这项工作得到了美国国家科学基金会研究生研究奖学金的支持。DGE-1745301 （S.Y.）、皮尤-斯图尔特学者奖 （S.C.）、Searle 学者奖 （S.C.）、Shurl 和 Kay Curci 基金会研究补助金 （SC）、Merkin 创新种子补助金 （SC）、Mallinckrodt 研究补助金 （SC） 和 Margaret E.早期医学研究信托基金 2024 年赠款（南卡罗来纳州）。SC 还得到了 NIH/NCI 的支持，奖项编号为 P30CA016042。

1. 细胞中蛋白质的标记
<ol><li>在所需细胞系中表达与 HaloTag 融合的目标蛋白。</li>
	<li>使用转座子或病毒转导在与 1.1 相同类型的细胞中稳定表达带有 Halo 标签的 H2B。</li>
</ol>2. 盖玻片的制备
<ol><li>在使用盖玻片进行细胞培养之前，清洁盖玻片以去除自发荧光污染物。<ol><li>将直径为 25 mm 的 #1.5 盖玻片安装在陶瓷染色架上，然后放入聚丙烯容器中。</li>
		<li>将盖玻片完全浸入1M的KOH溶液中并超声处理1小时。</li>
		<li>用双蒸水（ddH2O）冲洗盖玻片三次。</li>
		<li>将盖玻片完全浸入100%乙醇中并超声处理1小时。</li>
		<li>将盖玻片完全浸入用ddH2O稀释的20%乙醇中，并在4°C下储存直至使用。</li>
	</ol></li>
</ol>3. 显微镜细胞的制备
注意：在生物安全柜中执行本节中的步骤，以防止细胞污染。
<ol><li>将细胞铺在清洁的盖玻片上。如果要瞬时表达 Halo 标记的蛋白质，请在成像前 2 天进行。如果 Halo 标记的蛋白质稳定或内源性表达，则在成像前 1 天进行。<ol><li>使用无菌镊子将盖玻片放入 6 孔板（每个细胞样品一个盖玻片/孔）中，并让残留的乙醇蒸发。</li>
		<li>用适当数量的细胞铺板每个孔，以在成像时达到 70% 的汇合度。用具有相同靶汇合度稳定表达 H2B-Halo 的细胞再铺一块孔。</li>
		<li>仅当瞬时表达感兴趣的 Halo 标记蛋白时，才执行此步骤。将细胞在37°C和5%CO2下孵育24小时。然后，使用适当的转染试剂并遵循制造商的指南，用编码目标标记蛋白的质粒转染细胞。</li>
		<li>将细胞在37°C和5%CO2下孵育24小时。</li>
	</ol></li>
	<li>用不可光活化的 Halo 配体（例如 JFX549）和可光活化的 Halo 配体（例如 PA-JF646）对表达目标 Halo 标记蛋白的细胞进行染色。仅用光活化 Halo 配体染色表达 H2B-Halo 的细胞。 注意：此处列出的 Halo 配体浓度应用作起点;最佳浓度将取决于目标蛋白的表达水平和缩合物局部浓度。<ol><li>对于表达目标 Halo 标记蛋白的每个细胞样品，将 100 nM JFX549 Halo 配体35 和 20 nM PA-JF646 Halo 配体36 稀释在 500 μL 适当的细胞培养基中。对于每个表达 H2B-Halo 的样品，在 500 μL 适当的细胞培养基中仅稀释 20 nM PA-JF646 Halo 配体。</li>
		<li>对于每个孔，吸出现有培养基，加入 2 mL 1x PBS，然后吸出 PBS（在方案的其余部分称为“冲洗”），并替换为含有适当 Halo 配体的培养基。</li>
		<li>在37°C和5%CO2下孵育1小时。</li>
		<li>用 PBS 冲洗四次，然后用不含 Halo 配体的新鲜培养基替换。</li>
		<li>在37°C和5%CO2下孵育15分钟。</li>
		<li>重复步骤 3.2.4 和 3.2.5 三次（总共四个冲洗孵育周期）。</li>
		<li>使用无菌镊子将装有细胞的盖玻片转移到与显微镜载物台兼容的细胞培养盖玻片室中。</li>
		<li>将无酚红培养基加入腔室并进行成像。在37°C和5%CO2 下孵育不立即成像的样品，直到需要。</li>
	</ol></li>
</ol>4. 单分子成像
注意：测量H2B和目标蛋白停留时间的独立实验应在多（≥3）天内进行，以产生具有统计学意义的结果。在显微镜上对细胞样品进行成像之前，使用0.1μm染色微球（材料表）或类似的校准标准品对准两个相机。
<ol><li>准备显微镜。<ol><li>打开显微镜和控制显微镜的计算机。</li>
		<li>在显微镜上打开活细胞培养组件（加热器和CO2），并将其设置为37°C和5%CO2。让系统达到平衡。</li>
		<li>将一滴适当的油滴在显微镜上的 100 倍 TIRF 油浸物镜上，然后将细胞样品加载到显微镜载物台上。</li>
	</ol></li>
	<li>识别要成像的单元格。<ol><li>在明场照明下对细胞样品进行成像，并调整 z 位置，直到细胞聚焦。</li>
		<li>识别具有健康细胞形态特征的细胞。</li>
		<li>裁剪视场 （FOV），使其仅捕获目标细胞核。</li>
		<li>使用激光照明并调整TIRF角度，直到达到具有最佳单分子信噪比的HILO照明41 。</li>
	</ol></li>
	<li>成像活细胞中的 H2B-Halo。 注意：本节中使用的激光功率特定于此实验，并作为示例包含在内。应根据讨论中列出的标准选择适当的激光功率。<ol><li>按如下方式设置采集配置。将曝光时间设置为 500 毫秒。在每次 500 ms 曝光期间，用 9.1 mW、640 nm 光束激发 PA-JF646 标记的 H2B-Halo 分子。在帧之间的死区时间（此处使用的成像系统为158.01 μs），用111 μW，405 nm光束光活化分子。</li>
		<li>在这些设置下连续捕获 2000 帧。当光活化分子的数量不足时，在采集过程中逐渐增加 405 nm 光束的功率。</li>
	</ol></li>
	<li>对活细胞中感兴趣的 Halo 标记蛋白进行成像。<ol><li>使用长通二向色镜在两个相机之间分离发射波长，一个用于检测 PA-JF646，另一个用于检测 JFX549。在摄像机前使用适当的发射滤光片。</li>
		<li>使用 4.3.1 中描述的相同采集配置，并进行以下修改。添加一个额外的 JFX549 通道，以跟踪 Halo 标记的蛋白质缩合物随时间推移的位置。每 10 秒在 JFX549 通道中采集一帧（500 ms，2.1 mW，561 nm 激发），同时保持对 PA-JF646 通道的采集。这将导致渗漏到PA-JF646通道中，这将在采集后的数据分析中得到处理。</li>
		<li>在这些设置下连续捕获 2000 帧。当光活化分子的数量不足时，在采集过程中逐渐增加 405 nm 光束的功率。</li>
	</ol></li>
</ol>5. 单分子成像数据分析
注意：第 5 节中使用的参数特定于此实验，并作为示例包含在内。应根据讨论中列出的标准选择适当的参数。
<ol><li>准备原始成像数据进行处理。<ol><li>对于目标蛋白质的数据，使用ImageJ或类似的图像处理软件将原始成像数据文件中的每个通道转换为独立的.tif文件。对于H2B的数据，以同样的方式将原始成像数据文件中的单通道转换为.tif文件。 注意：根据所使用的采集软件，冷凝水电影中可能存在空帧，因为每 10 秒只拍摄一个冷凝水跟踪帧。空帧应填充最新的冷凝水帧（某些采集软件会自动执行此操作）。此外，如果在这些缩合物跟踪帧期间，从缩合物 （JFX549） 通道到单分子 （PA-JF646） 通道有明显的渗漏，则相应的单分子帧应替换为最近的单分子帧。</li>
		<li>如果由于上述原因，任一电影中有任何帧需要替换，请通过修改（如有必要）并运行 pretracking_comb.txt（Chong 等人 1 中可用的 ImageJ 宏）并按照提示将其替换为该电影中的最新帧。</li>
	</ol></li>
	<li>使用SPT软件生成单粒子轨迹，例如，SLIMfast39，Teves等人42中可用的MTT算法38的GUI实现。<ol><li>从 5.1.2 加载包含 SLIMfast 中单分子电影的文件，方法是单击 Load > Imagestack。</li>
		<li>设置参数（本地化错误率：10-6;通货紧缩循环：3） 通过单击 OPT > 工作表：本地化进行定位。</li>
		<li>设置参数（峰值发射：664 nm; 滞后时间：500 ms），通过单击 OPT > Sheet： Acquisition 进行采集。</li>
		<li>在单个帧中可视化所有分子的定位，以确保选择的参数是适当的（TEST LOC）。如果不合适，请修改步骤 5.2.2 和 5.2.3 中的参数并重复此步骤。</li>
		<li>生成一个文件，其中包含每帧中所有分子的定位 （LOC ALL）。</li>
		<li>在 SLIMfast 中从 5.2.5 加载文件，方法是单击 > SLIMfast >加载粒子数据。</li>
		<li>设置参数（最大预期扩散系数：0.1μm2/s; 最大参赛者数量：5）通过单击 OPT生成轨迹 > 表：跟踪。</li>
		<li>生成一个包含所有分子轨迹的文件 （GEN TRAJ）。</li>
	</ol></li>
	<li>使用 Drosopoulos 等人 43 中提供的 evalSPT39 过滤掉短于 tmin（2.5 秒）的轨迹，以解释导致人为缩短轨迹的跟踪误差。<ol><li>将文件从 5.2.8 加载到 evalSPT 中（+ > 5.2.8 中的文件>正常）。</li>
		<li>设置参数（最小：5 帧; max：从 5.2.8 开始的文件的最大轨迹长度）以过滤掉短于 2.5 秒的轨迹。</li>
		<li>生成包含所有过滤轨迹的文件 （EXPORT DATA）。</li>
	</ol></li>
	<li>仅针对目标蛋白质的轨迹遵循此步骤。将轨迹分类为冷凝水和冷凝水外的轨迹，然后提取平均冷凝水内停留时间。 注意：本节的所有代码都可以在 Chong et al.1 中找到。<ol><li>对 JFX549 通道中采集的影片的所有帧进行阈值，以生成一个延时短片，该短片填充了时间演进的二进制蒙版，通过运行 ImageJ 宏、细胞核和聚类 mask_v2.txt，突出显示凝聚物位置，并按照提示进行操作。</li>
		<li>重新格式化 5.3.3 中生成的单分子轨迹。使用 MATLAB 脚本 ConvertASCII_SlowTracking_css3.m，并根据需要调整文件名、路径和参数。（参数： 曝光，0.5秒; 分辨率，0.16 μm/像素）。</li>
		<li>使用 MATLAB 脚本 categorization_v4.m 根据给定分子在凝聚物 F 中度过的寿命分数对轨迹进行排序，并根据需要调整文件名和路径。</li>
		<li>仅使用冷凝水轨迹继续。</li>
	</ol></li>
	<li>提取 kobs，s 和 kpb 并计算校正后的平均停留时间 <img alt="Equation 6" src="/files/ftp_upload/66169/66169eq05.jpg" style="margin: auto;">。<ol><li>使用 MATLAB 脚本 PLOT_ResidenceHist_css.m 从目标缩合物蛋白轨迹中提取 kobs，s，并根据需要调整文件名、路径和参数。 （参数： 曝光，0.5秒; StartFrameForFit，5 帧）。</li>
		<li>使用 MATLAB 脚本 PLOT_ResidenceHist_css.m 从 H2B 轨迹中提取 kpb，并根据需要调整文件名、路径和参数。 （参数： 曝光，0.5秒; StartFrameForFit，5 帧）。</li>
		<li>计算与其缩合物特异性结合的目标蛋白质的校正平均停留时间， <img alt="Equation 6" src="/files/ftp_upload/66169/66169eq05.jpg" style="margin: auto;">如下所示 <img alt="Equation 7" src="/files/ftp_upload/66169/66169eq07v3.jpg" style="margin: auto;"> 注意：应执行对照以验证足够长以模糊移动颗粒的不同暴露时间，例如300ms和800ms，仍然导致目标蛋白质的平均停留时间相同。</li>
	</ol></li>
</ol>

基于活细胞单分子成像的蛋白质相互作用定量

作者没有什么可透露的。

这里介绍的协议是为Irgen-Gioro等人40中研究的系统而设计的。根据应用的不同，可以修改方案的某些组件，例如，生成荧光标记细胞系的方法、荧光标记系统以及使用的盖玻片样式。细胞中蛋白质的光晕标记可以使用两种策略来完成，具体取决于哪种策略更适合给定的实验。1） 外源表达：将目标蛋白融合到 HaloTag 中，并在靶细胞系中使用转染瞬时表达或使用转座子或病毒转导稳?...

越来越多的研究表明，生物分子凝聚物在细胞组织和众多细胞功能中起着重要作用，例如转录调控1、2、3、4、5、DNA 损伤修复6、7、8、染色质组织9、10、11、12、X 染色体失活 13,14,15 和细胞内信号转导 16,17,18。此外，生物分子缩合物的失调与许多疾病有关，包括癌症19,20,21和神经退行性疾病22,23,24,25,26。缩合物的形成通常由瞬时、选择性和多价蛋白质-蛋白质、蛋白质-核酸或核酸-核酸相互作用驱动27。在某些条件下，这些相互作用会导致液-液相分离（LLPS），这是一种密度跃迁，可局部富集无膜液滴中的特定生物分子。这种多价相互作用通常由蛋白质的固有无序区域 （IDR） 介导 1,28,29。鉴于凝聚物在分子水平上的普遍存在，在分子水平上对这些相互作用的生物物理表征对于我们理解许多健康和异常的细胞功能至关重要。尽管基于共聚焦荧光显微镜的技术，例如光漂白后的荧光恢复（FRAP）30,31,32，已被广泛用于定性地表明凝聚物与周围细胞环境之间的分子交换是动态的，但使用传统的共聚焦显微镜或单分子通常无法量化凝聚物中特定生物分子的相互作用动力学没有专门数据分析方法的显微镜。该协议中描述的单颗粒跟踪（SPT）技术基于活细胞单分子显微镜33，并提供了一种独特而强大的工具来量化缩合物中特定蛋白质之间的相互作用动力学。用于此类测量的 SPT 读数是目标蛋白质在缩合物中的平均停留时间。
该协议可分为两部分 - 数据采集和数据分析。成像数据采集的第一步是在细胞中表达与 HaloTag34 融合的目标蛋白质。这样就可以用两个荧光团标记感兴趣的蛋白质，其中大多数蛋白质分子用不可光活化的荧光团（例如，JFX549 Halo 配体35）标记，其中一小部分用光谱不同的、可光活化的荧光团（例如，PA-JF646 Halo 配体36).这允许同时采集细胞中的所有凝聚物位置，并采集目标蛋白质与凝聚物结合和解结合的单分子电影。同时，相同类型的细胞被修饰以稳定表达带有晕标记的H2B，这是一种在染色质上基本不动的组蛋白。然后用 PA-JF646 Halo 配体对细胞进行染色，以实现 H2B 的单分子成像。正如下面将详细讨论的那样，该实验解释了光漂白的贡献，以便能够精确定量目标蛋白质的相互作用动力学。然后，必须在干净的盖玻片上培养用于成像实验的细胞，用HaloTag配体染色，并组装到活细胞成像室中。从那里，样品在能够进行双通道成像和单分子检测的全内反射荧光 （TIRF） 显微镜上在高度倾斜和层压光学片 （HILO） 照明下成像。然后将发射物分成两个摄像头，一个跟踪冷凝物位置，一个跟踪单个分子。采集以较长的整合时间（大约数百毫秒）进行，以模糊自由扩散的蛋白质，并且仅捕获由于与细胞中的稳定结构结合而移动性较差的蛋白质37。
数据分析的第一步是使用已建立的单粒子跟踪 （SPT） 算法38,39 在电影的每一帧中定位单个蛋白质分子，并在其可检测的生命周期内将定位组装成每个分子的轨迹。然后，通过将分子在整个轨迹中的定位与相应时间所有凝聚物的定位进行比较，将轨迹分为代表凝聚物内部分子的轨迹和表示凝聚物外部分子的轨迹 1.
接下来，使用所有冷凝水轨迹的长度生成生存曲线 （1 - CDF）。然后通过将存活曲线拟合到以下蛋白质结合的双组分指数模型来提取分子的表观平均停留时间，
<img align="center" alt="Equation 1" src="/files/ftp_upload/66169/66169eq01.jpg" style="margin: auto;">,
其中 A 为非特异性结合分子的分数， kobs，ns 和 kobs，s 分别为非特异性结合分子和特异性结合分子的观察到的解离速率。从这里开始只考虑 kobs，s 。蛋白质解离 ktrue，s 和荧光团 kpb 的光漂白的动力学有助于 kobs，s 作为
<img alt="Equation 2" src="/files/ftp_upload/66169/66169eq02.jpg" style="margin: auto;">;
因此，为了分离蛋白质解离的影响，测量了前面提到的细胞系中H2B-Halo的特异性解离速率。
<img alt="Equation 3" src="/files/ftp_upload/66169/66169eq03.jpg" style="margin: auto;">
H2B 是一种稳定整合到染色质中的蛋白质，在单分子电影采集的时间尺度上经历最小的解离37。其比解离速率等于 PA-JF646 Halo 配体的光漂白速率，或
<img alt="Equation 4" src="/files/ftp_upload/66169/66169eq04.jpg" style="margin: auto;">.
目标 <img alt="Equation 5" src="/files/ftp_upload/66169/66169eq05.jpg" style="margin: auto;">蛋白的平均缩合物停留时间为
<img alt="Equation 6" src="/files/ftp_upload/66169/66169eq06.jpg" style="margin: auto;">.
显示了Irgen-Gioro等人40 的代表性结果，其中应用该协议来证明将寡聚结构域与IDR融合会导致IDR在其缩合物中的停留时间更长。这一结果表明，添加的寡聚化结构域稳定了驱动LLPS的IDR的同型相互作用。原则上，具有略微修改方案的相同方法可用于表征参与任何类型缩合物形成的任何蛋白质的同型或异型相互作用。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.1 &#181;m TetraSpeck microsphere</td>
			<td>Invitrogen</td>
			<td>T7279</td>
			<td>Single-molecule imaging</td>
		</tr>
		<tr>
			<td>25 mm Diameter, #1.5 Coverslips</td>
			<td>Marienfeld Superior</td>
			<td>111650</td>
			<td>Preparation of coverslips</td>
		</tr>
		<tr>
			<td>593/40 nm bandpass filter</td>
			<td>Semrock</td>
			<td>FF01-593/40-25</td>
			<td>Single-molecule imaging</td>
		</tr>
		<tr>
			<td>676/37 nm bandpass filter</td>
			<td>Semrock</td>
			<td>FF01-676/37-25</td>
			<td>Single-molecule imaging</td>
		</tr>
		<tr>
			<td>6-Well TC Plate</td>
			<td>Genesee</td>
			<td>25-105MP</td>
			<td>Preparation of cells for microscopy</td>
		</tr>
		<tr>
			<td>Cell Line: U-2 OS</td>
			<td>ATCC</td>
			<td>HTB-96</td>
			<td>Labeling of proteins in cells</td>
		</tr>
		<tr>
			<td>ConvertASCII_SlowTracking_css3 
			.m</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data: Available in Chong et al., 2018</td>
		</tr>
		<tr>
			<td>Coverglass Staining Rack</td>
			<td>Thomas</td>
			<td>24957</td>
			<td>Preparation of coverslips</td>
		</tr>
		<tr>
			<td>Deuterated Janelia Fluor 549 (JFX549)</td>
			<td>Janelia Research Campus</td>
			<td></td>
			<td>Preparation of cells for microscopy</td>
		</tr>
		<tr>
			<td>DMEM, Low Glucose</td>
			<td>Gibco</td>
			<td>10-567-022</td>
			<td>Labeling of proteins in cells: Growth media used: DMEM with 5% fetal bovine serum, 1% penstrep</td>
		</tr>
		<tr>
			<td>Eclipse Ti2-E Inverted Microscope</td>
			<td>Nikon</td>
			<td></td>
			<td>Single-molecule imaging</td>
		</tr>
		<tr>
			<td>Ethanol 200 Proof</td>
			<td>Lab Alley</td>
			<td>EAP200-1GAL</td>
			<td>Preparation of coverslips</td>
		</tr>
		<tr>
			<td>evalSPT</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data: Available in Drosopoulos et al., 2020</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>Cytiva</td>
			<td>SH30396.03</td>
			<td>Labeling of proteins in cells: Growth media used: DMEM with 5% fetal bovine serum, 1% penstrep</td>
		</tr>
		<tr>
			<td>Fiji</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data</td>
		</tr>
		<tr>
			<td>Ikon Ultra CCD Camera</td>
			<td>Andor</td>
			<td>X-13723</td>
			<td>Single-molecule imaging</td>
		</tr>
		<tr>
			<td>Longpass dichroic beamsplitter</td>
			<td>Semrock</td>
			<td>Di02-R635-25x36</td>
			<td>Single-molecule imaging: Red/Far Red beamsplitter</td>
		</tr>
		<tr>
			<td>LUN-F Laser Unit</td>
			<td>Nikon</td>
			<td></td>
			<td>Single-molecule imaging: 405/488/561/640</td>
		</tr>
		<tr>
			<td>MatTek glass-bottom dish</td>
			<td>MatTek</td>
			<td>P35G-1.5-20-C</td>
			<td>Preparation of cells for microscopy: 35 mm, #1.5 coverslip dish for cell culture.</td>
		</tr>
		<tr>
			<td>NIS-Elements</td>
			<td>Nikon</td>
			<td></td>
			<td>Single-molecule imaging: Microscope acquisition software</td>
		</tr>
		<tr>
			<td>nucleus and cluster mask_v2.txt</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data: Available in Chong et al., 2018</td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>Gibco</td>
			<td>15-140-122</td>
			<td>Labeling of proteins in cells: Growth media used: DMEM with 5% fetal bovine serum, 1% penstrep</td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline</td>
			<td>Thermo Fisher Scientific</td>
			<td>18912014</td>
			<td>Labeling of proteins in cells</td>
		</tr>
		<tr>
			<td>Photoactivatable Janelia Fluor 646 (PA-JF646)</td>
			<td>Janelia Research Campus</td>
			<td></td>
			<td>Preparation of cells for microscopy</td>
		</tr>
		<tr>
			<td>PLOT_ResidenceHist_css.m</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data: Available in Chong et al., 2018</td>
		</tr>
		<tr>
			<td>Potassium Hydroxide</td>
			<td>Mallinckrodt Chemicals</td>
			<td>6984-06</td>
			<td>Preparation of coverslips</td>
		</tr>
		<tr>
			<td>pretracking_comb.txt</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data: Available in Chong et al., 2018</td>
		</tr>
		<tr>
			<td>SLIMfast</td>
			<td></td>
			<td></td>
			<td>Analysis of single-molecule imaging data: Available in Teves et al., 2016</td>
		</tr>
		<tr>
			<td>Stage-top incubation system</td>
			<td>Tokai Hit</td>
			<td></td>
			<td>Single-molecule imaging: For live-cell imaging</td>
		</tr>
		<tr>
			<td>TwinCam dual emission image splitter</td>
			<td>Cairn Research</td>
			<td></td>
			<td>Single-molecule imaging</td>
		</tr>
		<tr>
			<td>Ultrasonic Cleaner</td>
			<td>Branson</td>
			<td>5800</td>
			<td>Preparation of coverslips</td>
		</tr>
	</tbody>
</table>

single-molecule measurement of protein interaction dynamics within biomolecular condensates

通过液-液相分离 （LLPS） 形成的生物分子缩合物被认为在细胞组织和越来越多的细胞功能中至关重要。表征活细胞中的LLPS也很重要，因为异常凝聚与许多疾病有关，包括癌症和神经退行性疾病。LLPS 通常由内在无序蛋白质之间的选择性、瞬时性和多价相互作用驱动。非常令人感兴趣的是参与LLPS的蛋白质的相互作用动力学，通过测量它们的结合停留时间（RT），即它们在缩合物中结合的时间，可以很好地总结出来。在这里，我们提出了一种基于活细胞单分子成像的方法，该方法使我们能够测量缩合物中特定蛋白质的平均RT。我们同时可视化单个蛋白质分子及其与之相关的缩合物，使用单颗粒跟踪 （SPT） 绘制单分子轨迹，然后将轨迹拟合到蛋白质-液滴结合模型以提取蛋白质的平均 RT。最后，我们展示了代表性结果，其中应用这种单分子成像方法比较蛋白质在其 LLPS 缩合物下融合和未融合到寡聚结构域时的平均 RT。该协议广泛适用于测量参与LLPS的任何蛋白质的相互作用动力学。

A growing body of work suggests that biomolecular condensates play an important role in cellular organization and numerous cellular functions, e.g., transcriptional regulation1,2,3,4,5, DNA damage repair6,7,8, chromatin organization9,10,11,12, X-chromosome inactivation13,14,15, and intracellular signaling16,17,18. In addition, the dysregulation of biomolecular condensates is implicated in many diseases, including cancers19,20,21 and neurodegenerative disorders22,23,24,25,26. Condensate formation is often driven by transient, selective, and multivalent protein-protein, protein-nucleic acid, or nucleic acid-nucleic acid interactions27. Under certain conditions, these interactions can lead to liquid-liquid phase separation (LLPS), a density transition that locally enriches specific biomolecules in membraneless droplets. Such multivalent interactions are often mediated by the intrinsically disordered regions (IDRs) of proteins1,28,29. Biophysical characterization of these interactions at the molecular level is critical to our understanding of numerous healthy and aberrant cellular functions, given the pervasiveness of condensates across them. Although techniques based on confocal fluorescence microscopy, e.g., fluorescence recovery after photobleaching (FRAP)30,31,32, have been widely used to qualitatively show that the molecular exchanges between condensates and the surrounding cellular environment are dynamic, quantifying the interaction dynamics of specific biomolecules within condensates is generally not possible using conventional confocal microscopy or single-molecule microscopy without specialized data analysis methods. The single-particle tracking (SPT) technique described in this protocol is based on live-cell single-molecule microscopy33 and provides a uniquely powerful tool to quantify the interaction dynamics between specific proteins within condensates. The readout of SPT for such measurement is the mean residence time of a protein of interest in the condensates.
The protocol can be broken down into two parts - data acquisition and data analysis. The first step of imaging data acquisition is to express in cells a protein of interest that is fused to a HaloTag34. This enables labeling of the protein of interest with two fluorophores, where a majority of the protein molecules are to be labeled with a non-photoactivatable fluorophore (e.g., JFX549 Halo ligand35) and a small fraction of them are to be labeled with a spectrally distinct, photoactivatable fluorophore (e.g., PA-JF646 Halo ligand36). This allows for the simultaneous acquisition of all condensate locations in the cell and the acquisition of single-molecule movies of the protein of interest binding and unbinding to the condensates. Meanwhile, the same type of cells are modified to stably express Halo-tagged H2B, a histone that is largely immobile on chromatin. The cells are then stained with the PA-JF646 Halo ligand to enable single-molecule imaging of H2B. As will be discussed in detail below, this experiment accounts for the contribution of photobleaching to enable precise quantification of the interaction dynamics of the protein of interest. Cells for imaging experiments must then be cultured on clean coverslips, stained with HaloTag ligand(s), and assembled into a live-cell imaging chamber. From there, the sample is imaged under highly inclined and laminated optical sheet (HILO) illumination on a total internal reflection fluorescence (TIRF) microscope capable of two-channel imaging and single-molecule detection. The emission is then split onto two cameras, one tracking condensate positions and one tracking single molecules. Acquisition is performed with a long integration time (on the order of hundreds of ms) to blur out freely-diffusing proteins and only capture proteins that are less mobile due to binding to stable structures in the cell37.
The first step of data analysis is using an established single-particle tracking (SPT) algorithm38,39 to localize individual protein molecules in each frame of the movie and assemble the localizations into a trajectory for each molecule over its detectable lifetime. The trajectories are then sorted into those representing molecules inside and those representing molecules outside the condensates by comparing the localizations of the molecules throughout their trajectories to the localizations of all the condensates at the corresponding times1.
Next, a survival curve (1 - CDF) is generated using the lengths of all the in-condensate trajectories. The apparent mean residence time of the molecules is then extracted by fitting the survival curve to the following two-component exponential model of protein binding,
<img align="center" alt="Equation 1" src="/files/ftp_upload/66169/66169eq01.jpg" style="margin: auto;" />,
with A as the fraction of molecules non-specifically bound and with kobs,ns and kobs,s as the observed dissociation rates of the non-specifically bound and specifically bound molecules, respectively. Only kobs,s is considered from here onward. The dynamics of both protein dissociation, ktrue,s, and photobleaching of the fluorophore, kpb, contribute to kobs,s as
<img alt="Equation 2" src="/files/ftp_upload/66169/66169eq02.jpg" style="margin: auto;" />;
thus, to isolate the effects of protein dissociation, the specific dissociation rate of H2B-Halo in the cell line mentioned prior is measured.
<img alt="Equation 3" src="/files/ftp_upload/66169/66169eq03.jpg" style="margin: auto;" />
H2B is a protein that is stably integrated into chromatin and that experiences minimal dissociation in the time scale of a single-molecule movie acquisition37. Its specific dissociation rate is then equal to the photobleaching rate of the PA-JF646 Halo ligand, or
<img alt="Equation 4" src="/files/ftp_upload/66169/66169eq04.jpg" style="margin: auto;" />.
The mean in-condensate residence time of the protein of interest, <img alt="Equation 5" src="/files/ftp_upload/66169/66169eq05.jpg" style="margin: auto;" />, is then
<img alt="Equation 6" src="/files/ftp_upload/66169/66169eq06.jpg" style="margin: auto;" />.
Representative results from Irgen-Gioro et al.40 are shown, where this protocol was applied to demonstrate that fusing an oligomerization domain to IDR results in longer residence times of the IDR in its condensates. This result suggests that the added oligomerization domain stabilizes the homotypic interactions of the IDR that drives LLPS. In principle, the same method with slightly modified protocols can be applied to characterize the homotypic or heterotypic interactions of any protein that participates in the formation of any types of condensates.

作者聚焦：活人细胞中蛋白质-凝聚物动力学的评估

生物分子缩合物中蛋白质相互作用动力学的单分子测量

1 Our lab aims to understand the interaction behaviors<v>2 of intrinsically disordered protein regions 3 and how they play roles in transcriptional regulation 4 in healthy and diseased human cells. 5 In our lab, we develop and use novel 6 single molecule microscopy techniques 7 in combination with molecular biology, 8 biochemical and proteomic approaches 9 to study biomolecular condensates. 10 This protocol enables the quantification of the dynamics 11 by which a specific protein 12 binds to a particular type of condensate 13 in live human cells, 14 and is broadly applicable 15 to measuring the interaction dynamics of any protein 16 that participates in liquid-liquid phase separation.

在这里，我们介绍了Irgen-Gioro等人40的代表性结果，我们使用该SPT协议比较了两种蛋白质在各自的自组装LLPS缩合物中的相互作用动力学。TAF15（TATA-box 结合蛋白相关因子 15）含有一个 IDR，在人细胞中过表达时可以发生 LLPS。我们假设将 TAF15（IDR） 与形成 24 个亚基寡聚体的 FTH1（铁蛋白重链 1）融合将导致更稳定的同型蛋白质-蛋白质相互作用，从而驱动 LLPS。为了验证这一假设，我...

Watch this Scientific Journal Video about Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells at JoVE.com

许多固有的无序蛋白质已被证明参与高动态生物分子凝聚物的形成，这种行为对许多细胞过程很重要。在这里，我们提出了一种基于单分子成像的方法，用于量化活细胞中生物分子缩合物中蛋白质相互作用的动力学。

Biomolecular condensates formed via liquid-liquid phase separation (LLPS) have been considered critical in cellular organization and an increasing number ...

single-molecule-measurement-protein-interaction-dynamics-within

Research

JoVE Journal

Biology

Single-Molecule Measurement of Protein Interaction Dynamics Within Biomolecular Condensates

2.6K 次观看.  California Institute of Technology. 1 我们的实验室旨在了解固有无序蛋白质区域 3 的相互作用行为 2 （interaction behaviors），以及它们如何在健康和患病人类细胞的转录调节 4 中发挥作用。5 在我们的实验室中，我们开发和使用新颖的 6 单分子显微镜技术 7 与分子生物学、8 生化和蛋白质组学方法 9 相结合来研究生物分子凝聚物。10 该协议能够量化动力学 11 通过特定的蛋白质 12 与活?...

视频: 作者聚焦：活人细胞中蛋白质-凝聚物动力学的评估

Biomolecular condensates formed via liquid-liquid phase separation (LLPS) have been considered critical in cellular organization and an increasing number of cellular functions. Characterizing LLPS in live cells is also important because aberrant condensation has been linked to numerous diseases, including cancers and neurodegenerative disorders. LLPS is often driven by selective, transient, and multivalent interactions between intrinsically disordered proteins. Of great interest are the interaction dynamics of proteins participating in LLPS, which are well-summarized by measurements of their binding residence time (RT), that is, the amount of time they spend bound within condensates. Here, we present a method based on live-cell single-molecule imaging that allows us to measure the mean RT of a specific protein within condensates. We simultaneously visualize individual protein molecules and the condensates with which they associate, use single-particle tracking (SPT) to plot single-molecule trajectories, and then fit the trajectories to a model of protein-droplet binding to extract the mean RT of the protein. Finally, we show representative results where this single-molecule imaging method was applied to compare the mean RTs of a protein at its LLPS condensates when fused and unfused to an oligomerizing domain. This protocol is broadly applicable to measuring the interaction dynamics of any protein that participates in LLPS.

Many intrinsically disordered proteins have been shown to participate in the formation of highly dynamic biomolecular condensates, a behavior important for numerous cellular processes. Here, we present a single-molecule imaging-based method for quantifying the dynamics by which proteins interact with each other in biomolecular condensates in live cells.

科研

生物学

Halo-Ligand Staining of Cells for Protein Condensate-Interaction Studies Using Single-Molecule Imaging

1 After creating the cell lines<v>2 expressing the HaloTag protein of interest and Halo-H2B, 3 prepare the halo ligand staining reagent 4 separately for both of them. 5 Rinse the cells with two milliliters of PBS. 6 Add halo ligand containing media 7 and incubate them at 37 degrees Celsius 8 with 5%carbon dioxide for one hour.9 After removing the staining media, 10 rinse the cells four times with PBS 11 and incubate them with fresh media devoid of halo ligands. 12 After four such rinse incubation cycles, 13 transfer the cover slip with cells 14 to a microscope stage compatible chamber 15 with sterile forceps. 16 Add phenol red-free media to the chamber before imaging.

使用单分子成像对细胞进行卤配体染色，用于蛋白质缩合物相互作用研究

Single-Molecule Imaging of Halo-Tagged Proteins to Study Protein-Condensate Interactions

1 To begin, obtain ligand stain cells<v>2 expressing halo-tagged protein of interest and halo H2B. 3 After turning on the microscope system, 4 set the live cell incubation parameters 5 and let the system equilibrate. 6 Put a drop of the oil onto 7 a 100x TIRF objective on the microscope 8 and load the cell sample onto the microscope stage.9 To identify a morphologically healthy cell, 10 adjust the Z position 11 and image the cell sample under brightfield illumination. 12 Crop the field of view to capture the target cell nucleus. 13 Using laser illumination, adjust the TIRF angle 14 to achieve hilo illumination with an optimal 15 signal to noise ratio of a single molecule.16 Then, set the exposure time to 500 milliseconds. 17 During the dead time between frames, 18 photo activate the molecules 19 with a 111 microwatt 405 nanometer beam, 20 and during each exposure, excite H2B halo molecules 21 with a 9.1 milliwatt 640 nanometer beam. 22 Initiate image capturing for 2000 frames continuously 23 and gradually increase the power of the 405 nanometer beam 24 upon reduction of photo activated molecules.25 For a halo-tagged protein of interest, 26 split emission wavelengths between two cameras 27 using a long pass dichroic mirror. 28 To the same acquisition parameters demonstrated earlier, 29 add JFX549 channel to acquire one frame every 10 seconds 30 and capture 2000 frames continuously.

光晕标记蛋白的单分子成像，以研究蛋白质-缩合物相互作用

Analysis of Single-Molecule Imaging Data to Study Interactions of Halo-Tagged Proteins

1 To begin, perform live cell<v>2 single molecule imaging of HaloTag protein condensates. 3 Using ImageJ, convert each channel 4 from the raw imaging data file 5 into an independent TIFF file. 6 If necessary, run pre-tracking comb.text 7 and follow the prompts 8 to replace any frames with the most recent ones. 9 To load the single molecule of movie file in SlimFast, 10 click on load, followed by image stack. 11 Set the parameters for localization 12 and acquisition under respective option sheets.13 Visualize the localizations of all the molecules. 14 And using lock all, 15 generate a file containing the localizations 16 of all the molecules in every frame. 17 Next, go to load particle data 18 and select SlimFast to load the file 19 with localization and acquisition settings.20 Using option and sheet tracking, 21 adjust the parameters for trajectory generation. 22 Click on gen traj to generate a file 23 containing the trajectories of all the molecules. 24 Load the sheet tracked file into eval SPT 25 and set the parameters 26 to filter out the trajectories shorter than 2.5 seconds.27 Using export data, generate a file 28 with all the filtered trajectories. 29 Running the ImageJ macro, nucleus, 30 and cluster mask version two. text, 31 threshold all the frames of the movie acquired 32 in the JFX549 channel to generate a time lapse movie 33 populated with the time evolving binary mask 34 highlighting condensate locations.35 Using convert ASCII slow tracking CSS 3. M, 36 reformat the trajectories 37 and run categorization version 4. M 38 to sort them based on the lifetime 39 a molecule spends in a condensate.40 Next, using plot residence hist CSS. M, 41 extract the observed dissociation rate 42 of specifically bound molecules 43 and the photobleaching rate from the in condensate 44 protein of interest and the H2B trajectories. 45 Finally, calculate the corrected mean residence time 46 of the protein of interest, 47 specifically bound to its condensates.48 A frame from two-color single molecule movie 49 of TAF15 IDR-Halo-FTH1 shows signals 50 from the nucleus in both PA-JF646 and JFX549 channels. 51 Upon assembling and sorting, 52 a clear distinction was observed for the trajectories 53 of PA-JF646 detected molecules bound to the condensates. 54 The calculated mean residence times 55 after correction for photobleaching 56 were more for TAF15-Halo-FTH1 than Halo-TAF15.