Use of image cytometry for quantification of pathogenic fungi in association with host cells.

Studies of the cellular pathogenesis mechanisms of pathogenic yeasts such as Candida albicans, Histoplasma capsulatum, and Cryptococcus neoformans commonly employ infection of mammalian hosts or host cells (i.e. macrophages) followed by yeast quantification using colony forming unit analysis or flow cytometry. While colony forming unit enumeration has been the most commonly used method in the field, this technique has disadvantages and limitations, including slow growth of some fungal species on solid media and low and/or variable plating efficiencies, which is of particular concern when comparing growth of wild-type and mutant strains. Flow cytometry can provide rapid quantitative information regarding yeast viability, however, adoption of flow cytometric detection for pathogenic yeasts has been limited for a number of practical reasons including its high cost and biosafety considerations. Here, we demonstrate an image-based cytometric methodology using the Cellometer Vision (Nexcelom Bioscience, LLC) for the quantification of viable pathogenic yeasts in co-culture with macrophages. Our studies focus on detection of two human fungal pathogens: Histoplasma capsulatum and Candida albicans. H. capsulatum colonizes alveolar macrophages by replicating within the macrophage phagosome, and here, we quantitatively assess the growth of H. capsulatum yeasts in RAW 264.7 macrophages using acridine orange/propidium iodide staining in combination with image cytometry. Our method faithfully recapitulates growth trends as measured by traditional colony forming unit enumeration, but with significantly increased sensitivity. Additionally, we directly assess infection of live macrophages with a GFP-expressing strain of C. albicans. Our methodology offers a rapid, accurate, and economical means for detection and quantification of important human fungal pathogens in association with host cells.

Rapid quantification of pathogenic fungi by Cellometer image-based cytometry.

The objective of this study was to develop an image-based cytometric methodology for the quantification of viable pathogenic yeasts, which can offer increased sensitivity and efficiency when compared to the traditional colony forming unit (CFU) assay. Live/dead yeast quantification by flow cytometry has been previously demonstrated, however, adoption of flow cytometric detection of pathogenic yeasts has been limited for a number of practical reasons including its high cost and biosafety considerations. Our studies focus on detection of two human fungal pathogens: Histoplasma capsulatum and Candida albicans. H. capsulatum colonizes alveolar macrophages by replicating within the macrophage phagosome. Here, we quantitatively assess the growth of H. capsulatum yeasts within RAW 264.7 macrophages using acridine orange/propidium iodide staining in combination with Cellometer image-based cytometry; this method faithfully recapitulates growth trends as measured by traditional CFU enumeration, but with significantly increased sensitivity. Additionally, we directly assess infection of bone marrow-derived macrophages with a GFP-expressing strain of C. albicans. To demonstrate that image-based cytometry can be used as a tool to assess the susceptibility of fungi to antifungal drugs, we perform dose response experiments with the antifungal drugs amphotericin B and itraconazole and show that image-based cytometry allows rapid assessment of the kinetics of cytotoxicity induced by these antifungals. Our methodology offers a rapid, accurate, and economical means for detection and quantification of important human fungal pathogens, either alone or in association with host cells.

Novel image cytometric method for detection of physiological and metabolic changes in Saccharomyces cerevisiae.

The studying and monitoring of physiological and metabolic changes in Saccharomyces cerevisiae (S. cerevisiae) has been a key research area for the brewing, baking, and biofuels industries, which rely on these economically important yeasts to produce their products. Specifically for breweries, physiological and metabolic parameters such as viability, vitality, glycogen, neutral lipid, and trehalose content can be measured to better understand the status of S. cerevisiae during fermentation. Traditionally, these physiological and metabolic changes can be qualitatively observed using fluorescence microscopy or flow cytometry for quantitative fluorescence analysis of fluorescently labeled cellular components associated with each parameter. However, both methods pose known challenges to the end-users. Specifically, conventional fluorescent microscopes lack automation and fluorescence analysis capabilities to quantitatively analyze large numbers of cells. Although flow cytometry is suitable for quantitative analysis of tens of thousands of fluorescently labeled cells, the instruments require a considerable amount of maintenance, highly trained technicians, and the system is relatively expensive to both purchase and maintain. In this work, we demonstrate the first use of Cellometer Vision for the kinetic detection and analysis of vitality, glycogen, neutral lipid, and trehalose content of S. cerevisiae. This method provides an important research tool for large and small breweries to study and monitor these physiological behaviors during production, which can improve fermentation conditions to produce consistent and higher-quality products.

A novel image-based cytometry method for autophagy detection in living cells.

Autophagy is an important cellular catabolic process that plays a variety of important roles, including maintenance of the amino acid pool during starvation, recycling of damaged proteins and organelles, and clearance of intracellular microbes. Currently employed autophagy detection methods include fluorescence microscopy, biochemical measurement, SDS-PAGE and western blotting, but they are time consuming, labor intensive, and require much experience for accurate interpretation. More recently, development of novel fluorescent probes have allowed the investigation of autophagy via standard flow cytometry. However, flow cytometers remain relatively expensive and require a considerable amount of maintenance. Previously, image-based cytometry has been shown to perform automated fluorescence-based cellular analysis comparable to flow cytometry. In this study, we developed a novel method using the Cellometer image-based cytometer in combination with Cyto-ID(®) Green dye for autophagy detection in live cells. The method is compared with flow cytometry by measuring macroautophagy in nutrient-starved Jurkat cells. Results demonstrate similar trends of autophagic response, but different magnitude of fluorescence signal increases, which may arise from different analysis approaches characteristic of the two instrument platforms. The possibility of using this method for drug discovery applications is also demonstrated through the measurement of dose-response kinetics upon induction of autophagy with rapamycin and tamoxifen. The described image-based cytometry/fluorescent dye method should serve as a useful addition to the current arsenal of techniques available in support of autophagy-based drug discovery relating to various pathological disorders.

Rapid image-based cytometry for comparison of fluorescent viability staining methods.

The ability to accurately measure cell viability is important for any cell-based research. Traditionally, viability measurements have been performed using trypan blue exclusion method on hemacytometer, which allowed researchers to visually distinguish viable from nonviable cells. However, the trypan blue method is often limited to only cell lines or primary cells that have been rigorously purified. In the recent years, small desktop image-based cell counters have been developed for rapid cell concentration and viability measurement due to advances in imaging and optics technologies as well as novel fluorescent stains. In this work, we employed the Cellometer image-based cytometer to demonstrate the ability to simplify viability detection compared to the current methods. We compared various fluorescence viability detection methods using single- or dual-staining technique. Single-staining method using nucleic acid stains including ethidium bromide, propidium iodide, 7AAD, DAPI, Sytox Green and Sytox Red, and enzymatic stains including CFDA and Calcein AM were performed. All stains produced comparable results to trypan blue exclusion method for cell line samples. Dual-staining method using AO/PI, CFDA/PI, Calcein AM/PI and Hoechst 33342/PI that enumerates viable and non-viable cells was tested on primary cell samples with high debris contents. This method allowed exclusion of cellular debris and non-nucleated cells from analysis, which can eliminate the need to perform purification step during sample preparation, and improves the efficiency of viability detection method. Overall, these image-based fluorescent cell counters can simplify assay procedures as well as capture images for visual confirmation.