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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Tol 5 mediating high adhesiveness to various abiotic surfaces.</a> PLoS One. 7, e48830 (2012).</li><li>Inoue, T., 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=Molecular+characterization+of+low-molecular-weight+component+protein,+Flp,+in+Actinobacillus+actinomycetemcomitans+fimbriae.">Molecular characterization of low-molecular-weight component protein, Flp, in Actinobacillus actinomycetemcomitans fimbriae.</a> Medical Microbiology and Immunology. 42 (4), 253-258 (1998).</li><li>Boddey, J. A., Flegg, C. P., Day, C. J., Beacham, I. R., Peak, I. 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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bistable+auto-aggregation+phenotype+in+Lactiplantibacillus+plantarum+emerges+after+cultivation+in+in+vitro+colonic+microbiota.">Bistable auto-aggregation phenotype in Lactiplantibacillus plantarum emerges after cultivation in in vitro colonic microbiota.</a> BMC Microbiology. 21, 268 (2021).</li><li>Kos, B., 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=Adhesion+and+aggregation+ability+of+probiotic+strain+Lactobacillus+acidophilus+M92.">Adhesion and aggregation ability of probiotic strain Lactobacillus acidophilus M92.</a> Journal of Applied Microbiology. 94 (6), 981-987 (2003).</li><li>Zommiti, M., 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+vitro+assessment+of+the+probiotic+properties+and+bacteriocinogenic+potential+of+Pediococcus+pentosaceus+MZF16+isolated+from+artisanal+tunisian+meat+&amp;#34;Dried+Ossban&amp;#34;.">In vitro assessment of the probiotic properties and bacteriocinogenic potential of Pediococcus pentosaceus MZF16 isolated from artisanal tunisian meat &amp;#34;Dried Ossban&amp;#34;.</a> Frontiers in Microbiology. 9, 2607 (2018).</li><li>Suissa, R., 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=Metabolic+rewiring+of+the+probiotic+bacterium+rhamnosus&lt;/em&gt;+GG+contributes+to+cell-wall+remodeling+and+antimicrobials+production.">Metabolic rewiring of the probiotic bacterium rhamnosus&lt;/em&gt; GG contributes to cell-wall remodeling and antimicrobials production.</a> bioRxiv. , (2023).</li><li>Connell, J. L., Kim, J., Shear, J. B., Bard, A. J., Whiteley, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+monitoring+of+quorum+sensing+in+3D-printed+bacterial+aggregates+using+scanning+electrochemical+microscopy.">Real-time monitoring of quorum sensing in 3D-printed bacterial aggregates using scanning electrochemical microscopy.</a> Proceedings of the National Academy of Sciences of the United States of America. 111 (51), 18255-18260 (2014).</li><li>Misawa, N., Blaser, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+and+characterization+of+autoagglutination+activity+by+Campylobacter+jejuni.">Detection and characterization of autoagglutination activity by Campylobacter jejuni.</a> Infection and Immunity. 68 (11), 6168-6175 (2000).</li><li>Sherlock, O., Schembri, M. A., Reisner, A., Klemm, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+roles+for+the+AIDA+adhesin+from+diarrheagenic+Escherichia+coli:+cell+aggregation+and+biofilm+formation.">Novel roles for the AIDA adhesin from diarrheagenic Escherichia coli: cell aggregation and biofilm formation.</a> Journal of Bacteriology. 186 (23), 8058-8065 (2004).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+flow+cytometry.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolism+characteristics+of+lactic+acid+bacteria+and+the+expanding+applications+in+food+industry.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+dialogue+between+the+human+gut+microbiota+and+the+host:+a+Lactobacillus+and+Bifidobacterium+perspective.">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., Ratajczak, M. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ImageStream+System:+a+key+step+to+a+new+era+in+imaging.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+flow+cytometry+for+phytoplankton+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacillus+subtilis+colonization+of+arabidopsis+thaliana+roots+induces+multiple+biosynthetic+clusters+for+antibiotic+production.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+flow+cytometry+reveals+a+dual+role+for+exopolysaccharides+in+biofilms:+To+promote+self-adhesion+while+repelling+non-self-community+members.">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><li>Konieczny, M., Rhein, P., Czaczyk, K., Bialas, W., Juzwa, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+flow+cytometry+to+study+biofilm-associated+microbial+aggregates.">Imaging flow cytometry to study biofilm-associated microbial aggregates.</a> Molecules. 26 (23), 7096 (2021).</li><li>Niederdorfer, R., Peter, H., Battin, T. 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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 bacteria<sup clas...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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>&#160;Details available at: https://www.merckmillipore.com/INTL/en/20150212_144049?ReferrerURL=https%3A%2F%2Fwww.google.com%2F&#38;bd=1</td>
		</tr>
		<tr>
			<td>ImageStream X Mark II</td>
			<td>Amnis/EMD Millipore</td>
			<td>N/A</td>
			<td>&#160;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.

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

Imaging Flow Cytometry to Study Microbial Autoaggregation

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.

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.
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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 ...

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

Research

JoVE Journal

Biology

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.

Flow Cytometric Data Acquisition of Microbial Aggregates

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.

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

Quantitative Analysis of Microbial Autoaggregation Using Imaging Flow Cytometry

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