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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. Raman spectroscopy is increasingly being used in biological assays and studies. This protocol provides guidance for performing chemometric analysis to detect and extract information relating to the chemical differences between biological samples.

Stimulated Raman scattering (SRS) microscopy in combination with innovative tagging strategies offers great potential as a universal high-throughput biomedical imaging tool. Here, we report rationally tailored small molecular monomers containing triple-bond units with large Raman scattering cross-sections, which can be polymerized at the nanoscale for enhancement of SRS contrast with smaller but brighter optical nanotags with artificial fingerprint output. From this, a class of triple-bond rich polymer nanoparticles (NPs) was engineered by regulating the relative dosages of three chemically different triple-bond monomers in co-polymerization. The bonding strategy allowed for 15 spectrally distinguishable triple-bond combinations. These accurately structured nano molecular aggregates, rather than long-chain macromolecules, could establish a universal method for generating small-sized biological SRS imaging tags with high sensitivity for high-throughput multi-color biomedical imaging.

Raman spectroscopy is an analytical method to identify medical samples of bacteria. Because Raman spectroscopy detects the biochemical properties of a cell, there are many factors that can influence and modify the Raman spectra of bacteria. One possible influence is a proper method for isolation of the bacteria. Medical samples in particular never occur in purified form, so a Raman-compatible isolation method is needed which does not affect the bacteria and thus the resulting spectra. In this study, we present a Raman-compatible method for isolation of bacteria from bronchoalveolar lavage (BAL) fluid using density gradient centrifugation. In addition to measuring the bacteria from a patient sample, the yield and the spectral influence of the isolation on the bacteria were investigated. Bacteria isolated from BAL fluid show additional peaks in comparison to pure culture bacteria, which can be attributed to components in the BAL sample. The isolation gradient itself has no effect on the spectra, and with a yield of 63% and 78%, the method is suitable for isolation of low concentrations of bacteria from a complex matrix. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00216-021-03488-0.

The functionality of active centers in proteins is governed by the secondary and higher structure of proteins which often lead to structures in the active center that are different from the structures found in protein-free models of the active center. To elucidate this structure–function relationship, it is therefore necessary to investigate both the protein structure and the local structure of the active center. In this work, we investigate the application of hetero (resonance) Raman two-dimensional correlation spectroscopy (2D-COS) to this problem. By employing a combination of near-infrared-Fourier transform-Raman- and vis-resonance Raman spectroscopy, we could show that this combination of techniques is able to directly probe the structure–function relationship of proteins. We were able to correlate the transition of the heme center in cytochrome c from low to high spin with changes in the secondary structure with the above mentioned two spectroscopic in situ techniques and without sample preparation. Thereby, we were able to reveal that the combination of a spectroscopic method to selectively observe the active center with a technique that monitors the whole system offers a promising toolkit to investigate the structure–function relationship of proteins with photoactive centers in general., Graphical Abstract

Raman-stable isotope labeling using heavy water (Raman-D2O) is attracting great interest as a fast technique with various applications ranging from the identification of pathogens in medical samples to the determination of microbial activity in the environment. Despite its widespread applications, little is known about the fundamental processes of hydrogen-deuterium (H/D) exchange, which are crucial for understanding molecular interactions in microorganisms. By combining two-dimensional (2D) correlation spectroscopy and Raman deuterium labeling, we have investigated H/D exchange in bacterial cells under time dependence. Most C-H stretching signals decreased in intensity over time, prior to the formation of the C-D stretching vibration signals. The intensity of the C-D signal gradually increased over time, and the shape of the C-D signal was more uniform after longer incubation times. Deuterium uptake showed high variability between the bacterial genera and mainly led to an observable labeling of methylene and methyl groups. Thus, the C-D signal encompassed a combination of symmetric and antisymmetric CD2 and CD3 stretching vibrations, depending on the bacterial genera. The present study allowed for the determination of the sequential order of deuterium incorporation into the functional groups of proteins, lipids, and nucleic acids and hence understanding the process of biomolecule synthesis and the growth strategies of different bacterial taxa. We present the combination of Raman-D2O labeling and 2D correlation spectroscopy as a promising approach to gain a fundamental understanding of molecular interactions in biological systems.

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. Supplementary Information The online version contains supplementary material available at 10.1007/s00216-021-03360-1.

Shifted excitation Raman difference spectroscopy (SERDS) can be used as an instrumental baseline correction technique to retrieve Raman bands in highly fluorescent samples. Genipin (GE) cross-linked equine pericardium (EP) was used as a model system since a blue pigment is formed upon cross-linking, which results in a strong fluorescent background in the Raman spectra. EP was cross-linked with 0.25% GE solution for 0.5 h, 2 h, 4 h, 6 h, 12 h, and 24 h, and compared with corresponding untreated EP. Raman spectra were collected with three different excitation wavelengths. For the assessment of the SERDS technique, the preprocessed SERDS spectra of two excitation wavelengths (784 nm-786 nm) were compared with the mathematical baseline-corrected Raman spectra at 785 nm excitation using extended multiplicative signal correction, rubberband, the sensitive nonlinear iterative peak and polynomial fitting algorithms. Whereas each baseline correction gave poor quality spectra beyond 6 h GE crosslinking with wave-like artefacts, the SERDS technique resulted in difference spectra, that gave superior reconstructed spectra with clear collagen and resonance enhanced GE pigment bands with lower standard deviation. Key for this progress was an advanced difference optimization approach that is described here. Furthermore, the results of the SERDS technique were independent of the intensity calibration because the system transfer response was compensated by calculating the difference spectrum. We conclude that this SERDS strategy can be transferred to Raman studies on biological and non-biological samples with a strong fluorescence background at 785 nm and also shorter excitation wavelengths which benefit from more intense scattering intensities and higher quantum efficiencies of CCD detectors.

This work presents the synthesis and characterization of easily tuneable shape-memory metallopolymers. Furthermore, the structural design enables excellent rewriting properties. For this purpose, two different polymers were synthesized using either 2-ethyl hexyl- or butyl methacrylate monomers featuring both histidine and terpyridine ligands in their side chains. The addition of metal salt resulted in the selective formation of stable complexes with the terpyridine ligands (NiCl2 or FeSO4) and addition of a second metal ion led to labile ones with the histidine ligands (Zn(TFMS)2 or NiCl2). As a consequence, a dual supramolecular crosslinked metallopolymer network was formed, which was confirmed using Raman spectroscopy. Due to the use of different metal salts, it is possible to tune the stability of both complexes independently and, thus, the switching and rewriting temperature of the shape-memory polymer. Fixity and recovery rates up to 99% were obtained by this approach. Furthermore, thermo mechanical analyses revealed the rewriting possibility without reducing the shape-memory abilities.

This work elaborates the effect of dynamic irradiation on light-driven molecular water oxidation to counteract catalyst deactivation. It highlights the importance of overall reaction engineering to overcome limiting factors in artificial photosynthesis reactions. Systematic investigation of a homogenous three component ruthenium-based water oxidation system revealed significant potential to enhance the overall catalytic efficiency by synchronizing the timescales of photoreaction and mass transport in a capillary flow reactor. The overall activity could be improved by a factor of more than 10 with respect to the turnover number and a factor of 31 referring to the external energy efficiency by controlling the local availability of photons. Detailed insights into the mechanism of light driven water oxidation could be obtained using complementary methods of investigation like Raman, IR and UV-vis/emission spectroscopy, unraveling the importance of avoiding high concentrations of excited photosensitizers.

The Beer‐Lambert law is unquestionably the most important law in optical spectroscopy and indispensable for the qualitative and quantitative interpretation of spectroscopic data. As such, every spectroscopist should know its limits and potential pitfalls, arising from its application, by heart. It is the goal of this work to review these limits and pitfalls, as well as to provide solutions and explanations to guide the reader. This guidance will allow a deeper understanding of spectral features, which cannot be explained by the Beer‐Lambert law, because they arise from electromagnetic effects/the wave nature of light. Those features include band shifts and intensity changes based exclusively upon optical conditions, i. e. the method chosen to record the spectra, the substrate and the form of the sample. As such, the review will be an essential tool towards a full understanding of optical spectra and their quantitative interpretation based not only on oscillator positions, but also on their strengths and damping constants., If August Beer would have had a chance to combine Pierre Bouguer's findings and Johann Heinrich Lambert's law with his own results under consideration of James Clerk Maxwell's relations, he would have ensured the Bouguer‐Beer‐Lambert law to be well‐funded on electromagnetic theory. At least, he would have made users well aware of its limitations. Since he died too early, it is the goal of this review to complete his work in this regard.

Neutrophils are important cells of the innate immune system and the major leukocyte subpopulation in blood. They are responsible for recognizing and neutralizing invading pathogens, such as bacteria or fungi. For this, neutrophils are well equipped with pathogen recognizing receptors, cytokines, effector molecules, and granules filled with reactive oxygen species (ROS)-producing enzymes. Depending on the pathogen type, different reactions are triggered, which result in specific activation states of the neutrophils. Here, we aim to establish a label-free method to indirectly detect infections and to identify the cause of infection by spectroscopic characterization of the neutrophils. For this, isolated neutrophils from human peripheral blood were stimulated in an

The design of nanoparticles for application in medical diagnostics and therapy requires a thorough understanding of various aspects of nanoparticle-cell interactions. In this work, two unconventional methods for the study of nanoparticle effects on cells, Raman spectroscopy and atomic force microscopy (AFM), were employed to track the molecular and morphological changes that are caused by the interaction between cervical carcinoma-derived HeLa cells and two types of cerium dioxide (CeO2) nanoparticles, ones with dextran coating and the others with no coating. Multivariate statistical analyses of Raman spectra, such as principal component analysis and partial least squares regression, were applied in order to extract the variations in the vibrational features of cell biomolecules and through them, the changes in biomolecular content and conformation. Both types of nanoparticles induced changes in DNA, lipid and protein contents of the cell and variations of the protein secondary structure, whereas dextran-coated CeO2 affected the cell-growth rate to a higher extent. Atomic force microscopy showed changes in cell roughness, cell height and nanoparticle effects on surface molecular layers. The method differentiated between the impact of dextran-coated and uncoated CeO2 nanoparticles with higher precision than performed viability tests. Due to the holistic approach provided by vibrational information on the overall cell content, accompanied by morphological modifications observed by high-resolution microscopy, this methodology offers a wider picture of nanoparticle-induced cell changes, in a label-free single-cell manner.

Multimodal optical imaging of tissue has significant potential to become an indispensable diagnostic tool in clinical pathology. Conventional bright-field microscopy provides contrast based on attenuation or reflectance of light, having no depth-related information and no molecular specificity. Recent developments in biomedical optics have introduced a variety of optical modalities, such as Raman spectroscopy (RS), fluorescence lifetime imaging microscopy (FLIM) of endogenous fluorophores, optical coherence tomography (OCT), and others, which provide a distinct characteristic, i.e., molecular, chemical, and morphological information, of the sample. To harvest the full analytical potential of those modalities, we have developed a novel multimodal imaging system, which allows the co-registered acquisition of OCT/FLIM/RS on a single device. The present implementation allows the investigation of biological tissues in the mesoscale range, 0.1-5 mm in a correlated manner. Due to the co-registered acquisition of the modalities, it is possible to directly compare and evaluate the corresponding information between the three modalities. Moreover, by additionally preparing and characterizing entire pathological hematoxylin and eosin (H&E) slides of head and neck biopsies, it is also possible to correlate the multimodal spectroscopic information to any location of the ground truth H&E information. To the best of our knowledge, this is the first development and implementation of a compact and clinically applicable multimodal scanning microscope, which combines OCT, FLIM, and RS together with the possibility for co-registering H&E information for a morpho-chemical tissue characterization and a correlation with the pathological ground truth (H&E) of the underlying signal origin directly in a clinical environment.

Raman spectroscopy probes the biochemical composition of samples in a non-destructive, non-invasive and label-free fashion yielding specific information on a molecular level. Nevertheless, the Raman effect is very weak. The detection of all inelastically scattered photons with highest efficiency is therefore crucial as well as the identification of all noise sources present in the system. Here we provide a study for performance comparison and assessment of different spectrometers for confocal Raman spectroscopy in biosensor applications. A low-cost, home-built Raman spectrometer with a complementary metal-oxide-semiconductor (CMOS) camera, a middle price-class mini charge-coupled device (CCD) Raman spectrometer and a laboratory grade confocal Raman system with a deeply cooled CCD detector are compared. It is often overlooked that the sample itself is the most important “optical” component in a Raman spectrometer and its properties contribute most significantly to the signal-to-noise ratio. For this purpose, different representative samples: a crystalline silicon wafer, a polypropylene sample and E. coli bacteria were measured under similar conditions using the three confocal Raman spectrometers. We show that biosensor applications do not in every case profit from the most expensive equipment. Finally, a small Raman database of three different bacteria species is set up with the middle price-class mini CCD Raman spectrometer in order to demonstrate the potential of a compact setup for pathogen discrimination.

As derived by Max Planck in 1903 from dispersion theory, Beer's law has a fundamental limitation. The concentration dependence of absorbance can deviate from linearity, even in the absence of any interactions or instrumental nonlinearities. Integrated absorbance, not peak absorbance, depends linearly on concentration. The numerical integration of the absorbance leads to maximum deviations from linearity of less than 0.1 %. This deviation is a consequence of a sum rule that was derived from the Kramers‐Kronig relations at a time when the fundamental limitation of Beer's law was no longer mentioned in the literature. This sum rule also links concentration to (classical) oscillator strengths and thereby enables the use of dispersion analysis to determine the concentration directly from transmittance and reflectance measurements. Thus, concentration analysis of complex samples, such as layered and/or anisotropic materials, in which Beer's law cannot be applied, can be achieved using dispersion analysis., Absorbance at a particular spectral point does not necessarily depend linearly on the concentration. Based on electromagnetic theory, it can be demonstrated, however, that for the integrated absorbance, Beer's law still holds.

Typical nanoparticle-based plasmonic index sensors detect the spectral shift of localized surface plasmon resonance (LSPR) upon the change of environmental index. Therefore, they require broadband illumination and spectrometers. The sensitivity and flexibility of nanoparticle-based index sensors are usually limited because LSPR peaks are usually broad and the spectral position cannot be freely designed. Here, we present a fully designable index sensing platform using a plasmonic Doppler grating (PDG), which provides broadband and azimuthal angle-dependent grating periodicities. Different from LSPR, the PDG index sensor is based on the momentum matching between photons and surface plasmons via the lattice momentum of the grating. Therefore, index change is translated into the variation of in-plane azimuthal angle for photon-to-plasmon coupling, which manifests as directly observable dark bands in the reflection image. The PDG can be freely designed to optimally match the range of index variation for specific applications. In this work, we demonstrate PDG index sensors for large (n = 1.00 to 1.52) and small index variation (n = 1.3330 to 1.3650). The tiny and nonlinear index change of water-ethanol mixture has been clearly observed and accurately quantified. Since the PDG is a dispersive device, it enables on-site and single-color index sensing without a spectrometer and provides a promising spectroscopic platform for on-chip analytical applications., Comment: July 22, 2019 published on Analytical Chemistry

This work reports the development and application of two liquid-core microstructured polymer optical fibers (LC-mPOF) with different microstructure sizes. They are used in a fiber-enhanced Raman spectroscopy sensing platform, with the aim of detecting glucose in aqueous solutions in the clinically relevant range for sodium–glucose cotransporter 2 inhibitor therapy. The sensing platform is tested for low-concentration glucose solutions using each LC-mPOF. Results confirm that a significant enhancement of the Raman signal is achieved in comparison to conventional Raman spectroscopy. Additional measurements are carried out to obtain the valid measurement range, the resolution, and the limit of detection, showing that the LC-mPOF with 66-μm-diameter central hollow core has the highest potential for future clinical applications. Finally, preliminary tests successfully demonstrate glucose identification in urine.

Raman spectroscopy can provide the biomolecular fingerprint of a cell in a label-free manner. Although a variety of clinical and biomedical applications have been demonstrated, the method remains largely a niche technology. The two main problems are the complexity of data acquisition and the complexity of data analysis. Generally, Raman measurements are performed manually and require a substantial amount of time. This, on the other hand, frequently results in a low number of samples and hence with questionable statistical evaluation. Here, we propose an automated high content screening Raman spectroscopy (HCS-RS) platform, which can perform a series of experiments without human interaction, significantly increasing the number of measured samples and making the measurement more reliable. The automated image processing of bright field images in combination with automatic spectral acquisition of the molecular fingerprint of cells exposed to different physiological conditions enables label-free high content screening applications. The performance of the developed HCS-RS platform is demonstrated by investigating the effect of panitumumab on SW48 and SW480 colorectal cancer cells with wild-type and mutated K-RAS, respectively, in a series of concentrations. Our result indicates that the increased content of panitumumab prohibits the activation of the MAP kinase of the colorectal cancer cells with wild-type K-RAS strongly, whereas there is no significant effect on the K-RAS mutated cells. Moreover, the relative amount of the panitumumab content present in the cells is determined from the Raman spectral information, which could be beneficial for personalized patient treatment.

Raman stable isotope labeling with 2 H, 13 C or 15 N has been reported as an elegant approach to investigate cellular metabolic activity, which is of great importance to reveal the functions of microorganisms in native environments. A new strategy termed Raman 18 O-labeling was developed to probe the metabolic activity of bacteria. Raman 18 O-labeling refers to the combination of Raman microspectroscopy with 18 O-labeling using H2 18 O. At an excitation wavelength of 532 nm, the incorporation of 18 O into the amide I group of proteins and DNA/RNA bases was observed in Escherichia coli cells, while for an excitation wavelength electronically resonant with DNA or aromatic amino acid absorption at 244 nm 18 O assimilation was detected using chemometric tools rather than visual inspection. Raman 18 O-labeling at 532 nm combined with 2D correlation analysis confirmed the assimilation of 18 O in proteins and nucleic acids and revealed the growth strategy of E. coli cells; they underwent protein synthesis followed by nucleic acid synthesis. Independent cultural replicates at different incubation times corroborated the reproducibility of these results. The variations in spectral features of 18 O-labeled cells revealed changes in physiological information of cells. Hence, Raman 18 O-labeling could provide a powerful tool to identify metabolically active bacterial cells.

Powerful detection schemes in bioanalytics are associated with molecular specificity, high sensitivity, high spatial resolution and fast detection times. Within this presentation we will demonstrate that surface enhanced Raman spectroscopy (SERS) is able to meet all those requirements. We start with introducing various SERS detection schemes (direct SERS, SERS labels, molecular sensors) to identify bacterial infections or for the detection of atherosclerotic plaques. At the end we will present a tip enhanced Raman approach (TERS) to identify nanometer small virus particles. In order to further enhance the detection limit a novel coherent anti-Stokes Raman scattering approach using tip-enhanced techniques will be introduced.

Using the multimodal combination of optical coherence tomography (OCT) with Raman spectroscopy (RS) for obtaining morpho-molecular tissue characteristics of suspicious bladder cancer lesions for improved diagnostics and signal origin characterization. We present endoscopically acquired co-localized OCT-RS data on bladder cancer biopsies. The correlated OCT and RS data enables a new way of interpretation and understanding of signal origin for both imaging modalities. Histopathological findings serve as ground truth. Molecular signal contributions can be directly correlated to identified morphological features from the OCT and lead to a better understanding of underlying biological structures.

Here we will highlight our recent efforts in translating Raman spectroscopy towards routine clinical applications by developing compact clinically usable automated Raman spectroscopic instrumentation and their combination with other spectroscopic / optical modalities, to provide a multimodal approach with high TRL levels. We will start with novel multimodal spectroscopic instrumentation for precise surgical guidance and intraoperative histopathological examination of tissue and their combination with laser tissue ablation for tissue specific laser surgery and for therapy monitoring. Finally, we report on the application of non-resonant Raman spectroscopy for an early diagnosis of neurodegenerative diseases directly in the fundus of the eye.

Novel concepts of vibrational spectroscopic sensors for biophotonic applications are introduced: (I) Raman point-of-care sensors for microbial analysis (e.g. rapid detection of pathogens and their antibiotic resistance pattern together with host response, detection of pathogens in food); (II) cavity enhanced and fiber enhanced Raman sensors for on-site environmental and drug monitoring; (III) linear and non-linear Raman fiber probes for intraoperative histopathological tissue screening; (IV) surface enhanced Raman spectroscopy (SERS) and surface enhanced IR absorption (SEIRA) sensors for ultrasensitive bio analysis (e.g. detection of antibiotics or disease metabolites in body liquids, forbidden substances in food or metamaterial concepts for chiral biosensing).

In this work, we introduce the platform of plasmonic Doppler grating (PDG) to experimentally investigate the enhancement effect of plasmonic gratings in the input and output beams of nonlinear surface-enhanced coherent anti-Stokes Raman scattering (SECARS). PDGs are designable azimuthally chirped gratings that provide broadband and spatially dispersed plasmonic enhancement. Therefore, they offer the opportunity to observe and compare the overall enhancement from different combinations of enhancement in individual input and output beams. We first confirm PDG's capability of spatially separating the input and output enhancement in linear surface-enhanced fluorescence and Raman scattering. We then investigate spatially resolved enhancement in nonlinear SECARS, where coherent interaction of the pump, Stokes, and anti-Stokes beams is enhanced by the plasmonic gratings. By mapping the SECARS signal and analyzing the azimuthal angle-dependent intensity, we characterize the enhancement at individual frequencies. Together with theoretical analysis, we show that while simultaneous enhancement in the input and output beams is important for SECARS, the enhancement in the pump and anti-Stokes beams plays a more critical role in the overall enhancement than that in the Stokes beam. This work provides an insight into the enhancement mechanism of plasmon-enhanced spectroscopy, which is important for the design and optimization of plasmonic gratings. The PDG platform may also be applied to study enhancement mechanisms in other nonlinear light-matter interactions or the impact of plasmonic gratings on the fluorescence lifetime., ACS Nano, published online on 23 December 2020

We demonstrate the ability of nondestructive optical imaging techniques such as second-harmonic generation (SHG), two-photon fluorescence (TPF), fluorescence lifetime imaging (FLIM), and Raman spectroscopy (RS) to monitor biochemical and mechanical alterations in tissues upon collagen degradation. Decellularized equine pericardium (EP) was treated with 50 mu g/mL bacterial collagenase at 37 degrees C for 8, 16, 24, and 32 h. The SHG ratio (defined as the normalized ratio between SHG and TPF signals) remained unchanged for untreated EP (stored in phosphate-buffered solution (PBS)), whereas treated EP showed a trend of a decreasing SHG ratio with increasing collagen degradation. In the fluorescence domain, treated EP experienced a red-shifted emission and the fluorescence lifetime had a trend of decreasing lifetime with increasing collagen digestion. RS monitors collagen degradation, the spectra had less intense Raman bands at 814, 852, 938, 1242, and 1270 cm(-1). Non-negative least-squares (NNLS) modeling quantifies collagen loss and relative increase of elastin. The Young's modulus, derived from atomic force microscope-based nanoindentation experiments, showed a rapid decrease within the first 8 h of collagen degradation, whereas more gradual changes were observed for optical modalities. We conclude that optical imaging techniques like SHG, RS, and FLIM can monitor collagen degradation in a label-free manner and coarsely access mechanical properties in a nondestructive manner.

Current implementations of fiber-optic Raman spectroscopy probes are frequently based on non-contact probes with a fixed focus and thus and have to precisely maintain the probe-to-sample distance to ensure a sufficient signal collection. We propose and experimentally demonstrate a novel hand-held fiber-optic Raman probe design, which is based on a liquid lens autofocusing unit, combined with a distance sensor and an in-house developed algorithm to precisely determine the probe-to-sample distance. The reported probe significantly improves the signal stability even for hand-held operation, while reducing distance-dependent artifacts for the acquisition of Raman spectra and can improve the acquisition of Raman spectra in a variety of applications.

The ease of fabrication, large surface area, tunable pore size and morphology as well surface modification capabilities of a porous silicon (PSi) layer make it widely used for sensoric applications. The pore size of a PSi layer can be an important parameter when used as a matrix for creating surface-enhanced Raman scattering (SERS) surfaces. Here, we evaluated the SERS activity of PSi with pores ranging in size from meso to macro, the surface of which was coated with gold nanoparticles (Au NPs). We found that different pore diameters in the PSi layers provide different morphology of the gold coating, from an almost monolayer to 50 nm distance between nanoparticles. Methylene blue (MB) and 4-mercaptopyridine (4-MPy) were used to describe the SERS activity of obtained Au/PSi surfaces. The best Raman signal enhancement was shown when the internal diameter of torus-shaped Au NPs is around 35 nm. To understand the role of plasmonic resonances in the observed SERS spectrum, we performed electromagnetic simulations of Raman scattering intensity as a function of the internal diameter. The results of these simulations are consistent with the obtained experimental data.

Biomaterials used in tissue engineering and regenerative medicine applications benefit from longitudinal monitoring in a non-destructive manner. Label-free imaging based on fluorescence lifetime imaging (FLIm) and Raman spectroscopy were used to monitor the degree of genipin (GE) cross-linking of antigen-removed bovine pericardium (ARBP) at three incubation time points (0.5, 1.0, and 2.5 h). Fluorescence lifetime decreased and the emission spectrum redshifted compared to that of uncross-linked ARBP. The Raman signature of GE-ARBP was resonance-enhanced due to the GE cross-linker that generated new Raman bands at 1165, 1326, 1350, 1380, 1402, 1470, 1506, 1535, 1574, 1630, 1728, and 1741 cm&minus, 1. These were validated through density functional theory calculations as cross-linker-specific bands. A multivariate multiple regression model was developed to enhance the biochemical specificity of FLIm parameters fluorescence intensity ratio (R2 = 0.92) and lifetime (R2 = 0.94)) with Raman spectral results. FLIm and Raman spectroscopy detected biochemical changes occurring in the collagenous tissue during the cross-linking process that were characterized by the formation of a blue pigment which affected the tissue fluorescence and scattering properties. In conclusion, FLIm parameters and Raman spectroscopy were used to monitor the degree of cross-linking non-destructively.

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags.

Bovine pericardium (BP) is a vascular biomaterial used in cardiovascular surgery that is typically cross-linked for masking antigenicity and enhance stability. There is a need for biochemical evaluation of the tissue properties prior to implantation to ensure that quality and reliability standards are met. Here, engineered antigen removed BP (ARBP) that was cross-linked with 0.2% and 0.6% glutaraldehyde (GA), and further calcified in vitro to simulate graft calcifications upon implantation was characterized nondestructively using fluorescence lifetime imaging (FLIm) to identify regions of interest which were then assessed by Raman spectroscopy. We observed that the tissue fluorescence lifetime shortened, and that Raman bands at 856, 935, 1282, and 1682 cm(−1) decreased, and at 1032 and 1627 cm(−1) increased with increasing GA cross-linking. Independent classification analysis based on fluorescence lifetime and on Raman spectra discriminated between GA-ARBP and untreated ARBP with an accuracy of 91% and 66%, respectively. Pearson’s correlation analysis showed a strong correlation between pyridinium cross-links measured with high-performance liquid chromatography and fluorescence lifetime measured at 380–400 nm (R = −0.76, p = 0.00094), as well as Raman bands at 856 cm(−1) for hydroxy-proline (R = −0.68, p = 0.0056) and at 1032 cm(−1) for hydroxy-pyridinium (R = 0.74, p = 0.0016). Calcified areas of GA cross-linked tissue showed characteristic hydroxyapatite (959 and 1038 cm(−1)) bands in the Raman spectrum and fluorescence lifetime shortened by 0.4 ns compared to uncalcified regions. FLIm-guided Raman imaging could rapidly identify degrees of cross-linking and detected calcified regions with high chemical specificity, an ability that can be used to monitor tissue engineering processes for applications in regenerative medicine.

Background: Fluorescence lifetime imaging (FLIm) is a spectroscopic imaging technique able to characterize the composition of luminal surface of arterial vessels. Studies of human coronary samples demonstrated that distinct atherosclerotic lesion types are characterized by FLIm features associate with distinct tissue molecular makeup. While conventional histology has provided indications about potential sources of molecular contrast, specific information about the origin of FLIm signals is lacking. Here we investigate whether Raman spectroscopy, a technique able to evaluate chemical content of biological samples, can provide additional insight into the origin of FLIm contrast. Methods: Six human coronary artery samples were imaged using FLIm (355 nm excitation)-Raman spectroscopy (785 nm excitation) via a multimodal fiber optic probe. The spatial distribution of molecular contrast in FLIm images was analyzed in relationship with histological findings. Raman data was investigated using an endmember technique and compared with histological findings. A descriptive modeling approach based on multivariate regression was used to identify Raman bands related with changes in lifetime in four spectral channels (violet: 387/35 nm, blue: 443/29 nm, green: 546/38 nm, and red: 628/53 nm). Results: Fluorescence lifetime variations in the violet, blue and green spectral bands were observed for distinct areas of each tissue sample associated with distinct pathologies. Analysis of Raman signals from areas associated with normal, pathological intimal thickening, and fibrocalcific regions demonstrated the presence of hydroxyapatite, collagenous proteins, carotene, cholesterol, and triglycerides. The FLIm and Raman descriptive modeling analysis indicated that lifetime increase in the violet spectral band was associated with increased presence of cholesterol and carotenes, a new finding consistent with LDL accumulation in atherosclerotic lesions, and not with collagen proteins, as expected from earlier studies. Conclusions: The systematic, quantitative analysis of the multimodal FLIm-Raman dataset using a descriptive modeling approach led to the identification of LDL accumulation as the primary source of lifetime contrast in atherosclerotic lesions in the violet spectral range. Earlier FLIm validation studies relying on histopathological findings had associated this contrast to increased collagen content, also present in advanced lesions, thus demonstrating the benefits of alternative validation methods.

Advanced wavefront-shaping methods can be used to transform a simple multimode fiber into an ultra-thin laser scanning microscope. Here, we extend this technique to label-free non-linear microscopy with chemical contrast using coherent anti-Stokes Raman scattering (CARS) through a multimode fiber endoscope, which opens up new avenues for instant and in-situ diagnosis of potentially malignant tissue. We use a commercial, 125 μm diameter, 0.29 NA, GRIN fiber as the endoscopic probe. Wavefront shaping on a spatial light modulator is used to create a focus, where the 1-2 ps long pump and Stokes pulses are overlapped in time, which is scanned behind the fiber facet across the sample. The chemical selectivity is demonstrated by imaging 2 μm polystyrene and 2.5 μm PMMA beads with per pixel integration time as low as 1 ms for epi-detection. Epi-detection through the fiber is possible despite the fact that the CARS signal is emitted mainly in the forward direction, away from the fiber facet. Detecting the back-scattered signal from the underlying tissue, requires a large detector aperture to be efficient. By detecting through both the core and the cladding of the fiber, we obtain sufficient detection efficiency.

Significance Surface features of a virus are very important in determining its virility. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the ACE2 receptor site of the host cell with a much stronger affinity than did the original SARS virus. Thus, it is clearly important to understand the virion surface structure. To that end, the present paper combines the spatial resolution of atomic force microscopy and the spectral resolution of coherent Raman spectroscopy. This combination of tip-enhanced microscopy using femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering (FAST CARS) with enhanced resolution (FASTER CARS) allows us to map a single virus particle with nanometer resolution and chemical specificity., From the famous 1918 H1N1 influenza to the present COVID-19 pandemic, the need for improved viral detection techniques is all too apparent. The aim of the present paper is to show that identification of individual virus particles in clinical sample materials quickly and reliably is near at hand. First of all, our team has developed techniques for identification of virions based on a modular atomic force microscopy (AFM). Furthermore, femtosecond adaptive spectroscopic techniques with enhanced resolution via coherent anti-Stokes Raman scattering (FASTER CARS) using tip-enhanced techniques markedly improves the sensitivity [M. O. Scully, et al., Proc. Natl. Acad. Sci. U.S.A. 99, 10994–11001 (2002)].

Genipin cross-linked engineered tissues are 10000 times less toxic than glutaraldehyde cross-linked tissues. Hence, genipin is a better fixative to support the recellularization of tissue-engineered constructs such as vascular grafts. Here, we demonstrate the ability of fiber-based Fluorescence Lifetime Imaging (FLIm) guided Raman spectroscopy to monitor the quality of genipin cross-linked vascular grafts with high speed and specificity. Current results indicate that the fluorescence lifetime of AR-BP shortens upon GE cross-linking. Raman spectroscopy reveals secondary structural changes occurring in the extracellular matrix of pericardia that correspond to Amide I, Amide III and C-C stretch vibrations. We conclude that FLIm guided Raman imaging can detect cross-linking signatures with biochemical specificity and that this imaging modality provides a non-destructive and label-free method to assess the quality of vascular grafts

Extracellular vesicles (EVs) in body fluids are promising biomarkers for cancer and other diseases. Due to their small size in the range between 50 and 800 nm, spectroscopic characterization is challenging. First Raman studies of single EVs suffered from weak signal intensities which complicated detection of small variations between different EVs. New Raman results will be presented on EVs from the blood of prostate carcinoma patients and control patients. Three EV fractions were prepared by sequential gradient centrifugation at 5000 g, 12000 g, and 120000 g called EV5, EV12, and EV120, respectively. Additionally, an EV-depleted fraction was obtained from the EV120 supernatant after additional overnight centrifugation. Nanoparticle tracking analysis and electron microscopy were used to determine particle concentration and control quality. High quality Raman spectra were collected from dried pellets using a Raman microscope at 785 nm excitation. Main spectral contributions were assigned to proteins. Protein secondary structure changes distinguished EV fractions from non-cancer and cancer patients consistent with results reported in an earlier paper. Suspensions with aggregated silver nanoparticles increased the band intensities due to the surface enhanced Raman scattering (SERS) effect at 785 nm excitation. Non-cancer EV fractions showed typical SERS bands of proteins. SERS spectra of cancer EV fractions showed an intense signature of new bands at 713, 853, 1004, 1132, 1238 and 1392 cm-1 . No SERS enhancement was observed in the EV-depleted fraction. We concluded that fractions EV12 and EV120 containing small EVs are most applicable for Raman and SERS measurements.

In this chapter, several spectroscopic and spectrometric techniques applied in environmental gas analysis are presented. Focus is placed on the underlying mechanisms in analysis and instrumentation. Advantages and limitations of all techniques are thoroughly discussed. Mass spectrometric techniques, ion-mobility spectroscopy and laser spectroscopic methods including several IR spectroscopic, absorption, and fluorescence methods as well as Raman spectroscopy are covered to give the reader a good overview of currently available and established techniques for environmental gas analysis.

Raman spectroscopy is a powerful bioanalytical method with high molecular specificity. The relatively low sensitivity of spontaneous Raman scattering can be overcome by signal enhancement effects such as surface enhanced Raman scattering and coherent Raman scattering, which is the collective term for coherent anti-Stokes Raman scattering and stimulated Raman scattering. Applications of Raman-based approaches for cancer diagnostics are summarized in the first part of the chapter. In the second part, complementary optical methods including autofluorescence, optical coherence tomography, second harmonic generation, and two-photon excited fluorescence were combined with Raman scattering as multi-contrast modalities. After training of multivariate chemometric classification models, Raman spectroscopy with or without other modalities has been used for pathological screening of cancer cells and tissues under ex vivo and in vivo conditions. Each modality is briefly introduced and typical examples are described.

The presence of biomarkers characteristic for Alzheimer’s disease in the retina is a controversial topic. Raman spectroscopy offers information on the biochemical composition of tissues. Thus, it could give valuable insight into the diagnostic value of retinal analysis. Within the present study, retinas of a double transgenic mouse model, that expresses a chimeric mouse/human amyloid precursor protein and a mutant form of human presenilin 1, and corresponding control group were subjected to ex vivo Raman imaging. The Raman data recorded on cross sections of whole eyes highlight the layered structure of the retina in a label-free manner. Based on the Raman information obtained from en face mounted retina samples, a discrimination between healthy and Alzheimer’s disease retinal tissue can be done with an accuracy of 85.9%. For this a partial least squares-linear discriminant analysis was applied. Therefore, although no macromolecular changes in form of, i.e., amyloid beta plaques, can be noticed based on Raman spectroscopy, subtle biochemical changes happening in the retina could lead to Alzheimer’s disease identification.

A GRIN multimode fiber (125 μm diameter, 0.29 NA) is transformed into a laser scanning microscope using wave-front shaping on an SLM. We demonstrate label-free non-linear microscopy, namely coherent anti-Stokes Raman scattering (CARS) and SHG.

The sensitive detection of organic pollutants is introduced by employing advanced nanostructures in electrochemical, colorimetric, and optical sensing schemes. As target analytes, hydrocarbons (e.g., polycyclic aromatic hydrocarbons (PAHs)), organic solvents, volatile organic compounds (VOCs), persistent organic pollutants such as pesticides as well as relevant compounds from industrial production and wastewater treatment are discussed. For the molecule classes the most applied sensing strategy is identified, for example, surface-enhanced Raman spectroscopy for the detection of PAHs or resistance-based techniques for the analysis of VOCs. In tabular overviews, the limits of detection as well as sensing platform and measurement conditions are summarized for the largely addressed targets, that is, PAHs, VOCs, and pesticides.

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.

Surface-enhanced Raman spectroscopy (SERS) is a modern tool for chemical analysis. SERS utilizes plasmonic metal nanostructures (SERS substrates) to enhance the molecular specific Raman signal by several orders of magnitude (104‒108). SERS substrates based on noble metals (gold and silver) are currently the gold standard due to their unique properties and high chemical stability. However, the application of noble metals significantly restricts widespread implementation of SERS in practice because of their high costs. To overcome this limitation, researchers are developing noble metal-free SERS substrates, and copper-based substrates are a highly promising alternative. The aim of this review is to summarize and critically discuss the available information regarding copper-based SERS substrates (fabrication, Raman enhancement properties, and fields of application) and to estimate their applicability for chemical analysis.

Raman microspectroscopy displays a label-free and non-destructive modality providing highly specific information on single-cell level. Thus, it bears great potential as a standard tool in biomedical studies. We have developed a high-throughput screening Raman spectroscopy platform combining both automated imaging microscopy and spectra acquisition. The system allows for rapid screening of whole cell populations enabling differential white blood cell count and circulating tumor cell detection.

Fluorescence emission is one of the major obstacles to apply Raman spectroscopy in biological investigations. It is usually several orders more intense than Raman scattering and hampers further analysis. In cases where the fluorescence emission is too intense to be efficiently removed via routine mathematical baseline correction algorithms, an alternative approach is needed. One alternative approach is shifted-excitation Raman difference spectroscopy (SERDS), where two Raman spectra are recorded with two slightly different excitation wavelengths. Ideally, the fluorescence emission at the two excitations does not change while the Raman spectrum shifts according to the excitation wavelength. Hence the fluorescence is removed in the difference of the two recorded Raman spectra. For better interpretability a spectral reconstruction procedure is necessary to recover the fluorescence-free Raman spectrum. This is challenging due to the intensity variations between the two recorded Raman spectra caused by unavoidable experimental changes as well as the presence of noise. Existent approaches suffer from drawbacks like spectral resolution loss, fluorescence residual, and artefacts. In this contribution, we proposed a reconstruction method based on non-negative least squares (NNLS), where the intensity variations between the two measurements are utilized in the reconstruction model. The method achieved fluorescence-free reconstruction on three real-world SERDS datasets without significant information loss. Thereafter, we quantified the performance of the reconstruction based on artificial datasets from four aspects: reconstructed spectral resolution, precision of reconstruction, signal-to-noise-ratio (SNR), and fluorescence residual. The artificial datasets were constructed with varied Raman to fluorescence intensity ratio (RFIR), SNR, full-width at half-maximum (FWHM), excitation wavelength shift, and fluorescence variation between the two spectra. It was demonstrated that the NNLS approach provides a faithful reconstruction without significantly changing the spectral resolution. Meanwhile, the reconstruction is almost robust to fluorescence variations between the two spectra. Last but not the least the SNR was improved after reconstruction for extremely noisy SERDS datasets.

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.