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.

Isolation of bacteria from artificial bronchoalveolar lavage fluid using density gradient centrifugation and their accessibility by Raman spectroscopy

Raman spectroscopy is an analytical method to identify medical samples of bacteria. Because Raman spectroscopy detects the biochemical properties of a cell, there are many factors that can influence and modify the Raman spectra of bacteria. One possible influence is a proper method for isolation of the bacteria. Medical samples in particular never occur in purified form, so a Raman-compatible isolation method is needed which does not affect the bacteria and thus the resulting spectra. In this study, we present a Raman-compatible method for isolation of bacteria from bronchoalveolar lavage (BAL) fluid using density gradient centrifugation. In addition to measuring the bacteria from a patient sample, the yield and the spectral influence of the isolation on the bacteria were investigated. Bacteria isolated from BAL fluid show additional peaks in comparison to pure culture bacteria, which can be attributed to components in the BAL sample. The isolation gradient itself has no effect on the spectra, and with a yield of 63% and 78%, the method is suitable for isolation of low concentrations of bacteria from a complex matrix.

A centrifugation-based clearing method allows high-throughput acidification and growth-rate measurements in milk

The turbidity of milk prohibits the use of optical density measurements for strain characterizations. This often limits research to laboratory media. Here, we cleared milk through centrifugation to remove insoluble milk solids. This resulted in a clear liquid phase, termed milk serum, in which optical density measurements can be used to track microbial growth until a pH of 5.2 is reached. At pH 5.2 coagulation of the soluble protein occurs, making the medium opaque again. We found that behavior in milk serum was predictive of that in milk for 39 Lactococcus lactis (R² = 0.81) and to a lesser extent for 42 Lactiplantibacillus plantarum (formerly Lactobacillus plantarum; R² = 0.49) strains. Hence, milk serum can be used as an optically clear alternative to milk for comparison of microbial growth and metabolic characteristics. Characterization of the growth rate, specific acidification rate for optical density at a wavelength of 600 nm, and the amount of acid produced per unit of biomass for all these strains in milk serum, showed that almost all strains could grow in milk, with higher specific acidification and growth rates of Lc. lactis strains compared with Lb. plantarum strains. Nondairy Lc. lactis isolates had a lower growth and specific acidification rate than dairy isolates. The amount of acid produced per unit biomass was relatively high and similar for Lc. lactis dairy and nondairy isolates, as opposed to Lb. plantarum isolates. Lactococcus lactis ssp. lactis showed slightly lower growth and acidification rates when compared with ssp. cremoris. For Lc. lactis strains a doubling of the specific acidification rate occurred with a doubling of the maximum growth rate. This relation was not found for Lb. plantarum strains, where the acidification rate remained relatively constant across 39 strains with growth rates ranging from 0.2 h^-1 to 0.3 h^-1. We conclude that milk serum is a valuable alternative to milk for high-throughput strain characterization during milk fermentation.

Bacterial phenotype dependency from CO2 measured by Raman spectroscopy

In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO₂, as this never remains stable in nature, but always adjusts itself in a dynamic equilibrium. So, it is obvious that the concentration of CO₂ cannot be controlled but it might have a big impact on the bacteria and bacterial composition in medical samples. When using a phenotypic method like Raman spectroscopy it is also important to know the influence of CO₂ to the dataset. To investigate the influence of CO₂ towards Raman spectra we cultivated E. coli at different concentration of CO₂ since this bacterium is able to switch metabolism from aerobic to microaerophilic conditions. After applying statistic methods small changes in the spectra became visible and it was even possible to observe the change of metabolism in this species according to the concentration of CO₂.

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.

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.

Sample preparation for Raman microspectroscopy

Raman spectroscopy and its variants allow for the investigation of a wide range of biological and biomedical samples, i. e. tissue sections, single cells and small molecules. The obtained information is on a molecular level. By making use of databases and chemometrical approaches, the chemical composition of complex samples can also be defined. The measurement procedure is straight forward, however most often sample preparation protocols must be implemented. While pure samples, such as high purity powders or highly concentrated chemicals in aqueous solutions, can be directly measured without any prior sample purification step, samples of biological origin, such as tissue sections, pathogens in suspension or biofluids, food and beverages often require pre-processing steps prior to Raman measurements. In this book chapter, different strategies for handling and processing various sample matrices for a subsequent Raman microspectroscopic analysis were introduced illustrating the high potential of this promising technique for life science and medical applications. The presented methods range from standalone techniques, such as filtration, centrifugation or immunocapture to innovative platform approaches which will be exemplary addressed. Therefore, the reader will be introduced to methods that will simplify the complexity of the matrix in which the targeted molecular species are present allowing direct Raman measurements with bench top or portable setups.

Isolation matters-processing blood for Raman microspectroscopic identification of bacteria

Bacteremia with its high mortality is a frequent case in clinical health care. Further, bacteremia includes the considerable risk of progressing to a sepsis. Even in case of survival, sepsis still entails diminished quality of life for the survivors and high indirect cost for the society. The crucial factor in sepsis is time. Therefore, timely description of adequate antibiotics is vital to reduce mortality and improve quality of life after survival. Despite that, the current gold standard of clinical bacteria diagnostic is based on cultivation of bacteria, which requires an average of 13-h cultivation. Consequently, there is a necessity for culture free identification methods without sacrificing the range of bacteria strains which can be identified. Raman microspectroscopy in general requires only single bacteria cells and has proven to offer high identification accuracies. However, the prerequisite for Raman microspectroscopy is a suitable isolation strategy to obtain single unharmed bacteria cells free from matrix. Moreover, in blood, bacteria are outnumbered by billions of blood cells. In this study, we present an isolation strategy to recover single bacteria cells from blood and evaluate their suitability for Raman microspectroscopic identification. Graphical abstract.

Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Raman spectroscopy is a label-free and non-destructive spectroscopic technique that has been explored for bacterial identification. However, noise often interferes with the interesting Raman peaks because the Raman signal is inherently weak, especially for bacterial samples. Although this problem can be solved by increasing the exposure time or the power of the excitation laser, a longer acquisition time is required or the risk of sample damage is increased. In contrast, short exposure time and low laser power often lead to inadequate acquisition of Raman scattering, in which the Raman spectra with low signal-to-noise ratio (SNR) is difficult to be further analyzed. In order to quickly and accurately characterize biological samples by using low SNR Raman measurements, a weighted spectral reconstruction based method was developed and tested on Raman spectra with low SNR from 20 bacterial samples of two species. Principal component analysis followed by support vector machine was applied on the reference Raman spectra and the spectra recovered from the low SNR Raman measurements by the proposed method, the traditional spectral reconstruction method, and four other commonly used de-noising methods for the discrimination of bacterial species. The results showed that a classification accuracy of 90% was achieved based on our method, which was comparable to that of the reference Raman spectra and showed significant advantages over other spectral recovery methods. Therefore, the weighted spectral reconstruction method can preserve the most biochemical information for the bacterial species' identification while removing the noise from the low SNR Raman spectra, in which the advantages of lesser sample damage and shorter acquisition time would promote wider biomedical applications of Raman spectroscopy.

Raman spectra recovered from low SNR Raman measurements by weighted spectral reconstruction method show excellent preservation of information about bacterial identification.

Pioneering particle-based strategy for isolating viable bacteria from multipart soil samples compatible with Raman spectroscopy

The study of edaphic bacteria is of great interest, particularly for evaluating soil remediation and recultivation methods. Therefore, a fast and simple strategy to isolate various bacteria from complex soil samples using poly(ethyleneimine) (PEI)-modified polyethylene particles is introduced. The research focuses on the binding behavior under different conditions, such as the composition, pH value, and ionic strength, of the binding buffer, and is supported by the characterization of the surface properties of particles and bacteria. The results demonstrate that electrostatic forces and hydrophobicity are responsible for the adhesion of target bacteria to the particles. Distinct advantages of the particle-based isolation strategy include simple handling, enrichment efficiency, and the preservation of viable bacteria. The presented isolation method allows a subsequent identification of the bacteria using Raman microspectroscopy in combination with chemometrical methods. This is demonstrated with a dataset of five different bacteria (Escherichia coli, Bacillus subtilis, Pseudomonas fluorescens, Streptomyces tendae, and Streptomyces acidiscabies) which were isolated from spiked soil samples. In total 92% of the Raman spectra could be identified correctly.

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.

Isolation and identification of bacteria by means of Raman spectroscopy

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed. The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view.

Toward culture-free Raman spectroscopic identification of pathogens in ascitic fluid

The identification of pathogens in ascitic fluid is standardly performed by ascitic fluid culture, but this standard procedure often needs several days. Additionally, more than half of the ascitic fluid cultures are negative in case of suspected spontaneous bacterial peritonitis (SBP). It is therefore important to identify and characterize the causing pathogens since not all of them are covered by the empirical antimicrobial therapy. The aim of this study is to show that pathogen identification in ascitic fluid is possible by means of Raman microspectroscopy and chemometrical evaluation with the advantage of strongly increased speed. Therefore, a Raman database containing more than 10000 single-cell Raman spectra of 34 bacterial strains out of 13 different species was built up. The performance of the used statistical model was validated with independent bacterial strains, which were grown in ascitic fluid.

Raman spectroscopy applied to the horizontal methods ISO 6579:2002 to identify Salmonella spp. in the food industry

Food safety is a major concern for suppliers in the food chain to ensure the safety of their products. The identification procedure requested by norms is tedious, and it often requires systematic controls and qualified staff to perform the necessary analyses. Raman spectroscopy offers new opportunities to rapidly and efficiently ascertain the presence of pathogens in samples. Nevertheless, this technique requires a standardized procedure to be applied in the industrial context. Our study shows that the variability between spectral fingerprints is related to the physiological state of the microbial species and the growth phase of the bacteria plays a crucial role in its identification by Raman spectroscopy. To improve the discrimination between closely related bacterial species, a procedure based on the selection of bacterial spectra in the exponential growth phase was proposed. Different ways to introduce Raman spectroscopy in the ISO 6579:2002 standards are also proposed from the entire process to a shorter protocol. In the latter case, the identification of bacterial colonies after the selective enrichment step was proposed with the advantages of this path in terms of simplicity and rapidity (analysis time is reduced up to 50 h from the 100 h required by the standard). The protocol validated using six food categories from industrial partners have presented a good correlation by confirmation with other laboratory classical methods. In the future, this procedure could be introduced to the control system of the food production chain with a reliable database for various microorganisms encountered in this field.

Identification of water pathogens by Raman microspectroscopy

Legionella species can be found living in water mostly in a viable but nonculturable state or associated with protozoa and complex biofilm formations. Isolation and afterwards identification of these pathogens from environmental samples by using common identification procedures based on cultivation are extremely difficult and prolonged. The development of fast and sensitive method based on the cultivation free identification of bacteria is necessary. In this study Raman microspectroscopy combined with multiclass support vector machines have been used to discriminate between Legionella and other common aquatic bacteria, to distinguish among clinically relevant Legionella species and to classify unknown Raman spectra for a fast and reliable identification. Recorded Raman spectra of the twenty-two Legionella species as well as the Raman spectra of Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa were utilized to build the classification model. Afterwards, independent Raman spectra of eleven species were used to identify them on the basis of the classification model that was created. The present study shows that Raman microspectroscopy can be used as a rapid and reliable method to distinguish between Legionella species recognized as human pathogens and to identify samples which are unknown to the model based on multiclass support vector machines (MC-SVM).

Isolation and enrichment of pathogens with a surface-modified aluminium chip for Raman spectroscopic applications

We developed a Raman-compatible chip for isolating microorganisms from complex media. The isolation of bacteria is achieved by using antibodies as capture molecules. Due to the very specific interaction with the targets, this approach is promising for isolation of bacteria even from complex matrices such as body fluids. Our choice of capture molecules also enabled the investigation of samples containing yet unidentified bacteria, as the antibodies can capture a large variety of bacteria based on their analogue cell wall surface structures. The capability of our system is demonstrated for a broad range of different Gram-positive and Gram-negative germs. Subsequent identification is done by recording Raman spectra of the bacteria. Further, it is shown that classification with chemometric methods is possible.

Biomarker discovery and applications for foods and beverages: proteomics to nanoproteomics

Foods and beverages have been at the heart of our society for centuries, sustaining humankind - health, life, and the pleasures that go with it. The more we grow and develop as a civilization, the more we feel the need to know about the food we eat and beverages we drink. Moreover, with an ever increasing demand for food due to the growing human population food security remains a major concern. Food safety is another growing concern as the consumers prefer varied foods and beverages that are not only traded nationally but also globally. The 21st century science and technology is at a new high, especially in the field of biological sciences. The availability of genome sequences and associated high-throughput sensitive technologies means that foods are being analyzed at various levels. For example and in particular, high-throughput omics approaches are being applied to develop suitable biomarkers for foods and beverages and their applications in addressing quality, technology, authenticity, and safety issues. Proteomics are one of those technologies that are increasingly being utilized to profile expressed proteins in different foods and beverages. Acquired knowledge and protein information have now been translated to address safety of foods and beverages. Very recently, the power of proteomic technology has been integrated with another highly sensitive and miniaturized technology called nanotechnology, yielding a new term nanoproteomics. Nanoproteomics offer a real-time multiplexed analysis performed in a miniaturized assay, with low-sample consumption and high sensitivity. To name a few, nanomaterials - quantum dots, gold nanoparticles, carbon nanotubes, and nanowires - have demonstrated potential to overcome the challenges of sensitivity faced by proteomics for biomarker detection, discovery, and application. In this review, we will discuss the importance of biomarker discovery and applications for foods and beverages, the contribution of proteomic technology in this process, and a shift towards nanoproteomics to suitably address associated issues. This article is part of a Special Issue entitled: Translational plant proteomics.

Raman spectroscopy as a potential tool for detection of Brucella spp. in milk

Detection of Brucella, causing brucellosis, is very challenging, since the applied techniques are mostly time-demanding and not standardized. While the common detection system relies on the cultivation of the bacteria, further classical typing up to the biotype level is mostly based on phenotypic or genotypic characteristics. The results of genotyping do not always fit the existing taxonomy, and misidentifications between genetically closely related genera cannot be avoided. This situation gets even worse, when detection from complex matrices, such as milk, is necessary. For these reasons, the availability of a method that allows early and reliable identification of possible Brucella isolates for both clinical and epidemiological reasons would be extremely useful. We evaluated micro-Raman spectroscopy in combination with chemometric analysis to identify Brucella from agar plates and directly from milk: prior to these studies, the samples were inactivated via formaldehyde treatment to ensure a higher working safety. The single-cell Raman spectra of different Brucella, Escherichia, Ochrobactrum, Pseudomonas, and Yersinia spp. were measured to create two independent databases for detection in media and milk. Identification accuracies of 92% for Brucella from medium and 94% for Brucella from milk were obtained while analyzing the single-cell Raman spectra via support vector machine. Even the identification of the other genera yielded sufficient results, with accuracies of >90%. In summary, micro-Raman spectroscopy is a promising alternative for detecting Brucella. The measurements we performed at the single-cell level thus allow fast identification within a few hours without a demanding process for sample preparation.