Chemotaxonomic identification of single bacteria by micro-Raman spectroscopy: application to clean-room-relevant biological contaminations

Symbiodiniaceae and Ruegeria sp. Co-Cultivation to Enhance Nutrient Exchanges in Coral Holobiont

The symbiotic relationship between corals and their associated microorganisms is crucial for the health of coral reef eco-environmental systems. Recently, there has been a growing interest in unraveling how the manipulation of symbiont nutrient cycling affects the stress tolerance in the holobiont of coral reefs. However, most studies have primarily focused on coral-Symbiodiniaceae-bacterial interactions as a whole, neglecting the interactions between Symbiodiniaceae and bacteria, which remain largely unexplored. In this study, we proposed a hypothesis that there exists an inner symbiotic loop of Symbiodiniaceae and bacteria within the coral symbiotic loop. We conducted experiments to demonstrate how metabolic exchanges between Symbiodiniaceae and bacteria facilitate the nutritional supply necessary for cellular growth. It was seen that the beneficial bacterium, Ruegeria sp., supplied a nitrogen source to the Symbiodiniaceae strain Durusdinium sp., allowing this dinoflagellate to thrive in a nitrogen-free medium. The Ruegeria sp.-Durusdinium sp. interaction was confirmed through 15N-stable isotope probing-single cell Raman spectroscopy, in which 15N infiltrated into the bacterial cells for intracellular metabolism, and eventually the labeled nitrogen source was traced within the macromolecules of Symbiodiniaceae cells. The investigation into Symbiodiniaceae loop interactions validates our hypothesis and contributes to a comprehensive understanding of the intricate coral holobiont. These findings have the potential to enhance the health of coral reefs in the face of global climate change.

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

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.

Understanding Raman Spectral Based Classifications with Convolutional Neural Networks Using Practical Examples of Fungal Spores and Carotenoid-Pigmented Microorganisms

Numerous publications showing that robust prediction models for microorganisms based on Raman micro-spectroscopy in combination with chemometric methods are feasible, often with very precise predictions. Advances in machine learning and easier accessibility to software make it increasingly easy for users to generate predictive models from complex data. However, the question regarding why those predictions are so accurate receives much less attention. In our work, we use Raman spectroscopic data of fungal spores and carotenoid-containing microorganisms to show that it is often not the position of the peaks or the subtle differences in the band ratios of the spectra, due to small differences in the chemical composition of the organisms, that allow accurate classification. Rather, it can be characteristic effects on the baselines of Raman spectra in biochemically similar microorganisms that can be enhanced by certain data pretreatment methods or even neutral-looking spectral regions can be of great importance for a convolutional neural network. Using a method called Gradient-weighted Class Activation Mapping, we attempt to peer into the black box of convolutional neural networks in microbiological applications and show which Raman spectral regions are responsible for accurate classification.

Surface-enhanced Raman spectroscopy for the characterization of pellets of biofilm forming bacterial strains of Staphylococcus epidermidis

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an effective tool for identifying biofilm forming bacterial strains. Biofilm forming bacteria are considered a major issue in the health sector because they have strong resistance against antibiotics. Staphylococcus epidermidis is commonly present on intravascular devices and prosthetic joints, catheters and wounds.

OBJECTIVES

To identify and characterize biofilm forming and non-biofilm forming bacterial strains, surface- enhanced Raman spectroscopy with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used.

METHODS

Surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles were employed for the analysis and characterization of biofilm forming bacterial strains. SERS is used to differentiate between non biofilm forming (five samples), medium biofilm forming (five samples) and strong biofilm forming (five samples) bacterial strains by applying silver nanoparticles (AgNPs) as SERS substrate. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) were used to discriminate between non, medium and strong biofilm ability of bacterial strains.

RESULTS

Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been used to identify the biochemical differences in the form of SERS features which can be used to differentiate between biofilm forming and non-biofilm forming bacterial strains. PLS-DA provides successful differentiation and classification of these different strains with 94.5% specificity, 96% sensitivity and 89% area under the curve (AUC).

CONCLUSIONS

Surface-enhanced Raman spectroscopy can be utilized to differentiate between non, medium and strong biofilm forming bacterial strains.

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy

Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.

Analyzing macromolecular composition of E. Coli O157:H7 using Raman-stable isotope probing

Metabolic dynamics of bacterial cells is needed for understanding the correlation between changes in environmental conditions and cell metabolic activity. In this study, Raman spectroscopy combined with deuterium labelling was used to analyze the metabolic activity of a single Escherichia coli O157:H7 cell. The incorporation of deuterium from heavy water into cellular biomolecules resulted in the formation of carbon-deuterium (CD) peaks in the Raman spectra, indicating the cell metabolic activity. The broad vibrational peaks corresponding to CD and CH peaks encompassed different specific shifts of macromolecules such as protein, lipids, and nucleic acid. The utilization of tryptophan and oleic acid by the cell as the sole carbon source led to changes in cell lipid composition, as indicated by new peaks in the second derivative spectra. Thus, the proposed method could semi-quantitatively determine total metabolic activity, macromolecule specific identification, and lipid and protein metabolism in a single cell.

Raman Microscopic Identification of Microorganisms on Metal Surfaces via Support Vector Machines

An easy, inexpensive, and rapid method to identify microorganisms is in great demand in various areas such as medical diagnostics or in the food industry. In our study, we show the development of several predictive models based on Raman spectroscopy combined with support vector machines (SVM) for 21 species of microorganisms. The microorganisms, grown under standardized conditions, were placed on a silver mirror slide to record the data for model development. Additional data was obtained from microorganisms on a polished stainless-steel slide in order to validate the models in general and to assess possible negative influences of the material change on the predictions. The theoretical prediction accuracies for the most accurate models, based on a five-fold cross-validation, are 98.4%. For practical validation, new spectra (from stainless-steel surfaces) have been used, which were not included in the calibration data set. The overall prediction accuracy in practice was about 80% and the inaccurate predictions were only due to a few species. The development of a database provides the basis for further investigations such as the application and extension to single-cell analytics and for the characterization of biofilms.

Integration of porous graphitic carbon and carbon fiber framework for ultrahigh sulfur-loading lithium-sulfur battery

A lithium-sulfur battery, a potential next-generation secondary battery, is affected by poor conductivity of sulfur and the dissolution of intermediate polysulfides. Here we report a lithium-sulfur battery with ultrahigh sulfur loading and excellent cycling stability using porous graphitic carbon (PGC) as a high-conductivity carrier of sulfur and carbon fiber with crisscross conductive framework as an electric attachment site of sulfur. PGC is fabricated through a simple and environmentally friendly synthesis process involving high-temperature graphitization in a N₂ atmosphere followed by an annealing process in air. Due to the presence of porous graphitic structure, with C-O termination groups, PGC endows the lithium-sulfur battery system with excellent cycling performance. The lithium-sulfur battery cathode constructed by PGC with a sulfur loading of 2.5 mg cm^-2 still retains a high specific capacity of 734.4 mA h g^-1 after 200 cycles. Meanwhile, an ultrahigh sulfur loading of 12.8 mg cm^-2 for a CR2025 coin cell is achieved, which is the highest sulfur loading reported in the literature for the coin cell. The ultrahigh sulfur loading cell also shows good electrochemical properties, profiting from the mesopores terminated with C-O groups, high specific surface area of 1129.9 m² g^-1 and high-conductivity graphitic structure.

An ensemble approach to the structure-function problem in microbial communities

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Simultaneous Production of Antibacterial Protein and Lipopeptides in Bacillus tequilensis, Detected by MALDI-TOF and GC Mass Analyses

As antibiotic resistance is nowadays one of the important challenges, efforts are crucial for the discovery of novel antibacterial drugs. This study aimed to evaluate antimicrobial/anticancerous activities of halophilic bacilli from the human microbiota. A spore-forming halotolerant bacterium with antibacterial effect against Staphylococcus aureus was isolated from healthy human feces. The antibacterial protein components of the extracted supernatant were identified by SDS-PAGE and zymography. The MALDI-TOF, GC mass, and FTIR analyses were used for peptide and lipopeptide identification, respectively. The stability, toxicity, and anticancerous effects were investigated using MTT and Flow cytometry methods. According to the molecular analysis, the strain was identified as Bacillus tequilensis and showed potential probiotic properties, such as bile and acid resistance, as well as eukaryotic cell uptake. SDS-PAGE and zymography showed that 15 and 10-kDa fragments had antibacterial effects. The MALDI-TOF mass analysis indicated that the 15-kDa fragment was L1 ribosomal protein, which was the first report of the RpL1 in bacilli. GC-mass and FTIR analyses confirmed the lipopeptide nature of the 10-kDa fragment. Both the extracted fractions (precipitation or "P" and chloroform or "C" fractions) were stable at < 100 °C for 10 min, and their antibacterial effects were preserved for more than 6 months. Despite its non-toxicity, the P fraction had anticancer activities against MCF7 cells. The anticancer and antibacterial properties of B. tequilensis, along with its non-toxicity and stability, have made it a potential candidate for studying the beneficial probiotic properties for humans and drug production.

Surface-enhanced Raman spectroscopy for identification of food processing bacteria

Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.

Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification

Invasive fungal infections caused by yeasts of the genus Candida carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among Candida species, the most concerning specie being Candida auris, which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for Candida species face a severe trade-off between testing speed and accuracy. Here, we propose and validate a machine-learning approach adapted to Raman spectroscopy as a rapid, precise, and labor-efficient method of clinical microbiology for C. auris identification and drug efficacy assessments. This paper demonstrates that the combination of Raman spectroscopy and machine learning analyses can provide an insightful and flexible mycology diagnostic tool, easily applicable on-site in the clinical environment.

Graphene edge method for three‐dimensional probing of Raman microscopes focal volumes

In this work, a layer of graphene was used as a standard material for the measurement of the dimensions of Raman microscopes focal volumes of different confocal Raman spectrometers equipped with different objectives and excitation laser wavelengths. This method consists in probing the volume near the focal point of the system by using a flat graphene monolayer sheet with a straight edge. Graphene was selected because of its high Raman cross section and mechanically and chemically stability, allowing fast measurements and easy manipulation. In this paper, a method to employ graphene to accurately and precisely measure the three dimensions of the focal volume of a Raman microscope is presented; scanning along the axial and lateral directions, it is possible to reconstruct the three dimensions of the focal volume. Furthermore, these operations can be combined in a single procedure which allows the measurement of projections of the volume on planes parallel to the optical axis. Knowledge of these parameters enable absolute quantification of Raman‐active molecules and support high‐resolution Raman imaging.

Resonance Raman Spectra for the In Situ Identification of Bacteria Strains and Their Inactivation Mechanism

The resonance Raman spectra of bacterial carotenoids have been employed to identify bacterial strains and their intensity changes as a function of ultraviolet (UV) radiation dose have been used to differentiate between live and dead bacteria. In addition, the resonance-enhanced Raman spectra enabled us to detect bacteria in water at much lower concentrations (∼10⁸ cells/mL) than normally detected spectroscopically. A handheld spectrometer capable of recording resonance Raman spectra in situ was designed, constructed, and was used to record the spectra. In addition to bacteria, the method presented in this paper may also be used to identify fungi, viruses, and plants, in situ, and detect infections within a very short period of time.

Sphingopyxis microcysteis sp. nov., a novel bioactive exopolysaccharides-bearing Sphingomonadaceae isolated from the Microcystis phycosphere

During the study into the microbial biodiversity and bioactivity of the Microcystis phycosphere, a new yellow-pigmented, non-motile, rod-shaped bacterium containing polyhydroxybutyrate granules designated as strain Z10-6^T was isolated from highly-toxic Microcystis aeruginosa Kützing M.TN-2. The new isolate produces active bioflocculating exopolysaccharides. Phylogenetic analysis based on 16S rRNA gene sequences indicated strain Z10-6^T belongs to the genus Sphingopyxis with highest similarity to Sphingopyxis solisilvae R366^T (98.86%), and the similarity to other Sphingopyxis members was less than 98.65%. However, both low values obtained by phylogenomic calculation of average nucleotide identity (ANI, 85.5%) and digital DNA-DNA hybridization (dDDH, 29.8%) separated the new species from its closest relative. The main polar lipids were sphingoglycolipid, phosphatidylethanolamine, phosphatidylglycerol, diphosphatidylglycerol, one unidentified glycolipid and one unidentified aminophospholipid. The predominant fatty acids were summed feature 8, C_17:1ω6c, summed feature 3, C_16:0, C_18:1ω7c 11-methyl and C_14:0 2-OH. The respiratory quinone was ubiqunone-10, with spermidine as the major polyamine. The genomic DNA G + C content was 64.8 mol%. Several biosynthesis pathways encoding for potential new bacterial bioactive metabolites were found in the genome of strain Z10-6^T. The polyphasic analyses clearly distinguished strain Z10-6^T from its closest phylogenetic neighbors. Thus, it represents a novel species of the genus Sphingopyxis, for which the name Sphingopyxis microcysteis sp. nov. is proposed. The type strain is Z10-6^T (= CCTCC AB2017276^T = KCTC 62492^T).

Decoding Optical Data with Machine Learning

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.

Abiotic stressors impact outer membrane vesicle composition in a beneficial rhizobacterium: Raman spectroscopy characterization

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria have roles in cell-to-cell signaling, biofilm formation, and stress responses. Here, the effects of abiotic stressors on OMV contents and composition from biofilm cells of the plant health-promoting bacterium Pseudomonas chlororaphis O6 (PcO6) are examined. Two stressors relevant to this root-colonizing bacterium were examined: CuO nanoparticles (NPs)-a potential fertilizer and fungicide- and H2O2-released from roots during plant stress responses. Atomic force microscopy revealed 40-300 nm diameter OMVs from control and stressed biofilm cells. Raman spectroscopy with linear discriminant analysis (LDA) was used to identify changes in chemical profiles of PcO6 cells and resultant OMVs according to the cellular stressor with 84.7% and 83.3% accuracies, respectively. All OMVs had higher relative concentrations of proteins, lipids, and nucleic acids than PcO6 cells. The nucleic acid concentration in OMVs exhibited a cellular stressor-dependent increase: CuO NP-induced OMVs > H2O2-induced OMVs > control OMVs. Biochemical assays confirmed the presence of lipopolysaccharides, nucleic acids, and protein in OMVs; however, these assays did not discriminate OMV composition according to the cellular stressor. These results demonstrate the sensitivity of Raman spectroscopy using LDA to characterize and distinguish cellular stress effects on OMVs composition and contents.

Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms

Microscale processes are critically important to soil ecology and biogeochemistry yet are difficult to study due to soil's opacity and complexity. To advance the study of soil processes, we constructed transparent soil microcosms that enable the visualization of microbes via fluorescence microscopy and the non-destructive measurement of microbial activity and carbon uptake in situ via Raman microspectroscopy. We assessed the polymer Nafion and the crystal cryolite as optically transparent soil substrates. We demonstrated that both substrates enable the growth, maintenance, and visualization of microbial cells in three dimensions over time, and are compatible with stable isotope probing using Raman. We applied this system to ascertain that after a dry-down/rewetting cycle, bacteria on and near dead fungal hyphae were more metabolically active than those far from hyphae. These data underscore the impact fungi have facilitating bacterial survival in fluctuating conditions and how these microcosms can yield insights into microscale microbial activities.

Identification of a resonance Raman marker for cytochrome to monitor stress responses in Escherichia coli

Raman spectroscopy and resonance Raman spectroscopy are widely used to study bacteria and their responses to different environmental conditions. In the present study, the identification of a novel resonance Raman peak for Escherichia coli, recorded with 633 nm laser excitation is discussed. A peak at 740 cm^-1 is observed exclusively with 633 nm excitation but not with 514 nm or 785 nm excitation. This peak is absent in the lag phase but appears in the log phase of bacterial growth. The intensity of the peak increases at high temperature (45 °C) compared with growth at low temperature (25 °C) or the physiological temperature (37 °C). Although osmotic stress lowered bacterial growth, the intensity of this peak was unaffected. However, treatment with chemical uncouplers of oxidative phosphorylation resulted in significantly lower intensity of this Raman band, indicating its possible involvement in respiration. Cytochromes, a component of bacterial respiration' can show resonance enhancement at 633 nm due to the presence of a shoulder in that region depending on the type and conformation of cytochrome. Therefore, the peak intensity was monitored in different genetic mutants of E. coli lacking cytochromes. This peak is absent in the Escherichia coli mutant lacking cydB, but not ccmE, demonstrating the contribution of cytochrome bd subunit II in the peak's origin. In future, this newly found cytochrome marker can be used for biochemical assessment of bacteria exposed to various conditions. Overall, this finding opens the scope for use of red laser excitation in resonance Raman in monitoring stress and respiration in bacteria. Graphical abstract.

Evaluation of the impact of buffered peptone water composition on the discrimination between Salmonella enterica and Escherichia coli by Raman spectroscopy

The detection of Salmonella spp. in food samples is regulated by the ISO 6579:2002 standard, which requires that precise procedures are followed to ensure the reliability of the detection process. This standard requires buffered peptone water as a rich medium for the enrichment of bacteria. However, the effects of different brands of buffered peptone water on the identification of microorganisms by Raman spectroscopy are unknown. In this regard, our study evaluated the discrimination between two bacterial species, Salmonella enterica and Escherichia coli, inoculated and analyzed with six of the most commonly used buffered peptone water brands. The results showed that bacterial cells behaved differently according to the brand used in terms of biomass production and the spectral fingerprint. The identification accuracy of the analyzed strains was between 85% and 100% depending on the given brand. Several batches of two brands were studied to evaluate the classification rates between the analyzed bacterial species. The chemical analysis performed on these brands showed that the nutrient content was slightly different and probably explained the observed effects. On the basis of these results, Raman spectroscopy operators are encouraged to select an adequate culture medium and continue its use throughout the identification process to guarantee optimal recognition of the microorganism of interest.

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Infectious diseases caused by bacteria still pose major diagnostic challenges in spite of the availability of various molecular approaches. Irrespective of the type of infection, rapid identification of the causative pathogen with a high degree of sensitivity and specificity is essential for initiating appropriate treatment. While existing methods like PCR possess high sensitivity, they are incapable of identifying the viability status of the pathogen and those which can, like culturing, are inherently slow. To overcome these limitations, we developed a diagnostic platform based on Raman microspectroscopy, capable of detecting biochemical signatures from a single bacterium for identification as well as viability assessment. The study also establishes a decontamination protocol for handling live pathogenic bacteria which does not affect identification and viability testing, showing applicability in the analysis of sputum samples containing pathogenic mycobacterial strains. The minimal sample processing along with multivariate analysis of spectroscopic signatures provides an interface for automatic classification, allowing the prediction of unknown samples by mapping signatures onto available datasets. Also, the novelty of the current work is the demonstration of simultaneous identification and viability assessment at a single bacterial level for pathogenic bacteria. Graphical abstract.

The effect of excitation laser power in Raman spectroscopic measurements of the degree of conversion of resin composites

OBJECTIVES

To evaluate the effect of excitation laser power in Raman spectrometry by comparing the spectra and the degree of conversion (DC) values obtained using excitation powers between 300 and 1000mW.

METHODS

Five commercial and three experimental resin composites were light cured at 1200mW/cm² for 10-20s from a commercial blue-violet LED dental curing unit. Raman spectra were collected from composite specimens within 9min after light-curing. The excitation laser (1064nm) was focused on the spot of 0.4mm in diameter. The following powers were used for specimen excitation (mW): 300, 400, 600, 800, and 1000. From Raman spectra, the DC values were calculated and compared among different laser powers. Also, vector-normalized Raman spectra collected using the lowest excitation power (300mW) were compared to those collected using the maximum excitation power (1000mW).

RESULTS

Varying the excitation laser power between 300 and 1000mW resulted in statistically significant differences in both the DC values and the intensity of particular spectral features. The effect of varying laser power on Raman spectra and obtained DC values was material-dependent. The DC values measured within an individual material using different laser powers varied between 3.2 and 7.2% (absolute DC difference). The spectral bands affected by variations in laser power were assigned to symmetric and asymmetric stretching of -CH₂ (2900-3100cm^-1), symmetric stretching of aliphatic CC (1640cm^-1) and scissoring of C-H (1458cm^-1).

SIGNIFICANCE

The DC can be artificially elevated through increasing excitation laser power. This effect should be considered in Raman spectroscopic evaluations of DC in specimens during ongoing post-cure polymerization.

High Correlation Between Structure Development and Chemical Variation During Biofilm Formation by Vibrio parahaemolyticus

The complex three-dimensional structure of biofilms is supported by extracellular polymeric substances (EPSs) and additional insight on chemical variations in EPS and biofilm structure development will inform strategies for control of biofilms. Vibrio parahaemolyticus VPS36 biofilm development was studied using confocal laser scanning microscopy (CLSM) and Raman spectroscopy (RM). The structural parameters of the biofilm (biovolume, mean thickness, and porosity) were characterized by CLSM and the results showed that VPS36 biofilm formed dense structures after 48 h incubation. There were concurrent variations in carbohydrates and nucleic acids contents in the EPS as evidenced by RM. The Raman intensities of the chemical component in EPS, measured using Pearson's correlation coefficient, were positively correlated with biovolume and mean thickness, and negatively correlated with porosity. The Raman intensity for carbohydrates correlated closely with mean thickness (p-value < 0.01) and the Raman intensity for nucleic acid correlated closely with porosity (p-value < 0.01). Additional evidence for these correlations were confirmed using scanning electron microscopic (SEM) and crystal violet staining.

Toward Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity

This study reports a proof-of concept study to demonstrate the novel approach of phenotyping microbial communities in enhanced biological phosphorus removal (EBPR) systems using single cell Raman microspectroscopy and link it with phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell Raman spectral fingerprints and intracellular polymer signatures to separate and classify the functionally relevant populations in EBPR systems, namely polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), as well as other microbial populations. We then investigated the link between Raman-based community phenotyping and 16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems with varying solid retention time (SRT) to gain insights into possible genotype-function relationships. Combined and simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs, and their association with EBPR performance. The phenotypic diversity and plasticity of PAO populations, which otherwise could not be obtained with phylogenetic analysis alone, showed complex but potentially crucial association with EBPR process stability.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Identification of natural product compounds as quorum sensing inhibitors in Pseudomonas fluorescens P07 through virtual screening

Pseudomonas fluorescens, a Gram-negative psychrotrophic bacteria, is the main microorganism causing spoilage of chilled raw milk and aquatic products. Quorum sensing (QS) widely exists in bacteria to monitor their population densities and regulate numerous physiological activities, such as the secretion of siderophores, swarming motility and biofilm formation. Thus, searching for quorum sensing inhibitors (QSIs) may be another promising way to control the deterioration of food caused by P. fluorescens. Here, we screened a traditional Chinese medicine (TCM) database to discover potential QSIs with lesser toxicity. The gene sequences of LuxI- and LuxR-type proteins of P. fluorescens P07 were obtained through whole-genome sequencing. In addition, the protein structures built by homology modelling were used as targets to screen for QSIs. Twenty-one compounds with a dock score greater than 6 were purchased and tested by biosensor strains (Chromobacterium violaceum CV026 and Agrobacterium tumefaciens A136). The results showed that 10 of the compounds were determined as hits (hit rate: 66.67%). Benzyl alcohol, rhodinyl formate and houttuynine were effective QSIs. The impact of the most active compound (benzyl alcohol) on the phenotypes of P. fluorescens P07, including swimming and swarming motility, production of extracellular enzymes and siderophores, N-acylhomoserine lactone (AHLs) content and biofilm formation were determined. The inhibitory mechanism of benzyl alcohol on the QS system of P. fluorescens P07 is further discussed. This study reveals the feasibility of searching for novel QSIs through virtual screening.

Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress

Analyzing the cells in various body fluids can greatly deepen the understanding of the mechanisms governing the cellular physiology. Due to the variability of physiological and metabolic states, it is important to be able to perform such studies on individual cells. Therefore, we developed an optofluidic system in which we precisely manipulated and monitored individual cells of Escherichia coli. We tested optical micromanipulation in a microfluidic chamber chip by transferring individual bacteria into the chambers. We then subjected the cells in the chambers to antibiotic cefotaxime and we observed the changes by using time-lapse microscopy. Separately, we used laser tweezers Raman spectroscopy (LTRS) in a different micro-chamber chip to manipulate and analyze individual cefotaxime-treated E. coli cells. Additionally, we performed conventional Raman micro-spectroscopic measurements of E. coli cells in a micro-chamber. We found observable changes in the cellular morphology (cell elongation) and in Raman spectra, which were consistent with other recently published observations. The principal component analysis (PCA) of Raman data distinguished between the cefotaxime treated cells and control. We tested the capabilities of the optofluidic system and found it to be a reliable and versatile solution for this class of microbiological experiments.

Raman Spectroscopy as a Tool for Quality and Sterility Analysis for Tissue Engineering Applications like Cartilage Transplants

At present, the production of tissue engineered cartilage requires the concurrent production of two identical transplants. One transplant is used for destructive quality control and the second one is implanted into the patient. A non-invasive characterization of such tissue engineering samples would be a promising tool to achieve a production process of just one transplant that is both implanted and tested. Raman spectroscopy is a method that satisfies this requirement by analyzing cells without lysis, fixation or the use of any chemicals. This pure optical technique is based on inelastic scattering of laser photons by molecular vibrations of biopolymers. Characteristic peaks in Raman spectra of cells could be assigned to typical biochemical molecules present in biological samples. For the analysis of chondrocytes present in cartilage transplants, the determination of the cell vitality as well as the discrimination of another cell type have been studied by Raman spectroscopy. Another bottleneck in such biological processes under GMP conditions is sterility control, as most of the commonly used methods require long cultivation times. Raman spectroscopy provides a good alternative to conventional methods in terms of time saving. In this study, the potential of Raman spectroscopy as a quality and sterility control tool for tissue engineering applications was studied by analyzing and comparing the spectra of cell and bacteria cultures.

Diagnostic Bacteriology: Raman Spectroscopy

Current clinical methodology for identification of bacterial infections relies predominantly on culturing microbes from patient material and performing biochemical tests. This can often be an inefficient and lengthy process, which has a significant detrimental effect upon patient care. Techniques used in other aspects of molecular research have the potential to revolutionize the way in which diagnostic tests are used and delivered in the clinical setting. The need for rapid, accurate, and cost-effective molecular techniques in the diagnostic laboratory is imperative to improving patient care, preventing the spread of drug resistance and decreasing the overall burden associated with nosocomial infections. Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) are powerful vibrational spectroscopy techniques that are being developed for highly sensitive pathogen identification in complex clinical samples. Raman spectroscopy is a molecular technique that is capable of probing samples noninvasively and nondestructively. It has been used with high specificity to assess tissue and bacterial samples at the molecular level with diverse clinical and diagnostic applications. SERS has recently developed out of the advances in the Raman spectroscopy arena. This technique is designed to amplify Raman scattering and allows for better differentiation of bacterial isolates. Although the current parameters for the use of SERS require a pure culture and are relatively monoparametric, current breakthroughs and testing are pushing the technology to new levels and thus changing the face of modern bacterial diagnostics.

Culture-Free Detection of Crop Pathogens at the Single-Cell Level by Micro-Raman Spectroscopy

The rapid and sensitive identification of invasive plant pathogens has important applications in biotechnology, plant quarantine, and food security. Current methods are far too time-consuming and need a pre-enrichment period ranging from hours to days. Here, a micro-Raman spectroscopy-based bioassay for culture-free pathogen quarantine inspection at the single cell level within 40 min is presented. The application of this approach can readily and specifically detect plant pathogens Burkholderia gladioli pv. alliicola and Erwinia chrysanthemi that are closely related pathogenically. Furthermore, the single-bacterium detection was able to discriminate them from a reference Raman spectral library including multiple quarantine-relevant pathogens with broad host ranges and an array of pathogenic variants. To show the usefulness of this assay, Burkholderia gladioli pv. alliicola and Erwinia chrysanthemi are detected at single-bacterium level in plant tissue lesions without pre-enrichment. The results are confirmed by the plate-counting method and a genetic molecular approach, which display comparable recognition ratios to the Raman spectroscopy-based bioassay. The results represent a critical step toward the use of micro-Raman spectroscopy in rapid and culture-free discrimination of quarantine relevant plant pathogens.

Purple spot damage dynamics investigated by an integrated approach on a 1244 A.D. parchment roll from the Secret Vatican Archive

Ancient parchments are commonly attacked by microbes, producing purple spots and detachment of the superficial layer. Neither standard cultivation nor molecular methods (DGGE) solved the issue: causative agents and colonization model are still unknown. To identify the putative causal agents, we describe the 16 S rRNA gene analysis (454-pyrosequencing) of the microbial communities colonizing a damaged parchment roll dated 1244 A.D. (A.A. Arm. I-XVIII 3328, Vatican Secret Archives). The taxa in damaged or undamaged areas of the same document were different. In the purple spots, marine halotolerant Gammaproteobacteria, mainly Vibrio, were found; these microorganisms are rare or absent in the undamaged areas. Ubiquitous and environmental microorganisms were observed in samples from both damaged and undamaged areas. Pseudonocardiales were the most common, representing the main colonizers of undamaged areas. We hypothesize a successional model of biodeterioration, based on metagenomic data and spectroscopic analysis of pigments, which help to relate the damage to a microbial agent. Furthermore, a new method (Light Transmitted Analysis) was utilized to evaluate the kind and entity of the damage to native collagen. These data give a significant advance to the knowledge in the field and open new perspectives to remediation activity on a huge amount of ancient document.

Accurate and Rapid Differentiation of Acinetobacter baumannii Strains by Raman Spectroscopy: a Comparative Study

In recent years, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has become the standard for routine bacterial species identification due to its rapidity and low costs for consumables compared to those of traditional DNA-based methods. However, it has been observed that strains of some bacterial species, such as Acinetobacter baumannii strains, cannot be reliably identified using mass spectrometry (MS). Raman spectroscopy is a rapid technique, as fast as MALDI-TOF, and has been shown to accurately identify bacterial strains and species. In this study, we compared hierarchical clustering results for MS, genomic, and antimicrobial susceptibility test data to hierarchical clustering results from Raman spectroscopic data for 31 A. baumannii clinical isolates labeled according to their pulsed-field gel electrophoresis data for strain differentiation. In addition to performing hierarchical cluster analysis (HCA), multiple chemometric methods of analysis, including principal-component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were performed on the MS and Raman spectral data, along with a variety of spectral preprocessing techniques for best discriminative results. Finally, simple HCA algorithms were performed on all of the data sets to explore the relationships between, and natural groupings of, the strains and to compare results for the four data sets. To obtain numerical comparison values of the clustering results, the external cluster evaluation criteria of the Rand index of the HCA dendrograms were calculated. With a Rand index value of 0.88, Raman spectroscopy outperformed the other techniques, including MS (with a Rand index value of 0.58).

Plasmonic nanoparticles in chemical analysis

In this review various analytical techniques utilising the plasmonic properties of silver and gold nanoparticles have been presented.

Exploring Morphological and Biochemical Linkages in Fungal Growth with Label-Free Light Sheet Microscopy and Raman Spectroscopy

Imaging tip growth in fungal hyphae is highly warranted to unravel the molecular mechanism of this extraordinarily precise and localized phenomenon. In situ probing of fungal cultures, however, have been challenging due to their inherent complexity and light penetration issues associated with conventional optical imaging. In this work, we report a label-free approach using a combination of light sheet microscopy and Raman spectroscopy to obtain concomitant morphological and biochemical information from the growing specimen. We show that the variance in morphology in the symbiotic fungus Piriformospora indica are rooted in the underlying differences in chemical composition in the specific growth zones. Our findings suggest that this potent two-pronged approach can comprehensively characterize growth areas and elucidate microbe interactions in still developing colonies with high sensitivity and multiplexing capability.

Single-cell level methods for studying the effect of antibiotics on bacteria during infection

Considerable evidence about phenotypic heterogeneity among bacteria during infection has accumulated during recent years. This heterogeneity has to be considered if the mechanisms of infection and antibiotic action are to be understood, so we need to implement existing and find novel methods to monitor the effects of antibiotics on bacteria at the single-cell level. This review provides an overview of methods by which this aim can be achieved. Fluorescence label-based methods and Raman scattering as a label-free approach are discussed in particular detail. Other label-free methods that can provide single-cell level information, such as impedance spectroscopy and surface plasmon resonance, are briefly summarized. The advantages and disadvantages of these different methods are discussed in light of a challenging in vivo environment.

A study on identification of bacteria in environmental samples using single-cell Raman spectroscopy: feasibility and reference libraries

We report on our recent efforts towards identifying bacteria in environmental samples by means of Raman spectroscopy. We established a database of Raman spectra from bacteria submitted to various environmental conditions. This dataset was used to verify that Raman typing is possible from measurements performed in non-ideal conditions. Starting from the same dataset, we then varied the phenotype and matrix diversity content included in the reference library used to train the statistical model. The results show that it is possible to obtain models with an extended coverage of spectral variabilities, compared to environment-specific models trained on spectra from a restricted set of conditions. Broad coverage models are desirable for environmental samples since the exact conditions of the bacteria cannot be controlled.

Continuous cell sorting in a flow based on single cell resonance Raman spectra

Single cell Raman spectroscopy measures a spectral fingerprint of the biochemistry of cells, and provides a powerful method for label-free detection of living cells without the involvement of a chemical labelling strategy. However, as the intrinsic Raman signals of cells are inherently weak, there is a significant challenge in discriminating and isolating cells in a flowing stream. Here we report an integrated Raman-microfluidic system for continuous sorting of a stream of cyanobacteria, Synechocystis sp. PCC6803. These carotenoid-containing microorganisms provide an elegant model system enabling us to determine the sorting accuracy using the subtly different resonance Raman spectra of microorganism cultured in a (12)C or (13)C carbon source. Central to the implementation of continuous flow sorting is the use of "pressure dividers" that eliminate fluctuations in flow in the detection region. This has enabled us to stabilise the flow profile sufficiently to allow automated operation with synchronisation of Raman acquisition, real-time classification and sorting at flow rates of ca. <100 μm s(-1), without the need to "trap" the cells. We demonstrate the flexibility of this approach in sorting mixed cell populations with the ability to achieve 96.3% purity of the selected cells at a speed of 0.5 Hz.

Raman activated cell sorting

Single cell Raman spectra (SCRS) are intrinsic biochemical profiles and 'chemical images' of single cells which can be used to characterise phenotypic changes, physiological states and functions of cells. On the base of SCRS, Raman activated cell sorting (RACS) provides a label-free cell sorting approach, which can link single cells to their chemical or phenotypic profiles. Overcoming naturally weak Raman signals, establishing Raman biomarker as sorting criteria to RACS and improving specific sorting technology are three challenges of developing RACS. Advances on Raman spectroscopy such as stimulated Raman scattering (SRS) and pre-screening helped to increase RACS sorting speed. Entire SCRS can be characterised using pattern recognition methods, and specific Raman bands can be extracted as biomarkers for RACS. Recent advances on cell sorting technologies based on microfluidic device and surface-ejection enable accurate and reliable single cell sorting from complex samples. A high throughput RACS will be achievable in near future by integrating fast Raman detection system such as SRS with microfluidic RACS and Raman activated cell ejection (RACE).

Detection of Explosives Using Differential Laser-Induced Perturbation Spectroscopy with a Raman-based Probe

Explosives detection is carried out with a novel spectral analysis technique referred to as differential laser-induced perturbation spectroscopy (DLIPS) on thin films of TNT, RDX, HMX, and PETN. The utility of Raman spectroscopy for detection of explosives is enhanced by inducing deep ultraviolet laser perturbation on molecular structures in combination with a differential Raman sensing scheme. Principal components analysis (PCA) is used to quantify the DLIPS method as benchmarked against a traditional Raman scattering probe, and the related photo-induced effects on the molecular structure of the targeted explosives are discussed in detail. Finally, unique detection is observed with TNT samples deposited on commonly available background substrates of nylon and polyester. Overall, the data support DLIPS as a noninvasive method that is promising for screening explosives in real-world environments and backgrounds.

Characterization of carotenoids in soil bacteria and investigation of their photodegradation by UVA radiation via resonance Raman spectroscopy

A soil habitat consists of an enormous number of pigmented bacteria with the pigments mainly composed of diverse carotenoids. Most of the pigmented bacteria in the top layer of the soil are photoprotected from exposure to huge amounts of UVA radiation on a daily basis by these carotenoids. The photostability of these carotenoids depends heavily on the presence of specific features like a carbonyl group or an ionone ring system on its overall structure. Resonance Raman spectroscopy is one of the most sensitive and powerful techniques to detect and characterize these carotenoids and also monitor processes associated with them in their native system at a single cell resolution. However, most of the resonance Raman profiles of carotenoids have very minute differences, thereby making it extremely difficult to confirm if these differences are attributed to the presence of different carotenoids or if it is a consequence of their interaction with other cellular components. In this study, we devised a method to overcome this problem by monitoring also the photodegradation of the carotenoids in question by UVA radiation wherein a differential photodegradation response will confirm the presence of different carotenoids irrespective of the proximities in their resonance Raman profiles. Using this method, the detection and characterization of carotenoids in pure cultures of five species of pigmented coccoid soil bacteria is achieved. We also shed light on the influence of the structure of the carotenoid on its photodegradation which can be exploited for use in the characterization of carotenoids via resonance Raman spectroscopy.

Interrogating chemical variation via layer-by-layer SERS during biofouling and cleaning of nanofiltration membranes with further investigations into cleaning efficiency

Periodic chemical cleaning is an essential step to maintain nanofiltration (NF) membrane performance and mitigate biofouling, a major impediment in high-quality water reclamation from wastewater effluent. To target the important issue of how to clean and control biofouling more efficiently, this study developed surface-enhanced Raman spectroscopy (SERS) as a layer-by-layer tool to interrogate the chemical variations during both biofouling and cleaning processes. The fact that SERS only reveals information on the surface composition of biofouling directly exposed to cleaning reagents makes it ideal for evaluating cleaning processes and efficiency. SERS features were highly distinct and consistent with different biofouling stages (bacterial adhesion, rapid growth, mature and aged biofilm). Cleaning was performed on two levels of biofouling after 18 h (rapid growth of biofilm) and 48 h (aged biofilm) development. An opposing profile of SERS bands between biofouling and cleaning was observed and this suggests a layer-by-layer cleaning mode. In addition, further dynamic biochemical and infrastructural changes were demonstrated to occur in the more severe 48-h biofouling, resulting in the easier removal of sessile cells from the NF membrane. Biofouling substance-dependent cleaning efficiency was also evaluated using the surfactant sodium dodecyl sulfate (SDS). SDS appeared more efficient in cleaning lipid than polysaccharide and DNA. Protein and DNA were the predominant residual substances (irreversible fouling) on NF membrane leading to permanent flux loss. The chemical information revealed by layer-by-layer SERS will lend new insights into the optimization of cleaning reagents and protocols for practical membrane processes.

Disperse Orange 3 as a resonance Raman probe for measuring membrane order

Resonance Raman spectra of azobenzene derivatives were examined in the presence of lipid membranes to find a probe that can distinguish different membrane phases. The NO2 symmetric stretching band of 4-(4-nitrophenylazo)aniline, also known as Disperse Orange 3 (DO3), is downshifted by about 4 cm(-1) on the phase transition of phosphatidylcholine membranes from the liquid crystalline to the gel phase. A comparable downshift also occurs when DO3 is bound to cholesterol-containing membranes in the liquid-ordered phase. Our results demonstrate that Raman spectrum of DO3 is a unique tool for measuring the molecular order of lipids in membranes.