Rapid Detection of Clostridium difficile Toxins in Stool by Raman Spectroscopy

An integrated computational pipeline for machine learning-driven diagnosis based on Raman spectra of saliva samples

Raman Spectroscopy promises the ability to encode in spectral data the significant differences between biological samples belonging to patients affected by a disease and samples of healthy patients (controls). However, the decoding and interpretation of the Raman spectral fingerprint is still a difficult and time-consuming procedure even for domain experts. In this work, we test an end-to-end deep-learning diagnostic pipeline able to classify spectral data from saliva samples. The pipeline has been validated against the SARS-COV-2 Infection and for the screening of neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. The proposed system can be used for the fast prototyping of promising non-invasive, cost and time-efficient diagnostic screening tests.

Rapid discrimination between clinical Clostridioides difficile infection and colonization by quantitative detection of TcdB toxin using a real-time cell analysis system

Objectives

It is important to accurately discriminate between clinical Clostridioides difficile infection (CDI) and colonization (CDC) for effective antimicrobial treatment.

Methods

In this study, 37 stool samples were collected from 17 CDC and 20 CDI cases, and each sample were tested in parallel through the real-time cell analysis (RTCA) system, real-time PCR assay (PCR), and enzyme-linked immunosorbent assay (ELISA).

Results

RTCA-measured functional and toxical C. difficile toxin B (TcdB) concentrations in the CDI group (302.58 ± 119.15 ng/mL) were significantly higher than those in the CDC group (18.15 ± 11.81 ng/mL) (p = 0.0008). Conversely, ELISA results revealed no significant disparities in TcdB concentrations between the CDC (26.21 ± 3.57 ng/mL) and the CDI group (17.07 ± 3.10 ng/mL) (p = 0.064). PCR results indicated no significant differences in tcdB gene copies between the CDC (774.54 ± 357.89 copies/μL) and the CDI group (4,667.69 ± 3,069.87 copies/μL) (p = 0.407). Additionally, the functional and toxical TcdB concentrations secreted from C. difficile isolates were measured by the RTCA. The results from the CDC (490.00 ± 133.29 ng/mL) and the CDI group (439.82 ± 114.66 ng/mL) showed no significant difference (p = 0.448). Notably, RTCA-measured functional and toxical TcdB concentration was significantly decreased when mixed with pooled CDC samples supernatant (p = 0.030).

Conclusion

This study explored the novel application of the RTCA assay in effectively discerning clinical CDI from CDC cases.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

Development and clinical application of a rapid, visually interpretable polymerase spiral reaction for tcdB gene of Clostridioides difficile in fecal cultures

In the surveillance of outbreaks of Clostridioides difficile infection, the rapid detection and diagnosis of C. difficile remain a major challenge. Polymerase spiral reaction (PSR) is a nucleic acid amplification technique that uses mixed primers and the strand displacement activity of Bst DNA polymerase to achieve a pair of primers and a single enzyme in an isothermal environment. The primer design is simple, the reaction is efficient, and a color indicator can be used to visualize the result. In this study, we developed a rapid and visually interpretable PSR to detect C. difficile by analyzing artificially contaminated feces samples and clinical isolates from patient feces samples. We designed two pairs of primers for a PSR that specifically targeted the conserved tcdB gene of C. difficile. The amplification results were visualized with the chromogenic dye hydroxynaphthol blue. The entire process was accomplished in 50 min at 64°C, with high specificity. The limit of detection of C. difficile with PSR was 150 fg/μl genomic DNA or 2 × 10 CFU/ml in artificially contaminated feces samples. With this method, we analyzed four clinical isolates and also compared the PSR with an isolation-and-culture detection method, polymerase chain reaction, and the Sanger sequencing. The four clinical isolates were found positive for tcdB, which confirmed the high specificity of the primers. The positive rates of tcdB in toxigenic C. difficile detected with PSR, PCR, and Sanger sequencing were 100%. The proportions of toxin types in these clinical C. difficile strains were 50% tcdA+tcdB+CDT- and 50% tcdA+tcdB+CDT+. The assay described should extend our understanding of the incidence of C. difficile. This may allow the rapid diagnosis and screening of C. difficile-related disease outbreaks in the field.

Detection and Characterization of Nodularin by Using Label-Free Surface-Enhanced Spectroscopic Techniques

Nodularin (NOD) is a potent toxin produced by Nodularia spumigena cyanobacteria. Usually, NOD co-exists with other microcystins in environmental waters, a class of cyanotoxins secreted by certain cyanobacteria species, which makes identification difficult in the case of mixed toxins. Herein we report a complete theoretical DFT-vibrational Raman characterization of NOD along with the experimental drop-coating deposition Raman (DCDR) technique. In addition, we used the vibrational characterization to probe SERS analysis of NOD using colloidal silver nanoparticles (AgNPs), commercial nanopatterned substrates with periodic inverted pyramids (Klarite^TM substrate), hydrophobic Tienta^® SpecTrim^TM slides, and in-house fabricated periodic nanotrenches by nanoimprint lithography (NIL). The 532 nm excitation source provided more well-defined bands even at LOD levels, as well as the best performance in terms of SERS intensity. This was reflected by the results obtained with the Klarite^TM substrate and the silver-based colloidal system, which were the most promising detection approaches, providing the lowest limits of detection. A detection limit of 8.4 × 10^-8 M was achieved for NOD in solution by using AgNPs. Theoretical computation of the complex vibrational modes of NOD was used for the first time to unambiguously assign all the specific vibrational Raman bands.

Growth Kinetics Monitoring of Gram-Negative Pathogenic Microbes Using Raman Spectroscopy

Optical density based measurements are routinely performed to monitor the growth of microbes. These measurements solely depend upon the number of cells and do not provide any information about the changes in the biochemical milieu or biological status. An objective information about these parameters is essential for evaluation of novel therapies and for maximizing the metabolite production. In the present study, we have applied Raman spectroscopy to monitor growth kinetics of three different pathogenic Gram-negative microbes Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. Spectral measurements were performed under 532 nm excitation with 5 seconds of exposure time. Spectral features suggest temporal changes in the "peptide" and "nucleic acid" content of cells under different growth stages. Using principal component analysis (PCA), successful discrimination between growth phases was also achieved. Overall, the findings are supportive of the prospective adoption of Raman based approaches for monitoring microbial growth.

Spectroscopic investigation of faeces with surface-enhanced Raman scattering: a case study with coeliac patients on gluten-free diet

Surface-enhanced Raman scattering (SERS) spectra of faecal samples can be obtained by adding AuNP to their methanol extracts according to the reported protocol, and display bands that are due to bilirubin-like species but also to xanthine and hypoxanthine, two metabolic products secreted by gut bacteria. A total of 27 faecal samples from three different groups, i.e. coeliac patients (n = 9), coeliac patients on gluten-free diet (n = 10) and a control group (n = 8), were characterized with both SERS spectroscopy and 16S rRNA sequencing analysis. Significant differences are present between SERS spectra of coeliac patients and those on gluten-free diet, with a marked increase in the relative intensity of both xanthine and hypoxanthine for the latter. Interestingly, these differences do not correlate with bacterial composition as derived from 16S rRNA sequencing.

DFT Modelling of Molecular Structure, Vibrational and UV-Vis Absorption Spectra of T-2 Toxin and 3-Deacetylcalonectrin

This paper discusses the applicability of optical and vibrational spectroscopies for the identification and characterization of the T-2 mycotoxin. Vibrational states and electronic structure of the T-2 toxin molecules are simulated using a density-functional quantum-mechanical approach. A numerical experiment aimed at comparing the predicted structural, vibrational and electronic properties of the T-2 toxin with analogous characteristics of the structurally similar 3-deacetylcalonectrin is performed, and the characteristic spectral features that can be used as fingerprints of the T-2 toxin are determined. It is shown that theoretical studies of the structure and spectroscopic features of trichothecene molecules facilitate the development of methods for the detection and characterization of the metabolites.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical "fingerprint" with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

Recent evolutions of machine learning applications in clinical laboratory medicine

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.