本研究得到了贵州省科技项目（ZK2023-297）、贵州省卫健委科技基金（gzwkj2023-565）、吉林省教育厅科技项目（JJKH20220245KJ JJKH20220226SK）、国家自然科学基金（U19A2061）、吉林省大数据智能计算重点实验室等单位的支持（20180622002JC）和中央高校基础科研基金（JLU.我们衷心感谢审稿编辑和三位匿名审稿人的建设性批评，这些批评在大大提高本协议的严谨性和清晰度方面发挥了重要作用。

注意：以下协议描述了主要模块的信息学分析过程和 Python 命令的详细信息。图 2 说明了该协议中使用的示例命令的三个主要步骤，有关更多技术细节，请参阅以前发表的作品26,38。在计算机系统中的普通用户帐户下执行以下协议，并避免使用管理员或 root 帐户。这是一个计算协议，没有生物医学危害因素。
1. 准备 Python 环境
<ol><li>创建虚拟环境。<ol><li>本研究使用了 Python 编程语言和 Python 3.7 的 Python 虚拟环境 （VE）。请按照下列步骤操作（图 3A）： conda create -n healthmodel python=3.7 conda create 是用于创建新 VE 的命令。参数 -n 指定新环境的名称，在本例中为 healthmodel。 python=3.7 指定要安装的 Python 版本。选择支持上述命令的任何首选名称和 Python 版本。</li>
		<li>运行命令后，输出类似于 图 3B。输入 y 并等待该过程完成。</li>
	</ol></li>
	<li>激活虚拟环境<ol><li>在大多数情况下，使用以下命令激活创建的 VE（图 3C）： conda 激活 healthmodel</li>
		<li>如果某些平台要求用户上传特定于平台的配置文件进行激活，请按照特定于平台的说明进行 VE 激活。</li>
	</ol></li>
	<li>安装 PyTorch 1.13.1<ol><li>PyTorch 是用于人工智能 （AI） 算法的流行 Python 包。以基于 CUDA 11.7 GPU 编程平台的 PyTorch 1.13.1 为例。在 https://pytorch.org/get-started/previous-versions/ 查找其他版本。使用以下命令（图 3D）： pip3 安装 torch torchvision torchaudio 注意：强烈建议使用 PyTorch 版本 1.12 或更高版本。否则，安装所需的软件包 torch_geometric 可能具有挑战性，如torch_geometric官方网站上所述：https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html。</li>
	</ol></li>
	<li>安装火炬几何的其他软件包
	<ol><li>按照 https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html 中的准则，使用命令安装以下软件包：torch_scatter、torch_sparse、torch_cluster 和 torch_spline_conv（图 3E）： pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu117.html</li>
	</ol></li>
	<li>安装 torch-geometric 软件包。<ol><li>本研究需要 火炬几何 包的特定版本 2.2.0。运行命令（图 3F）： pip 安装 torch_geometric==2.2.0</li>
	</ol></li>
	<li>安装其他软件包。<ol><li>像 pandas 这样的软件包通常默认可用。如果没有，请使用 pip 命令安装它们。例如，要安装 pandas 和 xgboost，请运行： pip 安装 pandas pip 安装 xgboost</li>
	</ol></li>
</ol>2. 使用预训练的 HealthModel 生成 mqTrans 特征
<ol><li>下载代码和预训练模型。<ol><li>从网站下载代码和预训练的 HealthModel：http://www.healthinformaticslab.org/supp/resources.php，该网站名为 HealthModel-mqTrans-v1-00.tar.gz （图 4A）。下载的文件可以解压缩为用户指定的路径。已实施协议的详细表述和支持数据可在26 中找到。</li>
	</ol></li>
	<li>引入参数以运行 HealthModel。<ol><li>首先，在命令行中将工作目录更改为 HealthModel-mqTrans 文件夹。使用以下语法运行代码： python main.py <数据文件夹> <模型文件夹> <输出文件夹> 有关每个参数以及数据、模型和输出文件夹的详细信息如下： data 文件夹：这是源数据文件夹，每个数据文件都是 csv 格式。此数据文件夹包含两个文件（请参阅步骤 2.3 和 2.4 中的详细说明）。这些文件需要替换为个人数据。 data.csv：转录组基质文件。第一行列出了特征（或基因）ID，第一列给出了样本ID。基因列表包括调控因子（TF 和 lincRNA）和调控的 mRNA 基因。 label.csv：示例标签文件。第一列列出示例 ID，名称为“label”的列提供示例标签。 model 文件夹：用于保存模型信息的文件夹： HealthModel.pth：预先训练的 HealthModel。 regulatory_geneIDs.csv：本研究中使用的调控基因 ID。 target_geneIDs.csv：本研究中使用的靶基因。 adjacent_matrix.csv：调控基因的相邻基质。 输出文件夹：输出文件写入此文件夹，由代码创建。 test_target.csv：Z-归一化和插补后靶基因的基因表达值。 pred_target.csv：靶基因的预测基因表达值。 mq_target.csv：靶基因的预测基因表达值。</li>
	</ol></li>
	<li>准备csv格式的转录组学矩阵文件。<ol><li>每行代表一个样本，每列代表一个基因（图4B）。将转录组数据矩阵文件命名为 data.csv 在 data 文件夹中。 注意：此文件可以通过从 Microsoft Excel 等软件中以 .csv 格式手动保存数据矩阵来生成。转录组基质也可以通过计算机编程生成。</li>
	</ol></li>
	<li>准备 csv 格式的标签文件。<ol><li>与转录组基质文件类似，将标签文件命名为数据文件夹中的label.csv（图4C）。 注意：第一列给出了示例名称，每个示例的类标签在标题 为标签的列中给出。标签列中的 0 值表示此样本为负数，1 表示为阳性样本。</li>
	</ol></li>
	<li>生成 mqTrans 特征。<ol><li>运行以下命令以生成 mqTrans 特征并获取 图 4D 所示的输出。mqTrans 特征将生成为文件 ./output/mq_targets.csv，标签文件将重新保存为文件 ./output/label.csv。为了便于进一步分析，mRNA基因的原始表达值也被提取为文件 ./output/test_target.csv。 python ./Get_mqTrans/code/main.py ./data ./Get_mqTrans/model ./输出</li>
	</ol></li>
</ol>3. 选择 mqTrans 功能
<ol><li>功能选择代码的语法<ol><li>首先，将工作目录更改为 HealthModel-mqTrans 文件夹。使用以下语法： python ./FS_classification/testMain.py <in-data-file.csv> <in-label-file.csv> <output folder> <select_feature_number> <test_size> <combine> <combine 文件> 各参数详情如下： in-data-file：输入数据文件 in-label-file：输入数据文件的标签 输出文件夹：此文件夹中保存两个输出文件，包括 Output-score.xlsx （特征选择方法和对应分类器的精度）和 Output-SelectedFeatures.xlsx （每个特征选择算法的所选特征名称）。<ol><li>select_feature_number：选择要素数，范围从 1 到数据文件的特征数。</li>
			<li>test_size：设置要拆分的测试样品的比例。例如，0.2 表示输入数据集按 0.8：0.2 的比例随机拆分为 train： 测试子集。</li>
			<li>combine：如果为 true，则将两个数据文件组合在一起以进行特征选择，即原始表达式值和 mqTrans 特征。如果为 false，则仅使用一个数据文件进行特征选择，即原始表达式值或 mqTrans 特征。</li>
			<li>合并文件：如果合并为 true，请提供此文件名以保存合并的数据矩阵。 注： 此管道旨在演示生成的 mqTrans 特征在分类任务中如何执行，并直接使用第 2 节生成的文件进行以下操作。</li>
		</ol></li>
	</ol></li>
	<li>运行特征选择算法以进行 mqTrans 特征选择。<ol><li>如果用户选择 mqTrans 特征或原始特征，则将 combine = False 。</li>
		<li>首先，选择 800 个原始特征，并将数据集拆分为训练：test=0.8：0.2： python ./FS_classification/testMain.py ./output/test_target.csv ./output/label.csv ./result 800 0.2 False</li>
		<li>如果用户希望将 mqTrans 特征与原始表达式值组合以选择特征，则将 combine = True 转为。在这里，示范示例是选择 800 个特征并将数据集拆分为训练：test=0.8：0.2： python ./FS_classification/testMain.py ./output/mq_targets.csv ./output/label.csv ./result_combine 800 0.2 True ./output/test_target.csv 注意： 图 5 显示了输出信息。此协议所需的补充文件位于HealthModel-mqTrans-v1-00.tar文件夹（补充编码文件 1）中。</li>
	</ol></li>
</ol>

用于识别转录组中暗生物标志物的 GAT 框架

作者没有什么可透露的。

该协议的第 2 部分（使用预先训练的 HealthModel 生成 mqTrans 特征）是该协议中最关键的步骤。在第 1 节中准备计算工作环境后，第 2 节基于预训练的大型参考模型生成转录组数据集的 mqTrans 视图。第 3 节是为生物标志物检测和预测任务选择生成的 mqTrans 特征的示范性示例。用户可以使用自己的工具或代码在此 mqTrans 数据集上进行其他转录组学分析。
原始 HealthModel 框架可以使用...

转录组由样品中所有基因的表达组成，可通过微阵列和 RNA-seq1 等高通量技术进行分析。数据集中一个基因的表达水平称为转录组学特征，表型组和对照组之间转录组特征的差异表示将该基因定义为该表型的生物标志物 2,3。转录组生物标志物在疾病诊断4、生物学机制5和生存分析6、7等研究中得到广泛应用。
健康组织中的基因活性模式携带有关生命的重要信息8,9。这些模式提供了宝贵的见解，并作为理解良性疾病10,11和致命疾病12的复杂发展轨迹的理想参考。基因相互作用，转录组代表其复杂相互作用后的最终表达水平。这种模式被表述为转录调控网络13和代谢网络14等。信使RNA（mRNA）的表达可以通过转录因子（TFs）和长基因间非编码RNA（lincRNA）进行转录调控15,16,17。传统的差异表达分析忽略了这种复杂的基因相互作用，并假设特征间独立性18,19。
图神经网络 （GNN） 的最新进展表明，在从基于 OMIC 的数据中提取重要信息用于癌症研究20 方面具有非凡的潜力，例如，识别共表达模块21。GNNs的先天能力使它们成为模拟基因之间错综复杂的关系和依赖关系的理想选择22,23。
生物医学研究通常侧重于准确预测对照组的表型。这些任务通常表述为二元分类24,25,26。在这里，两个类标签通常编码为 1 和 0、true 和 false，甚至正负27。
本研究旨在提供一种易于使用的协议，用于基于预训练的图注意力网络 （GAT） 参考模型生成转录组数据集的转录调控 （mqTrans） 视图。先前发表的工作26 中的多任务 GAT 框架用于将转录组学特征转换为 mqTrans 特征。使用来自加州大学圣克鲁斯分校 （UCSC） Xena 平台28 的大型健康转录组数据集对参考模型 （HealthModel） 进行预训练，该模型定量测量了从调节因子（TF 和 lincRNA）到靶 mRNA 的转录调控。生成的 mqTrans 视图可用于构建预测模型和检测暗生物标志物。该协议利用来自癌症基因组图谱（TCGA）数据库29 的结肠腺癌（COAD）患者数据集作为说明性示例。在这种情况下，I 期或 II 期的患者被归类为阴性样本，而 III 期或 IV 期的患者被视为阳性样本。还比较了 26 种 TCGA 癌症类型中深色和传统生物标志物的分布。
HealthModel 管道的说明 该协议中采用的方法基于先前发布的框架26，如 图 1 所示。首先，用户需要准备输入数据集，将其输入到建议的 HealthModel 管道中，并获取 mqTrans 特征。详细的数据制备说明在协议部分的第 2 节中提供。随后，用户可以选择将 mqTrans 特征与原始转录组特征相结合，或者仅继续使用生成的 mqTrans 特征。然后，生成的数据集将进行特征选择过程，用户可以灵活地在 k 倍交叉验证中选择他们的首选 k 值进行分类。该协议中使用的主要评估指标是准确性。
HealthModel26 将转录组学特征分为三个不同的组：TF（转录因子）、lincRNA（长基因间非编码 RNA）和 mRNA（信使 RNA）。TF 特征是根据人类蛋白质图谱30,31 中可用的注释定义的。这项工作利用了 GTEx 数据集32 中 lincRNA 的注释。属于 KEGG 数据库33 中第三级通路的基因被认为是 mRNA 特征。值得注意的是，如果 mRNA 特征表现出 TRRUST 数据库34 中记录的靶基因的调控作用，则将其重新分类为 TF 类。
该协议还手动生成调节因子 （regulatory_geneIDs.csv） 和靶标 mRNA （target_geneIDs.csv） 的基因 ID 的两个示例文件。调控特征（TFs和lincRNAs）之间的成对距离矩阵由Pearson相关系数计算，并通过流行的工具加权基因共表达网络分析（WGCNA）36 （adjacent_matrix.csv）进行聚类。用户可以直接将 HealthModel 管道与这些示例配置文件一起使用，以生成转录组数据集的 mqTrans 视图。
HealthModel的技术细节 HealthModel 将 TF 和 lincRNA 之间的复杂关系表示为图形，输入特征用作 V 表示的顶点，顶点间边矩阵指定为 E。每个样品都具有 K 调控特征，符号为 VK×1。具体来说，数据集包含 425 个 TF 和 375 个 lincRNA，样本维数为 K = 425 + 375 = 800。为了建立边矩阵 E，这项工作使用了流行的工具 WGCNA35。连接两个顶 <img alt="Equation 1" src="/files/ftp_upload/66030/66030eq01.jpg" style="margin: auto;"> 点的成对权重表示为 和 <img alt="Equation 2" src="/files/ftp_upload/66030/66030eq02.jpg" style="margin: auto;">，由 Pearson 相关系数确定。基因调控网络表现出无标度拓扑结构36，其特征在于存在具有关键功能作用的枢纽基因。我们使用拓扑重叠度量 （TOM） 计算两个要素或顶点 <img alt="Equation 1" src="/files/ftp_upload/66030/66030eq01.jpg" style="margin: auto;"> 之间的相关性，如下 <img alt="Equation 2" src="/files/ftp_upload/66030/66030eq02.jpg" style="margin: auto;">所示：
<img alt="Equation 3" src="/files/ftp_upload/66030/66030eq03.jpg" style="margin: auto;">(1)
<img alt="Equation 4" src="/files/ftp_upload/66030/66030eq04.jpg" style="margin: auto;">(2)
软阈值β是使用 WGCNA 软件包中的“pickSoft Threshold”函数计算的。应用幂指数函数 a ij，其中<img alt="Equation 5" src="/files/ftp_upload/66030/66030eq05.jpg" style="margin: auto;">表示不包括 i 和 j 的基因，并表示<img alt="Equation 6" src="/files/ftp_upload/66030/66030eq06.jpg" style="margin: auto;">顶点连通性。WGCNA 使用常用的差异度量将转录组学特征的表达谱聚类到多个模块中 （<img alt="Equation 7" src="/files/ftp_upload/66030/66030eq07.jpg" style="margin: auto;">37.
HealthModel 框架最初被设计为多任务学习架构26.该协议仅利用模型预训练任务来构建转录组mqTrans视图。用户可以选择使用额外的特定任务转录组样本在多任务图注意力网络下进一步完善预训练的 HealthModel。
特征选择和分类的技术细节 特征选择池实现了 11 种特征选择 （FS） 算法。其中，基于滤波器的FS算法有三种：使用最大信息系数（SK_mic）选择K个最佳特征，根据MIC的FPR（SK_fpr）选择K个特征，选择MIC错误发现率最高的K个特征（SK_fdr）。此外，三种基于树的 FS 算法使用具有 Gini 指数 （DT_gini）、自适应提升决策树 （AdaBoost） 和随机森林 （RF_fs） 的决策树来评估单个特征。该池还包含两种包装方法：使用线性支持向量分类器 （RFE_SVC） 的递归特征消除和使用逻辑回归分类器 （RFE_LR） 的递归特征消除。最后，包括两种嵌入算法：具有排名靠前的 L1 特征重要性值 （lSVC_L1） 的线性 SVC 分类器和具有排名靠前的 L1 特征重要性值 （LR_L1） 的逻辑回归分类器。
分类器池使用七种不同的分类器来构建分类模型。这些分类器包括线性支持向量机 （SVC）、高斯朴素贝叶斯 （GNB）、逻辑回归分类器 （LR）、k 最近邻（k 默认设置为 5 （KNN）、XGBoost、随机森林 （RF） 和决策树 （DT）。
可以在命令行中设置数据集的随机拆分为训练：测试子集。演示的示例使用火车的比率：test = 8：2。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Anaconda</td>
			<td>Anaconda</td>
			<td>version 2020.11</td>
			<td>Python programming platform</td>
		</tr>
		<tr>
			<td>Computer</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Any general-purpose computers satisfy the requirement</td>
		</tr>
		<tr>
			<td>GPU card</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Any general-purpose GPU cards with the CUDA computing library</td>
		</tr>
		<tr>
			<td>pytorch</td>
			<td>Pytorch</td>
			<td>version 1.13.1</td>
			<td>Software</td>
		</tr>
		<tr>
			<td>torch-geometric</td>
			<td>Pytorch</td>
			<td>version 2.2.0</td>
			<td>Software</td>
		</tr>
	</tbody>
</table>

generating the transcriptional regulation view of transcriptomic features for prediction task and dark biomarker detection on small datasets

转录组代表样品中许多基因的表达水平，已广泛应用于生物学研究和临床实践中。研究人员通常专注于在表型组和对照组样本之间具有差异表示的转录组生物标志物。本研究提出了一个多任务图-注意力网络（GAT）学习框架来学习参考样本的复杂基因间相互作用。在健康样本 （HealthModel） 上预训练了一个示范参考模型，该模型可直接用于生成独立测试转录组的基于模型的定量转录调控 （mqTrans） 视图。通过预测任务和暗生物标志物检测证明了生成的转录组 mqTrans 视图。创造的术语“暗生物标志物”源于其定义，即暗生物标志物在 mqTrans 视图中显示差异表示，但在其原始表达水平上没有差异表达。由于缺乏差异表达，在传统的生物标志物检测研究中，深色生物标志物总是被忽视。管道 HealthModelPipe 的源代码和手册可以从 http://www.healthinformaticslab.org/supp/resources.php 下载。

评估转录组数据集的 mqTrans 视图 测试代码使用 11 种特征选择 （FS） 算法和 7 种分类器来评估转录组数据集生成的 mqTrans 视图对分类任务的贡献（图 6）。测试数据集由来自癌症基因组图谱 （TCGA） 数据库29 的 317 个结肠腺癌 （COAD） 组成。I期或II期的COAD患者被视为阴性样本，而III期或IV期的COAD患者为阳性样本。
测试代?...

Watch this Scientific Journal Video about Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers at JoVE.com

作者聚焦：基因间相互作用对疾病识别深色生物标志物的影响

在这里，我们介绍了一种将转录组数据转换为mqTrans视图的协议，从而能够识别暗生物标志物。虽然在常规转录组学分析中没有差异表达，但这些生物标志物在 mqTrans 视图中表现出差异表达。该方法是对传统方法的补充技术，揭示了以前被忽视的生物标志物。

生成转录组学特征的转录调控视图，用于小型数据集上的预测任务和暗生物标志物检测

Transcriptome represents the expression levels of many genes in a sample and has been widely used in biological research and clinical practice. ...

我们的研究重点是疾病的遗传间相互作用。我们发现许多基因彼此之间有着复杂、相互交织的关系。通过探索这些关系，我们的目标是在疾病治疗和管理中做出更精确的诊断。我们发现许多传统、传统方法忽略的黑暗生物标志物，没有得到许多医学文献的支持。这表明该方法可以进一步帮助生物学家缩短生物标志物筛选的时间周期。实验过程中最大的挑战是解决不同疾病类型的小样本量问题。为了克服这个困难，我们收集了尽可能多的健康样本来训练参考模型，我们称之为健康模型。

generating-transcriptional-regulation-view-transcriptomic-features

Research

JoVE Journal

Biology

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

574 次观看.  Jilin University. 我们的研究重点是疾病的遗传间相互作用。我们发现许多基因彼此之间有着复杂、相互交织的关系。通过探索这些关系，我们的目标是在疾病治疗和管理中做出更精确的诊断。我们发现许多传统、传统方法忽略的黑暗生物标志物，没有得到许多医学文献的支持。这表明该方法可以进一步帮助生物学家缩短生物标志物筛选的时间周期。实验过程中最大的挑战是解决不同疾?...

视频: 作者聚焦：基因间相互作用对疾病识别深色生物标志物的影响

Light/dark Transition Test for Mice

Although all of the mouse genome sequences have been determined, we do not yet know the functions of most of these genes. Gene-targeting techniques, however, can be used to delete or manipulate a specific gene in mice. The influence of a given gene on a specific behavior can then be determined by conducting behavioral analyses of the mutant mice. As a test for behavioral phenotyping of mutant mice, the light/dark transition test is one of the most widely used tests to measure anxiety-like behavior in mice. The test is based on the natural aversion of mice to brightly illuminated areas and on their spontaneous exploratory behavior in novel environments. The test is sensitive to anxiolytic drug treatment. The apparatus consists of a dark chamber and a brightly illuminated chamber. Mice are allowed to move freely between the two chambers. The number of entries into the bright chamber and the duration of time spent there are indices of bright-space anxiety in mice. To obtain phenotyping results of a strain of mutant mice that can be readily reproduced and compared with those of other mutants, the behavioral test methods should be as identical as possible between laboratories. The procedural differences that exist between laboratories, however, make it difficult to replicate or compare the results among laboratories. Here, we present our protocol for the light/dark transition test as a movie so that the details of the protocol can be demonstrated. In our laboratory, we have assessed more than 60 strains of mutant mice using the protocol shown in the movie. Those data will be disclosed as a part of a public database that we are now constructing.

Visualization of the protocol will facilitate understanding of the details of the entire experimental procedure, allowing for standardization of the protocols used across laboratories and comparisons of the behavioral phenotypes of various strains of mutant mice assessed using this test.

The light/dark transition test is one of the most widely used tests to measure anxiety-like behavior in mice. Here, we present a movie that shows detailed procedures on how we conduct the test.

光/暗转换测试小鼠

Although all of the mouse genome sequences have been determined, we do not yet know the functions of most of these genes. Gene-targeting techniques, ...

科研

生物学

Semi-automated Optical Heartbeat Analysis of Small Hearts

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our Semi-automatic Optical Heartbeat Analysis (SOHA) uses a novel movement detection algorithm that is able to detect cardiac movements associated with individual contractile and relaxation events. The program provides a host of physiologically relevant readouts including systolic and diastolic intervals, heart rate, as well as qualitative and quantitative measures of heartbeat arrhythmicity. The program also calculates heart diameter measurements during both diastole and systole from which fractional shortening and fractional area changes are calculated. Output is provided as a digital file compatible with most spreadsheet programs. Measurements are made for every heartbeat in a record increasing the statistical power of the output. We demonstrate each of the steps where user input is required and show the application of our methodology to the analysis of heart function in all three genetically tractable heart models.

We have developed a Semi-automated Optical Heartbeat Analysis method (SOHA) for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts. We demonstrate the application of our methodology to the analysis of heart function in fruit fly and embryonic mouse hearts.

半自动小红桃光学心跳分析

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our ...

Analyzing the Function of Small GTPases by Microinjection of Plasmids into Polarized Epithelial Cells

Epithelial cells polarize their plasma membrane into biochemically and functionally distinct apical and basolateral domains where the apical domain faces the 'free' surfaces and the basolateral membrane is in contact with the substrate and neighboring cells. Both membrane domains are separated by tight junctions, which form a diffusion barrier. Apical-basolateral polarization can be recapitulated successfully in culture when epithelial cells such as Madin-Darby Canine Kidney (MDCK) cells are seeded at high density on polycarbonate filters and cultured for several days 1 2. Establishment and maintenance of cell polarity is regulated by an array of small GTPases of the Ras superfamily such as RalA, Cdc42, Rab8, Rab10 and Rab13 3 4 5 6 7. Like all GTPases these proteins cycle between an inactive GDP-bound state and an active GTP-bound state. Specific mutations in the nucleotide binding regions interfere with this cycling 8. For example, Rab13T22N is permanently locked in the GDP-form and thus dubbed 'dominant negative', whereas Rab13Q67L can no longer hydrolyze GTP and is thus locked in a 'dominant active' state 7. To analyze their function in cells both dominant negative and dominant active alleles of GTPases are typically expressed at high levels to interfere with the function of the endogenous proteins 9. An elegant way to achieve high levels of overexpression in a short amount of time is to introduce the plasmids encoding the relevant proteins directly into the nuclei of polarized cells grown on filter supports using microinjection technique. This is often combined with the co-injection of reporter plasmids that encode plasma membrane receptors that are specifically sorted to the apical or basolateral domain. A cargo frequently used to analyze cargo sorting to the basolateral domain is a temperature sensitive allele of the vesicular stomatitis virus glycoprotein (VSVGts045) 10. This protein cannot fold properly at 39&deg;C and will thus be retained in the endoplasmic reticulum (ER) while the regulatory protein of interest is assembled in the cytosol. A shift to 31&deg;C will then allow VSVGts045 to fold properly, leave the ER and travel to the plasma membrane 11. This chase is typically performed in the presence of cycloheximide to prevent further protein synthesis leading to cleaner results. Here we describe in detail the procedure of microinjecting plasmids into polarized cells and subsequent incubations including temperature shifts that allow a comprehensive analysis of regulatory proteins involved in basolateral sorting.

This article details the procedures involved in overexpression and analysis of small GTPases in polarized epithelial cells using microinjection technique.

为极化上皮细胞注射质粒的小GTP酶的功能分析

Epithelial cells polarize their plasma membrane into biochemically and functionally distinct apical and basolateral domains where the apical domain faces ...

In vitro Reconstitution of the Active T. castaneum Telomerase

Efforts to isolate the catalytic subunit of telomerase, TERT, in sufficient quantities for structural studies, have been met with limited success for more than a decade. Here, we present methods for the isolation of the recombinant Tribolium castaneum TERT (TcTERT) and the reconstitution of the active T. castaneum telomerase ribonucleoprotein (RNP) complex in vitro.
Telomerase is a specialized reverse transcriptase1 that adds short DNA repeats, called telomeres, to the 3' end of linear chromosomes2 that serve to protect them from end-to-end fusion and degradation. Following DNA replication, a short segment is lost at the end of the chromosome3 and without telomerase, cells continue dividing until eventually reaching their Hayflick Limit4. Additionally, telomerase is dormant in most somatic cells5 in adults, but is active in cancer cells6 where it promotes cell immortality7.
The minimal telomerase enzyme consists of two core components: the protein subunit (TERT), which comprises the catalytic subunit of the enzyme and an integral RNA component (TER), which contains the template TERT uses to synthesize telomeres8,9. Prior to 2008, only structures for individual telomerase domains had been solved10,11. A major breakthrough in this field came from the determination of the crystal structure of the active12, catalytic subunit of T. castaneum telomerase, TcTERT1.
Here, we present methods for producing large quantities of the active, soluble TcTERT for structural and biochemical studies, and the reconstitution of the telomerase RNP complex in vitro for telomerase activity assays. An overview of the experimental methods used is shown in Figure 1.

In vitro Reconstitution of the Active T. castaneum Telomerase

Efforts to isolate the catalytic subunit of telomerase, TERT, in sufficient quantities for structural studies, have been met with limited success for more than a decade. Here, we present methods for the isolation of the recombinant Tribolium castaneum TERT (TcTERT) and the reconstitution of the active T. castaneum telomerase ribonucleoprotein (RNP) complex in vitro.

在体外重组活跃T。谷盗端粒酶

Efforts to isolate the catalytic subunit of telomerase, TERT, in sufficient quantities for structural studies, have been met with limited success for more ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

基于计算机的蛋白质结构与功能预测议定书

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.

Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.

成年小鼠心肌细胞的单细胞转录谱

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation ...

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular biology has been greatly fueled by the discovery of noncoding RNAs as major players in post-transcriptional regulation. Such a revolutionary perspective has been accompanied and triggered by the development of powerful technologies for profiling short RNAs expression, both at the high-throughput level (genome-wide identification) or as single-candidate analysis (steady state accumulation of specific species). Although several state-of-art strategies are currently available for dosing or visualizing such fleeing molecules, Northern Blot assay remains the eligible approach in molecular biology for immediate and accurate evaluation of RNA expression. It represents a first step toward the application of more sophisticated, costly technologies and, in many cases, remains a preferential method to easily gain insights into RNA biology. Here we overview an efficient protocol (Enhanced Northern Blot) for detecting weakly expressed microRNAs (or other small regulatory RNA species) from Drosophila melanogaster whole embryos, manually dissected larval/adult tissues or in vitro cultured cells. A very limited amount of RNA is required and the use of material from flow cytometry-isolated cells can be also envisaged.

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

The aim of this publication is to visualize and discuss the operative steps of an Enhanced Northern Blot protocol on RNA extracted from Drosophila melanogaster embryos, cells, and tissues. This protocol is particularly useful for the efficient detection of small RNA species.

增强Northern印迹检测小RNA种类的<em&gt;果蝇</em

The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular ...

Methods to Discover Alternative Promoter Usage and Transcriptional Regulation of Murine Bcrp1

Gene expression in different tissues is often controlled by alternative promoters that result in the synthesis of mRNA with unique &#8212; usually untranslated &#8212; first exons. Bcrp1 (Abcg2), the murine orthologue of the ABC transporter Breast Cancer Resistance Protein (BCRP, ABCG2), has at least four alternative promoters that are designated by the corresponding four alternative first exons produced: E1U, E1A, E1B, and E1C. Herein, in-silico protocols are presented to predict alternative promoter usage for Bcrp1. Furthermore, reporter assay methods are described to produce reporter constructs for alternative promoters and to determine the functionality of putative promoters upstream of the alternative first exons that are identified.

With the murine ABC transporter Bcrp1 (Abcg2) as an example, in-silico protocols are presented to detect alternative promoter usage in genes expressed in mouse tissues, and to evaluate the functionality of the alternative promoters identified using reporter assays.

方法发现小鼠BCRP1的另类用法子和转录调控

Gene expression in different tissues is often controlled by alternative promoters that result in the synthesis of mRNA with unique &#8212; usually ...

SA-&#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell culture, termed senolytics, can reduce the senescent cell burden in vivo and extend healthspan. Multiple classes of senolytics have been identified to date including HSP90 inhibitors, Bcl-2 family inhibitors, piperlongumine, a FOXO4 inhibitory peptide and the combination of Dasatinib/Quercetin. Detection of SA-&#946;-Gal at an increased lysosomal pH is one of the best characterized markers for the detection of senescent cells. Live cell measurements of senescence-associated &#946;-galactosidase (SA-&#946;-Gal) activity using the fluorescent substrate C12FDG in combination with the determination of the total cell number using a DNA intercalating Hoechst dye opens the possibility to screen for senotherapeutic drugs that either reduce overall SA-&#946;-Gal activity by killing of senescent cells (senolytics) or by suppressing SA-&#946;-Gal and other phenotypes of senescent cells (senomorphics). Use of a high content fluorescent image acquisition and analysis platform allows for the rapid, high throughput screening of drug libraries for effects on SA-&#946;-Gal, cell morphology and cell number.

Cellular senescence is the key factor in the development of chronic age-related pathologies. Identification of therapeutics that target senescent cells show promise for extending healthy aging. Here, we present a novel assay to screen for the identification of senotherapeutics based on measurement of senescence associated &#946;-Galactosidase activity in single cells.

SA-β-加拉基沙酶筛查检测,用于识别治疗药物

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell ...

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Recent discoveries have revealed that cells perform direct, long-range, intercellular transfer via nano-scale, actin-membrane conduits, namely &#34;tunneling nanotubes&#34; (TNTs). TNTs are defined as open-ended, lipid bilayer-encircled membrane extensions that mediate continuity between neighboring cells of diameters ranging between 50 nm and 1 &#181;m. TNTs were demonstrated initially in neuronal cells, but successive studies have revealed the existence of TNTs in several cell types and diseases, such as neurodegenerative diseases, viral infections, and cancer. Several studies have referred to close-ended, electrically coupled membrane nanostructures between neighboring cells as TNTs or TNT-like structures.
The elucidation of ultrastructure in terms of membrane continuity at the endpoint is technically challenging. In addition, studies on cell-cell communication are challenging in terms of the characterization of TNTs using conventional methods due to the lack of specific markers. TNTs are primarily defined as F-actin-based, open-ended membrane protrusions. However, one major limitation is that F-actin is present in all types of protrusions, making it challenging to differentiate TNTs from other protrusions. One of the notable characteristics of F-actin-based TNTs is that these structures hover between two cells without touching the substratum. Therefore, distinct F-actin-stained TNTs can conveniently be distinguished from other protrusions such as filopodia and neurites based on their hovering between cells.
We have recently shown that the internalization of oligomeric amyloid-&#946;1-42 (oA&#946;) via actin-dependent endocytosis stimulates activated p21-activated kinase-1 (PAK1), which mediates the formation of F-actin-containing TNTs coexpressed with phospho-PAK1 between SH-SY5Y neuronal cells. This protocol outlines a 3D volume analysis method to identify and characterize TNTs from the captured z-stack images of F-actin- and phospho-PAK1-immunostained membrane protrusions in oA&#946;-treated neuronal cells. Further, TNTs are distinguished from developing neurites and neuronal outgrowths based on F-actin- and &#946;-III tubulin-immunostained membrane conduits.

Tunneling nanotubes (TNTs) are primarily open-ended F-actin membrane nanotubes that connect neighboring cells, facilitating intercellular communication. The notable characteristic that distinguishes TNTs from other cell protrusions is the hovering nature of the nanotubes between cells. Here, we characterize TNTs by constructing a 3D volume view of confocal z-stack images.