Vorremmo ringraziare i membri di Vancouver Istituto di Visual Analytics (VIVA) per offrire commenti e consigli per il progetto. In particolare, vorremmo ringraziare John Dill, Brian Fisher e David Darvill. Vorremmo anche ringraziare i membri del laboratorio Kollmann per il sostegno e utili discussioni. Questo lavoro è stato sostenuto in parte dal National Institute of Allergy and Infectious Diseases, Istituto Superiore di Sanità di Grant N01 AI50023; Borse di NCE Allergen 07-07-A1A e B2B, e il Michael Smith Fondazione per la Ricerca Sanitaria. TRK è sostenuto in parte da un Premio alla carriera nella scienza biomedica dal annuncio Burroughs Wellcome Fondo da un Istituti canadesi per Grant formazione per la ricerca in sanità canadese Health Program Scientist Bambino Clinico, in collaborazione con Sick-Kids Foundation, Child and Family Research Institute ( British Columbia), Donne e salute dei bambini Research Institute (Alberta), e Manitoba Institute of Child Health.

1. Esplorazione a base di analisi su Tableau <ol><li> Hai un set di dati e si desidera esplorare le varie relazioni tra le colonne all&#39;interno dei dati. <ol><li> (Figura 1) Importare il set di dati cliccando su &#39;Connect ai dati&#39;. Scegliere il tipo di origine dati e seguire le istruzioni per importare i set di dati. </li><li> (Figura 1) Verificare che la mensola dimensioni contiene colonne categorico, mentre la mensola misure contiene colonne numeriche dal set di dati. A volte una colonna dimensione come identificatore del soggetto possono essere elencati come misura se è numerata. Modificarlo in dimensioni facendo clic destro su di esso e scegliere &#39;Converti in Dimension&#39;. Nella Figura 1, colonne categorico dal set di dati come il livello di concentrazione e stimolo identificatori soggetto sono correttamente collocati in scaffali dimensioni, e la concentrazione delle citochine osservate sono correttamente collocati in misure di ripiano. </li><li> (Figura 2) Se durante il processo di analisi, un campo calcolato è necessario, basta fare clic destro in entrambe le dimensioni o misure di ripiano, e seleziona &#39;Crea campo calcolato&#39;. Inserire il calcolo nella casella della formula con i campi e le numerose funzioni o operazioni matematiche. La figura 2 mostra come creare un nuovo campo chiamato PFD&gt; 2 aggregando PFD2, PFD3 e PFD4 valori. </li><li> (Figura 3) Generare un 2-D trama in Tableau, in genere con una dimensione e una misura di interesse. Poiché l&#39;interfaccia offre semplice drag-and-drop interazione, la scelta della dimensione e misura può essere facilmente cambiata. Le dimensioni sono di solito collocati in scaffali colonne, e le misure di ripiano righe. Nella Figura 3, la mensola dimensioni contiene concentrazione a livello di stimolo, e la mensola contiene misure di livello osservato concentrazione delle citochine. </li><li> (Figura 3) Per differenziare la visualizzazione di una dimensione specifica, posto che l&#39;etichetta dimensione in scaffale testo, il colore o la dimensione, a seconda della natura della dimensione. Scaffale testo tentativi per differenziare i dati nella visualizzazione, mediante l&#39;etichettatura con informazioni testuali fornite dal dimensione selezionata. Mensola a colori e le dimensioni automaticamente differenziare i dati per colore e dimensioni, che può essere regolato con la barra di scorrimento nella parte inferiore di ogni ripiano. In figura 3, ponendo genotipo a scaffale colore porta alla separazione di tre genotipi di colori distinti. </li><li> (Figura 3) Filtrare la visualizzazione dalle variabili colonna specifica in altra dimensione. Questo può essere fatto trascinando l&#39;etichetta dimensione nello scaffale filtro. Apparirà una finestra con l&#39;elenco di valori tra cui scegliere. Il valore selezionato saranno gli unici valori rimasti nella visualizzazione. </li><li> (Figura 4) Si può unire visualizzazione con altra dimensione o misura per la produzione di una matrice di visualizzazione. Questo può essere fatto facendo cadere più dimensioni in scaffale colonne, e le misure di ripiano in più righe. Figura 4 contiene un esempio di due colonne della matrice di visualizzazione mettendo due dimensioni diverse nello scaffale colonne. </li><li> Per produrre una visualizzazione simile, fate clic destro sul foglio corrente sul basso a sinistra, e selezionate &#39;foglio duplicato&#39;. </li><li> Iterare il processo di analisi per identificare tendenze interessanti o valori anomali che possono aiutare a generare nuove ipotesi sui dati. </li></ol></li><li> Hai set di dati multipli generati dalla stessa fonte, e vogliono esplorare le possibili connessioni tra questi gruppi di dati. <ol><li> Luogo più set di dati in una stessa fonte, luogo cioè due fogli di calcolo nella stessa cartella di lavoro, per consentire Tableau di connettersi a questi set di dati attraverso la stessa connessione. </li><li> (Figura 5) Collegare il set di dati attraverso la logica di unire valori chiave, facendo attenzione che le dimensioni che sono le stesse per più set di dati selezionati. Nella Figura 5, ci sono cinque valori chiave per unire: identificatore tipo di cellula, la concentrazione a livello di stimolo, la fase o gruppo, stimolo, e soggetto. </li><li> Effettuare analisi simile alla sezione 1.1. </li></ol></li></ol> 2. Presentazione a base di bisogni <ol><li> Sapete le relazioni tra dati, ma si vuole essere in grado di generare rapidamente un elenco di visualizzazioni per l&#39;uso in presentazione. <ol><li> Seguire il protocollo di cui al punto 1.1, e annotare le visualizzazioni di conseguenza. </li><li> Utilizzare la funzione di esportazione in Tableau per produrre immagini di visualizzazioni. </li></ol></li><li> Lei ha generato una serie di visualizzazioni potente nella cartella di lavoro Tableau e vogliono condividere tale cartella di lavoro. <ol><li> Salvare la cartella di lavoro come cartella di lavoro Tableau confezionati e condividere questo file. </li><li> Colleghi senza Desktop Tableau Tableau Reader può essere scaricato per aprire la cartella di lavoro confezionato. Reader permette tableau tuoi colleghi di interagire con le visualizzazioni che hai creato. </li></ol></li></ol> 3. RAPPRESENTANTE RISULTATI <img src="/files/ftp_upload/2397/2397fig1.jpg" alt="Figura 1" /> Figura 1. Uno screenshotdi Tableau dopo l&#39;importazione il foglio di calcolo chiamato NFKBIA dal demo.xls file di Excel. Le dimensioni e le mensole misure sono state opportunamente compilato con i dati categorici e numerici, rispettivamente. <img src="/files/ftp_upload/2397/2397fig2.jpg" alt="Figura 2" /> Figura 2. La finestra di campo calcolato è invocato per creare un campo calcolato speciale da usare in Tableau. La lista in basso a sinistra della finestra consente di individuare possibili campi, e la lista sulla destra contiene un&#39;abbreviazione di funzioni che possono essere utilizzati nella formula. In questo esempio, abbiamo voluto aggiungere i valori per PFD4, PFD3 e PFD2 per ottenere il valore finale che noi chiamiamo PFD&gt; 2 <img src="/files/ftp_upload/2397/2397fig3.jpg" alt="Figura 3" /> Figura 3. Visualizzazione del livello di concentrazione stimolo contro la concentrazione delle citochine osservato. La visualizzazione mostra un grafico dei diversi livelli di concentrazione dello stimolo 3M-002 contro la concentrazione osservata della citochina TNF-α. I colori delle linee di consultare i genotipi differenti per un polimorfismo a singolo nucleotide nel gene NFKBIA degli individui nel nostro studio immunitario innato. <img src="/files/ftp_upload/2397/2397fig4.jpg" alt="Figura 4" /> Figura 4. Uno screenshot di un due colonne della matrice di visualizzazione. Abbiamo generato una matrice a due colonne per facilitare un side-by-side di confronto delle risposte a due stimoli, 3M-003 e LPS. L&#39;x-assi sono i diversi livelli di concentrazione dei due stimoli, e l&#39;asse y trame i valori del campo calcolato, PFD&gt; 2. <img src="/files/ftp_upload/2397/2397fig5.jpg" alt="Figura 5" /> Figura 5. Tableau Queste finestre di dialogo illustrano come collegare i dati registrati in fogli di calcolo diversi. Connessione dati da fogli di calcolo che può essere svolto dalla combinazione di questi con logiche unire le clausole dei valori chiave. <table border="1"><tbody><tr><td></td><td colspan="10"> Visualizzazione e strumento di analisi </td></tr><tr><td> Funzione </td><td> Quadro </td><td> VIS-STAMP </td><td> xmdvtool </td><td> GGobi </td><td> Luce stellare </td><td> Gapminder </td><td> Visulab </td><td> InfoVis Toolkit </td><td> Geotime </td><td> Ispirare </td></tr><tr><td> Grafici delle coordinate parallele </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td><td> No </td><td> No </td></tr><tr><td> Scatter plot matrici </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> No </td><td> No </td><td> No </td></tr><tr><td> Riduzione dimensionale </td><td> No </td><td> No </td><td> Sì </td><td> Sì </td><td> No </td><td> No </td><td> No </td><td> No </td><td> No </td><td> No </td></tr><tr><td> Dimensioni temporali </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td><td> No </td></tr><tr><td> Geospaziali dimensioni </td><td> Sì </td><td> Sì </td><td> No </td><td> No </td><td> Sì </td><td> Sì </td><td> No </td><td> No </td><td> Sì </td><td> No </td></tr><tr><td> Text mining </td><td> No </td><td> No </td><td> No </td><td> No </td><td> Sì </td><td> No </td><td> No </td><td> No </td><td> No </td><td> Sì </td></tr><tr><td> La manipolazione diretta dei dati </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> No </td></tr><tr><td> Filtraggio </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td></tr><tr><td> Estensibilità ad altre piattaforme (ad esempio, R) </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> No </td><td> Sì </td><td> No </td><td> No </td></tr><tr><td> CSV tavolo formati </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td><td> No </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td></tr><tr><td> Formati di dati XML </td><td> Sì </td><td> No </td><td> No </td><td> Sì </td><td> Sì </td><td> No </td><td> Sì </td><td> Sì </td><td> Sì </td><td> No </td></tr><tr><td> Sa muoversi con 10000 + file </td><td> Sì </td><td> No </td><td> No </td><td> No </td><td> Sì </td><td> No </td><td> No </td><td> No </td><td> No </td><td> Sì </td></tr><tr><td> Documentazione </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td><td> Sì </td></tr><tr><td> Prodotto commerciale </td><td> Sì </td><td> No </td><td> No </td><td> No </td><td> Sì </td><td> No </td><td> No </td><td> No </td><td> Sì </td><td> Sì </td></tr></tbody></table> Tabella 1. Elenco degli strumenti di analisi visiva e alcune delle loro caratteristiche.

<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>

Nessun conflitto di interessi dichiarati.

L&#39;avvento della tecnologia high-throughput nella moderna ricerca biomedica portato a un&#39;esplosione dei dati di ricerca che richiede un modo più efficiente di analisi. Visual analytics (VA) è la scienza del ragionamento analitico facilitato da interfacce visuali interattivi (1). L&#39;approccio VA ripristina la potenza analitica nelle mani di analista umano, in contrasto con l&#39;approccio tradizionale di individuare modelli di computer. Analisi visiva è stata applicata alla ricerca in vari campi, come la dif...

facilitating the analysis of immunological data with visual analytic techniques

Visual analytics (VA) è emerso come un nuovo modo di analizzare insieme di dati di grandi dimensioni attraverso interattivo di visualizzazione. Abbiamo dimostrato l&#39;utilità e la flessibilità di un approccio VA nell&#39;analisi dei set di dati biologici. Esempi di questi set di dati in immunologia comprendono citometria a flusso, i dati di Luminex, e la genotipizzazione (ad esempio, singolo polimorfismo del nucleotide) di dati. Contrariamente all&#39;approccio tradizionale visualizzazione delle informazioni, VA ripristina la potenza di analisi nelle mani di analista, consentendo l&#39;analista di impegnarsi in tempo reale processo di esplorazione dei dati. Abbiamo scelto il software VA chiamato Tableau dopo aver valutato diversi strumenti VA. Due tipi di attività di analisi di analisi all&#39;interno e tra i set di dati sono state dimostrate nel video di presentazione con un approccio chiamato analisi associati. Analisi appaiati, come definito in VA, è un approccio di analisi in cui un esperto strumento VA lavora fianco a fianco con un esperto di dominio durante l&#39;analisi. L&#39;esperto di dominio è colui che capisce il significato dei dati, e pone le domande che i dati raccolti potrebbero affrontare. L&#39;esperto strumento crea poi visualizzazioni per aiutare a trovare modelli nei dati che potrebbero rispondere a queste domande. Il breve intervallo di tempo tra la generazione di ipotesi e la visualizzazione rapida visiva dei dati è il principale vantaggio di un approccio 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

Visual analytics (VA) è un nuovo approccio di analisi dei dati in modo interattivo. In questo video si discute il problema di sovraccarico di dati causata da esperimenti biologici high-throughput, VA e proporre come soluzione a tale problema. Il video mostra l&#39;analisi all&#39;interno e tra le serie di dati immunologici usando uno strumento chiamato Tableau VA.

Facilitare l&#39;analisi dei dati immunologici con Visual tecniche analitiche

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 Views.  University of British Columbia. Visual analytics (VA) è un nuovo approccio di analisi dei dati in modo interattivo. In questo video si discute il problema di sovraccarico di dati causata da esperimenti biologici high-throughput, VA e proporre come soluzione a tale problema. Il video mostra l'analisi all'interno e tra le serie di dati immunologici usando uno strumento chiamato 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.

Facilitare Drug Discovery: un sistema automatico di alta Tenore infiammazione 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 ...

Ricerca

Immunologia e infezioni

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.

Visualizzazione della immunologica Synapse da Dual Color Time-gated emissione stimolata Depletion (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-Resolution Imaging di cellule Natural Killer immunologica Sinapsi su un vetro supportato Planar doppio strato lipidico

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 modello cellule endoteliali Planar for Imaging immunologiche Synapse Dynamics

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.

L&#39;analisi dei linfociti stravaso utilizzo di un<em&gt; In Vitro</em&gt; Modello del Sangue-Cervello umano Barriera

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: Un'interfaccia utente grafica per semplificare l'analisi dei dati Cytometry alto-dimensionali

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 complementare di tecniche microscopiche e lettura di fluorescenza nello studio delle interazioni 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.

Anticorpli uccisione test per valutare le risposte immunologiche contro batteri patogeni

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.

Applicazione di perturbazioni costanti simili a massaggi sui vitelli per topi e monitoraggio dei conseguenti cambiamenti di pressione intramuscolare

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.

Funzionalizzazione dei cantilever a microscopio a forza atomica con cellule A T singole o a particella singola per la spettroscopia immunologica a forza a cella singola

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