use-image-cytometry-for-quantification-pathogenic-fungi-association

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 ...

longitudinal-morphological-physiological-monitoring-three-dimensional

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Tumor spheroids have been developed as a three-dimensional (3D) cell culture model in cancer research and anti-cancer drug discovery. However, currently, ...

Merrimack College

Nexcelom Bioscience LLC

To ensure cell-based assays are performed properly, both cell concentration and viability have to be determined so that the data can be normalized to generate meaningful and comparable results. Cell-based assays performed in immuno-oncology, toxicology, or bioprocessing research often require measuring of multiple samples and conditions, thus the current automated cell counter that uses single disposable counting slides is not practical for high-throughput screening assays. In the recent years, a plate-based image cytometry system has been developed for high-throughput biomolecular screening assays. In this work, we demonstrate a high-throughput AO/PI-based cell concentration and viability method using the Celigo image cytometer. First, we validate the method by comparing directly to Cellometer automated cell counter. Next, cell concentration dynamic range, viability dynamic range, and consistency are determined. The high-throughput AO/PI method described here allows for 96-well to 384-well plate samples to be analyzed in less than 7 min, which greatly reduces the time required for the single sample-based automated cell counter. In addition, this method can improve the efficiency for high-throughput screening assays, where multiple cell counts and viability measurements are needed prior to performing assays such as flow cytometry, ELISA, or simply plating cells for cell culture. 

A high-throughput AO/PI-based cell concentration and viability detection method using the Celigo image cytometry.

Brettanomyces spp. can present unique cell morphologies comprised of excessive pseudohyphae and budding, leading to difficulties in enumerating cells. The current cell counting methods include manual counting of methylene blue-stained yeasts or measuring optical densities using a spectrophotometer. However, manual counting can be time-consuming and has high operator-dependent variations due to subjectivity. Optical density measurement can also introduce uncertainties where instead of individual cells counted, an average of a cell population is measured. In contrast, by utilizing the fluorescence capability of an image cytometer to detect acridine orange and propidium iodide viability dyes, individual cell nuclei can be counted directly in the pseudohyphae chains, which can improve the accuracy and efficiency of cell counting, as well as eliminating the subjectivity from manual counting. In this work, two experiments were performed to demonstrate the capability of Cellometer image cytometer to monitor Brettanomyces concentrations, viabilities, and budding/pseudohyphae percentages. First, a yeast propagation experiment was conducted to optimize software counting parameters for monitoring the growth of Brettanomyces clausenii, Brettanomyces bruxellensis, and Brettanomyces lambicus, which showed increasing cell concentrations, and varying pseudohyphae percentages. The pseudohyphae formed during propagation were counted either as multiple nuclei or a single multi-nuclei organism, where the results of counting the yeast as a single multi-nuclei organism were directly compared to manual counting. Second, a yeast fermentation experiment was conducted to demonstrate that the proposed image cytometric analysis method can monitor the growth pattern of B. lambicus and B. clausenii during beer fermentation. The results from both experiments displayed different growth patterns, viability, and budding/pseudohyphae percentages for each Brettanomyces species. The proposed Cellometer image cytometry method can improve efficiency and eliminate operator-dependent variations of cell counting compared with the traditional methods, which can potentially improve the quality of beverage products employing Brettanomyces yeasts.

A novel concentration and viability detection method for Brettanomyces using the Cellometer image cytometry.

The ability to accurately measure cell viability is important for any cell-based assay. Traditionally, viability measurements have been performed using the trypan blue exclusion method on a hemacytometer, which allows researchers to visually distinguish viable from nonviable cells. While the trypan blue method can work for cell lines or primary cells that have been rigorously purified, in more complex samples such as PBMCs, bone marrow, whole blood, or any sample with low viability, this method can lead to errors. In recent years, advances in optics and fluorescent dyes have led to the development of automated benchtop image-based cell counters for rapid cell concentration and viability measurement. In this work, we demonstrate the use of image-based cytometry for cell viability detection using single-, dual-, or multi-stain techniques. Single-staining methods using nucleic acid stains such as EB, PI, 7-AAD, DAPI, SYTOX Green, and SYTOX Red, and enzymatic stains such as CFDA and Calcein AM, were performed. Dual-staining methods using AO/PI, CFDA/PI, Calcein AM/PI, Hoechst/PI, Hoechst/DRAQ7, and DRAQ5/DAPI that enumerate viable and nonviable cells were also performed. Finally, Hoechst/Calcein AM/PI was used for a multi-staining method. Fluorescent viability staining allows exclusion of cellular debris and nonnucleated cells from analysis, which can eliminate the need to perform purification steps during sample preparation and improve efficiency. Image cytometers increase speed and throughput, capture images for visual confirmation of results, and can greatly simplify cell count and viability measurements.

Leo Li-Ying Chan

Center for Biotechnology and Biomedical Sciences

Merrimack College

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography