Fluorescent protein (FP) tagging is widely used to differentiate bacterial populations in mixed-species and surface-associated systems. However, FP expression can impose a metabolic burden and alter cellular physiology, raising concerns about whether FP-tagged strains reliably represent their wild-type counterparts. Understanding the biochemical consequences of FP expression is therefore essential when interpreting data from fluorescence-based studies. In this study, we employed Fourier-transform infrared (FTIR) microscopy and Raman confocal microscopy to evaluate the impact of FP tagging in Gram-positive Staphylococcus aureus and Gram-negative Pseudomonas aeruginosa labelled with eGFP- and mCherry-fluorescent proteins, respectively. Principal component analysis (PCA) of FTIR spectra revealed clear species-level discrimination driven by differences in lipid, protein, polysaccharide, and phosphate content characteristic of Gram-positive and Gram-negative bacteria. Importantly, no detectable spectral differences were observed between wild-type and FP-expressing strains. Raman spectroscopy offered stronger species-specific contrast owing to resonance enhancement of carotenoids in S. aureus and cytochromes in P. aeruginosa. PCA applied to Raman spectra from GFP-labelled Staphylococcus aureus strain and its wild-type counterpart again showed no difference. Together, these results demonstrate that FP expression under the experimental conditions used in this study did not induce detectable changes in vibrational spectral datasets, thus enabling species-level differentiation to be retained in the presence of FP tagging. This paves the way forward for correlative fluorescence and vibrational mapping of bacterial populations at surfaces.

The Briggs-Rauscher reaction is a prototypical oscillating system and is based on chemical mixtures of iodate (IO3-), hydrogen peroxide, malonic acid and Mn2+ ions in an acidic solution. Although the Briggs-Rauscher oscillatory behaviour can be seen visibly, conventional analytical methods lack molecular detail of the exact chemical reactions within this highly dynamic system. Using real-time resonance Raman spectroscopy at 785 nm excitation, we track iodine speciation through distinct triiodide and pentaiodide signatures. Fourier-transform analysis of these time-series Raman data reveals that increasing temperature accelerates oscillation frequency while destabilising oscillatory regularity at elevated temperatures (43 °C); higher iodate concentrations promote regular oscillations at intermediate levels but suppress them through excessive oxidation at high concentrations; and starch actively modulates the reaction by stabilising polyiodide formation and doubling the oscillation period relative to starch-free conditions. This study demonstrates Raman spectroscopy as a precise physicochemical tool for analysing chemical oscillations.

Cortisol plays a central role in maintaining physiological homeostasis, and both cortisol excess and deficiency are associated with life-threatening conditions. Accurate diagnosis, adequate treatment and monitoring of disorders of cortisol secretion are essential for good health, normal growth and development. Although commercially available lateral flow immunoassay (LFI) strips can be used to measure cortisol, they have limitations, especially low sensitivity and limited quantitative performance, inhibiting their use in clinical settings. Here, we present a novel LFI platform integrated with surface-enhanced Raman scattering (SERS), employing precisely size-controlled gold nanoparticle clusters functionalised with Raman reporter molecules to overcome these limitations. The approach achieves exceptional sensitivity, covering the relevant therapeutic range in humans, with a limit of detection (LOD) for cortisol of 0.014 pg mL-1, which is >500 times more sensitive than conventional LFI strips. The platform also showed high specificity for cortisol. The diagnostic potential was confirmed by testing with human saliva samples (n = 28), cross-validated with UPLC-MS/MS, showing excellent correlation (R2 = 0.9977). Bland-Altman analysis demonstrated strong agreement, with all samples falling within the 95% limits and yielding a mean bias of -3.5% ± 13.2% relative to UPLC-MS/MS. Given its sensitivity, specificity and simplicity, this LFI-SERS platform offers strong potential for clinical translation to enable convenient cortisol monitoring.

Rheumatoid arthritis (RA) is an auto-immune disease which causes irreversible damage to tissue and cartilage within synovial joints. Rapid diagnosis and treatment with disease-modifying therapies is essential to reduce inflammation and prevent joint destruction. RA is a heterogeneous disease, and many patients do not respond to front-line therapies, requiring escalation of treatment onto biologics, of which TNF inhibitors (TNF-i) are the most common. In this study we determined whether serum metabolomics, using nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopy, could discriminate RA blood sera from healthy human controls and whether the technologies could be used to predict response or non-response to TNF inhibitor (TNF-i) therapy. NMR spectroscopy identified 35 metabolites in RA sera, with acetic acid being significantly lower in RA sera compared to healthy controls (HC, FDR < 0.05). PLS-DA modelling identified 2-hydroxyisovalericacetic acid, acetoacetic acid, mobile lipids, alanine and leucine as important metabolites for discrimination of RA and HC sera by 1H NMR spectroscopy (averaged 83.1

Determining the geographic origin of palm oil in West Africa is vital for economic, environmental, and health reasons. It enhances traceability, protects local farmers, supports conservation by monitoring deforestation, and reduces food fraud, ensuring quality and regulatory compliance. Portable Raman spectroscopy offers a rapid method to identify the origin of palm oils from West Africa. Using principal component analysis (PCA), distinct clusters in scores plots were observed which reflected the geographic origin of the palm oils, with loadings from the first principal component (PC-1) highlighting β-carotene as a major source of variation among the samples. To quantify β-carotene content, a partial least squares regression (PLS-R) model was developed in coconut oil as the base oil as it is known to be β-carotene free. Once calibrated, PLS-R was used to rank the palm oil from West Africa based on their β-carotene levels. The resulting models in coconut oil demonstrated strong linearity and predictive performance, with R² and Q² values of 0.9848 and 0.9552, respectively, alongside low root mean square errors of cross-validation (0.1282 mM) and prediction (0.0747 mM); moreover, this model allows the palm oils to be ranked based on β-carotene content which was entirely reflective of the oils position in PC-1 from PCA. These findings underscore the potential of Raman spectroscopy as an effective tool for authenticating the geographic origin of palm oil from West Africa.

Rapid detection and identification of pathogens and antimicrobial susceptibility is essential for guiding appropriate antimicrobial therapy and reducing morbidity and mortality associated with sepsis. The metabolic response of clinical isolates of Klebsiella oxytoca exposed to different concentrations of ciprofloxacin (the second generation of quinolones antibiotics) were studied in order to investigate underlying mechanisms associated with antimicrobial resistance (AMR). Metabolomics investigations were performed using Fourier-transform infrared (FT-IR) spectroscopy as a metabolic fingerprinting approach combined with gas chromatography-mass spectrometry (GC-MS) for metabolic profiling. Our findings demonstrated that metabolic fingerprints provided by FT-IR analysis allowed for the differentiation of susceptible and resistant isolates. GC-MS analysis validated these findings, while also providing a deeper understanding of the metabolic alterations caused by exposure to ciprofloxacin. GC-MS metabolic profiling detected 176 metabolic features in the cellular extracts cultivated on BHI broth, and of these, 137 could be identified to Metabolomics Standards Initiative Level 2. Data analysis showed that 40 metabolites (30 Level 2 and 10 unknown) were differentiated between susceptible and resistant isolates. The identified metabolites belonging to central carbon metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; and pyruvate metabolism. Univariate receiver operating characteristic (ROC) curve analyses revealed that six of these metabolites (glycerol-3-phosphate, O-phosphoethanolamine, asparagine dehydrate, maleimide, tyrosine, and alanine) have a crucial role in distinguishing susceptible from resistant isolates (AUC > 0.84) and contributing to antimicrobial resistance in K. oxtytoca. Our study provides invaluable new insights into the mechanisms underlying development of antimicrobial resistance in K. oxytoca suggests potential therapeutic targets for prevention and identification of AMR in K. oxytoca infections.

Abstract In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm−1 and 1285 cm−1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist’s interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes.

Axillary malodour is caused by the microbial conversion of human-derived precursors to volatile organic compounds. Thiols strongly contribute to this odour but are hard to detect as they are present at low concentrations. Additionally, thiols are highly volatile and small making sampling and quantification difficult, including by gas chromatography-mass spectrometry. In this study, surface-enhanced Raman scattering (SERS), combined with chemometrics, was utilised to simultaneously quantify four malodourous thiols associated with axillary odour, both in individual and multiplex solutions. Univariate and multivariate methods of partial least squares regression (PLS-R) were used to calculate the limit of detection (LoD) and results compared. Both methods yielded comparable LoD values, with LoDs using PLS-R ranging from 0.0227 ppm to 0.0153 ppm for the thiols studied. These thiols were then examined and quantified simultaneously in 120 mixtures using PLS-R. The resultant models showed high linearity (Q2 values between 0.9712 and 0.9827 for both PLS-1 and PLS-2) and low values of root mean squared error of predictions (0.0359 ppm and 0.0459 ppm for PLS-1 and PLS-2, respectively). To test this approach further, these models were challenged with 15 new blind test samples, collected independently from the initial samples. This test demonstrated that SERS combined with PLS-R could be used to predict the unknown concentrations of these thiols in a mixture. These results display the ability of SERS for the simultaneous multiplex detection and quantification of analytes and its potential for future development for detecting gaseous thiols produced from skin and other body sites. SERS in combination with chemometric techniques has been used to simultaneously detect and quantify malodourous thiols which are associated with axillary malodour in multiplex solutions.

Microbial communities play crucial roles in shaping natural ecosystems, impacting human well-being, and driving advancements in industrial biotechnology. However, associating specific metabolic functions with bacteria proves challenging due to the vast diversity of microorganisms within these communities. In the past decades stable isotope probing (SIP) approaches, coupled with vibrational spectroscopy, have emerged as a novel method for revealing microbial metabolic roles and interactions in complex communities. In this study, we employed various combinations of heavy stable isotopes (D, 13C, 15N, and 18O), to evaluate all possible isotopic spectral shifts in the mid-IR region using Fourier-Transform Infrared (FT-IR) and Optical Photothermal Infrared (O-PTIR) spectroscopy, at both community and single-cell levels. Additionally, we conducted a time-course study to explore the kinetics of CD vibration in Escherichia coli bacteria, allowing time-based sampling and assessment of isotopic labeling kinetics. The FT-IR and O-PTIR, along with the second derivative spectra of E. coli cells cultured in minimal medium supplemented with various combinations of heavy isotopes exhibited notable similarities. Several spectral shifts in primary vibrational peaks were observed due to the incorporation of heavy isotopes into various biomolecules. Remarkably, the incorporation of deuterium into amide groups, resulting in the formation of nitrogen-deuterium bonds, caused a shift in amide A and B into the silent region, overlapping with CD signature peaks. The incorporation of 18O into the ester group of lipids and the carbonyl group of proteins resulted in a notable shift to the lower wavenumber region. Additionally, the second derivative of FT-IR spectral data highlighted the integration of 18O into α-helix and β-sheet structures. Furthermore, the spectra, second derivative, and PC-DFA scores and loadings plot of FT-IR data collectively illustrated the practicality of monitoring 13C and D incorporation into E. coli bacterial cells within the first 30-min incubation period. The findings of this study suggest that FT-IR and O-PTIR can serve as efficient tools for monitoring the incorporation of heavy isotopes into bacteria at both the population and single-cell levels. Additionally, the SIP approach allowed us to assign two new deuterium-associated vibrational peaks to their corresponding functional groups, which to the best of our knowledge have not been reported previously.

The universe is a vast store of organic abiotic carbon that could potentially drive heterotrophy on habitable planets. Meteorites are one of the transporters of this carbon to planetary surfaces. Meteoritic material was accumulating on early Earth when life emerged and proliferated. Yet it is not known if this organic carbon from space was accessible to life. In this research, an anaerobic microbial community was grown with the CM2 carbonaceous chondrite Aguas Zarcas as the sole carbon, energy and nutrient source. Using a reversed 13 C-stable isotope labelling experiment in combination with optical photothermal infrared (O-PTIR) spectroscopy of single cells, this paper demonstrates the direct transfer of carbon from meteorite into microbial biomass. This implies that meteoritic organics could have been used as a carbon source on early Earth and other habitable planets, and supports the potential for a heterotrophic metabolism in early living systems.

Raman analysis has been found to provide otherwise hard to obtain information on inverse vulcanised polymers, including their homogeneity, sulfur rank, and unpolymerised sulfur content.

Paracetamol (also known as acetaminophen) is an over-the-counter (OTC) drug that is commonly used as an analgesic for mild pain, headache, cold and flu. While in the short term it is a safe and effective medicine, it is sometimes used for attempted suicides particularly in young adults. In such circumstances it is important for rapid diagnosis of overdoses as antidotes can be given to limit liver damage from one of its primary metabolites N-acetyl-p-benzoquinone imine (NAPQI). Unfortunately, the demand for rapid and sensitive analytical techniques to accurately monitor the abuse of OTC drugs has significantly risen. Ideally these techniques would be highly specific, sensitive, reproducible, portable and rapid. In addition, an ideal point of care (PoC) test would enable quantitative detection of drugs and their metabolites present in body fluids. While Raman spectroscopy meets these specifications, there is a need for enhancement of the signal because the Raman effect is weak. In this study, we developed a surface-enhanced Raman scattering (SERS) methodology in conjunction with chemometrics to quantify the amount of paracetamol and its main primary metabolites (viz., paracetamol sulfate, p-acetamidophenyl β-D-glucuronide and NAPQI) in water and artificial urine. The enhancement of the SERS signals was achieved by mixing the drug or xenometabolites with a gold nanoparticle followed by aggregation with 0.045 M NaCl. We found that the SERS data could be collected directly, due to immediate analyte association with the Au surface and colloid aggregation. Accurate and precise measurements were generated, with a limit of detection (LoD) of paracetamol in water and artificial urine at 7.18 × 10-6 M and 2.11 × 10-5 M, respectively, which is well below the limit needed for overdose and indeed normal levels of paracetamol in serum after taking 1 g orally. The predictive values obtained from the analysis of paracetamol in water and artificial urine were also excellent, with the coefficient of determination (Q2) being 0.995 and 0.996, respectively (1 suggests a perfect model). It was noteworthy that when artificial urine was spiked with paracetamol, no aggregating agent was required due to the salt rich medium, which led to spontaneous aggregation. Moreover, for the xenometabolites of paracetamol excellent LoDs were obtained and these ranged from 2.6 × 10-4 M to 5 × 10-5 M with paracetamol sulfate and NAPQI having Q2 values of 0.934 and 0.892 and for p-acetamidophenyl β-D-glucuronide this was slightly lower at 0.6437.

Here, we aim to better characterize diabetes mellitus (DM) by analyzing 149 urine spectral samples, comprising of diabetes versus healthy control groups employing ATR-FTIR spectroscopy, combined with a 3D discriminant analysis machine learning approach. Our results depict that the model is highly precise with accuracy close to 100%.

Surface-enhanced Raman scattering (SERS) is a powerful technique for detecting trace amounts of analytes. However, the performance of SERS substrates depends on many variables including the enhancement factor, morphology, consistency, and interaction with target analytes. In this study, we investigated, for the first time, the use of electrospray deposition (ESD) combined with a novel ambient focusing DC ion funnel to deposit a high density of gold nanoparticles (AuNPs) to generate large-area, uniform substrates for highly sensitive SERS analysis. We found that the combination of ambient ion focusing with ESD facilitated high-density and intact deposition of non-spherical NPs. This also allowed us to take advantage of a polydisperse colloidal solution of AuNPs (consisting of nanospheres and nanorods), as confirmed by finite-difference time domain (FDTD) simulations. Our SERS substrate exhibited excellent capture capacity for model analyte molecules, namely 4-aminothiophenol (4-ATP) and Rhodamine 6G (R6G), with detection limits in the region of 10-11 M and a relative standard deviation of <6% over a large area (∼500 × 500 μm2). Additionally, we assessed the quantitative performance of our SERS substrate using the R6G probe molecule. The results demonstrated excellent linearity (R2 > 0.99) over a wide concentration range (10-4 M to 10-10 M) with a detection limit of 80 pM.

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Phenotypic heterogeneity is commonly found among bacterial cells within microbial populations due to intrinsic factors as well as equipping the organisms to respond to external perturbations. The emergence of phenotypic heterogeneity in bacterial populations, particularly in the context of using these bacteria as microbial cell factories, is a major concern for industrial bioprocessing applications. This is due to the potential impact on overall productivity by allowing the growth of subpopulations consisting of inefficient producer cells. Monitoring the spread of phenotypes across bacterial cells within the same population at the single-cell level is key to the development of robust, high-yield bioprocesses. Here, we discuss the novel development of optical photothermal infrared (O-PTIR) spectroscopy to probe phenotypic heterogeneity within Bacillus strains by monitoring the production of the bioplastic poly-3-hydroxybutyrate (PHB) at the single-cell level. Measurements obtained on single-point and in imaging mode show significant variability in the PHB content within bacterial cells, ranging from whether or not a cell produces PHB to variations in the intragranular biochemistry of PHB within bacterial cells. Our results show the ability of O-PTIR spectroscopy to probe PHB production at the single-cell level in a rapid, label-free, and semiquantitative manner. These findings highlight the potential of O-PTIR spectroscopy in single-cell microbial metabolomics as a whole-organism fingerprinting tool that can be used to monitor the dynamic of bacterial populations as well as for understanding their mechanisms for dealing with environmental stress, which is crucial for metabolic engineering research.

The rise and extensive spread of antimicrobial resistance (AMR) has become a growing concern, and a threat to the environment and human health globally. The majority of current AMR identification methods used in clinical setting are based on traditional microbiology culture-dependent techniques which are time-consuming or expensive to be implemented, thus appropriate antibiotic stewardship is provided retrospectively which means the first line of treatment is to hope that a broad-spectrum antibiotic works. Hence, culture-independent and single-cell technologies are needed to allow for rapid detection and identification of antimicrobial-resistant bacteria and to support a more targeted and effective antibiotic therapy preventing further development and spread of AMR. In this study, for the first time, a non-destructive phenotyping method of optical photothermal infrared (O-PTIR) spectroscopy, coupled with deuterium isotope probing (DIP) and multivariate statistical analysis was employed as a metabolic fingerprinting approach to detect AMR in Uropathogenic Escherichia coli (UPEC) at both single-cell and population levels. Principal component-discriminant function analysis (PC-DFA) of FT-IR and O-PTIR spectral data showed clear clustering patterns as a result of distinctive spectral shifts (C–D signature peaks) originating from deuterium incorporation into bacterial cells, allowing for rapid detection and classification of sensitive and resistant isolates at the single-cell level. Furthermore, the single-frequency images obtained using the C–D signature peak at 2,163 cm −1 clearly displayed the reduced ability of the trimethoprim-sensitive strain for incorporating deuterium when exposed to this antibiotic, compared to the untreated condition. Hence, the results of this study indicated that O-PTIR can be employed as an efficient tool for the rapid detection of AMR at the single-cell level.