我们要感谢 d. 斯蒂芬斯 (布里斯托尔大学, 布里斯托尔, 英国) 的 TPST1-EGFP DNA 质粒, 以及纳史木汗戈文达拉扬为帮助软件优化。这项工作得到了国家医学研究理事会 (NMRC/CBRG/007/2012)、教育部 (AcRF Tier1 rg 18/11、rg 48/13 和 RG132/15 和 AcRF Tier2 MOE2015-T2-2-073) 的资助, 以法学

注: 下面是 GLIM 的 step-by 步协议, 用于确定 EGFP 标记的 tyrosylprotein 3-o 1 (TPST1) 的 LQ, 在 HeLa 细胞中是高尔基体的驻留酶。
1. 荧光标记高尔基体微型叠的制备
<ol><li>
准备玻璃片
	<ol>
<li>分0.3 毫升无菌 Dulbecco 改良鹰的培养基 (DMEM) 到一个24井板在一个组织培养罩的井。</li>
		<li>将Φ 12 mm no. 1.5 玻璃片用用70% 乙醇轻擦的软组织纸擦拭, 去除玻璃表面的碎片片。用一双锋利的镊子简单地浸泡在70% 乙醇中的片, 将其转移到24井板中, 并将其沉入含有无菌 DMEM 的井底。 注: 玻璃片被燃烧的常规消毒。然而, 片在这种处理下容易破裂在随后处理。70% 乙醇浸泡对玻璃片灭菌效果良好, 无损伤。</li>
		<li>盖上盖子, 离开24井板在37° c CO2孵化器直到使用。</li>
	</ol>
</li>
	<li>
种子细胞
	<ol>
<li>培养 HeLa 细胞 (以下细胞) 在 T-25 烧瓶中的 DMEM 补充10% 胎牛血清 (FBS) (以下完整培养基) 在一个37° c co2孵化器提供 5% co2。这里不需要抗生素。</li>
		<li>当细胞达到〜 80% 70-100, 吸取培养基。在烧瓶中加入1毫升的0.25% 胰蛋白酶-EDTA, 并在37° c CO2孵化器中孵育细胞2分钟. 将1毫升的完整培养基放入烧瓶中, 然后用移轻轻冲洗瓶壁上的细胞。</li>
		<li>将分离的细胞转移到一个不育的离心管中, 在 500 x g 上旋转2分钟以使细胞颗粒。在1毫升完全培养基中, 吸取上清和悬浮细胞颗粒。</li>
		<li>在井中吸出含有灭菌玻璃片和种子 ~ 1 x 105细胞的24井板井中的培养基。用完整的培养基到0.5 毫升的井容量。孵育在一个37° c co2孵化器提供 5% co2。培养细胞到〜 80% 70-100。</li>
	</ol>
</li>
	<li>
染细胞 注: 良好的扩散细胞有利于成像分散的高尔基 mini-stacks。<ol>
<li>染 ~ 80% 汇合细胞与 80 ng mCherry7和 320 ng TPST1-EGFP (参见材料目录) 脱氧核糖核酸质粒使用转染试剂根据制造商提供的协议。孵育在一个37° c CO2孵化器。4-6 h 潜伏期后改变培养基。细胞准备好了 ~ 12 小时后</li>
	</ol>
</li>
	<li>
Nocodazole 治疗产生高尔基体 mini-stacks
	<ol>
<li>在1毫升二甲基亚砜中溶解10毫克 nocodazole 粉末, 制备 nocodazole 的溶液 (33 毫米)。分库存解决方案和存储在-20 ° c 的长期存储。</li>
		<li>稀释1µL nocodazole 溶液 (33 毫米) 在1毫升37° c 完全介质 (最终浓度33µM)。离心机在最高速度, 以消除颗粒物质。</li>
		<li>在井中吸入培养基, 加入0.5 毫升的33µM nocodazole 含有完整培养基。孵育37° c CO2孵化器中的细胞 3 h. 进行免疫荧光标记 (1.5 节)。</li>
	</ol>
</li>
	<li>
免疫荧光标记 注意: 保持细胞在黑暗中避免漂白 mCherry 和 TPST1-EGFP。<ol>
<li>
制备免疫荧光标记试剂
		<ol>
<li>准备4% 甲醛溶液, 溶解 4% (w/v) 甲醛粉末在热1X 磷酸盐缓冲盐水 (PBS) 和过滤解决方案通过0.45 µm 过滤器。溶液可贮存在-20 ° c。 注意: 甲醛是有毒和致癌的, 它会引起皮肤刺激。穿戴适当的个人防护设备 (PPE)。</li>
			<li>在 1x PBS 中制备荧光稀释缓冲液 (FDB), 混合 5% (v/v) FBS 和 2% (含五) 牛血清白蛋白 (BSA)。通过0.45 µm 过滤器过滤解决方案, 并将解决方案存储在-20 ° c。</li>
			<li>在1升水中溶解5.35 克 nh4cl 粉末, 以准备 100 mM nh4cl, 并在室温下存储该溶液。</li>
			<li>在水中溶解 10% (含钒) 皂苷粉, 制备10% 皂素, 分, 并在-20 ° c 贮存溶液。</li>
		</ol>
</li>
		<li>
固定
		<ol>
<li>冲洗井一次与0.5 毫升 1x PBS 解决方案, 并添加0.5 毫升4% 甲醛解决方案。室温孵育20分钟。冲洗井两次与0.5 毫升 1x pbs 和两次与0.5 毫升 100 mM NH4Cl. 冲洗两次与0.5 毫升 1x pbs。细胞可在4° c 的 1x PBS 在黑暗中过夜。</li>
		</ol>
</li>
		<li>
荧光标记
		<ol>
<li>在500µL FDB 中稀释1µL 小鼠 anti-GM130 主抗体, 含有0.1% 的皂苷。翻转24井板的盖子, 并在盖子上应用10µL 主抗体混合物。</li>
			<li>用一双锋利的镊子将玻璃片到抗体混合物的下落, 确保细胞侧与混合物接触。在室温下, 用主抗体混合物孵育1小时的细胞。 注: 与片的盖子可以放在一个湿的塑料袋在黑暗中。</li>
			<li>使用一对锋利的镊子提取和转移片到井与细胞侧向上和冲洗它在0.5 毫升 1x PBS 三次在≥ 30 min. 摇晃是不必要的。</li>
			<li>稀释1µL 荧光共轭山羊 anti-mouse IgG (二次抗体) 在500µL FDB 中含有0.1% 的皂甙。</li>
			<li>清洗和清洁24井板盖的背面。在盖子上应用10µL 远红色二级抗体混合物。重复步骤 1.5.3.2-1.5.3.3。</li>
		</ol>
</li>
		<li>
安装
		<ol>
<li>通过混合1克甘油, 2.2 克聚 (乙烯醇) (兆瓦〜 3.1万 Da), 1.2 毫升 1 M 三 (pH 8) 和8.8 毫升 H2o 在60° c 水浴中溶解混合物, 偶尔涡流。将解决方案存储在-20 ° c。</li>
			<li>将安装介质解冻, 并将10µL 转到玻璃滑梯上。将片 (单元侧向下) 叠加到安装介质的下落上。将玻璃玻片在37° c 下孵育30分钟或室温1小时, 以硬化安装介质。用无色指甲油封住片, 在-20 ° c 的时候将玻片储存起来。</li>
		</ol>
</li>
	</ol>
</li>
</ol>2. 用于色移校正的荧光微珠的制备
<ol><li>
片清洗
	<ol>
<li>小心地将一块25毫米 no. 1.5 玻璃片放在塑料架上。</li>
		<li>将机架与片到一个400毫升的烧杯中, 用300毫升1米的氢氧化钠和几种在浴缸 sonicator (35 瓦特) 中进行15分钟的清洗, 用400毫升去离子水冲洗一个300毫升烧杯中的机架。将机架转到400毫升的烧杯中, 300 毫升99% 乙醇和几种在浴 sonicator 中, 15 分钟用400毫升去离子水冲洗货架。</li>
		<li>重复步骤2.1.2 两次。</li>
		<li>用300毫升去离子水在400毫升的烧杯中用片冲洗机架10分钟. 重复两次步骤。将机架转至60° c 烘箱, 干燥片1小时, 将干燥的片在无尘培养皿中贮存。</li>
	</ol>
</li>
	<li>
玻璃片上的荧光珠固定
	<ol>
<li>稀释 110 nm 多色荧光珠80倍, 在 1x PBS 含有0.1 µg/µL BSA。简要地涡流管分散珠团聚体。将60µL 稀释的珠子涂在清洗过的Φ 25 mm no. 1.5 玻璃片上 (见第2.1 节) 使用吸管尖端。干燥片在一个干燥连接到一个真空泵在黑暗中, 并安装在50µL 安装媒体的玻璃幻灯片 (见步骤 1.5.4.2) 的片。</li>
	</ol>
</li>
</ol>3. 图像采集
注: GLIM 需要高信噪比 (SNR) 的高精度质心计算图像。该图像可由传统的显微镜, 如激光扫描共焦, 旋转圆盘共聚焦或广域显微镜。可使用具有计划物镜物镜和低噪声图像传感器 (如电荷耦合器件 (CCD) 和科学互补金属氧化物半导体 (sCMOS)) 的广域显微镜。对图像传感器的参数进行了调整, 以确保低读噪声和高动态范围。该显微镜必须配有绿色、mCherry 和远红荧光的荧光过滤器的最佳配置, 并且必须有可忽略的荧光相声。理想情况下, 成像系统在 x、y 和 z 轴上实现奈奎斯特采样率, 这通常要求像素的 x、y 和 z 大小分别小于100、100和 200 nm。体素的 x 和 y 大小总是相等的, 被称为 pixel_size。pixel_size 可以通过将相机传感器大小除以系统放大率来计算。
<ol><li>
图像多色珠
	<ol>
<li>图像多色珠在绿色, 红色和远红色的渠道在开始和每一个影像会议结束。 注: 在这里, 图像是通过一个传统的广域 epi 荧光显微镜 (倒置), 配备 100X NA 1.4 计划-物镜目标, 机动阶段, 200 瓦特金属卤化物光源和16位 sCMOS 相机。绿色通道过滤器立方体的励磁滤波器 (带通)、分色镜 (长传球) 和发射滤波器 (带通) 的波长为 465-495、505和 515-555 nm;那些为红色渠道过滤器立方体是 528-553, 565 和 578-633 毫微米;并且那些为远的红色渠道过滤器立方体是 590-650, 660 和 663-738 毫微米。在获取过程中, 沿 x、y 和 z 轴的体素大小分别为64、64和 200 nm。</li>
		<li>找到一个视野良好的珠密度。</li>
		<li>获取绿色、红色和远红色通道中的3D 图像堆栈 (通道G、通道R、通道B分别)。在最佳焦平面上方和下方3节 (每栈7节)。将三堆栈保存为三 TIFF 文件。 注意: 每个通道的曝光时间是根据经验确定的, 以最大化动态范围, 避免像素饱和和最小化漂白。其他参数, 如滤镜和体素的 x、y 和 z 大小, 都是在上面所讨论的选择。</li>
	</ol>
</li>
	<li>
图像高尔基 mini-stacks
	<ol>
<li>使用 TPST1-EGFP 和高尔特-mCherry 转染和荧光标记 (1.5 节) 的幻灯片, 以找到表达 TPST1-EGFP 和低或中级高级别 mCherry 的细胞。获取3D 图像堆栈, 如步骤3.1.3 中所述。</li>
	</ol>
</li>
</ol>4. 图像分析
<ol><li>获取荧光珠的中心
	<ol>
<li>在 ImageJ 中, 打开一组由三 TIFF 文件 (文件 > 图像 > 打开) 组成的珠子图像。</li>
		<li>通过图像 > 堆栈 > Z 项目, 在通道R中的最佳聚焦截面上平均连续3节。输入 "开始切片" 和 "停止切片" 的节数, 并在 "投影类型" 的选项中选择 "平均强度"。</li>
		<li>使用 "多边形选择" 绘制图像中不含珠子的感兴趣区域 (roi)。在 "分析 > 设置测量" 中, 只检查 "平均灰度值" 和 "标准偏差"。然后执行 "分析 > 测量" 以获得 ROI 的平均值和 SD。</li>
		<li>将背景强度计算为 "mean+6×SD".通过 "过程 > 数学 > 减法", 用相应的背景强度值减去图像, 输入对应于该通道的背景强度值。</li>
		<li>通过使用相同的开始和停止切片以及相同的背景 ROI, 对通道G和通道B图像堆栈重复步骤4.1.2 到4.1.4。请注意, 步骤4.1.3 中使用的 ROI 可以复制到通道G和通道B图像.
</li>
		<li>通过选择频道R为红色, 将通道G作为绿色和通道B作为蓝色, 合并三背景减去图像 (图像 > 颜色 > 合并通道)。将获得由三通道组成的单个复合图像。</li>
		<li>启动 roi 管理器 (分析 > 工具 > roi 管理器)。在单个珠子周围画一个正方形的 roi, 确保每个 roi 中只有一个珠子。通过在键盘上按 "t", 将 roi 添加到 roi 管理器中。重复此过程并添加尽可能多的 roi。</li>
		<li>在 "分析 > 设置测量" 中, 只检查 "质量中心"。</li>
		<li>选择频道R, 在 ROI 管理器中, 单击 "测量" 以获取中心;两列对应于 roi 中心的 x 和 y 坐标将显示在 "结果" 窗口中。将两列复制并粘贴到电子表格中。</li>
		<li>对通道G和通道B重复步骤4.1.9。</li>
		<li>按以下顺序排列单个电子表格中中心的坐标: xr、yr、xg、yg、xb和 yb。将 ". csv" 格式的电子表格保存为 "珠. csv"。在文件名中不留空格。</li>
	</ol></li>
	<li>选择和测量高尔基体 mini-stacks
	<ol>
<li>在 ImageJ 中安装两个宏 (附加在补充材料中), "宏高尔基 ROI 检测" 和 "宏输出3通道数据", 通过插件 > 安装 "。清除 ROI 管理器和结果窗口。</li>
		<li>打开一组高尔基体 mini-stack 图像, 由三 TIFF 文件 (文件 > 图像 > 打开) 组成。</li>
		<li>重复步骤 4.1.2-4.1.5 并保存三背景减去的图像以备以后使用。将通道R、通道G和通道b的背景 SDs 分别记录为 sdr、sdg和 sdb, 供以后使用。</li>
		<li>复制由步骤4.2.3 生成的三背景减去的图像 (ctrl+ Shift + D)。</li>
		<li>在 "过程 > 图像计算器" 中, 首先添加背景减去通道G以通道R图像。将生成的图像添加到背景减去通道B图像中。对于生成的图像, 在 "图像 > 调整 > 阈值" 中, 选择 "设置" 以输入 "1" 为 "最低阈值级别"。按下 "应用" 后, 产生黑白二进制图像。</li>
		<li>在 "分析 > 分析粒子", 输入大小范围为 "大小 (pixel^2)"。输入 "50-无穷大"。检查 "在边上排除" 和 "添加到管理器"。包含高尔基 mini-stacks 的 roi 现在添加到 ROI 管理器中。 注意: 大小范围必须是经验主义地确定排除通常小的噪声。</li>
		<li>通过选择频道R为红色, 将通道G作为绿色和通道B作为蓝色, 将步骤4.2.3 中的三背景减去图像 (图像 > 颜色 > 合并通道) 合并。生成由三通道组成的单个复合图像。</li>
		<li>通过选择 "插件 > 宏观-高尔基的 roi 检查" 来运行宏观的 "宏观-高尔基 roi 检测"。在 "交互式" 对话框中, 直观地检查每个 ROI 以保留或拒绝它。在所有三通道中选择包含单个对象的 roi。 注意: 在运行此工具后, 被拒绝的 roi 将从 ROI 管理器中删除。</li>
		<li>在 "分析 > 设置测量" 中只检查 "区域"、"平均灰度值" 和 "质量中心"。通过启动宏工具 "宏输出3通道数据" (插件 > 宏输出3通道数据) 获取数据。 注意: 在 "结果" 窗口中显示通道R、通道G和通道B中的 roi 的区域、平均强度和中心 (x 和 y)。</li>
		<li>将中心的 x 和 y 坐标复制到电子表格中, 并按以下顺序排列: xr、yr、xg、yg、xb和 yb。将电子表格保存为 ". csv" 文件 (ministacks. csv)。在文件名中不留空格。进行中心的色移校正 (4.3 节) 和 LQ 计算 (第4.4 节)。</li>
	</ol></li>
	<li>中心的色移校正
	<ol>
<li>安装 Matlab 编译器运行时 (MCR)。</li>
		<li>在专用工作文件夹中安装以下文件: my_train 和 my_test. exe。在同一个文件夹中复制并粘贴 "珠. csv" 和 "ministacks. csv" 文件。</li>
		<li>通过键入以下命令 " cd path_of_working_folder"启动 Windows 的 "命令提示符" 并转到工作文件夹。</li>
		<li>使用珠的中心生成色移校准文件。键入以下命令 "my_train 珠子. csv校准. 垫1。在工作文件夹中创建名为 "校准. 席子" 的文件。忽略生成的其他文件。</li>
		<li>通过键入以下命令 "my_test. exe ministacks. csv corrected_ministacks. csv校准. 垫1 注意: 在工作文件夹中创建名为 "corrected_ministacks. csv" 的文件。它包含了中心的色移校正坐标, 它们按 xg、yg、xb和 yb的顺序排列。忽略创建的其他文件。请注意, 红色通道定义为无色差, 因此 xr和 yr与原始数据相同。</li>
	</ol></li>
	<li>LQs 的计算
	<ol>
<li>启动数据分析软件, 并复制和粘贴, 在下面的序列中, 平均灰度值, 区域, 从步骤4.2.3 获得的背景 SDs (放置在1行) 和彩色移位校正的 x 和 y 高尔基体 mini-stacks 在通道R中的坐标转换为以列 A 到 E 的工作表。同样, 分别将信道G和通道B的相应数据传送到 F 到 J 和 K 到 O 的列。</li>
		<li>添加八新的 P 到 W, 命名为 "综合强度的通道 R", "综合强度的通道 G", "综合强度的通道 B", d1, dx, abs (棕褐色), abs (tan B) 和 LQ。<ol>
<li>右键单击相应列的顶部, 然后选择 "设置列值" 以计算每列的值。对于 P 列, 输入 "(a) 上校 (B)";对于列 Q, 输入 "(F) 上校 (G)";对于 R 列, 输入 "(K) 上校 (L)";对于列 S, 输入 "pixel_size * 距离 (D), 上校 (E), 上校 (N), 上校 (O))", 其中 "pixel_size" 是像素的大小在 nm;对于 T 列, 输入 "pixel_size" ((距离 (I), (J), (n), 上校 (o)) ^ 2 + 距离 (d), 上校 (E), 上校 (N), 上校 (o)) ^ 2 距离 (d), (e)、(I)、(J)) ^ 2)/(2 距离 (D)、(e)、上校 (N)、上校 (O))) ";对于 U 型, 输入 "Abs (助理 ((距离 (I), 上校 (J), 上校 (n), 上校 (O)) ^ 2 + 距离 (上校 (D), 上校 (E), 上校 (n), (O)) ^ 2 距离 (D), 上校 (E), 上校 (I), 上校 (J)) ^ 2)/(2 距离 (i), 上校 (J), 上校 (N), (O) 距离 (D)、(E)、(N)、上校 (O)))) ";对于第 V, 输入 "Abs" (助理 ((d), (四), 上校 (e), (i), 上校 (J)) ^ 2 + 距离 ((d), 上校 (e), 上校 (N), (o) ^ 2-距离 (I), 上校 (J), 上校 (N), 上校 (o)) ^ 2)/(2 距离 (d)、(E)、上校 (i)、上校 (J) 距离 (d)、(E)、(N)、上校 (O)))) ";对于 W 列, 输入 "(T)/上校 (S)"。</li>
		</ol>
</li>
		<li>按简介中描述的三标准筛选高尔基 mini-stacks。<ol>
<li>在数据分析软件中, 转到 "工作表 > 工作表查询", 并为 "If" 测试选择列变量。将下列别名分别分配给 I1、I2、I3、d1、a 和 B, 用于列 "通道 R 的综合强度"、"通道 G 的综合强度"、"通道 B 的综合强度"、"d1"、"abs" (tan a) 和 abs (tan b)。在 "If 条件" 框中, 输入 "I1 > = 30 * 单元 (13) 和 I2 > = 30 * 单元 (18) 和 I3 > = 30 * 单元 (113) 和 d1 > = 70 和 (一个 < = 0.3 或 B < = 0.3)"。</li>
			<li>选择 "提取到新工作表", 单击 "应用"。柱分析高尔基体 mini-stacks 被提取到一个新的工作表。</li>
		</ol>
</li>
		<li>在新工作表中, 右键单击列 W 的顶部, 选择 "列 > 打开对话框上的统计信息"。检查 "N 总计", "平均值", "SE", "直方图"。然后显示 LQs 的统计分析。</li>
	</ol></li>
</ol>

<ol>
	<li>Glick, B. S., Luini, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Models+for+Golgi+traffic:+a+critical+assessment.">Models for Golgi traffic: a critical assessment.</a> Cold Spring Harb Perspect Biol. 3 (11), 005215 (2011).</li><li>Klumperman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Architecture+of+the+mammalian+Golgi.">Architecture of the mammalian Golgi.</a> Cold Spring Harb Perspect Biol. 3 (7), (2011).</li><li>Lu, L., Hong, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+endosomes+to+the+trans-Golgi+network.">From endosomes to the trans-Golgi network.</a> Semin Cell Dev Biol. , (2014).</li><li>De Matteis, M. A., Luini, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exiting+the+Golgi+complex.">Exiting the Golgi complex.</a> Nat Rev Mol Cell Biol. 9 (4), 273-284 (2008).</li><li>Lu, L., Ladinsky, M. S., Kirchhausen, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cisternal+organization+of+the+endoplasmic+reticulum+during+mitosis.">Cisternal organization of the endoplasmic reticulum during mitosis.</a> Mol Biol Cell. 20 (15), 3471-3480 (2009).</li><li>Schermelleh, L., Heintzmann, R., Leonhardt, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+guide+to+super-resolution+fluorescence+microscopy.">A guide to super-resolution fluorescence microscopy.</a> J Cell Biol. 190 (2), 165-175 (2010).</li><li>Tie, H. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+imaging+method+for+quantitative+Golgi+localization+reveals+differential+intra-Golgi+trafficking+of+secretory+cargoes.">A novel imaging method for quantitative Golgi localization reveals differential intra-Golgi trafficking of secretory cargoes.</a> Mol Biol Cell. 27 (5), 848-861 (2016).</li><li>Trucco, A., 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=Secretory+traffic+triggers+the+formation+of+tubular+continuities+across+Golgi+sub-compartments.">Secretory traffic triggers the formation of tubular continuities across Golgi sub-compartments.</a> Nat Cell Biol. 6 (11), 1071-1081 (2004).</li><li>Cole, N. B., Sciaky, N., Marotta, A., Song, J., Lippincott-Schwartz, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Golgi+dispersal+during+microtubule+disruption:+regeneration+of+Golgi+stacks+at+peripheral+endoplasmic+reticulum+exit+sites.">Golgi dispersal during microtubule disruption: regeneration of Golgi stacks at peripheral endoplasmic reticulum exit sites.</a> Mol Biol Cell. 7 (4), 631-650 (1996).</li><li>Rogalski, A. A., Bergmann, J. E., Singer, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+microtubule+assembly+status+on+the+intracellular+processing+and+surface+expression+of+an+integral+protein+of+the+plasma+membrane.">Effect of microtubule assembly status on the intracellular processing and surface expression of an integral protein of the plasma membrane.</a> J Cell Biol. 99 (3), 1101-1109 (1984).</li><li>Van De Moortele, S., Picart, R., Tixier-Vidal, A., Tougard, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nocodazole+and+taxol+affect+subcellular+compartments+but+not+secretory+activity+of+GH3B6+prolactin+cells.">Nocodazole and taxol affect subcellular compartments but not secretory activity of GH3B6 prolactin cells.</a> Eur J Cell Biol. 60 (2), 217-227 (1993).</li><li>Nakamura, N., 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=Characterization+of+a+cis-Golgi+matrix+protein,+GM130.">Characterization of a cis-Golgi matrix protein, GM130.</a> J Cell Biol. 131, 1715-1726 (1995).</li><li>Roth, J., Berger, E. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunocytochemical+localization+of+galactosyltransferase+in+HeLa+cells:+codistribution+with+thiamine+pyrophosphatase+in+trans-Golgi+cisternae.">Immunocytochemical localization of galactosyltransferase in HeLa cells: codistribution with thiamine pyrophosphatase in trans-Golgi cisternae.</a> J Cell Biol. 93 (1), 223-229 (1982).</li><li>Baeuerle, P. A., Huttner, W. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tyrosine+sulfation+is+a+trans-Golgi-specific+protein+modification.">Tyrosine sulfation is a trans-Golgi-specific protein modification.</a> J Cell Biol. 105 (6), 2655-2664 (1987).</li><li>Honda, A., Al-Awar, O. S., Hay, J. C., Donaldson, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+of+Arf-1+to+the+early+Golgi+by+membrin,+an+ER-Golgi+SNARE.">Targeting of Arf-1 to the early Golgi by membrin, an ER-Golgi SNARE.</a> J Cell Biol. 168 (7), 1039-1051 (2005).</li><li>Lavieu, G., Zheng, H., Rothman, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stapled+Golgi+cisternae+remain+in+place+as+cargo+passes+through+the+stack.">Stapled Golgi cisternae remain in place as cargo passes through the stack.</a> Elife. 2, 00558 (2013).</li><li>Zhao, X., Lasell, T. K., Melancon, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Localization+of+large+ADP-ribosylation+factor-guanine+nucleotide+exchange+factors+to+different+Golgi+compartments:+evidence+for+distinct+functions+in+protein+traffic.">Localization of large ADP-ribosylation factor-guanine nucleotide exchange factors to different Golgi compartments: evidence for distinct functions in protein traffic.</a> Mol Biol Cell. 13 (1), 119-133 (2002).</li><li>Dejgaard, S. Y., Murshid, A., Dee, K. M., Presley, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Confocal+microscopy-based+linescan+methodologies+for+intra-Golgi+localization+of+proteins.">Confocal microscopy-based linescan methodologies for intra-Golgi localization of proteins.</a> J Histochem Cytochem. 55 (7), 709-719 (2007).</li><li>Fourriere, L., Divoux, S., Roceri, M., Perez, F., Boncompain, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microtubule-independent+secretion+requires+functional+maturation+of+Golgi+elements.">Microtubule-independent secretion requires functional maturation of Golgi elements.</a> J Cell Sci. 129 (17), 3238-3250 (2016).</li><li>Volchuk, A., 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=Countercurrent+distribution+of+two+distinct+SNARE+complexes+mediating+transport+within+the+Golgi+stack.">Countercurrent distribution of two distinct SNARE complexes mediating transport within the Golgi stack.</a> Mol Biol Cell. 15 (4), 1506-1518 (2004).</li><li>Popoff, V., Adolf, F., Brugger, B., Wieland, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=COPI+budding+within+the+Golgi+stack.">COPI budding within the Golgi stack.</a> Cold Spring Harb Perspect Biol. 3 (11), 005231 (2011).</li></ol>

作者声明他们没有竞争的金融利益。

以前, 在光显微镜下的高尔基体蛋白的定位, 主要是通过与已知的高尔基体标记的图像相关或重叠程度的蛋白质的图像进行量化,15,16, 17。由此产生的相关或重叠系数反映了测试蛋白在空间上对高尔基标记的接近程度。此方法至少有三警告。首先, 相关或重叠系数是非线性的, 不直接表示空间距离。第二, 相关性的程度严重依赖于?...

高尔基复合体在哺乳动物细胞分泌/吞的蛋白质和脂质 (以下) 的贩运中起着重要作用1,2,3。在高尔基, 货物不仅排序到各种细胞舱室, 但也修改了不同类型的糖基化。哺乳动物高尔基复合体包括许多侧向连接的高尔基栈, 通常由 4-11 紧密相邻和扁平膜囊称为泡。连续堆叠高尔基体泡进一步分类, 从一端到另一个, 作为cis, 内侧和反式-泡。在高尔基体的反式一侧,反式-大多数膜囊发展成管状和网状膜网络, 称为反式-高尔基网络 (TGN)4。在分泌通路中, 来自内质网 (ER) 的货物在其cis端进入高尔基体, 然后依次通过内侧和反式-泡。货物最终退出高尔基体在反式-高尔基或 TGN 注定的细胞膜, 体或分泌颗粒。
的分子和细胞机制如何运输的高尔基体和高尔基体保持其池组织仍然神秘, 目前仍在激烈辩论1。这一领域的难点之一是, 高尔基泡只能在电子显微镜下解决 (EM), 因为光学显微镜 (200 nm) 的分辨率不足以解决单个高尔基泡 (< 100 纳米池厚度和距离)。因此, 亚高尔基定位的居民蛋白和转运货物是常规确定的免疫金 EM。然而, 免疫金 EM 是非常严格的技术要求, 它是超出了能力的大多数细胞生物学实验室。虽然 em 的分辨率可以 sub-nanometer, 免疫金 em 所提供的分辨率很大程度上受阻于抗体复合体 (主辅抗体) 和金粒子的大小, 而且可能比 20 nm 差。此外, EM 图像是从2D 薄剖面, 而不是一个3D 的全球视野的高尔基, 这可能导致错误的结论取决于相对位置和方向的2D 节5。例如, 研究 EM 开路不能可靠地区分一个小泡从一个小管的正交视图, 因为两者都可以显示相同的圆形膜剖面。最近出现的超分辨率显微镜技术, 如 3 d 结构照明显微镜 (3 d SIM), 刺激发射损耗 (STED), 光定位显微镜 (手掌) 和随机光学重建显微镜 (风暴), 使得有可能解决亚高尔基体在光显微镜下的结构6。然而, 至少有四缺点, 可以极大地限制他们的使用在细胞生物学研究高尔基。1) 目前超分辨率技术需要昂贵和特殊的硬件配置, 这是超出大多数细胞生物学实验室。2) 某些超分辨率技术需要特殊的荧光标记协议。3) 虽然在最佳条件下, 这些技术在空间分辨率上要求 20-110 nm, 但实际样品中获得的实用分辨率可能更差。4) 与常规显微镜相比, 这些超分辨率技术在进行多色、3D 或活细胞成像方面仍有困难, 无论是单独还是联合进行。可能最重要的是, 免疫金 EM 和超分辨显微镜技术的产生定性而不是定量的本地化数据。
试图部分解决上述问题, 我们最近开发了一个传统的光学显微镜的方法, 这是命名为高尔基体蛋白质的成像中心的质量 (GLIM), 系统和定量本地化高尔基蛋白质的分辨率相当于免疫金 EM7。在这种方法中, 高尔基体在培养的哺乳动物细胞被分散作为高尔基体 mini-stacks 通过治疗 nocodazole, 一个微管聚药物。广泛的研究表明, nocodazole 诱导高尔基 mini-stacks (以下高尔基体 mini-stacks) 在组织和细胞功能上都与本机的基尔基体紧密类似8,9,10, 11。测试蛋白的定位商 (LQ) 可以通过 GLIM 获得, 它表示定量的亚尔基体定位。对 LQs 的数值进行了比较, 得到了超过25高尔基标记的 LQ 数据库。
在 GLIM, 高尔基 mini-stacks 是三重标记的内生或外部表达 GM130, 高尔特 mCherry 和测试蛋白 (x)。GM130 和高尔特-mCherry, cis-和反式-高尔基标记分别12,13, 提供参考点。三重荧光, 红色 (R), 绿色 (G) 和远红色 (B), 被人为地显示红色, 绿色和蓝色, 分别。采用荧光质量中心 (以下简称中心) 实现亚像素分辨率。高尔基轴被定义为从 GM130 中心到高尔 mCherry 的向量。高尔基 mini-stack 被建模为一个圆柱形结构, 在高尔基轴周围有无限的旋转对称。因此, 高尔基体 mini-stack 可以被进一步建模为沿高尔基轴的 one-dimensional 结构。测试蛋白 x 的 LQ 定义为 dx/d1, 其中 dx是从 x 中心到 GM130 的距离, 而 d1是从 mCherry 中心到 GM130 的距离。如果 x 轴的中心是轴向的, 它的投影轴线距离用于计算。变量, 包括高尔基轴, 轴向角, dx, d1, 角α和角β, 为 GLIM 的示意图在图 1中说明。LQ 是独立的高尔基轴向角, 虽然高尔基 mini-stacks 东方随机在一个细胞。
高尔基体 mini-stacks 在图像中呈现不均匀。我们制定了三标准, 以选择分析高尔基 mini-stacks GLIM。1) 信噪比判据, 在每个通道中, 高尔基体 mini-stack 的总强度与背景的标准差 (SD) 的比值为≥30。这一标准是确保质量中心的定位精度, 这取决于高尔基体 mini-stacks 的信噪比。2) 轴向角度或距离标准, 要求 d1≥ 70 nm。d1随着高尔基轴向角的增大而减小。当轴向角度接近90°或垂直方向时, mini-stack 变为 non-resolvable, 因为 d1接近0。d1≥ 70 nm 可以有效地排除垂直高尔基体附近的 mini-stacks。3) co-linearity 标准, 其中任一 | tan α |或 | tan β |≤0.3。这一标准确保了 mini-stack 的三中心足以共我们的 one-dimensional 模型的高尔基 mini-stack。所有的光显微镜都有色差, 严重地扭曲了红色、绿色和远红光荧光中心的相对位置。对显微镜系统的色差进行了实验标定, 成像110纳米珠, 这是红, 绿和远红色荧光三标。对于每个珠子图像, 红色中心被定义为珠的真实位置, 绿色和远红色中心的色移由一阶多项式函数拟合。高尔基体 mini-stacks 的中心受多项式函数的作用, 以纠正绿色和远红通道中的色移。
通过 GLIM, 我们可以在标准条件下在高尔基轴上实现约 30 nm 的分辨率。重要的是, 它提供了一种系统的方法来定量绘制任何高尔基蛋白。GLIM 可由常规显微镜 (如广域或共聚焦显微镜) 使用普通荧光标记协议进行。图像处理和数据处理可能会缩短一小时。通过 GLIM, 我们直接演示了分泌货物从cis-到高尔基体7的跨端的逐步过渡。

<table>
	<tbody>
		<tr>
			<td class="xl67">fluorescence beads. Commercial name: TetraSpeck beads</td>
			<td class="xl67">Invitrogen</td>
			<td class="xl75">T7279</td>
			<td class="xl67">As multi-color beads to calibrate chromatic-shift of the microscope.</td>
		</tr>
		<tr>
			<td class="xl67">Glass coverslip &#934; 12 mm (No. 1.5)</td>
			<td class="xl67">Menzel</td>
			<td class="xl75">CB00120RAC</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">Glass coverslip &#934; 25 mm (No. 1.5)</td>
			<td class="xl67">Menzel</td>
			<td class="xl75"></td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">DMEM</td>
			<td class="xl67">Capricon</td>
			<td class="xl75">DMEM-HPA-P50</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">Trypsin-EDTA</td>
			<td class="xl67"></td>
			<td class="xl75"></td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">FBS</td>
			<td class="xl67">GE Hyclone</td>
			<td class="xl75">SV30160.03</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">Nocodazole</td>
			<td class="xl67">Merck</td>
			<td class="xl75">487928</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">transfection reagent. Commercial name: Lipofectamine 2000</td>
			<td class="xl67">Invitrogen</td>
			<td class="xl75">11668-019</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">transfection medium. Commercial name: OptiMEM</td>
			<td class="xl67">Invitrogen</td>
			<td class="xl75">31985070</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">TPST1-EGFP</td>
			<td class="xl67">Addgene</td>
			<td class="xl75">66617</td>
			<td class="xl67">A gift from D. Stephens (University of Bristol, Bristol, United Kingdom)</td>
		</tr>
		<tr>
			<td class="xl67">GalT-mCherry</td>
			<td class="xl67"></td>
			<td class="xl75"></td>
			<td class="xl67">Made in our lab.</td>
		</tr>
		<tr>
			<td class="xl67">paraformaldehyde</td>
			<td class="xl67">Merck</td>
			<td class="xl75">1.04005.1000</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">saponin</td>
			<td class="xl67">Sigma-Aldrich</td>
			<td class="xl75">47036</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">poly(vinyl alcohol) (Mw ~31,000). Commercial name: Mowiol-488</td>
			<td class="xl67">CALBIOCHEM</td>
			<td class="xl75">475904</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">BSA</td>
			<td class="xl67">Sigma-Aldrich</td>
			<td class="xl75">A9647</td>
			<td class="xl67"></td>
		</tr>
		<tr>
			<td class="xl67">Mouse anti-GM130</td>
			<td class="xl67">BD Biosciences</td>
			<td class="xl75">610823</td>
			<td class="xl67">Primary antibody for human GM130</td>
		</tr>
		<tr>
			<td class="xl67">far-red fluorescence conjugated goat anti-mouse IgG. Commercial name: Alexa Fluor 647 conjugated goat anti-mouse IgG</td>
			<td class="xl67">Invitrogen</td>
			<td class="xl75">A-21235</td>
			<td class="xl67">Far red fluorescence conjugated secondary antibody</td>
		</tr>
	</tbody>
</table>

quantitative localization of a golgi protein by imaging its center of fluorescence mass

高尔基复合体由连续堆积的膜泡组成, 可进一步划分为亚高尔基区, 包括cis-高尔基体、中高尔基、反式-高尔基和反式-高尔基网络。高尔基体的细胞功能由其驻留蛋白的特征分布决定。传统光学显微镜的空间分辨率过低, 无法解决亚高尔基结构或泡。因此, 免疫金电子显微镜是一种选择的方法, 本地化的蛋白质在亚高尔基水平。然而, 该技术和仪器超出了大多数细胞生物学实验室的能力。我们在这里描述我们最近开发的超分辨率的方法称为高尔基蛋白的成像中心的质量 (GLIM) 系统和定量本地化高尔基体蛋白。GLIM 是基于标准荧光标记协议和传统的广域或共聚焦显微镜。它涉及对显微系统的色移像差的标定、图像采集和后分析。将测试蛋白的亚尔基体定位定量地表达为定位商。GLIM 有四主要优点;它是快速的, 根据传统的方法和工具, 本地化的结果是定量的, 它提供了〜 30 nm 的实际分辨率沿高尔基轴。在这里, 我们描述了 GLIM 的详细协议, 本地化一个测试高尔基蛋白。

现代研究级光学显微镜配有计划物镜透镜, 如我们实验室使用的, 显示最小色差 (图 2a)。然而, 仔细检查多色荧光珠图像可以揭示同一珠子的不同颜色图像的移位 (图 2B)。我们定义红色通道没有色差, 因此红色荧光的中心是珠的真实位置。绿色和远红光荧光的相对色位移可以用来自红色荧光中心的绿色和?...

Watch this Scientific Journal Video about Quantitative Localization of a Golgi Protein by Imaging Its Center of Fluorescence Mass at JoVE.com

Quantitative Localization of a Golgi Protein by Imaging Its Center of Fluorescence Mass

高尔基体的精确定位对于了解高尔基体的细胞功能是至关重要的。然而, 传统的光学显微镜无法解决亚尔基体的结构。在这里, 我们描述了一个传统的基于显微镜的超分辨率方法的协议, 定量地确定蛋白质的亚高尔基体定位。

高尔基蛋白质荧光质量中心的定量定位

The Golgi complex consists of serially stacked membrane cisternae which can be further categorized into sub-Golgi regions, including the cis-Golgi, ...

quantitative-localization-golgi-protein-imaging-its-center

Research

JoVE Journal

Biology

10.7K 次观看.  Nanyang Technological University. 高尔基体的精确定位对于了解高尔基体的细胞功能是至关重要的。然而, 传统的光学显微镜无法解决亚尔基体的结构。在这里, 我们描述了一个传统的基于显微镜的超分辨率方法的协议, 定量地确定蛋白质的亚高尔基体定位。

视频: Quantitative Localization of a Golgi Protein by Imaging Its Center of Fluorescence Mass

Time-lapse Imaging of Mitosis After siRNA Transfection

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and cellular content. Hallmark events of mitosis, such as chromosome movement, can be readily tracked on an individual cell basis using time-lapse fluorescence microscopy of mammalian cell lines expressing specific GFP-tagged proteins. In combination with RNAi-based depletion, this can be a powerful method for pinpointing the stage(s) of mitosis where defects occur after levels of a particular protein have been lowered. In this protocol, we present a basic method for assessing the effect of depleting a potential mitotic regulatory protein on the timing of mitosis. Cells are transfected with siRNA, placed in a stage-top incubation chamber, and imaged using an automated fluorescence microscope. We describe how to use software to set up a time-lapse experiment, how to process the image sequences to make either still-image montages or movies, and how to quantify and analyze the timing of mitotic stages using a cell-line expressing mCherry-tagged histone H2B. Finally, we discuss important considerations for designing a time-lapse experiment. This strategy is complementary to other approaches and offers the advantages of 1) sensitivity to changes in kinetics that might not be observed when looking at cells as a population and 2) analysis of mitosis without the need to synchronize the cell cycle using drug treatments. The visual information from such imaging experiments not only allows the sub-stages of mitosis to be assessed, but can also provide unexpected insight that would not be apparent from cell cycle analysis by FACS.

Here we describe a basic protocol to image and quantify the mitotic timing of live mammalian tissue culture cells after siRNA transfection.

有丝分裂的siRNA转染后的时间推移成像

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and ...

科研

生物学

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Living cells control and regulate their biological processes through the coordinated action of a large number of proteins that assemble themselves into an array of dynamic, multi-protein complexes1. To gain a mechanistic understanding of the various cellular processes, it is crucial to determine the structure of such protein complexes, and reveal how their structural organization dictates their function. Many aspects of multi-protein complexes are, however, difficult to characterize, due to their heterogeneous nature, asymmetric structure, and dynamics. Therefore, new approaches are required for the study of the tertiary levels of protein organization.
One of the emerging structural biology tools for analyzing macromolecular complexes is mass spectrometry (MS)2-5. This method yields information on the complex protein composition, subunit stoichiometry, and structural topology. The power of MS derives from its high sensitivity and, as a consequence, low sample requirement, which enables examination of protein complexes expressed at endogenous levels. Another advantage is the speed of analysis, which allows monitoring of reactions in real time. Moreover, the technique can simultaneously measure the characteristics of separate populations co-existing in a mixture. 
Here, we describe a detailed protocol for the application of structural MS to the analysis of large protein assemblies. The procedure begins with the preparation of gold-coated capillaries for nanoflow electrospray ionization (nESI). It then continues with sample preparation, emphasizing the buffer conditions which should be compatible with nESI on the one hand, and enable to maintain complexes intact on the other. We then explain, step-by-step, how to optimize the experimental conditions for high mass measurements and acquire MS and tandem MS spectra. Finally, we chart the data processing and analyses that follow. Rather than attempting to characterize every aspect of protein assemblies, this protocol introduces basic MS procedures, enabling the performance of MS and MS/MS experiments on non-covalent complexes. Overall, our goal is to provide researchers unacquainted with the field of structural MS, with knowledge of the principal experimental tools.

Mass spectrometry has proven to be a valuable tool for analyzing large protein complexes. This method enables insights into the composition, stoichiometry and overall architecture of multi-subunit assemblies. Here, we describe, step-by-step, how to perform a structural mass spectrometry analysis, and characterize macromolecular structures.

大蛋白复合物结构质谱分析

Living cells control and regulate their biological processes through the coordinated action of a large number of proteins that assemble themselves into an ...

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available1. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment2. Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice3. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown4, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)5 and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants.
Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for 15N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein7 (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state8. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE19.

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

We present a variation of the QUICK (QUantitative Immunoprecipitation Combined with Knockdown) approach that was introduced previously to distinguish between true and false protein-protein interactions. Our approach is based on 15N metabolic labeling, the modulation of affinities of protein-protein interactions by the presence/absence of ATP, immunoprecipitation, and quantitative mass spectrometry.

一个协议，用于基于蛋白质 - 蛋白质相互作用的鉴定<sup&gt; 15</sup&gt; N代谢标记，免疫沉淀法，的定量质谱法和亲和调制

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

采用先进的3D荧光显微镜定量分析自噬

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

双分子荧光互补流式细胞仪分析:高通量的定量方法研究蛋白质 - 蛋白质相互作用

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled single molecules within a sample with a spatial resolution of tens of nanometers. Using either photoactivatable (PAFP) or&#160;photoswitchable (PSFP) fluorescent proteins fused to proteins of interest, or organic dyes conjugated to antibodies or other molecules of interest, fluorescence photoactivation localization microscopy (FPALM) can simultaneously image multiple species of molecules within single cells. By using the following approach, populations of large numbers (thousands to hundreds of thousands) of individual molecules are imaged in single cells and localized with a precision of ~10-30 nm. Data obtained can be applied to understanding the nanoscale spatial distributions of multiple protein types within a cell. One primary advantage of this technique is the dramatic increase in spatial resolution: while diffraction limits resolution to ~200-250 nm in conventional light microscopy, FPALM can image length scales more than an order of magnitude smaller. As many biological hypotheses concern the spatial relationships among different biomolecules, the improved resolution of FPALM can provide insight into questions of cellular organization which have previously been inaccessible to conventional fluorescence microscopy. In addition to detailing the methods for sample preparation and data acquisition, we here describe the optical setup for FPALM. One additional consideration for researchers wishing to do super-resolution microscopy is cost: in-house setups are significantly cheaper than most commercially available imaging machines. Limitations of this technique include the need for optimizing the labeling of molecules of interest within cell samples, and the need for post-processing software to visualize results. We here describe the use of PAFP and PSFP expression to image two protein species in fixed cells. Extension of the technique to living cells is also described.

We demonstrate the use of fluorescence photo activation localization microscopy (FPALM) to simultaneously image multiple types of fluorescently labeled molecules&#160;within cells. The techniques described yield the localization of thousands to hundreds of thousands of individual fluorescent labeled proteins, with a precision of tens of nanometers&#160;within single cells.

Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

快速分析和荧光显微镜图像的探索

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

Characterization of G Protein-coupled Receptors by a Fluorescence-based Calcium Mobilization Assay

For more than 20 years, reverse pharmacology has been the preeminent strategy to discover the activating ligands of orphan G protein-coupled receptors (GPCRs). The onset of a reverse pharmacology assay is the cloning and subsequent transfection of a GPCR of interest in a cellular expression system. The heterologous expressed receptor is then challenged with a compound library of candidate ligands to identify the receptor-activating ligand(s). Receptor activation can be assessed by measuring changes in concentration of second messenger reporter molecules, like calcium or cAMP. The fluorescence-based calcium mobilization assay described here is a frequently used medium-throughput reverse pharmacology assay. The orphan GPCR is transiently expressed in human embryonic kidney 293T (HEK293T) cells and a promiscuous G&#945;16 construct is co-transfected. Following ligand binding, activation of the G&#945;16 subunit induces the release of calcium from the endoplasmic reticulum. Prior to ligand screening, the receptor-expressing cells are loaded with a fluorescent calcium indicator, Fluo-4 acetoxymethyl. The fluorescent signal of Fluo-4 is negligible in cells under resting conditions, but can be amplified more than a 100-fold upon the interaction with calcium ions that are released after receptor activation. The described technique does not require the time-consuming establishment of stably transfected cell lines in which the transfected genetic material is integrated into the host cell genome. Instead, a transient transfection, generating temporary expression of the target gene, is sufficient to perform the screening assay. The setup allows medium-throughput screening of hundreds of compounds. Co-transfection of the promiscuous G&#945;16, which couples to most GPCRs, allows the intracellular signaling pathway to be redirected towards the release of calcium, regardless of the native signaling pathway in endogenous settings. The HEK293T cells are easy to handle and have proven their efficacy throughout the years in receptor deorphanization assays. However, optimization of the assay for specific receptors may remain necessary.

The here described fluorescence-based calcium mobilization assay is a medium-throughput reverse pharmacology screening system for the identification of functionally activating ligand(s) of orphan G protein-coupled receptors (GPCRs).

G蛋白偶联受体的基于荧光的钙动员含量表征

For more than 20 years, reverse pharmacology has been the preeminent strategy to discover the activating ligands of orphan G protein-coupled receptors ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

在造血干细胞和祖细胞的成年小鼠颅骨骨髓体内四维跟踪

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.

Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.