Validating the utility of heavy water (Deuterium Oxide) as a potential Raman spectroscopic probe for identification of antibiotic resistance

The impact of microbial infections is increasing over time, and it is one of the major reasons for death in both developed and developing countries. colistin is considered as the antibiotic of last choice for infections brought by major multidrug-resistant (MDR), gram-negative bacteria such as Enterobacter species, Acinetobacter species, and Pseudomonas aeruginosa. Existing approaches to diagnose these resistant species are relatively slow and take up to 2 to 3 days. In this work, we propose a novel interdisciplinary method based on Raman spectroscopy and heavy water to identify colistin-resistant microbes. Our hypothesis is based on the fact that resistant bacteria will be metabolically active in the culture medium containing antibiotics and heavy water, and these bacteria will take up deuterium instead of hydrogen to newly synthesized lipids and proteins. This effect will generate a 'C - D' bond-specific Raman spectral marker. Successful identification of this band in the spectral profile can confirm the presence of colistin-resistant bacteria. We have validated the efficacy of this approach in identifying colistin-resistant bacteria spiked in artificial urine and have compared sensitivity at different bacterial concentrations. Overall findings suggest that heavy water can potentially serve as a suitable Raman probe for identifying metabolically active colistin-resistant bacteria via urine under clinically implementable time and can be used in clinical settings after validation.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

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.

Specific Instantaneous Detection of Klebsiella pneumoniae for UTI Diagnosis with a Plasmonic Gold Nanoparticle Conjugated Aptasensor

Urinary tract infection (UTI), which can be caused by various pathogens, if not detected at an early stage can be fatal. It is essential to identify the specific pathogen responsible for UTI for appropriate treatment. This study describes a generic approach to the fabrication of a prototype for the noninvasive detection of a specific pathogen using a tailor-made plasmonic aptamer-gold nanoparticle (AuNP) assay. The assay is advantageous because the adsorbed specific aptamers passivate the nanoparticle surfaces and reduce and/or eliminate false-positive responses to nontarget analytes. Based on the localized surface plasmon resonance (LSPR) phenomena of AuNP, a point-of-care aptasensor was designed that shows specific changes in the absorbance in the visible spectra in the presence of a target pathogen for robust and fast screening of UTI samples. In this study, we demonstrate the specific detection of Klebsiella pneumoniae bacteria with LoD as low as 3.4 × 103 CFU/mL.

Rapid Identification of Pathogens Causing Bloodstream Infections by Raman Spectroscopy and Raman Tweezers

The search for the "Holy Grail" in clinical diagnostic microbiology-a reliable, accurate, low-cost, real-time, easy-to-use method-has brought up several methods with the potential to meet these criteria. One is Raman spectroscopy, an optical, nondestructive method based on the inelastic scattering of monochromatic light. The current study focuses on the possible use of Raman spectroscopy for identifying microbes causing severe, often life-threatening bloodstream infections. We included 305 microbial strains of 28 species acting as causative agents of bloodstream infections. Raman spectroscopy identified the strains from grown colonies, with 2.8% and 7% incorrectly identified strains using the support vector machine algorithm based on centered and uncentred principal-component analyses, respectively. We combined Raman spectroscopy with optical tweezers to speed up the process and captured and analyzed microbes directly from spiked human serum. The pilot study suggests that it is possible to capture individual microbial cells from human serum and characterize them by Raman spectroscopy with notable differences among different species. IMPORTANCE Bloodstream infections are among the most common causes of hospitalizations and are often life-threatening. To establish an effective therapy for a patient, the timely identification of the causative agent and characterization of its antimicrobial susceptibility and resistance profiles are essential. Therefore, our multidisciplinary team of microbiologists and physicists presents a method that reliably, rapidly, and inexpensively identifies pathogens causing bloodstream infections-Raman spectroscopy. We believe that it might become a valuable diagnostic tool in the future. Combined with optical trapping, it offers a new approach where the microorganisms are individually trapped in a noncontact way by optical tweezers and investigated by Raman spectroscopy directly in a liquid sample. Together with the automatic processing of measured Raman spectra and comparison with a database of microorganisms, it makes the whole identification process almost real time.

Biochemical changes of macrophages and U87MG cells occurring as a result of the exposure to iron oxide nanoparticles detected with the Raman microspectroscopy

The core size of iron oxide nanoparticles (IONPs) is a crucial factor defining not only their magnetic properties but also toxicological profile and biocompatibility. On the other hand, particular IONPs may induce different biological response depending on the dose, exposure time, but mainly depending on the examined system. New light on this problem may be shed by the information concerning biomolecular anomalies appearing in various cell lines in response to the action of IONPs with different core diameters and this was accomplished in the present study. Using Raman microscopy we studied the abnormalities in the accumulation of proteins, lipids and organic matter within the nucleus, cytoplasm and cellular membrane of macrophages, HEK293T and U87MG cell line occurring as a result of 24-hour long exposure to PEG-coated magnetite IONPs. The examined nanoparticles had 5, 10 and 30 nm cores and were administered in doses 5 and 25 μg Fe/ml. The obtained results showed significant anomalies in biochemical composition of macrophages and the U87MG cells, but not the HEK293T cells, occurring as a result of exposure to all of the examined nanoparticles. However, IONPs with 10 nm core diminished the accumulation of biomolecules in cells only when they were administered at a larger dose. The Raman spectra recorded for the macrophages subjected to 30 nm IONPs and for the U87MG cells exposed to 5 and 10 nm showed the presence of additional bands in the wavenumber range 1700-2400 cm^-1, probably resulting from the appearance of Fe adducts within cells. Our results indicate, moreover, that smaller IONPs may be effectively internalized into the U87MG cells, which points at their diagnostic/therapeutic potential in the case of glioblastoma multiforme.

Raman Spectroscopy—A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings

Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful—quick, non-invasive, and non-destructive —tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.

Raman spectroscopy-a tool for rapid differentiation among microbes causing urinary tract infections

Urinary tract infections belong to the most common infections in the world. Besides community-acquired infections, nosocomial infections pose a high risk especially for patients having indwelling catheters, undergoing urological surgeries or staying at hospital for prolonged time. They can be often complicated by antimicrobial resistance and/or biofilm formation. Therefore, a rapid diagnostic tool enabling timely identification of a causative agent and its susceptibility to antimicrobials is a need. Raman spectroscopy appears to be a suitable method that allows rapid differentiation among microbes and provides a space for further analyses, such as determination of capability of biofilm formation or antimicrobial susceptibility/resistance in tested strains. Our work here presents a possibility to differ among most common microbes causing urinary tract infections (belonging to 20 species). We tested 254 strains directly from colonies grown on Mueller-Hinton agar plates. The results show that it is possible to distinguish among the tested species using Raman spectroscopy, which proves its great potential for future use in clinical diagnostics. Moreover, we present here a pilot study of a real-time analysis and identification (in less than 10 min) of single microbial cells directly in urine employing optical tweezers combined with Raman spectroscopy.

Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters

Urinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (10⁵ CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

Surface plasmon resonance based biomimetic sensor for urinary tract infections

Tailor-made Escherichia coli (E. coli) receptors were created with microcontact imprinted technique and binding events of E. coli were carried out by a surface plasmon resonance (SPR) sensor in aqueous solution and in urine mimic in real time and label-free. N-methacryloyl-(l)-histidine methyl ester (MAH) was selected as a functional monomer to design tailor-made E. coli receptors on the polymeric film and during the formation of the polymeric film on a chip surface, Ag nanoparticles (AgNPs) were entrapped into the polymer mixture in order to lower the detection limit of biomimetic SPR based sensor. The polymeric film was characterized with atomic force microscopy (AFM), scanning electron microscopy (SEM), ellipsometer and contact angle measurements. Limit of detection (LOD) was found 0.57 CFU/mL and feasibility of the biomimetic sensor was investigated in urine mimic.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.

Emerging nanotechnology based strategies for diagnosis and therapeutics of urinary tract infections: A review

At present, various diagnostic and therapeutic approaches are available for urinary tract infections. But, still the quest for development of more rapid, accurate and reliable approach is an unending process. The pathogens, especially uropathogens are adapting to new environments and antibiotics day by day rapidly. Therefore, urinary tract infections are evolving as hectic and difficult to eradicate, increasing the economic burden to the society. The technological advances should be able to compete the adaptability characteristics of microorganisms to combat their growth in new environments and thereby preventing their infections. Nanotechnology is at present an extensively developing area of immense scientific interest since it has diverse potential applications in biomedical field. Nanotechnology may be combined with cellular therapy approaches to overcome the limitations caused by conventional therapeutics. Nanoantibiotics and drug delivery using nanotechnology are currently growing areas of research in biomedical field. Recently, various categories of antibacterial nanoparticles and nanocarriers for drug delivery have shown their potential in the treatment of infectious diseases. Nanoparticles, compared to conventional antibiotics, are more beneficial in terms of decreasing toxicity, prevailing over resistance and lessening costs. Nanoparticles present long term therapeutic effects since they are retained in body for relatively longer periods. This review focuses on recent advances in the field of nanotechnology, principally emphasizing diagnostics and therapeutics of urinary tract infections.

Rapid urinary tract infection diagnostics by surface-enhanced Raman spectroscopy (SERS): identification and antibiotic susceptibilities

SERS spectra of 12 bacterial strains of urinary tract infection (UTI) clinical isolates grown and enriched from urine are reported. A partial least squares-discriminant analysis (PLS-DA) classification treatment of these SERS spectra results in strain level identification with >95% sensitivity and >99% specificity. The classification model successfully identified the SERS spectra of a urine-cultured strain not used to build this statistical model. Enrichment was accomplished by a filtration and centrifugation protocol. The predetermined drug susceptibility profiles of these clinical isolates thus allowed the SERS methodology to provide appropriate UTI antibiotic information in less than 1 h. Most of this time was used for sample preparation procedures (enrichment and washing) for this proof of principle study. SERS spectra of the enriched bacterial samples are dominated by nucleotide degradation metabolites: adenine, hypoxanthine, xanthine, guanine, uric acid, AMP, and guanosine. Strain-specific specificity is due to the different relative amounts of these purines contributing to the corresponding SERS spectra of these clinical isolates. All measurements were made at the minimal bacterial concentration in urine for UTI diagnosis (10⁵ cfu/mL). Graphical abstract The relative contribution of each of the seven purines found to contribute to the bacterial SERS spectra are summarized in this bar graph. Although strain specific differences are evident, it can be see how the pattern of contributing purines is more different between the four species than between strains of a given species.