Kendra Liu, Narine Arabyan, Nguyet Kong, Marie-Pierre Forquin Gomez, and Bart C. Weimer Department of Population Health and Reproduction School of Veterinary Medicine, University of California – Davis, CA, USA ACKNOWLEDGMENTS I am grateful to Dr. Weimer for his guidance and helpful discussions. I also acknowledge and appreciate all the help and support received from everyone in the Weimer Lab. CONCLUSION Alternative method to quantify bacterial and eukaryotic cells. Numbers determined by this new method were similar to those obtained from traditional methods of plating the cells for bacteria or hemocytometer for eukaryotic cells. The physiological state of the cells were differentiated into live and dead cells with the dyes SYTO62 and SYTOX, respectively. INTRODUCTION Quantification and detection of the physiological state of the cells is fundamental to microbiological studies. Traditional methods of bacterial enumeration and detection of physiological state via microscopic observations are labor and time intensive (Ikeda et al. 2009). Using traditional culturing methods, compromised or dead cells would not be able to be enumerated. Therefore, a new, fast, and accurate method of cell enumeration and detection of physiological state of the cell is necessary to develop. This new method needs to be culture independent. One suitable way is the use of fluorescent dyes to stain the cells and flow cytometry. This instrument uses two-color fluorescence detection. The red laser diode has an excitation range of 620-645nm with max emission of 630nm and detection range of 674-696nm (Sakamoto et al. 2005). The blue LED has an excitation range of 458-482nm with max emission of 470nm and detection range of 510-540nm (Sakamoto et al. 2005). Microchip based analysis has many advantages over the conventional methods (Müller & Nebe‐von‐Caron 2010). This provides a rapid, sensitive, and reliable quantification of individual cells. The analysis is small scale and is completed in a shorter time period. The assay takes only thirty minutes to run six samples and it counts 2500 cells in four minutes (Sakamoto et al. 2005). The consumption of the samples and reagents are low. Lastly, preparation of samples is simple and the analysis is performed with high reproducibility. The aim of this investigation was to develop an assay that will quantify and detect the physiological state of the cells: Madin- Darby Canine Kidney (MDCK) epithelial cell line and Salmonella enterica Typhimurium ST14028 using Agilent 2100 Bioanalyzer System on-chip flow cytometry. ABSTRACT This study describes the use of cell fluorescence assays on the Agilent 2100 Bioanalyzer System. The Agilent 2100 Bioanalyzer System is the first commercially available instrument capable of measuring cell fluorescence for cell sorting. This instrument uses two- color fluorescence detection—the red laser diode and blue LED. The cell assay makes use of microfluidic chip- based flow cytometry to quantify and detect the physiological state of the cells. Salmonella enterica spp. enterica serovar Typhimurium ST14028 and Madin-Darby Canine Kidney (MDCK) epithelial cells were quantified using the Agilent 2100 Bioanalyzer System . The physiological states of the cells were detected using SYTO62 and SYTOX dyes for live and dead cells, respectively. Numbers determined by this method were similar to those obtained by CFU counts from plates and microscopic count using a hemocytometer, respectively. With low cell and reagent consumption and running up to six samples at a time, data analysis using the software on the Agilent 2100 Bioanalyzer is quick and simple. . EXPERIMENTAL APPROACH RESULTS Figure 1. Detection of live bacterial cells with SYTO62 red fluorescent dye. 10μM is the optimum concentration of SYTO62 to detect live bacterial cells. Figure 3. Optimization of SYTOX green fluorescent dye. 5μM is the optimum concentration of SYTOX to detect dead bacterial cells. Grow cells to mid- exponential phase Development of an assay to quantify and detect the physiological state of the cells CONTACT INFORMATION Bart C. Weimer, Ph.D. ([email protected] ) Narine Arabyan ([email protected] ) Kendra Liu ([email protected] ) UC Davis (VM:PHR) VetMed3B – Room 4016 1089 Veterinary Medicine Dr. Davis, CA 95616 (530) 752-6426 http://weimermicrolab.wix.com/thelab REFERENCES Ikeda, M., Yamaguchi, Nobuyasu & Nasu, Masao, 2009. Rapid On-chip flow Cytometric Detection of Listeria monocytogenes in Milk. Journal of Health Science, 55(5), pp.851-856. Johnson, S., Nguyen, V. & Coder, D., 2001 Assessment of Cell Viability. Current Protocols in Cytometry. Müller, S. & Nebe-von-Caron, G., 2010. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiology Reviews, 34(4), pp.554- 587. Sakamoto, C., Yamaguchi, N. & Nasu, M., 2005. Rapid and Simple Quantification of Bacterial Cells by Using a Microfluidic Device. Applied and Environmental Microbiology, 71(2), pp.1117- 1121. Figure 4. Quantification of dead bacterial cells. Figure 7. Differentiation of live and dead MDCK cells when new and old/overgrown MDCK cells were used with both 1 μM SYTOX and 2 μM SYTO62. Figure 8. Quantification of MDCK cells. Bacterial cells Salmonella enterica Typhimurium ST14028 Grow cells for 14 hours Eukaryotic cells Madin-Darby Canine Kidney epithelial cells Incubate with SYTO62 / SYTOX for 1 hour at room temperature in the dark Count cells using hemocytometer Grow cells for 24 hours 30 minutes for 6 samples Dilute cells to 10 6 cell/mL Live Cells: SYTO62 Dead Cells: SYTOX Load onto the cell chip Calculate number of cells from event number Red laser diode Blue LED Detection of Live Bacterial Cells With SYTO62 Detection of Dead Bacterial Cells With SYTOX Figure 2. Quantification of live cells from event numbers. Detection of Live and Dead MDCK Cells With Both SYTO62 and SYTOX Detection of Live and Dead Bacterial Cells With Both SYTO62 and SYTOX Figure 5. Detection of Live and Dead cells with 10 μM SYTO62 and 5 μM SYTOX. Figure 6. Quantification of Live and Dead cells with 10 μM SYTO62 and 5 μM SYTOX. FUTURE DIRECTIONS Identify the different populations of cells during Salmonella infection