Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

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 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). These directions allow the algorithm to determine a threshold unique to each image by taking the average pixel intensity value and adding the standard deviation. Any pixel with an intensity value higher than said threshold is considered white and any with an intensity value lower is considered black. The resulting image allows a clear and crisp distinction of where the capsule ends (Figure 1B). The algorithm then has a robust white-on-black contrast based circle to detect for the capsule and the original image provides a robust black-on-white circle for cell bodies. A representative visualization of the detected circles is generated as part of the program (Figure 1C). This allows the user to quickly parse through processed images and ensure the results appear accurate.

It is essential to follow the exact protocol to acquire optimal images. Sub-optimal images usually result from a lack of contrast between capsule and Ink (Figure 2A). Optimal contrast and acquisition parameters will vary based on sample, experiment, camera and microscope models, etc. An optimal image is one where yeast cells are separated if possible, where the outer edge of the capsule has clear contrast to the India Ink stain, and where the cell body is focused to appear as a dark band which contrasts sharply with its background. If too many cells are in the field of view or if the stain is too light, the pixel intensity values will cluster and the program will not be able to establish a threshold capable of elucidating capsule size (Figure 2B). When sub-optimal images are used the program can detect circles of incorrect sizes, not be able to detect any circles, or detect multiple pseudo-circles based on artifacts in the threshold image (Figure 2C).

Detecting cell bodies poses a similar problem in that the cell body must be visualized as a robust black circle on the white background of the capsule. This problem is addressed in the protocol when describing the desired focal plane for image acquisition. The best focal plane to use is one in which the cell body appears as a dark, concentrated band (Figure 3A). This focal plane is acceptable as a standard because it properly focuses the cell. This was confirmed by visualizing the cell wall with Uvitex, a chitin stain (Figure 3B). The Uvitex stain clearly shows a crisp, focused cell wall with weak signal in the center (where the top and bottom of the cell body would be stained, out of focus).

In developing this method it was also important to confirm that this algorithm could accurately determine capsule and cell body measurements. While the representative circles in the previous figures are promising, actual measurements were compared between computer and human measurements. When the protocol is followed accurately and optimal images are obtained, the algorithm is accurate and reliable (Figure 4A). 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 (Figure 4B). Since this technique was originally developed for a specific individual (first author), a different individual was asked to use it to determine if differences between staining and measurement styles would affect accuracy despite following protocol. The results showed that this technique was widely applicable as long as the protocol was followed as instructed (Figure 4C). Similarly, this technique can be used with any microscope capable of acquiring phase contrast images of the prepared India Ink slides. To ensure the algorithm remains accurate with other microscope setups this experiment was repeated by a third investigator using a different bright field microscope coupled with a camera and compared detected capsule diameters with manually measured capsule diameters. No significant difference was observed between computer and human measurements (Figure 4D).

Finally, future applications of this algorithm were explored in the context of fluorescence staining. Since fluorescence imaging is still based on signal to noise ratios of pixel intensity values the algorithm should be readily applicable to fluorescence images so long as the stain is circular in nature. This application was confirmed when the algorithm was able to successfully detect Uvitex stained cell bodies from the previously described images (Figure 5A, 5B). Users should be careful to optimize the algorithm parameters for their experiment, and standardized protocols should be developed in the future for any new applications.

Figure 1: Representative results of an optimally obtained image. A. The initial image acquired by bright field microscopy of an India Ink slide. B. The binary image created using a threshold calculated from the average pixel intensity value added to the standard deviation. All values above this threshold are considered white and all below are considered black. C. A visualization of the capsules (green) and cell bodies (blue) detected by the algorithm. All images were obtained at 40x magnification with 2×2 binning. Please click here to view a larger version of this figure.

Figure 2: Representative results of a sub-optimally obtained image. A. The initial India Ink slide acquired via bright field microscopy. Uneven and inadequate staining results in a particularly bright background with gradients of intensity. B. The resulting binary image, in which capsules cannot clearly be distinguished due to high background signal. C. Several capsules are undetectable. All images were obtained at 40x magnification with 2×2 binning. Please click here to view a larger version of this figure.

Figure 3: Detecting cell bodies. A. Bright field image of capsule centered at the preferred focal plane. B. Uvitex fluorescence image of the cell body at the preferred focal plane. Images were obtained at 100x magnification with 2×2 binning. Please click here to view a larger version of this figure.

Figure 4: Comparisons of results for algorithm (black) and human (gray) analyses. A. When slides are prepared according to protocol and optimal images are obtained there is no appreciable difference between measurements. B. If the protocol is not followed the algorithm cannot accurately identify and measure capsules. Here specifically the background was not consistent and there was not clear contrast between capsules and background. Significant variations calculated via t-test is noted as * for P values < 0.05. C. An independent investigator was asked to follow the protocol and compare their analyses with the algorithm. Reliability is maintained across observers as long as the images are properly acquired. D. A second independent investigator with a second microscope was used to confirm the algorithm retains accuracy across individuals and hardware. Please click here to view a larger version of this figure.

Figure 5: A potential alternative application of the algorithm: identification and measurement of fluorescence microscopy. Cell bodies were stained and imaged with Uvitex, then identified by algorithm. The detected circle extends past what would be labeled as the cell wall because the detection is based on the fluorescence signal. The detection algorithm can be tailored to identify specifically the brightest accumulation of signal (the cell wall) by modifying the threshold used to generate the initial binary image represented in Figures 1B and 2B. Please click here to view a larger version of this figure.

Supplemental Figure 1: A representative screenshot of the application running in a Windows environment. Please click here to view a larger version of this figure.

Supplemental Figure 2: Representations of detected budding yeast cells. Scale bar represents 10 µm. A. A parent cell and bud each counted as separate cells within their own capsule. B. A parent cell and bud each counted twice, once for their own capsule and once for the other cell capsule, resulting in four total cells detected from one parent and one bud. Please click here to view a larger version of this figure.

Supplemental Figure 3: A representation of the final data provided after cell detection and measurement. Image file refers to the name of the original image. Total radius refers to the radius of the representative green circles, capsule and cell body combined. Capsule x and Capsule y refer to the coordinates of the capsule for which this data applies to. Body radius refers to the radius of the representative blue circles. Capsule radius refers to the total radius minus the body radius, resulting in the radius of just the capsule. These values will be in microns so long as the user included a pixel-to-micron conversion ratio in step 3.2.5. Please click here to view a larger version of this figure.