Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Flexible nanohybrid substrates utilizing gold nanocubes/nano mica platelets with 3D lightning-rod effect for highly efficient bacterial biosensors based on surface-enhanced Raman scattering

In this study, gold nanocubes (AuNCs) were quickly synthesized using the seed-mediated growth method and reduced onto the surface of two-dimensional (2D) delaminated nano mica platelets (NMPs), enabling the development of AuNCs/NMPs nanohybrids with a 3D lightning-rod effect. First, the growth-solution amount can be changed to easily adjust the AuNCs average-particle size within a range of 30-70 nm. The use of the cationic surfactant cetyltrimethylammonium chloride as a protective agent allowed the surface of AuNCs and nanohybrids to be positively charged. Positively charged nanohybrid surfaces presented a good adsorption effect for detecting molecules with negative charges on the surface. Additionally, the NMP surfaces were rich in ionic charges and provided a large specific surface area for stabilizing the growth of AuNCs. Delaminated AuNCs/NMPs nanohybrids can generate a 3D hotspot effect through self-assembly to enhance the Raman signal. Surface-enhanced Raman scattering (SERS) is highly sensitive in detecting adenine biomolecules. Its limit of detection (LOD) and Raman enhancement factor reached 10-9 M and 3.6 × 108, respectively. Excellent reproducibility was obtained owing to the relatively regular arrangement of AuNC particles, and the relative standard deviation (RSD) was 10.7%. Finally, the surface of NMPs was modified by adding the hydrophilic poly(oxyethylene)-diamine (POE2000) and amphiphilic PIB-POE-PIB copolymer at different weight ratios. The adjustment of the surface hydrophilicity and hydrophobicity of AuNCs/NMPs nanohybrids led to better adsorption and selectivity for bacteria. AuNCs/POE/NMPs and AuNCs/PIB-POE-PIB/NMPs were further applied to the SERS detection of hydrophilic Staphylococcus aureus and hydrophobic Escherichia coli, respectively. The SERS-detection results suggest that the LOD of hydrophilic Staphylococcus aureus and hydrophobic Escherichia coli reached 92 CFU mL-1 and 1.6 × 102 CFU mL-1, respectively. The AuNCs/POE/NMPs and AuNCs/PIB-POE-PIB/NMPs nanohybrids had different hydrophilic-hydrophobic affinities, which greatly improved the selectivity and sensitivity for detecting bacteria with different hydrophilicity and hydrophobicity. Therefore, fast, highly selective, and highly sensitive SERS biological-detection results were obtained.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.

Cultureless enumeration of live bacteria in urinary tract infection by single-cell Raman spectroscopy

Urinary tract infections (UTIs) are the most common outpatient infections. Obtaining the concentration of live pathogens in the sample is crucial for the treatment. Still, the enumeration depends on urine culture and plate counting, which requires days of turn-around time (TAT). Single-cell Raman spectra combined with deuterium isotope probing (Raman-DIP) has been proven to identify the metabolic-active bacteria with high accuracy but is not able to reveal the number of live pathogens due to bacteria replication during the Raman-DIP process. In this study, we established a new approach of using sodium acetate to inhibit the replication of the pathogen and applying Raman-DIP to identify the active single cells. By combining microscopic image stitching and recognition, we could further improve the efficiency of the new method. Validation of the new method on nine artificial urine samples indicated that the exact number of live pathogens obtained with Raman-DIP is consistent with plate-counting while shortening the TAT from 18 h to within 3 h, and the potential of applying Raman-DIP for pathogen enumeration in clinics is promising.