Classification and identification of pigmented cocci bacteria relevant to the soil environment via Raman spectroscopy

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.

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.

Impact of key parameters involved with plant-microbe interaction in context to global climate change

Anthropogenic activities have a critical influence on climate change that directly or indirectly impacts plant and microbial diversity on our planet. Due to climate change, there is an increase in the intensity and frequency of extreme environmental events such as temperature rise, drought, and precipitation. The increase in greenhouse gas emissions such as CO2, CH4, NOx, water vapor, increase in global temperature, and change in rainfall patterns have impacted soil–plant-microbe interactions, which poses a serious threat to food security. Microbes in the soil play an essential role in plants’ resilience to abiotic and biotic stressors. The soil microbial communities are sensitive and responsive to these stressors. Therefore, a systemic approach to climate adaptation will be needed which acknowledges the multidimensional nature of plant-microbe-environment interactions. In the last two scores of years, there has been an enhancement in the understanding of plant’s response to microbes at physiological, biochemical, and molecular levels due to the availability of techniques and tools. This review highlights some of the critical factors influencing plant-microbe interactions under stress. The association and response of microbe and plants as a result of several stresses such as temperature, salinity, metal toxicity, and greenhouse gases are also depicted. New tools to study the molecular complexity of these interactions, such as genomic and sequencing approaches, which provide researchers greater accuracy, reproducibility, and flexibility for exploring plant-microbe–environment interactions under a changing climate, are also discussed in the review, which will be helpful in the development of resistant crops/plants in present and future.

Label-Free Multiphoton Imaging of Microbes in Root, Mineral, and Soil Matrices with Time-Gated Coherent Raman and Fluorescence Lifetime Imaging

Imaging biogeochemical interactions in complex microbial systems─such as those at the soil-root interface─is crucial to studies of climate, agriculture, and environmental health but complicated by the three-dimensional (3D) juxtaposition of materials with a wide range of optical properties. We developed a label-free multiphoton nonlinear imaging approach to provide contrast and chemical information for soil microorganisms in roots and minerals with epi-illumination by simultaneously imaging two-photon excitation fluorescence (TPEF), coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), and sum-frequency mixing (SFM). We used fluorescence lifetime imaging (FLIM) and time gating to correct CARS for the autofluorescence background native to soil particles and fungal hyphae (TG-CARS) using time-correlated single-photon counting (TCSPC). We combined TPEF, TG-CARS, and FLIM to maximize image contrast for live fungi and bacteria in roots and soil matrices without fluorescence labeling. Using this instrument, we imaged symbiotic arbuscular mycorrhizal fungi (AMF) structures within unstained plant roots in 3D to 60 μm depth. High-quality imaging was possible at up to 30 μm depth in a clay particle matrix and at 15 μm in complex soil preparation. TG-CARS allowed us to identify previously unknown lipid droplets in the symbiotic fungus, Serendipita bescii. We also visualized unstained putative bacteria associated with the roots of Brachypodium distachyon in a soil microcosm. Our results show that this multimodal approach holds significant promise for rhizosphere and soil science research.

Investigation of Raman Spectroscopic Signatures with Multivariate Statistics: An Approach for Cataloguing Microbial Biosignatures

Spectroscopic instruments are increasingly being implemented in the search for extraterrestrial life. However, microstructural spectral analyses of alien environments could prove difficult without knowledge on the molecular identification of individual spectral signatures. To bridge this gap, we introduce unsupervised K-means clustering as a statistical approach to discern spectral patterns of biosignatures without prior knowledge of spectral regions of biomolecules. Spectral profiles of bacterial isolates from analogous polar ice sheets were measured with Raman spectroscopy. Raman analysis identified carotenoid and violacein pigments, and key cellular features including saturated and unsaturated fats, triacylglycerols, and proteins. Principal component analysis and targeted spectra integration biplot analysis revealed that the clustering of bacterial isolates was attributed to spectral biosignatures influenced by carotenoid pigments and ratio of unsaturated/saturated fat peaks. Unsupervised K-means clustering highlighted the prevalence of the corresponding spectral peaks, while subsequent supervised permutational multivariate analysis of variance provided statistical validation for spectral differences associated with the identified cellular features. Establishing a validated catalog of spectral signatures of analogous biotic and abiotic materials, in combination with targeted supervised tools, could prove effective at identifying extant biosignatures.

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.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes

Microalgae are among the most genetically and metabolically diverse organisms on earth, yet their identification and metabolic profiling have generally been slow and tedious. Here, we established a reference ramanome database consisting of single-cell Raman spectra (SCRS) from >9000 cells of 27 phylogenetically diverse microalgal species, each under stationary and exponential states. When combined, prequenching ("pigment spectrum" (PS)) and postquenching ("whole spectrum" (WS)) signals can classify species and states with 97% accuracy via ensemble machine learning. Moreover, the biosynthetic profile of Raman-sensitive metabolites was unveiled at single cells, and their interconversion was detected via intra-ramanome correlation analysis. Furthermore, not-yet-cultured cells from the environment were functionally characterized via PS and WS and then phylogenetically identified by Raman-activated sorting and sequencing. This PS-WS combined approach for rapidly identifying and metabolically profiling single cells, either cultured or uncultured, greatly accelerates the mining of microalgae and their products.

Characterization of Clinically Relevant Fungi via SERS Fingerprinting Assisted by Novel Chemometric Models

Nonculture-based tests are gaining popularity and upsurge in the diagnosis of invasive fungal infections (IFI) fostered by their main asset, the reduced analysis time, which enables a more rapid diagnosis. In this project, three different clinical isolates of relevant filamentous fungal species were discriminated by using a rapid (less than 5 min) and sensitive surface-enhanced Raman scattering (SERS)-based detection method, assisted by chemometrics. The holistic evaluation of the SERS spectra was performed by employing appropriate chemometric tools-classical and fuzzy principal component analysis (FPCA) in combination with linear discriminant analysis (LDA) applied to the first relevant principal components. The efficiency of the proposed robust algorithm is illustrated on the data set including three fungal isolates (Aspergillus fumigatus sensu stricto, cryptic A. fumigatus complex species, and Rhizomucor pusillus) that were isolated from patient materials. The accurate and reliable discrimination between species of common fungal pathogen strains suggest that the developed method has the potential as an alternative, spectroscopic-based routine analysis tool in IFI diagnosis.

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.

Raman chemical imaging of the rhizosphere bacterium Pantoea sp. YR343 and its co-culture with Arabidopsis thaliana

Chemical imaging of plant-bacteria co-cultures makes it possible to characterize bacterial populations and behaviors and their interactions with proximal organisms, under conditions closest to the environment in the rhizosphere. Here Raman micro-spectroscopy and confocal Raman imaging are used as minimally invasive probes to study the rhizosphere bacterial isolate, Pantoea sp. YR343, and its co-culture with model plant Arabidopsis thaliana by combining enhanced Raman spectroscopies with electron microscopy and principal component analysis (PCA). The presence of carotenoid pigments in the wild type Pantoea sp. YR343 was characterized using resonance Raman scattering, which was also used to confirm successful disruption of the crtB gene in an engineered carotenoid mutant strain. Other components of the Pantoea sp. YR343 cells were imaged in the presence of resonantly enhanced pigments using a combination of surface enhanced Raman imaging and PCA. Pantoea sp. YR343 cells decorated with Ag colloid synthesized ex situ gave spectra dominated by carotenoid scattering, whereas colloids synthesized in situ produced spectral signatures characteristic of flavins in the cell membrane. Scanning electron microscopy (SEM) of whole cells and transmission electron microscopy (TEM) images of thinly sliced cross-sections were used to assess structural integrity of the coated cells and to establish the origin of spectral signatures based on the position of Ag nanoparticles in the cells. Raman imaging was also used to characterize senescent green Arabidopsis thaliana plant roots inoculated with Pantoea sp. YR343, and PCA was used to distinguish spectral contributions from plant and bacterial cells, thereby establishing the potential of Raman imaging to visualize the distribution of rhizobacteria on plant roots.