Automated Measurement of <em>Cryptococcal</em> Species Polysaccharide Capsule and Cell Body

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.

Cryptococcus neoformans capsule regrowth experiments reveal dynamics of enlargement and architecture

“…Capsule measurements were produced using the exclusion zone produced with India Ink and the Quantitative Capture Analysis program developed by the lab (31). A minimum of 100 yeast cells were measured for each strain and condition.…”

“…For each of the three methods we measured: (i) reduction in capsule radius (ii) cell survival (iii) freed polysaccharide as measured by capture ELISA and Phenol Sulfuric Acid (PSA) assays and (iv) size of polymers in isolated CPS. Cells were analyzed for capsule-size reduction using Quantitative Capsule Analysis (QCA), a computer program developed in our laboratory that scans microscopic images to provide capsule dimension information (31). All three capsule removal methods produced a significant reduction in capsule size (Figure 2a), but DMSO killed the cells while both sonication and glucanex digestion were significantly less toxic, reducing viability to ~90% and ~60%, respectively (Figure 2b).…”

Bet-hedging antimicrobial strategies in macrophage phagosome acidification drive the dynamics ofCryptococcus neoformansintracellular escape mechanisms

“…Slides were prepared using 8 μL of cell mixture and 1.5 μL India ink, then imaged on an Olympus AX70 at 20x objective. Capsules and cell bodies were measured using a previously published measuring program 18 .…”

“…Capsule and cell body size of C. neoformans isolated 24 hours after ingestion by BMDMs. Capsules and cell bodies are measured by preparing and imaging India Ink slides and a previously published 18 measuring code. No significant differences were found between the polarization states of host macrophages.…”

The Buoyancy of Cryptococcus neoformans Is Affected by Capsule Size

“…The cells were visualized and imaged with India ink negative staining under an Olympus AX70 microscope at ×20 magnification and ×40 magnification. The capsule and cell body sizes were estimated using automated measurement Python software ( 59 ) or by the use of ImageJ when cells were observed to be aggregated.…”