Updated: Jul 22, 2019
Cryptococcus neoformans is a pathogenic intracellular yeast and the causative agent of cryptococcosis, a systemic infectious disease with a higher prevalence in immunocompromised individuals. The polysaccharide capsule is the main virulence factor and phenotypic feature of C. neoformans and plays an important role in the survival of the yeast in phagocytic cells. Since increased capsule diameter is correlated with increased virulence it is essential to quickly and accurately quantify capsule measurements. This study conducted by the labs of Dr. David Nelson and Dr. Erin McClelland at Middle Tennessee State University along with Hoyin Lai (DRVISION) have illustrated capsule induction in Cryptococcus neoformans and developed computational methods to measure the capsule diameter.
The cells of C. neoformans increase their capsular diameter in response to diverse stressors so data on capsule size of different strains under varying growth conditions can provide important information about the pathogen and its responses to different stimuli. By measuring the linear diameter of the capsule the authors of the study quantified the capsule size using India ink combined with conventional light microscopy (a more cost-effective and widely used procedure), and with fluorescent dyes combined with confocal microscopy.
Transforming complex imaging data into quantitative information
After quantifying the capsule size by measuring the linear diameter of the capsule, the authors captured images of hundreds of C. neoformans cells with digital microscopes. So far researchers have calculated the whole body size (capsule and cell body) manually, which is both time-consuming and subject to human error. Since precise and reliable analysis is essential for extracting quantitative information from complex and large data sets, automated computational image analysis is crucial for researchers. However, measurement of the capsule in India Ink stained images has been difficult to automate with available tools and accurate identification of the interface between the cell wall and capsule has been difficult to resolve.
In this study, the authors used Aivia for automated measurement of capsule diameter from images of India Ink stained cells that produced results comparable to manual measurement methods. Aivia uses machine learning technology for image segmentation and object classification. The Colony Analysis and Cell Proliferation recipes were used by the authors to detect, segment, and measure 2D images of C. neoformans cells and capsule diameters, which was compared to manual measurements of the same data sets (Fig. 2).
The authors found that using Aivia for automated measurement of the capsule diameter was a viable alternative to manual measurement because it offered the researchers the benefit of reproducibility and eliminated much of the human variability that is associated with manual measurements along with time-saving opportunities (by at least 50%) at high degree of accuracy and efficiency.
The C. neoformans capsule remains an enigmatic structure, and the present study demonstrates an important computational approach for the automation of the measurement of a critical virulence factor that circumvents experimenter bias and improves reproducibility.
Guess, T., Lai, H., Smith, S. E., Sircy, L., Cunningham, K., Nelson, D. E., McClelland, E. E. Size Matters: Measurement of Capsule Diameter in Cryptococcus neoformans. J. Vis. Exp. (132), e57171, doi:10.3791/57171 (2018).