Identification of Bacillus anthracis via Raman Spectroscopy and Chemometric Approaches

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.

Comparative Analysis Using Raman Spectroscopy of the Cellular Constituents of Lacticaseibacillus paracasei Zhang in a Normal and Viable but Nonculturable State

This study aimed to investigate the molecular composition of a viable but nonculturable (VBNC) state of a probiotic strain, Lacticaseibacillus paracasei Zhang (L. paracasei Zhang), using single-cell Raman spectroscopy (SCRS). Fluorescent microcopy with live/dead cell staining (propidium iodide and SYTO 9), plate counting, and scanning electron microscopy were used in combination to observe bacteria in an induced VBNC state. We induced the VBNC state by incubating the cells in de Man, Rogosa, and Sharpe broth (MRS) at 4 °C. Cells were sampled for subsequent analyses before VBNC induction, during it, and up to 220 days afterwards. We found that, after cold incubation for 220 days, the viable plate count was zero, but active cells could still be observed (as green fluorescent cells) under a fluorescence microscope, indicating that Lacticaseibacillus paracasei Zhang entered the VBNC state under these conditions. Scanning electron microscopy revealed the altered ultra-morphology of the VBNC cells, characterized by a shortened cell length and a wrinkled cell surface. Principal component analysis of the Raman spectra profiles revealed obvious differences in the intracellular biochemical constituents between normal and VBNC cells. Comparative analysis of the Raman spectra identified 12 main differential peaks between normal and VBNC cells, corresponding to carbohydrates, lipids, nucleic acids, and proteins. Our results suggested that there were obvious cellular structural intracellular macromolecular differences between normal and VBNC cells. During the induction of the VBNC state, the relative contents of carbohydrates (such as fructose), saturated fatty acids (such as palmitic acid), nucleic acid constituents, and some amino acids changed obviously, which could constitute a bacterial adaptive mechanism against adverse environmental conditions. Our study provides a theoretical basis for revealing the formation mechanism of a VBNC state in lactic acid bacteria.

Hypervirulent R20291 Clostridioides difficile spores show disinfection resilience to sodium hypochlorite despite structural changes

BACKGROUND

Clostridioides difficile is a spore forming bacterial species and the major causative agent of nosocomial gastrointestinal infections. C. difficile spores are highly resilient to disinfection methods and to prevent infection, common cleaning protocols use sodium hypochlorite solutions to decontaminate hospital surfaces and equipment. However, there is a balance between minimising the use of harmful chemicals to the environment and patients as well as the need to eliminate spores, which can have varying resistance properties between strains. In this work, we employ TEM imaging and Raman spectroscopy to analyse changes in spore physiology in response to sodium hypochlorite. We characterize different C. difficile clinical isolates and assess the chemical's impact on spores' biochemical composition. Changes in the biochemical composition can, in turn, change spores' vibrational spectroscopic fingerprints, which can impact the possibility of detecting spores in a hospital using Raman based methods.

RESULTS

We found that the isolates show significantly different susceptibility to hypochlorite, with the R20291 strain, in particular, showing less than 1 log reduction in viability for a 0.5% hypochlorite treatment, far below typically reported values for C. difficile. While TEM and Raman spectra analysis of hypochlorite-treated spores revealed that some hypochlorite-exposed spores remained intact and not distinguishable from controls, most spores showed structural changes. These changes were prominent in B. thuringiensis spores than C. difficile spores.

CONCLUSION

This study highlights the ability of certain C. difficile spores to survive practical disinfection exposure and the related changes in spore Raman spectra that can be seen after exposure. These findings are important to consider when designing practical disinfection protocols and vibrational-based detection methods to avoid a false-positive response when screening decontaminated areas.

Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities

Microbial persisters are the featured tiny sub-population of microorganisms that are highly tolerant to multiple antimicrobials. Currently, studies on persisters remain a considerable challenge owing to technical limitations. Here, we explored the application of single-cell Raman spectroscopy (SCRS) in the investigation of persisters. Escherichia coli (ATCC 25922) cells were treated with a lethal dosage of ampicillin (100 μg/mL, 32 × MIC, 4 h) for the formation of persisters. The biochemical characters of E. coli and its persisters were assessed by SCRS, and their metabolic activities were labeled and measured with D₂O-based single-cell Raman spectroscopy (D₂O-Ramanometry). Notable differences in the intensity of Raman bands related to major cellular components and metabolites were observed between E. coli and its ampicillin-treated persisters. Based on their distinct Raman spectra, E. coli and its persister cells were classified into different projective zones through the principal component analysis and t-distributed stochastic neighbor embedding. According to the D₂O absorption rate, E. coli persisters exhibited higher metabolic activities than those of untreated E. coli. Importantly, after the termination of ampicillin exposure, these persister cells showed a temporal pattern of D₂O intake that was distinct from non-persister cells. To our knowledge, this is the first report on identifying E. coli persisters and assessing their metabolic activities through the integrated SCRS and D₂O-Ramanometry approach. These novel findings enhance our understanding of the phenotypes and functionalities of microbial persister cells. Further investigations could be extended to other pathogens by disclosing microbial pathogenicity mechanisms for developing novel therapeutic strategies and approaches.

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.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Bacillus thuringiensis Cells Selectively Captured by Phages and Identified by Surface Enhanced Raman Spectroscopy Technique

In this work, the results on the detection and identification of Bacillus thuringiensis (Bt) cells by using surface-enhanced Raman spectroscopy (SERS) are presented. Bt has been chosen as a harmless surrogate of the pathogen Bacillus anthracis (Ba) responsible for the deadly Anthrax disease, because of their genetic similarities. Drops of 200 μL of Bt suspensions, with concentrations 10² CFU/mL, 10⁴ CFU/mL, 10⁶ CFU/mL, were deposited on a SERS chip and sampled after water evaporation. To minimize the contribution to the SERS data given by naturally occurring interferents present in a real scenario, the SERS chip was functionalized with specific phage receptors BtCS33, that bind Bt (or Ba) cells to the SERS surface and allow to rinse the chip removing unwanted contaminants. Different chemometric approaches were applied to the SERS data to classify spectra from Bt-contaminated and uncontaminated areas of the chip: Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). The first two was tested and trained by using data from both contaminated and un-contaminated chips, the last was trained by using data from un-contaminated chips only and tested with all the available data. All of them were able to correctly classify the SERS spectra with great accuracy, the last being suitable for an automated recognition procedure.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Advances in single cell Raman spectroscopy technologies for biological and environmental applications

The increasing sophistication of single cell Raman spectroscopy (SCRS) via its integrations with other advanced analytical techniques and modern data analytics, enable unprecedented exploration of complex biological and environmental samples with significantly improved specificity, sensitivity, and resolution. Because of the merits of being high-resolution, label-free, non-invasive, molecular-specific, culture-independent, and suitable for in situ, in vitro or in vivo analysis, the SCRS-derived techniques offer abilities superior to conventional bulk measurements for environmental and biological studies. Here, we provide a comprehensive and critical review of the most recent advances in the development and application of SCRS-enabled technologies, with focus on those biomolecular and cellular high-resolution applications in environmental and biological fields. The basic principles, unique advantages, and suitable applications, as well as recognized limitations for each technology are recapitulated. The remaining challenges, research needs and future outlook are discussed. We predict that SCRS-enabled technologies are earning its place as a routine and powerful tool in many and rapidly expanding applications across disciplines.

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.

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.

Nanoparticle-Based Contrast Agents for 129Xe HyperCEST NMR and MRI Applications

Spin hyperpolarization techniques have enabled important advancements in preclinical and clinical MRI applications to overcome the intrinsic low sensitivity of nuclear magnetic resonance. Functionalized xenon biosensors represent one of these approaches. They combine two amplification strategies, namely, spin exchange optical pumping (SEOP) and chemical exchange saturation transfer (CEST). The latter one requires host structures that reversibly bind the hyperpolarized noble gas. Different nanoparticle approaches have been implemented and have enabled molecular MRI with 129Xe at unprecedented sensitivity. This review gives an overview of the Xe biosensor concept, particularly how different nanoparticles address various critical aspects of gas binding and exchange, spectral dispersion for multiplexing, and targeted reporter delivery. As this concept is emerging into preclinical applications, comprehensive sensor design will be indispensable in translating the outstanding sensitivity potential into biomedical molecular imaging applications.

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.

Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications

The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.

Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell level

Background

As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment.

Methods

In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS.

Results

Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%.

Conclusions

To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.

Microfluidic dielectrophoresis device for trapping, counting and detecting Shewanella oneidensis at the cell level

Shewanella oneidensis, a model organism for electrochemical activity bacteria, has been widely studied at the biofilm level. However, to obtain more information regarding this species, it is essential to develop an approach to trap and detect S. oneidensis at the cell level. In this study, we report a rapid and label-free microfluidic platform for trapping, counting and detecting S. oneidensis cells. A microfluidic chip was integrated with a modified dielectrophoresis (DEP) trapping technique and hole arrays of different hole sizes. By numerical simulation and an elaborate electric field distribution design, S. oneidensis cells were successfully trapped and positioned in the hole arrays. Real time fluorescence imaging was also used to observe the trapping process. With the aid of a homemade image program, the trapped bacteria were accurately counted, and the results demonstrated that the amount of bacteria correlated with the hole sizes. As one of the significant applications of the device, Raman identification and detection of countable S. oneidensis cells was accomplished in two kinds of holes. The microfluidic platform provides a quantitative sample preparation and analysis method at the cell level that could be widely applied in the environmental and energy fields.

Review: Microbial analysis in dielectrophoretic microfluidic systems

Infections caused by various known and emerging pathogenic microorganisms, including antibiotic-resistant strains, are a major threat to global health and well-being. This highlights the urgent need for detection systems for microbial identification, quantification and characterization towards assessing infections, prescribing therapies and understanding the dynamic cellular modifications. Current state-of-the-art microbial detection systems exhibit a trade-off between sensitivity and assay time, which could be alleviated by selective and label-free microbial capture onto the sensor surface from dilute samples. AC electrokinetic methods, such as dielectrophoresis, enable frequency-selective capture of viable microbial cells and spores due to polarization based on their distinguishing size, shape and sub-cellular compositional characteristics, for downstream coupling to various detection modalities. Following elucidation of the polarization mechanisms that distinguish bacterial cells from each other, as well as from mammalian cells, this review compares the microfluidic platforms for dielectrophoretic manipulation of microbials and their coupling to various detection modalities, including immuno-capture, impedance measurement, Raman spectroscopy and nucleic acid amplification methods, as well as for phenotypic assessment of microbial viability and antibiotic susceptibility. Based on the urgent need within point-of-care diagnostics towards reducing assay times and enhancing capture of the target organism, as well as the emerging interest in isolating intact microbials based on their phenotype and subcellular features, we envision widespread adoption of these label-free and selective electrokinetic techniques.

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.

Green mitigation strategy for cultural heritage: bacterial potential for biocide production

Several biosurfactants with antagonistic activity are produced by a variety of microorganisms. Lipopeptides (LPPs) produced by some Bacillus strains, including surfactin, fengycin and iturin are synthesized nonribosomally by mega-peptide synthetase (NRPS) units and they are particularly relevant as antifungal agents. Characterisation, identification and evaluation of the potentials of several bacterial isolates were undertaken in order to establish the production of active lipopeptides against biodeteriogenic fungi from heritage assets. Analysis of the iturin operon revealed four open reading frames (ORFs) with the structural organisation of the peptide synthetases. Therefore, this work adopted a molecular procedure to access antifungal potential of LPP production by Bacillus strains in order to exploit the bioactive compounds synthesis as a green natural approach to be applied in biodegraded cultural heritage context. The results reveal that the bacterial strains with higher antifungal potential exhibit the same morphological and biochemical characteristics, belonging to the genera Bacillus. On the other hand, the higher iturinic genetic expression, for Bacillus sp. 3 and Bacillus sp. 4, is in accordance with the culture antifungal spectra. Accordingly, the adopted methodology combining antifungal screening and molecular data is represent a valuable tool for quick identification of iturin-producing strains, constituting an effective approach for confirming the selection of lipopeptides producer strains.

Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells

BACKGROUND

Current approaches for quantification of major energy-storage forms in microalgae, including starch, protein and lipids, generally require cell cultivation to collect biomass followed by tedious and time-consuming analytical procedures. Thus, label-free, non-destructive and simultaneous quantification of such macromolecules at single-cell resolution is highly desirable in microalgal feedstock development and bioprocess control.

RESULTS

Here, we established a method based on single-cell Raman spectra (SCRS) that simultaneously quantifies the contents of starch, protein, triacylglycerol (TAG) and lipid unsaturation degree in individual Chlamydomonas reinhardtii cells. Measurement accuracy for the contents based on full SCRS spectrum each reached 96.86-99.24%, all significantly higher than single peak-based models. However, accuracy and reliability of measurement are dependent on the number of cells sampled, thus a formal mathematical framework was proposed and validated to rationally define "minimal sampling depth" for a given state of cellular population. Furthermore, a barcode consisting of 13 marker Raman peaks was proposed to characterize the temporal dynamics of these energy-storage products, which revealed that the average contents of starch and TAG increased, while their heterogeneity indices decreased, with those of protein being exactly the opposite. Finally, our method is widely applicable, as measurements among cells from liquid suspension culture, wet paste and frozen dried powder all exhibited excellent consistency.

CONCLUSIONS

When sampled at proper depth, SCRS can serve as a quantitative and generally applicable tool for characterization and screening of strains and bioprocesses based on the profile of energy-storage macromolecules and their among-cell heterogeneity.

Label-free, rapid and quantitative phenotyping of stress response in E. coli via ramanome

Rapid profiling of stress-response at single-cell resolution yet in a label-free, non-disruptive and mechanism-specific manner can lead to many new applications. We propose a single-cell-level biochemical fingerprinting approach named “ramanome”, which is the collection of Single-cell Raman Spectra (SCRS) from a number of cells randomly selected from an isogenic population at a given time and condition, to rapidly and quantitatively detect and characterize stress responses of cellular population. SCRS of Escherichia coli cells are sensitive to both exposure time (eight time points) and dosage (six doses) of ethanol, with detection time as early as 5 min and discrimination rate of either factor over 80%. Moreover, the ramanomes upon six chemical compounds from three categories, including antibiotics of ampicillin and kanamycin, alcohols of ethanol and n-butanol and heavy metals of Cu²⁺ and Cr⁶⁺, were analyzed and 31 marker Raman bands were revealed which distinguish stress-responses via cytotoxicity mechanism and variation of inter-cellular heterogeneity. Furthermore, specificity, reproducibility and mechanistic basis of ramanome were validated by tracking stress-induced dynamics of metabolites and by contrasting between cells with and without genes that convey stress resistance. Thus ramanome enables rapid prediction and mechanism-based screening of cytotoxicity and stress-response programs at single-cell resolution.

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.

Raman-activated cell sorting based on dielectrophoretic single-cell trap and release

Raman-activated cell sorting (RACS) is a promising single-cell technology that holds several significant advantages, as RACS is label-free, information-rich, and potentially in situ. To date, the ability of the technique to identify single cells in a high-speed flow has been limited by inherent weakness of the spontaneous Raman signal. Here we present an alternative pause-and-sort RACS microfluidic system that combines positive dielectrophoresis (pDEP) for single-cell trap and release with a solenoid-valve-suction-based switch for cell separation. This has allowed the integration of trapping, Raman identification, and automatic separation of individual cells in a high-speed flow. By exerting a periodical pDEP field, single cells were trapped, ordered, and positioned individually to the detection point for Raman measurement. As a proof-of-concept demonstration, a mixture of two cell strains containing carotenoid-producing yeast (9%) and non-carotenoid-producing Saccharomyces cerevisiae (91%) was sorted, which enriched the former to 73% on average and showed a fast Raman-activated cell sorting at the subsecond level.

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.

On-line SERS detection of single bacterium using novel SERS nanoprobes and a microfluidic dielectrophoresis device

The integration of novel surface-enhanced Raman scattering (SERS) nanoprobes and a microfluidic dielectrophoresis (DEP) device is developed for rapid on-line SERS detection of Salmonella enterica serotype Choleraesuis and Neisseria lactamica. The SERS nanoprobes are prepared by immobilization of specific antibody onto the surface of nanoaggregate-embedded beads (NAEBs), which are silica-coated, dye-induced aggregates of a small number of gold nanoparticles (AuNPs). Each NAEB gives highly enhanced Raman signals owing to the presence of well-defined plasmonic hot spots at junctions between AuNPs. Herein, the on-line SERS detection and accurate identification of suspended bacteria with a detection capability down to a single bacterium has been realized by the NAEB-DEP-Raman spectroscopy biosensing strategy. The practical detection limit with a measurement time of 10 min is estimated to be 70 CFU mL(-1) . In comparison with whole-cell enzyme-linked immunosorbent assay (ELISA), the SERS-nanoprobe-based biosensing method provides advantages of higher sensitivity and requiring lower amount of antibody in the assay (100-fold less). The total assay time including sample pretreatment is less than 2 h. Hence, this sensing strategy is promising for faster and effective on-line multiplex detection of single pathogenic bacterium by using different bioconjugated SERS nanoprobes.

Following drug uptake and reactions inside Escherichia coli cells by Raman microspectroscopy

Raman microspectroscopy combined with Raman difference spectroscopy reveals the details of chemical reactions within bacterial cells. The method provides direct quantitative data on penetration of druglike molecules into Escherichia coli cells in situ along with the details of drug-target reactions. With this label-free technique, clavulanic acid and tazobactam can be observed as they penetrate into E. coli cells and subsequently inhibit β-lactamase enzymes produced within these cells. When E. coli cells contain a β-lactamase that forms a stable complex with an inhibitor, the Raman signature of the known enamine acyl-enzyme complex is detected. From Raman intensities it is facile to measure semiquantitatively the number of clavulanic acid molecules taken up by the lactamase-free cells during growth.

Direct identification of clinically relevant bacterial and yeast microcolonies and macrocolonies on solid culture media by Raman spectroscopy

Decreasing turnaround time is a paramount objective in clinical diagnosis. We evaluated the discrimination power of Raman spectroscopy when analyzing colonies from 80 strains belonging to nine bacterial and one yeast species directly on solid culture medium after 24-h (macrocolonies) and 6-h (microcolonies) incubation. This approach, that minimizes sample preparation and culture time, would allow resuming culture after identification to perform downstream antibiotic susceptibility testing. Correct identification rates measured for macrocolonies and microcolonies reached 94.1% and 91.5%, respectively, in a leave-one-strain-out cross-validation mode without any correction for possible medium interference. Large spectral differences were observed between macrocolonies and microcolonies, that were attributed to true biological differences. Our results, conducted on a very diversified panel of species and strains, were obtained by using simple and robust sample preparation and preprocessing procedures, while still confirming published results obtained by using more complex elaborated protocols. Instrumentation is simplified by the use of 532-nm laser excitation yielding a Raman signal in the visible range. It is, to our knowledge, the first side-by-side full classification study of microorganisms in the exponential and stationary phases confirming the excellent performance of Raman spectroscopy for early species-level identification of microorganisms directly from an agar culture.

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).

Quantitative dynamics of triacylglycerol accumulation in microalgae populations at single-cell resolution revealed by Raman microspectroscopy

BACKGROUND

Rapid, real-time and label-free measurement of the cellular contents of biofuel molecules such as triacylglycerol (TAG) in populations at single-cell resolution are important for bioprocess control and understanding of the population heterogeneity. Raman microspectroscopy can directly detect the changes of metabolite profile in a cell and thus can potentially serve these purposes.

RESULTS

Single-cell Raman spectra (SCRS) of the unicellular oleaginous microalgae Nannochloropsis oceanica from the cultures under nitrogen depletion (TAG-producing condition) and nitrogen repletion (non-TAG-producing condition) were sampled at eight time points during the first 96 hours upon the onset of nitrogen depletion. Single N. oceanica cells were captured by a 532-nm laser and the SCRS were acquired by the same laser within one second per cell. Using chemometric methods, the SCRS were able to discriminate cells between nitrogen-replete and nitrogen-depleted conditions at as early as 6 hours with >93.3% accuracy, and among the eight time points under nitrogen depletion with >90.4% accuracy. Quantitative prediction of TAG content in single cells was achieved and validated via SCRS and liquid chromatography-mass spectrometry (LC-MS) analysis at population level. SCRS revealed the dynamics of heterogeneity in TAG production among cells in each isogenic population. A significant negative correlation between TAG content and lipid unsaturation degree in individual microalgae cells was observed.

CONCLUSIONS

Our results show that SCRS can serve as a label-free and non-invasive proxy for quantitatively tracking and screening cellular TAG content in real-time at single-cell level. Phenotypic comparison of single cells via SCRS should also help investigating the mechanisms of functional heterogeneity within a cellular population.

Bacterial spore detection and analysis using hyperpolarized 129Xe chemical exchange saturation transfer (Hyper-CEST) NMR

Previously, we reported hyperpolarized ¹²⁹Xe chemical exchange saturation transfer (Hyper-CEST) NMR techniques for the ultrasensitive (i.e., 1 picomolar) detection of xenon host molecules known as cryptophane. Here, we demonstrate a more general role for Hyper-CEST NMR as a spectroscopic method for probing nanoporous structures, without the requirement for cryptophane or engineered xenon-binding sites. Hyper-CEST ¹²⁹Xe NMR spectroscopy was employed to detect Bacillus anthracis and Bacillus subtilis spores in solution, and interrogate the layers that comprise their structures. ¹²⁹Xe-spore samples were selectively irradiated with radiofrequency pulses; the depolarized ¹²⁹Xe returned to aqueous solution and depleted the ¹²⁹Xe-water signal, providing measurable contrast. Removal of the outermost spore layers in B. anthracis and B. subtilis (the exosporium and coat, respectively) enhanced ¹²⁹Xe exchange with the spore interior. Notably, the spores were invisible to hyperpolarized ¹²⁹Xe NMR direct detection methods, highlighting the lack of high-affinity xenon-binding sites, and the potential for extending Hyper-CEST NMR structural analysis to other biological and synthetic nanoporous structures.

Combined dielectrophoresis-Raman setup for the classification of pathogens recovered from the urinary tract

Rapid and effective methods of pathogen identifications are of major interest in clinical microbiological analysis to administer timely tailored antibiotic therapy. Raman spectroscopy as a label-free, culture-independent optical method is suitable to identify even single bacteria. However, the low bacteria concentration in body fluids makes it difficult to detect their characteristic molecular fingerprint directly in suspension. Therefore, in this study, Raman spectroscopy is combined with dielectrophoresis, which enables the direct translational manipulation of bacteria in suspensions with spatial nonuniform electrical fields so as to perform specific Raman spectroscopic characterization. A quadrupole electrode design is used to capture bacteria directly from fluids in well-defined microsized regions. With live/dead fluorescence viability staining, it is verified, that the bacteria survive this procedure for the relevant range of field strengths. The dielectrophoretic enrichment of bacteria allows for obtaining high quality Raman spectra in dilute suspensions with an integration time of only one second. As proof-of-principle study, the setup was tested with Escherichia coli and Enterococcus faecalis, two bacterial strains that are commonly encountered in urinary tract infections. Furthermore, to verify the potential for dealing with real world samples, pathogens from patients' urine have been analyzed. With the additional help of multivariate statistical analysis, a robust classification model could be built and allowed the classification of those two strains within a few minutes. In contrast, the standard microbiological diagnostics are based on very time-consuming cultivation tests. This setup holds the potential to reduce the crucial parameter diagnosis time by orders of magnitude.

Identification of meat-associated pathogens via Raman microspectroscopy

The development of fast and reliable sensing techniques to detect food-borne microorganisms is a permanent concern in food industry and health care. For this reason, Raman microspectroscopy was applied to rapidly detect pathogens in meat, which could be a promising supplement to currently established methods. In this context, a spectral database of 19 species of the most important harmful and non-pathogenic bacteria associated with meat and poultry was established. To create a meat-like environment the microbial species were prepared on three different agar types. The whole amount of Raman data was taken as a basis to build up a three level classification model by means of support vector machines. Subsequent to a first classifier that differentiates between Raman spectra of Gram-positive and Gram-negative bacteria, two decision knots regarding bacterial genus and species follow. The different steps of the classification model achieved accuracies in the range of 90.6%-99.5%. This database was then challenged with independently prepared test samples. By doing so, beef and poultry samples were spiked with different pathogens associated with food-borne diseases and then identified. The test samples were correctly assigned to their genus and for the most part down to the species-level i.e. a differentiation from closely-related non-pathogenic members was achieved.