Abstract

Identification and classification of microbes are vital for maintenance of normal and altered state of human health and have applications in pharmaceutical industries, food processing, clinical analysis, and treatment. Development of methods aimed towards achieving these goals must be rapid and reliable. Conventional physiochemical and morphology-based methods of identification are often ambiguous, while newer molecular methods such as flow cytometry and polymerase chain reaction, though reliable, are time and resource intensive. Spectroscopic methods provide advantages over conventional methods as these can be fast, non-destructive, and highly specific. Surface charge of bacteria is an important parameter which can reveal composition of cell wall and is attributed to the presence of carboxyl and phosphoryl groups. Interaction of the cell with the solvent and response to various stresses can hence be measured by the changes in surface charge. In this study, we have obtained auto-fluorescence spectra (tryptophan) and dynamic light scattering (DLS) measurements from common pathogenic strains of Pseudomonas aeruginosa and Staphylococcus aureus. Fluorescence emission spectra were obtained in the range of 300–550 nm at excitation wavelength of 280 nm and DLS measurements comprised zeta potential and size parameters. Both types of measurements were performed in physiological and stress-induced conditions such as heat, sonication, and antibiotic treatment with vancomycin and cetylpyridinium chloride. Effects of these antibiotics on membrane integrity and cell viability, as obtained by DLS measurements, were statistically significant and comparable with conventional methods. Multivariate analysis enabled clustering of 83% of the samples at the genera level, based on variances from auto-fluorescence and DLS measurements.

Original languageEnglish
JournalLasers in Medical Science
DOIs
Publication statusAccepted/In press - 01-01-2019

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Fluorescence
Cetylpyridinium
Anti-Bacterial Agents
Physiological Stress
Food Handling
Sonication
Drug Industry
Vancomycin
Cell Communication
Tryptophan
Cell Wall
Pseudomonas aeruginosa
Cluster Analysis
Staphylococcus aureus
Cell Survival
Flow Cytometry
Multivariate Analysis
Hot Temperature
Maintenance
Bacteria

All Science Journal Classification (ASJC) codes

  • Surgery
  • Dermatology

Cite this

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abstract = "Identification and classification of microbes are vital for maintenance of normal and altered state of human health and have applications in pharmaceutical industries, food processing, clinical analysis, and treatment. Development of methods aimed towards achieving these goals must be rapid and reliable. Conventional physiochemical and morphology-based methods of identification are often ambiguous, while newer molecular methods such as flow cytometry and polymerase chain reaction, though reliable, are time and resource intensive. Spectroscopic methods provide advantages over conventional methods as these can be fast, non-destructive, and highly specific. Surface charge of bacteria is an important parameter which can reveal composition of cell wall and is attributed to the presence of carboxyl and phosphoryl groups. Interaction of the cell with the solvent and response to various stresses can hence be measured by the changes in surface charge. In this study, we have obtained auto-fluorescence spectra (tryptophan) and dynamic light scattering (DLS) measurements from common pathogenic strains of Pseudomonas aeruginosa and Staphylococcus aureus. Fluorescence emission spectra were obtained in the range of 300–550 nm at excitation wavelength of 280 nm and DLS measurements comprised zeta potential and size parameters. Both types of measurements were performed in physiological and stress-induced conditions such as heat, sonication, and antibiotic treatment with vancomycin and cetylpyridinium chloride. Effects of these antibiotics on membrane integrity and cell viability, as obtained by DLS measurements, were statistically significant and comparable with conventional methods. Multivariate analysis enabled clustering of 83{\%} of the samples at the genera level, based on variances from auto-fluorescence and DLS measurements.",
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