Immunology and Infection
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Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body
Chapters
Summary January 11th, 2018
This technique describes an automated batch image processor designed to measure polysaccharide capsule and body radii. While initially designed for Cryptococcus neoformans capsule measurements the automated image processor can also be applied to other contrast based detection of circular objects.
Transcript
The overall goal of this technique is to automate tedious manual measurements. This method can help answer key questions in the cryptococcal field, such as the relationship between capsule or cell body size, and the different aspects of virulence. The main advantage of this technique is that it allows analysis of enormous sample volumes without fatigue.
The implications of this technique extend toward therapy of cryptococcosis, because capsule and cell body size are important aspects of the pathogen. To prepare an India Ink Slide, begin by pipetting 10 microliters of cryptococcal sample onto a slide. Any circular yi strain with work, but for this experiment, H99 was the only strain used.
Pipette two microliters of India ink stain onto the sample and mix it by physically pushing the pipette tip into the sample and moving in a circular motion until the India ink appears evenly distributed. Place a cover slip over the sample by holding down the left edge of the cover slip against the surface of the slide. Then, gently and evenly lower the opposite side of the cover slip over the sample.
Allow the slide to air dry for five minutes. Then, apply a light layer of nail polish to the cover slip border to form a seal and preserve the India ink stain. To image the samples, place a slide under a bright-field microscope with the camera attachment and known pixel to micron conversion.
Adjust the filters, objectives and contrasts, and ensure the field of view is dense, but not overpopulated, with cryptococcal cells with clear contrast between the cell capsule and the background, and properly focused with the cell body visualized as a dark band. After taking images, save them to a single directory with clear titles, as the measurement algorithm will be run on images in a single directory and the output data would be organized according to the names of the image files. Following the setup of the algorithm according to the text protocol, run the application by double-clicking on QCA.py.
While following the steps outlined in the program, input the extension type of the image files, preceded by a period and separated by semicolons. Then, click the Enter button. Choose the directory where the image files are located by clicking the Select Directory button and select the folder that contains the images.
Generate the list of image files in the directory by clicking the Generate Image List button. The images will be listed in the text box on the right. Review and ensure the list is accurate and complete.
Next, select a random image from the list to use as a preview by clicking the Select Random Image button. Then, input the microscope objective and binning settings. If the default settings do not match the microscope used, select Custom Pixel Conversion and input the pixel to micrometer conversion for the image files.
Once selected, click the Calculate Conversion button and ensure the conversion is correct according to text box on the right. To input the algorithm parameters for circle detection, input the minimum and maximum radius detectable for the outer capsule detection, as the min and max capsule radius entries. Then, input the minimum and maximum radius detectable for cell body detection as the min and max cell body radius entries.
Now, move the capsule and cell body sensitivity sliders to adjust the sensitivity threshold of the algorithm. A low sensitivity will be strict, and reduce false positive circle detection, but may also detect fewer real circles. Conversely, a high sensitivity will increase the detection rate, but may also result in false positive circles.
Maximize the number of bodies and capsules detected and visually inspect whether the circles appear to fit correctly. The number of bodies within capsules detected will be displayed in the text box on the right. Otherwise, manipulate the algorithm parameters until the results are accurate.
Run the detection algorithm on the entire directory of image files by clicking the Begin Analysis button. Each image will be analyzed and the program will display Finished in the text box to the right when all images have been analyzed. Then, click the Match and Cleanup button.
This will match detected cell bodies to the detected capsules they reside in, and calculate the true capsule radius by subtracting the body from the capsule. In the image directory, locate the completed data in the CleanedOutput. csv file.
If only one piece of data was selected, the file will be labeled CleanedBodies. csv, or CleanedCapsules.csv. As seen here in this microscopic image of an India ink slide, the cells are separated and in sufficiently low density not to overwhelm the field of view.
Enough stain has also been used to create contrast between the cells and the background. The algorithm determines a threshold unique to each image by taking the average intensity value and adding the standard deviation. Pixel intensities with an intensity higher than threshold are considered white, and with a lower intensity, are considered black.
The cell body must be visualized as a robust, black circle on the white background of the capsule. The best focal plane to use is one in which the cell body appears as a dark, concentrated band. This focal plane is acceptable as a standard, because it properly focuses the cell.
This one confirmed by visualizing the cell wall with UVITEX, a kind of stain that clearly a crisp, focused cell wall with a weak signal in the center. When the protocol is followed accurately and optimal images are obtained, the algorithm is accurate and reliable. However, if the protocol is followed incorrectly and suboptimal images are obtained due to poor staining, overcrowding or other previously mentioned parameters, the algorithm loses accuracy and cannot recapitulate human measurements.
Once mastered, this technique can be done in about 10 minutes if performed properly. While attempting this procedure, it's important to remember to gain a feel for acquiring optimal images ahead of time, so you're prepared when acquiring real samples. After it's development, this technique paved the way for researchers in the field of cryptococcal research to explore capsule and cell body sizes in any imaging system in which the two are clearly visualized.
After watching this video, you should have a good understanding of how to measure cryptococcal capsules and cell bodies. Don't forget that working with cryptococcus can be extremely hazardous, and precautions such as basic BSL2 safety should always be taken while performing this procedure.
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