This study utilized Lactobacillaceae 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 Lactobacillaceae species to simple carbohydrates from the diet. The most used methods for measuring autoaggregation are spectrophotometry, which measures microbial suspension, turbidity, or optical density, and traditional microscopy techniques, such as bright field, phase contrast, and fluorescent microscopy.
When talking about high-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.
Start by inoculating five milliliters of Lactobacilli MRS Broth with a single colony of the Lactobacillaceae strain. Incubate it at 37 degrees Celsius under static conditions overnight. Dilute the cell culture in a 1-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 microcentrifuge tube. 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 bright field 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 Lactobacillaceae 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 pop-up 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 bright field 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 bright field 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 tab 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.
