Nos gustaría agradecer a los miembros de Vancouver Instituto de Visual Analytics (VIVA) para ofrecer comentarios y consejos para el proyecto. En particular, nos gustaría agradecer a John Dill, Brian Fisher, y Darvill David. También nos gustaría agradecer a los miembros del laboratorio de Kollmann por su apoyo y sus útiles comentarios. Este trabajo fue apoyado en parte por el Instituto Nacional de Alergias y Enfermedades Infecciosas, Instituto Nacional de Salud Grant N01 AI50023; Becas alérgenos NCE 07-A1A y 07 B2B-, y la Michael Smith Fundación para la Investigación en Salud. TRK es apoyado en parte por una concesión de carrera en la ciencia biomédica del anuncio Fondo Burroughs Wellcome por Institutos Canadienses de Salud de subvención en el Programa de Capacitación en Investigación Canadiense de Salud Infantil Científico Médica, en colaboración con la Fundación Niños Enfermos, Niño y la Familia del Instituto de Investigación ( Columbia Británica), la Mujer y la Infancia del Instituto de Investigación de la Salud (Alberta), Manitoba y el Instituto de Salud Infantil.

1. Exploración basada en el análisis de Tableau <ol><li> Usted tiene un conjunto de datos y desea explorar las diversas relaciones entre las columnas de los datos. <ol><li> (Figura 1) Importar el conjunto de datos, haga clic en &quot;Conectar a los datos. Seleccione el tipo de fuente de datos y siga las instrucciones para importar el conjunto de datos. </li><li> (Figura 1) Asegúrese de que la plataforma contiene columnas de dimensiones categóricas a la vez la plataforma de medidas contiene las columnas numéricas del conjunto de datos. En ocasiones una columna de dimensión, tales como identificador de objeto se puede enumerar como medida si es numerado. El cambio a las dimensiones haciendo clic derecho sobre él y seleccione &quot;Convertir a la dimensión&quot;. En la Figura 1, las columnas del conjunto de datos categóricos como el nivel de concentración de estímulo y los identificadores de objeto se colocan correctamente en la plataforma dimensiones, y la concentración observada de las citoquinas están correctamente colocados en el estante medidas. </li><li> (Figura 2) Si durante el proceso de análisis, un campo calculado que se necesita, simplemente haga clic derecho en cualquiera de las dimensiones o la plataforma de medidas, y seleccione &quot;Crear un campo calculado. De entrada para el cálculo en el cuadro de fórmula con campos combinados con las funciones u operaciones matemáticas. La figura 2 muestra cómo crear un nuevo campo llamado PFD&gt; 2 mediante la agregación de PFD2, PFD3, y los valores PFD4. </li><li> (Figura 3) Generar un gráfico 2-D en el Tableau, por lo general con una dimensión y una medida de interés. Puesto que la interfaz ofrece una sencilla función de arrastrar y soltar la interacción, la elección de la dimensión y la medida puede ser cambiado fácilmente. Las dimensiones se suelen colocar en el estante columnas, y las medidas de conservación en las filas. En la figura 3, la plataforma dimensiones contiene un nivel de concentración de estímulo, y la plataforma contiene las medidas de nivel de concentración observada de las citoquinas. </li><li> (Figura 3) Para diferenciar la visualización de una dimensión específica, lugar que marca la dimensión en la plataforma de texto, color o tamaño, dependiendo de la naturaleza de la dimensión. Estante texto intenta diferenciar los datos en la visualización, mediante el etiquetado con información textual proporcionada por la dimensión seleccionada. Estante de color y el tamaño automáticamente diferenciar los datos por color y tamaño, que se puede ajustar con la barra de desplazamiento situada en la parte inferior de cada estante. En la figura 3, colocando en el estante de color genotipo conduce a la separación de los tres genotipos de distintos colores. </li><li> (Figura 3) Filtrar la visualización de las variables de columna específica en otra dimensión. Esto se puede hacer arrastrando la etiqueta de dimensión en la plataforma de filtrado. Aparecerá una ventana con la lista de valores para elegir. El valor seleccionado será el único valor que queda en la visualización. </li><li> (Figura 4) Usted puede combinar la visualización con otra dimensión o medida para producir una matriz de visualización. Esto puede hacerse dejando caer las múltiples dimensiones en el estante columnas, y múltiples medidas de conservación filas. Figura 4 muestra un ejemplo de dos columnas de la matriz de visualización mediante la colocación de dos dimensiones diferentes en la plataforma columnas. </li><li> Para producir una visualización similar, haga clic derecho en la hoja actual en la esquina inferior izquierda, y selecciona &quot;hoja duplicada. </li><li> Iterar el proceso de análisis para identificar las tendencias interesantes o atípicos que pueden ayudarle a generar nuevas hipótesis sobre los datos. </li></ol></li><li> Usted tiene múltiples conjuntos de datos generados a partir de la misma fuente, y desea explorar las posibles conexiones entre estos conjuntos de datos. <ol><li> Coloque varios conjuntos de datos en una misma fuente, es decir, dos hojas de cálculo a cabo en el mismo libro, para que Tableau para conectarse a estos datos a través de la misma conexión. </li><li> (Figura 5) Conectar los conjuntos de datos a través de unirse lógica de los valores clave, asegurándose de que las dimensiones que son los mismos para varios conjuntos de datos seleccionados. En la figura 5, hay cinco valores clave para ingresar: identificador del tipo de célula, nivel de concentración de estímulo, etapa o grupo, el estímulo, y el sujeto. </li><li> Realizar un análisis similar a la sección 1.1. </li></ol></li></ol> 2. Basado en la presentación-las necesidades <ol><li> Usted sabe las relaciones entre los datos, pero desea ser capaz de generar rápidamente una lista de visualizaciones para su uso en la presentación. <ol><li> Siga el protocolo descrito en la sección 1.1, y anotar las visualizaciones en consecuencia. </li><li> Utilice la función de exportación en el Tableau para producir imágenes de visualizaciones. </li></ol></li><li> Que han generado una serie de visualizaciones de gran alcance en el libro de Tableau y queremos compartir ese libro. <ol><li> Guarde el libro como libro Tableau envasados ​​y compartir este archivo. </li><li> Colegas sin escritorio Tableau puede descargar lector de Tableau para abrir el libro envasados. Lector de Tableau permite a sus colegas para interactuar con las visualizaciones que ha creado. </li></ol></li></ol> 3. Los resultados representativos <img src="/files/ftp_upload/2397/2397fig1.jpg" alt="Figura 1" /> Figura 1. Una captura de pantallade Tableau después de importar la hoja de cálculo llamada NFKBIA de la demo.xls archivo de Excel. Las dimensiones y medidas de los estantes estaban pobladas correctamente con los datos categóricos y numéricos, respectivamente. <img src="/files/ftp_upload/2397/2397fig2.jpg" alt="Figura 2" /> Figura 2. La ventana de campo calculado que se invoca para crear un campo especial calculada para su uso en el Tapete. La lista en la parte inferior izquierda de caja ayuda a identificar posibles campos, y la lista de la derecha contiene la abreviatura de las funciones que se pueden utilizar en la fórmula. En este ejemplo, queremos sumar los valores de PFD4, PFD3 y PFD2 para obtener el valor final que nos referimos como PFD&gt; 2 <img src="/files/ftp_upload/2397/2397fig3.jpg" alt="Figura 3" /> Figura 3. Visualización del nivel de estímulo de concentración frente a la concentración de citoquinas observados. La visualización muestra un gráfico de los diferentes niveles de concentración del estímulo 3M-002 frente a la concentración observada de la citoquina TNF-α. Los colores de las líneas se refieren a los diferentes genotipos para un polimorfismo de un solo nucleótido en el gen NFKBIA de los individuos en nuestro estudio inmunológico innato. <img src="/files/ftp_upload/2397/2397fig4.jpg" alt="Figura 4" /> Figura 4. Una captura de pantalla de visualización de una matriz de dos columnas. Hemos generado una matriz de dos columnas para facilitar una comparación lado a lado de las respuestas a dos estímulos, 3M-003 y LPS. El x-ejes son los diferentes niveles de concentración de los dos estímulos, y las parcelas del eje y los valores del campo calculado, PFD&gt; 2. <img src="/files/ftp_upload/2397/2397fig5.jpg" alt="Figura 5" /> Figura 5. Estas ventanas de diálogo Tableau ilustran cómo conectar los datos registrados en las hojas de cálculo diferente. Conexión de datos de hojas de cálculo diferente se puede lograr mediante la combinación de estos con lógica unirse a las cláusulas de los valores clave. <table border="1"><tbody><tr><td></td><td colspan="10"> Visualización y análisis de herramientas </td></tr><tr><td> Función </td><td> Cuadro </td><td> VIS-STAMP </td><td> xmdvtool </td><td> GGobi </td><td> Luz de las estrellas </td><td> Gapminder </td><td> Visulab </td><td> InfoVis Toolkit </td><td> Geotime </td><td> Inspirar </td></tr><tr><td> Paralelo coordinar las parcelas </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td><td> No </td><td> No </td></tr><tr><td> Matrices gráfico de dispersión </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> No </td><td> No </td><td> No </td></tr><tr><td> Reducción dimensional </td><td> No </td><td> No </td><td> Sí </td><td> Sí </td><td> No </td><td> No </td><td> No </td><td> No </td><td> No </td><td> No </td></tr><tr><td> Dimensiones temporales </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td><td> No </td></tr><tr><td> Geoespaciales dimensiones </td><td> Sí </td><td> Sí </td><td> No </td><td> No </td><td> Sí </td><td> Sí </td><td> No </td><td> No </td><td> Sí </td><td> No </td></tr><tr><td> De minería de texto </td><td> No </td><td> No </td><td> No </td><td> No </td><td> Sí </td><td> No </td><td> No </td><td> No </td><td> No </td><td> Sí </td></tr><tr><td> La manipulación directa de los datos </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> No </td></tr><tr><td> Filtración </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td></tr><tr><td> De ampliación para otras plataformas (por ejemplo, R) </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> No </td><td> Sí </td><td> No </td><td> No </td></tr><tr><td> CSV formatos de tabla </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td><td> No </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td></tr><tr><td> XML formatos de datos </td><td> Sí </td><td> No </td><td> No </td><td> Sí </td><td> Sí </td><td> No </td><td> Sí </td><td> Sí </td><td> Sí </td><td> No </td></tr><tr><td> Puede hacer frente a una0000 + filas </td><td> Sí </td><td> No </td><td> No </td><td> No </td><td> Sí </td><td> No </td><td> No </td><td> No </td><td> No </td><td> Sí </td></tr><tr><td> Documentación </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td><td> Sí </td></tr><tr><td> Producto comercial </td><td> Sí </td><td> No </td><td> No </td><td> No </td><td> Sí </td><td> No </td><td> No </td><td> No </td><td> Sí </td><td> Sí </td></tr></tbody></table> Tabla 1. Lista de herramientas visuales de análisis y algunas de sus características.

<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>

El advenimiento de la tecnología de alto rendimiento en la investigación biomédica moderna llevó a una explosión de datos de investigación que requiere de una forma más eficiente de los análisis. La analítica visual (AV) es la ciencia del razonamiento analítico facilitada por interfaces interactivas visuales (1). El enfoque VA restaura el poder analítico en las manos del analista humano, a diferencia del enfoque tradicional para detectar patrones por ordenador. La analítica visual se ha aplicado a la investi...

facilitating the analysis of immunological data with visual analytic techniques

La analítica visual (VA) ha surgido como una nueva forma de analizar la gran base de datos a través de pantallas de visualización interactiva. Hemos demostrado la utilidad y la flexibilidad de un enfoque de VA en el análisis de conjuntos de datos biológicos. Ejemplos de estos conjuntos de datos en inmunología incluyen la citometría de flujo, los datos de Luminex, y el genotipo (por ejemplo, el polimorfismo de nucleótido único) de datos. A diferencia del enfoque tradicional de visualización de información, VA restaura el poder de análisis en las manos del analista, al permitir que el analista de participar en tiempo real proceso de exploración de datos. Hemos seleccionado el software VA llamado Tableau después de evaluar varias herramientas de VA. Hay dos tipos de tareas de análisis de análisis dentro y entre los conjuntos de datos se demuestra en el video de presentación usando un método llamado análisis de pares. El análisis de pares, tal como se define en el VA, es un método de análisis en el que un experto en la herramienta VA trabaja codo a codo con un experto en el campo durante el análisis. El experto en el campo es el que entiende la importancia de los datos, y hace las preguntas que los datos recogidos podrían abordar. El experto herramienta crea visualizaciones para ayudar a encontrar patrones en los datos que puedan responder a estas preguntas. El corto tiempo de retardo entre la generación de hipótesis y la visualización rápida visual de los datos es la principal ventaja de un enfoque de VA.

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

La analítica visual (AV) es un nuevo enfoque de análisis de datos de forma interactiva. En este video, se discute el problema de la sobrecarga de datos causada por experimentos de alto rendimiento biológico y proponer VA como una solución a tal problema. El video muestra el análisis dentro y entre los conjuntos de datos inmunológicos utilizando una herramienta llamada Tableau VA.

Facilitar el análisis de los datos inmunológicos con Visual técnicas analíticas

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

Research

JoVE Journal

Immunology and Infection

10.0K vistas.  University of British Columbia. La analítica visual (AV) es un nuevo enfoque de análisis de datos de forma interactiva. En este video, se discute el problema de la sobrecarga de datos causada por experimentos de alto rendimiento biológico y proponer VA como una solución a tal problema. El video muestra el análisis dentro y entre los conjuntos de datos inmunológicos utilizando una herramienta llamada Tableau VA.

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

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish

Zebrafish larvae are particularly amenable to whole animal small molecule screens1,2 due to their small size and relative ease of manipulation and observation, as well as the fact that compounds can simply be added to the bathing water and are readily absorbed when administered in a &lt;1% DMSO solution. Due to the optical clarity of zebrafish larvae and the availability of transgenic lines expressing fluorescent proteins in leukocytes, zebrafish offer the unique advantage of monitoring an acute inflammatory response in vivo. Consequently, utilizing the zebrafish for high-content small molecule screens aiming at the identification of immune-modulatory compounds with high throughput has been proposed3-6, suggesting inflammation induction scenarios e.g. localized nicks in fin tissue, laser damage directed to the yolk surface of embryos7 or tailfin amputation3,5,6. The major drawback of these methods however was the requirement of manual larva manipulation to induce wounding, thus preventing high-throughput screening. Introduction of the chemically induced inflammation (ChIn) assay8 eliminated these obstacles. Since wounding is inflicted chemically the number of embryos that can be treated simultaneously is virtually unlimited. Temporary treatment of zebrafish larvae with copper sulfate selectively induces cell death in hair cells of the lateral line system and results in rapid granulocyte recruitment to injured neuromasts. The inflammatory response can be followed in real-time by using compound transgenic cldnB::GFP/lysC::DsRED26,9 zebrafish larvae that express a green fluorescent protein in neuromast cells, as well as a red fluorescent protein labeling granulocytes.
In order to devise a screening strategy that would allow both high-content and high-throughput analyses we introduced robotic liquid handling and combined automated microscopy with a custom developed software script. This script enables automated quantification of the inflammatory response by scoring the percent area occupied by red fluorescent leukocytes within an empirically defined area surrounding injured green fluorescent neuromasts. Furthermore, we automated data processing, handling, visualization, and storage all based on custom developed MATLAB and Python scripts.
In brief, we introduce an automated HC/HT screen that allows testing of chemical compounds for their effect on initiation, progression or resolution of a granulocytic inflammatory response. This protocol serves a good starting point for more in-depth analyses of drug mechanisms and pathways involved in the orchestration of an innate immune response. In the future, it may help identifying intolerable toxic or off-target effects at earlier phases of drug discovery and thereby reduce procedural risks and costs for drug development.

Here we describe a novel high-content chemically induced inflammation assay aiming at the identification of immune-modulatory bioactives. We have successfully combined automated microscopy with custom developed software scripts enabling automated quantification of the inflammatory response as well as further data processing, analysis, mining, and storage.

Facilitar el descubrimiento de fármacos: Un sistema automatizado de alta el contenido del ensayo La inflamación en el pez cebra

Zebrafish larvae are particularly amenable to whole animal small molecule screens1,2 due to their small size and relative ease of manipulation and ...

Investigación

Inmunología e Infección

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.

La visualización de la sinapsis inmunológica mediante gated-Tiempo color dual Emisión Estimulada de Agotamiento (STED) Nanoscopia

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

Super-resolution Imaging of the Natural Killer Cell Immunological Synapse on a Glass-supported Planar Lipid Bilayer

The glass-supported planar lipid bilayer system has been utilized in a variety of disciplines. One of the most useful applications of this technique has been in the study of immunological synapse formation, due to the ability of the glass-supported planar lipid bilayers to mimic the surface of a target cell while forming a horizontal interface. The recent advances in super-resolution imaging have further allowed scientists to better view the fine details of synapse structure. In this study, one of these advanced techniques, stimulated emission depletion (STED), is utilized to study the structure of natural killer (NK) cell synapses on the supported lipid bilayer. Provided herein is an easy-to-follow protocol detailing: how to prepare raw synthetic phospholipids for use in synthesizing glass-supported bilayers; how to determine how densely protein of a given concentration occupies the bilayer's attachment sites; how to construct a supported lipid bilayer containing antibodies against NK cell activating receptor CD16; and finally, how to image human NK cells on this bilayer using STED super-resolution microscopy, with a focus on distribution of perforin positive lytic granules and filamentous actin at NK synapses. Thus, combining the glass-supported planar lipid bilayer system with STED technique, we demonstrate the feasibility and application of this combined technique, as well as intracellular structures at NK immunological synapse with super-resolution.

We describe here a combination of the glass-supported lipid bilayer technique of forming immunological synapses with the super-resolution imaging technique of stimulated emission depletion (STED) microscopy. The goal of this protocol is to provide users with the instructions necessary to successfully carry out these two techniques.

Super-resolución de imagen de la célula Natural Killer inmunológica Synapse en un apoyada-Glass planar bicapa lipídica

The glass-supported planar lipid bilayer system has been utilized in a variety of disciplines. One of the most useful applications of this technique has ...

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'.

Un modelo de la célula endotelial planar for Imaging inmunológica Synapse Dinámica

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.

Análisis de linfocitos extravasación El uso de una<em&gt; In Vitro</em&gt; Modelo de la barrera sangre-cerebro humano

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.

ExCYT: Una interfaz gráfica de usuario para simplificar el análisis de datos multidimensional de citometría de

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

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.

Uso complementario de técnicas microscópicas y lectura de fluorescencia en el estudio de las interacciones Cryptococcus-Amoeba

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.

Opsonofagocítico asesinato ensayo para evaluar las respuestas inmunológicas contra patógenos bacterianos

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.

Aplicación de Perturbaciones Consistentes Similares al Masaje en Los terneros de ratón y Monitoreo de los Cambios de Presión Intramuscular Resultantes

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.

Funcionalización de voladizos de microscopio de fuerza atómica con células de una t o de una sola partícula para espectroscopia de fuerza de una sola célula inmunológica

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