Raman Based Molecular Imaging and Analytics: A Magic Bullet for Biomedical Applications!?

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

Strategies and Progress of Raman Technologies for Cellular Uptake Analysis of the Drug Delivery Systems

Nanoparticle (NP)-based drug delivery systems have the potential to significantly enhance the pharmacological and therapeutic properties of drugs. These systems enhance the bioavailability and biocompatibility of pharmaceutical agents via enabling targeted delivery to specific tissues or organs. However, the efficacy and safety of these systems are largely dependent on the cellular uptake and intracellular transport of NPs. Thus, it is crucial to monitor the intracellular behavior of NPs within a single cell. Yet, it is challenging due to the complexity and size of the cell. Recently, the development of the Raman instrumentation offers a versatile tool to allow noninvasive cellular measurements. The primary objective of this review is to highlight the most recent advancements in Raman techniques (spontaneous Raman scattering, bioorthogonal Raman scattering, coherence Raman scattering, and surface-enhanced Raman scattering) when it comes to assessing the internalization of NP-based drug delivery systems and their subsequent movement within cells.

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.

Compact oblique-incidence nonlinear widefield microscopy with paired-pixel balanced imaging

Nonlinear (vibrational) microscopy has emerged as a successful tool for the investigation of molecular systems as it combines label-free chemical characterization with spatial resolution on the sub-micron scale. In addition to the molecular recognition, the physics of the nonlinear interactions allows in principle to obtain structural information on the molecular level such as molecular orientations. Due to technical limitations such as the relatively complex imaging geometry with the required oblique sample irradiation and insufficient sensitivity of the instrument this detailed molecular information is typically not accessible using widefield imaging. Here, we present, what we believe to be, a new microscope design that addresses both challenges. We introduce a simplified imaging geometry that enables the measurement of distortion-free widefield images with free space oblique sample irradiation achieving high spatial resolution (∼1 µm). Furthermore, we present a method based on a paired-pixel balanced detection system for sensitivity improvement. With this technique, we demonstrate a substantial enhancement of the signal-to-noise ratio of up to a factor of 10. While both experimental concepts presented in this work are very general and can, in principle, be applied to various microscopy techniques, we demonstrate their performance for the specific case of heterodyned, sum frequency generation (SFG) microscopy.

Correction for Extrinsic Background in Raman Hyperspectral Images

Raman hyperspectral microscopy is a valuable tool in biological and biomedical imaging. Because Raman scattering is often weak in comparison to other phenomena, prevalent spectral fluctuations and contaminations have brought advancements in analytical and chemometric methods for Raman spectra. These chemometric advances have been key contributors to the applicability of Raman imaging to biological systems. As studies increase in scale, spectral contamination from extrinsic background, intensity from sources such as the optical components that are extrinsic to the sample of interest, has become an emerging issue. Although existing baseline correction schemes often reduce intrinsic background such as autofluorescence originating from the sample of interest, extrinsic background is not explicitly considered, and these methods often fail to reduce its effects. Here, we show that extrinsic background can significantly affect a classification model using Raman images, yielding misleadingly high accuracies in the distinction of benign and malignant samples of follicular thyroid cell lines. To mitigate its effects, we develop extrinsic background correction (EBC) and demonstrate its use in combination with existing methods on Raman hyperspectral images. EBC isolates regions containing the smallest amounts of sample materials that retain extrinsic contributions that are specific to the device or environment. We perform classification both with and without the use of EBC, and we find that EBC retains biological characteristics in the spectra while significantly reducing extrinsic background. As the methodology used in EBC is not specific to Raman spectra, correction of extrinsic effects in other types of hyperspectral and grayscale images is also possible.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study

The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups. To show NGF's effect, we measured the level of oxidative loads such as Total Antioxidant Level (TAS), Total Oxidant Level (TOS), and Oxidative Stress Index (OSI) with Beckman Coulter AU system and biochemical assays. We find a correlation between oxidative load and NGF level. Oxidative load mainly causes structural changes in the blood. Therefore, we obtained Raman measurements of the participant's serum. Then we selected two Raman regions, 800 and 1800 cm^-1, and between 2700 cm^-1 and 3000 cm^-1, to see chemical changes. We noted that Raman spectra obtained for RPL and healthy women differed. The findings confirm that the imbalance between reactive oxygen species and antioxidants has important implications for the pathogenesis of RPL and that NGF levels accompany the level of oxidative load in the RPL state. Biomolecular structure and composition were determined using Raman spectroscopy and machine learning methods, and the correlation of these parameters was studied alongside machine learning technologies to advance toward clinical translation. Here we determined and validated the development of instrumentation for the Analysis of RPL patients' serum that can differentiate from control individuals with an accuracy of 100% using the Raman region corresponding to structural changes. Furthermore, this study found a correlation between traditional biochemical parameters and Raman data. This suggests that Raman spectroscopy is a sensitive tool for detecting biochemical changes in serum caused by RPL or other diseases.

A Novel Method for Detecting Duchenne Muscular Dystrophy in Blood Serum of mdx Mice

Duchenne muscular dystrophy (DMD) is the most common form of muscular dystrophy, typically affecting males in infancy. The disease causes progressive weakness and atrophy of skeletal muscles, with approximately 20,000 new cases diagnosed yearly. Currently, methods for diagnosing DMD are invasive, laborious, and unable to make accurate early detections. While there is no cure for DMD, there are limited treatments available for managing symptoms. As such, there is a crucial unmet need to develop a simple and non-invasive method for accurately detecting DMD as early as possible. Raman spectroscopy with chemometric analysis is shown to have the potential to fill this diagnostic need.

Deep Learning for Raman Spectroscopy: A Review

Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.

Towards Raman imaging of centimeter scale tissue areas for real-time opto-molecular visualization of tissue boundaries for clinical applications

Raman spectroscopy combined with augmented reality and mixed reality to reconstruct molecular information of tissue surface.

Insights and prospects for ion mobility-mass spectrometry in clinical chemistry

INTRODUCTION

Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements.

AREAS COVERED

Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds.

EXPERT OPINION

Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.

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.

Micro-Raman spectroscopy of lipid halo and dense-core amyloid plaques: aging process characterization in the Alzheimer's disease APPswePS1ΔE9 mouse model

The deposition of amyloid plaques is considered one of the main microscopic features of Alzheimer's disease (AD). Since plaque formation can precede extensive neurodegeneration and it is the main clinical manifestation of AD, it constitutes a relevant target for new treatment and diagnostic approaches. Micro-Raman spectroscopy, a label-free technique, is an accurate method for amyloid plaque identification and characterization. Here, we present a high spatial resolution micro-Raman hyperspectral study in transgenic APPswePS1ΔE9 mouse brains, showing details of AD tissue biochemical and histological changes without staining. First we used stimulated micro-Raman scattering to identify the lipid-rich halo surrounding the amyloid plaque, and then proceeded with spontaneous (conventional) micro-Raman spectral mapping, which shows a cholesterol and sphingomyelin lipid-rich halo structure around dense-core amyloid plaques. The detailed images of this lipid halo relate morphologically well with dystrophic neurites surrounding plaques. Principal Component Analysis (PCA) of the micro-Raman hyperspectral data indicates the feasibility of the optical biomarkers of AD progression with the potential for discriminating transgenic groups of young adult mice (6-month-old) from older ones (12-month-old). Frequency-specific PCA suggests that plaque-related neurodegeneration is the predominant change captured by Raman spectroscopy, and the main differences are highlighted by vibrational modes associated with cholesterol located majorly in the lipid halo.

A polyyne toxin produced by an antagonistic bacterium blinds and lyses a Chlamydomonad alga

Algae live in association with microbes that interact by a variety of chemical mediators, resulting in mutualistic or antagonistic relationships. Although algae are key contributors to carbon fixation and are fundamental for food webs, we still know little about the underlying molecular mechanisms affecting their fitness. This study investigates the interaction between an antagonistic bacterium and a unicellular alga. It demonstrates multiple roles of a polyyne, protegencin, that is used by the bacteria to attack green algal cells. It is a highly effective toxin that alters a subcellular algal compartment used for vision, bleaches, and lyses the algal cells. These results expand our knowledge of the arsenal of chemical mediators in bacteria and their modes of action in algal communities.

Algae are key contributors to global carbon fixation and form the basis of many food webs. In nature, their growth is often supported or suppressed by microorganisms. The bacterium Pseudomonas protegens Pf-5 arrests the growth of the green unicellular alga Chlamydomonas reinhardtii, deflagellates the alga by the cyclic lipopeptide orfamide A, and alters its morphology [P. Aiyar et al., Nat. Commun. 8, 1756 (2017)]. Using a combination of Raman microspectroscopy, genome mining, and mutational analysis, we discovered a polyyne toxin, protegencin, which is secreted by P. protegens, penetrates the algal cells, and causes destruction of the carotenoids of their primitive visual system, the eyespot. Together with secreted orfamide A, protegencin thus prevents the phototactic behavior of C. reinhardtii. A mutant of P. protegens deficient in protegencin production does not affect growth or eyespot carotenoids of C. reinhardtii. Protegencin acts in a direct and destructive way by lysing and killing the algal cells. The toxic effect of protegencin is also observed in an eyeless mutant and with the colony-forming Chlorophyte alga Gonium pectorale. These data reveal a two-pronged molecular strategy involving a cyclic lipopeptide and a conjugated tetrayne used by bacteria to attack select Chlamydomonad algae. In conjunction with the bloom-forming activity of several chlorophytes and the presence of the protegencin gene cluster in over 50 different Pseudomonas genomes [A. J. Mullins et al., bioRxiv [Preprint] (2021). https://www.biorxiv.org/content/10.1101/2021.03.05.433886v1 (Accessed 17 April 2021)], these data are highly relevant to ecological interactions between Chlorophyte algae and Pseudomonadales bacteria.

Cell-free hemoglobin is a marker of systemic inflammation in mouse models of sepsis: a Raman spectroscopic study

Sepsis is a life-threatening condition caused by heightened host immune responses post infection. Despite intensive research, most of the existing diagnostic methods remain non-specific, labour-intensive, time-consuming or are not sensitive enough for rapid and timely diagnosis of the onset and progression of sepsis. The present work was undertaken to explore the potential of Raman spectroscopy to identify the biomarkers of sepsis in a label-free and minimally invasive manner using different mouse models of inflammation. The sera of BALB/c mice infected with Salmonella Typhimurium reveal extensive hemolysis, as indicated by the Raman bands that are characteristic of the porphyrin ring of hemoglobin (668, 743, 1050, 1253 and 1397 cm-1) which increase in a kinetic manner. These markers are also observed in a lipopolysaccharide-induced endotoxic shock model, but not in a thioglycollate-induced sterile peritonitis model. These data demonstrate that hemolysis is a signature of systemic, but not localised, inflammation. To further validate our observations, sepsis was induced in the nitric oxide synthase 2 (Nos2-/-) deficient strain which is more sensitive to infection. Interestingly, Nos2-/- mice exhibit a higher degree of hemolysis than C57BL/6 mice. Sepsis-induced hemolysis was also confirmed using resonance Raman spectroscopy with 442 nm excitation which demonstrated a pronounced increase in the resonant Raman bands at 670 and 1350 cm-1 in sera of the infected mice. This is the first study to identify inflammation-induced hemolysis in mouse models of sepsis using Raman spectral signatures for hemoglobin. The possible implications of this method in detecting hemolysis in different inflammatory pathologies, such as the ongoing COVID-19 pandemic, are discussed.

Comparison of functional and discrete data analysis regimes for Raman spectra

Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.

Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms

BACKGROUND

Breast cancer screening is time consuming, requires expensive equipment, and has demanding requirements for doctors. Hence, a large number of breast cancer patients may miss screening and early treatment, which greatly threatens their health around the world. Infrared spectroscopy may be able to be used as a screening tool for breast cancer detection. Fourier transform infrared (FT-IR) spectroscopy of serum was combined with traditional machine learning algorithms to achieve an auxiliary diagnosis that could quickly and accurately distinguish patients with different stages of breast cancer, including stage 1 disease, from control subjects without breast cancer.

MATERIALS AND METHODS

FT-IR spectroscopy were performed on the serum of 114 non-cancer control subjects, 35 patients with stage I, 43 patients with stage II, and 29 patients with stage III & IV breast cancer. Due to the experimental sample imbalance, we used the oversampling to process the four classes of sample. The oversampling selected Synthetic Minority Oversampling Technique (SMOTE). Subsequently, we used the random discarding method in undersampling to do experiments as well. The average FT-IR spectroscopy results for the four groups showed differences in phospholipids, nucleic acids, lipids, and proteins between non-cancer control subjects and breast cancer patients at different stages. Based on these differences, four classification models were used to classify stage I, II, III & IV breast cancer patients and non-cancer control subjects. First, standard normal variate transformation (SNV) was used to preprocess the original data, and then partial least squares (PLS) was used for feature extraction. Finally, the five models were established including extreme learning machine (ELM), k-nearest neighbor (KNN), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM) and grid search-support vector machine (GS-SVM).

CONCLUSION

In oversampling experiment, the GS-SVM classifier obtained the highest average classification accuracy of 95.45 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 90 %; breast cancer stage II was 84.62 %; and breast cancer stage III & IV was 100 %. In undersampling experiment, the GA-SVM model obtained the highest average classification accuracy of 100 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 100 %; breast cancer stage II was 100 %; and breast cancer stage III & IV was 100 %. The results show that FT-IR spectroscopy combined with powerful classification algorithms has great potential in distinguishing patients with different stages of breast cancer from non-cancer control subjects. In addition, this research provides a reference for future multiclassification studies of cervical cancer, ovarian cancer and other female high-incidence cancers through serum FT-IR spectroscopy.

Remote near infrared identification of pathogens with multiplexed nanosensors

Infectious diseases are worldwide a major cause of morbidity and mortality. Fast and specific detection of pathogens such as bacteria is needed to combat these diseases. Optimal methods would be non-invasive and without extensive sample-taking/processing. Here, we developed a set of near infrared (NIR) fluorescent nanosensors and used them for remote fingerprinting of clinically important bacteria. The nanosensors are based on single-walled carbon nanotubes (SWCNTs) that fluoresce in the NIR optical tissue transparency window, which offers ultra-low background and high tissue penetration. They are chemically tailored to detect released metabolites as well as specific virulence factors (lipopolysaccharides, siderophores, DNases, proteases) and integrated into functional hydrogel arrays with 9 different sensors. These hydrogels are exposed to clinical isolates of 6 important bacteria (Staphylococcus aureus, Escherichia coli,…) and remote (≥25 cm) NIR imaging allows to identify and distinguish bacteria. Sensors are also spectrally encoded (900 nm, 1000 nm, 1250 nm) to differentiate the two major pathogens P. aeruginosa as well as S. aureus and penetrate tissue (>5 mm). This type of multiplexing with NIR fluorescent nanosensors enables remote detection and differentiation of important pathogens and the potential for smart surfaces.

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.

Data mining in Raman imaging in a cellular biological system

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Working flow of data mining in Raman imaging of cell system described.

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Pre-processing, pattern recognition and validation discussed.

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Machine learning methods applied at each step discussed.

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Single-cell visualization, cell type classification and quantification applications.

The distribution and dynamics of biomolecules in the cell is of critical interest in biological research. Raman imaging techniques have expanded our knowledge of cellular biological systems significantly. The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity. As well as instrumental developments, data mining plays a significant role in revealing the complicated chemical information contained within the spectral data. A number of data mining methods have been applied to extract the spectral information and translate them into biological information. Single-cell visualization, cell classification and biomolecular/drug quantification have all been achieved by the application of data mining to Raman imaging data. Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis. The characteristics of these methods are described in relation to their application in Raman imaging of the cell. Furthermore, we summarize the software that can facilitate the implementation of these methods. Through its careful selection and application, data mining can act as an essential tool in the exploration of information-rich Raman spectral data.

Broadband stimulated Raman scattering microscopy with wavelength-scanning detection

We introduce a high-sensitivity broadband stimulated Raman scattering (SRS) setup featuring wide spectral coverage (up to 500 cm-1) and high-frequency resolution (≈20 cm-1). The system combines a narrowband Stokes pulse, obtained by spectral filtering an Yb laser, with a broadband pump pulse generated by a home-built optical parametric oscillator. A single-channel lock-in amplifier connected to a single-pixel photodiode measures the stimulated Raman loss signal, whose spectrum is scanned rapidly using a galvanometric mirror after the sample. We use the in-line balanced detection approach to suppress laser fluctuations and achieve close to shot-noise-limited sensitivity. The setup is capable of measuring accurately the SRS spectra of several solvents and of obtaining hyperspectral data cubes consisting in the broadband SRS microscopy images of polymer beads test samples as well as of the distribution of different biological substances within plant cell walls.

Multiplex micro-SERS imaging of cancer-related markers in cells and tissues using poly(allylamine)-coated Au@Ag nanoprobes

Surface-enhanced Raman scattering (SERS) nanoprobes based on Au@Ag core@shell nanoparticles coated with poly(allylamine) were functionalized with small targeting molecules to evaluate simultaneously the level of expression of two cancer-related markers, both in cells and in tissues. The Au@Ag nanoparticles provide a high SERS signal enhancement in the visible range when combined with resonant Raman-active molecules. The poly(allylamine) coating plays a dual key role in (i) protecting the metal surface against the complex biological medium, leading to a stable signal of the Raman-active molecules, and (ii) enabling specific biofunctionalization through its amine functions. Using small targeting molecules linked to the polymer coating, two different nanoprobes (duplex approach) were designed. Each was able to specifically target a particular cancer-related marker: folate receptors (FRs) and sialic acid (SA). We demonstrate that the level of expression of these targeted markers can be evaluated following the SERS signal of the probes incubated on cells or tissues. The potential overexpression of folate receptors and of sialic acid was evaluated and measured in breast and ovarian cancerous tissue sections. In addition, FR and/or SA overexpression in the tumor region can be visualized with high contrast with respect to the healthy region and with high spatial accuracy consistent with histology by SERS imaging of the nanoprobe signal. Owing to the unique spectral signature of the designed nanoprobes, this approach offers an efficient tool for the spatially resolved, in situ measurement of the expression level of several cancer-related markers in tumors at the same time.Graphical abstract.

Diagnosis of a model of Duchenne muscular dystrophy in blood serum of mdx mice using Raman hyperspectroscopy

Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy and affects boys in infancy or early childhood. Current methods for diagnosing DMD are often laborious, expensive, invasive, and typically diagnose the disease late in its progression. In an effort to improve the accuracy and ease of diagnosis, this study focused on developing a novel method for diagnosing DMD which combines Raman hyperspectroscopic analysis of blood serum with advanced statistical analysis. Partial least squares discriminant analysis was applied to the spectral dataset acquired from blood serum of a mouse model of Duchenne muscular dystrophy (mdx) and control mice. Cross-validation showed 95.2% sensitivity and 94.6% specificity for identifying diseased spectra. These results were verified via external validation, which achieved 100% successful classification accuracy at the donor level. This proof-of-concept study presents Raman hyperspectroscopic analysis of blood serum as an easy, fast, non-expensive, and minimally invasive detection method for distinguishing control and mdx model mice, with a strong potential for clinical diagnosis of DMD.

A fingerprint of amyloid plaques in a bitransgenic animal model of Alzheimer's disease obtained by statistical unmixing analysis of hyperspectral Raman data

The global prevalence of Alzheimer's disease (AD) points to endemic levels, especially considering the increase of average life expectancy worldwide. AD diagnosis based on early biomarkers and better knowledge of related pathophysiology are both crucial in the search for medical interventions that are able to modify AD progression. In this study we used unsupervised spectral unmixing statistical techniques to identify the vibrational spectral signature of amyloid β aggregation in neural tissues, as early biomarkers of AD in an animal model. We analyzed spectral images composed of a total of 55 051 Raman spectra obtained from the frontal cortex and hippocampus of five bitransgenic APPswePS1ΔE9 mice, and colocalized amyloid β plaques by other fluorescence techniques. The Raman signatures provided a multifrequency fingerprint consistent with the results of synthesized amyloid β fibrils. The fingerprint obtained from unmixed analysis in neural tissues is shown to provide a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers for non-invasive early diagnosis and pathophysiology studies in AD on the retina.

Towards Raman spectroscopy of urine as screening tool

For the screening purposes urine is an especially attractive biofluid, since it offers easy and noninvasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave-one-mouse-out cross-validation approach. Considering kidney disorders, the accuracy of 100% was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27% and 78.57%. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and noninvasive diagnosis of kidney disorders, but also holds some promises for the screening of nonurinary tract diseases.

Non-invasive Imaging Techniques: From Histology to In Vivo Imaging : Chapter of Imaging in Oncology

In this chapter, we will introduce and review molecular-sensitive imaging techniques, which close the gap between ex vivo and in vivo analysis. In detail, we will introduce spontaneous Raman spectral imaging, coherent anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS), second-harmonic generation (SHG) and third-harmonic generation (THG), two-photon excited fluorescence (TPEF), and fluorescence lifetime imaging (FLIM). After reviewing these imaging techniques, we shortly introduce chemometric methods and machine learning techniques, which are needed to use these imaging techniques in diagnostic applications.

Multispectral near infrared absorption imaging for histology of skin cancer

Multispectral imaging combines the spectral resolution of spectroscopy with the spatial resolution of imaging and is therefore very useful for biomedical applications. Currently, histological diagnostics use mainly stainings with standard dyes (eg, hematoxylin + eosin) to identify tumors. This method is not applicable in vivo and provides low amounts of chemical information. Biomolecules absorb near infrared light (NIR, 800-1700 nm) at different wavelengths, which could be used to fingerprint tissue. Here, we built a NIR multispectral absorption imaging setup to study skin tissue samples. NIR light (900-1500 nm) was used for homogenous wide-field transmission illumination and detected by a cooled InGaAs camera. In this setup, images I(x, y, λ) from dermatological samples (melanoma, nodular basal-cell carcinoma, squamous-cell carcinoma) were acquired to distinguish healthy from diseased tissue regions. In summary, we show the potential of multispectral NIR imaging for cancer diagnostics.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Inkjet-printed micro-calibration standards for ultraquantitative Raman spectral cytometry

Herein we report the development of a cytometric analysis platform for measuring the contents of individual cells in absolute (picogram) scales; this study represents the first report of Raman-based quantitation of the absolute mass - or the total amount - of multiple endogenous biomolecules within single-cells. To enable ultraquantitative calibration, we engineered single-cell-sized micro-calibration standards of known composition by inkjet-printer deposition of biomolecular components in microarrays across the surface of silicon chips. We demonstrate clinical feasibility by characterizing the compositional phenotype of human skin fibroblast and porcine alveolar macrophage cell populations in the respective contexts of Niemann-Pick disease and drug-induced phospholipidosis: two types of lipid storage disorders. We envision this microanalytical platform as the foundation for many future biomedical applications, ranging from diagnostic assays to pathological analysis to advanced pharmaco/toxicokinetic research studies.

Improving Sensitivity in Raman Imaging for Thin Layered and Powdered Food Analysis Utilizing a Reflection Mirror

Raman imaging has been proven to be a powerful analytical technique for the characterization and visualization of chemical components in a range of products, particularly in the food and pharmaceutical industries. The conventional backscattering Raman imaging technique for the spatial analysis of a deep layer suffers from the presence of intense fluorescent and Raman signals originating from the surface layer which mask the weaker subsurface signals. Here, we demonstrated the application of a new reflection amplifying method using a background mirror as a sample holder to increase the Raman signals from a deep layer. The approach is conceptually demonstrated on enhancing the Raman signals from the subsurface layer. Results show that when bilayer samples are scanned on a reflection mirror, the average signals increase 1.62 times for the intense band at 476 cm⁻¹ of starch powder, and average increases of 2.04 times (for the band at 672 cm⁻¹) for a subsurface layer of high Raman sensitive melamine powder under a 1 mm thick teflon sheet. The method was then applied successfully to detect noninvasively the presence of small polystyrene pieces buried under a 2 mm thick layer of food powder (a case of powdered food adulteration) which otherwise are inaccessible to conventional backscattering Raman imaging. In addition, the increase in the Raman signal to noise ratio when measuring samples on a mirror is an important feature in many applications where high-throughput imaging is of interest. This concept is also applicable in an analogous manner to other disciplines, such as pharmaceutical where the Raman signals from deeper zones are typically, substantially diluted due to the interference from the surface layer.

Raman spectroscopy reveals LPS-induced changes of biomolecular composition in monocytic THP-1 cells in a label-free manner

The human innate immune system is able to recognize pathogen-associated molecular patterns like lipopolysaccharides (LPS) leading to the activation of signal cascades and the release of different cytokines. Activation of the immune cells can be assessed in different ways which are either indirect (ELISA of cytokine release), require staining protocols (flow cytometry) or lysis of the cells (mRNA analysis). Here, Raman spectroscopy as a non-destructive spectroscopic method is presented to enable direct and label-free monitoring of changes in cellular metabolism, biomolecular composition as well as morphology. Exemplarily, the potential of Raman spectroscopy is presented for the characterization of LPS-stimulation of monocytic THP-1 cells over a time course of 16 h. The cell culture stimulation model is characterized using gene transcription and expression of the two cytokines TNFα and IL-1β. After 1 h, 3 h, 8 h and 16 h specific Raman spectroscopic fingerprints are generated which encode cell activation pattern after TLR4 stimulation. Most prevalent changes in the spectra occur after 8 h, but slight differences are already detectable after 1 h. Spatially highly resolved Raman scans are used to generate false-color Raman images which provide spatial information of the biochemical state of the cells and changes over time. One of the most significant observed differences is an increase in Raman signal from DNA/RNA content in LPS-stimulated cells when compared to unstimulated cells. The systematic assignment of Raman spectroscopic profiles of LPS-activated cells to cellular activation assessed by cytokine gene transcription and expression opens new ways for label-free and direct immunological studies of specific pathogen recognizing receptors and their downstream signaling pathways.

Analyzing Raman spectroscopic data

This chapter is a short introduction into the data analysis pipeline, which is typically utilized to analyze Raman spectra. We empathized in the chapter that this data analysis pipeline must be tailored to the specific application of interest. Nevertheless, the tailored data analysis pipeline consists always of the same general procedures applied sequentially. The utilized procedures correct for artefacts, standardize the measured spectral data and translate the spectroscopic signals into higher level information. These computational procedures can be arranged into separate groups namely data pre-treatment, pre-processing and modeling. Thereby the pre-treatment aims to correct for non-sample-dependent artefacts, like cosmic spikes and contributions of the measurement device. The block of procedures, which needs to be applied next, is called pre-processing. This group consists of smoothing, baseline correction, normalization and dimension reduction. Thereafter, the analysis model is constructed and the performance of the models is evaluated. Every data analysis pipeline should be composed of procedures of these three groups and we describe every group in this chapter. After the description of data pre-treatment, pre-processing and modeling, we summarized trends in the analysis of Raman spectra namely model transfer approaches and data fusion. At the end of the chapter we tried to condense the whole chapter into guidelines for the analysis of Raman spectra.

Raman tweezers microspectroscopy of circa 100 nm extracellular vesicles

The technique of Raman tweezers microspectroscopy (RTM) for the global biomolecular content characterization of a single extracellular vesicle (EV) or a small number of EVs or other nanoscale bioparticles in an aqueous dispersion in the difficult-to-access size range of near 100 nm is described in detail. The particularities and potential of RTM are demonstrated using the examples of DOPC liposomes, exosomes from human urine and rat hepatocytes, and a mixed sample of the transfection reagent FuGENE in diluted DNA solution. The approach of biomolecular component analysis for the estimation of the main biomolecular contributions (proteins, lipids, nucleic acids, carotenoids, etc.) is proposed and discussed. Direct Raman evidence for strong intra-sample biomolecular heterogeneity of individual optically trapped EVs, due to variable contributions from nucleic acids and carotenoids in some preparations, is reported. On the basis of the results obtained, we are making an attempt to convince the scientific community that RTM is a promising method of single-EV research; to our knowledge, it is the only technique available at the moment that provides unique information about the global biomolecular composition of a single vesicle or a small number of vesicles, thus being capable of unravelling the high diversity of EV subpopulations, which is one of the most significant urgent challenges to overcome. Possible RTM applications include, among others, searching for DNA biomarkers, cancer diagnosis, and discrimination between different subpopulations of EVs, lipid bodies, protein aggregates and viruses.

Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855-886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm^-1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications.

An Expandable Mechanopharmaceutical Device (3): a Versatile Raman Spectral Cytometry Approach to Study the Drug Cargo Capacity of Individual Macrophages

PURPOSE

To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules.

METHODS

The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples.

RESULTS

Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning.

CONCLUSIONS

This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.

Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example

The goal of sample-size planning (SSP) is to determine the number of measurements needed for statistical analysis. This SSP is necessary to achieve robust and significant results with a minimal number of measurements that need to be collected. SSP is a common procedure for univariate measurements, whereas for multivariate measurements, like spectra or time traces, no general sample-size-planning method exists. Sample-size planning becomes more important for biospectroscopic data because the data generation is time-consuming and costly. Additionally, ethical reasons do not allow the use of unnecessary samples and the measurement of unnecessary spectra. In this paper, a general sample-size-planning algorithm is presented that is based on learning curves. The learning curve quantifies the improvement of a classifier for an increasing training-set size. These curves are fitted by the inverse-power law, and the parameters of this fit can be utilized to predict the necessary training-set size. Sample-size planning is demonstrated for a biospectroscopic task of differentiating six different bacterial species, including Escherichia coli, Klebsiella terrigena, Pseudomonas stutzeri, Listeria innocua, Staphylococcus warneri, and Staphylococcus cohnii, on the basis of their Raman spectra. Thereby, we estimate the required number of Raman spectra and biological replicates to train a classification model, which consists of principal-component analysis (PCA) combined with linear-discriminant analysis (LDA). The presented algorithm revealed that 142 Raman spectra per species and seven biological replicates are needed for the above-mentioned biospectroscopic-classification task. Even though it was not demonstrated, the learning-curve-based sample-size-planning algorithm can be applied to any multivariate data and in particular to biospectroscopic-classification tasks.

Raman micro-spectroscopy monitors acquired resistance to targeted cancer therapy at the cellular level

Monitoring the drug efficacy or resistance in vitro is usually carried out by measuring the response of single few proteins. However, observation of single proteins instead of an integral cell response may lead to results that are not consistent with patient’s response to a drug. We present a Raman spectroscopic method that detects the integral cell response to drugs such as tyrosine kinase inhibitors (TKIs). Non-small cell lung cancer (NSCLC) patients with EGFR mutations develop acquired resistance to first (erlotinib)- and third (osimertinib)-generation TKIs. Large erlotinib-induced differences were detected by Raman micro-spectroscopy in NSCLC cells without T790M EGFR mutation but not in cells with this mutation. Additionally, Raman difference spectra detected the response of NSCLC cells with T790M EGFR mutation to second- (neratinib) and third-generation (osimertinib) TKIs, and the resistance of cells with T790M/C797S EGFR mutation to osimertinib. Thus, the in vitro Raman results indicated that NSCLC cells with T790M and T790M/C797S EGFR mutations are resistant to erlotinib- and osimertinib, respectively, consistent with the observed responses of patients. This study shows the potential of Raman micro-spectroscopy to monitor drug resistance and opens a new door to in vitro companion diagnostics for screening personalized therapies.

Raman spectroscopy-based identification of toxoid vaccine products

Vaccines are complex biomedicines. Manufacturing is time consuming and requires a high level of quality control (QC) to guarantee consistent safety and potency. An increasing global demand has led to the need to reduce time and cost of manufacturing. The evolving concepts for QC and the upcoming threat of falsification of biomedicines define a new need for methods that allow the fast and reliable identification of vaccines. Raman spectroscopy is a non-destructive technology already established in QC of classical medicines. We hypothesized that Raman spectroscopy could be used for identification and differentiation of vaccine products. Raman maps obtained from air-dried samples of combination vaccines containing antigens from tetanus, diphtheria and pertussis (DTaP vaccines) were summarized to compile product-specific Raman signatures. Sources of technical variance were emphasized to evaluate the robustness and sensitivity in downstream data analysis. The data management approach corrects for spatial inhomogeneities in the dried sample while offering a proper representation of the original samples inherent chemical signature. Reproducibility of the identification was validated by a leave-one-replicate-out cross-validation. The results highlighted the high specificity and sensitivity of Raman measurements in identifying DTaP vaccine products. The results pave the way for further exploitation of the Raman technology for identification of vaccines in batch release and cases of suspected falsification.

A light-based identification method offers a fast, reliable and non-destructive method to analyze vaccines. Using vaccines for tetanus, diphtheria, and pertussis, a German research team led by the Paul Ehrlich Institute and University of Jena and Leibniz-IPHT showed that Raman spectroscopy — which identifies substances based on how they scatter laser light — is able to identify distinctive signatures of vaccines. In their experiments, Raman spectroscopy was sensitive enough to detect subtle differences in vaccine formulation, such as the specific combination of vaccine and adjuvant components. However, a data analytics technique was required to correct for sample quality variation caused by their preparation. Raman spectroscopy already sees use in classical medicines, and its application to vaccines could help to reduce the time and cost of quality control while benefitting the unmet need for rapid analysis of vaccine quality and identity.

Tissue degeneration in ALS affected spinal cord evaluated by Raman spectroscopy

The Raman spectral features from spinal cord tissue sections of transgenic, ALS model mice and non-transgenic mice were compared using 457 nm excitation line, profiting from the favourable signal intensity obtained in the molecular fingerprint region at this wavelength. Transverse sections from four SOD1G93A mice at 75 days and from two at 90 days after birth were analysed and compared with sections of similarly aged control mice. The spectra acquired within the grey matter of tissue sections from the diseased mice is markedly different from the grey matter signature of healthy mice. In particular, we observe an intensity increase in the spectral windows 450-650 cm-1 and 1050-1200 cm-1, accompanied by an intensity decrease in the lipid contributions at ~1660 cm-1, ~1440 cm-1 and ~1300 cm-1. Axons demyelination, loss of lipid structural order and the proliferation and aggregation of branched proteoglycans are related to the observed spectral modifications. Furthermore, the grey and white matter components of the spinal cord sections could also be spectrally distinguished, based on the relative intensity of characteristic lipid and protein bands. Raman spectra acquired from the white matter regions of the SOD1G93A mice closely resembles those from control mice.

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.

Graphene-Based Raman Spectroscopy for pH Sensing of X-rays Exposed and Unexposed Culture Media and Cells

Graphene provides a unique way of sensing the local pH level of substances on the micrometric scale, with important implications for the monitoring of cellular metabolic activities where proton excretion could occur. Accordingly, an innovative biosensing approach for the quantification of the pH value of biological fluids, to be used also with small amounts of fluids, was realized and tested. It is based on the use of micro-Raman spectroscopy to detect the modifications of the graphene doping level induced by the contact of the graphene with the selected fluids. The approach was preliminarily tested on aqueous solutions of known pH values. It was then used to quantify the pH values of cell culture media directly exposed to different doses of X-ray radiation and to media exposed to X-ray-irradiated cells. The Raman response of cells placed on graphene layers was also examined.

Optimizing laser crater enhanced Raman scattering spectroscopy

The laser crater enhanced Raman scattering (LCERS) spectroscopy technique has been systematically studied for chosen sampling strategy and influence of powder material properties on spectra intensity enhancement. The same nanosecond pulsed solid state Nd:YAG laser (532 nm, 10 ns, 0.1-1.5 mJ/pulse) was used for laser crater production and Raman scattering experiments for l-aspartic acid powder. Increased sampling area inside crater cavity is the key factor for Raman signal improvement for the LCERS technique, thus Raman signal enhancement was studied as a function of numerous experimental parameters including lens-to-sample distance, wavelength (532 and 1064 nm) and laser pulse energy utilized for crater production. Combining laser pulses of 1064 and 532 nm wavelengths for crater ablation was shown to be an effective way for additional LCERS signal improvement. Powder material properties (particle size distribution, powder compactness) were demonstrated to affect LCERS measurements with better results achieved for smaller particles and lower compactness.

Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products.

Optimizing laser crater enhanced Raman spectroscopy

Raman signal enhancement by laser crater production was systematically studied for 785 nm continuous wave laser pumping. Laser craters were produced in L-aspartic acid powder by a nanosecond pulsed solid state neodymium-doped yttrium aluminum garnet laser (532 nm, 8 ns, 1 mJ/pulse), while Raman spectra were then acquired by using a commercial spectrometer with 785 nm laser beam pumping. The Raman signal enhancement effect was studied in terms of the number of ablating pulses used, the lens-to-sample distance, and the crater-center-laser-spot offset. The influence of the experiment parameters on Raman signal enhancement was studied for different powder materials. Maximum Raman signal enhancement reached 11 fold for loose powders but decreased twice for pressed tablets. Raman signal enhancement was demonstrated for several diverse powder materials like gypsum or ammonium nitrate with better results achieved for the samples tending to give narrow and deep craters upon the laser ablation stage. Alternative ways of cavity production (steel needle tapping and hole drilling) were compared with the laser cratering technique in terms of Raman signal enhancement. Drilling was found to give the poorest enhancement of the Raman signal, while both laser ablation and steel needle tapping provided comparable results. Here, we have demonstrated for the first time, to the best of our knowledge, that a Raman signal can be enhanced 10 fold with the aid of simple cavity production by steel needle tapping in rough highly reflective materials. Though laser crater enhancement Raman spectroscopy requires an additional pulsed laser, this technique is more appropriate for automatization compared to the needle tapping approach.