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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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 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.

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

608 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

Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of the visual system provide more empirical evidence for functional applications. Investigation into the sensory system provides information about the sensory capacity, learning and memory ability, and establishes known baseline behaviour in which to gauge deviations (Burghardt, 1977). However, unlike mammalian or avian systems, testing for learning and memory in a reptile species is difficult. Furthermore, using an operant paradigm as a psychophysical measure of sensory ability is likewise as difficult. Historically, reptilian species have responded poorly to conditioning trials because of issues related to motivation, physiology, metabolism, and basic biological characteristics. Here, I demonstrate an operant paradigm used a novel model lizard species, the Jacky dragon (Amphibolurus muricatus) and describe how to test peripheral sensitivity to salient speed and motion characteristics. This method uses an innovative approach to assessing learning and sensory capacity in lizards. I employ the use of random-dot kinematograms (RDKs) to measure sensitivity to speed, and manipulate the level of signal strength by changing the proportion of dots moving in a coherent direction. RDKs do not represent a biologically meaningful stimulus, engages the visual system, and is a classic psychophysical tool used to measure sensitivity in humans and other animals. Here, RDKs are displayed to lizards using three video playback systems. Lizards are to select the direction (left or right) in which they perceive dots to be moving. Selection of the appropriate direction is reinforced by biologically important prey stimuli, simulated by computer-animated invertebrates.

Testing visual sensitivity in lizards using an operant conditioning paradigm that employs video playback of random-dot kinematograms and computer-generated invertebrates.

Badania wizualne Wrażliwość na prędkość i kierunek ruchu w Jaszczurki

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of ...

IP-FCM:  Immunoprecipitation Detected by Flow Cytometry

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic principle extends that of sandwich ELISA, wherein the captured primary analyte can be detected together with other molecules physically associated within multiprotein complexes. The procedure involves covalent coupling of polystyrene latex microbeads with immunoprecipitating monoclonal antibodies (mAb) specific for a protein of interest, incubating these beads with cell lysates, probing captured protein complexes with fluorochrome-conjugated probes, and analyzing bead-associated fluorescence by flow cytometry. IP-FCM is extremely sensitive, allows analysis of proteins in their native (non-denatured) state, and is amenable to either semi-quantitative or quantitative analysis. As additional advantages, IP-FCM requires no genetic engineering or specialized equipment, other than a flow cytometer, and it can be readily adapted for high-throughput applications.

The IP-FCM method is presented, which allows a sensitive, robust, biochemical assessment of native protein-protein interactions, without requiring genetic engineering or large sample sizes.

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic ...

Quantitative Measurement of GLUT4 Translocation to the Plasma Membrane by Flow Cytometry

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces glucose uptake by insulin-sensitive tissues, most notably the brain, skeletal muscle, and adipocytes. Patients suffering from type-2 diabetes and/or obesity often develop insulin resistance and are unable to control their glucose homeostasis. New insights into the mechanisms of insulin resistance may provide new treatment strategies for type-2 diabetes.
The GLUT family of glucose transporters consists of thirteen members distributed on different tissues throughout the body1. Glucose transporter type 4 (GLUT4) is the major transporter that mediates glucose uptake by insulin sensitive tissues, such as the skeletal muscle. Upon binding of insulin to its receptor, vesicles containing GLUT4 translocate from the cytoplasm to the plasma membrane, inducing glucose uptake. Reduced GLUT4 translocation is one of the causes of insulin resistance in type-2 diabetes2,3.
The translocation of GLUT4 from the cytoplasm to the plasma membrane can be visualized by immunocytochemistry, using fluorophore-conjugated GLUT4-specific antibodies.
Here, we describe a technique to quantify total amounts of GLUT4 translocation to the plasma membrane of cells during a chosen duration, using flow cytometry. This protocol is rapid (less than 4 hours, including incubation with insulin) and allows the analysis of as few as 3,000 cells or as many as 1 million cells per condition in a single experiment. It relies on anti-GLUT4 antibodies directed to an external epitope of the transporter that bind to it as soon as it is exposed to the extracellular medium after translocation to the plasma membrane.

This protocol describes a rapid technique to quantify the translocation of GLUT4 from the cytoplasm to the plasma membrane of cells by flow cytometry.

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces ...

Flow Cytometry Purification of Mouse Meiotic Cells

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the unique differentiation programs that are manifest during meiosis. Two principal methods have been developed to purify, to varying degrees, various meiotic fractions from both adult and immature animals: elutriation or Staput (sedimentation) using BSA and/or percoll gradients. Both of these methods rely on cell size and density to separate meiotic cells1-5. Overall, except for few cell populations6, these protocols fail to yield sufficient purity of the numerous meiotic cell populations that are necessary for detailed molecular analyses. Moreover, with such methods usually one type of meiotic cells can be purified at a given time, which adds an extra level of complexity regarding the reproducibility and homogeneity when comparing meiotic cell samples.
Here, we describe a refined method that allows one to easily visualize, identify, and purify meiotic cells, from germ cells to round spermatids, using FACS combined with Hoechst 33342 staining7,8. This method provides an overall snapshot of the entire meiotic process and allows one to highly purify viable cells from most stage of meiosis. These purified cells can then be analyzed in detail for molecular changes that accompany progression through meiosis, for example changes in gene expression9,10and the dynamics of nucleosome occupancy at hotspots of meiotic recombination11.

An efficient method to obtain highly purified viable meiotic fractions from mouse testis is described, which combines a refined cell dissociation protocol with fluorescent activated cell sorting (FACS). This method takes advantage of differences in the DNA content and nuclear density of discrete meiotic fractions.

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the ...

Flow Cytometry-based Purification of S. cerevisiae Zygotes

Zygotes are essential intermediates between haploid and diploid states in the life cycle of many organisms, including yeast (Figure 1) 1. S. cerevisiae zygotes result from the fusion of haploid cells of distinct mating type (MATa, MATalpha) and give rise to corresponding stable diploids that successively generate as many as 20 diploid progeny as a result of their strikingly asymmetric mitotic divisions 2. Zygote formation is orchestrated by a complex sequence of events: In this process, soluble mating factors bind to cognate receptors, triggering receptor-mediated signaling cascades that facilitate interruption of the cell cycle and culminate in cell-cell fusion. Zygotes may be considered a model for progenitor or stem cell function.
Although much has been learned about the formation of zygotes and although zygotes have been used to investigate cell-molecular questions of general significance, almost all studies have made use of mating mixtures in which zygotes are intermixed with a majority population of haploid cells 3-8. Many aspects of the biochemistry of zygote formation and the continuing life of the zygote therefore remain uninvestigated.
Reports of purification of yeast zygotes describe protocols based on their sedimentation properties 9; however, this sedimentation-based procedure did not yield nearly 90% purity in our hands. Moreover, it has the disadvantage that cells are exposed to hypertonic sorbitol. We therefore have developed a versatile purification procedure. For this purpose, pairs of haploid cells expressing red or green fluorescent proteins were co-incubated to allow zygote formation, harvested at various times, and the resulting zygotes were purified using a flow cytometry-based sorting protocol. This technique provides a convenient visual assessment of purity and maturation. The average purity of the fraction is approximately 90%. According to the timing of harvest, zygotes of varying degrees of maturity can be recovered. The purified samples provide a convenient point of departure for "-omic" studies, for recovery of initial progeny, and for systematic investigation of this progenitor cell.

Flow Cytometry-based Purification of S. cerevisiae Zygotes

To purify zygotes of S. cerevisiae, haploid cells of opposite mating type were engineered to express red or green fluorescent proteins, co-incubated to allow zygote formation, and fractionated using a flow cytometry-based protocol. The highly-enriched fraction enables subsequent "-omic" studies, recovery of initial progeny, and systematic investigation of zygote morphogenesis.

Zygotes are essential intermediates between haploid and diploid states in the life cycle of many organisms, including yeast (Figure 1) 1. S. cerevisiae ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Spermatozoa are male reproductive cells especially designed to reach, recognize and fuse with the egg. To perform these tasks, sperm cells must be prepared to face a constantly changing environment and to overcome several physical barriers. Being in essence transcriptionally and translationally silent, these motile cells rely profoundly on diverse signaling mechanisms to orient themselves and swim in a directed fashion, and to contend with challenging environmental conditions during their journey to find the egg. In particular, Ca2+-mediated signaling is pivotal for several sperm functions: activation of motility, capacitation (a complex process that prepares sperm for the acrosome reaction) and the acrosome reaction (an exocytotic event that allows sperm-egg fusion). The use of fluorescent dyes to track intracellular fluctuations of this ion is of remarkable importance due to their ease of application, sensitivity, and versatility of detection. Using one single dye-loading protocol we utilize four different fluorometric techniques to monitor sperm Ca2+ dynamics. Each technique provides distinct information that enables spatial and/or temporal resolution, generating data both at single cell and cell population levels.

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Intracellular Ca2+ dynamics are very important in sperm physiology and Ca2+-sensitive fluorescent dyes constitute a versatile tool to study them. Population experiments (fluorometry and stopped flow fluorometry) and single cell experiments (flow cytometry and single cell imaging) are used to track spatio-temporal [Ca2+] changes in human sperm cells.

Spermatozoa  are male reproductive cells especially designed to reach, recognize and fuse  with the egg. To perform these tasks, sperm cells must be ...

Identification of a Murine Erythroblast Subpopulation  Enriched in Enucleating Events by Multi-spectral Imaging Flow Cytometry

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not yet been fully elucidated. A common problem encountered when studying the localization of key proteins and structures within enucleating erythroblasts by microscopy is the difficulty to observe a sufficient number of cells undergoing enucleation.&#160;We have developed a novel analysis protocol using multiparameter high-speed cell imaging in flow (Multi-Spectral Imaging Flow Cytometry), a method that combines immunofluorescent microscopy with flow cytometry, in order to identify efficiently a significant number of enucleating events, that allows to obtain measurements and perform statistical analysis.
We first describe here two in vitro erythropoiesis culture methods used in order to synchronize murine erythroblasts and increase the probability of capturing enucleation at the time of evaluation.&#160;Then, we describe in detail the staining of erythroblasts after fixation and permeabilization in order to study the localization of intracellular proteins or lipid rafts during enucleation by multi-spectral imaging flow cytometry.&#160;Along with size and DNA/Ter119 staining which are used to identify the orthochromatic erythroblasts, we utilize the parameters &#8220;aspect ratio&#8221; of a cell in the bright-field channel that aids in the recognition of elongated cells and &#8220;delta centroid XY Ter119/Draq5&#8221; that allows the identification of cellular events in which the center of Ter119 staining (nascent reticulocyte) is far apart from the center of Draq5 staining (nucleus undergoing extrusion), thus indicating a cell about to enucleate.&#160;The subset of the orthochromatic erythroblast population with high delta centroid and low aspect ratio is highly enriched in enucleating cells.

The present protocol describes a novel method of identifying a population of enucleating orthochromatic erythroblasts by multi-spectral imaging flow cytometry, providing a visualization of the erythroblast enucleation process.

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not ...

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

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

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

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

LED Thermo Flow &#8212; Combining Optogenetics with Flow Cytometry

Optogenetic tools allow isolated, functional investigations of almost any signaling molecule within complex signaling pathways. A major obstacle is the controlled delivery of light to the cell sample and hence the most popular tools for optogenetic studies are microscopy-based cell analyses and in vitro experiments. The flow cytometer has major advantages over a microscope, including the ability to rapidly measure thousands of cells at single cell resolution. However, it is not yet widely used in optogenetics. Here, we present a device that combines the power of optogenetics and flow cytometry: the LED Thermo Flow. This device illuminates cells at specific wavelengths, light intensities and temperatures during flow cytometric measurements. It can be built at low cost and be used with most common flow cytometers. To demonstrate its utility, we characterized the photoswitching kinetics of Dronpa proteins in vivo and in real time. This protocol can be adapted to almost all optically controlled substances and substantially expands the set of possible experiments. More importantly, it will greatly simplify the discovery and development of new optogenetic tools.

Optically controlled substances are powerful tools to study signaling pathways. To expand the spectrum of possible experiments, we developed a device for studying optically controlled substances in real time using flow cytometry: the LED Thermo Flow.

Optogenetic tools allow isolated, functional investigations of almost any signaling molecule within complex signaling pathways. A major obstacle is the ...

Imaging Flow Cytometry to Study Microbial Autoaggregation

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Badania wizualne Wrażliwość na prędkość i kierunek ruchu w Jaszczurki

IP-FCM: Immunoprecipitation Detected by Flow Cytometry

Quantitative Measurement of GLUT4 Translocation to the Plasma Membrane by Flow Cytometry

Flow Cytometry Purification of Mouse Meiotic Cells

Flow Cytometry-based Purification of S. cerevisiae Zygotes

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Measuring Intracellular Ca²⁺ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Identification of a Murine Erythroblast Subpopulation Enriched in Enucleating Events by Multi-spectral Imaging Flow Cytometry

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

LED Thermo Flow — Combining Optogenetics with Flow Cytometry