Agradecemos ao Dr. Tanja Maritzen e ao Dr. Fabian Feutlinske (Leibniz Institute of Molecular Pharmacology, Berlim, Alemanha) por compartilhar conosco sua versão do plugin Fuji ImageJ Clock Scan e nos inspirar a desenvolver esta versão do programa. Também estamos gratos ao Dr. Fritz Melchers (Departamento de Desenvolvimento de Linfócitos, Instituto Max Planck para Biologia Infecciosa) por sua amável permissão para usar as imagens do banco de dados de seu departamento com o objetivo de testar e melhorar o plugin. Apoio: Centro de Neurociências Translacionais; Empréstimo NIH: P30-GM110702-03.

1. Instalação de software <ol><li> Instale as versões mais recentes do Java agrupado e ImageJ ou Fiji ImageJ conforme recomendado nos respectivos sites (consulte a tabela de materiais para obter links para os sites correspondentes). No texto abaixo, ambos os programas são referidos como &quot;ImageJ&quot;. </li><li> Copie os arquivos de plugin &quot;Clock_Scan-1.0.1. Jar&quot; e &quot;Multi_Clock_Scan-1.0.1.jar&quot; usando o link fornecido na tabela de materiais e cole-os no diretório do plugin ImageJ. Como alternativa, use a opção de menu &quot;Plugins | Instalar o plugin&quot; para instalar esses arquivos depois de terem sido salvos no disco rígido do computador. </li></ol> 2. Análise de varredura do relógio <ol><li> Plugin de verificação de relógio padrão ( Figura 1 ): <ol><li> Use o comando de menu ImageJ &quot;Arquivo | Abrir&quot; para abrir uma imagem de interesse. </li><li> Clique na ferramenta &#39;polígono&#39; ou &#39;seleção de linha segmentada&#39;Ferramenta, e depois desenhe a imagem para descrever todo o ROI ou um segmento dessa região. Veja a Figura 1 A para um exemplo de seleção de polígono (contorno de traço interno). NOTA: Outras ferramentas de seleção, disponíveis no software (seleção de linha retangular, oval e a mão livre) também podem ser usadas. </li><li> Selecione &quot;Plugins | Clock Scan&quot; no menu para abrir a janela de opção pop-up do protocolo de varredura do relógio padrão. Observe que esse comando também abrirá a janela ROI Manager com o contorno adicionado automaticamente a ele. </li><li> Use a janela de opções do plugin para fazer o seguinte. <ol><li> Revise e altere as coordenadas X e Y do centro ROI (calculado automaticamente como coordenadas do centro de massa física) usando barras de rolagem ou alterando os valores nas caixas de entrada correspondentes. Veja a Figura 1 B. </li><li> Dependendo de quanto da região de fundo fora do objeto shoVocê deve estar coberto pela digitalização, ajuste os limites de digitalização usando a barra de rolagem &quot;limite de digitalização&quot;. Veja a Figura 1 A. NOTA: O limite de digitalização é o número fracionado que representa a distância que a verificação deve prosseguir além da borda dos objetos em qualquer direção; O valor padrão é 1.20, indicando que o comprimento da varredura será 20% maior do que o raio do objeto na direção da varredura; Veja a Figura 1 A , linha tracejada externa). </li><li> Modifique a saída do plugin usando as caixas de seleção &quot;raio real&quot;, &quot;subtrair fundo&quot;, &quot;transformação polar&quot; e / ou &quot;traçar com desvio padrão&quot;. </li><li> Clique em &quot;OK&quot; para executar o plugin. Veja a Figura 1 C-H . NOTA: Exemplos da saída do protocolo com &quot;trama com desvio padrão&quot; e &quot;transformada polar&quot; ou &quot;raio real&quot; e &quot;polar transfOrm &quot;selecionadas são mostradas na Figura 1 C e 1D e Figura 1 E e 1F , respectivamente. Observe que os valores de desvio padrão calculado (SD) representam a variação entre varreduras de intensidade de pixel radial individuais do objeto. Observe também a&quot; seleção ROI Comprimento &quot;na janela do plugin, que exibe as informações no comprimento do contorno ROI medido em pixels. </li></ol></li><li> No &quot;Lote de perfil de digitalização de relógio&quot; gerado, use o comando &quot;Listar&quot; para traçar valores exibidos em duas colunas de dados X e Y para imagens em escala de cinza e em X e quatro colunas Y de imagens RGB, das quais Y0, As colunas Y1, Y2 e Y3 serão preenchidas com valores de intensidade de pixel do canal de cor integral e individual (vermelho, verde e azul). </li></ol></li><li> Múltiplo ROI Clock Scan plugin - trabalhando com ROI múltiplo ( <strongClass = &quot;xfig&quot;&gt; Figura 2 ): <ol><li> Abra uma imagem contendo vários ROI. </li><li> Abra o ROI Manager clicando em &quot;Analisar | Ferramentas | Gerenciador de ROI&quot;. </li><li> Sequencialmente descreva (veja o passo 2.1.2) e adicione cada ROI ao ROI Manager, clicando em &quot;Adicionar&quot; na janela ROI Manager; Faça isso para todos os ROIs dentro da imagem. Use o comando &quot;Analisar | Medir&quot; se as métricas ROI forem de interesse. <ol><li> Veja a Figura 2 A para um exemplo de múltiplas seleções de linha segmentada e Figura 2 E para um exemplo de múltiplas seleções de polígono. </li></ol></li><li> Selecione &quot;Multi Clock Scan&quot; no menu &quot;Plugins&quot; para abrir a janela pop-up das opções de protocolo. </li><li> Use a janela de opção de protocolo para fazer o seguinte. <ol><li> Se necessário, reponha o limite de digitalização conforme o passo 2.1.4.2; O valor padrão é 1.20. </li><li> Se necessário, selecione a opçãoPara traçar o perfil médio de varredura do relógio com barras SD, verificando a caixa &quot;Traçar com desvio padrão&quot;. Veja a Figura 2 C e D. NOTA: Os valores de SD calculados representarão variação entre perfis de varredura de relógio integral de objetos diferentes. Além disso, observe a linha na janela do plugin que exibe informações no &quot;número de ROIs selecionados&quot;. </li><li> Clique em &quot;OK&quot; para executar o protocolo. </li></ol></li><li> No &quot;Roteiro de perfil de verificação de relógio&quot; gerado, use o comando &quot;Listar&quot; para traçar os valores exibidos na janela &quot;Lote de valores&quot;. Consulte a legenda da janela &quot;Multi Clock Scan Profile Plot&quot; para a designação da coluna por canal de cores. </li><li> Observe que os ROIs são numerados e seus perfis de varredura de relógio para qualquer canal de cor são traçados na mesma seqüência em que os ROIs foram delineados e adicionados ao &quot;Gerenciador de ROI&quot;. </li></ol></li><li> MulPlugin de verificação do relógio ROI da Tiple - trabalhando com uma pilha de imagens ( Figura 3 ): <ol><li> Abra uma imagem-pilha de interesse. </li><li> Abra o ROI Manager clicando em &quot;Analisar | Ferramentas | Gerenciador de ROI&quot;. </li><li> Descreva o ROI das imagens dentro da pilha e adicione-o ao gerenciador de ROI como descrito nas etapas 2.1.2 e 2.2.3. Use o comando &quot;Analisar | Medir&quot; se as métricas ROI forem de interesse. </li><li> Selecione &quot;Multi Clock Scan&quot; no menu &quot;Plugins&quot; para abrir a janela pop-up das opções de protocolo. </li><li> Use a janela de opção de protocolo para fazer o seguinte. <ol><li> Repor o limite de digitalização conforme descrito na etapa 2.1.4.2; O valor padrão é 1.20. </li><li> Selecione a opção para traçar o perfil médio de varredura do relógio com barras SD, verificando a caixa &#39;Traçar com desvio padrão&#39;. NOTA: Os valores de SD calculados representarão a variação entre diferentes instâncias do objeto selecionado na imagem staCk. Além disso, observe a linha na janela do plugin que exibe informações sobre o &quot;número de imagens na pilha&quot;. </li><li> Clique em &quot;OK&quot; para executar o protocolo. </li></ol></li><li> Na janela &quot;Relógio Scan Profile Plot&quot;, clique em &quot;List&quot; para traçar os valores exibidos na janela &quot;Plot Values&quot;, onde o número da coluna Y representa a posição da imagem dentro da pilha - 1. </li></ol></li></ol>

<ol>
	<li>Dobretsov, M., Romanovsky, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term="Clock-scan"+protocol+for+image+analysis.">"Clock-scan" protocol for image analysis.</a> Am J Physiol Cell Physiol. 291, 869-879 (2006).</li><li>Feutlinske, F., Browarski, M., Ku, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stonin1+mediates+endocytosis+of+the+proteoglycan+NG2+and+regulates+focal+adhesion+dynamics+and+cell+motility.">Stonin1 mediates endocytosis of the proteoglycan NG2 and regulates focal adhesion dynamics and cell motility.</a> Nat Commun. 6, 8535 (2015).</li><li>Schneider, C. A., Rasband, W. S., Eliceiri, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIH+Image+to+ImageJ:+25+years+of+image+analysis.">NIH Image to ImageJ: 25 years of image analysis.</a> Nat Methods. 9, 671-675 (2012).</li><li>Schindelin, J., Arganda-Carreras, I., Frise, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nat Methods. 9, 676-682 (2012).</li><li>Dobretsov, M., Hastings, S. L., Stimers, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-uniform+expression+of+alpha+subunit+isoforms+of+the+Na+/K++pump+in+rat+dorsal+root+ganglia+neurons.">Non-uniform expression of alpha subunit isoforms of the Na+/K+ pump in rat dorsal root ganglia neurons.</a> Brain Res. 821, 212-217 (1999).</li><li>Hayar, A., Gu, C., Al-Chaer, E. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+improved+method+for+patch+clamp+recording+and+calcium+imaging+of+neurons+in+the+intact+dorsal+root+ganglion+in+rats.">An improved method for patch clamp recording and calcium imaging of neurons in the intact dorsal root ganglion in rats.</a> J Neurosci Methods. 173, 74-82 (2008).</li><li>Dobretsov, M., Pierce, D., Light, K. E., Kockara, N. T., Kozhemyakin, M., Wight, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transgenic+mouse+model+to+selectively+identify+alpha3+Na,K-ATPase+expressing+cells+in+the+nervous+system.">Transgenic mouse model to selectively identify alpha3 Na,K-ATPase expressing cells in the nervous system.</a> Society for Neuroscience. , 1 (2015).</li><li>Lein, E. S., Hawrylycz, M. J., Ao, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+atlas+of+gene+expression+in+the+adult+mouse+brain.">Genome-wide atlas of gene expression in the adult mouse brain.</a> Nature. 445, 168-176 (2007).</li><li>Romanovsky, D., Mrak, R. E., Dobretsov, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-dependent+decline+in+density+of+human+nerve+and+spinal+ganglia+neurons+expressing+the+alpha3+isoform+of+Na/K-ATPase.">Age-dependent decline in density of human nerve and spinal ganglia neurons expressing the alpha3 isoform of Na/K-ATPase.</a> Neuroscience. 310, 342-353 (2015).</li><li>Campbell, J., Singh, D., Hollett, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatially+selective+photoconductive+stimulation+of+live+neurons.">Spatially selective photoconductive stimulation of live neurons.</a> Front Cell Neurosci. 8, 142 (2014).</li><li>Yuryev, M., Pellegrino, C., Jokinen, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+Calcium+Imaging+of+Evoked+Calcium+Waves+in+the+Embryonic+Cortex.">In vivo Calcium Imaging of Evoked Calcium Waves in the Embryonic Cortex.</a> Front Cell Neurosci. 9, 500 (2015).</li><li>Qiao, M., Sanes, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+Method+for+Labeling+Electrically+Coupled+Cells:+Application+to+Retina.">Genetic Method for Labeling Electrically Coupled Cells: Application to Retina.</a> Front Mol Neurosci. 8, 81 (2015).</li></ol>

Os autores declaram que não têm interesses financeiros concorrentes ou outros conflitos de interesse.

Protocolo de varredura de relógio: o protocolo de varredura de relógio é uma ferramenta rápida e simples de análise de imagem. As vantagens deste protocolo, em comparação com abordagens comuns existentes de análise de imagem (como varreduras de intensidade de pixel linear ou cálculo da intensidade média de pixels do ROI), foram descritas em detalhes nas publicações anteriores 1 , 9 . Resumidamente, este protocolo permite a geração ...

O objetivo deste trabalho é apresentar um protocolo de varredura de relógio que seja livre de plataforma e livremente disponível para qualquer pesquisador interessado neste tipo de análise de imagem. O protocolo Clock Scan foi desenvolvido originalmente em 2006 1 , com o objetivo de melhorar os métodos existentes de quantificação de intensidade de pixels dentro de regiões de interesse convexas (ROI), um método que possui melhor capacidade de integração e resolução espacial melhorada. Durante a aquisição, o protocolo coleciona seqüencialmente vários perfis de intensidade de pixel radial, digitalizados do centro ROI para a sua borda ou a uma distância predeterminada fora do ROI com o objetivo de medir a intensidade do pixel &quot;background&quot;. O protocolo dimensiona esses perfis de acordo com o raio da célula, medido na direção da varredura. Assim, a distância do centro para a margem ROI de cada varredura radial individual é sempre 100% da escala X. Finalmente, o programa mede esse indivíduoPerfis em um perfil de intensidade de pixel radial integral. Devido à escala, o perfil médio de intensidade de pixel, produzido pelo protocolo &quot;Clock Scan&quot;, não depende nem do tamanho do ROI nem, dentro de limites razoáveis, na forma ROI. Este método permite comparação direta ou, se necessário, média ou subtração de perfis de ROIs diferentes. O protocolo também permite a correção dos perfis integrados de intensidade de pixels, de qualquer objeto para o ruído de fundo, por uma simples subtração da intensidade média dos pixels localizados fora do objeto. Embora apenas tenha sido testado em amostras biológicas, nosso protocolo fornece uma adição valiosa a outras ferramentas de análise de imagem existentes usadas em estudos de imagens de processos físicos ou químicos dispostos em torno de um ponto de origem (como a difusão de substâncias de uma fonte pontual ) 1 . No entanto, a principal limitação do método de análise de imagem original foi que o protocolo foi dev.Eliminou-se como um Visual Basic 6 (VB6) (código e, portanto, era dependente da plataforma e difícil de distribuir (exigindo VB6). Para resolver este problema e juntar-se a esforços recentes semelhantes de outros investigadores 2 , convertimos o VB6 Clock Scan Codifique o programa em dois plugins baseados em Java, compatíveis com os programas de análise de imagem independentes da plataforma open-source patrocinados pelo NIH e livremente disponíveis, ImageJ 3 e Fiji ImageJ 4. Além disso, esses plugins possuem várias funções novas que expandem a capacidade Do protocolo original para processar vários ROIs e pilhas de imagens. Muitas aplicações de análise de imagem não são fáceis de usar, no que diz respeito à análise estatística de múltiplos objetos e, portanto, muitas vezes apenas dados representativos são mostrados. Com o plugin Multi Clock Scan ImageJ, É possível facilitar a análise de objetos múltiplos simultaneamente. Uma avaliação estatística robusta dos dados de microscopia,No que diz respeito à distribuição da intensidade do sinal em células / objetos únicos, agora é possível com esta extensão de plugin. Aqui, descrevemos os plugins do Clock Scan e mostramos exemplos de suas aplicações na análise de imagens.

<table border="0" cellpadding="0" cellspacing="0" width="896">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Computer</td>
			<td style="width:71px;">Any</td>
			<td style="width:289px;"></td>
			<td style="width:320px;">compatible with software listed below</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ImageJ or Fiji ImageJ</td>
			<td style="width:71px;">NIH</td>
			<td>https://imagej.nih.gov/ij/ or https://fiji.sc/</td>
			<td>bundled with Java 1.8 or higher</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Clock-scan plugins</td>
			<td style="width:71px;">freeware</td>
			<td>https://sourceforge.net/projects/clockscan/</td>
			<td>Clock_Scan-1.0.1 jar and Multi_Clock_Scan-1.0.1/ jar</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:216px;">Origin 9.0</td>
			<td>OriginLab</td>
			<td style="width:289px;">Northampton, MA, USA</td>
			<td style="width:320px;">This program was used to generate some graphs of the original Clock Scan data. Any other graphic software can be used to perform this function</td>
		</tr>
	</tbody>
</table>

clock scan protocol for image analysis: imagej plugins

O protocolo de varredura do relógio para análise de imagem é uma ferramenta eficiente para quantificar a intensidade média de pixels dentro, na borda e fora (fundo) de uma região de interesse convexa fechada ou segmentada, levando à geração de um pixel radial integral integral, Perfil de intensidade. Este protocolo foi originalmente desenvolvido em 2006, como um script básico visual 6, mas, como tal, teve distribuição limitada. Para resolver esse problema e se juntar a esforços recentes semelhantes por parte de outros, convertimos o código original do protocolo de varredura do relógio em dois plugins baseados em Java compatíveis com programas de análise de imagens patrocinados pelo NIH e livremente disponíveis, como ImageJ ou Fiji ImageJ. Além disso, esses plugins possuem várias funções novas, expandindo ainda mais a gama de recursos do protocolo original, como análise de múltiplas regiões de interesse e pilhas de imagens. O último recurso do programa é especialmente útil em aplicativos em que é importante determinar as mudanças relacionadasAo tempo e à localização. Assim, a análise da varredura do relógio de pilhas de imagens biológicas pode potencialmente ser aplicada à disseminação de Na + ou Ca ++ em uma única célula, bem como à análise da atividade de espalhamento ( por exemplo , ondas de Ca ++ ) em populações de forma sináptica - células acopladas ou juntas separadas. Aqui, descrevemos esses novos plugins de verificação de relógio e mostramos alguns exemplos de suas aplicações na análise de imagens.

As imagens que são usadas aqui para fins ilustrativos são tiradas de bancos de dados criados durante nossos estudos biológicos biológicos de células e tecidos anteriores 5 , 6 , 7 e do Allen Mouse Brain Atlas 8 . Ambos os plugins foram testados com sucesso usando ImageJ 1.50i / Java 1.8.0_77, ImageJ 2.0.0-rc-44 / 1.50e / Java 1.8.9_66 e Fiji ImageJ 2.0.0-rc54 / 1.51g...

Watch this Scientific Journal Video about Clock Scan Protocol for Image Analysis: ImageJ Plugins at JoVE.com

Clock Scan Protocol for Image Analysis: ImageJ Plugins

Este artigo descreve dois novos plugins ImageJ para análise de imagem &#39;Clock Scan&#39;. Esses plugins expandem a funcionalidade do programa original visual 6 básico e, o mais importante, disponibilizam o programa para uma grande comunidade de pesquisa, agrupando-o com o pacote de software de análise de imagem gratuito ImageJ.

Protocolo de varredura do relógio para análise de imagem: plugins ImageJ

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a ...

clock-scan-protocol-for-image-analysis-imagej-plugins

Research

JoVE Journal

Biology

17.3K visualizações.  University of Arkansas for Medical Sciences.  Este artigo descreve dois novos plugins ImageJ para análise de imagem 'Clock Scan'. Esses plugins expandem a funcionalidade do programa original visual 6 básico e, o mais importante, disponibilizam o programa para uma grande comunidade de pesquisa, agrupando-o com o pacote de software de análise de imagem gratuito ImageJ. 

Vídeo: Clock Scan Protocol for Image Analysis: ImageJ Plugins

Protocol for Mosquito Rearing (A. gambiae)

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night cycle. For this procedure, you'll need mosquito cages, 10% sterile sucrose solution, paper towels, beaker, whatman filter paper, glass feeders, human blood and serum, water bath, parafilm, distilled water, clean plastic trays, mosquito food (described below), mosquito net to cover the trays, vacuum, and a collection chamber to collect adults.

Protocol for Mosquito Rearing A. gambiae

This video illustrates the general techniques used to rear Anopheles gambiae in the laboratory. The methods for caring for laboratory mosquitoes are demonstrated through all stages of the organism's life cycle from larvae to pupae to blood-feeding adults.

Protocolo para a Criação Mosquito (A. gambiae)

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night ...

Biologia

Neutrophil Isolation Protocol

Neutrophil polymorphonuclear granulocytes (PMN) are the most abundant leukocytes in humans and among the first cells to arrive on the site of inflammatory immune response. Due to their key role in inflammation, neutrophil functions such as locomotion, cytokine production, phagocytosis, and tumor cell combat are extensively studied. To characterize the specific functions of neutrophils, a clean, fast, and reliable method of separating them from other blood cells is desirable for in vitro studies, especially since neutrophils are short-lived and should be used within 2-4 hours of collection. Here, we demonstrate a standard density gradient separation method to isolate human neutrophils from whole blood using commercially available separation media that is a mixture of sodium metrizoate and Dextran 500. The procedure consists of layering whole blood over the density gradient medium, centrifugation, separation of neutrophil layer, and lysis of residual erythrocytes. Cells are then washed, counted, and resuspended in buffer to desired concentration. When performed correctly, this method has been shown to yield samples of >95% neutrophils with >95% viability.

Neutrophils are among the first cells to arrive on the site of inflammatory immune response, and their functions and mechanisms have been studied extensively in vitro. We demonstrate a standard density gradient separation method to isolate human neutrophils from whole blood using commercially available separation media.

Isolamento de neutrófilos Protocolo

Neutrophil polymorphonuclear granulocytes (PMN) are the most abundant leukocytes in humans and among the first cells to arrive on the site of inflammatory ...

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

A Neuronal e astrocitária Ensaio Co-Cultura de Análise de Conteúdo de Alta de neurotoxicidade

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Robótica e Análise de Imagem Dinâmica de Estudos de Expressão Gênica em Tecidos Vegetais

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

ReAsH/FlAsH Labeling and Image Analysis of Tetracysteine Sensor Proteins in Cells

Fluorescent proteins and dyes are essential tools for the study of protein trafficking, localization and function in cells. While fluorescent 
proteins such as green fluorescence protein (GFP) have been extensively used as fusion partners to proteins to track the properties of a protein of interest1, recent 
developments with smaller tags enable new functionalities of proteins to be examined in cells such as conformational change and protein-association 2, 3. One small 
tag system involves a tetracysteine motif (CCXXCC) genetically inserted into a target protein, which binds to biarsenical dyes, ReAsH (red fluorescent) and FlAsH 
(green fluorescent), with high specificity even in live cells 2. The TC/biarsenical dye system offers far less steric constraints to the host protein than 
fluorescent proteins which has enabled several new approaches to measure conformational change and protein-protein interactions 4-7. We recently developed 
a novel application of TC tags as sensors of oligomerization in cells expressing mutant huntingtin, which when mutated aggregates in neurons in Huntington 
disease 7. Huntingtin was tagged with two fluorescent dyes, one a fluorescent protein to track protein location, and the second a TC tag which only 
binds biarsenical dyes in monomers. Hence, changes in colocalization between protein and biarsenical dye reactivity enabled submicroscopic oligomer 
content to be spatially mapped within cells. Here, we describe how to label TC-tagged proteins fused to a fluorescent protein (Cherry, GFP or CFP) 
with FlAsH or ReAsH in live mammalian cells and how to quantify the two color fluorescence (Cherry/FlAsH, CFP/FlAsH or GFP/ReAsH combinations).

The biarsenical dyes FlAsH and ReAsH bind specifically to tetracysteine motifs in proteins and can selectively label proteins in live cells. Recently this labeling strategy has been used to develop sensors for different protein conformations or oligomeric states. We describe the labeling approach and methods to quantitatively analyze binding.

ReAsH / Flash Rotulagem e Análise de Imagem Sensor de Proteínas em Células Tetracysteine

Fluorescent proteins and dyes are essential tools for the study of protein trafficking, localization and function in cells.  While fluorescent 
proteins ...

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools5,6. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS)7. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices8. To date, leaf shape programs (e.g. SHAPE9, LAMINA10, LeafAnalyzer11, LEAFPROCESSOR12) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size13. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.

LeafJ: um plugin ImageJ para semi-automatizado de medição forma de folha

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the ...

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ imaging data is time consuming and subject to bias. Automated signal analysis algorithms based on region of interest (ROI) detection have been implemented for one-dimensional line scan measurements, but there is no current algorithm which integrates optimized identification and analysis of ROIs in two-dimensional image sequences. Here an algorithm for rapid acquisition and analysis of ROIs in image sequences is described. It utilizes ellipses fit to noise filtered signals in order to determine optimal ROI placement, and computes Ca2+ signal parameters of amplitude, duration and spatial spread. This algorithm was implemented as a freely available plugin for ImageJ (NIH) software. Together with analysis scripts written for the open source statistical processing software R, this approach provides a high-capacity pipeline for performing quick statistical analysis of experimental output. The authors suggest that use of this analysis protocol will lead to a more complete and unbiased characterization of physiologic Ca2+ signaling.

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.

Automated Análise de Ca dinâmico<sup&gt; 2 +</sup&gt; Sinais em seqüências de imagens

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ ...

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application &#8211; SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min&#160;on a minimally configured laptop, or around 1 min&#160;on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary &#8220;Manual Initialize&#8221; and &#8220;Hand Draw&#8221; tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.

We present a high-throughput image analysis software application to measure the size of three-dimensional tumor spheroids imaged with bright-field microscopy. This application provides a fast and effective way to examine the effects of therapeutic drugs on spheroids, which is beneficial for researchers who wish to use spheroids in drug screens.

De alta capacidade Análise de Imagem de esferóides tumorais: uma aplicação de software de fácil utilização para medir o tamanho de esferóides automaticamente e com precisão

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to ...

A Protocol for Lentiviral Transduction and Downstream Analysis of Intestinal Organoids

Intestinal crypt-villus structures termed organoids, can be kept in sustained culture three dimensionally when supplemented with the appropriate growth factors. Since organoids are highly similar to the original tissue in terms of homeostatic stem cell differentiation, cell polarity and presence of all terminally differentiated cell types known to the adult intestinal epithelium, they serve as an essential resource in experimental research on the epithelium. The possibility to express transgenes or interfering RNA using lentiviral or retroviral vectors in organoids has increased opportunities for functional analysis of the intestinal epithelium and intestinal stem cells, surpassing traditional mouse transgenics in speed and cost. In the current video protocol we show how to utilize transduction of small intestinal organoids with lentiviral vectors illustrated by use of doxycylin inducible transgenes, or IPTG inducible short hairpin RNA for overexpression or gene knockdown. Furthermore, considering organoid culture yields minute cell counts that may even be reduced by experimental treatment, we explain how to process organoids for downstream analysis aimed at quantitative RT-PCR, RNA-microarray and immunohistochemistry. Techniques that enable transgene expression and gene knock down in intestinal organoids contribute to the research potential that these intestinal epithelial structures hold, establishing organoid culture as a new standard in cell culture.

In this video protocol we give a step by step explanation of lentiviral transduction in organoids of primary intestinal epithelium and of processing and downstream analysis of these cultures by quantitative RT-PCR, RNA-microarray and immunohistochemistry.

Um Protocolo de Transdução Lentivirus e Análise de Downstream da Intestinal Organóides

Intestinal crypt-villus structures termed organoids, can be kept in sustained culture three dimensionally when supplemented with the appropriate growth ...

Rapid Analysis of Circadian Phenotypes in Arabidopsis Protoplasts Transfected with a Luminescent Clock Reporter

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable biotic and abiotic stress. Conversely, disruption of circadian timekeeping severely compromises plant health and reduces agricultural crop yields. It is therefore imperative that we understand the intricate regulation of circadian rhythms in plants, including the factors that affect motion of the transcriptional clockwork itself.
Testing circadian defects in the model plant Arabidopsis thaliana (Arabidopsis) traditionally involves crossing specific mutant lines to a line rhythmically expressing firefly luciferase from a circadian clock gene promoter. This approach is laborious, time-consuming, and could be fruitless if a mutant has no circadian phenotype. The methodology presented here allows a rapid initial assessment of circadian phenotypes. Protoplasts derived from mutant and wild-type Arabidopsis are isolated, transfected with a rhythmically expressed luminescent reporter, and imaged under constant light conditions for 5 days. Luminescent traces will directly reveal whether the free-running period of mutant plants is different from wild-type plants. The advantage of the method is that any Arabidopsis line can efficiently be screened, without the need for generating a stably transgenic luminescent clock marker line in that mutant background.

The circadian clock regulates about a third of the Arabidopsis transcriptome, but the percentage of genes that feed back into timekeeping remains unknown. Here we visualize a method to rapidly assess circadian phenotypes in any mutant line of Arabidopsis using luminescent imaging of a circadian reporter transiently expressed in protoplasts.

Rápida análise de fenótipos circadianos em Arabidopsis Protoplastos transfectadas com um relógio Reporter luminescentes

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable ...