Optical Fiber-Based Steady State and Fluorescence Lifetime Spectroscopy for Rapid Identification and Classification of Bacterial Pathogens Directly from Colonies on Agar Plates

Fluorescence Spectroscopy Based Characterization of Pseudomonas Aeruginosa Suspension

In this article, optical characterization of Pseudomonas aeruginosa (PA) suspension has been performed by using Fluorescence spectroscopy. Optical density (OD) and plate count methods have been employed as a reference for the analysis of emission spectra of Pseudomonas aeruginosa in water suspension. Emission spectra of PA suspension has been acquired by using excitation wavelengths from 270 to 420 nm with step of 10 nm to explore its spectral behavior. It has been found that emission spectra of tryptophan, tyrosine, NADH and FAD, the intracellular biomolecules of bacteria, can be used as finger prints for the detection of Pseudomonas aeruginosa. Furthermore, the effect of water matrix on the spectral emission of Pseudomonas aeruginosa has been investigated that might be one of the limitation of Fluorescence spectroscopy for complex water matrices. Moreover, a calibration curve has been produced between ODs600 of Pseudomonas aeruginosa suspensions of different bacterial load and integrated intensities of the emission spectra of same samples. These ODs600 and integrating intensities have been further vetted through plate count method by determining their corresponding colony forming units per ml (CFU/ml). This calibration curve may be used to determine CFU/ml of Pseudomonas aeruginosa in water sample by determining integrating intensity of its emission spectrum.

Characterization and classification of pathogenic bacteria using native fluorescence and spectral deconvolution

Identification and classification of pathogenic bacterial strains is of current interest for the early treatment of diseases. In this work, protein fluorescence from eight different pathogenic bacterial strains were characterized using steady state and time resolved fluorescence spectroscopy. The spectral deconvolution method was also employed to decompose the emission contribution from different intrinsic fluorophores and extracted various key parameters, such as intensity, emission maxima, emission line width of the fluorophores, and optical redox ratio. The change in average lifetime values across different bacterial strains exhibits good statistical significance (p ≤ 0.01). The variations in the photophysical characteristics of bacterial strains are due to the different conformational states of the proteins. The stepwise multiple linear discriminate analysis of fluorescence emission spectra at 280 nm excitation across eight different bacterial strains classifies the original groups and cross validated group with 100% and 99.5% accuracy, respectively.

A Clinical Study to Evaluate Autofluorescence Imaging of Diabetic Foot Ulcers Using a Novel Artificial Intelligence Enabled Noninvasive Device

Diabetic foot ulcers, with worldwide prevalence ranging from 12%-25%, are an important cause of nontraumatic lower limb amputation. Evidence-based assessment of early infection can help the clinician provide the right first line treatment thus helping improve the wound closure rate. Illuminate®, a novel point of care device working on multispectral autofluorescence imaging, helps in the rapid identification and classification of bacteria. This study was aimed to evaluate the diagnostic accuracy of the device in detecting bacterial gram type against standard culture methods. A total of 178 patients from a tertiary care center for diabetes was recruited and 203 tissue samples were obtained from the wound base by the plastic surgeon. The device was handled by the trained investigator to take wound images. The tissue samples were taken from the color-coded infected region as indicated by the device's Artificial Intelligence algorithm and sent for microbial assessment. The results were compared against the Gram type inferred by the device and the device was found to have an accuracy of 89.54%, a positive predictive value of 86.27% for detecting Gram-positive bacteria, 80.77% for Gram-negative bacteria, and 91.67% for no infection. The negative predictive value corresponded to 87.25% for Gram-positive, 92% for Gram-negative, and 96.12% for no infection. The Results exhibited the accuracy of this novel autofluorescence device in identifying and classifying the gram type of bacteria and its potential in significantly aiding clinicians towards early infection assessment and treatment.

Fluorescence Spectroscopy Based Identification of Pseudomonas Aeruginosa and Escherichia Coli Suspensions

In this article, Fluorescence spectroscopy has been employed for the identification of Pseudomonas aeruginosa (PA) and Escherichia coli (E. coli) in water suspension. Emission spectra of PA and E. coli suspensions have been acquired by using excitation wavelengths from 270 to 420 nm with steps of 10 nm to explore their spectral features. It has been found that the emission spectra of tryptophan, tyrosine, NADH and FAD, being the intracellular biomolecules present in both bacteria, can be used as fingerprints for their identification, differentiation and quantification. Both bacterial strains can clearly be differentiated from water and from each other by using λex 270-290 nm through spectral analysis and from λex: 300-500 nm by applying statistical analysis. Furthermore, calibration curves for different bacterial loads of PA and E. coli suspensions have been produced between colonies forming units per ml (CFUs/ml) the integrated intensities of their emission spectra. CFUs/ml of both bacterial suspensions have been determined through plate count method which was used as cross-reference for the analysis of emission spectra of both bacterial suspensions. These curves may be used to estimate CFU/ml of both PA and E. coli in unknown water suspensions by determining the integrating intensity of their emission spectra.

Fluorescence Spectroscopy for the Assessment of Microbial Load in UVC Treated Water

In this article, Fluorescence spectroscopy has been employed for the assessment of microbial load and it has been compared with the gold standard colony forming unit (CFU) and optical density (OD) methods. In order to develop a correlation between three characterization techniques, water samples of different microbial loads have been prepared by UVC disinfection method through an indigenously developed NUVWater sterilizer, which operates in close cycle flow configuration. A UV dose of 58.9 mJ/cm2 has been determined for 99.99% disinfection for a flow rate of 0.8 l/min. The water samples were excited at 270 nm which results in the tryptophan like fluorescence at 360 nm that decreases gradually with increase of UVC dose, indicating the bacterial degradation and it has been confirmed by OD and CFU methods. In addition, it has been proved that a close cycle water flow around UV lamp is imperative so that an appropriate dose must be delivered to microorganisms for an efficient disinfection. It has been found that due to the sensitive nature of Fluorescence spectroscopy, it yields immediate results, whereas, for CFU and OD methods, water samples needs to be inoculated for 24 h. Fluorescence spectroscopy, therefore, provide a fast, online, reliable and sensitive method for the monitoring of pathogenic quantification in drinking water.

Metabolic fingerprinting of bacteria by fluorescence lifetime imaging microscopy

Bacterial populations exhibit a range of metabolic states influenced by their environment, intra- and interspecies interactions. The identification of bacterial metabolic states and transitions between them in their native environment promises to elucidate community behavior and stochastic processes, such as antibiotic resistance acquisition. In this work, we employ two-photon fluorescence lifetime imaging microscopy (FLIM) to create a metabolic fingerprint of individual bacteria and populations. FLIM of autofluorescent reduced nicotinamide adenine dinucleotide (phosphate), NAD(P)H, has been previously exploited for label-free metabolic imaging of mammalian cells. However, NAD(P)H FLIM has not been established as a metabolic proxy in bacteria. Applying the phasor approach, we create FLIM-phasor maps of Escherichia coli, Salmonella enterica serovar Typhimurium, Pseudomonas aeruginosa, Bacillus subtilis, and Staphylococcus epidermidis at the single cell and population levels. The bacterial phasor is sensitive to environmental conditions such as antibiotic exposure and growth phase, suggesting that observed shifts in the phasor are representative of metabolic changes within the cells. The FLIM-phasor approach represents a powerful, non-invasive imaging technique to study bacterial metabolism in situ and could provide unique insights into bacterial community behavior, pathology and antibiotic resistance with sub-cellular resolution.