Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

Summary

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.

Abstract

The purpose of this technique is to provide a consistent, accurate, and manageable process for large numbers of polysaccharide capsule measurements.

First, a threshold image is generated based on intensity values uniquely calculated for each image. Then, circles are detected based on contrast between the object and background using the well-established Circle Hough Transformation (CHT) algorithm. Finally, the detected cell capsules and bodies are matched according to center coordinates and radius size, and data is exported to the user in a manageable spreadsheet.

The advantages of this technique are simple but significant. First, because these calculations are performed by an algorithm rather than a human both accuracy and reliability are increased. There is no decline in accuracy or reliability regardless of how many samples are analyzed. Second, this approach establishes a potential standard operating procedure for the Cryptococcus field instead of the current situation where capsule measurement varies by lab. Third, given that manual capsule measurements are slow and monotonous, automation allows rapid measurements on large numbers of yeast cells that in turn facilitates high throughput data analysis and increasingly powerful statistics.

The major limitations of this technique come from how the algorithm functions. First, the algorithm will only generate circles. While Cryptococcus cells and their capsules take on a circular morphology, it would be difficult to apply this technique to non-circular object detection. Second, due to how circles are detected the CHT algorithm can detect enormous pseudo-circles based on the outer edges of several clustered circles. However, any misrepresented cell bodies caught within the pseudo-circle can be easily detected and removed from the resulting data sets.

This technique is meant for measuring the circular polysaccharide capsules of Cryptococcus species based on India Ink bright field microscopy; though it could be applied to other contrast based circular object measurements.

Introduction

Cryptococcus neoformans is a pathogenic yeast found ubiquitously around the globe that is associated with human disease primarily in immunosuppressed populations. C. neoformans most notably accounts for a significant cause of total annual deaths in sub-Saharan Africa due to infectious disease¹. The major clinical manifestation of cryptococcal infection is meningoencephalitis, which follows invasion of the central nervous system by transport in infected macrophages (Trojan horse manner) or direct crossing of the blood-brain barrier. C. neoformans expresses several virulence factors including the ability to replicate at human body temperature, urease activity, melanization, and formation of a polysaccharide capsule². The polysaccharide capsule is composed of repeating glucuronoxylomannan and glucoronoxylomannangalactan polymers and functions as a protective barrier against factors such as environmental stress and host immune responses².

Although the size of the cryptococcal polysaccharide capsule size has not consistently been associated with virulence, there is evidence that it is a factor in pathogenesis^2,3,4,5,6,7. Capsule size is associated with meningitis pathology⁶, can affect macrophage ability to control Cryptococcus infection⁵, and can result in loss of virulence if absent⁸. Hence, capsule size measurements are common in cryptococcal research, but there is no fieldwide standard for a method of capsule measurement.

Currently, C. neoformans polysaccharide capsule measurement is based on manual measurements of microscopy images, and the exact methods of both image and measurement acquisitions vary across laboratories^9,10,11. An immediate concern to this method is that some studies require the acquisition of thousands of individual measurements, which makes maintaining accuracy and reliability difficult. Furthermore, even when the results are published, there is often inadequate description of the measurement method. Many publications do not explain how their measurements were obtained, what focal plane was used, how they determined the threshold for capsule identification, whether they used radius or diameter, whether they used one measurement or averaged several, or other details. Some publications only state their method as which program was used, e.g., "Adobe Photoshop CS3 was used to measure the cells"¹¹. This lack of standardization and reporting detail can make reproducibility difficult if not impossible. Differences in human eyesight, computer brightness, microscope settings, slide lighting, and other factors can vary not only between individuals but between samples, whereas calculations based on ratios of pixel intensity values will remain constant and applicable between samples. This technique was generated in the context of providing a standardized, accurate, rapid, and simple technique to measure capsules sizes for a field in which there was none before.

As previously mentioned, the CHT algorithm is long-established, and scripts to automatically detect circles have been written before. This method improves in two areas where other scripts would fall short. First, simply detecting circles is not enough, because with cryptococcal cells two distinct circles must be detected in relation to each other. This method specifically detects cell bodies within capsules, discriminates between the two, and performs calculations only on the relevant body-capsule pairs. Second, even when following the same protocol, different investigators will end up with different acquired images. By allowing the investigator control over every algorithm parameter, this tool can be adjusted to match a broad range of acquisition methods. There is no need for a standardized scope, objective, filter, and so on.

This technique can be readily applied to any situation in which the investigator needs to detect circles within an image that contrast with their background. Both circles lighter and darker than their background can be detected, counted, and measured using this technique.

Protocol

1. Preparation of India Ink Slide

1. Pipette 10 µL of cryptococcal sample onto a slide. Any circular yeast strain will work but for this experiment H99 was the only strain used.
   NOTE: If the sample is directly from culture media, diluting 1:2 with PBS or water can help prevent the India Ink from clumping.
2. Pipette 2 µL of India Ink stain onto the sample and mix by physically pushing the pipette tip to the sample and moving in a circular motion until the India Ink appears evenly distributed.
3. Place a coverslip over the sample by holding down the left edge of the coverslip against the surface of the slide, then gently and evenly lowering the opposite side of the coverslip over the sample.
4. Allow the slide to air dry for 5 min.
5. Gently apply a light layer of nail polish to coverslip border to form a seal and preserve the India Ink stain.

2. Imaging Slide

1. Place slides in a bright field microscope with a camera attachment and known pixel-to-micron conversion. Adjust filters, objectives, and contrast so that cell capsules are clear and cell bodies are focused, dark bands.
   NOTE: Various filters, objectives, and contrast settings will work but a 20x objective, Ph1 filter, and 2x2 binning is recommended.
2. Ensure the field of view is dense but not overpopulated with cryptococcal cells, with clear contrast between cell capsule and background, and properly focused with the cell body visualized as a dark band.
   NOTE: The exact number of cells for an optimal image will vary depending on the sample and objective being used. The important aspects are to ensure cells are not clustered or overlapping, that the cell focal planes do not vary significantly, and that there is significant India Ink stain clearly visible in the background (at least 25% of the field).
3. Save images to a single directory with clear titles, as the measurement algorithm will be run on images in a single directory and the output data will be organized according to the names of the image files.

3. Algorithm Setup

1. Install Python version 2.7 from
2. Install additional python libraries by running the commands "pip install pillow" and "pip install openpyxl".
3. Install MATLAB by following the instructions at
4. Build the MATLAB python library by following the instructions provided at https://www.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html.
5. Download the three required files included in this manuscript's Supplementary Materials ("QCA.py", "Analysis2.m", and "TestRun.m").
   Note: These files can be extracted to any location, but all three must be in the same directory.

4. Use of Algorithm

1. Run the application by double clicking on QCA.py.
   ​NOTE: The application may take several minutes to start. The "QCA.py" file contains the program structure that calls the ".m" files to run the actual algorithm.
2. Follow the steps outlined in the program.
   1. Input the extension type of the image files preceded by a period and separated by semicolons (ex. ".TIF;.jpeg") then click the Enter button.
   2. Choose the directory in which the image files are located by clicking the Select Directory button and selecting the folder which contains the images.
   3. 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 to the right. Review and ensure the list is accurate and complete.
   4. Select a random image from the list to use as a preview by clicking the Select Random Image button.
      NOTE: If the image is unable to open due to an "incorrect image mode error," the algorithm will still function properly despite not displaying the image. After step 4.2.7, the test image will still display.
   5. Input microscope objective and binning settings. If the default settings do not match the microscope used, select "Custom Pixel Conversion" and input the pixel-to-um conversion for the image files. Once selected, click the Calculate Conversion button and ensure the conversion is correct according to the text box on the right.
      ​NOTE: Representative images shown in Figure 1 were calculated with 40x magnification and 2x2 binning selected.
   6. Input algorithm parameters for circle detection.
      1. Input the minimum and maximum radius detectable for the outer capsule detection as the Min and Max Capsule Radius entries. A smaller range will allow more accurate results.
      2. Input the minimum and maximum radius detectable for cell body detection as the Min and Max Cell Body Radius entries.
         NOTE: All four of these entries should be numbers representing their respective values in pixels, according to the source images.
      3. 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.
         ​NOTE: Representative results were obtained with a minimum capsule radius of 7, maximum capsule radius of 45, minimum body radius of 4, maximum body radius of 30, capsule sensitivity of 87, and body sensitivity of 87.
   7. Test the parameters on the randomly selected image by clicking the Run Test button. The results will be displayed in the upper center of the program, replacing the original image. If the results look accurate, it is recommended to select an additional random image to try as well to ensure the selected parameters will fit all selected images.
      1. 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 to the right. Otherwise, manipulate the algorithm parameters until the results are accurate.
         NOTE: The thick colored circles are only to help with visualization. The actual circles generated for measurement is a mathematical curve calculated by the HCT algorithm.
   8. 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.
   9. 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 body from capsule.
      NOTE: If the algorithm is being used to detect fluorescence or another situation in which only one circle is detected this step is unnecessary. Instead click the Only Pull Bodies or the Only Pull Capsules button to collect the data for only the blue or only the green circles, respectively. It is important to note that if only the capsule data is retrieved the radius will refer to the total radius of the cell as the radius of the cell body could not be subtracted.
   10. Locate the completed data in the "CleanedOuput.csv" file in the image directory. If only one piece of data was selected, the file will be labeled "CleanedBodies.csv" or "CleanedCapsules.csv".

Results

Images are first obtained by microscopy of India Ink slides using a bright field microscope coupled with a camera (Figure 1A). It is important to have cells separated and in sufficiently low density not to overwhelm the field of view, as well as to use enough stain to create contrast between cells and background. As stated in the protocol, the exact number of cells for an optimal image will vary depending on the sample, microscope, and object...

Discussion

The critical steps of this technique are preparing the India Ink slide and acquiring the microscope images. While the algorithm has been successfully tested with a variety of slide and image techniques the recommended protocol is described in this manuscript. The polysaccharide capsule is detected based on the exclusion of India Ink particles from the domain of the capsule as these particles are too large to penetrate the polysaccharide fibril network. India Ink exclusion results in a bright circle on top of a dark backg...

Materials

Name	Company	Catalog Number	Comments
India Ink	Becton, Dickinson and Co.	261194
Fisherbrand Superfrost Microscope Slides	Fisher Scientific	12-550-143	25x75x1
Fisherfinest Premium Cover Glass	Fisher Scientific	12-548-B	22x22-1
Sally Hansen HardasNails Xtreme Wear Nail Polish	Sally Hansen	N/A	109 invisible
SAB Media	Sigma	S3306
Cryptotoccus neoformans	ATCC	208821	H99 strain
Olympus AX70 Microscope	Olympus	AX70TRF	Discontinued ; Bright Field Microscope
Qimaging Retiga 1300	Qimaging	N/A	Discontinued ; Camera Microscope Attachment
MATLAB	MathWorks	N/A	Most recent version recommended
Python Programming Language	Python	N/A	Version 2 necessary ; 2.7 recommended
Microsoft Excel	Microsoft	N/A	Most recent version recommended
Phosphate Buffered Saline (PBS)	Sigma	P3813