Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Abstract

A novel strategy for the rapid detection and identification of traditional and emerging Campylobacter strains based upon Raman spectroscopy (532 nm) is presented here. A total of 200 reference strains and clinical isolates of 11 different Campylobacter species recovered from infected animals and humans from China and North America were used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter identification to the species level and cross validated for its feasibility to predict Campylobacter -associated food-borne outbreaks. Bayesian probability coupled with Monte Carlo estimation was employed to validate the established Raman classification model on the basis of the selected principal components, mainly protein secondary structures, on the Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson index of diversity of 0.968. Intra- and interlaboratory reproducibility with different instrumentation yielded differentiation index values of 4.79 to 6.03 for wave numbers between 1,800 and 650 cm −1 and demonstrated the feasibility of using this spectroscopic method at different laboratories. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture (regression coefficient, >0.98; residual prediction deviation, >7.88). A standard protocol for sample preparation, spectral collection, model validation, and data analyses was established for the Raman spectroscopic technique. Raman spectroscopy may have advantages over traditional genotyping methods for bacterial epidemiology, such as detection speed and accuracy of identification to the species level.

Detection of Receptor-Induced Glycoprotein Conformational Changes on Enveloped Virions by Using Confocal Micro-Raman Spectroscopy

“…Spectral reproducibility was determined by calculating the differentiation index (D y1y2 ) value, as described in our previous studies (19,37 (19). Furthermore, spectra of both fresh NiV samples and frozen and then thawed NiV samples were collected (data not shown), and spectral reproducibility was suitable (D y1y2 value of Ͻ300).…”

“…The application of the Bayesian probability approach to construction of chemometric cluster-based models for infrared and Raman spectroscopy of bacteria has been extensively demonstrated by our group and others (37,46). Additionally, Monte Carlo estimation was performed to determine the stability of the cluster models created by the Bayesian probability approach.…”

“…In the current study, we employed Raman spectroscopy, principal component (PC) analysis (PCA), and chemometric methods to successfully discriminate glycoproteins (i.e., F and G) on the membrane of NiV VLPs or NiV/VSV pseudotyped virions (19,37). In addition, dynamic receptor-or temperature-induced conformational changes of these glycoproteins on NiV pseudovirions were monitored in situ by using a Raman spectroscopy-based twodimensional (2D) correlation synchronous plotting method.…”

Investigating the Responses of Cronobacter sakazakii to Garlic-Drived Organosulfur Compounds: a Systematic Study of Pathogenic-Bacterium Injury by Use of High-Throughput Whole-Transcriptome Sequencing and Confocal Micro-Raman Spectroscopy

“…The preprocessing of the raw Raman spectra can separate and subsequently eliminate the side effects which may influence the quality of spectroscope-based chemometric models and multivariate analyses. We first conducted a polynomial background fit (45) combined with baseline subtraction using identification and discrimination of minima via adaptive and least-squares thresholding (46) to remove fluorescence background derived from C. sakazakii cells on gold-coated microarray slides, Gaussian noise, white noise, CCD background noise, and cosmic spikes (47)(48)(49). Besides fluorescence background, most of the spectral interference is contributed by CCD background noise, which is generated due to the thermal fluctuations on the CCD detector.…”

“…Spectral binning (2 cm Ϫ1 ) and smoothing (9-point Savitzky-Golay algorithm) were subsequently applied, followed by normalization of the spectra based upon the intensity of the C-H band in the wave numbers of 3,100 to 2,950 cm Ϫ1 , the indication of the total biomass of C. sakazakii cells. Recent studies conducting Raman spectral normalization of C. jejuni (48) and E. coli and Staphylococcus spp. (50) validated that using this C-H vibrational band as the standard for normalization effectively removed the spectral fluctuation derived from the small focal volume of bacterial cells and yielded the best results for spectral baseline correction.…”