Name: scanlag: high-throughput quantification of colony growth and lag time
Uploaded: 2014-07-15T13:00:00
Duration: 7 min 47 s
Description: Watch this Scientific Journal Video about ScanLag: High-throughput Quantification of Colony Growth and Lag Time at JoVE.com

We thank Eliq Oster for the images of the Bacillus subtillis. The work is supported by the European Research Council (# 260871) and the Israel Science Foundation (#592/10).

1. Build the Setup
<ol>
	<li>Choose a flatbed Scanner with optical resolution: 4,800 dpi; Hardware resolution:&#160;4,800 x 9,600 dpi; color bit depth: 48 bit, that can operate at the relevant temperature and humidity levels.</li>
	<li>Petri dishes have significant volume, unlike paper usually used on commercial scanners. The colonies lie on top of the agar layer, about 5 mm above the scanner surface. Note: For most scanners the colonies will be in focus.</li>
	<li>Some scanner companies do not allow connection of more than one instance of scanner of the same type to the same computer. Make sure that more than one scanner can be attached and identified uniquely.</li>
	<li>Prepare the accessories as detailed below:
	<ol>
		<li>Prepare pieces of sterile black felt cloth to cover the plates (Figure 1 step 2).</li>
		<li>To hold the Petri dishes in place, prepare white plate holders (Materials and Methods) that fit the scanners and that have 6 holes the size of the dishes (Figure 1 step 3).</li>
	</ol>
	</li>
	<li>If needed for the growth of the microorganism, put the scanners in a temperature controlled environment. Connect the scanners to the computer.</li>
	<li>Optional: Connect the relay to the computer and bypass the on/off switch of the scanner to overcome temperature gradient. 
	NOTE: The relay unit is for extremely temperature sensitive microorganisms. It switches off the scanner so that the electronics do not heat the surface unevenly. Using only part of the scanner surface may achieve similar results.</li>
</ol>
2. Install the Scanning Manager
<ol>
	<li>Copy all &#39;Scanning Manager&#39; application files into a designated directory from http://www.phys.huji.ac.il/bio_physics/nathalie/publications.html.</li>
	<li>Configure ScanningManager.exe.config file according to the scanners names as they appear in Computer Management-&#62;Device Manager-&#62;Imaging Devices (Figure 2).</li>
	<li>For troubleshooting of the &#39;Scanning Manager&#39; application, see the read me file.</li>
	<li>For further documentation on the &#39;Scanning Manager&#39; application, see Documentation.html.</li>
</ol>
3. Perform an Experiment
<ol>
	<li>Prepare the Petri dishes (Figure 1):
	<ol>
		<li>Dilute the culture to approximately 2,000 CFU/ml. Plate 0.1 ml of culture uniformly on the plate surface.</li>
		<li>To gain good contrast between the colonies and the dish and to absorb moisture, cover the plate with a piece of sterile black felt cloth. Put lid on the plate.</li>
	</ol>
	</li>
	<li>Place the plates in the holders on the scanners.</li>
	<li>Start the Scanning Manager (Figure 3): Choose the participating scanners from the list of attached scanners. Choose the number of images to be taken (Repetitions), the time interval between subsequent images (Time gap), and the time delay before starting the experiment (Start After).</li>
</ol>
4. Analysis of the Images
NOTE: An outline of computational methods is provided for analyzing the images in MATLAB using &#8220;ScanLag&#8221;, which is freely available for non-commercial use at http://www.phys.huji.ac.il/bio_physics/nathalie/publications.html. Further functions available are detailed in the software manual, and an example code is supplemented.
<ol>
	<li>Sort the images from each scanner into separate folders.</li>
	<li>Using Matlab&#8217;s command window, run the following procedures. The parameters required to run these procedures are defined in the software manual.
	<ol>
		<li>Run PreparePictures to perform preprocessing: In preprocessing, the images from a scanner are aligned. Each Petri dish is cropped from each sequence of images. The time at which each image was collected is retrieved from the timestamp.</li>
		<li>Run TLAllPlates to perform detection and tracking In each Petri dish separately, colonies are detected and assigned an identifying number.</li>
		<li>Analysis of the extracted data: The sizes of every detected colony are measured as a function of time. To extract the appearance distribution of the colonies use the following functions in Matlab&#8217;s command window (further information is available in the software manual):
		<ol>
			<li>Run ScanLagApp to display the analysis of a certain Petri dish alongside with the graph of the colonies size versus time (Figure 4).
			<ol>
				<li>Use the slider to change the current time of the plate. The timing will also be indicated by a matching vertical line on the colonies area graph.</li>
				<li>Click on a colony to see its associated curve, and vice versa, click on a curve to find its associated colony. After identifying the colony, the phenotype can be retrieved by picking the colony from the identified Petri dish.</li>
				<li>Filter defects in the automatic analysis by choosing a colony and clicking the exclude button. When clicking exclude the number of the colony will turn yellow.</li>
			</ol>
			</li>
			<li>After going through the results of the analysis of each plate, sum up the results:
			<ol>
				<li>Run AddHistograms to create a histogram of appearance times.</li>
				<li>Run plotDeathCurve to create a survival curve representation of the distribution.</li>
				<li>Run GetAppearanceTimes to get the appearance time of all colonies in the experiment.</li>
			</ol>
			</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol>
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O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+rate+and+generation+time+of+bacteria,+with+special+reference+to+continuous+culture.">Growth rate and generation time of bacteria, with special reference to continuous culture.</a> J Gen Microbiol. 15, 492-511 (1956).</li><li>Elfwing, A., LeMarc, Y., Baranyi, J., Ballagi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observing+growth+and+division+of+large+numbers+of+individual+bacteria+by+image+analysis.">Observing growth and division of large numbers of individual bacteria by image analysis.</a> Applied and Environmental Microbiology. 70, 675-678 (2004).</li><li>Hertog, A., 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=Simplified+automated+image+analysis+for+detection+and+phenotyping+of+Mycobacterium+tuberculosis+on+porous+supports+by+monitoring+growing+microcolonies.">Simplified automated image analysis for detection and phenotyping of Mycobacterium tuberculosis on porous supports by monitoring growing microcolonies.</a> PloS one. 5, (2010).</li><li>Levin-Reisman, I., 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=Automated+imaging+with+ScanLag+reveals+previously+undetectable+bacterial+growth+phenotypes.">Automated imaging with ScanLag reveals previously undetectable bacterial growth phenotypes.</a> Nature Methods. 7, (2010).</li><li>Lewis, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Persister+cells.">Persister cells.</a> Annual review of microbiology. 64, 357-372 (2010).</li><li>Metris, A., George, S. 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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=Influence+of+environmental+stress+on+distributions+of+times+to+first+division+in+Escherichia+coli+populations,+as+determined+by+digital-image+analysis+of+individual+cells.">Influence of environmental stress on distributions of times to first division in Escherichia coli populations, as determined by digital-image analysis of individual cells.</a> Applied and Environmental Microbiology. 74, 3757-3763 (2008).</li><li>Pin, C., Baranyi, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+and+population+lag+times+as+a+function+of+cell+age.">Single-cell and population lag times as a function of cell age.</a> Applied and Environmental Microbiology. 74, 2534-2536 (2008).</li><li>Guillier, L., Pardon, P., Augustin, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+stress+on+individual+lag+time+distributions+of+Listeria+monocytogenes.">Influence of stress on individual lag time distributions of Listeria monocytogenes.</a> Applied and Environmental Microbiology. 71, 2940-2948 (2005).</li><li>Metris, A., George, S. M., Peck, M. W., Baranyi, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distribution+of+turbidity+detection+times+produced+by+single+cell-generated+bacterial+populations.">Distribution of turbidity detection times produced by single cell-generated bacterial populations.</a> Journal of Microbiological Methods. 55, 821-827 (2003).</li><li>Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., Leibler, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacterial+persistence+as+a+phenotypic+switch.">Bacterial persistence as a phenotypic switch.</a> Science. 305, 1622-1625 (2004).</li><li>Glaser, D. A., Wattenburg, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+system+for+the+growth+and+analysis+of+large+numbers+of+bacterial+colonies+using+an+environmental+chamber+and+a+computer-controlled+flying-spot+scanner.">An automated system for the growth and analysis of large numbers of bacterial colonies using an environmental chamber and a computer-controlled flying-spot scanner.</a> Ann N Y Acad Sci. 139, 243-257 (1966).</li><li>Guillier, L., Pardon, P., Augustin, J. 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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+analysis+of+persister+fractions+suggests+different+mechanisms+of+formation+among+environmental+isolates+of+E.+coli.">Quantitative analysis of persister fractions suggests different mechanisms of formation among environmental isolates of E. coli.</a> BMC Microbiol. 13, 25 (2013).</li><li>Moyed, H. S., Bertrand, K. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hipa+a+Newly+Recognized+Gene+of+Escherichia-Coli+K-12+That+Affects+Frequency+of+Persistence+after+Inhibition+of+Murein+Synthesis.">Hipa a Newly Recognized Gene of Escherichia-Coli K-12 That Affects Frequency of Persistence after Inhibition of Murein Synthesis.</a> Journal of Bacteriology. 155, 768-775 (1983).</li><li>Balaban, N. 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The authors have nothing to disclose.

Microscopic methods based on direct observation are often regarded as the &#8220;golden standard&#8221; for studying single-cell behavior. ScanLag, which enables measurement of the distribution of lag times in bacterial populations, provides data in good agreement with the distribution obtained from single-cell analysis and achieves much higher statistics.
Several critical steps have to be performed for this method to work well: first, do not compromise on the scanner resolution, which should be 4,800 x 9,600 to gain good images. Second, be aware that without the power management module (step 1.6), temperature gradient may develop on the flatbed surface and affect growth. Another important step is the filtration of defects such as dust that may have been mistakenly counted as colonies by the software. The built-in background subtraction of the software usually overcomes these defects, but sometimes &#8220;false&#8221; colonies have to be manually filtered.
The main troubleshooting steps might address inter-plate variation in the setup, due to several reasons: (a) The growth rate of microorganisms is affected by temperature. Different temperature and humidity conditions can occur due to uneven ventilation or location of the dish in the incubator. (b) The electronics of the scanner might heat up and cause a temperature gradient across the surface of the scanner. Figure 10 shows the influence of such spatial heat gradient on Bacillus bacteria, together with the measurements of the temperature on the scanner surface before and after implementing the power management module (step 1.6). Note that this module might not be necessary for newer scanners, and if only part of the scanner surface is used, power management may be unnecessary. (c) Plates with different opacity of the agar nutrient might lead to different levels of detection. Figure 11 shows the tolerance for different volumes of nutrient agar that result in different opacities.
Experimentally the ScanLag technique is easy to perform and requires only to plate microorganisms on standard Petri dishes, and to place them on the scanner&#8217;s surface. The software that was developed controls the entire procedure. The image acquisition is done automatically, and the image analysis for colony tracking is also automatic. Furthermore, the system can be scaled-up to measure many different conditions at the same time. Finally, the method relies on commercial office scanners and, therefore, is low cost.
This technique can be extended to the study of microorganisms which differ from E. coli K-12, however several considerations have to be made. First, the influence of early appearing colonies on the later ones has to be assessed, as described in detail in a previous publication7. For E. Coli K-12, the maximal density of CFU per plate is 200. Other strains with different sizes of colonies, and other possible cross talk between colonies, might require a different density. Second, the analysis of the plates is calibrated to specific software based thresholds that might need to be modified, depending on the contrast between the solid medium and the colonies. The software can be extended also to extract other colony characteristics, beyond lag and growth rate. The software code is open and can be modified to extract colony shape, brightness, smoothness and color.
Because measurements are made on colonies that originate from single cells, the data reveal new phenotypes that cannot be measured by population-level measurements. The importance of the lag time distribution is revealed in the presence of antibiotics. Many antibiotics are known to be effective only against growing cells; therefore as long as cells remain in the lag phase and do not grow, they are protected from effects of those antibiotics21. When the numbers of survivor bacteria are evaluated at time intervals during antibiotic treatment15,22,23, a subpopulation of bacteria, called persisters, is often revealed. In certain cases, immunity to antibiotic treatment originates from prolonged lag. Using this setup, this lag is revealed and often has a non-trivial distribution24 (Figure 6). This method also enables the retrieval of rare mutants. For example, after plating a mutagenized population, mutants can be identified based on phenotype and isolated directly, without selection. The setup could also monitor cell-cell interactions by measuring the growth of colonies as a function of the density of nearby colonies and to quantify phenomena such as quorum sensing or the spread of swarming bacteria. Multidimensional information uncovered by future experiments using this method will lead to better characterization of microbial populations and to advances in medical and environmental research.

Bacteria have evolved to respond to many stressful conditions. A general feature of adaptation to stress is a change in growth dynamics1,2. Growth dynamics are characterized mostly by two parameters: the exponential growth rate and the lag time, namely the time needed to adapt to new conditions. Whereas a large number of high throughput methods focus on measurements of the growth rate, the lag time is often an overlooked parameter, although it is the crucial parameter for characterization of the adaptation to new conditions. Quantification of the adaptation to changing conditions has traditionally been performed using laborious manual methods3,4. More recently, advanced techniques have been developed for analysis of single cells5,6. However, it is still difficult to distinguish normal growth following a delay in growth (namely a lag time)2,3 from slow growth. An automated method was constructed, ScanLag7, to discriminate between these two similar phenotypes.
A striking example of the importance of the study of the lag time distribution is revealed under antibiotic treatment. Many antibiotics and other stresses kill only actively growing cells, and, therefore, cells that are &#8220;lagging&#8221; are protected8. To monitor lag time, one can observe single cells under a microscope and monitor the time to first division. A major drawback of this method is that the dynamic time range is limited; those cells that are growing early cover the surface at an exponential rate, effectively decreasing the growth of cells with longer lag time. Use of flow chambers somewhat circumvents this5, but a limited number of cells can be tracked and the fraction of bacteria that exit the lag phase after the majority of the population has started growing cannot be observed. Despite these limitations, several studies have evaluated the influence of the level of different stresses on lag time distribution using single cell microscopy9-12. Another way to monitor lag time distribution is through turbidity measurements13,14. Many parallel cultures, each started from a single cell, are grown in an optical density reader that measures the number of bacteria in the culture over time. This method is limited by the precision of the extrapolation and to the number of wells in a plate.
An automated method was developed, called ScanLag, that enables lag times and growth time distributions to be assessed, even for the small percentage of bacteria in a population that have very long lag times. The method is based on detection of colonies on conventional nutrient agar plates15 (Figure 1). To automate detection16,17 an array of commercial scanners18 that are directed by in house software to periodically acquire images of the plates, was engineered. Software was designed to automatically analyze the images and extract quantitative parameters19,20, such as time of appearance of each colony and growth time of each colony, which is defined here as the time to grow from 20 pixels to 80 pixels. Here we present the method in detail, including the setup of the system and the use of the automated image analysis software which has been improved with a better user interface.
This method can be adapted to measure other characteristics of colonies, such as morphology and color. Measurement of these types of parameters will allow the system&#8217;s use in multidimensional phenomics. As the method detects colonies from single cells, the system can reveal new phenotypes that cannot be measured by population-level measurements. The technique enables the detection and retrieval of rare variants, and facilitates screening for desired traits.

<table border="0" cellpadding="0" cellspacing="0" width="573">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Name of Material</td>
			<td style="width:71px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td style="width:167px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Bacto Agar</td>
			<td style="width:71px;">BD</td>
			<td align="right" style="width:119px;">214010</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Difco LB broth</td>
			<td style="width:71px;">BD</td>
			<td align="right" style="width:119px;">240230</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Petri dish 90mm</td>
			<td style="width:71px;">Miniplast</td>
			<td style="width:119px;">20090-01</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;"></td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Name of Equipment</td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="126">
			<td height="126" style="height:126px;width:216px;">Epson Perfection v37</td>
			<td style="width:71px;">Epson</td>
			<td style="width:119px;">B11B207201</td>
			<td style="width:167px;">Flatbed Scanner with Optical resolution: 4800 dpi, Hardware Resolution:&#160;4800 x 9600 dpi, color bit depth 48-bit.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Epson Perfection v200</td>
			<td style="width:71px;">Epson</td>
			<td style="width:119px;">B11B188011</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Epson Perfection 3490</td>
			<td style="width:71px;">Epson</td>
			<td style="width:119px;">B11B177011</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ADU200 USB Relay</td>
			<td style="width:71px;">ONTRAK</td>
			<td style="width:119px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">pieces of sterile black felt cloth</td>
			<td style="width:71px;">custon made</td>
			<td style="width:119px;"></td>
			<td>approximatly 100 x100 mm</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">white plate holders</td>
			<td style="width:71px;">custon made</td>
			<td style="width:119px;"></td>
			<td>surface size, with 6 x 99mm diameter hole</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;">Delerin rings the size of the Petri dish</td>
			<td style="width:71px;">custon made</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">inner diameter: 72 mm 
			outer diameter: 86 mm 
			top outer diameter: 90 mm</td>
		</tr>
	</tbody>
</table>

scanlag: high-throughput quantification of colony growth and lag time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are affected both by the doubling time of the microorganism and by any delay in growth upon transfer from one condition to another, the lag. The ScanLag method enables the characterization of these two independent properties at the level of colonies originating each from a single cell, generating a two-dimensional distribution of the lag time and of the growth time. In ScanLag, measurement of the time it takes for colonies on conventional nutrient agar plates to be detected is automated on an array of commercial scanners controlled by an in house application. Petri dishes are placed on the scanners, and the application acquires images periodically. Automated analysis of colony growth is then done by an application that returns the appearance time and growth rate of each colony. Other parameters, such as the shape, texture and color of the colony, can be extracted for multidimensional mapping of sub-populations of cells. Finally, the method enables the retrieval of rare variants with specific growth phenotypes for further characterization. The technique could be applied in bacteriology for the identification of long lag that can cause persistence to antibiotics, as well as a general low cost technique for phenotypic screens.

As an example of lag time distribution extraction, Figure 5 shows the ability of ScanLag to identify and track each colony with specific characteristics on one plate over time, as would be done in a screening assay.
When the culture of the microorganism is heterogeneous, the different subpopulations might reveal themselves during the assay. For example, Figure 6 shows the appearance quantification and thus reveals bimodal lag time distribution in a mutant strain of E. coli.
The growth rate of the cells influences the appearance time of the colony. When colonies grow at the same rate, the late appearance can be attributed to the lag time (Figure 7).
To validate that the method indeed measures the lag time of single cells, we compared ScanLag results with those obtained using single-cell microscopy; this validation is described in detail in a previous publication7. This method enables monitoring of many more cells than can be evaluated with microscopy. The distributions obtained using microscopy and obtained using ScanLag largely overlap. The ScanLag distribution is slightly broader; theoretical analyses predict broadening to be of the order of the standard deviation of the division time. If growth time is different for each strain, the origin of the delay in appearance must be further investigated using other methods.
The influence of early-appearing colonies on the appearance of later colonies was examined. Control experiments7 confirmed that later appearance was not affected as long as the total number of colonies per plate did not exceed 200 (Figure 8). Another control experiment confirmed that the location of the colony on the plate was not affecting its appearance time 7.
The analysis of the plates is calibrated to specific thresholds that might need modification depending on nutrients present in growth medium or the type of microorganism. Nevertheless, the analysis is quite robust for a large range of thresholds as shown in Figure 9.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig1highres.jpg" width="500" /> 
Figure 1. A schematic diagram of the steps needed to create a colony appearance distribution.
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig2highres.jpg" width="500" /> 
Figure 2. A screen shot of the Device Manager showing the names of the attached scanners, and of the configuration file. <a href="https://www.jove.com/files/ftp_upload/51456/51456fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig3highres.jpg" width="500" /> 
Figure 3. A screen shot of the Scanning Manager.
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig4highres.jpg" width="500" /> 
Figure 4. A screen shot of the ScanLagApp. An image of the plate is on the left pane, and the growth curves of each colony are on the right pane, as detailed in the manual. The spikes in some of the colonies area curves occur when two or more colonies merge. When merging of colonies happens, the area of these colonies is considered as the joint area.
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig5highres.jpg" width="500" /> 
Figure 5. A representative result of the analysis of one plate. (A) An image of the output of the detection application at the end of the experiment. Each colony area is identified, colored and assigned with a unique ID number. The arrows point to representative colonies measured in B. The colony is detected based on intensity threshold (For E. coli colonies on LB agar or M9 agar this threshold is 0.03. For other media or bacteria, this threshold can be adjusted in the function ProcessPictures). Only objects above 10 pixels are counted (this threshold can be modified in the function MatchColonies). Detection of a colony starts at approximately 105 bacteria. (B) Plots of the area in pixels versus time of four representative colonies in this plate. The &#8216;appearance time&#8217; of each colony is when the colony is detected. The &#8216;growth time&#8217; of each colony is defined here as the time to grow from 20 pixels to 80 pixels (those boundaries are adjustable using the function getAppearanceGrowthByVec). The software excludes the colonies that merged before reaching the upper bound. Identification of a specific colony according to its characteristics is easy thanks to the identifying number and the color assigned to each colony. For example colonies # 1, 56, 77 and 124 are highlighted in both graphs.
<img alt="Figure 6" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig6highres.jpg" width="500" /> 
Figure 6. Appearance quantification can reveal bimodal lag time distribution. Comparison of ScanLag analysis histograms of appearance times for two different strains. Blue line: wild type strain exponentially growing (Total: 1,320 colonies); red line- high persistence mutant enriched with lagging bacteria (Total: 1,529 colonies). (A) Normalized appearance histograms. The peak of appearance of exponentially growing cells is the typical time to grow to a detectable colony. Inset: same histogram of the mutant strain after subtracting the time of the peak of the exponential culture to get the actual lag time on log-spaced bins showing the bimodal lag time distribution. (B) Survival function of the same data as in (A) on a logarithmic scale. This representation enhances the late appearance tail of the mutant strain.
<img alt="Figure 7" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig7highres.jpg" width="500" /> 
Figure 7. Appearance distribution and growth time distribution. (A) and (B) show two dimensional histograms of the appearance time and the growth time of the two different conditions (The software excludes colonies that merged before reaching the upper bound). (C) Comparing the histograms of the two strains show the difference in appearance time, whereas the growth times (D) are similar. <a href="https://www.jove.com/files/ftp_upload/51456/51456fig7highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig8highres.jpg" width="500" /> 
Figure 8. The appearance of early colonies does not interfere with the appearance of later colonies. (A) lag time distribution of wild&#8208;type cells plated alone. (B) lag time distribution of a cold sensitive strain alone, after transfer to permissive temperature. (C) lag time distribution of both strains plated together under the same conditions. The black line represents the expected distribution based on the data obtained when measuring each strain separately. A good agreement between expected and measured distributions is obtained as long as the total number of colonies per plate is below 200.
<img alt="Figure 9" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig9highres.jpg" width="500" /> 
Figure 9. The detection threshold does not affect the shape of the colony appearance distribution. The colony appearance distribution was analyzed for the same data set using two different threshold sizes; (A) threshold size is 10 pixels; (B) threshold size is 50 pixels. Number of cells per histogram: approx. 1,500. The different detection threshold results only in a shift of the detection time.
<img alt="Figure 10" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig10highres.jpg" width="500" /> 
Figure 10. Temperature stability measurements across the scanner&#8217;s surface. (A) Image of a plate with colonies of Bacillus subtillis grown on the scanner without Power Management. The surface is warmer on the bottom and therefore the colonies grow faster. The gradual line beside the plate is a schematic representation of the heat gradient. (B) The temperature stability was measured with two thermocouples placed across the scanner&#8217;s surface. The scanner heats up during each scan and over the time. (C) With Power Management, the temperature is uniform and the colonies grow at similar rates. (D) Same as (B) with the Power Management module. Temperature stability of &#177;0.2 &#176;C.
<img alt="Figure 11" fo:content-width="5in" src="/files/ftp_upload/51456/51456fig11highres.jpg" width="500" /> 
Figure 11. Differences in the appearance distribution may be due to differences in the volumes of solid medium. The same bacterial culture was plated on plates with different volumes of LB agar, resulting in a different height of agar surface. The different heights led to different opacity of the plates, and therefore a different detection time. The inter plates difference was checked and the difference between different height conditions. (A) Typical differences between plates of equal volumes of 30&#177;0.5 ml of LB Agar. (B) Difference between different height conditions: the appearance distribution was measured for plates with volumes of 20&#177;0.5 ml (red), of 30&#177;0.5 ml (green) and of 40&#177;0.5 ml (blue). Each condition is the average of 6 different plates. As long as the volume of the plates is within &#177;5 ml range, the opacity is similar and leads to similar appearance time distribution.

Watch this Scientific Journal Video about ScanLag: High-throughput Quantification of Colony Growth and Lag Time at JoVE.com

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

ScanLag is a high-throughput method for measuring the delay in growth, namely lag time, as well as the growth rate of colonies for thousands of cells in parallel. Screening using ScanLag enables the discrimination between long lag-time and slow growth at the level of a single variant.

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are ...

scanlag-high-throughput-quantification-of-colony-growth-and-lag-time

Research

JoVE Journal

Immunology and Infection

High-throughput Detection Method for Influenza Virus

Influenza virus is a respiratory pathogen that causes a high degree of morbidity and mortality every year in multiple parts of the world. Therefore, precise diagnosis of the infecting strain and rapid high-throughput screening of vast numbers of clinical samples is paramount to control the spread of pandemic infections. Current clinical diagnoses of influenza infections are based on serologic testing, polymerase chain reaction, direct specimen immunofluorescence and cell culture 1,2.
Here, we report the development of a novel diagnostic technique used to detect live influenza viruses. We used the mouse-adapted human A/PR/8/34 (PR8, H1N1) virus 3 to test the efficacy of this technique using MDCK cells 4. MDCK cells (104 or 5 x 103 per well) were cultured in 96- or 384-well plates, infected with PR8 and viral proteins were detected using anti-M2 followed by an IR dye-conjugated secondary antibody. M2 5 and hemagglutinin 1 are two major marker proteins used in many different diagnostic assays. Employing IR-dye-conjugated secondary antibodies minimized the autofluorescence associated with other fluorescent dyes. The use of anti-M2 antibody allowed us to use the antigen-specific fluorescence intensity as a direct metric of viral quantity. To enumerate the fluorescence intensity, we used the LI-COR Odyssey-based IR scanner. This system uses two channel laser-based IR detections to identify fluorophores and differentiate them from background noise. The first channel excites at 680 nm and emits at 700 nm to help quantify the background. The second channel detects fluorophores that excite at 780 nm and emit at 800 nm. Scanning of PR8-infected MDCK cells in the IR scanner indicated a viral titer-dependent bright fluorescence. A positive correlation of fluorescence intensity to virus titer starting from 102-105 PFU could be consistently observed. Minimal but detectable positivity consistently seen with 102-103 PFU PR8 viral titers demonstrated the high sensitivity of the near-IR dyes. The signal-to-noise ratio was determined by comparing the mock-infected or isotype antibody-treated MDCK cells.
Using the fluorescence intensities from 96- or 384-well plate formats, we constructed standard titration curves. In these calculations, the first variable is the viral titer while the second variable is the fluorescence intensity. Therefore, we used the exponential distribution to generate a curve-fit to determine the polynomial relationship between the viral titers and fluorescence intensities. Collectively, we conclude that IR dye-based protein detection system can help diagnose infecting viral strains and precisely enumerate the titer of the infecting pathogens.

This method describes the use of Infrared dye based imaging system for detection of H1N1 in bronchioalveolar lavage (BAL) fluid of infected mice at a high sensitivity. This methodology can be performed in a 96- or 384-well plate, requires &lt;10 &mu;l volume of test material and has the potential for concurrent screening of multiple pathogens.

Influenza virus is a respiratory pathogen that causes a high degree of morbidity and mortality every year in multiple parts of the world. Therefore, ...

A Microscopic Phenotypic Assay for the Quantification of Intracellular Mycobacteria Adapted for High-throughput/High-content Screening

Despite the availability of therapy and vaccine, tuberculosis (TB) remains one of the most deadly and widespread bacterial infections in the world. Since several decades, the sudden burst of multi- and extensively-drug resistant strains is a serious threat for the control of tuberculosis. Therefore, it is essential to identify new targets and pathways critical for the causative agent of the tuberculosis, Mycobacterium tuberculosis (Mtb) and to search for novel chemicals that could become TB drugs. One approach is to set&#160;up methods suitable for the genetic and chemical screens of large scale libraries enabling the search of a needle in a haystack. To this end, we developed a phenotypic assay relying on the detection of fluorescently labeled Mtb within fluorescently labeled host&#160;cells using automated confocal microscopy. This in vitro assay allows an image based quantification of the colonization process of Mtb&#160;into the host and was optimized for the 384-well microplate format, which is proper for screens of siRNA-, chemical compound- or Mtb mutant-libraries. The images are then processed for multiparametric analysis, which provides read&#160;out inferring on the pathogenesis of Mtb&#160;within host cells.

Here, we describe a phenotypic assay applicable to the High-throughput/High-content screens of small-interfering synthetic RNA (siRNA), chemical compound, and Mycobacterium tuberculosis mutant libraries. This method relies on the detection of fluorescently labeled Mycobacterium tuberculosis within fluorescently labeled host&#160;cell using automated confocal microscopy.

Despite the availability of therapy and vaccine, tuberculosis (TB) remains one of the most deadly and widespread bacterial infections in the world. Since ...

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses

RNA viruses are responsible for major human diseases such as flu, bronchitis, dengue, Hepatitis C or measles. They also represent an emerging threat because of increased worldwide exchanges and human populations penetrating more and more natural ecosystems. A good example of such an emerging situation is chikungunya virus epidemics of 2005-2006 in the Indian Ocean. Recent progresses in our understanding of cellular pathways controlling viral replication suggest that compounds targeting host cell functions, rather than the virus itself, could inhibit a large panel of RNA viruses. Some broad-spectrum antiviral compounds have been identified with host target-oriented assays. However, measuring the inhibition of viral replication in cell cultures using reduction of cytopathic effects as a readout still represents a paramount screening strategy. Such functional screens have been greatly improved by the development of recombinant viruses expressing reporter enzymes capable of bioluminescence such as luciferase. In the present report, we detail a high-throughput screening pipeline, which combines recombinant measles and chikungunya viruses with cellular viability assays, to identify compounds with a broad-spectrum antiviral profile.

In vitro assays to measure virus replication have been greatly improved by the development of recombinant RNA viruses expressing luciferase or other enzymes capable of bioluminescence. Here we detail a high-throughput screening pipeline that combines such recombinant strains of measles and chikungunya viruses to isolate broad-spectrum antivirals from chemical libraries.

RNA viruses are responsible for major human diseases such as flu, bronchitis, dengue, Hepatitis C or measles. They also represent an emerging threat ...

High-throughput Quantitative Real-time RT-PCR Assay for Determining Expression Profiles of Types I and III Interferon Subtypes

Described in this report is a qRT-PCR assay for the analysis of seventeen human IFN subtypes in a 384-well plate format that incorporates highly specific locked nucleic acid (LNA) and molecular beacon (MB) probes, transcript standards, automated multichannel pipetting, and plate drying. Determining expression among the type I interferons (IFN), especially the twelve IFN-&#945; subtypes, is limited by their shared sequence identity; likewise, the sequences of the type III IFN, especially IFN-&#955;2 and -&#955;3, are highly similar. This assay provides a reliable, reproducible, and relatively inexpensive means to analyze the expression of the seventeen interferon subtype transcripts.

This protocol describes a high-throughput qRT-PCR assay for the analysis of type I and III IFN expression signatures. The assay discriminates single base pair differences between the highly similar transcripts of these genes. Through batch assembly and robotic pipetting, the assays are consistent and reproducible.

Described in this report is a qRT-PCR assay for the analysis of seventeen human IFN subtypes in a 384-well plate format that incorporates highly specific ...

High-throughput Gene Tagging in Trypanosoma brucei

Improvements in mass spectrometry, sequencing and bioinformatics have generated large datasets of potentially interesting genes. Tagging these proteins can give insights into their function by determining their localization within the cell and enabling interaction partner identification. We recently published a fast and scalable method to generate Trypanosoma brucei cell lines that express a tagged protein from the endogenous locus. The method was based on a plasmid we generated that, when coupled with long primer PCR, can be used to modify a gene to encode a protein tagged at either terminus. This allows the tagging of dozens of trypanosome proteins in parallel, facilitating the large-scale validation of candidate genes of interest. This system can be used to tag proteins for localization (using a fluorescent protein, epitope tag or electron microscopy tag) or biochemistry (using tags for purification, such as the TAP (tandem affinity purification) tag). Here, we describe a protocol to perform the long primer PCR and the electroporation in 96-well plates, with the recovery and selection of transgenic trypanosomes occurring in 24-well plates. With this workflow, hundreds of proteins can be tagged in parallel; this is an order of magnitude improvement to our previous protocol and genome scale tagging is now possible.

High-throughput Gene Tagging in Trypanosoma brucei

Addition of a tag to a protein is a powerful way of gaining insight into its function. Here, we describe a protocol to endogenously tag hundreds of Trypanosoma brucei proteins in parallel such that genome scale tagging is achievable.

Improvements in mass spectrometry, sequencing and bioinformatics have generated large datasets of potentially interesting genes. Tagging these proteins ...

High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity

Traditional measures of intracellular antimicrobial activity and eukaryotic cell cytotoxicity rely on endpoint assays. Such endpoint assays require several additional experimental steps prior to readout, such as cell lysis, colony forming unit determination, or reagent addition. When performing thousands of assays, for example, during high-throughput screening, the downstream effort required for these types of assays is considerable. Therefore, to facilitate high-throughput antimicrobial discovery, we developed a real-time assay to simultaneously identify inhibitors of intracellular bacterial growth and assess eukaryotic cell cytotoxicity. Specifically, real-time intracellular bacterial growth detection was enabled by marking bacterial screening strains with either a bacterial lux operon (1st generation assay) or fluorescent protein reporters (2nd generation, orthogonal assay). A non-toxic, cell membrane-impermeant, nucleic acid-binding dye was also added during initial infection of macrophages. These dyes are excluded from viable cells. However, non-viable host cells lose membrane integrity permitting entry and fluorescent labeling of nuclear DNA (deoxyribonucleic acid). Notably, DNA binding is associated with a large increase in fluorescent quantum yield that provides a solution-based readout of host cell death. We have used this combined assay to perform a high-throughput screen in microplate format, and to assess intracellular growth and cytotoxicity by microscopy. Notably, antimicrobials may demonstrate synergy in which the combined effect of two or more antimicrobials when applied together is greater than when applied separately. Testing for in vitro synergy against intracellular pathogens is normally a prodigious task as combinatorial permutations of antibiotics at different concentrations must be assessed. However, we found that our real-time assay combined with automated, digital dispensing technology permitted facile synergy testing. Using these approaches, we were able to systematically survey action of a large number of antimicrobials alone and in combination against the intracellular pathogen, Legionella pneumophila.

A high throughput, real-time assay was developed to simultaneously identify (1) eukaryotic cell-penetrant antimicrobials targeting an intracellular bacterial pathogen, and (2) assess eukaryotic cell cytotoxicity. A variation on the same technology was thereafter combined with digital dispensing technology to enable facile, high-resolution, dose-response, and two- and three-dimensional synergy studies.

Traditional measures of intracellular antimicrobial activity and eukaryotic cell cytotoxicity rely on endpoint assays. Such endpoint assays require ...

A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.

Fecal-oral transmission of acute gastroenteritis occurs from time to time, especially when people who handled food and water are infected by Salmonella spp./Shigella spp. The gold standard method for the detection of Salmonella spp./Shigella spp. is direct culture but this is labor-intensive and time-consuming. Here, we describe a high-throughput platform for Salmonella spp./Shigella spp. screening, using real-time polymerase chain reaction (PCR) combined with guided culture. There are two major stages: real-time PCR and the guided culture. For the first stage (real-time PCR), we explain each step of the method: sample collection, pre-enrichment, DNA extraction and real-time PCR. If the real-time PCR result is positive, then the second stage (guided culture) is performed: selective culture, biochemical identification and serological characterization. We also illustrate representative results generated from it. The protocol described here would be a valuable platform for the rapid, specific, sensitive and high-throughput screening of Salmonella spp./Shigella spp.

A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.

Salmonella spp./Shigella spp. are common pathogens attributed to diarrhea. Here, we describe a high-throughput platform for the screening of Salmonella spp./Shigella spp. using real-time PCR combined with guided culture.

Fecal-oral transmission of acute gastroenteritis occurs from time to time, especially when people who handled food and water are infected by Salmonella ...

High-throughput Measurement of Plasma Membrane Resealing Efficiency in Mammalian Cells

In their physiological environment, mammalian cells are often subjected to mechanical and biochemical stresses that result in plasma membrane damage. In response to these damages, complex molecular machineries rapidly reseal the plasma membrane to restore its barrier function and maintain cell survival. Despite 60 years of research in this field, we still lack a thorough understanding of the cell resealing machinery. With the goal of identifying cellular components that control plasma membrane resealing or drugs that can improve resealing, we have developed a fluorescence-based high-throughput assay that measures the plasma membrane resealing efficiency in mammalian cells cultured in microplates. As a model system for plasma membrane damage, cells are exposed to the bacterial pore-forming toxin listeriolysin O (LLO), which forms large 30-50 nm diameter proteinaceous pores in cholesterol-containing membranes. The use of a temperature-controlled multi-mode microplate reader allows for rapid and sensitive spectrofluorometric measurements in combination with brightfield and fluorescence microscopy imaging of living cells. Kinetic analysis of the fluorescence intensity emitted by a membrane impermeant nucleic acid-binding fluorochrome reflects the extent of membrane wounding and resealing at the cell population level, allowing for the calculation of the cell resealing efficiency. Fluorescence microscopy imaging allows for the enumeration of cells, which constitutively express a fluorescent chimera of the nuclear protein histone 2B, in each well of the microplate to account for potential variations in their number and allows for eventual identification of distinct cell populations. This high-throughput assay is a powerful tool expected to expand our understanding of membrane repair mechanisms via screening for host genes or exogenously added compounds that control plasma membrane resealing.

Here we describe a high-throughput fluorescence-based assay that measures the plasma membrane resealing efficiency through fluorometric and imaging analyses in living cells. This assay can be used for screening drugs or target genes that regulate plasma membrane resealing in mammalian cells.

In their physiological environment, mammalian cells are often subjected to mechanical and biochemical stresses that result in plasma membrane damage. In ...

A High-throughput Shigella-specific Bactericidal Assay

Serum bactericidal assays (SBAs) measure the functional activity of antibodies and have been used for many decades. SBAs directly measure antibody killing activity by assessing the ability of antibodies in serum to bind to bacteria and activate complement. This complement activation results in the lysis and killing of the target bacteria. These assays are valuable because they go beyond quantifying antibody production to elucidate the biological functions that these antibodies have, allowing researchers to study the role that antibodies may play in preventing infection. SBAs have been used to study immune responses for many human pathogens, but there is no widely accepted methodology for Shigella at present. Historically, SBAs have been very labor-intensive, requiring many time-consuming steps to accurately quantify surviving bacteria. This protocol describes a simple, robust, and high-throughput assay that measures functional antibodies specific for Shigella in serum in vitro. The method described here offers many advantages over traditional SBAs, including the use of frozen bacterial stocks, 96 well assay plates, a micro-culture system, and automated colony-counting. All of these modifications make this assay less labor-intensive and more high-throughput. This protocol is simpler and faster to perform than traditional SBAs while still using simple technologies and readily available reagents. The protocol has been successfully applied in multiple independent laboratories and the assay is robust and reproducible. The assay can be used to assess immune responses in pre-clinical as well as clinical studies. Quantifying shigellacidal antibody titers both before and after antigen exposure (either by immunization or infection) allows for a broader understanding of how functional antibody responses are generated and their contribution to protective immunity. The development of this standardized, well-characterized assay may greatly facilitate Shigella vaccine design.

A High-throughput Shigella-specific Bactericidal Assay

Here we present a protocol to measure Shigellacidal activity of antibodies in serum. Serum is mixed with bacteria and exogenous complement, incubated, and the reaction mixture is plated on agar plates. Viable bacteria form colonies which are counted, using an automated colony enumerator, and used to determine the bactericidal titer.

Serum bactericidal assays (SBAs) measure the functional activity of antibodies and have been used for many decades. SBAs directly measure antibody killing ...

High Throughput Co-culture Assays for the Investigation of Microbial Interactions

The study of interactions between microorganisms has led to numerous discoveries, from novel antimicrobials to insights in microbial ecology. Many approaches used for the study of microbial interactions require specialized equipment and are expensive and time intensive. This paper presents a protocol for co-culture interaction assays that are inexpensive, scalable to large sample numbers, and easily adaptable to numerous experimental designs. Microorganisms are cultured together, with each well representing one pairwise combination of microorganisms. A test organism is cultured on one side of each well and first incubated in monoculture. Subsequently, target organisms are simultaneously inoculated onto the opposite side of each well using a 3D-printed inoculation stamp. After co-culture, the completed assays are scored for visual phenotypes, such as growth or inhibition. These assays can be used to confirm phenotypes or identify patterns among isolates of interest. Using this simple and effective method, users can analyze combinations of microorganisms rapidly and efficiently. This co-culture approach is applicable to antibiotic discovery as well as culture-based microbiome research and has already been successfully applied to both applications.

The co-culture interaction assays presented in this protocol are inexpensive, high throughput, and simple. These assays can be used to observe microbial interactions in co-culture, identify interaction patterns, and characterize the inhibitory potential of a microbial strain of interest against human and environmental pathogens.