Name: quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using imagej
Uploaded: 2018-06-05T14:00:00.000+00:00
Duration: 8 min 44 s
Description: Watch this Scientific Journal Video about Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ at JoVE.com

这项研究获得了 NINR (F32NR013611) 的财政支持。我们希望进一步确认和感谢 AnalyzeSkeleton (2 维/3 d) 和 FracLac (阿甘达卡雷拉斯 et) 和 Karperien et等) 的开发人员, 没有这些方法, 此处描述的分析是不可能的。

所有实验均根据亚利桑那大学动物保育和使用委员会制定的指导方针和美国国立卫生研究院关于护理和使用实验动物的指导方针予以批准和执行。注意尽量减少动物的疼痛和不适。安乐死的方法是根据批准的协议, 包括宫颈斩首在异氟醚麻醉下。
1. 组织准备
注意: 对固定的 cryoprotected 组织标本进行小胶质细胞形态学分析, 以保留细胞膜形态学。以下为荧光 IHC 制备和直接切片固定组织的标准协议。
<ol><li>在所需实验后, 根据标准实验室协议, 从安乐死动物中取出老鼠或老鼠的大脑。将大脑放入一个10毫升的小瓶中, 其中含有5毫升4% 多聚甲醛溶液, 24 小时4摄氏度。然后冲洗并放入5-10 毫升的30% 蔗糖磷酸盐缓冲溶液 (PBS, 0.01 米) 为72小时在4摄氏度。存储整个大脑在-80 °c, 直到组织切片与恒温器, 或在4摄氏度, 如果切片与切片。 注意: 本协议尚未使用石蜡嵌入组织切片进行测试。</li>
	<li>用恒温器或切片在抗冻溶液中 (50 毫米 PBS、乙二醇、甘油) 在-20 摄氏度处储存自由浮动部分, 将脑组织按所需断面厚度和方向进行切片。 注意: 该协议已成功地进行了冠状组织切片范围从50µm 到200µm 的厚度。少于50µm 的组织切片可能无法捕获小胶质细胞过程的全跨度, 而在厚的组织切片中, 由于抗体渗入组织, IHC 染色可能不完善。组织可以切片, 无论是在冠状或矢状方向, 这一选择将取决于实验目标和大脑区域 (s) 的研究。</li>
</ol>2. 免疫组化
注: 骨架和分形分析方法可应用于荧光或 3,3′-diaminobenzidine (IHC)。以下是标准荧光 IHC 协议, 可根据需要替换。与 IHC 相比, 荧光 IHC 产生了细胞过程的卓越可视化。
<ol><li>将组织切片放入4毫升玻璃瓶 (最多15只老鼠脑切片/小瓶), 并孵育1毫升溶液, 含多达10% 匹马血清, PBS (0.01 米), 0.5% 海卫, 0.04% 南3在室温 (23 °c) 为 1 h。</li>
	<li>室温下用 PBS (0.01 米) 清洗5分钟。</li>
	<li>用主抗体 (Iba1, 1:1, 000) 在室温下孵育72小时, 含 PBS (0.01 米), 0.5% 海卫, 0.04% 南3, 并覆盖 (以保留 NaN3有效性)。</li>
	<li>室温下用 PBS (0.01 米) 清洗5分钟。</li>
	<li>孵育与二级抗体 (抗兔 488, 1:250) 在室温下覆盖4小时1毫升的解决方案, 包括 PBS (0.01 米), 0.5% 海卫, 0.04% 南3
</li>
	<li>室温下用 PBS (0.01 米) 清洗5分钟。</li>
	<li>将脑组织部分 (基于首选项的数量和方向) 安装到地基幻灯片上, 将软集安装介质应用于幻灯片, 并将盖玻片放在组织上。将幻灯片存储在摄氏4摄氏度。 注: 共焦成像需要1.5 玻璃盖玻片厚度。软集是首选的安装介质, 因为高粘度不压缩组织, 最好保留的形态与硬集安装介质相比。</li>
</ol>3. 成像
<ol><li>图像 Iba1 脑组织部分阳性细胞使用一个明亮的领域或共聚焦显微镜, 具有 z 堆栈获取能力使用20X 目标或更大。 注: 实验中所有显微照片的成像参数和软件设置应该是恒定的。使用40X 或63X 目标获得优秀的工艺细节。可以将本文描述的骨架分析协议应用于一个较大的显微照片中的整个显微照片或多单元 ROI。<ol>
<li>使用特定于显微镜软件的适当软件设置获取8位图像。 注意: 将文件转换为8位后获取可能会扭曲数据收集。</li>
		<li>使用特定于显微镜软件的适当软件设置, 获取至少30µm z 堆栈, 其图像之间不超过2µm 间隔。 注: 显微镜和软件应允许在 X、Y 和 Z 轴上进行图像采集。z 栈可以增加, 时间间隔可以减少, 以提供额外的小胶质细胞细节。作为交换, 成像时间会增加。在可能的荧光显微镜下使用采样取样。</li>
	</ol>
</li>
	<li>将所有文件保存为 Tif 文件或显微镜软件的要求。</li>
	<li>在 ImageJ 中打开 Tif 文件, 然后使用工具栏通过单击图像 |颜色 |拆分通道, 并通过单击图像来堆叠图像|栈 |X 项目 |最大强度投影酌情。另存为. tif 文件。</li>
</ol>4. 骨架分析
<ol><li>从 < https://imagej.net/Fiji/Downloads > 下载斐济或 ImageJ。对于单个插件, 从 < http://imagej.net/AnalyzeSkeleton >15下载 AnalyzeSkeleton (2 维/3 d), 然后从 < https://imagej.nih.gov/ij/plugins/fraclac/fraclac.html >16下载 FracLac。 注意: 将显微照片转换为二进制骷髅图像的整个过程需要少于1分钟。</li>
	<li>如果使用荧光显微照片, 请确保图像为8位, 并转换为灰度, 以最佳的可视化所有正染色。使用工具栏并单击图像 |查阅表格 |灰色。如果使用民建联的明亮现场照片, 首先使用 FFT 带通滤波器插件 (使用工具栏单击进程 |FFT |带通滤波器), 然后转换为灰度。 注意: 为了本协议的目的, ImageJ 的 FFT 带通滤波器的默认设置是足够的 (最多可筛选3个像素, 降低到40个, 不带条纹抑制)。应用 FFT 带通滤波器消除噪音 (小功能), 同时保持图像的整体较大的方面。这在明亮的场图像中尤其有用, 因为在这种情况下, 组织中的分裂和裂纹可以作为背景出现, 从而使骨架分析18复杂化。</li>
	<li>调整亮度和对比度, 如果图像太暗, 小胶质细胞过程无法可视化。使用工具栏并单击图像 |调整 |亮度/对比度。根据需要调整最小或最大滑块, 直至直方图的边缘, 但不进一步。 注意: 在 ImageJ 中, 通过更新图像的查阅表格 (查找表) 来更改亮度和对比度, 因此像素值不变。最大和最小滑块控制显示范围的下限和上限, 像素值在255以上出现白色和像素值, 低于0出现黑色18。在荧光显微照片的情况下, 应使用最大滑块, 而最小滑块将用于显微照片染色小胶质细胞。</li>
	<li>通过单击 "进程 |", 运行 Unsharp 掩码筛选器以进一步提高对比度. 过滤器 |Unsharp 掩码。为了本协议的目的, 使用 ImageJ 的默认设置 (像素半径为3和掩码重量为 0.6)。 注意: Unsharp 掩码通过从原始图像中减去图像 (Unsharp 掩码) 的模糊版本来锐化和增强图像的边缘特征, 然后将生成的图像与原始映像的高对比度版本合并。因此, Unsharp 掩码筛选器不会创建详细信息, 而是澄清图像中的现有详细信息。radius 设置会更改 Unsharp 掩码的模糊程度 (以及将删除多少模糊), 而掩码权重设置会更改 Unsharp 掩码将与之合并的对比度 (从而调整最终图像的对比度)。</li>
	<li>执行去斑步骤以清除 Unsharp 掩码生成的椒盐噪音。使用工具栏并单击进程 |噪声 |去斑。 注: 去斑在 3 x 3 邻域中用中值替换每个像素, 消除了椒盐噪声。其效果是, 强度中的异常值被中间像素替换, 而不影响边缘的锐度18。</li>
	<li>使用工具栏将图像转换为二进制文件, 方法是单击图像 |调整 |阈值。 注意: 阈值 stratifies 将灰度图像转换为兴趣与背景的特征, 并将图像转换为二进制18。</li>
	<li>应用去斑、关闭和移除异常函数: 在生成的二进制图像中, 可能存在单像素背景噪声和进程之间的间隙。<ol>
<li>通过单击 "进程 |", 使用工具栏应用去斑函数噪声 |去斑。 注意: 将去斑应用于二进制图像将删除任何剩余的单像素噪声。</li>
		<li>通过单击 "进程 |二进制 |关闭。 注: 此插件连接两个暗像素, 如果它们被分离最多2像素。</li>
		<li>通过单击 "进程 |噪声 |删除异常点。 注意: 为了本协议的目的, 明亮的异常点的目标是像素半径为 2, 阈值为50。如果它偏离了超过某个值 (阈值)18, 则该插件会在周围区域中的中点像素处替换明亮或暗的异常像素。</li>
	</ol>
</li>
	<li>将图像另存为单独的文件, 以供将来使用和/或分形分析, 并通过单击 "处理" (缩略) 工具栏来对图像进行使用. 二进制 |缩略。</li>
	<li>使用工具栏选择骷髅图像并运行插件 AnalyzeSkeleton (2 维/3 d), 方法是单击插件 |骨架 |分析骨架, 并检查分支信息框。 注意: 图像处理很可能需要通过添加或删除上述建议的步骤来优化。在这个过程中, 通过创建骨架和原始图像的叠加来评估骷髅图像的准确性。Somas 应该是单一的起源点与进程产生的中心;循环 somas 混淆数据, 应通过协议调整来避免。图 1中说明了单个原点与圆形 somas 的示例。</li>
</ol>常见的问题导致非代表性的骨骼和建议的解决方案:
<ul><li>图像太暗: 转换为灰度, 调整亮度/对比度滑块, 并/或应用 Unsharp 掩码</li>
	<li>背景太多: 调整亮度/对比度滑块, 应用去斑和/或删除异常点</li>
	<li>骷髅图像中的圆形 somas (特别是荧光图像): 应用 FFT 带通滤波器和/或 Unsharp 掩码</li>
	<li>组织中的裂纹 (特别是明亮场图像): 应用 FFT 带通滤波器和/或去斑</li>
</ul><ol start="10"><li>复制结果中的所有数据并分支信息输出, 并将数据粘贴到 Excel 电子表格中。</li>
	<li>在 Excel 中, 修剪数据以删除 IHC 和图像获取所产生的骨架碎片。<ol>
<li>将实验工作簿与骨架分析中的原始数据输出复制, 并将修剪添加到文件名中。所有后续的数据修整都应出现在重复的工作簿中, 以保留原始数据以供将来使用和引用。</li>
		<li>通过在 ImageJ 中打开骷髅图像并选择 "线条" 工具, 确定将从数据集中裁剪的片段长度。测量几个片段, 记下平均长度, 并决定一个截止值。 注: 对于此处提供的数据, 不想要的碎片的截止长度为0.5。此值应在整个数据集中保持一致。</li>
		<li>自定义对 Excel 电子表格进行排序, 单击排序 & 筛选 |自定义排序。按 "端点素" 排序, 从最大到最小, 在一个新的层次, 由 "Mx 分支 pt" 从最大到最小。</li>
		<li>删除包含2个端点的每行, 其最大分支长度小于截止值(i. e, 0.5)。求和 "端点" 列中的数据, 以计算从图像中收集的终结点的总数。</li>
		<li>对分支信息数据重复: 按 "分支长度" 从最大到最小排序。滚动数据并删除每行的分支长度小于截止值(i. e, 0.5)。求和 "分支长度" 列中的值, 以计算从图像收集的所有分支的汇总长度。</li>
		<li>对每个图像/工作表重复步骤 4.11.3-4.11.5, 直到所有数据都已修剪和汇总为止。</li>
		<li>用相应图像中的小胶质 somas 的数量除以每个图像的数据 (端点数和汇总分支长度)。在统计软件中输入最终数据 (端点/单元格 & 分支长度/单元格)。 注: 汇总的分支长度/单元格数据可能需要从长度 (以像素为单位) 转换为微米。</li>
	</ol>
</li>
</ol>5. 分形分析
注意: FracLac 可以运行许多不同的形状分析, 这是不包括在本协议中。有关 FracLac 的各种功能的详细说明, 请参阅 < https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.htm > 和相关引用的 FracLac 手册2,16,19。分形分析利用上面描述的 4.1-4.7 协议步骤。
<ol><li>确定将用于所有分形分析的 ROI 的大小。使用矩形工具绘制 ROI。确保该框足够大, 足以捕获整个单元格, 并且可以在整个数据集中保持一致。 注意: 使用矩形选择而不是手绘选择, 以确保所有 ROIs 都是相同大小的矩形, 因此, 单元格具有相同的刻度。如果使用徒手选择, 则不会出现这种情况, 因为 ImageJ 将自动缩放平方 ROI 以适应不同大小的矩形窗口, 从而产生不同比例的单元格。当分形形状与尺度无关时, 使用 FracLac 对 ImageJ 的分形分析过程依赖于尺度16。因此, 在整个数据收集过程中, 需要有一个一致的大小 ROI。</li>
	<li>随机选择小胶质细胞进行分形分析, 在每个显微照片和相应的二进制图像。在 roi 管理器窗口中, 选择Update将 ROI 锁定到单元格的位置。使用ctrl+ Shift + D将 ROI 中的区域复制为新窗口, 然后保存裁剪的单元格。在相应的二进制图像 (从骨架分析) 中, 使用 ROI 管理器复制同一单元格区域。重复直到为分形分析随机选择足够数量的单元格, 并保存所有文件。 注: 随机数发生器和编号网格可用于随机选择单元格。</li>
	<li>使用单个单元格打开二进制图像。双击 "画笔" 工具, 将颜色设置为黑色, 并根据需要调整画笔宽度。使用匹配的显微照片作为参考, 使用画笔删除相邻的单元进程, 连接零碎的进程, 并隔离感兴趣的单元格。二进制单元格被隔离后, 保存二进制文件。 注意: 按住 "alt" 将画笔从前景色 (黑色) 切换到背景色 (白色)。</li>
	<li>使用工具栏通过 "进程 |" 将二进制单元格转换为轮廓二进制 |大纲。 注意: 图像 J 的 FracLac 可以在实体形状或形状轮廓上使用, 但是, 当前的约定是使用形状轮廓16。</li>
	<li>在工具栏中, 使用工具栏打开 FracLac, 方法是单击插件 |分形分析 |FracLac并选择BC (方框计数)。在网格设计设置中, 将Num G设置为4。在图形选项下, 选中度量值框以分析单元的凸壳和边界圆。完成后, 选择确定。 注: num g 是扫描期间使用的方框计数网格方向数, 并且建议的 num g 的范围为4-12。在 FracLac 手册16中全面审查了 Num G 设置和建议的范围。增加数 G 可以大大减慢计算时间。FracLac 设置将只需要设置一次每会话。输入设置后, 扫描按钮将变为可用。</li>
	<li>选择扫描按钮以对所选图像运行一个方框计数扫描。 注: 扫描将生成三个具有数据输出的窗口: 船体和圆形结果、方框计数摘要文件和扫描类型。"扫描类型" 窗口包含所用设置的日志, 以及某些度量值的标准偏差。为了本协议的目的, 不使用 "扫描类型" 窗口, 可能会关闭或保存以供将来参考。</li>
	<li>在赫尔和循环结果窗口中, 复制所有所需的数据 (i. e, 密度, 跨度比和圆形) 结果。在"框计数摘要" 窗口中, 复制所需的数据 (i. e, 分形维度和 lacunarity) 结果。将复制的数据传输到 Excel 文件或统计软件。 注意: 在 ImageJ 手动16的 FracLac 中提供并彻底解释了 FracLac 输出数据的完整列表。</li>
</ol>

<ol>
	<li>Davalos, D., 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=ATP+mediates+rapid+microglial+response+to+local+brain+injury+in+vivo.">ATP mediates rapid microglial response to local brain injury in vivo.</a> Nat Neurosci. 8 (6), 752-758 (2005).</li><li>Karperien, A., Ahammer, H., Jelinek, H. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitating+the+subtleties+of+microglial+morphology+with+fractal+analysis.">Quantitating the subtleties of microglial morphology with fractal analysis.</a> Front Cell Neurosci. 7 (3), eCollection (2013).</li><li>Taylor, S. E., Morganti-Kossmann, C., Lifshitz, J., Ziebell, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rod+microglia:+a+morphological+definition.">Rod microglia: a morphological definition.</a> PLoS One. 9 (5), e97096 (2014).</li><li>Nimmerjahn, A., Kirchhoff, F., Helmchen, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resting+microglial+cells+are+highly+dynamic+surveillants+of+brain+parenchyma+in+vivo.">Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo.</a> Science. 308 (5726), 1314-1318 (2005).</li><li>Abiega, O., 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=Neuronal+Hyperactivity+Disturbs+ATP+Microgradients,+Impairs+Microglial+Motility,+and+Reduces+Phagocytic+Receptor+Expression+Triggering+Apoptosis/Microglial+Phagocytosis+Uncoupling.">Neuronal Hyperactivity Disturbs ATP Microgradients, Impairs Microglial Motility, and Reduces Phagocytic Receptor Expression Triggering Apoptosis/Microglial Phagocytosis Uncoupling.</a> PLoS Biol. 14 (6), e1002466 (2016).</li><li>Wyatt-Johnson, S. K., Herr, S. A., Brewster, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Status+Epilepticus+Triggers+Time-Dependent+Alterations+in+Microglia+Abundance+and+Morphological+Phenotypes+in+the+Hippocampus.">Status Epilepticus Triggers Time-Dependent Alterations in Microglia Abundance and Morphological Phenotypes in the Hippocampus.</a> Front Neurol. 8 (700), eCollection (2017).</li><li>Morrison, H., Young, K., Qureshi, M., Rowe, R. K., Lifshitz, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+microglia+analyses+reveal+diverse+morphologic+responses+in+the+rat+cortex+after+diffuse+brain+injury.">Quantitative microglia analyses reveal diverse morphologic responses in the rat cortex after diffuse brain injury.</a> Sci Rep. 7 (1), 13211 (2017).</li><li>Morrison, H. W., Filosa, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+spatiotemporal+analysis+of+microglia+morphology+during+ischemic+stroke+and+reperfusion.">A quantitative spatiotemporal analysis of microglia morphology during ischemic stroke and reperfusion.</a> J Neuroinflammation. 10 (4), (2013).</li><li>Gyoneva, S., Traynelis, S. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Norepinephrine+modulates+the+motility+of+resting+and+activated+microglia+via+different+adrenergic+receptors.">Norepinephrine modulates the motility of resting and activated microglia via different adrenergic receptors.</a> J Biol Chem. 288 (21), 15291-15302 (2013).</li><li>Xu, H., 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=Environmental+Enrichment+Potently+Prevents+Microglia-Mediated+Neuroinflammation+by+Human+Amyloid+beta-Protein+Oligomers.">Environmental Enrichment Potently Prevents Microglia-Mediated Neuroinflammation by Human Amyloid beta-Protein Oligomers.</a> J Neurosci. 36 (35), 9041-9056 (2016).</li><li>Rodriguez, J. J., Noristani, H. N., Verkhratsky, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglial+response+to+Alzheimer's+disease+is+differentially+modulated+by+voluntary+wheel+running+and+enriched+environments.">Microglial response to Alzheimer's disease is differentially modulated by voluntary wheel running and enriched environments.</a> Brain Struct Funct. 220 (2), 941-953 (2015).</li><li>Soltys, Z., 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=Quantitative+morphological+study+of+microglial+cells+in+the+ischemic+rat+brain+using+principal+component+analysis.">Quantitative morphological study of microglial cells in the ischemic rat brain using principal component analysis.</a> J Neurosci Methods. 146 (1), 50-60 (2005).</li><li>Orlowski, D., Soltys, Z., Janeczko, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Morphological+development+of+microglia+in+the+postnatal+rat+brain.+A+quantitative+study.">Morphological development of microglia in the postnatal rat brain. A quantitative study.</a> Int J Dev Neurosci. 21 (8), 445-450 (2003).</li><li>Morrison, H. W., Filosa, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sex+differences+in+astrocyte+and+microglia+responses+immediately+following+middle+cerebral+artery+occlusion+in+adult+mice.">Sex differences in astrocyte and microglia responses immediately following middle cerebral artery occlusion in adult mice.</a> Neuroscience. 339, (2016).</li><li>Arganda-Carreras, I., Fernandez-Gonzalez, R., Munoz-Barrutia, A., Ortiz-De-Solorzano, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+reconstruction+of+histological+sections:+Application+to+mammary+gland+tissue.">3D reconstruction of histological sections: Application to mammary gland tissue.</a> Microsc Res Tech. 73 (11), 1019-1029 (2010).</li><li>Davis, B. M., Salinas-Navarro, M., Cordeiro, M. F., Moons, L., De Groef, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterizing+microglia+activation:+a+spatial+statistics+approach+to+maximize+information+extraction.">Characterizing microglia activation: a spatial statistics approach to maximize information extraction.</a> Sci Rep. 7 (1), 1576 (2017).</li><li>. ImageJ User Guide Available from: <a target="_blank" href="https://imagej.nih.gov/ij/docs/guide/">https://imagej.nih.gov/ij/docs/guide/</a> (2014)</li><li>Karperien, A. L., Jelinek, H. F., , <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fractal,+Multifractal,+and+Lacunarity+Analysis+of+Microglia+in+Tissue+Engineering.">Fractal, Multifractal, and Lacunarity Analysis of Microglia in Tissue Engineering.</a> Front Bioeng Biotechnol. 3 (51), eCollection (2015).</li><li>Martyanova, E. K., Tishkina, A. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+quantitative+analysis+of+microglial+morphology.">3D quantitative analysis of microglial morphology.</a> available as conference preceedings SkoltechOn. , (2015).</li><li>Fernandez-Arjona, M. D. M., Grondona, J. M., Granados-Duran, P., Fernandez-Llebrez, P., Lopez-Avalos, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglia+Morphological+Categorization+in+a+Rat+Model+of+Neuroinflammation+by+Hierarchical+Cluster+and+Principal+Components+Analysis.">Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis.</a> Front Cell Neurosci. 11 (235), eCollection (2017).</li></ol>

作者没有什么可透露的。

小胶质细胞被精细地调谐到其微域内的生理和病理, 并在微妙的7、14和严重伤害8中显示不同的形态2 .使用 ImageJ 协议, 使小胶质细胞形态学量化的所有实验室可访问, 因为平台和插件是一个开源的图像处理软件。当描述的协议集中在图像处理和分析使用这个软件时, 数据收集、有效性和可靠性的一致性从优秀的 IHC 和显微学开始。此协议用于改进整个显微照片和单个单元格的二进制、骨架和轮廓表示, 但不能取代差的 IHC 染色和显微术, 从而产生低对比度、模糊或失真的图像。作为额外的考虑, 必须注意不要在储存期间压扁大脑组织, 在切片之前, 这不可逆转地改变小胶质细胞形态学。最后, 在每个实验中, 小胶质细胞必须使用相同的尺度和相同的显微镜成像。仪器、目标和软件各不相同, 因为显微镜会产生不同大小的显微照片, 尽管有类似的目标, 改变细节以及每个帧内的细胞数量。例如, 在徕卡 SPII 上使用40X 目标进行图像采集, 会使单元格数加倍, 而比使用蔡司880获得的细节要少。这对于从整个帧而不是单个单元收集的细胞分枝数据来说尤其重要, 因为这成为数据取样的问题。
总的来说, 利用整个显微照片的骨架分析在单细胞分形分析之前有两个原因。与单细胞分形分析相比, 测定显微照片中所有细胞的细胞衍生物是快速的, 如果时间是一个因素, 则可以将其视为筛选工具。此外, 在骨架分析中导出的二进制图像用于分形分析。一旦成像, 有许多关键步骤可能影响骨架分析结果, 并引入用户影响。在用户之间最易变的协议步骤是步骤 4.2 (增加图像亮度) 和步骤 4.5 (确定阈值)。在可能的情况下, 为所有图像和用户确定并保持常量的最佳数字, 以增加亮度 (介于0-255 之间的最大或最小滑块)。如果图像变异性很大, 则用户可以选择不同图像之间的亮度。或者, 如果图像是明亮的和对比度高, 那么增加亮度可以省略和阈值可以通过使用专业阈值过滤器 (e. g, 黄) 而不是更可变的默认值。一旦优化, 参数应遵守, 以尽量减少额外的用户影响。
图 5中显示了用户可变性的一个示例。用户1与用户2之间的数据值增加, 因此, 如果用户1和用户2都对数据收集作出贡献, 则可变性会增加。在用户1和用户2二进制和骷髅图像中的差异示例由彩色圆圈 (图 5) 突出显示。在这种情况下, 这两位用户都在小胶质细胞的专业技能有限的本科生进行了简单的培训。小胶质专家的定期监督和指导以及增加的协议培训2可以减少用户间的变异性。虽然在这里没有评估, 分形分析较少受用户之间的差异, 因为二进制细胞是手动和单独从显微照片分离, 而不是仅仅依靠阈值来确定小胶质的形状。但是, 所有方法都具有用户之间的某些可变性。因此, 单个用户 (理想情况下, 由小胶质细胞的一些专门知识训练) 应该完成整个数据集的数据采集。
可以很容易地对此协议进行其他修改, 这将取决于图像质量, 以及为减少噪音和确保过程连接而采取的努力。例如, 如果对比度足够, 则不需要 unsharp 掩码, 并且可以省略。在从整个集合中收集数据之前, 应谨慎地优化和最后确定一组特定图像 (包括实验用例和控件) 的协议。最后, 还可以使用其他插件来代替他人澄清或锐化本协议中未描述的图像, 如放大或锐化。
该协议的优点是它的普遍可用性和适应性。此外, 使用 AnalyzeSkeleton 评估细胞衍生物是快速的, 适用于整个显微照片。多细胞分析方法的一个优点是关注整个区域而不是单个细胞。因此, 可以快速评估图像中所有小胶质细胞的平均分枝 (端点和加工长度)。骨架分析提供了对多个细胞的分析: 一个数据抽样的细胞数, 不能匹配的分形分析由于需要的时间投资, 以隔离单个细胞从显微照片。一个例子, 这可能是最适合的, 将是筛选小胶质细胞的形态 proximities 的病灶损伤。一个限制是整个场图像渲染创建骨架模型的 IHC 显微照片是不完善的, 当与更多的时间消耗单细胞方法。此外, 区域分析不适用于小胶质细胞形态在同一领域内的显著差异的情况。最后, 该分析方法依赖于细胞计数, 这一参数在实验条件上可能有所不同。
分形分析是在单个细胞上进行的, 因此补充了骨架分析产生的平均细胞衍生物的数据输出。虽然花费更多的时间, 这种投资产生了广泛的形态计量数据。例如, 细胞密度、跨度比和圆形数据分别描述了细胞轮廓的大小、伸长率和形状。分形维数和 lacunarity 分别对细胞的复杂性和形态异质性进行了总结。有关如何计算每个参数以及如何解释数据的更深入的摘要, 请在交互式手册16中提供, 并应考虑具体的研究问题。所述的协议导致敏感工具, 以量化的小变化, 在2D 胶质细胞形态可能发生在生理和病理条件。如果生成3D 形状, 则可能有其他的形态学分析, 如坚固性、凸性和形状因子16、20 。
协议的开发和适应是连续的和用户驱动的。它已从荧光8扩展到民建联/亮场图像7 , 但尚未石蜡嵌入组织。此外, 它可以与专有软件 (如 Imaris) 结合使用, 以进行额外分析。该协议可应用于多种生理学, 不限于小胶质细胞, 但可应用于任何具有特定模式或形状, 可使用 IHC 方法识别的组织。最后, 有足够的样本大小, 可以根据形态学12,21, 对分层小胶质细胞进行多元或聚类分析;这是有意义的信息, 因为小胶质细胞形态学是胶质细胞功能和对周围环境反应的重要指标。对胶质形态学多样性的认识正在扩大, 对充分理解在健康和疾病期间神经元-胶质细胞-血管相互作用的重要性。这一领域的增长是通过良好的发展, 易于使用和可重现的协议, 以量化和总结小胶质细胞形态学使用多个连续变量。

小胶质细胞对大脑生理学的变化有一个直接而多样的形态学反应1沿连续的可能性, 范围从超衍生物和高度复杂的形态到分支和变形形貌2.小胶质细胞也可能成为极化和杆状的3。小胶质细胞衍生物通常被定义为具有多个过程的复杂形状, 并且经常被报告为每个单元的端点数和细胞进程的长度。由于小胶质细胞通过连续单元细胞交叉谈话和在体内运动4,5, 细微地调谐到神经细胞和胶质功能, 胶质细胞的形态可以作为多种功能和机能障碍的指标。在大脑中。定量的方法是必要的, 以充分描述这些形态学变化的多样性, 并区分发生微妙的生理扰动的分支细胞之间的差异 (如癫痫5,6和震荡7) 除了严重伤害 (如描边8)。形态学量化的增加使用7,8,9,10,11,12,13,14将揭示在健康和疾病期间小胶质细胞形态的完全多样性。本研究详述了在免疫组化 (IHC) 后对固定啮齿动物组织中的小胶质细胞进行荧光或非荧光显微照片的 ImageJ 插件的逐步使用。
这里描述的分析技术的中心是 ImageJ 插件 AnalyzeSkeleton (2 d/3 d)15, 2010年开发, 以量化大型乳腺结构, FracLac16, 2014年开发, 以整合 ImageJ 和分形分析, 以量化单个小胶质细胞形状。这些插件提供了一个快速分析的小胶质细胞衍生物在整个显微照片或多个小胶质细胞的定义 ROI 内的显微照片。此分析可单独使用, 也可以用分形分析进行补充。单细胞分形分析 (FracLac) 需要时间的投入, 但提供了关于小胶质的复杂性、形状和大小的多种形态学输出。这两种工具的使用都不是多余的, 因为细胞衍生物与细胞的复杂性相辅相成, 多个参数的组合可用于区分数据集12、17中不同的小胶质形态。

<table><tbody><tr><td>primary antibody anti-IBA1</td><td>Wako&amp;nbsp;</td><td>019-19741</td><td>rabbit host</td></tr><tr><td>Vectashield soft mount</td><td>Vector Labs</td><td>H-1000</td><td></td></tr><tr><td>Secondary antibody</td><td>Jackson ImmunorResearch</td><td>711-545-152</td><td>donkey host</td></tr><tr><td>4 mL glass vial</td><td>Wheaton</td><td>UX-08923-11</td><td></td></tr><tr><td>Triton X-100</td><td>Fisher Scientific&amp;nbsp;</td><td>BP151</td><td></td></tr><tr><td>Sodium Azide (NaN&lt;sub&gt;3&lt;/sub&gt;)</td><td>Sigma</td><td>S-8032</td><td></td></tr><tr><td>glass coverslip</td><td>Fisher Scientific&amp;nbsp;</td><td>12-544-G</td><td></td></tr></tbody></table>

quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using imagej

小胶质细胞是大脑吞噬, 参与大脑稳态, 并不断地调查其环境中的功能障碍, 损伤和疾病。作为第一反应者, 小胶质细胞具有减轻神经元和胶质细胞功能障碍的重要功能, 在这个过程中, 它们经历了广泛的形态学变化。小胶质细胞的形貌可以分类描写或, 或者, 可以量化为一个连续变量的参数, 如单元格的衍生物, 复杂性和形状。虽然将小胶质细胞量化的方法应用于单细胞, 但在整个显微照片中, 很少有技术适用于多个胶质。此方法的目的是使用现成的 ImageJ 协议量化多个和单个单元格。本协议是建议将荧光和亮场显微照片转换为具有代表性的二进制和骷髅图像的步骤和 ImageJ 插件的摘要, 并使用软件插件 AnalyzeSkeleton (2 维/3 d) 和 FracLac 进行分析。用于形态学数据收集。这些插件的输出根据过程端点、结点和长度以及复杂度、单元格形状和大小描述符来总结细胞形态学。本文描述的骨架分析协议非常适合于整个显微照片或区域 (ROI) 中的多个小胶质细胞的区域分析, 而 FracLac 提供了互补的个体单元分析。结合, 该协议提供了一个客观的, 敏感的, 全面的评估工具, 可用于分层在不同的胶质细胞形态存在的健康和受伤的大脑。

本文介绍的小胶质细胞形态学分析协议总结了处理荧光和显微照片的形态学分析的步骤。这些步骤在图 2和 图 3中直观地进行了总结。这些步骤的目的是创建一个具有代表性的二进制和骷髅图像, 适当地对原始显微照片建模, 以便累积的数据有效。在协议应用程序之后, AnalyzeSkeleton 插件将产生一个标记的骨架图像, 从中可以从生成的输出文件中总结出端点和分支 (i. e、进程) 的数量 lengthcan。然后使用端点和过程长度数据来估计显微照片或 ROI 中小胶质细胞衍生物的程度。图 4汇总了与协议应用程序一起收集的结果数据 (端点/单元格和进程长度/单元格)。虽然存在类似的趋势, 但图 4F中汇总的数据比图 4E中的要少。此外, 这些数据说明在应用协议时检测组之间的差异的灵敏度增强. 最后, 在适用《议定书》时, 必须注意用户间的可变性问题。此类差异由图 5汇总, 其中两个独立用户分析了同一数据集, 并应用了上面总结的相同协议。
其他形态学数据是从与协议应用程序中创建的二进制图像隔离的单个单元格中收集的。在图 6中总结了在使用 FracLac 插件之前分析小胶质细胞形态学的协议步骤。我们在未受伤的 (图 6A) 和受伤的 (图 6B) 组织中演示此分析。在图 6CF 中显示了具有代表性的二进制、轮廓、凸壳/封装圆的典型图像, 以及对每个单元格进行分析且没有协议应用程序的方框计数示例.这些图像有助于说明在图 6G中总结的形态学数据差异的起源。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57648/57648fig1.jpg"> 图1。图示骷髅小胶质细胞与一个圆形的躯体 (优) 与一个单一的起源躯体 (最佳) 和相应的覆盖之间的骷髅单元格和原始显微照片。缩放条 = 10 µm.<a href="/files/ftp_upload/57648/57648fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57648/57648fig2.jpg"> 图2。协议应用到荧光显微照片。将骨架分析协议的插图应用于带有单个单元格的荧光显微照片, 以显示详细信息。缩放条 = 10 µm.<a href="/files/ftp_upload/57648/57648fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57648/57648fig3.jpg"> 图3。协议应用于明亮领域的显微照片。将骨架分析协议的插图应用于明亮领域的显微照片, 用单个单元格裁剪来显示详细信息。缩放条 = 10 µm.<a href="/files/ftp_upload/57648/57648fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57648/57648fig4.jpg"> 图4。具有和不使用协议应用程序的数据分析.(A) 显微照片与 (B) 中黄色框对应的荧光 IHC 和裁剪单元格的示例.带有 (C) 和不带 (D) 的二进制和骷髅图像的示例, 该协议应用如所述。在未受伤 (白色) 和受伤 (灰色) 皮质组织 (E) 和没有 (F) 的情况下, 小胶质细胞终点/细胞和过程长度/单元的汇总数据应用了该协议。统计分析使用学生的 t 测试和 n = 3, ** 表示p < 0.01。缩放条 = 10 µm.<a href="/files/ftp_upload/57648/57648fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57648/57648fig5.jpg"> 图5。与协议应用程序的用户差异.用户1和用户2的原始图像和协议转换为二进制和骨架图像的示例。两个图像之间的差异用匹配的彩色圆圈突出显示。用用户1和用户2对未受伤和受伤脑区的小胶质细胞终点/单元格和过程长度/细胞数据的摘要图。方差统计分析和样本大小为 n = 3;*p < 0.05, **p < 0.01, ***p < 0.001。缩放条 = 10 µm.<a href="/files/ftp_upload/57648/57648fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57648/57648fig6.jpg"> 图6。具有和不带协议应用程序的分形分析.在未受伤害的 (A) 和受伤 (B) 皮质中有相应的二进制 (C) 和轮廓 (D) 的小胶质细胞的裁剪显微照片的示例, 这些图像的结果和不应用协议。对应的轮廓形状 (E) 的关联凸壳 (蓝色) 和封闭圆 (粉红色) 用于计算形状密度、跨度比和圆形 (G)。方框计数方法在 (F) 中说明, 用于分形维度 (DB) 和 lacunarity (λ) 计算 (G)。缩放条 = 10 µm.<a href="/files/ftp_upload/57648/57648fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ at JoVE.com

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

小胶质细胞是通过形态学变化对改变的大脑生理学进行调查和反应的脑免疫单元, 可以定量评估。本协议概述了一种基于 ImageJ 的分析协议, 它根据诸如细胞分枝、复杂性和形状等指标来表示小胶质形态学作为连续数据。

应用 ImageJ 对免疫组化制备组织显微照片小胶质细胞形态学的定量研究

Microglia are brain phagocytes that participate in brain homeostasis and continuously survey their environment for dysfunction, injury, and disease. As ...

quantifying-microglia-morphology-from-photomicrographs

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

66.5K 次观看.  University of Arizona. 小胶质细胞是通过形态学变化对改变的大脑生理学进行调查和反应的脑免疫单元, 可以定量评估。本协议概述了一种基于 ImageJ 的分析协议, 它根据诸如细胞分枝、复杂性和形状等指标来表示小胶质形态学作为连续数据。

视频: Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated micro-plates. Under these conditions, cells spread and divide in all directions resulting in an inherent variability in cell shape, morphology and behavior. The high cell-to-cell variance of the overall population impedes the success of HCA, especially for drug development. The ability of micropatterns to normalize the shape and internal polarity of every individual cell provides a tremendous opportunity for solving this critical bottleneck 1-2.
To facilitate access and use of the micropatterning technology, CYTOO has developed a range of ready to use micropatterns, available in coverslip and microwell formats. In this video article, we provide detailed protocols of all the procedures from cell seeding on CYTOOchip micropatterns, drug treatment, fixation and staining to automated acquisition, automated image processing and final data analysis. With this example, we illustrate how micropatterns can facilitate cell-based assays. Alterations of the cell cytoskeleton are difficult to quantify in cells cultured on homogenous substrates, but culturing cells on micropatterns results in a reproducible organization of the actin meshwork due to systematic positioning of the cell adhesion contacts in every cell. Such normalization of the intracellular architecture allows quantification of even small effects on the actin cytoskeleton as demonstrated in these set of protocols using blebbistatin, an inhibitor of the actin-myosin interaction.

Adhesive micropatterns that normalize cellular architecture can be used to increase sensitivity in the detection of drug effects, improve reproducibility and simplify automated image acquisition and analysis. Such technology will benefit drug/siRNA screening assays, performed on conventional cell culture supports and consequently suffering from excessive cell-to-cell variability.

改进的可视化和定量分析，药物使用Micropatterned细胞的影响

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated ...

科研

生物工程

An Engulfment Assay: A Protocol to Assess Interactions Between CNS Phagocytes and Neurons

Phagocytosis is a process in which a cell engulfs material (entire cell, parts of a cell, debris, etc.) in its surrounding extracellular environment and subsequently digests this material, commonly through lysosomal degradation. Microglia are the resident immune cells of the central nervous system (CNS) whose phagocytic function has been described in a broad range of conditions from neurodegenerative disease (e.g., beta-amyloid clearance in Alzheimer&#8217;s disease) to development of the healthy brain (e.g., synaptic pruning)1-6. The following protocol is an engulfment assay developed to visualize and quantify microglia-mediated engulfment of presynaptic inputs in the developing mouse retinogeniculate system7. While this assay was used to assess microglia function in this particular context, a similar approach may be used to assess other phagocytes throughout the brain (e.g., astrocytes) and the rest of the body (e.g., peripheral macrophages) as well as other contexts in which synaptic remodeling occurs (e.g. ,brain injury/disease).

Microglia are the resident immune cells of the central nervous system (CNS) with a high capacity to phagocytose or engulf material in their extracellular environment. Here, a broadly applicable, reliable, and highly quantitative assay for visualizing and measuring microglia-mediated engulfment of synaptic components is described.

一个吞噬含量：一个协议，以评估之间的中枢神经系统吞噬细胞和神经元的相互作用

Phagocytosis is a process in which a cell engulfs material (entire cell, parts of a cell, debris, etc.) in its surrounding extracellular environment and ...

神经科学

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with distinct functions and gene expression profiles. The roles of microglial activation in health, injury and disease remain incompletely understood due to their dynamic and complex regulation in response to changes in their microenvironment. Thus, it is critical to non-invasively monitor and analyze changes in microglial activation over time in the intact organism. In vivo studies of microglial activation have been delayed by technical limitations to tracking microglial behavior without altering the CNS environment. This has been particularly challenging during chronic neurodegeneration, where long-term changes must be tracked. The retina, a CNS organ amenable to non-invasive live imaging, offers a powerful system to visualize and characterize the dynamics of microglia activation during chronic disorders.
This protocol outlines methods for long-term, in vivo imaging of retinal microglia, using confocal ophthalmoscopy (cSLO) and CX3CR1GFP/+ reporter mice, to visualize microglia with cellular resolution. Also, we describe methods to quantify monthly changes in cell activation and density in large cell subsets (200-300 cells per retina). We confirm the use of somal area as a useful metric for live tracking of microglial activation in the retina by applying automated threshold-based morphometric analysis of in vivo images. We use these live image acquisition and analyses strategies to monitor the dynamic changes in microglial activation and microgliosis during early stages of retinal neurodegeneration in a mouse model of chronic glaucoma. This approach should be useful to investigate the contributions of microglia to neuronal and axonal decline in chronic CNS disorders that affect the retina and optic nerve.

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Microglia activation and microgliosis are key responses to chronic neurodegeneration. Here, we present methods for in vivo, long-term visualization of retinal CX3CR1-GFP+ microglial cells by confocal ophthalmoscopy, and for threshold and morphometric analyses to identify and quantify their activation. We monitor microglial changes during early stages of age-related glaucoma.

视网膜胶质细胞活化的体内动力学在神经退行性疾病:共聚焦成像眼底及细胞形态计量小鼠青光眼

Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with ...

医学

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The Drosophila larval neuromuscular junction (NMJ), a well-established model for glutamatergic synapses, has been extensively studied for decades. Identification of mutations causing NMJ morphological defects revealed a repertoire of genes that regulate synapse development and function. Many of these were identified in large-scale studies that focused on qualitative approaches to detect morphological abnormalities of the Drosophila NMJ. A drawback of qualitative analyses is that many subtle players contributing to NMJ morphology likely remain unnoticed. Whereas quantitative analyses are required to detect the subtler morphological differences, such analyses are not yet commonly performed because they are laborious. This protocol describes in detail two image analysis algorithms "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics", available as Fiji-compatible macros, for quantitative, accurate and objective morphometric analysis of the Drosophila NMJ. This methodology is developed to analyze NMJ terminals immunolabeled with the commonly used markers Dlg-1 and Brp. Additionally, its wider application to other markers such as Hrp, Csp and Syt is presented in this protocol. The macros are able to assess nine morphological NMJ features: NMJ area, NMJ perimeter, number of boutons, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones in the NMJ terminal.

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).

两种算法的高通量和多参数定量<em&gt;果蝇</em&gt;神经肌肉接头形态

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

基于面积的图像分析算法，用于巨噬细胞-成纤维细胞共生群的定量

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

It has become increasingly clear that astrocytes modulate neuronal function not only at the synaptic and single-cell levels, but also at the network level. Astrocytes are strongly connected to each other through gap junctions and coupling through these junctions is dynamic and highly regulated. An emerging concept is that astrocytic functions are specialized and adapted to the functions of the neuronal circuit with which they are associated. Therefore, methods to measure various parameters of astrocytic networks are needed to better describe the rules governing their communication and coupling and to further understand their functions.
Here, using the image analysis software (e.g., ImageJFIJI), we describe a method to analyze confocal images of astrocytic networks revealed by dye-coupling. These methods allow for 1) an automated and unbiased detection of labeled cells, 2) calculation of the size of the network, 3) computation of the preferential orientation of dye spread within the network, and 4) repositioning of the network within the area of interest.
This analysis can be used to characterize astrocytic networks of a particular area, compare networks of different areas associated to different functions, or compare networks obtained under different conditions that have different effects on coupling. These observations may lead to important functional considerations. For instance, we analyze the astrocytic networks of a trigeminal nucleus, where we have previously shown that astrocytic coupling is essential for the ability of neurons to switch their firing patterns from tonic to rhythmic bursting1. By measuring the size, confinement, and preferential orientation of astrocytic networks in this nucleus, we can build hypotheses about functional domains that they circumscribe. Several studies suggest that several other brain areas, including the barrel cortex, lateral superior olive, olfactory glomeruli, and sensory nuclei in the thalamus and visual cortex, to name a few, may benefit from a similar analysis.

Here we present a protocol to assess the organization of astrocytic networks. The described method minimizes bias to provide descriptive measures of these networks such as cell count, size, area, and position within a nucleus. Anisotropy is assessed with a vectorial analysis.

耦合星形胶质细胞网络的大小、形状和方向性分析

It has become increasingly clear that astrocytes modulate neuronal function not only at the synaptic and single-cell levels, but also at the network ...

Immunofluorescence Staining Using IBA1 and TMEM119 for Microglial Density, Morphology and Peripheral Myeloid Cell Infiltration Analysis in Mouse Brain

This is a protocol for the dual visualization of microglia and infiltrating macrophages in mouse brain tissue. TMEM119 (which labels microglia selectively), when combined with IBA1 (which provides an exceptional visualization of their morphology), allows investigation of changes in density, distribution, and morphology. Quantifying these parameters is important in providing insights into the roles exerted by microglia, the resident macrophages of the brain. Under normal physiological conditions, microglia are regularly distributed in a mosaic-like pattern and present a small soma with ramified processes. Nevertheless, as a response to environmental factors (i.e., trauma, infection, disease, or injury), microglial density, distribution, and morphology are altered in various manners, depending on the insult. Additionally, the described double-staining method allows visualization of infiltrating macrophages in the brain based on their expression of IBA1 and without colocalization with TMEM119. This approach thus allows discrimination between microglia and infiltrating macrophages, which is required to provide functional insights into their distinct involvement in brain homeostasis across various contexts of health and disease. This protocol integrates the latest findings in neuroimmunology that pertain to the identification of selective markers. It also serves as a useful tool for both experienced neuroimmunologists and researchers seeking to integrate neuroimmunology into projects.

This protocol describes a step-by-step workflow for immunofluorescent costaining of IBA1 and TMEM119, in addition to analysis of microglial density, distribution, and morphology, as well as peripheral myeloid cell infiltration in mouse brain tissue.

使用IBA1和TMEM119进行小鼠脑微胶质密度、形态学和周围骨髓细胞渗透分析的免疫荧光染色

This is a protocol for the dual visualization of microglia and infiltrating macrophages in mouse brain tissue. TMEM119 (which labels microglia ...

免疫与感染

Human Microglia-like Cells: Differentiation from Induced Pluripotent Stem Cells and In Vitro Live-cell Phagocytosis Assay using Human Synaptosomes

Microglia are the resident immune cells of myeloid origin that maintain homeostasis in the brain microenvironment and have become a key player in multiple neurological diseases. Studying human microglia in health and disease represents a challenge due to the extremely limited supply of human cells. Induced pluripotent stem cells (iPSCs) derived from human individuals can be used to circumvent this barrier. Here, it is demonstrated how to differentiate human iPSCs into microglia-like cells (iMGs) for in vitro experimentation. These iMGs exhibit the expected and physiological properties of microglia, including microglia-like morphology, expression of proper markers, and active phagocytosis. Additionally, documentation for isolating and labeling synaptosome substrates derived from human iPSC-derived lower motor neurons (i3LMNs) is provided. A live-cell, longitudinal imaging assay is used to monitor engulfment of human synaptosomes labeled with a pH-sensitive dye, allowing for investigations of iMG's phagocytic capacity. The protocols described herein are broadly applicable to different fields that are investigating human microglia biology and the contribution of microglia to disease.

Human Microglia-like Cells: Differentiation from Induced Pluripotent Stem Cells and In Vitro Live-cell Phagocytosis Assay using Human Synaptosomes

This protocol describes the differentiation process of human induced pluripotent stem cells (iPSCs) into microglia-like cells for in vitro experimentation. We also include a detailed procedure for generating human synaptosomes from iPSC-derived lower motor neurons that can be used as a substrate for in vitro phagocytosis assays using live-cell imaging systems.

人小胶质细胞样细胞:使用人突触体从诱导多能干细胞和 体外 活细胞吞噬测定中分化

Microglia are the resident immune cells of myeloid origin that maintain homeostasis in the brain microenvironment and have become a key player in multiple ...

基于流式细胞术的突触材料小胶质细胞吞噬检测

Quantification of Microglial Engulfment of Synaptic Material Using Flow Cytometry

Microglia play a pivotal role in synaptic refinement in the brain. Analysis of microglial engulfment of synapses is essential for comprehending this process; however, currently available methods for identifying microglial engulfment of synapses, such as immunohistochemistry (IHC) and imaging, are laborious and time-intensive. To address this challenge, herein we present in vitro and in vivo* assays that allow fast and high-throughput quantification of microglial engulfment of synapses using flow cytometry.
In the in vivo* approach, we performed intracellular vGLUT1 staining following fresh cell isolation from adult mouse brains to quantify engulfment of vGLUT1+ synapses by microglia. In the in vitro synaptosome engulfment assay, we used freshly isolated cells from the adult mouse brain to quantify the engulfment of pHrodo&#160;Red-labeled synaptosomes by microglia. These protocols together provide a time-efficient approach to quantifying microglial engulfment of synapses and represent promising alternatives to labor-intensive image analysis-based methods. By streamlining the analysis, these assays can contribute to a better understanding of the role of microglia in synaptic refinement in different disease models.

作者聚焦：通过心理神经免疫学的技术创新促进理解

Here we present two protocols to quantify microglial engulfment of vGLUT1-positive synapses and pHRodo&#160;Red-labeled crude synaptosomes using flow cytometry.

使用流式细胞术定量突触材料的小胶质细胞吞噬

Microglia play a pivotal role in synaptic refinement in the brain. Analysis of microglial engulfment of synapses is essential for comprehending this ...

应用 ImageJ 对免疫组化制备组织显微照片小胶质细胞形态学的定量研究

摘要

探索更多视频

此视频中的章节

改进的可视化和定量分析，药物使用Micropatterned细胞的影响

一个吞噬含量：一个协议，以评估之间的中枢神经系统吞噬细胞和神经元的相互作用

视网膜胶质细胞活化的体内动力学在神经退行性疾病:共聚焦成像眼底及细胞形态计量小鼠青光眼

两种算法的高通量和多参数定量

基于面积的图像分析算法，用于巨噬细胞-成纤维细胞共生群的定量

耦合星形胶质细胞网络的大小、形状和方向性分析

使用IBA1和TMEM119进行小鼠脑微胶质密度、形态学和周围骨髓细胞渗透分析的免疫荧光染色

人小胶质细胞样细胞:使用人突触体从诱导多能干细胞和体外活细胞吞噬测定中分化

使用流式细胞术定量突触材料的小胶质细胞吞噬

使用流式细胞术定量突触材料的小胶质细胞吞噬

应用 ImageJ 对免疫组化制备组织显微照片小胶质细胞形态学的定量研究

摘要

探索更多视频

此视频中的章节

改进的可视化和定量分析，药物使用Micropatterned细胞的影响

一个吞噬含量：一个协议，以评估之间的中枢神经系统吞噬细胞和神经元的相互作用

视网膜胶质细胞活化的体内动力学在神经退行性疾病:共聚焦成像眼底及细胞形态计量小鼠青光眼

两种算法的高通量和多参数定量

基于面积的图像分析算法，用于巨噬细胞-成纤维细胞共生群的定量

耦合星形胶质细胞网络的大小、形状和方向性分析

使用IBA1和TMEM119进行小鼠脑微胶质密度、形态学和周围骨髓细胞渗透分析的免疫荧光染色

人小胶质细胞样细胞:使用人突触体从诱导多能干细胞和 体外 活细胞吞噬测定中分化

使用流式细胞术定量突触材料的小胶质细胞吞噬

使用流式细胞术定量突触材料的小胶质细胞吞噬

人小胶质细胞样细胞:使用人突触体从诱导多能干细胞和体外活细胞吞噬测定中分化