M Ogawa1, K Tani, A Ochiai, N Yamaguchi, M Nasu. 1. Environmental Science and Microbiology, Graduate School of Pharmaceutical Sciences, Osaka University, Osaka, Japan.
Abstract
AIMS: To develop a rapid and simple multicolour digital image analysis system for simultaneous identification of bacteria and assessment of their metabolic activity. METHODS AND RESULTS: We developed an image analyser capable of distinguishing triple-stained bacterial cells. Bacteria were stained with a nucleic acid stain, a fluorescent antibody and a fluorescent metabolic indicator for enumeration, species identification and assessment of metabolic activity. This multicolour image analyser was used to simultaneously identify Escherichia coli O157:H7 in milk samples and assess their respiratory activity. The images of the triple-stained bacteria were captured using a combination of blue light and u.v. excitation and an epifluorescence microscope and were processed by our image analyser. We found a good correlation between the counts of actively respiring (r = 0.93) and total (r = 0.94) E. coli O157:H7 measured by digital image analysis and visual observation. CONCLUSION: The multicolour digital image analysis system described here was able to quantify active pathogenic micro-organisms within 2 h. SIGNIFICANCE AND IMPACT OF THE STUDY: This multicolour image analysis allows the rapid and simultaneous quantification of bacteria, identification of species and assessment of metabolic activity.
AIMS: To develop a rapid and simple multicolour digital image analysis system for simultaneous identification of bacteria and assessment of their metabolic activity. METHODS AND RESULTS: We developed an image analyser capable of distinguishing triple-stained bacterial cells. Bacteria were stained with a nucleic acid stain, a fluorescent antibody and a fluorescent metabolic indicator for enumeration, species identification and assessment of metabolic activity. This multicolour image analyser was used to simultaneously identify Escherichia coli O157:H7 in milk samples and assess their respiratory activity. The images of the triple-stained bacteria were captured using a combination of blue light and u.v. excitation and an epifluorescence microscope and were processed by our image analyser. We found a good correlation between the counts of actively respiring (r = 0.93) and total (r = 0.94) E. coli O157:H7 measured by digital image analysis and visual observation. CONCLUSION: The multicolour digital image analysis system described here was able to quantify active pathogenic micro-organisms within 2 h. SIGNIFICANCE AND IMPACT OF THE STUDY: This multicolour image analysis allows the rapid and simultaneous quantification of bacteria, identification of species and assessment of metabolic activity.