ), 如步骤4所述, 将为光学 disector 定位插入坐标. </li>
	<li> 计算光学 disector 位置的方法是使用 "电子表格模板", #34; 光学 disector 位置, #34; 如步骤4所述 </li>
	<li> 校准光学 disector (由克里斯托弗· s 沃德编写的宏, 补充文件 ) 通过单击 #34;P lugins 和 #34; #8594; #34; 宏和 #34; #8594; #34; 编辑和 #34; 图 4t ), 打开和 #34; opt_dis_grid.txt 和 #34; 文件 ( 图 4u ), 并定义 #34 的大小; usergrid 和 #34; 以像素为单位, 与光学 disector ( 图 4v ) 的 x、y 维度相对应。选择大小为50和 #181; m x 50 和 #181; m, 使 usergrid 的一侧的大小定义为212.5 像素 (50 x 4.25 像素). </li>
	<li> 通过插入定义光学 disector ( 图 4v ) 中心的 ImageX 和 ImageY 坐标来确定光盘 disector 在堆栈图像中的位置。运行宏 (#34; 宏和 #34; 和 #8594; #34; 运行宏和 #34;) ( 图 4w ). 
	注意: 网格将从堆栈图像 ( 图 3d 和 图 4x ) 中消失。在 z 平面 ( 图 3e 和 图 4x ) 中向下移动时, 光学 disector 将持续. </li>
	<li> 选择和 #34;P lugins 和 #34; #160; #8594; #34; 单元格计数器和 #34 启动单元计数器插件 ( 图 4y )。初始化单元格计数器并选择一个标记类型 ( 图 4z )。放大图像 (点击和 #34; + 和 #34;) 和执行视学评估的细胞数放大 200%. </li>
	<li> 通过圆形或卵球形的形状和细胞大小来识别多巴胺能神经元 perikarya ( 图 1a )。使用视学规则量化单元格的数量. 
	注意: 由于光学 disector 是一个3维多维数据集 ( 图 5a ), 因此将排除边定义为多维数据集的前、左和上侧 (红色)。因此, don 和 #39, 不要计算与红线 (左和前) 或顶部接触的 TH 细胞。将单元格计数的结果添加到已准备好的电子表格文件中, #34; 计算单元计数和 #34; (步骤 5). </li>
	<li> 根据 SN 图像, 计算下一个网格正方形, 用于将光学 disector 用于下一单元格计数, 使用 x、y 步骤覆盖上覆网格的大小 ( 图 5b ) 和 #34; 光学 disector 位置和 #34;电子表格模板, 如步骤4所述. </li>
	<li> 使用 and #34 对单元格数进行估计; 计算单元计数和 #34; 电子表格 (步骤 5). </li>
</ol> 4。光学 Disector 位置的计算 #34; 光学 Disector 位置和 #34; 电子表格模板 <ol> <li> 计算每个光学 Disector 的大小和位置, 将其放置在 SN 轮廓内的上覆盖网格中 ( 图 5b )使用和 #34; 光学 disector 位置. xlsx 和 #34; 电子表格文件 ( 补充文件 ). </li>
	<li> 插入按 #34 定义的每 #181 像素的转换; 设置缩放和 #34; 命令, 进入和 #34 的黄色标记位置; 光学 disector 位置. xlsx 和 #34; 文件 ( 图 6a 和 补充文件).</li>
	<li> 插入 disector 的计划大小和上覆网格的网格正方形的大小, 由一侧 (在和 #181; m) 的长度定义, 到橙色标记的位置. 
	注意: 结果在橙色方框旁边的红色方框中描述. </li>
	<li> 从作为起点的 SN 轮廓中的网格正方形开始 (在上面的步骤3.9 中选择一个网格正方形作为开始). </li>
	<li> 将 x 和 y 坐标 (如步骤3.9 所示) 插入文件的绿色框中 (通过使用这些值, 计算第一个正方形内的光学 disector 中心的 x、y 坐标, 并在蓝色框中描述结果)。运行 opt_dis_grid 的. txt 宏 ( 补充文件 ). </li>
	<li> 相对于第一个网格正方形 (棕色框), 通过插入网格平方距离 (以平方数度量) 来计算连续光学 disectors 的 x、y 坐标. 
	注: + x: 方格位于右侧;-x: 网格正方形位于左边;+ y: 网格正方形位于底部;-y: 网格广场位于顶端。结果显示在灰色框中. </li>
</ol> 5。使用 and #34 估计单元格数; 计算单元数和 #34; 电子表格 <ol> <li> 使用已发布的公式计算每个动物 (N) 的估计数 + SN 单元格个数由西部 et al ., 1991 3 , 在哪里和 #8721; Q - 定义为在一个脑部的所有光学 disectors 中计数的 TH + 神经元总数. 
	注意: 该方程中包含了光学 disector (h) 与截面 (t) 的测量厚度有关的高度。区域取样分数 (asf) 定义为在网格的平方大小 (a x, 光学 disector) 帧大小的光 disector (一个光学的) 的面积 (a) 的比例 (y 步) (光学 disector/x、y 步) ( 图 3b ) 和切片取样分数是指整个序列切脑部分的比例: <img alt="Equation 1" src="/files/ftp_upload/56103/56103eq1.jpg"/> 以确保高质量的定量估计, 系数错误 (CE) 应低于0.10。通过 Keuker et al 来确定 CE 的使用公式, 2001 10 : <img alt="Equation 2" src="/files/ftp_upload/56103/56103eq2.jpg"/> </li> <li> 视学估计每个鼠标, 插入有关每个鼠标生成的系列总数的指示信息, 光学 disector 的高度, 光学 disector 的 x、y 区域 (光学 disector, 在和 #181; m 2 ), 网格正方形的 x、y 区域 (x、y 步, #181; m 2 ), 以及使用 and #34 的黄色框中的截面 (#181; m) 的平均测量厚度; 计算单元数. xlsx 和 #34; 电子表格文件 ( 图 6b 和补充文件 ).</li>
	<li> 从同一动物中分析每个 SN 段, 如步骤5所述. </li>
	<li> 在灰色框中插入每个光学 disector 的单元格计数, 每一行引用一个 SN 节, 使用 and #34; 计算单元计数. xlsx 和 #34; 电子表格文件 ( 补充文件 ). 
	注意: 在蓝色方框 ( 图 6b ) 中描述了估计的单元数和计算出的 CE. </li>
</ol> 6。使用商业上可用的视学系统估计单元格数 <ol> <li> 通过单击 #34;P 长袍和 #34 开始估计多巴胺能 SNpc 细胞数; #8594; #34; 光学分馏塔工作流和 #34; 打开光学分馏塔工作流窗口. 
	注意: 将弹出一个新窗口, 以确定是否要计算新的 SN 系列. </li>
	<li> 通过单击和 #34 启动新的 "TH + SN" 系列; 开始新的主题和 #34; 在弹出窗口中. </li>
	<li> 遍历光分馏塔工作流的不同步骤。在第一步中设置主题。为此, 请插入调查员和 #39 的名称、主题和 #39; s 名称 (SN 组) 以及其他有用的信息。插入此 SN 要计数的节数。插入切口组织切片的厚度。插入节间隔, 在本例中为四。插入随机确定的节编号以开始. </li>
	<li> 在第二步中, 将显微镜设置为低放大率 (2X 目标), 并选择和 #34; 2X Mag 透镜和 #34; 从菜单中. </li>
	<li> 在步骤3中, 用鼠标左键跟踪感兴趣的区域, 这是 SNpc, 如 Baquet et al ., 2009 17 . </li>
	<li> 在步骤4中将显微镜设置为高倍放大倍数。为此, 将目标更改为 100X, 然后选择和 #34; 100X Mag 透镜和 #34; 从下拉菜单中. </li>
	<li> 通过聚焦到节的顶部和底部来测量步骤5中的已装入厚度. </li>
	<li> 在步骤6中, 将计数帧大小定义为 50 x 50 和 #181; m; 这是光学 disector (x、y) 的大小. </li>
	<li> 在步骤7中, 将系统随机抽样 (SRS) 网格的大小设置为 130 x 130 和 #181; m. </li>
	<li> 在步骤8中输入3和 #181; m 保护区域. </li>
	<li> 在步骤9中保存设置 </li>
	<li> 最后, 在光学分馏塔工作流的步骤10中, 计算 SRS 网格中每个光学 disectors 中的 TH + 多巴胺能细胞。为此, 请先点击 #34; 计数和 #34; 按钮. </li>
	<li> 完成一节后, 单击 #34; 开始下一节和 #34; 按钮以启动同一 sn 系列的下一个 sn 段的光学分馏塔工作流. </li>
	<li> 当一个 sn 系列的最后一个 sn 部分被量化后, 单击 #34; 我和 #39; 我已经完成计数. #34; </li>
	<li> 单击 #34; 查看结果和 #34; 按钮以显示视学采样的结果. </li>
</ol>

<ol>
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c.w.i. 为 Merz 制药、LLC 和 TEVA 的科学委员会服务;从易普森、Merz 制药、LLC 和 Allergan、Inc. 的旅费中获得资金;并已收到 Merz、TEVA、Allergan、Inc. 外的演讲者酬金。企业曾担任波士顿科学、敦和 AbbVie 的顾问, 并获得敦、波士顿科学、AbbVie、比亚尔、Allergan 和 GlobalKinetics 在提交的工作之外的酬金。

视学从组织加工开始。在视学分析过程中, 必须小心地进行 SN 组织的连续切割, 以防止节段丢失。另外, 一个重要的步骤是标记一个半球, 以便在执行视学时区分左 SN 的右侧。在脑干上部放置一个小孔, 在所提出的研究中产生了最好的结果。此外, 由于使用光学分馏塔方法, 要求组织被切割在厚的部分约30-40 µm, 而不是5-10 µm 更常用的免疫组化, 抗体和其他培养液组织渗透期间免疫组织化学染色必须?...

神经元细胞数的定量是临床前帕金森病研究的关键, 以确定黑质 (SN)1,2内神经的水平。对感兴趣区域中的单元格数的无偏视学估计被认为是金标准3、4、5。
在无偏视学的出现之前, 部分神经元的数量通过操纵计数的细胞剖面来进行评估, 以纠正神经元在一节中进入视线的可变概率。最常用的方法之一是修正由6所描述的量化单元计数。如果在相邻的薄片中发现同一单元格的碎片, 则此方法试图考虑到单元格可以多次量化。因此, 在计算单元的形状、大小和方向方面需要假设的公式7,8。但是, 这些假设通常没有实现, 因此导致了系统的错误和与实际单元格数 (即偏倚) 的背离。此外, 还不能通过附加采样3来减少偏差。
对于使用光学分馏塔的细胞数的视学估计, 应用数学原理直接在定义的3维体积中直接估计细胞数。这种方法的优点是它不涉及被计算单元的形状、大小和方向的假设。因此, 估计的单元格数更接近于真实值, 并且随着样本大小的增加 (即,无偏)3变得更接近。由于使用视学保持方法无偏时必须遵循许多规则, 因此已开发现成的商业视学系统 (供审查, 请参阅 20054)。专门的视学系统实现了设计视学方法与先验定义的探针和抽样方案的视学评估, 导致独立的形状, 大小, 空间分布, 和方向要分析的单元格4,9。然而, 商业上可用的视学系统是昂贵的;这可能会限制新研究的实施。
本研究的目的是开发一种可用的技术设计视学估计多巴胺能细胞计数在小鼠 SN, 采用光学分馏塔方法和使用标准显微镜设备 (即,光显微镜, 标准显微镜软件, 和机动的 x, y, z 阶段)。为此, 提出了如何处理小鼠脑组织和如何使用设计无偏视学估计 SN 细胞数的 step-by 步骤指南。此外, 还提供了计算估计单元数和误差系数 (CE) 的模板。
这里描述的方法不限于对 SN 的分析, 但可以适应在其他的解剖定义的老鼠或老鼠大脑区域使用。例如, 无偏视学被用来估计海马体的神经元细胞数10和轨迹斑11。此外, 还可以评估除神经元以外的其他细胞类型, 如星状细胞12和小胶质红细胞13。因此, 这种方法对于打算在研究中实现无偏视学的科学家是有用的, 但他们不愿意花很多钱购买视学系统。

<table border="0" cellpadding="0" cellspacing="0" width="1019">
	<tbody>
		<tr height="25">
			<td height="25" style="height:25px;width:216px;">Paxinos mouse atlas</td>
			<td style="width:217px;"></td>
			<td style="width:119px;"></td>
			<td style="width:467px;">The Mouse Brain George Paxinos Keith B.J.FranklinCopyright @2001 by Academic Press CD Rom designet &#38; created by Paul Halasz</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">brain matrix slicer mouse</td>
			<td style="width:217px;">Zivic Instruments&#160;</td>
			<td style="width:119px;">BSMAS 001-1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">paraformaldehyde</td>
			<td style="width:217px;">Merck</td>
			<td style="width:119px;">1040051000</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">sucrose /D(+) Saccharose&#160;</td>
			<td style="width:217px;">Roth</td>
			<td style="width:119px;">4621.1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">isopentane</td>
			<td style="width:217px;">Roth</td>
			<td style="width:119px;">3927.1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">glycerol</td>
			<td style="width:217px;">Merck</td>
			<td style="width:119px;">1040931000</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Ethanol</td>
			<td style="width:217px;">Sigma Aldrich</td>
			<td style="width:119px;">32205-1L</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Name</td>
			<td style="width:217px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">phosphate buffered saline ingredients:</td>
			<td style="width:217px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">sodium chloride</td>
			<td style="width:217px;">Sigma Aldrich</td>
			<td style="width:119px;">31434-1KG-R</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">potassium dihydrogen phosphate</td>
			<td style="width:217px;">Merck</td>
			<td style="width:119px;">1048731000</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">di-sodium hydrogen phosphate dihydrate</td>
			<td style="width:217px;">Merck</td>
			<td style="width:119px;">1065801000</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">potassium chloride</td>
			<td style="width:217px;">Merck</td>
			<td style="width:119px;">1049360500</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">normal goat serum</td>
			<td style="width:217px;">Dako</td>
			<td style="width:119px;">X0907</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">bovine serum albumin</td>
			<td style="width:217px;">Sigma&#160;</td>
			<td style="width:119px;">A4503-100G</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Triton X-100</td>
			<td style="width:217px;">Sigma Aldrich</td>
			<td style="width:119px;">X100-100ml</td>
			<td>detergent</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">3,3-Diaminobenzidine-tetrahydrochlorid/DAB tablets 10mg pH 7.0</td>
			<td style="width:217px;">Kem En Tec</td>
			<td style="width:119px;">4170</td>
			<td></td>
		</tr>
		<tr height="26">
			<td height="26" style="height:26px;width:216px;">H2O2/ Hydrogen peroxide 30%</td>
			<td style="width:217px;">Merck</td>
			<td style="width:119px;">1072090250</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">avidin/biotin reagent</td>
			<td style="width:217px;">Thermo Scientific</td>
			<td style="width:119px;">32050</td>
			<td>Standard Ultra Sensitive ABC Staining Kit, 1:100</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">rabbit anti mouse tyrosine hydroxylase antibody</td>
			<td style="width:217px;">abcam</td>
			<td style="width:119px;">Ab112</td>
			<td>1:1000</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">biotinylated goat-anti-rabbit IgG H+L</td>
			<td style="width:217px;">vector laboratories</td>
			<td style="width:119px;">BA-1000</td>
			<td>1:100</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">StereoInvestigator version 11.07</td>
			<td style="width:217px;">MBF</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">BX53 microscope</td>
			<td style="width:217px;">Olympus</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Visiview</td>
			<td style="width:217px;">Visitron Systems GmbH</td>
			<td style="width:119px;">3.3.0.2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Axiophot2</td>
			<td style="width:217px;">Zeiss</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ImageJ software</td>
			<td style="width:217px;">NIH</td>
			<td style="width:119px;">Version 4.7</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Tissue-TEK OCT</td>
			<td style="width:217px;">Sakura</td>
			<td style="width:119px;">4583</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">dry ice</td>
			<td style="width:217px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">grid overlay plugin</td>
			<td style="width:217px;">Wayne Rasband</td>
			<td style="width:119px;"></td>
			<td>https://imagej.nih.gov/ij/plugins/graphic-overlay.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">cell counter plugin</td>
			<td style="width:217px;">Kurt de Vos</td>
			<td style="width:119px;"></td>
			<td>https://imagej.nih.gov/ij/plugins/cell-counter.html).&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">optical disector macro</td>
			<td style="width:217px;">Christopher Ward</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">C57Bl/6N male mice</td>
			<td style="width:217px;">Charles River, Germany</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">SuperFrost Plus coated object slides</td>
			<td style="width:217px;">Langenbrinck, Germany</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">25G needle Microlance 3</td>
			<td style="width:217px;">BD&#160;</td>
			<td align="right" style="width:119px;">300400</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">REGLO Analog Infusion pump</td>
			<td style="width:217px;">Ismatec</td>
			<td style="width:119px;">ISM 829</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">StereoInvestigator system</td>
			<td style="width:217px;"></td>
			<td style="width:119px;"></td>
			<td style="width:467px;">StereoInvestigator version 11.07</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">BX53 microscope</td>
			<td style="width:217px;"></td>
			<td style="width:119px;"></td>
			<td style="width:467px;">BX53 microscope</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">self-assorted stereology</td>
			<td style="width:217px;"></td>
			<td style="width:119px;"></td>
			<td style="width:467px;">Visiview</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Axiophot2</td>
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			<td style="width:467px;">Axiophot2</td>
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stereological estimation of dopaminergic neuron number in the mouse substantia nigra using the optical fractionator and standard microscopy equipment

在临床前帕金森病的研究中, 对体道的分析, 包括对黑质中多巴胺能神经元损失的定量, 是必不可少的。为了估计总的多巴胺能神经元数, 采用光分馏法的无偏视学目前被认为是金标准。因为光分馏塔方法的理论是复杂的, 因为视学是难以实现的, 没有专门的设备, 一些商业上可用的完整的视学系统, 包括必要的软件确实存在, 纯粹细胞计数的原因。由于购买专门的视学设置并不总是可行的, 由于许多原因, 本报告描述了使用标准显微镜设备对多巴胺能神经元细胞计数进行视学估计的方法, 包括光学显微镜、带有成像软件的机动对象表 (x、y、z 平面) 和用于分析的计算机。给出了用光学分馏塔法进行视学量化的 step-by 步骤说明, 并给出了计算估计单元数的预编程文件。为了评估这种方法的准确性, 对从商用视学仪器中获得的数据进行了比较。使用此协议和视学设备发现可比较的单元数, 从而证明了该协议对于无偏视学的精确性。

使用所提出的方法, 右 sn 中的 TH + 多巴胺能神经元的估计数介于7363和7987细胞之间, 在左 sn 中, 7446 和7904细胞之间。因此, 平均多巴胺能神经元 (± SEM) 为右 sn 为7647±83细胞, 左 sn 为7675±66。每个动物的计算 CE 值低于 0.08 (范围: 0.073-0. 079) (图 7)。为了确定此方法与商用和专用视学系统的可比性, 还使用视学设置 (图 8) 对 SN 单元数进...

Watch this Scientific Journal Video about Stereological Estimation of Dopaminergic Neuron Number in the Mouse Substantia Nigra Using the Optical Fractionator and Standard Microscopy Equipment at JoVE.

Stereological Estimation of Dopaminergic Neuron Number in the Mouse Substantia Nigra Using the Optical Fractionator and Standard Microscopy Equipment

这项工作提出了一个 step-by 步协议的无偏视学估计多巴胺能神经元细胞数在小鼠黑质使用标准显微镜设备 (即,光显微镜, 机动对象表 (x, y, z平面) 和公有域软件进行数字图像分析。

用光学分馏塔和标准显微镜设备对小鼠黑质多巴胺能神经元数的视学估计

In pre-clinical Parkinson&#39;s disease research, analysis of the nigrostriatal tract, including quantification of dopaminergic neuron loss within the ...

stereological-estimation-dopaminergic-neuron-number-mouse-substantia

Universidad de Cartagena UDEC

Research

JoVE Journal

Neuroscience

17.9K 次观看.  University Hospital of Würzburg. 这项工作提出了一个 step-by 步协议的无偏视学估计多巴胺能神经元细胞数在小鼠黑质使用标准显微镜设备 (即,光显微镜, 机动对象表 (x, y, z平面) 和公有域软件进行数字图像分析。

视频: Stereological Estimation of Dopaminergic Neuron Number in the Mouse Substantia Nigra Using the Optical Fractionator and Standard Microscopy Equipment

In vivo Imaging of the Mouse Spinal Cord Using Two-photon Microscopy

In vivo imaging using two-photon microscopy 1 in mice that have been genetically engineered to express fluorescent proteins in specific cell types 2-3 has significantly broadened our knowledge of physiological and pathological processes in numerous tissues in vivo 4-7. In studies of the central nervous system (CNS), there has been a broad application of in vivo imaging in the brain, which has produced a plethora of novel and often unexpected findings about the behavior of cells such as neurons, astrocytes, microglia, under physiological or pathological conditions 8-17. However, mostly technical complications have limited the implementation of in vivo imaging in studies of the living mouse spinal cord. In particular, the anatomical proximity of the spinal cord to the lungs and heart generates significant movement artifact that makes imaging the living spinal cord a challenging task.
We developed a novel method that overcomes the inherent limitations of spinal cord imaging by stabilizing the spinal column, reducing respiratory-induced movements and thereby facilitating the use of two-photon microscopy to image the mouse spinal cord in vivo. This is achieved by combining a customized spinal stabilization device with a method of deep anesthesia, resulting in a significant reduction of respiratory-induced movements. This video protocol shows how to expose a small area of the living spinal cord that can be maintained under stable physiological conditions over extended periods of time by keeping tissue injury and bleeding to a minimum. Representative raw images acquired in vivo detail in high resolution the close relationship between microglia and the vasculature. A timelapse sequence shows the dynamic behavior of microglial processes in the living mouse spinal cord. Moreover, a continuous scan of the same z-frame demonstrates the outstanding stability that this method can achieve to generate stacks of images and/or timelapse movies that do not require image alignment post-acquisition. Finally, we show how this method can be used to revisit and reimage the same area of the spinal cord at later timepoints, allowing for longitudinal studies of ongoing physiological or pathological processes in vivo.

In vivo Imaging of the Mouse Spinal Cord Using Two-photon Microscopy

A minimally invasive protocol to stabilize the mouse spinal column and perform repetitive in vivo spinal cord imaging using two-photon microscopy is described. This method combines a spinal stabilization device and an anesthetic regimen to minimize respiratory-induced movements and produce raw imaging data that require no alignment or other post-processing.

在体内脊髓的鼠标线使用双光子显微镜成像

In vivo imaging using two-photon microscopy 1 in mice that have been genetically engineered to express fluorescent proteins in specific cell types 2-3 has ...

科研

神经科学

Imaging Odor-Evoked Activities in the Mouse Olfactory Bulb using Optical Reflectance and Autofluorescence Signals

In the brain, sensory stimulation activates distributed populations of neurons among functional modules which participate to the coding of the stimulus. Functional optical imaging techniques are advantageous to visualize the activation of these modules in sensory cortices with high spatial resolution. In this context, endogenous optical signals that arise from molecular mechanisms linked to neuroenergetics are valuable sources of contrast to record spatial maps of sensory stimuli over wide fields in the rodent brain.
Here, we present two techniques based on changes of endogenous optical properties of the brain tissue during activation. First the intrinsic optical signals (IOS) are produced by a local alteration in red light reflectance due to: (i) absorption by changes in blood oxygenation level and blood volume (ii) photon scattering. The use of in vivo IOS to record spatial maps started in the mid 1980's with the observation of optical maps of whisker barrels in the rat and the orientation columns in the cat visual cortex1. IOS imaging of the surface of the rodent main olfactory bulb (OB) in response to odorants was later demonstrated by Larry Katz's group2. The second approach relies on flavoprotein autofluorescence signals (FAS) due to changes in the redox state of these mitochondrial metabolic intermediates. More precisely, the technique is based on the green fluorescence due to oxidized state of flavoproteins when the tissue is excited with blue light. Although such signals were probably among the first fluorescent molecules recorded for the study of brain activity by the pioneer studies of Britton Chances and colleagues3, it was not until recently that they have been used for mapping of brain activation in vivo. FAS imaging was first applied to the somatosensory cortex in rodents in response to hindpaw stimulation by Katsuei Shibuki's group4.
The olfactory system is of central importance for the survival of the vast majority of living species because it allows efficient detection and identification of chemical substances in the environment (food, predators). The OB is the first relay of olfactory information processing in the brain. It receives afferent projections from the olfactory primary sensory neurons that detect volatile odorant molecules. Each sensory neuron expresses only one type of odorant receptor and neurons carrying the same type of receptor send their nerve processes to the same well-defined microregions of &tilde;100&mu;m3 constituted of discrete neuropil, the olfactory glomerulus (Fig. 1). In the last decade, IOS imaging has fostered the functional exploration of the OB5, 6, 7 which has become one of the most studied sensory structures. The mapping of OB activity with FAS imaging has not been performed yet.
Here, we show the successive steps of an efficient protocol for IOS and FAS imaging to map odor-evoked activities in the mouse OB.

This article presents the protocols of intrinsic optical signals and flavoproteins autofluorescence signals imaging to map odor-evoked activities at the surface of the olfactory bulb in mice.

在小鼠嗅球成像气味诱发的活动，利用光学反射和自体荧光信号

In the brain, sensory stimulation activates distributed populations of neurons among functional modules which participate to the coding of the stimulus. ...

Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area

Dopamine is a vigorously studied neurotransmitter in the CNS. Indeed, its involvement in locomotor activity and reward-related behaviour has fostered five decades of inquiry into the molecular deficiencies associated with dopamine regulation. The majority of these inquiries of dopamine regulation in the brain focus upon the molecular basis for its regulation in the terminal field regions of the nigrostriatal and mesoaccumbens pathways; striatum and nucleus accumbens. Furthermore, such studies have concentrated on analysis of dopamine tissue content with normalization to only wet tissue weight. Investigation of the proteins that regulate dopamine, such as tyrosine hydroxylase (TH) protein, TH phosphorylation, dopamine transporter (DAT), and vesicular monoamine transporter 2 (VMAT2) protein often do not include analysis of dopamine tissue content in the same sample. The ability to analyze both dopamine tissue content and its regulating proteins (including post-translational modifications) not only gives inherent power to interpreting the relationship of dopamine with the protein level and function of TH, DAT, or VMAT2, but also extends sample economy. This translates into less cost, and yet produces insights into the molecular regulation of dopamine in virtually any paradigm of the investigators' choice.
We focus the analyses in the midbrain. Although the SN and VTA are typically neglected in most studies of dopamine regulation, these nuclei are easily dissected with practice. A comprehensive readout of dopamine tissue content and TH, DAT, or VMAT2 can be conducted. There is burgeoning literature on the impact of dopamine function in the SN and VTA on behavior, and the impingements of exogenous substances or disease processes therein 1-5. Furthermore, compounds such as growth factors have a profound effect on dopamine and dopamine-regulating proteins, to a comparatively greater extent in the SN or VTA 6-8. Therefore, this methodology is presented for reference to laboratories that want to extend their inquiries on how specific treatments modulate behaviour and dopamine regulation. Here, a multi-step method is presented for the analyses of dopamine tissue content, the protein levels of TH, DAT, or VMAT2, and TH phosphorylation from the substantia nigra and VTA from rodent midbrain. The analysis of TH phosphorylation can yield significant insights into not only how TH activity is regulated, but also the signaling cascades affected in the somatodendritic nuclei in a given paradigm.
We will illustrate the dissection technique to segregate these two nuclei and the sample processing of dissected tissue that produces a profile revealing molecular mechanisms of dopamine regulation in vivo, specific for each nuclei (Figure 1).

Dopamine is distinctly regulated in the midbrain nuclei, which contain the cell bodies and dendrites of the dopamine neurons. Here we describe a dissection and sample-handling approach to maximize results, and thus conclusions and insights, on dopamine regulation in the midbrain nuclei of the substantia nigra (SN) and ventral tegmental area (VTA) in rodents.

在黑质和腹侧被盖区多巴胺调控的综合性能分析

Dopamine is a vigorously studied neurotransmitter in the CNS. Indeed, its involvement in locomotor activity and reward-related behaviour has fostered five ...

Directed Dopaminergic Neuron Differentiation from Human Pluripotent Stem Cells

Dopaminergic (DA) neurons in the substantia nigra pars compacta (also known as A9 DA neurons) are the specific cell type that is lost in Parkinson&#8217;s disease (PD). There is great interest in deriving A9 DA neurons from human pluripotent stem cells (hPSCs) for regenerative cell replacement therapy for PD. During neural development, A9 DA neurons originate from the floor plate (FP) precursors located at the ventral midline of the central nervous system. Here, we optimized the culture conditions for the stepwise differentiation of hPSCs to A9 DA neurons, which mimics embryonic DA neuron development. In our protocol, we first describe the efficient generation of FP precursor cells from hPSCs using a small molecule method, and then convert the FP cells to A9 DA neurons, which could be maintained in vitro for several months. This efficient, repeatable and controllable protocol works well in human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) from normal persons and PD patients, in which one could derive A9 DA neurons to perform in vitro disease modeling and drug screening and in vivo cell transplantation therapy for PD.

We, based on knowledge from developmental biology and published research, developed an optimized protocol to efficiently generate A9 midbrain dopaminergic neurons from both human embryonic stem cells and human induced pluripotent stem cells, which would be useful for disease modeling and cell replacement therapy for Parkinson&#8217;s disease.

从人多能干细胞定向多巴胺能神经元分化

Dopaminergic (DA) neurons in the substantia nigra pars compacta  (also known as A9 DA neurons) are the specific cell type that is lost in ...

The Neuromuscular Junction: Measuring Synapse Size, Fragmentation and Changes in Synaptic Protein Density Using Confocal Fluorescence Microscopy

The neuromuscular junction (NMJ) is the large, cholinergic relay synapse through which mammalian motor neurons control voluntary muscle contraction. Structural changes at the NMJ can result in neurotransmission failure, resulting in weakness, atrophy and even death of the muscle fiber. Many studies have investigated how genetic modifications or disease can alter the structure of the mouse NMJ. Unfortunately, it can be difficult to directly compare findings from these studies because they often employed different parameters and analytical methods. Three protocols are described here. The first uses maximum intensity projection confocal images to measure the area of acetylcholine receptor (AChR)-rich postsynaptic membrane domains at the endplate and the area of synaptic vesicle staining in the overlying presynaptic nerve terminal. The second protocol compares the relative intensities of immunostaining for synaptic proteins in the postsynaptic membrane. The third protocol uses Fluorescence Resonance Energy Transfer (FRET) to detect changes in the packing of postsynaptic AChRs at the endplate. The protocols have been developed and refined over a series of studies. Factors that influence the quality and consistency of results are discussed and normative data are provided for NMJs in healthy young adult mice.

The neuromuscular junction (NMJ) is altered in a variety of conditions that can sometimes culminate in synaptic failure. This report describes fluorescence microscope-based methods to quantify such structural changes.

神经肌肉接头处:测量突触大小，碎片和改变突触蛋白密度利用共聚焦荧光显微镜

The neuromuscular junction (NMJ) is the large, cholinergic relay synapse through which mammalian motor neurons control voluntary muscle contraction. ...

Optical Control of a Neuronal Protein Using a Genetically Encoded Unnatural Amino Acid in Neurons

Photostimulation is a noninvasive way to control biological events with excellent spatial and temporal resolution. New methods are desired to photo-regulate endogenous proteins expressed in their native environment. Here, we present an approach to optically control the function of a neuronal protein directly in neurons using a genetically encoded unnatural amino acid (Uaa). By using an orthogonal tRNA/aminoacyl-tRNA synthetase pair to suppress the amber codon, a photo-reactive Uaa 4,5-dimethoxy-2-nitrobenzyl-cysteine (Cmn) is site-specifically incorporated in the pore of a neuronal protein Kir2.1, an inwardly rectifying potassium channel. The bulky Cmn physically blocks the channel pore, rendering Kir2.1 non-conducting. Light illumination instantaneously converts Cmn into a smaller natural amino acid Cys, activating Kir2.1 channel function. We express these photo-inducible inwardly rectifying potassium (PIRK) channels in rat hippocampal primary neurons, and demonstrate that light-activation of PIRK ceases the neuronal firing due to the outflux of K+ current through the activated Kir2.1 channels. Using in utero electroporation, we also express PIRK in the embryonic mouse neocortex in vivo, showing the light-activation of PIRK in neocortical neurons. Genetically encoding Uaa imposes no restrictions on target protein type or cellular location, and a family of photoreactive Uaas is available for modulating different natural amino acid residues. This technique thus has the potential to be generally applied to many neuronal proteins to achieve optical regulation of different processes in brains. The current protocol presents an accessible procedure for intricate Uaa incorporation in neurons in vitro and in vivo to achieve photo control of neuronal protein activity on the molecular level.

Here, a procedure to selectively activate a neuronal protein with a short pulse of light by genetically encoding a photo-reactive unnatural amino acid into a target neuronal protein expressed in neurons in culture or in vivo is presented.

一个神经元蛋白在神经元使用遗传编码的非天然氨基酸的光控制

Photostimulation is a noninvasive way to control biological events with excellent spatial and temporal resolution. New methods are desired to ...

Optical Clearing of the Mouse Central Nervous System Using Passive CLARITY

Traditionally, tissue visualization has required that the tissue of interest be serially sectioned and imaged, subjecting each tissue section to unique non-linear deformations, dramatically hampering one's ability to evaluate cellular morphology, distribution and connectivity in the central nervous system (CNS). However, optical clearing techniques are changing the way tissues are visualized. These approaches permit one to probe deeply into intact organ preparations, providing tremendous insight into the structural organization of tissues in health and disease. Techniques such as Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging-compatible Tissue-hYdrogel (CLARITY) achieve this goal by providing a matrix that binds important biomolecules while permitting light-scattering lipids to freely diffuse out. Lipid removal, followed by refractive index matching, renders the tissue transparent and readily imaged in 3 dimensions (3D). Nevertheless, the electrophoretic tissue clearing (ETC) used in the original CLARITY protocol can be challenging to implement successfully and the use of a proprietary refraction index matching solution makes it expensive to use the technique routinely. This report demonstrates the implementation of a simple and inexpensive optical clearing protocol that combines passive CLARITY for improved tissue integrity and 2,2&#8242;-thiodiethanol (TDE), a previously described refractive index matching solution.

Optical clearing techniques are revolutionizing the way tissues are visualized. In this report we describe modifications of the original Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging-compatible Tissue-hYdrogel (CLARITY) protocol that yields more consistent and less expensive results.

鼠标中枢神经系统的光透明使用被动CLARITY

Traditionally, tissue visualization has required that the tissue of interest be serially sectioned and imaged, subjecting each tissue section to unique ...

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Stereological methods are designed to describe quantitative parameters without making assumptions about size, shape, orientation and distribution of cells or structures. These methods have been revolutionary for quantitative analysis of the mammalian brain, in which volumetric cell populations are too high to count manually, and stereology is now the technique of choice whenever estimates of three-dimensional quantities need to be extracted from measurements on two-dimensional sections. All stereological methods are in principle unbiased; however, they rely on proper knowledge about the structure of interest and the characteristics of the tissue. Stereology is based on Systematic Uniformly Random Sampling (SURS), with adjustment of sampling to the most efficient level in respect to precision, providing reliable, quantitative information about the whole structure of interest. Here we present the optical fractionator in conjunction with BrdU immunohistochemistry to estimate the production and survival of newly-formed neurons in the granule cell layer (including the sub-granular zone) of the rat hippocampus following electroconvulsive stimulation, which is among the most potent stimulators of neurogenesis. The optical fractionator technique is designed to provide estimates of the total number of cells from thick sections sampled from the full structure. Thick sections provide the opportunity to observe cells in their full 3-D extent and thus, allow for easy and robust cell classification based on morphological criteria. When correctly implemented, the sensitivity and efficiency of the optical fractionator provides accurate estimates with a fixed and predetermined precision.

Here, we present a stereological method, the optical fractionator, used to quantify the formation of new neurons, and their survival, in the rat hippocampus following electroconvulsive stimulation. When correctly implemented, the sensitivity and efficiency of stereological methods ensures accurate estimates with a fixed and predetermined precision.

用于估计电惊厥治疗大鼠模型中细胞数的光学分级技术

Stereological methods are designed to describe quantitative parameters without making assumptions about size, shape, orientation and distribution of cells ...

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Type 2 diabetes mellitus (T2DM) is a global health crisis which is characterized by insulin signaling impairment and chronic inflammation in peripheral tissues. The hypothalamus in the central nervous system (CNS) is the control center for energy and insulin signal response regulation. Chronic inflammation in peripheral tissues and imbalances of certain chemokines (such as CCL5, TNF&#945;, and IL-6) contribute to diabetes and obesity. However, the functional mechanism(s) connecting chemokines and hypothalamic insulin signal regulation still remain unclear.
In vitro primary neuron culture models are convenient and simple models which can be used to investigate insulin signal regulation in hypothalamic neurons. In this study, we introduced exogeneous GLUT4 protein conjugated with GFP (GFP-GLUT4) into primary hypothalamic neurons to track GLUT4 membrane translocation upon insulin stimulation. Time-lapse images of GFP-GLUT4 protein trafficking were recorded by deconvolution microscopy, which allowed users to generate high-speed, high-resolution images without damaging the neurons significantly while conducting the experiment. The contribution of CCR5 in insulin regulated GLUT4 translocation was observed in CCR5 deficient hypothalamic neurons, which were isolated and cultured from CCR5 knockout mice. Our results demonstrated that the GLUT4 membrane translocation efficiency was reduced in CCR5 deficient hypothalamic neurons after insulin stimulation.

This protocol describes a technique for observation of real-time Green Fluorescence Protein (GFP) tagged Glucose Transporter 4 (GLUT4) protein trafficking upon insulin stimulation and characterization of the biological role of CCR5 in the insulin&#8211;GLUT4 signaling pathway with Deconvolution Microscopy.

用反褶积显微镜观察小鼠原发性下丘脑神经元 GLUT4 蛋白的动态图像

Type 2 diabetes mellitus (T2DM) is a global health crisis which is characterized by insulin signaling impairment and chronic inflammation in peripheral ...

Stereological Estimation of Cholinergic Fiber Length in the Nucleus Basalis of Meynert of the Mouse Brain

The length of cholinergic or other neuronal axons in various brain regions are often correlated with the specific function of the region. Stereology is a useful method to quantify neuronal profiles of various brain structures. Here we provide a software-based stereology protocol to estimate the total length of cholinergic fibers in the nucleus basalis of Meynert (NBM) of the basal forebrain. The method uses a space ball probe for length estimates. The cholinergic fibers are visualized by choline acetyltransferase (ChAT) immunostaining with the horseradish peroxidase-diaminobenzidine (HRP-DAB) detection system. The staining protocol is also valid for fiber and cell number estimation in various brain regions using stereology software. The stereology protocol can be used for estimation of any linear profiles such as cholinoceptive fibers, dopaminergic/catecholaminergic fibers, serotonergic fibers, astrocyte processes, or even vascular profiles.

Neuronal fiber length within a three-dimensional structure of a brain region is a reliable parameter to quantify specific neuronal structural integrity or degeneration. This article details a stereological quantification method to measure cholinergic fiber length within the nucleus basalis of Meynert in mice as an example.

小鼠大脑细胞核巴沙里斯中胆碱能纤维长度的立体估计

The length of cholinergic or other neuronal axons in various brain regions are often correlated with the specific function of the region. Stereology is a ...