This work was supported by the Israeli Science Foundation (Grant 119/16) and&#160;IMoh grant&#160;(3-15656) to IKG. R.S. supported by the Kreitman fellowship.&#160;

The .ast file, with the template for Lacticaseibacillus rhamnosus GG (LGG) as an example, is provided in Supplementary Coding File 1.
1. Media preparation
<ol>
	<li>Prepare Lactobacilli MRS broth following the manufacturer&#39;s instructions (55 g in 1 L of deionized water) and Lactobacilli MRS agar plates with 1.5% (w/v) agar (see Table of Materials). After autoclave sterilization, the medium is ready to use directly or can be stored at 4 &#176;C.</li>
	<li>Prepare 50% (15 g in 1 L of deionized water) Tryptic soy broth (TSB) (see Table of Materials), TSB supplemented with 1% (w/v) D-(+)-glucose and 1% (w/v) D-(+)-raffinose, then autoclave to sterilize the medium. It is preferred to store it at 4 &#176;C to avoid contamination.</li>
	<li>For a buffered medium with glucose, prepare TSB supplemented with 1% (w/v) D-(+)-glucose, 5 mM of potassium phosphate monobasic together with potassium dibasic, and 100 mM MOPS (3-(N-morpholino) propane sulfonic acid, see Table of Materials) pH 7. Then autoclave to sterilize the medium.</li>
</ol>
2. Preparation of samples
NOTE: This step involves the evaluation of cellular aggregation in response to fermentable or non-fermentable sugar from our diet. A schematic representation of the sample preparation process is depicted in Figure 1.
<ol>
	<li>Streak the Lactobacillaceae strain from a frozen glycerol stock (50% glycerol) on a Lactobacilli MRS broth plate (1.5% agar). 
	NOTE: In the example experiment presented here, Lactobacillus acidophilus (ATCC 4356), Lacticaseibacillus casei (ATCC 393), Lacticaseibacillus casei subsp. paracasei (ATCC BAA-52), Lactiplantibacillus plantarum (ATCC 8014), and Lacticaseibacillus rhamnosus GG (ATCC 53103) (LGG) probiotic strains were used. To expand this methodology to additional probiotic members of the firmicutes phylum, Bacillus coagulans (ATCC 10545) was selected.</li>
	<li>Grow the cells overnight at 37 &#176;C under static conditions.</li>
	<li>Inoculate 5 mL of Lactobacilli MRS broth with a single colony and grow it at 37 &#176;C under static conditions overnight.</li>
	<li>Dilute the cell culture from the previous step (1:100) in 3 mL of 50% Tryptic soy broth (TSB), TSB supplemented with 1% (w/v) D-(+)-glucose, or TSB supplemented with 1% (w/v) D-(+)-raffinose.</li>
	<li>Incubate overnight at 37 &#176;C under static conditions.</li>
	<li>Prepare the sample by mixing the cells uniformly in the medium, and gently vortex the cell culture tube from step 2.5. Gently invert the test tube until complete mixing takes place. Transfer 200-300 &#181;L into a 1.5 mL microcentrifuge tube. 
	&#8203;NOTE: It is essential not to sonicate the sample to avoid breaking the aggregates. During the data acquisition from steps 3.4 to 3.10, there may be instances where the samples are too concentrated. If it is observed that the sample is running very slowly or not running at all, one can dilute the sample with the appropriate fresh medium.</li>
</ol>
3. Data acquisition
<ol>
	<li>Set up the imaging flow cytometer according to the instructions provided by the manufacturer (see Table of Materials).</li>
	<li>Set the 785 nm laser to 5 mW for darkfield measurement (equivalent to side scattering in conventional flow cytometry, abbreviated as SSC). Use a 60x lens with a numerical aperture (N.A) of 0.9, select high sensitivity, and set the speed to low.</li>
	<li>Before collecting any data, ensure the stability of the calibration beads flow in the SSC channel. Click the play button in the &#34;fluidics&#34; box and examine the quality of the bead images. The bead images should appear sharp and crisp.</li>
	<li>Press the Load button and place a tube with a sample (from step 2.6) into the holder.</li>
	<li>Create a new scatterplot in the &#34;workspace&#34; box. Click New scatterplot and select the area in the brightfield channel corresponding to the SSC channel intensity. Exclude the calibration beads that run in the instrument along with the sample. 
	NOTE: Beads can be excluded by loading a sample with buffer only and identifying the beads on the Area vs. SSC plot. Then draw a gate around the bead population using the Create Polygon Region button.</li>
	<li>In the &#34;Acquisition Settings&#34; box, enter the sample name, specify the location for the data storage, choose the population (all cells without beads) to record from, and set the number of events to be collected. Typically, 10,000-20,000 events are sufficient.</li>
	<li>Click the Record button located in the &#34;Acquisition&#34; box, and the acquisition process will automatically end once the specified number of events is reached.</li>
	<li>Click the Return button to unload the tube, and remove the tube from the holder when prompted in a small window.</li>
	<li>Repeat steps 3.5-3.9 for each sample.</li>
	<li>Save the template to maintain the uniformity of data acquisition for subsequent repeats.</li>
</ol>
4. Data analysis
<ol>
	<li>Measure the percentage (%) of autoaggregation from the population.
	<ol>
		<li>Analyze the data acquired on the imaging flow cytometer using IDEAS software (see Table of Materials).</li>
		<li>Create a data analysis file (.daf) from the raw file (.rif) by loading the (.rif) file into the analysis software. Click the File button and open the required (.rif) file. In the opened window, click on use acquisition analysis and then click OK.</li>
		<li>Create a histogram of gradient RMS (a measurement of image contrast and focus) in the brightfield channel to identify focused events. In the analysis area, click the New Histogram button. In the &#34;New Histogram&#34; window, choose the population from the acquisition to analyze, and in the &#34;x axis feature,&#34; select Gradient RMS of the brightfield channel.
		<ol>
			<li>Place a gate by clicking the Create Line Region button in the analysis area to exclude non-focused events (Figure 2A). 
			NOTE: Typically, the &#34;Focused&#34; line region should include 80%-90% of the events with the highest Gradient RMS score, but specific thresholds for &#34;in-focus&#34; cells should be determined for each experimental setup.</li>
		</ol>
		</li>
		<li>Create a scatterplot of the area (&#181;m2) versus the aspect ratio (width divided by the length of a best-fit ellipse) of the brightfield channel. Press the New Scatterplot button in the analysis area. In the &#34;New Scatterplot&#34; window, choose the Focused population from step 4.1.3.
		<ol>
			<li>Select Area of the brightfield channel for the x-axis feature and Aspect Ratio of the brightfield channel for the y-axis feature. This scatterplot allows gating on the focused population to distinguish singlets and small aggregate events from larger microbial aggregates and chains.</li>
		</ol>
		</li>
		<li>On this scatterplot, draw a gate using the Create Rectangle Region button (or Create Polygon Region) based on the area value for the aggregation events population and another gate for the singlets and small aggregates population (Figure 2B). 
		NOTE: In certain backgrounds, separating small aggregates from singlets may be challenging, so they can be combined into the same gate. The same applies to larger aggregates and chains (Figure 3).</li>
		<li>Manually review and evaluate images of events within each gate to verify the reliability of the gating strategy. If needed, adjust the area value for the gate. Perform this by selecting the reviewed population from the &#34;Populations&#34; drop-down menu. 
		NOTE: The gating has no specific area value, as it can vary depending on the characteristics of the bacteria being analyzed.</li>
		<li>Save the data analysis as a template. Click the File tab, select Save as Template.ast, and open the next sample (.rif) file under the same template to create the data analysis file (.daf) (Supporting .ast file as an example for LGG).</li>
		<li>Create a statistics table to enumerate key events for further analysis. Click on the Reports tab, then click on Define Statistics Report.</li>
		<li>In the new window, click Add Columns. Add the singlets/aggregates count and %gated statistics. Under &#34;statistics,&#34; choose %gated/count, and under the selected population, choose singlets/aggregates. Click Add Statistics to add the statistic to the list and save it as a template.</li>
		<li>Click on Generate statistic report and choose the statistic template and the (.daf) files for analysis.</li>
	</ol>
	</li>
	<li>Measure the size distribution of the aggregates.
	<ol>
		<li>To analyze the mean size of the aggregation events, plot a histogram of the area (&#181;m2) of the aggregation events population from step 4.1.5 (Figure 2C).</li>
		<li>Save the data analysis as a template. Click on the File tab, select Save as Template, and open the next sample (.rif) file under the same template for the data analysis file (.daf).</li>
		<li>Repeat steps 4.1.7-4.19. For the aggregates&#39; size, choose mean under statistics and select aggregates under the selected population. Under features, select Area of the brightfield channel. Click on Add Statistics to add the statistic to the list and save it as a template.</li>
		<li>Repeat step 4.1.9 to generate a statistic report.</li>
	</ol>
	</li>
</ol>

Quantitative Analysis of Microbial Autoaggregation Using Imaging Flow Cytometry

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<li>Isenring, J., Geirnaert, A., Lacroix, C., Stevens, M. J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Bistable+auto-aggregation+phenotype+in+Lactiplantibacillus+plantarum+emerges+after+cultivation+in+in+vitro+colonic+microbiota%5BTitle%5D)+AND+%22BMC+Microbiology%22%5BJournal%5D)">Bistable auto-aggregation phenotype in Lactiplantibacillus plantarum emerges after cultivation in in vitro colonic microbiota.</a> BMC Microbiology. 21, 268(2021).</li>
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<li>Misawa, N., Blaser, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+and+characterization+of+autoagglutination+activity+by+Campylobacter+jejuni%5BTitle%5D)+AND+%22Infection+and+Immunity%22%5BJournal%5D)">Detection and characterization of autoagglutination activity by Campylobacter jejuni.</a> Infection and Immunity. 68 (11), 6168-6175 (2000).</li>
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<li><a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Imaging+flow+cytometry%5BTitle%5D)+AND+%22Nature+Reviews+Methods+Primers%22%5BJournal%5D)">Imaging flow cytometry.</a> Nature Reviews Methods Primers. 2, 87(2022).</li>
<li>Wang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Metabolism+characteristics+of+lactic+acid+bacteria+and+the+expanding+applications+in+food+industry%5BTitle%5D)+AND+%22Frontiers+in+Bioengineering+and+Biotechnology%22%5BJournal%5D)">Metabolism characteristics of lactic acid bacteria and the expanding applications in food industry.</a> Frontiers in Bioengineering and Biotechnology. 9, 612285(2021).</li>
<li>Turroni, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+dialogue+between+the+human+gut+microbiota+and+the+host%3A+a+Lactobacillus+and+Bifidobacterium+perspective%5BTitle%5D)+AND+%22Cellular+and+Molecular+Life+Sciences%22%5BJournal%5D)">Molecular dialogue between the human gut microbiota and the host: a Lactobacillus and Bifidobacterium perspective.</a> Cellular and Molecular Life Sciences. 71, 183-203 (2014).</li>
<li>Zuba-Surma, E. K., Kucia, M., Abdel-Latif, A., Lillard, J. W. Jr, Ratajczak, M. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+ImageStream+System%3A+a+key+step+to+a+new+era+in+imaging%5BTitle%5D)+AND+%22Folia+Histochemica+et+Cytobiologica%22%5BJournal%5D)">The ImageStream System: a key step to a new era in imaging.</a> Folia Histochemica et Cytobiologica. 45, 279-290 (2007).</li>
<li>Dashkova, V., Malashenkov, D., Poulton, N., Vorobjev, I., Barteneva, N. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Imaging+flow+cytometry+for+phytoplankton+analysis%5BTitle%5D)+AND+%22Methods%22%5BJournal%5D)">Imaging flow cytometry for phytoplankton analysis.</a> Methods. 112, 188-200 (2017).</li>
<li>Maan, H., Gilhar, O., Porat, Z., Kolodkin-Gal, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Bacillus+subtilis+colonization+of+arabidopsis+thaliana+roots+induces+multiple+biosynthetic+clusters+for+antibiotic+production%5BTitle%5D)+AND+%22Frontiers+in+Cellular+and+Infection+Microbiology%22%5BJournal%5D)">Bacillus subtilis colonization of arabidopsis thaliana roots induces multiple biosynthetic clusters for antibiotic production.</a> Frontiers in Cellular and Infection Microbiology. 11, 722778(2021).</li>
<li>Maan, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Imaging+flow+cytometry+reveals+a+dual+role+for+exopolysaccharides+in+biofilms%3A+To+promote+self-adhesion+while+repelling+non-self-community+members%5BTitle%5D)+AND+%22Computational+and+Structural+Biotechnology+Journal%22%5BJournal%5D)">Imaging flow cytometry reveals a dual role for exopolysaccharides in biofilms: To promote self-adhesion while repelling non-self-community members.</a> Computational and Structural Biotechnology Journal. 20, 15-25 (2022).</li>
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Flow cytometry is a widely used method for quantifying fluorescence intensities in eukaryotic cells, but it may not provide accurate measurements for bacterial cells due to their larger size or small aggregates. These factors can significantly impact the precise quantification of autoaggregation and the basal level of aggregate formation in different conditions. To address this, imaging flow cytometry (IFC) was employed to gain a better resolution of how carbohydrates affect the aggregation of probiotic bacteria5. IFC combines the statistical significance of large sample sizes in traditional flow cytometry with the per-cell information content of standard microscopy by collecting numerous digital images per sample and extracting numerical image-based features16.
Previously, IFC has been used to study various bacterial behaviors, such as antibiotic production in Bacillus subtilis and the composition of dual-species biofilms18,19. While sporadic use of Imagestream flow cytometry for measuring autoaggregation has been reported20, it is believed to be the first demonstration of its usefulness in studying probiotic bacteria5. The findings indicate that IFC is efficient and reliable, serving as a replacement for both traditional flow cytometry and microscopy in studying microbial aggregation of LAB strains, especially considering the significant differences in basal levels of autoaggregation among strains5. This approach is particularly valuable for accurate high-throughput screening of multiple strains and growth conditions.
However, the IFC-based method has certain limitations and considerations. It requires a higher number of cells to generate data sets compared to traditional microscopy methods. Additionally, the optical and digital resolution of IFC images is relatively lower than that of conventional microscopy due to the inherent properties of the method. While traditional flow cytometry lacks the reliability and quality assurance of optical scans, Imagestream flow cytometry is commonly used as an alternative to conventional flow cytometry rather than fluorescent microscopy. Moreover, determining the area value that distinguishes single cells from aggregates is a critical step, and its accurate identification is crucial to avoid losing vital data. As mentioned in the protocol, this value varies across bacterial species and should be carefully determined.
Further developments can be made to expand the method's application, such as investigating aggregation under additional environmental factors. Combining image analysis with flow cytometry data allows for more comprehensive characterization of microbial aggregates, including size, shape, and composition. This method can also be applied to enhance the high-throughput characterization of microbial aggregates in naturally occurring aquatic habitats21. Furthermore, it can be easily adapted to examine aggregation in complex microbiome communities by labeling target species and cell types.
Overall, the findings highlight that this approach is straightforward, informative, and highly useful for evaluating the aggregation properties of bacteria in both mono-species and complex communities. It enables a better understanding of the molecular mechanisms underlying autoaggregation by comparing the number of cells and the size of aggregates among different deletion, overexpression strains, and various chemical and physical manipulations.

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.

<table><tbody><tr><td>14 mL culture tubes</td><td>Falcon</td><td>352051</td><td></td></tr><tr><td>15 mL centrifuge tube</td><td>Falcon</td><td>352096</td><td></td></tr><tr><td>Bacto Agar</td><td>Baeton,Dickinson and Company</td><td>214010</td><td></td></tr><tr><td>Bacto Typtic Soy Broth</td><td>Baeton,Dickinson and Company</td><td>211825</td><td></td></tr><tr><td>D-(+)-Glucose</td><td>Sigma</td><td>G7021-1KG</td><td></td></tr><tr><td>D-(+)-Raffinose pentahydrate</td><td>Sigma</td><td>83400-25G</td><td></td></tr><tr><td>Difco Lactobacilli MRS broth</td><td>Baeton,Dickinson and Company</td><td>288130</td><td></td></tr><tr><td>EASY-LOCK MICROPR. 1.5 mL (Eppendorf)</td><td>FL medical</td><td>23053</td><td></td></tr><tr><td>IDEAS Software</td><td>Amnis/EMD Millipore</td><td>N/A</td><td>&amp;nbsp;Details available at: https://www.merckmillipore.com/INTL/en/20150212_144049?ReferrerURL=https%3A%2F%2Fwww.google.com%2F&amp;amp;bd=1</td></tr><tr><td>ImageStream X Mark II</td><td>Amnis/EMD Millipore</td><td>N/A</td><td>&amp;nbsp;Details available at: https://www.merckmillipore.com/INTL/en/20150121_205948?ReferrerURL=https%3A%2F%2Fwww.google.com%2F</td></tr><tr><td>MOPS, 3-(N-morpholino)propanesulfonic acid</td><td>Fisher bioreagents</td><td>BP308-500</td><td></td></tr><tr><td>Potassium phosphate dibasic</td><td>Fisher Scientific, 174.18 g/mol</td><td>BP363-1</td><td></td></tr><tr><td>Potassium phosphate monobasic</td><td>Sigma, 136.09 g/mol</td><td>P0662-500G</td><td></td></tr></tbody></table>

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.

The results demonstrate that this method can easily measure the differences in autoaggregation in response to dietary sugars in LAB bacteria. By separating individuals from aggregates, the method allows calculating the percentage of the population of the aggregation events out of all events in response to fermentable or non-fermentable sugars from the diet. Additionally, it was possible to measure if there are differences in the mean size of the aggregate&#39;s population between treatments.
The representative images in Figure 3 demonstrate the gating strategy for each LAB bacterium. In LGG and L. paracasei, single cells, small aggregates, large aggregates, and chains were detected (Figure 3). However, chains and larger aggregates could not be separated.
The representative images in Figure 4 revealed substantial variations in the aggregation characteristics among different LAB strains presented in response to fermentable or non-fermentable sugars, which were measured by the population percentage and the average size of aggregation events5.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65788/65788fig01.jpg" /> 
Figure 1: Workflow of the sample preparation process. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65788/65788fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65788/65788fig02.jpg" /> 
Figure 2: Gating strategy for evaluating aggregation events population and size. (A) Gating of focused events from all events based on Gradient RMS using the brightfield channel. (B) Separation of singles and small aggregates from larger aggregates by plotting the area (&#181;m2) versus the aspect ratio (width divided by the length of a best-fit ellipse) of the brightfield channel. (C) The area of the brightfield channel among aggregation events provides information about the size of the particles. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65788/65788fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65788/65788fig03.jpg" /> 
Figure 3: Cell images of gating strategy. (A) LGG. (B) L. paracasei in TSB (control). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65788/65788fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65788/65788fig04.jpg" /> 
Figure 4: Representative brightfield images of aggregation events population in different conditions. (A) LGG. (B) L. paracasei. (C) L. plantarum. (D) L. casei. The conditions include TSB medium (control), TSB medium supplemented with glucose and raffinose (1% w/v), and TSB medium supplemented with glucose (1% w/v) and buffer. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65788/65788fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Coding File 1: The .ast file with the template for LGG as an example. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65788/Supporting .ast file as example for LGG .zip" target="_blank">Please click here to download this File.</a>

Read this Scientific Article Protocol about Author Spotlight: Advancing Research in Microbial Auto Aggregation Using Imaging Flow Cytometry at JoVE.com

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

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

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 ...

imaging-flow-cytometry-to-study-microbial-autoaggregation

Nanyang Technological University

Research

JoVE Journal

Biology

693 Views.  Ben-Gurion University of the Negev. This protocol describes a quantitative approach to measure microbial autoaggregation using imaging flow cytometry.

Video: Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

Nanoparticulate systems have emerged as valuable tools in vaccine delivery through their ability to efficiently deliver cargo, including proteins, to antigen presenting cells1-5. Internalization of nanoparticles (NP) by antigen presenting cells is a critical step in generating an effective immune response to the encapsulated antigen. To determine how changes in nanoparticle formulation impact function, we sought to develop a high throughput, quantitative experimental protocol that was compatible with detecting internalized nanoparticles as well as bacteria. To date, two independent techniques, microscopy and flow cytometry, have been the methods used to study the phagocytosis of nanoparticles. The high throughput nature of flow cytometry generates robust statistical data. However, due to low resolution, it fails to accurately quantify internalized versus cell bound nanoparticles. Microscopy generates images with high spatial resolution; however, it is time consuming and involves small sample sizes6-8. Multi-spectral imaging flow cytometry (MIFC) is a new technology that incorporates aspects of both microscopy and flow cytometry that performs multi-color spectral fluorescence and bright field imaging simultaneously through a laminar core. This capability provides an accurate analysis of fluorescent signal intensities and spatial relationships between different structures and cellular features at high speed.
	Herein, we describe a method utilizing MIFC to characterize the cell populations that have internalized polyanhydride nanoparticles or Salmonella enterica serovar Typhimurium. We also describe the preparation of nanoparticle suspensions, cell labeling, acquisition on an ImageStreamX system and analysis of the data using the IDEAS application. We also demonstrate the application of a technique that can be used to differentiate the internalization pathways for nanoparticles and bacteria by using cytochalasin-D as an inhibitor of actin-mediated phagocytosis.

In this article, we describe a method utilizing multi-spectral imaging flow cytometry to quantify the internalization of polyanhydride nanoparticles or bacteria by RAW 264.7 cells.

Nanoparticulate  systems have emerged as valuable tools in vaccine delivery through their  ability to efficiently deliver cargo, including proteins, to ...

Bioengineering

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Environment

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

Studying Interactions of Staphylococcus aureus with Neutrophils by Flow Cytometry and Time Lapse Microscopy

We present methods to study the effect of phenol soluble modulins (PSMs) and other toxins produced and secreted by Staphylococcus aureus on neutrophils. To study the effects of the PSMs on neutrophils we isolate fresh neutrophils using density gradient centrifugation. These neutrophils are loaded with a dye that fluoresces upon calcium mobilization. The activation of neutrophils by PSMs initiates a rapid and transient increase in the free intracellular calcium concentration. In a flow cytometry experiment this rapid mobilization can be measured by monitoring the fluorescence of a pre-loaded dye that reacts to the increased concentration of free Ca2+. Using this method we can determine the PSM concentration necessary to activate the neutrophil, and measure the effects of specific and general inhibitors of the neutrophil activation.	
	To investigate the expression of the PSMs in the intracellular space, we have constructed reporter fusions of the promoter of the PSM&alpha; operon to GFP. When these reporter strains of S. aureus are phagocytosed by neutrophils, the induction of expression can be observed using fluorescence microscopy.

Studying Interactions of Staphylococcus aureus with Neutrophils by Flow Cytometry and Time Lapse Microscopy

We present methods to study the effect of PSMs and other toxins secreted by Staphylococcus aureus on neutrophils using flow cytometry and fluorescence microscopy.

We present methods to study the effect of phenol soluble modulins (PSMs)  and other toxins produced and secreted by Staphylococcus  aureus on neutrophils. ...

Immunology and Infection

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

Granulocytes play a key role in the body&#8217;s innate immune response to bacterial and viral infections. While methods exist to measure granulocyte function, in general these are limited in terms of the information they can provide. For example, most existing assays merely provide a percentage of how many granulocytes are activated following a single, fixed length incubation. Complicating matters, most assays focus on only one aspect of function due to limitations in detection technology. This report demonstrates a technique for simultaneous measurement of granulocyte phagocytosis of bacteria and oxidative burst. By measuring both of these functions at the same time, three unique phenotypes of activated granulocytes were identified: 1) Low Activation (minimal phagocytosis, no oxidative burst), 2) Moderate Activation (moderate phagocytosis, some oxidative burst, but no co-localization of the two functional events), and 3) High Activation (high phagocytosis, high oxidative burst, co-localization of phagocytosis and oxidative burst). A fourth population that consisted of inactivated granulocytes was also identified. Using assay incubations of 10, 20, and 40-min the effect of assay incubation duration on the redistribution of activated granulocyte phenotypes was assessed. A fourth incubation was completed on ice as a control. By using serial time incubations, the assay may be able to able to detect how a treatment spatially affects granulocyte function. All samples were measured using an image-based flow cytometer equipped with a quantitative imaging (QI) option, autosampler, and multiple lasers (488, 642, and 785 nm).

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

This method demonstrates a technique for assessing granulocyte function by simultaneously measuring phagocytosis of bacteria and oxidative burst. Image-based flow cytometry allowed for the identification of three distinct subsets of activated granulocytes that all differed in their relative functional capacity.

Granulocytes play a key role in the body&#8217;s innate immune response to bacterial and viral infections. While methods exist to measure granulocyte ...

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins towards the immune synapse to assure a stable binding and signal exchange. Classical confocal, TIRF, or super-resolution microscopy have been used to study the immune synapse. Since these methods require manual image acquisition and time-consuming quantification, the imaging of rare events is challenging. Here, we describe a workflow that enables the morphological analysis of tens of thousands of cells. Immune synapses are induced between primary human T cells in pan-leukocyte preparations and Staphylococcus aureus enterotoxin B (SEB)-loaded Raji cells as APCs. Image acquisition is performed with imaging flow cytometry, also called In-Flow microscopy, which combines features of a flow cytometer and a fluorescence microscope. A complete gating strategy for identifying T cell/APC couples and analyzing the immune synapses is provided. As this workflow allows the analysis of immune synapses in unpurified pan-leukocyte preparations and hence requires only a small volume of blood (i.e., 1 mL), it can be applied to samples from patients. Importantly, several samples can be prepared, measured, and analyzed in parallel.

Here, we describe a complete workflow for the qualitative and quantitative analysis of immune synapses between primary human T cells and antigen-presenting cells. The method is based on imaging flow cytometry, which allows the acquisition and evaluation of several thousand cell images within a relatively short period of time.

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins ...

Quantifying Human Norovirus Virus-like Particles Binding to Commensal Bacteria Using Flow Cytometry

Commensal bacteria are well established to impact infection of eukaryotic viruses. Direct binding between the pathogen and the host microbiome is responsible for altering infection for many of these viruses. Thus, characterizing the nature of virus-bacteria binding is a foundational step needed for elucidating the mechanism(s) by which bacteria alter viral infection. For human norovirus, commensal bacteria enhance B cell infection. The virus directly binds to these bacteria, indicating that this direct interaction is involved in the mechanism of infection enhancement. A variety of techniques can be used to quantify interactions between bacteria and viruses including scintillation counting of radiolabeled viruses and polymerase chain reaction (PCR). Both methods require the use of live virus, which may need to be generated in the laboratory. Currently, none of the established in vitro culture systems available for human norovirus are robust enough to allow for generation of highly concentrated viral stocks. In lieu of live virus, virus-like particles (VLPs) have been used to characterize the interactions between norovirus and bacteria. Herein a flow cytometry method is described with uses virus specific antibodies to quantify VLP binding to gram-negative and gram-positive bacteria. Inclusion of both bacteria only and isotype controls allowed for optimization of the assay to reduce background antibody binding and accurate quantification of VLP attachment to the bacteria tested. High VLP:bacterium ratios result in VLPs binding to large percentages of the bacterial population. However, when VLP quantities are decreased, the percent of bacteria bound also decreases. Ultimately, this method can be employed in future experiments elucidating the specific conditions and structural components that regulate norovirus:bacterial interactions.

The goal of this protocol is to quantify binding of the eukaryotic pathogen human norovirus to bacteria. After performing an initial virus-bacterium attachment assay, flow cytometry is used to detect virally-bound bacteria within the population.

Commensal bacteria are well established to impact infection of eukaryotic viruses. Direct binding between the pathogen and the host microbiome is ...

Flow Cytometric Characterization of Murine B Cell Development

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells have been isolated and developed into well-established therapeutics. Validation of murine B cell development, in the context of autoimmune prone mice, or in mice with modified immune systems, is a crucial component of developing or testing therapeutic agents in mice and is an appropriate use of flow cytometry. Well established B cell flow cytometric parameters can be used to evaluate B cell development in the murine peritoneum, bone marrow, and spleen, but a number of best practices must be adhered to. In addition, flow cytometric analysis of B cell compartments should also complement additional readouts of B cell development. Data generated using this technique can further our understanding of wild type, autoimmune prone mouse models as well as humanized mice that can be used to generate antibody or antibody-like molecules as therapeutics.

We describe herein a simple analysis of the heterogeneity of the murine immune B cell compartment in the peritoneum, spleen, and bone marrow tissues by flow cytometry. The protocol can be adapted and extended to other mouse tissues.

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells ...

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Pseudomonas aeruginosa (Pa) is one of the most common opportunistic pathogens associated with cystic fibrosis (CF). Once Pa colonization is established, a large proportion of the infecting bacteria form biofilms within airway sputum. Pa biofilms isolated from CF sputum have been shown to grow in small, dense aggregates of ~10-1,000 cells that are spatially organized and exhibit clinically relevant phenotypes such as antimicrobial tolerance. One of the biggest challenges to studying how Pa aggregates respond to the changing sputum environment is the lack of nutritionally relevant and robust systems that promote aggregate formation. Using a synthetic CF sputum medium (SCFM2), the life history of Pa aggregates can be observed using confocal laser scanning microscopy (CLSM) and image analysis at the resolution of a single cell. This in vitro system allows the observation of thousands of aggregates of varying size in real time, three dimensions, and at the micron scale. At the individual and population levels, having the ability to group aggregates by phenotype and position facilitates the observation of aggregates at different developmental stages and their response to changes in the microenvironment, such as antibiotic treatment, to be differentiated with precision.

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Synthetic cystic fibrosis sputum medium (SCFM2) can be utilized in combination with both confocal laser scanning microscopy and fluorescence-activated cell sorting to observe bacterial aggregates at high resolution. This paper details methods to assess aggregate populations during antimicrobial treatment as a platform for future studies.

Pseudomonas aeruginosa (Pa) is one of the most common opportunistic pathogens associated with cystic fibrosis (CF). Once Pa colonization is established, a ...

Flow Cytometry Approach to Characterize Phagocytic Properties of Acutely-Isolated Adult Microglia and Brain Macrophages In Vitro

Microglia and central nervous system (CNS)-infiltrating macrophages, collectively called CNS mononuclear phagocytes (CNS-MPs), play central roles in neurological diseases including neurodegeneration and stroke. CNS-MPs are involved in phagocytic clearance of pathological proteins, debris and neuronal synapses, each with distinct underlying molecular pathways. Characterizing these phagocytic properties can provide a functional readout that compliments molecular profiling of microglia using traditional flow cytometry, transcriptomics and proteomics approaches. Phagocytic profiling of microglia has relied on microscopic visualization and in vitro cultures of mouse neonatal microglia. The former approach suffers from limited sampling while the latter approach is inherently poorly reflective of the true in vivo state of adult CNS-MPs. This paper describes optimized protocols to phenotype phagocytic properties of acutely-isolated mouse CNS-MPs by flow cytometry. CNS-MPs are acutely isolated from adult mouse brain using mechanical dissociation followed by density gradient centrifugation, incubated with fluorescent microspheres or fluorescent A&#946; fibrils, washed, and then labeled with panels of antibodies against surface markers (CD11b, CD45). Using this approach, it is possible to compare phagocytic properties of brain-resident microglia with CNS-infiltrating macrophages and then assess the effect of aging and disease pathology on these phagocytic phenotypes. This rapid method also holds potential to functionally phenotype acutely-isolated human CNS-MPs from post-mortem or surgical brain specimens. Additionally, specific mechanisms of phagocytosis by CNS-MP subsets can be investigated by inhibiting select phagocytic pathways.

Assessment of phagocytic properties of microglia and brain macrophages can provide a valuable functional dimension to molecular profiling studies. We describe a validated protocol that uses flow cytometry to rapidly and reliably quantify phagocytosis of fluorescent microspheres and amyloid beta fibrils by acutely-isolated microglia and brain macrophages from mouse models.

Microglia and central nervous system (CNS)-infiltrating macrophages, collectively called CNS mononuclear phagocytes (CNS-MPs), play central roles in ...

Imaging Flow Cytometry to Study Microbial Autoaggregation

Summary

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Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Studying Interactions of Staphylococcus aureus with Neutrophils by Flow Cytometry and Time Lapse Microscopy

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

Quantifying Human Norovirus Virus-like Particles Binding to Commensal Bacteria Using Flow Cytometry

Flow Cytometric Characterization of Murine B Cell Development

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

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