Gostaríamos de agradecer aos membros do Vancouver Institute of Visual Analytics (VIVA) para oferecer comentários e sugestões para o projeto. Em particular, gostaríamos de agradecer a John Dill, Brian Fisher, e David Darvill. Gostaríamos também de agradecer aos membros do laboratório Kollmann pelo apoio e discussões úteis. Este trabalho foi financiado em parte pelo Instituto Nacional de Alergia e Doenças Infecciosas, Instituto Nacional de Saúde Grant N01 AI50023; Grants Allergen NCE 07-07-A1A e B2B, eo Michael Smith Fundação para a Pesquisa em Saúde. TRK é suportado, em parte, por um Prémio Carreira na Ciência Biomédica da ad Burroughs Wellcome Fund por um Canadian Institutes for Health Research Grant Formação em Canadian Criança Programa de Saúde Clínico Scientist, em parceria com Doente-Kids Foundation, Criança e Família (Instituto de Pesquisa British Columbia), Saúde da Mulher e da Criança Research Institute (Alberta), e Manitoba Institute of Child Health.

1. Exploração baseada em Análise de Tableau <ol><li> Você tem um conjunto de dados e você deseja explorar as várias relações entre as colunas dentro dos dados. <ol><li> (Figura 1) Importe o conjunto de dados, clicando em &#39;Connect aos dados &quot;. Escolha o tipo de fonte de dados e siga as instruções para importar o seu conjunto de dados. </li><li> (Figura 1) Certifique-se que a plataforma dimensões contém colunas categóricas, enquanto a prateleira medidas contém colunas do conjunto de dados numéricos. Às vezes, uma coluna de dimensão, tais como identificador de assunto podem ser listados como medir se é numerada. Alterá-lo para dimensões clicando sobre ele e selecione &quot;Convert to Dimension&quot;. Na Figura 1, as colunas do conjunto de dados categóricos tais como o nível de concentração e estímulo identificadores sujeitos estão correctamente colocados na prateleira dimensões, ea concentração de citocinas são observadas corretamente colocado na prateleira de medidas. </li><li> (Figura 2) Se durante o processo de análise, um campo calculado é necessário, simplesmente clique direito em dimensões ou medidas de prateleira, e selecione &#39;Criar Campo Calculado. Entrada de cálculo da fórmula na caixa com os campos combinadas com funções ou operações matemáticas. A Figura 2 mostra como criar um novo campo chamado PFD&gt; 2 agregando PFD2, PFD3 e PFD4 valores. </li><li> (Figura 3) Gerar um lote 2-D no Tableau, tipicamente com uma dimensão e uma medida de interesse. Já que a interface oferece interação de arrastar e soltar-simples, a escolha da dimensão e medida pode ser facilmente alterada. Dimensões são normalmente colocadas em colunas de prateleira, e medidas em prateleira linhas. Na Figura 3, a plataforma dimensões contém nível de concentração de estímulo, ea plataforma medidas contém nível de concentração de citocinas observadas. </li><li> (Figura 3) Para diferenciar a visualização de uma dimensão específica, coloque esse rótulo dimensão na prateleira cor do texto, ou tamanho, dependendo da natureza da dimensão. Prateleira texto tentativas para diferenciar de dados na visualização, rotulando-os com informações textuais fornecidas pela dimensão selecionada. Prateleira Cor e tamanho será automaticamente diferenciar dados por cor e tamanho, que pode ser ajustado com a barra de rolagem localizada na parte inferior de cada prateleira. Na figura 3, colocando genótipo na prateleira cor leva à separação de três genótipos de cores distintas. </li><li> (Figura 3) Filtrar a visualização pelas variáveis ​​coluna específica em outra dimensão. Isto pode ser feito arrastando o rótulo dimensão na prateleira do filtro. Aparecerá uma janela com a lista de valores para escolher. O valor selecionado será os únicos valores restantes na visualização. </li><li> (Figura 4) Você pode combinar visualização com outra dimensão ou medida para produzir uma matriz de visualização. Isto pode ser feito por queda múltiplas dimensões na prateleira colunas e múltiplas medidas na prateleira linhas. Figura 4 contém um exemplo de duas colunas da matriz de visualização, colocando duas dimensões diferentes na prateleira colunas. </li><li> Para produzir uma visualização semelhante, clique direito sobre a planilha atual no canto inferior esquerdo, e selecionou &#39;página duplicada. </li><li> Iterar o processo de análise para identificar as tendências interessantes ou outliers que podem ajudar a gerar novas hipóteses sobre os dados. </li></ol></li><li> Você tem múltiplos dataset gerado a partir da mesma fonte, e querem explorar as possíveis ligações entre estes conjuntos de dados. <ol><li> Coloque vários conjuntos de dados em uma mesma fonte, lugar ou seja, duas planilhas no mesmo livro, para permitir Tableau para se conectar a estes dataset através da mesma conexão. </li><li> (Figura 5) Conecte os conjuntos de dados por meio de unir lógica de valores-chave, certificando-se que as dimensões que são as mesmas para vários conjuntos de dados são selecionados. Na Figura 5, existem cinco valores-chave para adesão: identificador de tipo de célula, nível de concentração de estímulo palco, ou grupo, de estímulo, e assunto. </li><li> Realizar uma análise semelhante a seção 1.1. </li></ol></li></ol> 2. Apresentação baseada em Necessidades <ol><li> Você sabe que as relações entre os dados, mas você quer ser capaz de gerar rapidamente uma lista de visualizações para uso em apresentações. <ol><li> Seguir o protocolo descrito na secção 1.1, e anotar as visualizações de acordo. </li><li> Use a função de exportação no Tableau para produzir imagens de visualizações. </li></ol></li><li> Você tem gerado um conjunto de visualizações poderoso livro Tableau e quero compartilhar essa pasta de trabalho. <ol><li> Guardar o livro como livro Tableau embalados e compartilhar esse arquivo. </li><li> Colegas sem desktop Tableau pode baixar Leitor Tableau para abrir o livro embalado. Leitor Tableau permite que os seus colegas de interagir com as visualizações que você criou. </li></ol></li></ol> 3. RESULTADOS REPRESENTANTE <img src="/files/ftp_upload/2397/2397fig1.jpg" alt="Figura 1" /> Figura 1. A screenshotTableau de depois de importar a planilha denominada NFKBIA do demo.xls arquivo do Excel. As dimensões e as prateleiras medidas foram devidamente preenchidos com os dados categóricos e numéricos, respectivamente. <img src="/files/ftp_upload/2397/2397fig2.jpg" alt="Figura 2" /> Figura 2. A janela de Campo Calculado é chamado para criar um campo especial calculado para uso em Tableau. A lista na caixa inferior esquerda ajuda a identificar possíveis campos, ea lista no lado direito contém abreviação de funções que podem ser usados ​​na fórmula. Neste exemplo, nós queríamos adicionar os valores para PFD4, PFD3 PFD2 e para obter o valor final que nos referimos como PFD&gt; 2 <img src="/files/ftp_upload/2397/2397fig3.jpg" alt="Figura 3" /> Figura 3. Visualização do nível de estímulo de concentração versus concentração de citocinas observadas. A visualização mostra um gráfico dos diferentes níveis de concentração do estímulo 3M-002 contra a concentração observada da citocina TNF-α. As cores das linhas referem-se os diferentes genótipos para o polimorfismo de nucleotídeo único no gene NFKBIA dos indivíduos em nosso estudo imunológico inato. <img src="/files/ftp_upload/2397/2397fig4.jpg" alt="Figura 4" /> Figura 4. Um screenshot de uma matriz de visualização de duas colunas. Geramos uma matriz de duas colunas para facilitar uma comparação lado a lado de respostas a dois estímulos, a 3M-003 e LPS. O x-eixos são os diferentes níveis de concentração dos dois estímulos, e as parcelas do eixo y os valores do campo calculado, PFD&gt; 2. <img src="/files/ftp_upload/2397/2397fig5.jpg" alt="Figura 5" /> Figura 5. Essas janelas diálogo Tableau ilustram como conectar dados registrados em planilhas diferentes. De dados conectando a partir de planilhas diferentes pode ser realizado através da combinação destes usando lógica juntar cláusulas de valores de chave. <table border="1"><tbody><tr><td></td><td colspan="10"> Ferramenta de visualização e análise </td></tr><tr><td> Função </td><td> Quadro </td><td> VIS-STAMP </td><td> xmdvtool </td><td> GGobi </td><td> Luz das estrelas </td><td> Gapminder </td><td> Visulab </td><td> InfoVis Toolkit </td><td> Geotime </td><td> Inspirar </td></tr><tr><td> Paralelo coordenar parcelas </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td><td> Não </td><td> Não </td></tr><tr><td> Enredo matrizes dispersão </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td></tr><tr><td> Redução dimensional </td><td> Não </td><td> Não </td><td> Sim </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td><td> Não </td><td> Não </td><td> Não </td></tr><tr><td> Dimensões temporais </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td><td> Não </td></tr><tr><td> Dimensões geoespaciais </td><td> Sim </td><td> Sim </td><td> Não </td><td> Não </td><td> Sim </td><td> Sim </td><td> Não </td><td> Não </td><td> Sim </td><td> Não </td></tr><tr><td> Mineração de texto </td><td> Não </td><td> Não </td><td> Não </td><td> Não </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td><td> Não </td><td> Sim </td></tr><tr><td> Manipulação direta de dados </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Não </td></tr><tr><td> Filtragem </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td></tr><tr><td> Extensibilidade para outras plataformas (por exemplo, R) </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Não </td><td> Sim </td><td> Não </td><td> Não </td></tr><tr><td> Tabela formatos CSV </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td><td> Não </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td></tr><tr><td> Formatos de dados XML </td><td> Sim </td><td> Não </td><td> Não </td><td> Sim </td><td> Sim </td><td> Não </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Não </td></tr><tr><td> Pode lidar com um0000 + linhas </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td><td> Não </td><td> Sim </td></tr><tr><td> Documentação </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td><td> Sim </td></tr><tr><td> Produto comercial </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td><td> Sim </td><td> Não </td><td> Não </td><td> Não </td><td> Sim </td><td> Sim </td></tr></tbody></table> Lista Tabela 1. Análises de ferramentas visuais e algumas de suas 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>

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O advento da tecnologia de alto rendimento na pesquisa biomédica moderna conduziu a uma explosão de dados de pesquisa que requer uma maneira mais eficiente de análise. Visual analytics (VA) é a ciência do raciocínio analítico facilitada pela interativo interfaces visuais (1). A abordagem VA restaura o poder analítico nas mãos do analista humano, ao contrário da abordagem tradicional para detectar padrões por computador. Visual analytics tem sido aplicada à pesquisa em vários campos, tais como a defesa de pe...

facilitating the analysis of immunological data with visual analytic techniques

Visual analytics (VA) tem emergido como uma nova forma de analisar grande volume de dados através de display visual interativo. Demonstramos a utilidade ea flexibilidade de uma abordagem VA na análise de conjuntos de dados biológicos. Exemplos destes conjuntos de dados em imunologia incluem citometria de fluxo, dados Luminex e genotipagem (por exemplo, o polimorfismo de nucleotídeo único) de dados. Contrariamente à abordagem tradicional de visualização de informação, VA restaura o poder de análise nas mãos do analista, permitindo que o analista se engajar em tempo real processo de exploração de dados. Nós selecionamos o software chamado VA Tableau após avaliar várias ferramentas VA. Dois tipos de tarefas de análise de análise dentro e entre os conjuntos de dados foram demonstrados na apresentação de vídeo utilizando uma abordagem chamada análise pareada. Análise pareada, conforme definido no VA, é uma abordagem de análise em que um especialista ferramenta VA trabalha lado a lado com um especialista de domínio durante a análise. O especialista de domínio é aquele que entende o significado dos dados, e faz as perguntas que os dados coletados possam endereço. O especialista ferramenta cria visualizações para ajudar a encontrar padrões nos dados que possam responder a estas perguntas. O curto intervalo de tempo entre a geração de hipóteses ea exibição rápida visual dos dados é a principal vantagem de uma abordagem 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) é uma nova abordagem de análise de dados de forma interativa. Neste vídeo, discutimos o problema de sobrecarga de dados causada por high-throughput experimentos biológicos, e propor VA como uma solução para esse problema. O vídeo demonstra a análise dentro e entre os conjuntos de dados imunológicos usando uma ferramenta chamada Tableau VA.

Facilitar a Análise de Dados com Visual imunológicos 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 visualizações.  University of British Columbia. Visual analytics (VA) é uma nova abordagem de análise de dados de forma interativa. Neste vídeo, discutimos o problema de sobrecarga de dados causada por high-throughput experimentos biológicos, e propor VA como uma solução para esse problema. O vídeo demonstra a análise dentro e entre os conjuntos de dados imunológicos usando uma ferramenta chamada Tableau VA.

Vídeo: 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 Drug Discovery: um ensaio automatizado de alta Inflamação conteúdo em Zebrafish

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

Imunologia e Infecção

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.

Visualização do imunológica Synapse por Dual Color Time-fechado Emissão Estimulada Exaustão (STED) Nanoscopia

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'.

Um modelo de celular endotelial Planar for Imaging imunológica 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.

Análise de Linfócitos extravasamento Usando um<em&gt; In Vitro</em&gt; Modelo do Sangue-Cérebro humano Barreira

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: Uma Interface gráfica do usuário para racionalizar a análise de dados de alta dimensão Cytometry

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 complementar de técnicas microscópicas e leitura de fluorescência no estudo de Cryptococcus-Amoeba interações

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.

Ensaio de matar Opsonophagocytic para avaliar as respostas imunológicas 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.

Aplicação de consistente massagem-como perturbações em bezerros do mouse e monitoramento das mudanças de pressão intramuscular resultante

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.

Functionalization dos Cantialavancos do microscópio da força atômica com únicas-células de T ou único-partícula para a espectroscopia imunológica da força da único-pilha

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).

Imagem da Sinapse Imunológica Humana

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) ...