We would like to thank the members of Vancouver Institute of Visual Analytics (VIVA) for offering comments and advice for the project. In particular, we would like to thank John Dill, Brian Fisher, and David Darvill. We would also like to thank the members of the Kollmann lab for their support and helpful discussions. This work was supported in part by National Institute of Allergy and Infectious Diseases, National Institute of Health Grant N01 AI50023; AllerGen NCE Grants 07-A1A and 07-B2B; and the Michael Smith Foundation for Health Research. T.R.K. is supported in part by a Career Award in the Biomedical Science from the Burroughs Wellcome Fund ad by a Canadian Institutes for Health Research Training Grant in Canadian Child Health Clinician Scientist Program, in partnership with Sick-Kids Foundation, Child and Family Research Institute (British Columbia), Women and Children's Health Research Institute (Alberta), and Manitoba Institute of Child Health.

1. 	Exploration-based Analysis on Tableau
<ol>
<li>You have a dataset and you want to explore the various relationships between columns within the data.
<ol>
<li>(Figure 1) Import the dataset by clicking on 'Connect to data'. Choose the data source type and follow instructions to import your dataset.</li>
<li>(Figure 1) Make sure that the dimensions shelf contains categorical columns while the measures shelf contains numerical columns from your dataset. Sometimes a dimension column such as subject identifier may be listed as measure if it is numerated. Change it to dimensions by right-clicking on it and select 'Convert to Dimension'. In Figure 1, categorical columns from the dataset such as stimulus concentration level and subject identifiers are correctly placed in dimensions shelf, and the observed concentration of cytokines are correctly placed in measures shelf.</li>
<li>(Figure 2) If during the analysis process, a calculated field is needed, simply right click in either dimensions or measures shelf, and select 'Create Calculated Field'. Input the calculation in the formula box with fields combined with functions or mathematical operations. Figure 2 shows how to create a new field called PFD &gt; 2 by aggregating PFD2, PFD3, and PFD4 values.</li>
<li>(Figure 3) Generate a 2-D plot in Tableau, typically with a dimension and a measure of interest. Since the interface offers simple drag-and-drop interaction, the choice of dimension and measure can be easily changed. Dimensions are typically placed in columns shelf, and measures in rows shelf. In Figure 3, the dimensions shelf contains stimulus concentration level, and the measures shelf contains observed concentration level of cytokines.</li>
<li>(Figure 3) To differentiate the visualization by a specific dimension, place that dimension label in text, color or size shelf, depending on the nature of the dimension. Text shelf attempts to differentiate data in the visualization by labeling them with textual information provided by the dimension selected. Colour and size shelf will automatically differentiate data by color and size, which can be adjusted with the scroll bar located at the bottom of each shelf. In figure 3, placing genotype in color shelf leads to the separation of three genotypes by distinct colors.</li>
<li>(Figure 3) Filter the visualization by the specific column variables in other dimension. This can be done by dragging the dimension label into the filter shelf. A window will appear with the list of values to choose from. The selected value will be the only values remaining in the visualization.</li>
<li>(Figure 4) You may combine visualization with other dimension or measure to produce a matrix of visualization. This can be done by dropping multiple dimensions in columns shelf, and multiple measures in rows shelf. Figure 4 contains an example of two-column visualization matrix by placing two different dimensions in the columns shelf.</li>
<li>To produce a similar visualization, right click on the current sheet on the bottom left hand corner, and selected 'duplicate sheet'.</li>
<li>Iterate the analysis process to identify interesting trends or outliers that may help you generate new hypothesis about the data.</li>
</ol>
</li>
<li>You have multiple dataset generated from the same source, and want to explore the possible connections between these datasets.
<ol>
<li>Place multiple datasets in a same source, i.e. place two spreadsheets into the same workbook, to allow Tableau to connect to these dataset through the same connection.</li>
<li>(Figure 5) Connect the datasets through logical join of key values, making sure that the dimensions that are the same for multiple datasets are selected. In Figure 5, there are five key values for joining: cell type, stimulus concentration level, stage or group, stimulus, and subject identifier.</li>
<li>Perform analysis similar to section 1.1.</li>
</ol>
</li>
</ol>
2. 	Presentation-based Needs
<ol>
<li>You know the relationships between data, but you want to be able to quickly generate a list of visualizations for use in presentation.
<ol>
<li>Follow protocol outlined in section 1.1, and annotate the visualizations accordingly.</li>
<li>Use the export function in Tableau to produce images of visualizations.</li>
</ol>
</li>
<li>You have generated a set of powerful visualizations in Tableau workbook and want to share that workbook.
<ol>
<li>Save the workbook as Tableau packaged workbook and share this file.</li>
<li>Colleagues without Tableau Desktop can download Tableau Reader to open the packaged workbook. Tableau Reader allows your colleagues to interact with the visualizations that you have created.</li>
</ol>
</li>
</ol>
3. REPRESENTATIVE RESULTS

 <img src="/files/ftp_upload/2397/2397fig1.jpg" alt="Figure 1" /> 
Figure 1. A screenshot of Tableau after importing the spreadsheet named NFKBIA from the Excel file demo.xls. The dimensions and measures shelves were properly populated with the categorical and numerical data, respectively.
 <img src="/files/ftp_upload/2397/2397fig2.jpg" alt="Figure 2" /> 
Figure 2. The Calculated Field window is invoked to create a special calculated field to use in Tableau. The list on the bottom left-hand box helps identify possible fields, and the list on the right-hand side contains abbreviation of functions that can be used in the formula. In this example, we wanted to add the values for PFD4, PFD3 and PFD2 to obtain the final value that we refer to as PFD &gt; 2
 <img src="/files/ftp_upload/2397/2397fig3.jpg" alt="Figure 3" /> 
Figure 3. Visualization of stimulus concentration level vs. observed cytokine concentration. The visualization shows a plot of the different concentration levels of the stimulus 3M-002 against the observed concentration of the cytokine TNF-&alpha;. The colors of the lines refer to the different genotypes for a single-nucleotide polymorphism in the NFKBIA gene of the individuals in our innate immune study.
 <img src="/files/ftp_upload/2397/2397fig4.jpg" alt="Figure 4" /> 
Figure 4. A screenshot of a two-column visualization matrix. We generated a two-column matrix to facilitate a side-by-side comparison of responses to two stimuli, 3M-003 and LPS. The x-axes are the different concentration levels of the two stimuli, and the y-axis plots the values of the calculated field, PFD &gt; 2. 
 <img src="/files/ftp_upload/2397/2397fig5.jpg" alt="Figure 5" /> 
Figure 5. These Tableau dialogue windows illustrate how to connect data recorded in different spreadsheets. Connecting data from different spreadsheets can be accomplished by combining these using logical join clauses of key values.

<table border="1">
<tbody>
 <tr>
 <td>&nbsp;</td>
 <td colspan="10">Visualization and Analysis Tool</td>
 </tr>
 <tr>
 <td>Function</td>
 <td>Tableau</td>
 <td>VIS-STAMP</td>
 <td>xmdvtool</td>
 <td>GGobi</td>
 <td>Starlight</td>
 <td>Gapminder</td>
 <td>Visulab</td>
 <td>InfoVis Toolkit</td>
 <td>Geotime</td>
 <td>Inspire</td>
 </tr>
 <tr>
 <td>Parallel coordinate plots</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Scatter plot matrixes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Dimensional reduction</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Temporal dimensions</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Geospatial dimensions</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Text mining</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>Direct manipulation of data</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Filtering</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>Extensibility to other platforms (e.g., R)</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 </tr>
 <tr>
 <td>CSV table formats</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 </tr>
 <tr>
 <td>XML data formats</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>No</td>
 </tr>
 <tr>
 <td>Can deal with 10000+ rows</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>Documentation</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>Commercial product</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>No</td>
 <td>No</td>
 <td>No</td>
 <td>Yes</td>
 <td>Yes</td>
 </tr>
</tbody>
</table>
Table 1. List of visual analytics tools and some of their features.

<ol>
	<li>Thomas, J., Cook, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Illuminating+the+Path:+The+Research+and+Development+Agenda+for+Visual+Analytics.">Illuminating the Path: The Research and Development Agenda for Visual Analytics.</a> , (2005).</li><li>Steed, C., Swan, J., Jankun-Kelly, T., Fitzpatrick, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guided+Analysis+of+Hurricane+Trends+Using+Statistical+Processes+Integrated+with+Interactive+Parallel+Coordinates.">Guided Analysis of Hurricane Trends Using Statistical Processes Integrated with Interactive Parallel Coordinates.</a> IEEE Symposium on Visual Analytics Science and Technology. , 19-26 (2009).</li><li>Saraiya, P., North, C., Lam, V., Duca, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+Insight-Based+Longitudinal+Study+of+Visual+Analytics.">An Insight-Based Longitudinal Study of Visual Analytics.</a> IEEE Transactions on Visualization and Computer Graphics. 12, 1511-1522 (2006).</li><li>Kollmann, T. R., Crabtree, J., Rein-Weston, A., Blimkie, D., Thommai, F., Wang, X. Y., Lavoie, P. M., Furlong, J., Fortuno, E. S., Hajjar, A. M., Hawkins, N. R., Self, S. G., Wilson, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neonatal+innate+TLR-mediated+responses+are+distinct+from+those+of+adults.">Neonatal innate TLR-mediated responses are distinct from those of adults.</a> J. Immunol. 183, 7150-7160 (2009).</li></ol>

No conflicts of interest declared.

The advent of high-throughput technology in modern biomedical research led to an explosion of research data that requires a more efficient way of analysis. Visual analytics (VA) is the science of analytical reasoning facilitated by interactive visual interfaces (1). The VA approach restores the analytical power in the hands of human analyst, contrary to the traditional approach to detect patterns by computer. Visual analytics has been applied to research in various fields, such as defense research (1) and hurricane trends (2). So far, there are only a few examples of VA applications in biology (3). We demonstrated in this video article that VA is an approach that can be added to the biologist's arsenal of analysis tools. Many VA softwares are available ranging from those that are in development in academic labs to those that are commercially-available. For our work on neonatal innate immunity (4), we chose Tableau because of its suitability in analyzing spreadsheet-style datasets available in the lab. Other VA tools, some of which we mentioned in our video article, may be more appropriate for other types of biological data. We listed the functions and characteristics of some of the more popular VA tools in Table 1. This list is not meant to be exhaustive because it is beyond the scope of our study, but it should be a good starting point for scientists to determine the suitable VA tool for their specific datasets.
There are two major points about VA that we would like to highlight. One, the VA approach is intended as an exploration process by helping the analyst quickly spot patterns such as general trends and outliers in the data. The main focus of VA is to provide a powerful visualization technique for large datasets. It is not an alternative to statistical analysis. In fact, most of the VA tools are very limited in their ability to perform statistical analysis although we anticipate this to change in the near future. The second point we want to mention is that the data pre-processing prior to importing the dataset to a VA tool is crucial for the success of the analysis. Bear in mind that data presented in a human-readable fashion in spreadsheets are sometimes different from a machine-readable format. Data pre-processing can be a time-consuming step, often requiring massive transformation of the data, if the original format of the data is not suitable to the VA tool. We highly recommend the careful planning of data entry and encoding to allow flexible and efficient uploads to downstream analysis software tools such as those available in VA.

facilitating the analysis of immunological data with visual analytic techniques

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the flexibility of a VA approach in the analysis of biological datasets. Examples of these datasets in immunology include flow cytometry, Luminex data, and genotyping (e.g., single nucleotide polymorphism) data. Contrary to the traditional information visualization approach, VA restores the analysis power in the hands of analyst by allowing the analyst to engage in real-time data exploration process. We selected the VA software called Tableau after evaluating several VA tools. Two types of analysis tasks analysis within and between datasets were demonstrated in the video presentation using an approach called paired analysis. Paired analysis, as defined in VA, is an analysis approach in which a VA tool expert works side-by-side with a domain expert during the analysis. The domain expert is the one who understands the significance of the data, and asks the questions that the collected data might address. The tool expert then creates visualizations to help find patterns in the data that might answer these questions. The short lag-time between the hypothesis generation and the rapid visual display of the data is the main advantage of a VA approach.

Watch this Scientific Journal Video about Facilitating the Analysis of Immunological Data with Visual Analytic Techniques at JoVE.com

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Visual analytics (VA) is a new approach of analyzing data interactively. In this video, we discuss the data overload problem brought on by high-throughput biological experiments, and propose VA as a solution to such problem. The video demonstrates analysis within and between immunological datasets using a VA tool called Tableau.

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the ...

facilitating-analysis-immunological-data-with-visual-analytic

Universidad San Sebastian

Research

JoVE Journal

Immunology and Infection

10.0K Views.  University of British Columbia. Visual analytics (VA) is a new approach of analyzing data interactively. In this video, we discuss the data overload problem brought on by high-throughput biological experiments, and propose VA as a solution to such problem. The video demonstrates analysis within and between immunological datasets using a VA tool called Tableau.

Video: Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Visualization of the Immunological Synapse by Dual Color Time-gated Stimulated Emission Depletion (STED) Nanoscopy

Natural killer cells form tightly regulated, finely tuned immunological synapses (IS) in order to lyse virally infected or tumorigenic cells. Dynamic actin reorganization is critical to the function of NK cells and the formation of the IS. Imaging of F-actin at the synapse has traditionally utilized confocal microscopy, however the diffraction limit of light restricts resolution of fluorescence microscopy, including confocal, to approximately 200 nm. Recent advances in imaging technology have enabled the development of subdiffraction limited super-resolution imaging. In order to visualize F-actin architecture at the IS we recapitulate the NK cell cytotoxic synapse by adhering NK cells to activating receptor on glass. We then image proteins of interest using two-color stimulated emission depletion microscopy (STED). This results in &#60;80 nm resolution at the synapse. Herein we describe the steps of sample preparation and the acquisition of images using dual color STED nanoscopy to visualize F-actin at the NK IS. We also illustrate optimization of sample acquisition using Leica SP8 software and time-gated STED. Finally, we utilize Huygens software for post-processing deconvolution of images.

Visualization of the Immunological Synapse by Dual Color Time-gated Stimulated Emission Depletion STED Nanoscopy

Here we illustrate the protocol for imaging by two-color STED nanoscopy the cytotoxic immune synapse of NK cells recapitulated on glass. Using this method we obtain sub-100 nm resolution of synapse proteins and the cytoskeleton.

Natural killer cells form tightly regulated, finely tuned immunological synapses (IS) in order to lyse virally infected or tumorigenic cells. Dynamic ...

An Endothelial Planar Cell Model for Imaging Immunological Synapse Dynamics

Adaptive immunity is regulated by dynamic interactions between T cells and antigen presenting cells (&#39;APCs&#39;) referred to as &#39;immunological synapses&#39;. Within these intimate cell-cell interfaces discrete sub-cellular clusters of MHC/Ag-TCR, F-actin, adhesion and signaling molecules form and remodel rapidly. These dynamics are thought to be critical determinants of both the efficiency and quality of the immune responses that develop and therefore of protective versus pathologic immunity. Current understanding of immunological synapses with physiologic APCs is limited by the inadequacy of the obtainable imaging resolution. Though artificial substrate models (e.g., planar lipid bilayers) offer excellent resolution and have been extremely valuable tools, they are inherently non-physiologic and oversimplified. Vascular and lymphatic endothelial cells have emerged as an important peripheral tissue (or stromal) compartment of &#39;semi-professional APCs&#39;. These APCs (which express most of the molecular machinery of professional APCs) have the unique feature of forming virtually planar cell surface and are readily transfectable (e.g., with fluorescent protein reporters). Herein a basic approach to implement endothelial cells as a novel and physiologic &#39;planar cellular APC model&#39; for improved imaging and interrogation of fundamental antigenic signaling processes will be described.

Adaptive immunity is controlled by dynamic 'immunological synapses' formed between T cells and antigen presenting cells. This protocol describes methods for investigating endothelial cells both as understudied physiologic APCs and as a novel type of 'planar cellular APC model'.

Adaptive immunity is regulated by dynamic interactions between T cells and antigen presenting cells (&#39;APCs&#39;) referred to as &#39;immunological ...

Analysis of Lymphocyte Extravasation Using an In Vitro Model of the Human Blood-brain Barrier

Lymphocyte extravasation into the central nervous system (CNS) is critical for immune surveillance. Disease-related alterations of lymphocyte extravasation might result in pathophysiological changes in the CNS. Thus, investigation of lymphocyte migration into the CNS is important to understand inflammatory CNS diseases and to develop new therapy approaches. Here we present an in vitro model of the human blood-brain barrier to study lymphocyte extravasation. Human brain microvascular endothelial cells (HBMEC) are confluently grown on a porous polyethylene terephthalate transwell insert to mimic the endothelium of the blood-brain barrier. Barrier function is validated by zonula occludens immunohistochemistry, transendothelial electrical resistance (TEER) measurements as well as analysis of evans blue permeation. This model allows investigation of the diapedesis of rare lymphocyte subsets such as CD56brightCD16dim/- NK cells. Furthermore, the effects of other cells, cytokines and chemokines, disease-related alterations, and distinct treatment regimens on the migratory capacity of lymphocytes can be studied. Finally, the impact of inflammatory stimuli as well as different treatment regimens on the endothelial barrier can be analyzed.

Analysis of Lymphocyte Extravasation Using an In Vitro Model of the Human Blood-brain Barrier

Here, we describe a human blood-brain barrier model enabling to investigate lymphocyte transmigration into the central nervous system in vitro.

Lymphocyte extravasation into the central nervous system (CNS) is critical for immune surveillance. Disease-related alterations of lymphocyte ...

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.

ExCYT is a MATLAB-based Graphical User Interface (GUI) that allows users to analyze their flow cytometry data via commonly employed analytical techniques for high-dimensional data including dimensionality reduction via t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots.

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically ...

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

This metagenome approach is used to identify plant viruses with circular DNA genomes and their transcripts. Often plant DNA viruses that occur in low titers in their host or cannot be mechanically inoculated to another host are difficult to propagate to achieve a greater titer of infectious material. Infected leaves are ground in a mild buffer with optimal pH and ionic composition recommended for purifying most bacilliform Para retroviruses. Urea is used to break up inclusion bodies that trap virions and to dissolve cellular components. Differential centrifugation provides further separation of virions from plant contaminants. Then proteinase K treatment removes the capsids. Then the viral DNA is concentrated and used for next-generation sequencing (NGS). The NGS data are used to assemble contigs which are submitted to NCBI-BLASTn to identify a subset of virus sequences in the generated dataset. In a parallel pipeline, RNA is isolated from infected leaves using a standard column-based RNA extraction method. Then ribosome depletion is carried out to enrich for a subset of mRNA and virus transcripts. Assembled sequences derived from RNA sequencing (RNA-seq) were submitted to NCBI-BLASTn to identify a subset of virus sequences in this dataset. In our study, we identified two related full-length badnavirus genomes in the two datasets. This method is preferred to another common approach which extracts the aggregate population of small RNA sequences to reconstitute plant virus genomic sequences. This latter metagenomic pipeline recovers virus related sequences that are retro-transcribing elements inserted into the plant genome. This is coupled to biochemical or molecular assays to further discern the actively infectious agents. The approach documented in this study, recovers sequences representative of replicating viruses that likely indicate active virus infection.

Here we present a new approach to identify plant viruses with double-strand DNA genomes. We use standard methods to extract DNA and RNA from infected leaves and carry out next-generation sequencing. Bioinformatic tools assemble sequences into contigs, identify contigs representing virus genomes and assign genomes to taxonomic groups.

This metagenome approach is used to identify plant viruses with circular DNA genomes and their transcripts. Often plant DNA viruses that occur in low ...

Complementary Use of Microscopic Techniques and Fluorescence Reading in Studying Cryptococcus-Amoeba Interactions

To simulate Cryptococcus infection, amoeba, which is the natural predator of cryptococcal cells in the environment, can be used as a model for macrophages. This predatory organism, similar to macrophages, employs phagocytosis to kill internalized cells. With the aid of a confocal laser-scanning microscope, images depicting interactive moments between cryptococcal cells and amoeba are captured. The resolution power of the electron microscope also helps to reveal the ultrastructural detail of cryptococcal cells when trapped inside the amoeba food vacuole. Since phagocytosis is a continuous process, quantitative data is then integrated in the analysis to explain what happens at the timepoint when an image is captured. To be specific, relative fluorescence units are read in order to quantify the efficiency of amoeba in internalizing cryptococcal cells. For this purpose, cryptococcal cells are stained with a dye that makes them fluoresce once trapped inside the acidic environment of the food vacuole. When used together, information gathered through such techniques can provide critical information to help draw conclusions on the behavior and fate of cells when internalized by amoeba and, possibly, by other phagocytic cells.

Complementary Use of Microscopic Techniques and Fluorescence Reading in Studying Cryptococcus-Amoeba Interactions

This paper details a protocol for preparing a co-culture of cryptococcal cells and amoebae that is studied using still, fluorescent images and high-resolution transmission electron microscope images. Illustrated here is how quantitative data can complement such qualitative information.

To simulate Cryptococcus infection, amoeba, which is the natural predator of cryptococcal cells in the environment, can be used as a model for ...

Opsonophagocytic Killing Assay to Assess Immunological Responses Against Bacterial Pathogens

A key aspect of the immune response to bacterial colonization of the host is phagocytosis. An opsonophagocytic killing assay (OPKA) is an experimental procedure in which phagocytic cells are co-cultured with bacterial units. The immune cells will phagocytose and kill the bacterial cultures in a complement-dependent manner. The efficiency of the immune-mediated cell killing is dependent on a number of factors and can be used to determine how different bacterial cultures compare with regard to resistance to cell death. In this way, the efficacy of potential immune-based therapeutics can be assessed against specific bacterial strains and/or serotypes. In this protocol, we describe a simplified OPKA that utilizes basic culture conditions and cell counting to determine bacterial cell viability after co-culture with treatment conditions and HL-60 immune cells. This method has been successfully utilized with a number of different pneumococcal serotypes, capsular and acapsular strains, and other bacterial species. The advantages of this OPKA protocol are its simplicity, versatility (as this assay is not limited to antibody treatments as opsonins), and minimization of time and reagents to assess basic experimental groups.

This opsonophagocytic killing assay is used to compare the ability of phagocytic immune cells to respond to and kill bacteria based on different treatments and/or conditions. Classically, this assay serves as the gold&#160;standard for assessing effector functions of antibodies raised against a bacterium as opsonin.

A key aspect of the immune response to bacterial colonization of the host is phagocytosis. An opsonophagocytic killing assay (OPKA) is an experimental ...

Application of Consistent Massage-Like Perturbations on Mouse Calves and Monitoring the Resulting Intramuscular Pressure Changes

Massage is generally recognized to be beneficial for relieving pain and inflammation. Although previous studies have reported anti-inflammatory effects of massage on skeletal muscles, the molecular mechanisms behind are poorly understood. We have recently developed a simple device to apply local cyclical compression (LCC), which can generate intramuscular pressure waves with varying amplitudes. Using this device, we have demonstrated that LCC modulates inflammatory responses of macrophages in situ and alleviates immobilization-induced muscle atrophy. Here, we describe protocols for the optimization and application of LCC as a massage-like intervention against immobilization-induced inflammation and atrophy of skeletal muscles of mouse hindlimbs. The protocol that we have developed can be useful for investigating the mechanism underlying beneficial effects of physical exercise and massage. Our experimental system provides a prototype of the analytical approach to elucidate the mechanical regulation of muscle homeostasis, although further development needs to be made for more comprehensive studies.

Here we describe the protocols for applying defined mechanical loads to mouse calves and for monitoring the concomitant intramuscular pressure changes. The experimental systems that we have developed can be useful for investigating the mechanism behind the beneficial effects of physical exercise and massage.

Massage is generally recognized to be beneficial for relieving pain and inflammation. Although previous studies have reported anti-inflammatory effects of ...

Functionalization of Atomic Force Microscope Cantilevers with Single-T Cells or Single-Particle for Immunological Single-Cell Force Spectroscopy

Atomic force microscopy based single cell force spectroscopy (AFM-SCFS) is a powerful tool for studying biophysical properties of living cells. This technique allows for probing interaction strengths and dynamics on a live cell membrane, including those between cells, receptor and ligands, and alongside many other variations. It also works as a mechanism to deliver a physical or biochemical stimulus on single cells in a spatiotemporally controlled manner, thus allowing specific cell activation and subsequent cellular events to be monitored in real-time when combined with live-cell fluorescence imaging. The key step in those AFM-SCFS measurements is AFM-cantilever functionalization, or in other words, attaching a subject of interest to the cantilever. Here, we present methods to modify AFM cantilevers with a single T cell and a single polystyrene bead respectively for immunological studies. The former involves a biocompatible glue that couples single T cells to the tip of a flat cantilever in a solution, while the latter relies on an epoxy glue for single bead adhesion in the air environment. Two immunological applications associated with each cantilever modification are provided as well. The methods described here can be easily adapted to different cell types and solid particles.

We present a protocol to functionalize atomic force microscope (AFM) cantilevers with a single T cell and bead particle for immunological studies. Procedures to probe single-pair T cell-dendritic cell binding by AFM and to monitor the real-time cellular response of macrophages to a single solid particle by AFM with fluorescence imaging are shown.

Atomic force microscopy based single cell force spectroscopy (AFM-SCFS) is a powerful tool for studying biophysical properties of living cells. This ...

Imaging the Human Immunological Synapse

The purpose of the method is to generate an immunological synapse (IS), an example of cell-to-cell conjugation formed by an antigen-presenting cell (APC) and an effector helper T lymphocyte (Th) cell, and to record the images corresponding to the first stages of the IS formation and the subsequent trafficking events (occurring both in the APC and in the Th cell). These events will eventually lead to polarized secretion at the IS. In this protocol, Jurkat cells challenged with Staphylococcus enterotoxin E (SEE)-pulsed Raji cells as a cell synapse model was used, because of the closeness of this experimental system to the biological reality (Th cell-APC synaptic conjugates). The approach presented here involves cell-to-cell conjugation, time-lapse acquisition, wide-field fluorescence microscopy (WFFM) followed by image processing (post-acquisition deconvolution). This improves the signal-to-noise ratio (SNR) of the images, enhances the temporal resolution, allows the synchronized acquisition of several fluorochromes in emerging synaptic conjugates and decreases fluorescence bleaching. In addition, the protocol is well matched with the end point cell fixation protocols (paraformaldehyde, acetone or methanol), which would allow further immunofluorescence staining and analyses. This protocol is also compatible with laser scanning confocal microscopy (LSCM) and other state-of-the-art microscopy techniques. As a main caveat, only those T cell-APC boundaries (called IS interfaces) that were at the right 90&#176; angle to the focus plane along the Z-axis could be properly imaged and analyzed. Other experimental models exist that simplify imaging in the Z dimension and the following image analyses, but these approaches do not emulate the complex, irregular surface of an APC, and may promote non-physiological interactions in the IS. Thus, the experimental approach used here is suitable to reproduce and to confront some biological complexities occurring at the IS.

This protocol images both the immunological synapse formation and the subsequent polarized secretory traffic towards the immunological synapse. Cellular conjugates were formed between a superantigen-pulsed Raji cell (acting as an antigen-presenting cell) and a Jurkat clone (acting as an effector helper T lymphocyte).

The purpose of the method is to generate an immunological synapse (IS), an example of cell-to-cell conjugation formed by an antigen-presenting cell (APC) ...