This work was supported by the Senior and Junior Technological Innovation Team (20210509055RQ), Guizhou Provincial Science and Technology Projects (ZK2023-297), the Science and Technology Foundation of Health Commission of Guizhou Province (gzwkj2023-565), Science and Technology Project of Education Department of Jilin Province (JJKH20220245KJ and JJKH20220226SK), the National Natural Science Foundation of China (U19A2061), the Jilin Provincial Key Laboratory of Big Data Intelligent Computing (20180622002JC), and the Fundamental Research Funds for the Central Universities, JLU. We extend our sincerest appreciation to the review editor and the three anonymous reviewers for their constructive critiques, which have been instrumental in substantially enhancing the rigor and clarity of this protocol.

NOTE: The following protocol describes the details of the informatics analytic procedure and Python commands of the major modules. Figure 2 illustrates the three major steps with example commands utilized in this protocol and refer to previously published works26,38 for more technical details. Do the following protocol under a normal user account in a computer system and avoid using the administrator or root account. This is a computational protocol and has no biomedical hazardous factors.
1. Prepare Python environment
<ol>
	<li>Create a virtual environment.
	<ol>
		<li>This study used the Python programming language and a Python virtual environment (VE) with Python 3.7. Follow these steps (Figure 3A): 
		conda create -n healthmodel python=3.7 
		conda create is the command to create a new VE. The parameter -n specifies the name of the new environment, in this case, healthmodel. And python=3.7 specifies the Python version to be installed. Choose any preferred name and Python version supporting the above command.</li>
		<li>After running the command, the output is similar to Figure 3B. Enter y and wait for the process to complete.</li>
	</ol>
	</li>
	<li>Activate the virtual environment
	<ol>
		<li>In most cases, activate the created VE with the following command (Figure 3C): 
		conda activate healthmodel</li>
		<li>Follow the platform-specific instructions for the VE activation, if some platforms require the user to upload the platform-specific configuration files for activation.</li>
	</ol>
	</li>
	<li>Install PyTorch 1.13.1
	<ol>
		<li>PyTorch is a popular Python package for artificial intelligence (AI) algorithms. Use PyTorch 1.13.1, based on the CUDA 11.7 GPU programming platform, as an example. Find other versions at &#160;https://pytorch.org/get-started/previous-versions/. Use the following command (Figure 3D): 
		pip3 install torch torchvision torchaudio 
		NOTE: Using PyTorch version 1.12 or newer is strongly recommended. Otherwise, installing the required package torch_geometric may be challenging, as noted on the official torch_geometric website:&#160;https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html.</li>
	</ol>
	</li>
	<li>Install additional packages for torch-geometric
	<ol>
		<li>Following the guidelines at https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html, install the following packages: torch_scatter, torch_sparse, torch_cluster, and torch_spline_conv using the command (Figure 3E): 
		pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f&#160;https://data.pyg.org/whl/torch-1.13.0+cu117.html</li>
	</ol>
	</li>
	<li>Install torch-geometric package.
	<ol>
		<li>This study requires a specific version, 2.2.0, of the torch-geometric package. Run the command (Figure 3F): 
		pip install torch_geometric==2.2.0</li>
	</ol>
	</li>
	<li>Install other packages.
	<ol>
		<li>Packages like pandas are usually available by default. If not, install them using the pip command. For instance, to install pandas and xgboost, run: 
		pip install pandas 
		&#8203;pip install xgboost</li>
	</ol>
	</li>
</ol>
2. Using the pre-trained HealthModel to generate the mqTrans features
<ol>
	<li>Download the code and the pre-trained model.
	<ol>
		<li>Download the code and the pre-trained HealthModel from the website: http://www.healthinformaticslab.org/supp/resources.php,&#160;which is named HealthModel-mqTrans-v1-00.tar.gz (Figure 4A). The downloaded file can be decompressed to a user-specified path. The detailed formulation and the supporting data of the implemented protocol can be found in26.</li>
	</ol>
	</li>
	<li>Introduce the parameters to run HealthModel.
	<ol>
		<li>Firstly, change the working directory to the HealthModel-mqTrans folder in the command line. Use the following syntax for running the code: 
		python main.py &#60;data folder&#62; &#60;model folder&#62; &#60;output folder&#62; 
		The details regarding each parameter and the data, model and output folders are as follows: 
		data folder: This is the source data folder, and each data file is in the csv format. This data folder has two files (see detailed descriptions in steps 2.3 and 2.4). These files need to be replaced with personal data. 
		data.csv: The transcriptomic matrix file. The first row lists the feature (or gene) IDs, and the first column gives the sample IDs. The list of genes includes the regulatory factors (TFs and lincRNAs), and the regulated mRNA genes. 
		label.csv: The sample label file. The first column lists the sample IDs, and the column with the name &#34;label&#34; gives the sample label. 
		model folder: The folder to save information about the model: 
		HealthModel.pth: The pre-trained HealthModel. 
		regulatory_geneIDs.csv: The regulatory gene IDs used in this study. 
		target_geneIDs.csv: The target genes used in this study. 
		adjacent_matrix.csv: The adjacent matrix of regulatory genes. 
		output folder: The output files are written to this folder, created by the code. 
		test_target.csv: The gene expression value of target genes after Z-Normalization and imputation. 
		pred_target.csv: The predicted gene expression value of target genes. 
		mq_target.csv: The predicted gene expression value of target genes.</li>
	</ol>
	</li>
	<li>Prepare the transcriptomic matrix file in the csv format.
	<ol>
		<li>Each row represents a sample, and each column represents a gene (Figure 4B). Name the transcriptomic data matrix file as data.csv in the data folder. 
		NOTE: This file may be generated by manually saving a data matrix in the .csv format from software such as Microsoft Excel. The transcriptomic matrix may also be generated by computer programming.</li>
	</ol>
	</li>
	<li>Prepare the label file in the csv format.
	<ol>
		<li>Similar to the transcriptomic matrix file, name the label file as label.csv in the data folder (Figure 4C). 
		NOTE: The first column gives the sample names, and the class label of each sample is given in the column titled label. The 0 value in the label column means this sample is negative, 1 means a positive sample.</li>
	</ol>
	</li>
	<li>Generate the mqTrans features.
	<ol>
		<li>Run the following command to generate the mqTrans features and get the outputs shown in Figure 4D. The mqTrans features are generated as the file ./output/mq_targets.csv, and the label file is resaved as the file ./output/label.csv. For the convenience of further analysis, the original expression values of the mRNA genes are also extracted as the file ./output/ test_target.csv. 
		python ./Get_mqTrans/code/main.py ./data ./Get_mqTrans/model ./output</li>
	</ol>
	</li>
</ol>
3. Select mqTrans Features
<ol>
	<li>Syntax of the feature selection code
	<ol>
		<li>Firstly, change the working directory to the HealthModel-mqTrans folder. Use the following syntax: 
		python ./FS_classification/testMain.py &#60;in-data-file.csv&#62; &#60;in-label-file.csv&#62; &#60;output folder&#62; &#60;select_feature_number&#62; &#60;test_size&#62; &#60;combine&#62; &#60;combine file&#62; 
		The details of each parameter are as follows: 
		in-data-file: The input data file 
		in-label-file: The label of the input data file 
		output folder: Two output files are saved in this folder, including Output-score.xlsx (the feature selection method and the accuracy of the corresponding classifier), and Output-SelectedFeatures.xlsx (the selected feature names for each feature selection algorithm).
		<ol>
			<li>select_feature_number: select the number of features, ranging from 1 to the number of the features of the data file.</li>
			<li>test_size: Set the ratio of the test sample to split. For example, 0.2 means that the input dataset is randomly split into the train: test subsets by the ratio of 0.8:0.2.</li>
			<li>combine: If true, combine two data files together for feature selection, i.e., the original expression values and the mqTrans features. If false, just use one data file for feature selection, i.e., the original expression values or the mqTrans features.</li>
			<li>combine file: If combine is true, provide this file name to save the combined data matrix. 
			NOTE: This pipeline aims to demonstrate how the generated mqTrans features perform on classification tasks, and it directly uses the file generated by section 2 for the following operations.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Run feature selection algorithm for mqTrans feature selection.
	<ol>
		<li>Turn combine =False if the user selects mqTrans features or original features.</li>
		<li>Firstly, select 800 original features and split the dataset into train: test=0.8:0.2: 
		python ./FS_classification/testMain.py ./output/test_target.csv ./output/label.csv ./result 800 0.2 False</li>
		<li>Turn combine =True, if the user wants to combine the mqTrans features with the original expression values to select features. Here, the demonstrative example is to select 800 features and split the dataset into train: 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 
		NOTE: Figure 5 shows the output information. The supplementary files required for this protocol are in HealthModel-mqTrans-v1-00.tar folder (Supplementary Coding File 1).</li>
	</ol>
	</li>
</ol>

GAT Framework for Identifying Dark Biomarkers in Transcriptomes

The authors have nothing to disclose.

Section 2 (Use the pre-trained HealthModel to generate the mqTrans features) of the protocol is the most critical step within this protocol. After preparing the computational working environment in section 1, section 2 generates the mqTrans view of a transcriptomic dataset based on the pre-trained large reference model. Section 3 is a demonstrative example of selecting the generated mqTrans features for biomarker detections and prediction tasks. The users can conduct other transcriptomic analyses on this mqTrans dataset ...

Transcriptome consists of the expressions of all the genes in a sample and may be profiled by high-throughput technologies like microarray and RNA-seq1. The expression levels of one gene in a dataset are called a transcriptomic feature, and the differential representation of a transcriptomic feature between the phenotype and control groups defines this gene as a biomarker of this phenotype2,3. Transcriptomic biomarkers have been extensively utilized in the investigations of disease diagnosis4, biological mechanism5, and survival analysis6,7, etc.
Gene activity patterns in the healthy tissues carry crucial information about the lives8,9. These patterns offer invaluable insights and act as ideal references for understanding the complex developmental trajectories of benign disorders10,11 and lethal diseases12. Genes interact with each other, and transcriptomes represent the final expression levels after their complicated interactions. Such patterns are formulated as transcriptional regulation network13 and metabolism network14, etc. The expressions of messenger RNAs (mRNAs) can be transcriptionally regulated by transcription factors (TFs) and long intergenic non-coding RNAs (lincRNAs)15,16,17. Conventional differential expression analysis ignored such complex gene interactions with the assumption of inter-feature independence18,19.
Recent advancements in graph neural networks (GNNs) demonstrate extraordinary potential in extracting important information from OMIC-based data for cancer studies20, e.g., identifying co-expression modules21. The innate capacity of GNNs renders them ideal for modeling the intricate relationships and dependencies among genes22,23.
Biomedical studies often focus on accurately predicting a phenotype against the control group. Such tasks are commonly formulated as binary classifications24,25,26. Here, the two class labels are typically encoded as 1 and 0, true and false, or even positive and negative27.
This study aimed to provide an easy-to-use protocol for generating the transcriptional regulation (mqTrans) view of a transcriptome dataset based on the pre-trained graph-attention network (GAT) reference model. The multitask GAT framework from a previously published work26 was used to transform transcriptomic features to the mqTrans features. A large dataset of healthy transcriptomes from the University of California, Santa Cruz (UCSC) Xena platform28 was used to pre-train the reference model (HealthModel), which quantitatively measured the transcription regulations from the regulatory factors (TFs and lincRNAs) to the target mRNAs. The generated mqTrans view could be used to build prediction models and detect dark biomarkers. This protocol utilizes the colon adenocarcinoma (COAD) patient dataset from The Cancer Genome Atlas (TCGA) database29 as an illustrative example. In this context, patients in stages I or II are categorized as negative samples, while those in stages III or IV are considered positive samples. The distributions of dark and traditional biomarkers across the 26 TCGA cancer types are also compared.
Description of the HealthModel pipeline 
The methodology employed in this protocol is based on the previously published framework26, as outlined in Figure 1. To commence, users are required to prepare the input dataset, feed it into the proposed HealthModel pipeline, and obtain mqTrans features. Detailed data preparation instructions are provided in section 2 of the protocol section. Subsequently, users have the option to combine mqTrans features with the original transcriptomic features or proceed with the generated mqTrans features only. The produced dataset is then subjected to a feature selection process, with users having the flexibility to choose their preferred value for k in k-fold cross-validation for classification. The primary evaluation metric utilized in this protocol is accuracy.
HealthModel26 categorizes the transcriptomic features into three distinct groups: TF (Transcription Factor), lincRNA (long intergenic non-coding RNA), and mRNA (messenger RNA). The TF features are defined based on the annotations available in the Human Protein Atlas30,31. This work utilizes the annotations of lincRNAs from the GTEx dataset32. Genes belonging to the third-level pathways in the KEGG database33 are considered as mRNA features. It is worth noting that if an mRNA feature exhibits regulatory roles for a target gene as documented in the TRRUST database34, it is reclassified into the TF class.
This protocol also manually generates the two example files for the gene IDs of regulatory factors (regulatory_geneIDs.csv) and target mRNA (target_geneIDs.csv). The pairwise distance matrix among the regulatory features (TFs and lincRNAs) is calculated by the Pearson correlation coefficients and clustered by the popular tool weighted gene co-expression network analysis (WGCNA)36 (adjacent_matrix.csv). Users can directly utilize the HealthModel pipeline together with these example configuration files to generate the mqTrans view of a transcriptomic dataset.
Technical details of HealthModel 
HealthModel represents the intricate relationships among TFs and lincRNAs as a graph, with the input features serving as the vertices denoted by V and an inter-vertex edge matrix designated as E. Each sample is characterized by K regulatory features, symbolized as VK&#215;1. Specifically, the dataset encompassed 425 TFs and 375 lincRNAs, resulting in a sample dimensionality of K = 425 + 375 = 800. To establish the edge matrix E, this work employed the popular tool WGCNA35. The pairwise weight linking two vertices represented as <img alt="Equation 1" src="/files/ftp_upload/66030/66030eq01.jpg" style="margin: auto;" /> and <img alt="Equation 2" src="/files/ftp_upload/66030/66030eq02.jpg" style="margin: auto;" />, is determined by the Pearson correlation coefficient. The gene regulatory network exhibits a scale-free topology36, characterized by the presence of hub genes with pivotal functional roles. We compute the correlation between two features or vertices, <img alt="Equation 1" src="/files/ftp_upload/66030/66030eq01.jpg" style="margin: auto;" /> and <img alt="Equation 2" src="/files/ftp_upload/66030/66030eq02.jpg" style="margin: auto;" />, using the topological overlap measure (TOM) as follows:
<img alt="Equation 3" src="/files/ftp_upload/66030/66030eq03.jpg" style="margin: auto;" />&#160; &#160; &#160;(1)
<img alt="Equation 4" src="/files/ftp_upload/66030/66030eq04.jpg" style="margin: auto;" />&#160; &#160; &#160;(2)
The soft threshold&#160;&#946; is calculated using the &#39;pickSoft Threshold&#39; function from the WGCNA package. The power exponential function aij is applied, where <img alt="Equation 5" src="/files/ftp_upload/66030/66030eq05.jpg" style="margin: auto;" />&#160;represents a gene excluding i&#160;and j, and <img alt="Equation 6" src="/files/ftp_upload/66030/66030eq06.jpg" style="margin: auto;" /> represents the vertex connectivity. WGCNA clusters the expression profiles of the transcriptomic features into multiple modules using a commonly employed dissimilarity measure (<img alt="Equation 7" src="/files/ftp_upload/66030/66030eq07.jpg" style="margin: auto;" />37.
The HealthModel framework was originally designed as a multitask learning architecture26. This protocol only utilizes the model pre-training task for the construction of the transcriptomic mqTrans view. The user may choose to further refine the pre-trained HealthModel under the multitask graph attention network with additional task-specific transcriptomic samples.
Technical details of feature selection and classification 
The feature selection pool implements eleven feature selection (FS) algorithms. Among them, three are filter-based FS algorithms: selecting K best features using the Maximal Information Coefficient (SK_mic), selecting K features based on the FPR of MIC (SK_fpr), and selecting K features with the highest false discovery rate of MIC (SK_fdr). Additionally, three tree-based FS algorithms assess individual features using a decision tree with the Gini index (DT_gini), adaptive boosted decision trees (AdaBoost), and random forest (RF_fs). The pool also incorporates two wrapper methods: Recursive feature elimination with the linear support vector classifier (RFE_SVC) and recursive feature elimination with the logistic regression classifier (RFE_LR). Finally, two embedding algorithms are included: linear SVC classifier with the top-ranked L1 feature importance values (lSVC_L1) and logistic regression classifier with the top-ranked L1 feature importance values (LR_L1).
The classifier pool employs seven different classifiers to build classification models. These classifiers comprise linear support vector machine (SVC), Gaussian Na&#239;ve Bayes (GNB), logistic regression classifier (LR), k-nearest neighbor, with k set to 5 by default (KNN), XGBoost, random forest (RF), and decision tree (DT).
The random split of the dataset into the train: test subsets can be set in the command line. The demonstrated example uses the ratio of train: 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

Transcriptome represents the expression levels of many genes in a sample and has been widely used in biological research and clinical practice. Researchers usually focused on transcriptomic biomarkers with differential representations between a phenotype group and a control group of samples. This study presented a multitask graph-attention network (GAT) learning framework to learn the complex inter-genic interactions of the reference samples. A demonstrative reference model was pre-trained on the healthy samples (HealthModel), which could be directly used to generate the model-based quantitative transcriptional regulation (mqTrans) view of the independent test transcriptomes. The generated mqTrans view of transcriptomes was demonstrated by prediction tasks and dark biomarker detection. The coined term &#34;dark biomarker&#34; stemmed from its definition that a dark biomarker showed differential representation in the mqTrans view but no differential expression in its original expression level. A dark biomarker was always overlooked in traditional biomarker detection studies due to the absence of differential expression. The source code and the manual of the pipeline HealthModelPipe can be downloaded from&#160;http://www.healthinformaticslab.org/supp/resources.php.

Evaluation of the mqTrans view of the transcriptomic dataset 
The test code uses eleven feature selection (FS) algorithms and seven classifiers to evaluate how the generated mqTrans view of the transcriptomic dataset contributes to the classification task (Figure 6). The test dataset consists of 317 colon adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) database29. The COAD patients at stages I or II are regarded as the negative samples,...

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

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Here, we introduce a protocol for converting transcriptomic data into a mqTrans view, enabling the identification of dark biomarkers. While not differentially expressed in conventional transcriptomic analyses, these biomarkers exhibit differential expression in the mqTrans view. The approach serves as a complementary technique to traditional methods, unveiling previously overlooked biomarkers.

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

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

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571 Views.  Jilin University. Here, we introduce a protocol for converting transcriptomic data into a mqTrans view, enabling the identification of dark biomarkers. While not differentially expressed in conventional transcriptomic analyses, these biomarkers exhibit differential expression in the mqTrans view. The approach serves as a complementary technique to traditional methods, unveiling previously overlooked biomarkers.

Video: Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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.

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, ...

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.

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.

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.

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.

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.

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.

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.

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.

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.