Antimicrobial susceptibility testing of Gram-positive and -negative bacterial isolates directly from spiked blood culture media with Raman spectroscopy

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest

Bacterial infections cause serious illnesses that are treated with antibiotics. Currently used methods for detecting bacterial antibiotic susceptibility consume 48-72 h, leading to overuse of antibiotics. Thus, many bacterial species have acquired resistance to a broad range of available antibiotics. There is an urgent need to develop efficient methods for rapid determination of bacterial susceptibility to antibiotics. The combination of machine learning and Fourier-transform infrared (FTIR) spectroscopy has generated a promising diagnostic approach in medicine and biology. Our main goal is to examine the potential of FTIR spectroscopy to determine the susceptibility of urinary tract infection-Proteus mirabilis to a specific range of antibiotics, within about 20 min after 24 h culture and identification. We measured the infrared spectra of 489 different P. mirabilis isolates and used random forest to analyze this spectral database. A classification success rate of ~84% was achieved in differentiating between the resistant and sensitive isolates based on their susceptibility to ceftazidime, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, ciprofloxacin, gentamicin, and sulfamethoxazole antibiotics in a time span of 24 h instead of 48 h.

Single Cell Raman Spectroscopy Deuterium Isotope Probing for Rapid Antimicrobial Susceptibility Test of Elizabethkingia spp

Nosocomial infection by multi-drug resistance Elizabethkingia spp. is an emerging concern with severe clinical consequences, particularly in immunocompromised individuals and infants. Efficient control of this infection requires quick and reliable methods to determine the appropriate drugs for treatment. In this study, a total of 31 Elizabethkingia spp., including two standard strains (ATCC 13253 and FMS-007) and 29 clinical isolates obtained from hospitals in China were subjected to single cell Raman spectroscopy analysis coupled with deuterium probing (single cell Raman-DIP). The results demonstrated that single cell Raman-DIP could determine antimicrobial susceptibility of Elizabethkingia spp. in 4 h, only one third of the time required by standard broth microdilution method. The method could be integrated into current clinical protocol for sepsis and halve the report time. The study also confirmed that minocycline and levofloxacin are the first-line antimicrobials for Elizabethkingia spp. infection.

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.

Clinical Perspective of Antimicrobial Resistance in Bacteria

Antimicrobial resistance (AMR) has become a global clinical problem in recent years. With the discovery of antibiotics, infections were not a deadly problem for clinicians as they used to be. However, worldwide AMR comes with the overuse/misuse of antibiotics and the spread of resistance is deteriorated by a multitude of mobile genetic elements and relevant resistant genes. This review provides an overview of the current situation, mechanism, epidemiology, detection methods and clinical treatment for antimicrobial resistant genes in clinical important bacteria including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), penicillin-resistant Streptococcus pneumoniae (PRSP), extended-spectrum β-lactamase-producing Enterobacteriaceae, acquired AmpC β-lactamase-producing Enterobacteriaceae, carbapenemase-producing Enterobacteriaceae (CPE), multidrug-resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa.

Fast Antibiotic Susceptibility Testing via Raman Microspectrometry on Single Bacteria: An MRSA Case Study

Despite recent advances in molecular diagnostics, ultrafast determination of the antibiotic susceptibility phenotype of pathogenic microorganisms is still a major challenge of in vitro diagnostics (IVD) of infectious diseases. Raman microspectroscopy has been proposed as a means to achieve this goal. Previous studies have shown that susceptibility phenotyping could be done through Raman analysis of microbial cells, either in large clusters or down to the single-cell level in the case of Gram-negative rods. Gram-positive cocci such as Staphylococcus aureus pose several challenges due to their size and their different metabolic and chemical characteristics. Using a tailored automated single-cell Raman spectrometer and a previously proposed sample preparation protocol, we acquired and analyzed 9429 S. aureus single cells belonging to three cefoxitin-resistant strains and two susceptible strains during their incubation in the presence of various concentrations of cefoxitin. We observed an effect on S. aureus spectra that is weaker than what was detected on previous bacteria/drug combinations, with a higher cell-to-cell response variability and an important impact of incubation conditions on the phenotypic resistance of a given strain. Overall, the proposed protocol was able to correlate strains' phenotype with a specific modification of the spectra using majority votes. We, hence, confirm that our previous results on single-cell Raman antibiotic susceptibility testing can be extended to the S. aureus case and further clarify potential limitations and development requirements of this approach in the move toward industrial applications.

Automated and rapid identification of multidrug resistant Escherichia coli against the lead drugs of acylureidopenicillins, cephalosporins, and fluoroquinolones using specific Raman marker bands

A Raman-based, strain-independent, semi-automated method is presented that allows the rapid (<3 hours) determination of antibiotic susceptibility of bacterial pathogens isolated from clinical samples. Applying a priori knowledge about the mode of action of the respective antibiotic, we identified characteristic Raman marker bands in the spectrum and calculated batch-wise weighted sum scores from standardized Raman intensity differences between spectra of antibiotic exposed and nonexposed samples of the same strains. The lead substances for three relevant antibiotic classes (fluoroquinolone ciprofloxacin, third-generation cephalosporin cefotaxime, ureidopenicillin piperacillin) against multidrug-resistant Gram-negative bacteria (MRGN) revealed a high sensitivity and specificity for the susceptibility testing of two Escherichia coli laboratory strains and 12 clinical isolates. The method benefits from the parallel incubation of control and treated samples, which reduces the variance due to alterations in cultivation conditions and the standardization of differences between batches leading to long-term comparability of Raman measurements.

Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.

Micro-Raman spectroscopy in medicine

A potential role of optical technologies in medicine including micro-Raman spectroscopy is diagnosis of bacteria, cells and tissues which is covered in this chapter. The main advantage of Raman-based methods to complement and augment diagnostic tools is that unsurpassed molecular specificity is achieved without labels and in a nondestructive way. Principles and applications of micro-Raman spectroscopy in the context of medicine will be described. First, Raman spectra of biomolecules representing proteins, nucleic acids, lipids and carbohydrates are introduced. Second, microbial applications are summarized with the focus on typing on species and strain level, detection of infections, antibiotic resistance and biofilms. Third, cytological applications are presented to classify single cells and study cell metabolism and drug–cell interaction. Fourth, applications to tissue characterization start with discussion of lateral resolution for Raman imaging followed by Raman-based detection of pathologies and combination with other modalities. Finally, an outlook is given to translate micro-Raman spectroscopy as a clinical tool to solve unmet needs in point-of-care applications and personalized treatment of diseases.

Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance

Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?

Phenotypic antibiotic susceptibility testing of pathogenic bacteria using photonic readout methods: recent achievements and impact

The development of antibiotic resistances in common pathogens is an increasing challenge for therapy of infections and especially severe complications like sepsis. To prevent administration of broad-spectrum and potentially non-effective antibiotics, the susceptibility spectrum of the pathogens underlying the infection has to be determined. Current phenotypic standard methods for antibiotic susceptibility testing (AST) require the isolation of pathogens from the patient and the subsequent culturing in the presence of antibiotics leading to results only after 24-72 h. Since the early initialization of an effective antibiotic therapy is crucial for positive treatment result in severe infections, faster methods of AST are urgently needed. A large number of different assay systems are currently tested for their practicability for fast detection of antibiotic resistance profiles. They can be divided into genotypic ones which detect the presence of certain genes or gene products associated with resistances and phenotypic assays which determine the effect of antibiotics on the pathogens. In this mini-review, we summarize current developments in fast phenotypic tests that use photonic approaches and critically discuss their status. We further outline steps that are required to bring these assays into clinical practice.

Culture-free Antibiotic-susceptibility Determination From Single-bacterium Raman Spectra

Raman spectrometry appears to be an opportunity to perform rapid tests in microbiological diagnostics as it provides phenotype-related information from single bacterial cells thus holding the promise of direct analysis of clinical specimens without any time-consuming growth phase. Here, we demonstrate the feasibility of a rapid antibiotic-susceptibility determination based on the use of Raman spectra acquired on single bacterial cells. After a two-hour preculture step, one susceptible and two resistant E. coli strains were incubated, for only two hours, in the presence of different bactericidal antibiotics (gentamicin, ciprofloxacin, amoxicillin) in a range of concentrations that included the clinical breakpoints used as references in microbial diagnostic. Spectra were acquired and processed to isolate spectral modifications associated with the antibiotic effect. We evidenced an “antibiotic effect signature” which is expressed with specific Raman peaks and the coexistence of three spectral populations in the presence of antibiotic. We devised an algorithm and a test procedure that overcome single-cell heterogeneities to estimate the MIC and determinate the susceptibility phenotype of the tested bacteria using only a few single-cell spectra in four hours only if including the preculture step.