flow cytometric data acquisition of microbial aggregates

Análise quantitativa da autoagregação microbiana usando citometria de fluxo de imagem

Bacterial autoaggregation is considered a primary step in biofilm formation. In this process (sometimes also called autoagglutination or flocculation), bacteria of the same type form multicellular clumps that eventually settle at the bottom of culture tubes or attach to their target tissue or surface1.
Autoaggregation is a widely observed phenomenon and has been shown so far in Gram-negative pathogens such as the opportunistic pathogen Acinetobacter baumannii2, the dental pathogen Aggregobacter actinomycetemcomitans3, and the emerging pathogen Burkholderia pseudomallei4. Autoaggregation has also been described in several probiotic Gram-positive strains5,6,7,8. In Lactobacillus (L.) acidophilus, autoaggregation was partially mediated by S-layer proteins and correlated with an adhesion to xylan7. Similarly, we found a correlation between glucose-dependent autoaggregation and induction of the adhesive properties (as judged by adhesion to mucin) of the probiotic species Lacticaseibacillus rhamnosus GG, Lacticaseibacillus casei, L. acidophilus, Lacticaseibacillus paracasei, and Lactiplantibacillus plantarum5. The increased autoaggregation most likely reflected changes in the expression of cellular adhesins in response to glucose and its catabolites9. While the molecular mechanisms of autoaggregation remain to be determined, it has been shown that this process alters the phenotype of the aggregated bacteria and grants them enhanced tolerance to environmental stressors1, as well as increased sensitivity to quorum sensing molecules10.
Several approaches have been used to measure autoaggregation; one experimental approach is to let cultures stand statically in narrow culture tubes for a given time. Control cultures remain turbid, whereas autoaggregation cultures will settle at the bottom of the tube. A more quantitative approach measures autoaggregation by sedimentation or settling assay11.
Flow cytometry has also been increasingly employed in recent years to investigate bacterial autoaggregation. This method is appropriate for analyzing particles between approximately 0.5 and 1000 &#181;m in size. The single bacterium or formed aggregates are suspended in fluid, fed into a stream, and can be detected one by one11. Recording forward scattered light allows measuring the relative size of the cell or aggregate. It is relatively fast and straightforward but cannot detect several parameters, such as aggregate size or the average number of cells in aggregates. Therefore, this approach can be complemented microscopically, allowing more parameters to be checked12. However, traditional microscopy is time-consuming and thereby limits the number of tested samples and the statistical power of the analysis. In general, imaging flow cytometry provides several features compared to traditional flow cytometry, such as simultaneous analysis of cell morphology and phenotype, conducting image-based analysis, detecting rare events, and validating flow cytometry data13. These advantages enhance the capabilities of flow cytometry and facilitate a more detailed examination of cell populations.
This study provides a valuable protocol for imaging flow cytometry to monitor autoaggregation in lactic acid bacteria (LAB). These Gram-positive rods are facultative anaerobes and belong to the LAB group. These efficient glucose fermenters generate lactic acid as their main end-product of carbohydrate metabolism14. These bacteria are beneficial core members of the microbiome and are naturally found in the gastrointestinal tract (GIT) of humans and animals, as well as in the urogenital tract of females15. Therefore, the exact characterization of their autoaggregation properties is of high biotechnological and clinical interest.
Our previous findings indicated that the basal level of autoaggregation differs between different probiotic strains. This heterogeneity is affected by the different carbohydrates used as a carbon source5. To overcome this fundamental property of probiotic bacteria, the effects of carbohydrates from the diet were monitored on autoaggregation on the single-cell level using IFC. This IFC-based approach combines the power and speed of traditional flow cytometers with the microscope&#39;s resolution. Therefore, it allows high-rate complex morphometric measurements in a phenotypically defined way16,17. This approach can be extended to other probiotic and pathogenic bacteria, combined with fluorescent reporters to monitor gene expression, and fluorescently labeled strains to monitor the presence and abundance of specific bacterial species in heterogeneous aggregates.

Autor em destaque: Avançando na pesquisa em autoagregação microbiana usando citometria de fluxo de imagem

Citometria de fluxo de imagem para estudar a autoagregação microbiana

This study utilized Lactobacillus species as a model to demonstrate the effectiveness of imaging flow cytometry analysis in studying microbial autoaggregation. The focus is on analyzing the response of autoaggregation of Lactobacillus species to simple carbohydrates from the diet. The most used methods for measuring autoaggregation are spectral photometry, which measures microbial suspensions, turbidity, or optical density, and traditional microscopy techniques, such as bright field, phase contrast, and fluorescent microscopy.When talking about hight-throughput imaging, one major experimental challenge is single event segmentation. In this protocol, it is a single aggregate. Imaging flow cytometry addresses this challenge elegantly and give researchers a powerful tool for studying autoaggregation.

Aquisição de dados de citometria de fluxo de agregados microbianos

Para iniciar a análise de citometria de fluxo, primeiro defina o laser de 785 nanômetros para uma potência de cinco miliwatts para medições de campo escuro. Use uma lente de 60X com uma abertura numérica de 0,9. Em seguida, selecione a configuração de alta sensibilidade e defina a velocidade como baixa.Para garantir a estabilidade do cordão de calibração, pressione o botão Reproduzir na caixa de fluidos e examine as imagens do cordão, que devem parecer nítidas e claras. Gere um novo gráfico de dispersão na caixa de espaço de trabalho selecionando Novo gráfico de dispersão. Em seguida, selecione a área no canal Campo claro que corresponde à intensidade do canal SSC e exclua os cordões de calibração que estão sendo executados simultaneamente na amostra.Em seguida, pressione o botão Carregar e posicione um tubo contendo a amostra de lactobacilos no suporte. Na caixa Configurações de aquisição, insira o nome da amostra e especifique o local para armazenamento de dados. Defina o número de eventos a serem coletados, normalmente dentro do intervalo de 10.000 a 20.000 eventos.Agora, inicie o processo de aquisição clicando no botão Gravar localizado na caixa Aquisição. O processo será interrompido após atingir o volume especificado de eventos. Pressione o botão Return para descarregar o tubo e removê-lo do suporte conforme solicitado na janela pop-up.Depois de repetir o processo para todas as amostras, salve o modelo para manter a uniformidade de aquisição de dados. Para análise de dados, para carregar o arquivo bruto no software de análise, clique em Arquivo e abra o arquivo rif. Na janela aberta, selecione Usar Análise de Aquisição e clique em OK. Em seguida, para criar um histograma do quadrado médio da raiz do gradiente, clique no botão Novo histograma e escolha a população da aquisição a ser analisada.No recurso Eixo X, selecione RMS de gradiente do canal Campo claro. Em seguida, coloque um portão para excluir eventos não focados clicando no botão Criar região de linha na área de análise. Crie um gráfico de dispersão da área versus a proporção clicando no botão Novo gráfico de dispersão na área de análise.Selecione a população focalizada na janela Novo gráfico de dispersão. Em seguida, selecione a área do canal Campo claro para o recurso Eixo X. Escolha a proporção para o recurso Eixo Y.Desenhe uma porta no gráfico de dispersão usando o botão Criar Retângulo com base no valor da área para a população de eventos agregados. Da mesma forma, desenhe outro portão para a população de solteiros e pequenos agregados. Salve sua análise de dados como um modelo clicando na guia Arquivo, escolhendo Salvar como modelo ast e abra o próximo arquivo de amostra no mesmo modelo para criar o arquivo de análise de dados.Para medir a distribuição de tamanho agregado, comece plotando um histograma da área da população de eventos de agregação. Salve a análise de dados clicando no arquivo e selecionando a opção Salvar como modelo. Depois de salvar, abra o próximo arquivo de amostra no mesmo modelo para o arquivo de análise de dados.Células únicas, pequenos agregados, grandes agregados e cadeias foram observados em LGG e L. paracasei, mas não foi possível distinguir entre cadeias e agregados maiores. Variações significativas nas características de agregação de diferentes cepas de LAB foram observadas em resposta a açúcares fermentáveis ou não fermentáveis.

flow-cytometric-data-acquisition-of-microbial-aggregates

Research

JoVE Journal

Biology

Flow Cytometric Data Acquisition of Microbial Aggregates

Imaging Flow Cytometry to Study Microbial Autoaggregation

Beneficial and probiotic bacteria play essential roles in their hosts, providing various health benefits, including immunity to infectious diseases. The Lactobacillaceae family consists of Gram-positive bacteria with confirmed probiotic properties. This study utilizes Lactobacillaceae species as a model to demonstrate the effectiveness of single-cell high throughput analysis in studying cellular aggregation. The focus is on analyzing the response of these beneficial species to simple carbohydrates from the diet.
The study showcases how Imaging Flow Cytometry (IFC) can overcome the fundamental differences in the assembly of probiotic bacteria in the presence and absence of carbohydrates. IFC combines the power and speed of conventional flow cytometry with the spatial resolution of microscopy, enabling high-rate complex morphometric measurements in a phenotypically defined manner across a library of beneficial bacterial strains and conditions. This protocol provides insights into the autoaggregation of Lactobacillaceae species and sheds light on their response to dietary carbohydrates, contributing to understanding the mechanisms behind the beneficial effects of these probiotic bacteria.

This protocol describes a quantitative approach to measure microbial autoaggregation using imaging flow cytometry.

Beneficial and probiotic bacteria play essential roles in their hosts, providing various health benefits, including immunity to infectious diseases. The ...

Biologia

Lactobacillaceae Sample Preparation for Flow Cytometric Analysis

Start by inoculating five milliliters of lactobacilli MRS broth with a single colony of the lactobacilli strain, incubated at 37 degrees Celsius under static conditions overnight. Dilute the cell culture in a one-to-100 ratio in three milliliters of 50%supplemented tryptic soy broth, vortex the culture, and incubate it. Then invert the tube to mix the cells in the medium before transferring 200 to 300 microliters of the sample into a 1.5 milliliter micro centrifuge tube.

Lactobacilas Preparação de amostras para análise por citometria de fluxo

To begin the flow cytometric analysis, first set the 785-nanometer laser to a power of five milliwatts for dark field measurements. Use a 60X lens with a numerical aperture of 0.9. Then select the high-sensitivity setting and set the speed to low.To ensure calibration bead stability, press the Play button in the fluidics box and examine the bead images, which should appear sharp and clear. Generate a new scatterplot in the workspace box by selecting New Scatterplot. Then select the area in the Brightfield channel that corresponds to the SSC channel intensity and exclude the calibration beads that are running concurrently within the sample.Next, press the Load button and position a tube containing the Lactobacilli sample in the holder. In the Acquisition Settings box, enter the sample name and specify the location for data storage. Set the number of events to be collected, typically within the range of 10, 000 to 20, 000 events.Now, begin the acquisition process by clicking the Record button located in the Acquisition box. The process will halt after reaching the specified volume of events. Press the Return button to unload the tube and remove it from the holder as prompted in the popup window.After repeating the process for all samples, save the template to maintain data acquisition uniformity. For data analysis, to load the raw file into the analysis software, click on File and open the rif file. In the opened window, select Use Acquisition Analysis and then click OK.Next, to create a histogram of the gradient root mean square, click the New Histogram button and choose the population from the acquisition to analyze.Under the X Axis feature, select Gradient RMS of the Brightfield channel. Next, place a gate to exclude non-focused events by clicking the Create Line Region button in the analysis area. Create a scatterplot of the area versus the aspect ratio by clicking the New Scatterplot button in the analysis area.Select the focused population in the New Scatterplot window. Then select the area of the Brightfield channel for the X-Axis feature. Choose the aspect ratio for the Y-Axis feature.Draw a gate on the scatterplot using the Create Rectangle button based on the area value for the aggregate events population. Similarly, draw another gate for the singles and small aggregates population. Save your data analysis as a template by clicking the File tab, choosing Save As Template ast, and then open the next sample file under the same template to create the data analysis file.To measure the aggregate size distribution, start by plotting a histogram of the area of the aggregation events population. Save the data analysis by clicking on the file and selecting the Save As Template option. After saving, open the next sample file under the same template for the data analysis file.Single cells, small aggregates, large aggregates, and chains were observed in LGG and L.paracasei but it was not possible to distinguish between chains and larger aggregates. Significant variations in the aggregation characteristics of different LAB strains were observed in response to fermentable or non-fermentable sugars.