The overall goal of this procedure is to quantify the lag distribution of a bacterial culture to allow discrimination between long lag time and slow growth at the level of a single variant. This is accomplished by first building a setup of an array of scanners that are identified by a single computer. The second step is to pla a defined colony forming unit to bacterial culture onto Petri dishes containing conventional nutrient agar.
Next, the dishes are placed into engineered holders on the scanners and time-lapse images of the colonies are taken with the scanners. The final step is to run the image analysis using the scan lag software, which automatically analyzes the images and extracts quantitative parameters such as time of appearance of each colony and growth time of each colony. Ultimately, MATLAB functions are used to generate the lag histogram using this data.
The implications of this technique extend towards diagnosis of persistent infections because it enables detection of slow variants. To begin, choose a flatbed scanner with an optical resolution of 4, 800 DPI hardware resolution of 4, 800 by 9, 600 DPI and the color bit depth of 48 bit that can operate at the relevant temperature and humidity levels. Choose a scanner that allows more than one scanner to be attached to the computer and identified.
Uniquely Petri dishes have significant volume. Unlike paper, which is usually used in commercial scanners, the colonies lie on top of the agar layer about five millimeters above the scanner's surface. For most scanners, the colonies will be in focus.
Prepare pieces of sterile black felt cloth to cover the plates to hold the Petri dishes in place. Prepare white plate holders that fit the scanners and have six holes that equal the size of the dishes if needed for the growth of the microorganism. Put the scanners in a temperature controlled environment.
Connect the scanners to the computer to install the scanning manager. Copy all scanning manager application files into a designated directory from the scan lag website. Use a computer with a Windows XP operating system and ensure that microsoft.
net framework version 2.0 is installed.Stalled. Find the scanner names in computer management device manager imaging devices. Then configure the scanning manager configuration file according to these names.
To perform an experiment, first, dilute the culture to approximately 2000 CFU per milliliter. Then plate 0.1, milliliter of culture uniformly onto Petri dishes prepared with conventional nutrient agar. Then cover the plate with a piece of sterile black felt cloth to gain good contrast between the colonies and the dish, and to absorb moisture.
Put the lid on the plate and place the plates into the holders on the scanners. To start the scanning manager. Choose the participating scanners from the list of attached scanners.
Then choose the number of images to be taken, the time interval between subsequent images and the time delay Before starting the experiment for image analysis. First, sort the images from each scanner into separate folders Using MATLAB's command window. First run prepare images to perform pre-processing.
In pre-processing, 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 run.
Time lapse for all plates to perform detection and tracking colonies are detected in each Petri dish and assigned an identifying number. The sizes of each detected colony are measured as a function of time. Extract the appearance distribution of the colonies using MATLAB's command window.
First run scan lag app to display the analysis of a certain Petri dish alongside with the graph of the size versus time for the colonies. Then use the slider to change the current time of the plate. The timing will also be indicated by a matching vertical line on the area graph of the colonies.
Click on a colony to see its associated curve and vice versa. After identifying the colony, the phenotype can be retrieved by picking the colony from the identified Petri dish. 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. After going through the results of the analysis of each plate, sum up the results. First run, add histograms to create a histogram of appearance.
Times then run plot death curve to create a survival curve representation of the distribution. An example of lag time distribution extract is shown here. Scan lag can 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 difference subpopulations might reveal themselves during the assay. This figure shows the appearance quantification and thus reveals bimodal lag time distribution in a mutant strain of e coli. Shown here are two dimensional histograms of the appearance time and the growth time of two different conditions.
Comparing the histograms of the two strains show the difference in appearance time, whereas the growth times are similar. When colonies grow at the same rate, the late appearance can be attributed to the lag time. The influence of early appearing colonies on the appearance of later colonies was examined.
Shown here are the lag time distribution of wild type cells, plated alone, and the lag time distribution of a cold sensitive strain. Plated alone after transfer to permissive temperature control experiments confirmed that later appearance was not affected as long as the total number of colonies per plate did not exceed 200. Following this procedure.
Other methods like whole genome sequencing or expression analysis can be performed on colonies with interesting growth phenotype in order to answer additional questions like whether the phenotype is genetic or epigenetic.
