The many facets of Raman spectroscopy for biomedical analysis.

Anal Bioanal Chem DOI 10.1007/s00216-014-8311-9

REVIEW

The many facets of Raman spectroscopy for biomedical analysis Christoph Krafft & Jürgen Popp

Received: 22 July 2014 / Revised: 20 October 2014 / Accepted: 31 October 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract A critical review is presented on the use of linear and nonlinear Raman microspectroscopy in biomedical diagnostics of bacteria, cells, and tissues. This contribution is combined with an overview of the achievements of our research group. Linear Raman spectroscopy offers a wealth of chemical and molecular information. Its routine clinical application poses a challenge due to relatively weak signal intensities and confounding overlapping effects. Nonlinear variants of Raman spectroscopy such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) have been recognized as tools for rapid image acquisition. Imaging applications benefit from the fact that contrast is based on the chemical composition and molecular structures in a label-free and nondestructive manner. Although not labelfree, surface enhanced Raman scattering (SERS) has also been recognized as a complementary biomedical tool to increase sensitivity. The current state of the art is evaluated, illustrative examples are given, future developments are pointed out, and important reviews and references from the current literature are selected. The topics are identification of bacteria and single cells, imaging of single cells, Raman activated cell sorting, diagnosis of tissue sections, fiber optic Raman

Published in the topical collection celebrating ABCs 13th Anniversary. For his notable achievements in the field of analytical chemistry in Europe, Prof. Dr. Jürgen Popp had the honor of holding the “Robert Kellner Lecture” at the Euroanalysis XVII conference in Warsaw in August 2013. C. Krafft (*) : J. Popp Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany e-mail: [email protected] J. Popp Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany

spectroscopy, and progress in coherent Raman scattering in tissue diagnosis. The roles of networks—such as Raman4clinics and CLIRSPEC on a European level—and early adopters in the translation, dissemination, and validation of new methods are discussed. Keywords IR spectroscopy . Raman spectroscopy . Spectroscopy/instrumentation . Clinical/biomedical analysis

Introduction The scope of biophotonics research is the development of optical approaches for biomedical diagnostics and life sciences. In particular, linear and nonlinear Raman techniques and multimodal imaging are developed to rapidly identify and characterize biomolecules, body fluids, bioparticles (such as bacteria, yeasts, viruses, and cells), and biological tissues to address important biomedical questions related to infectious diseases, cancer diagnostics, and other pathologies. Raman spectroscopy probes molecular vibrations without external labels by inelastic scattering of monochromatic light. The last two decades witnessed an increasing number of Raman-based applications to bioanalytical and biomedical sciences. However, Raman scattering faces two limitations when applied to diagnostics of cells and tissues. First, Raman images need to be recorded to resolve the inhomogeneous distribution of certain features such as cell nuclei or the delineation of tumor margins. As the Raman process suffers from weak cross sections, the integration time per spectrum is typically so long that Raman imaging is experimentally slow—in particular for large regions of interest. Second, the Stokes-shifted Raman photons are spectrally red-shifted relative to the incident photons. Consequently, the weak Raman signals are easily overwhelmed by autofluorescence of the sample itself or contaminations that have much larger cross

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sections. Though fluorescence can be suppressed by nearinfrared excitation this procedure further decreases the data acquisition speed as the scattered intensity depends on the forth power of the excitation frequency. Both limitations of Raman scattering—slow imaging and high fluorescence background—can be circumvented by coherent nonlinear Raman scattering. A recent tutorial [1] introduced the concepts of the linear vibrational Raman effect and summarized fundamental characteristics of coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS). They belong to the most important coherent nonlinear Raman scattering methodologies to emerge within recent years for rapid chemical imaging of biological samples. They can be combined with other label-free nonlinear optical techniques, namely second harmonic generation, two-photon absorption, and stimulated emission [2]. It is well accepted that fluorescence probes and molecular stains are powerful approaches to visualize the inner workings of cells. However, such labels have considerable drawbacks. Delivering labels can be a problem, particularly for whole organisms. Some labels work only for dead cells or perturb the very processes they are intended to study or suffer from limited lifetime as a result of photobleaching. Label-free microscopic techniques offer a way to investigate living cells and organisms while eliminating a slew of possible artefacts. Of course such techniques have their own limitations: whereas fluorescence labelling can often allow discrimination of single molecules, label-free techniques are less sensitive and specific. All but the most common substituents tend to be hidden in signals generated from a few abundant species. Here, the current state of the art of linear and nonlinear Raman spectroscopy is evaluated, important references are selected, and future developments and trends are pointed out.

Recent reviews in the field of biomedical Raman spectroscopy A large number of recent reviews in the PubMed database deal with Raman spectroscopy which indicates the high research interest in this methodology. The following reviews appeared in the field of biomedical Raman spectroscopy within the last 5 years. Instrumental methods, multivariate statistical analysis tools, and applications of Raman microspectroscopy to single cell analysis were highlighted [3]. Prominent applications encompass the characterization and identification of specific metabolic states of eukaryotic cells, stem cells, and bacterial cells. Another review by the same group also discussed coherent Raman scattering (CRS) techniques such as CARS and stimulated Raman scattering (SRS) for imaging of cells, tissues, and entire organisms in vitro and in vivo [4]. Multimodal nonlinear imaging in biomedical sciences was reviewed and, beside CRS, also encompassed second harmonic generation

(SHG), third harmonic generation (THG), and two-photon excited fluorescence (TPEF) [5]. The synergy of multiple contrast mechanisms has unique capabilities for label-freevisualization of tissue structure and chemical composition, high depth penetration, intrinsic 3D sectioning, diffractionlimited resolution, and low phototoxicity. A strategy was outlined that would integrate multiple advanced features to overcome all technical barriers simultaneously, effectively reduce trade-offs, and synergistically optimize CARS microscopy for clinical translation [6]. The operation of the envisioned system incorporates Raman microspectroscopy to identify vibrational biomolecular markers of disease and single frequency or multiplex coherent Raman imaging of these specific biomarkers for real-time in vivo diagnostics and monitoring. Recent topics were highlighted in spontaneous Raman imaging of cells and tissues such as studies of intracellular drug pharmacokinetics and oxygen saturation of blood capillaries [7]. A comprehensive insight was provided into the current progress in expanding the applicability of Raman spectroscopy for characterization of living cells and tissues [8]. The principles of Raman and CARS microspectroscopy were described and applications to cells and tissues were summarized that are expected to gain significance in the future such as the combination with imaging approaches, optical traps, and fiber optic probes [9]. A summary focusing on Raman fiber optic probes for biomedical applications was recently published [10]. The advances were discussed in the rapid identification of microbial cells for bulk samples and at the single cell level by Raman and the related vibrational spectroscopic technique—infrared spectroscopy [11]. The inherently weak Raman signal of small microbial samples can be increased up to several orders of magnitude by signal enhancement methods such as UV resonance Raman spectroscopy (RRS) with excitation in the UV region, surface enhanced Raman scattering (SERS), and tip enhanced Raman scattering. Raman microspectroscopy combined with stable isotope probing, fluorescent in situ hybridization, and optical tweezers offers a culture-independent approach to study the function and physiology of culturable and even unculturable microorganisms in the ecosystem. A review described these applications of Raman microspectroscopy to microbial research with particular emphasis on single bacterial cells [12]. The role of Raman spectroscopy in monitoring biological function, mapping the chemical microenvironment, and screening diseases was also reviewed [13]. Applications included endoscopy, SERS, and CRS. Raman spectroscopy was coupled with optical micromanipulation such as laser tweezers [14] and microfluidic systems [14]. A similar overview introduced first the research progress in enhancing the signal of spontaneous Raman spectroscopy including RRS, CARS, and SRS, and the associated applications including Raman activated cell sorting (RACS), Raman imaging, and mapping [15]. Cytopathology is the term for histopathological

The many facets of Raman spectroscopy for biomedical analysis

inspection of cells; histopathology is the term for the inspection of tissues. Spectral cytopathology and histopathology use vibrational spectroscopy, such as Raman and infrared spectroscopy, to complement standard tools. The background, fundamentals and applications of spectral cytopathology and histopathology were described [16]. Advantages of Ramanbased methods include that they can reveal information that is available by classical methods only by costly and timeconsuming procedures such as immunohistochemistry, polymerase chain reaction (PCR), or gene arrays. Disease recognition by Raman spectroscopy and infrared spectroscopy was reviewed with the focus on tumors of epithelial tissue, brain tumors, prion disease, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes, and osteoarthritis [17]. Another review illustrated the complementary advantage of Raman and infrared spectroscopy for cancer diagnostics together [18]. An overview was presented of the in vitro and in vivo work on the oncological application of Raman spectroscopy [19]. Also opportunities were discussed to integrate Raman spectroscopy in oncological cure and care as a realtime guidance tool during diagnostic (i.e., biopsy) and therapeutic (surgical resection) modalities. The benefits and limitations of Raman spectroscopy, CARS, and SRS were discussed with emphasis on applications both in vivo using fiber endoscopes and in vitro in optical microscopes [20]. The role of Raman spectroscopy as a diagnostic tool in biomedicine was summarized [21]. Here, studies in imaging, endoscopy, stem cell research, and recent developments such as spatially offset Raman scattering (SORS) were highlighted. A special review was dedicated to Raman spectroscopy for deep and non-invasive medical diagnosis [22]. Beside SORS, transmission Raman spectroscopy permits the assessment of diffusely scattering samples at depths several orders of magnitude deeper than possible with conventional Raman spectroscopy. A review about the diagnostic applications of Raman spectroscopy emphasized transplant allograft rejection, cancer detection, and implementation of Raman systems in the laboratory [23]. Another review explored the advancements of Raman and SERS specific to multiplex analysis [24]—the detection of several analytical targets at the same time. Here, full advantage is taken of the stronger signals of SERS and narrow spectral bands compared with emission of fluorescence labels. The authors described applications spanning from direct detection of analytes to multiplexed SERS-based imaging. SERS in cancer detection and imaging has recently been reviewed [25] including multiplexed detection and identification of new biomarkers, single-nucleotide polymorphism, and circulating tumor cells. Progress towards the integration of Raman spectroscopy with microfluidics was summarized and possible future directions of Raman microfluidic systems were presented [26]. This technique is increasingly utilized for single cell analysis as shown in “Raman activated cell sorting”.

Raman spectroscopic identification of bacteria The gold standard for identifying infectious microorganisms is bacterial culture. Alternative bacterial classification methods are PCR-based techniques and matrix-assisted laser desorption time-of-flight mass spectrometry that have found their way into clinical microbiology laboratories [27]. However, culture methods are time consuming and need at least 24 h until the results are available. Raman-based approaches have been shown to offer advantages over traditional genotyping for ultrafast, automated, and reliable bacterial identification, bacterial typing, and epidemiological surveillance of bacterial infections. Recent examples are given next. The SpectraCellRA bacterial strain analyzer (River Diagnostics, NL) was evaluated to type multiresistant Escherichia coli and Klebsiella pneumoniae isolates [28]. Raman spectroscopy was shown to discriminate outbreak-related isolates from clinical isolates that were not involved in an outbreak or transmission. In another study, the performance in terms of typeability, discriminatory power, reproducibility, workflow, and costs of the SpectraCellRA system was evaluated for typing of methicillin-resistant Staphylococcus aureus (MRSA) strains isolated from patients and patients’ household members who were infected with or colonized by MRSA. The results of Raman-based analysis on the strain level corresponded better than 90 % with epidemiological data, including pulse field gel electrophoresis. Recently, urinary tract infection (UTI) pathogens were investigated by Raman spectroscopy on a single cell level [29]. The Raman measurements were performed with a dedicated Raman instrument called BioParticleExplorer (RapID, Germany). Typical Raman spectra of bacterial species are shown in Fig. 1. The main spectral contributions are assigned to proteins and nucleic acids, in particular CH2/3 valence vibrations at 2,935 cm−1, CH2/3 deformation vibrations of aliphatic amino acids at 1,451 cm−1, the amide backbone at 1,241 and 1,665 cm−1, the phosphate backbone at 1,099 cm−1, amino acids at 1,004 and 1,606 cm−1, and nucleotides at 723, 781, and 1,573 cm−1. Careful inspection reveals spectral features that can be considered as specific fingerprints. A prerequisite was a database of Raman spectra from bacterial species that were grown in sterile filtered urine. Then, support vector machines (SVMs) generated a classification model which allowed the identification of unknown specimens. A restriction is that this approach only gives accurate results for the bacterial species in the training data set. In the report, the most common 11 species were studied and the model succeeded in determining the predominant bacterial species in infected urine samples of ten patients (seven E. coli and three Enterococcus faecalis) without a preceding culture step within 2 h. A large-scale database of Raman spectra of single bacterial endospores was compiled and classification functions were calculated to discriminate between endospores of 66 strains from 13

C. Krafft, J. Popp

Fig. 1 Mean Raman spectra of urinary tract infection microbial pathogens: Enterococcus faecalis (a), Enterococcus faecium (b), Staphylococcus epidermis (c), Staphylococcus haemolyticus (d), Staphylococcus hominis (e), Staphylococcus saprophyticus (f), Staphylococcus aureus (g), Escherichia coli (h), Klebsiella pneumoniae (i), Pseudomonas aeruginosa (j), and Proteus mirabilis (k)

Bacillus and Bacillus-related species including B. anthracis [30]. Endospores have been reported as difficult to process by nucleic acid based assays because their nuclei acid is encased in a very resistant shell. The classification approach was challenged by a test of 27 samples to simulate the case of a real-world scenario when suspicious samples are to be identified. The samples covered a diverse selection of species within the phylogenetically broad Bacillus genus and also included strains which were not incorporated in the database before. The Raman-based analysis required no biomass enrichment step and qualifies the approach to be a rapid analysis system for Bacillus endospore typing. Raman spectroscopic characterization of Bacillus endospores has also been successfully performed in an assay to sense them in complex matrixes such as baking powder or sand [31]. This publication exemplified the robustness of Raman microspectroscopy of environmental samples.

Raman spectroscopy in combination SERS of nanoparticles offers a culture-independent identification procedure [32]. The protocol described how to prepare shell-isolated nanoparticles with controlled core sizes, shapes, and shell thicknesses. Another way to enhance the Raman signal of bacteria is adsorption onto SERS-active substrates. Efforts to develop a SERS-based approach were described that encompasses sample preparation, SERS substrates, and portable Raman instrumentation and identification software [33]. The progress made in these areas was illustrated by a spiked infectious sample for UTI diagnostics. SERS was also coupled with a microfluidics chip to rapidly detect MSRA. First, clinical isolates were analyzed by PCR and multilocus sequence typing (MLST). A total of 17,400 SERS spectra of MSRA isolates were collected within 3.5 h using this optofluidic platform. SERS typing correlated well with MLST indicating that it has high sensitivity and selectivity and would be suitable for determining the origin and possible spread of MRSA [34]. The simultaneous detection and quantitation of three bacterial meningitis pathogens by SERS was reported [35]. The quantitative assay combined lambda exonuclease and SERS. The new assay format involves the simultaneous hybridization of two complementary DNA probes (one of them containing a SERS-active dye) to a target sequence followed by lambda exonuclease digestion of double-stranded DNA and SERS detection of the digestion product. SERS challenges current fluorescent-based detection methods in terms of both sensitivity and more importantly the detection of multiple components in a mixture. Three meningitis pathogens were successfully quantified in a multiplexed test with a calculated limit of detection in the picomolar range.

Raman spectroscopic identification of single cells Cell identification by Raman spectroscopy has been recognized to be an attractive complement to established optical techniques like fluorescence spectroscopy. In general, twoclass discriminations are easier than multiclass identification. Proper design of the experimental protocol should be considered because the Raman spectral changes are often small and might be affected by, e.g., batch-to-batch variations or daily variations of the instrument response. Consequently, multiple independent batches are recommended in Raman cell studies and validation should be on the batch level rather than cell level. Furthermore, if Raman data are collected during a period of several days, each cell type should collected on every day and the instrument calibration needs to be confirmed daily. Then, the results are reproducible and the classification model is expected to be robust. Typical Raman spectra of single cells in aqueous buffer are shown in Fig. 2. The cells were trapped by the Raman excitation laser that acted as optical tweezers. As in Fig. 1, the main spectral


Fig. 2 Mean Raman spectra of single cells that are optically tweezed by the Raman excitation laser. From top to bottom: leukocytes from blood, leukemia cell line OCI-AML3, breast cancer cell lines BT-20 and MCF-7

contributions are again due to proteins and nucleic acids. The broadening of the amide I band near 1,657 cm−1 is attributed to the water band near 1,640 cm−1. The most evident spectral change between normal leukocytes and tumor cells was the ratio of the bands at 1,320 and 1,342 cm−1. Each spectrum represents the average of 216–306 cells from three donors and three independent cultivation batches. The classification model SVMs distinguished tumor cells from healthy cells with a sensitivity of greater than 99.7 % and specificity of greater than 99.5 %. In addition, the correct types (leukocyte, OCIAML3, BT-20, and MCF-7) were predicted with an accuracy of approximately 92 % [36]. Recent applications since 2011 are summarized next. Raman spectroscopy and partial least squares (PLS) analysis discriminated peripheral cells affected by Huntington’s disease (HD)–a neurodegenerative disorder. The analysis of the plasma membrane might be a useful diagnostic biomarker for the detection of HD in readily accessible peripheral cells. Spectral differences were observed between plasma membranes extracted from HD and control fibroblasts [37]. Different subsets of lung cells were identified by Raman microspectroscopy. The data differentiated between lung cancer, lung epithelial cells, and lung fibroblasts. The results suggested that the main spectral differences reside in the cell nucleus [38]. Raman spectroscopy identified and characterized murine fibroblast cells and malignant fibroblast cells transformed by murine sarcoma virus cells. The best results for differentiation were achieved from spectra that were obtained from the membrane-rich sites [39]. A study

demonstrated that Raman microspectroscopy combined with image analysis can be used for rapid and label-free identification of normal sperm cells by providing both morphological and biochemical information. The standard analysis of semen to evaluate fertility disorders is susceptible to subjectivity due to the lack of objective criteria for sperm cell quality [40]. Raman spectroscopy was employed to identify specific bands that were assigned to proteoglycans (PG) within human and porcine cartilage samples and chondrocytes. To date, PGassociated alterations of extracellular matrix components are routinely diagnosed by invasive analytical methods. The report demonstrated the applicability of Raman microspectroscopy as an analytical and potential diagnostic tool for noninvasive cell and tissue state monitoring of cartilage [41]. Subcellular spectroscopic markers, topography, and nanomechanics of lung and breast cancer cells were examined by Raman microspectroscopy and atomic force microscopy (AFM). Differences were found in membrane chemical components by Raman spectroscopy and different membrane surface adhesion forces and cell spring constants were measured by AFM [42]. The distribution of different biomolecules was probed within adult human bone marrow-derived stromal stem cells and human embryonic stem cells by Raman spectroscopy. Reproducible differences were identified in Raman spectra that distinguished genetically abnormal and transformed stem cells from their normal counterparts. It was concluded that Raman spectroscopy can be prospectively employed to identify abnormal stem cells in ex vivo cultures prior to clinical transplantation [43]. Multivariate analysis of cell-type-specific Raman spectra enabled one to discriminate between living primary and immortalized keratinocytes. The authors further noninvasively distinguished between fibroblasts, keratinocytes, and melanocytes. These findings were relevant for the engineering of in vitro skin models and the production of artificial skin where both the biopsy and the transplant consist of several cell types [44]. Raman spectroscopy discriminated individual live cardiomyocytes (CMs) derived from human embryonic stem cells within highly heterogeneous cell populations. Retrospective immunostaining was used as the gold standard for phenotypic identification of each cell. It was concluded that glycogen is responsible for the discrimination of CMs whereas myofibril proteins have a lesser contribution [45].

Raman microscopic imaging of single cells Raman spectrometers for Raman microscopic imaging of single cells have been commercially available from several manufacturers for years. A frequently used excitation wavelength is 532 nm that probes in particular cytochrome in cells owing to resonance enhancement [46]. But laser irradiation in the range of 514.5 nm has been found to induce photodamage

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in living cells [47]. Therefore, 785-nm excitation has been found to be more appropriate for live cell studies owing to reduced phototoxic effects [48]. Estimating 1,000 spectra per Raman cell image at a step size of 0.5 μm and with a 0.5-s exposure time gives a typical total acquisition time of more than 10 min (500 s plus additional time for moving the sample or scanning the laser and saving the spectrum). Therefore, the cell numbers in most Raman imaging studies are low and ranged from six to study changes in stressed cells [49], 48 to classify tumor cells [50], to 70 to monitor the lipid uptake in macrophages [51]. Although line mapping and widefield or global imaging have been suggested to reduce acquisition time and improve the lateral resolution [52], these techniques have not frequently been applied so far. A related approach called slit scanning was introduced that allows Raman imaging of live cells with high temporal and spatial resolution [53]. The protocol described the optics, hardware, and software used to construct the Raman microscope. More cells can be studied by Raman spectroscopy if only one spectrum per cell—preferably with an expanded laser focus—is collected as shown in the previous or next paragraph. Beside the cell nucleus, lipid droplets can be resolved in Raman images of single cells. The metabolism of lipid droplets and their subsequent storage pattern are important in cellular processes such as atherogenesis, which means the deposition of lipids in plaques within arterial walls. In order to distinguish molecules of interest from other naturally occurring lipids spectroscopically, palmitic acid, oleic acid, arachidonic acid, and cholesterol were labeled with deuterium. The hydrogen isotope shows the same chemical behavior. However, the higher mass of deuterium shifts the CD vibrations to lower wavenumbers compared to their CH counterparts. An example is shown in Fig. 3. The spectral unmixing algorithm N-FINDR separated spectral contributions from proteins and lipids that are composed of CH bands near 2,880 and 1,439 cm−1, CD bands near 2,248 cm−1, C=C bands near 1,640 cm−1, and C=O bands near 1,743 cm−1. The color-coded scores represent the concentration of each component and are plotted in Fig. 3b. The Raman-based contrast of lipid droplets, cell nucleus, and nucleoli correlates well with the photomicrograph in Fig. 3a. In vitro formation of cellular lipid droplets is typically induced by the addition of free fatty acids to the cell culture medium or by mediation with serum albumin. Uptake dynamics were quantitatively evaluated by monitoring the increase in CD scattering intensities at time points 0.5, 1, 3, 6, 24, 30, and 36 h. Incubation times longer than 40 h resulted in death of cells, likely due to lipotoxicity. Uptake was stopped by chemically fixing the cells in formalin solution. Five cells were selected at each time point and exhibited very similar uptake patterns for palmitic acid, oleic acid, and cholesterol [51]. A similar Raman study was reported for deuterated polyunsaturated arachidonic acid [54]. The storage efficiency was found to be

Fig. 3 Photomicrograph (a), Raman image (b), and Raman spectra (c) of a macrophage cell. The Raman image was analyzed by N-FINDR. The concentrations and spectra of proteins (cyan) and lipids (red) are plotted in b and c, respectively.

lower than in cells incubated with deuterated palmitic acid and foam cell formation was less pronounced. A direct comparison of fatty acids ratios in single cellular lipid droplets of single liver HepG2 cells was achieved by combining CARS microscopy and Raman microspectroscopy [55]. First, CARS images of single cells resolved individual lipid droplets from which Raman spectra were subsequently collected. The fatty acids palmitic acid and oleic acid were distinguished by Raman spectroscopy by characteristic bands of carbon–carbon double bonds and the lipid composition was determined by asymmetric least squares fitting of pure fatty acid spectra.

Raman activated cell sorting Flow cytometry is a popular method to assess cell populations. Flow cytometers combine multiple detection techniques that are based on light scattering and fluorescence. Whereas light scattering probes morphological features of single cells, fluorescence probes the emission of autofluorophores or exogenous fluorescence markers. The variant fluorescence activated cell sorting is not yet successful in detecting circulating tumor cells (CTC) in body fluids such as the blood of cancer patients.


CTCs are of high diagnostic interest in order to monitor tumor spreading, assess the success of chemotherapeutic treatment, and characterize the parental tumor. However, CTCs are extremely rare (it is assumed that only one out of 103 to 107 normal blood cells is a tumor cell), they do not share common morphological features or antigens for which fluorescent antibodies can be applied. RACS is a candidate to complement existing methods. The main advantage is that Raman spectroscopy can identify the cell type in a label-free and nondestructive way as described in “Raman spectroscopic identification of single cells”. To demonstrate the principle of RACS, Raman spectrometers were combined with optical traps, microfluidic chips, and microhole arrays as shown in Fig. 4. Fig. 4 Scheme of setup for Raman activated cell sorting integrating a Raman spectrometer, fiber lasers, microfluidic pumps, and a holder with a microfluidic chip (a). The microfluidic chip encompasses channels for hydrodynamic focusing (1), cell injection (2), immersion fluid (IF), fiber lasers (FL), and cell sorting (3, 4) (b). Mean Raman spectra of cells trapped in microfluidic chip (c). From top to bottom: leukemia cells OCI-AML3, breast cancer cells MCF-7 and BT-20, and leukocytes

Design considerations were summarized previously [14, 56] and some of them are given next. Optical traps are realized by two counter-propagating fiber lasers and retain cells in the focus during acquisition of Raman spectra. The advantages of optical traps compared to optical tweezers are the drastically reduced power densities inside the dielectric objects and the additional scattering forces that stabilize the particle inside the trap [57]. If the dielectric particle is a biological cell, the high local intensities in the focus of optical tweezers harbor the risk of inducing unwanted cell damage. In the context of Raman spectroscopy, optical traps offer the possibility to hold and maneuver the cell relative to the excitation laser. If the trapping wavelength overlaps with the spectral range of the

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Raman spectrum, additional band-pass filters have to be integrated to suppress elastic scattering. Another confounding factor is the spectral contribution of the substrate of the chip material. Although polymer microfluidic chips can be fabricated at low cost, they are incompatible with Raman spectroscopy of single cells in a small-diameter microchannel because intense out-of-focus Raman signals of the polymer strongly overlap with weak Raman signals of the biological analytes. Consequently, microfluidic chips were made from glass wafers for Raman spectroscopy with 514-nm excitation [58] and from quartz wafers for 785-nm excitation [59]. A dedicated microfluidic chip incorporated three main operation units: a flow focusing unit for injecting single cells from a reservoir, the optical trap structure for single cell Raman spectroscopy, and a Y-shaped branching unit for flow switching and cell sorting between the two chip outlets (Fig. 4b). Four channels accommodate the laser fibers for trapping. Additional channels were integrated for rinsing with cleaning solvents and filling with immersion fluid. The fiber channels had a diameter of 130 μm and the fluidic channels had a width of 70 μm and a height of 50 μm. The chip was mounted in a holder that provides simple, accurate, and stable adjustment of chip, microfluidic connections, and the trapping laser fibers. The first samples were the four cell types of the circulating tumor cell model (leukocytes, OCI-AML3, MCF7, BT20) studied by laser tweezers Raman spectroscopy before and shown already in Fig. 3 [36]. Additional bands in the spectral range between 1,500 and 1,600 cm−1 and below 800 cm−1 are assigned to the side bands of the trapping laser and were not considered for classification. Typical acquisition times were 10 s per spectrum using 100-mW laser intensity. Mean sensitivity and specificity determined by iterated 10-fold cross validation were 96 % and 99 % for the distinction of cells in a data set of 405 spectra. Further innovations are required to achieve higher cell sorting rates. These innovations might include rapid presorting or Raman signal enhancement. If cells are immobilized onto a microhole array, they can be assessed by sensitive, but less specific optical tools, and subsequently tumor cell candidates can be identified by Raman spectroscopy. A microhole array made of silicon nitride was found to be compatible with Raman spectroscopy at 785-nm excitation [60]. Application of a gentle underpressure immobilized the cells nondestructively and enables sorting by a pipette micromanipulator. Another strategy is offered by SERS of gold or silver nanoparticles (NP) to engineer stable, reproducible, bright, and biocompatible labels for use with live cells. Because the plasmon resonance of silver NP is shifted towards shorter wavelengths relative to gold NP, SERS spectra are excited with green-emitting lasers in the range from 488 to 532 nm for silver NP and from 600 to 800 nm for gold NP. Challenges of SERS in the context of cell detection are unspecific binding and low reproducibility. These challenges

have been overcome by surface functionalization with cellspecific antibodies and the introduction of reporter molecules [61]. Successful implementations were reported by several authors. Starting points are assemblies of nanoparticles linked to dimers or small clusters in a process mediated by molecular linkers [62]. Such nanometer-scale junctions between nanoparticles serve as SERS hot spots and optimize SERS enhancement. Further processing gave nanocapsules that were shown to compete with fluorescence imaging in the multiplexed identification of cancer cell epitopes at the single cell and single nanotag level [62]. SERS labels were functionalized with an EGF antibody to quantify CTCs in blood samples [63]. HER2 is another frequently used antigen for SERS-based differentiation of tumor cells by hollow gold nanospheres [64], gold nanorods [65], silver nanoparticleembedded silica spheres [66], gold nanocorals [67], and in combination with EpCAM-labeled magnetic particles [68]. A quintuple-modality nanoprobe was developed with gold nanostars for SERS, magnetic resonance imaging, computed tomography, and two-photon luminescence imaging and photothermal therapy [69]. The synthesized gold nanostars were tagged with a SERS reporter and linked with an MRI contrast agent. The theranostic potential of this nanoprobe was demonstrated in in vitro experiments. Two sets of SERRS (surface enhanced resonance Raman scattering) peptide affinity tags were used in a ratiometric approach to distinguish between cancerous and noncancerous epithelial prostate cells in vitro [70]. One targeted the neuropilin-1 (NRP-1) receptors of cancer cells. The other functions as a positive control (PC) and binds to both noncancerous and cancer cells. Simultaneous incubation with the two sets of biotags and Raman imaging of single cells yielded an NRP-1/PC ratio from which a robust quantitative measure of the overexpression of NRP-1 by the cancer cell line was extracted. Advantages compared to fluorescence labels include that SERS labels show a very low spectral width of the Raman bands [71] which offers an excellent multiplexing potential [72]. For the detection of CTCs this enables a strategy to apply multiple SERS labels and simultaneously identify multiple tumor-specific antigens [23, 73]. As a proof of concept, the actively targeted multiplex in vitro detection of three intrinsic cancer biomarkers— EGFR, CD44, and TGFβRII—was demonstrated in a breast cancer model using three multiplexing capable, biocompatible SERS nanoparticles/nanotags [74]. Another work described the expression of EGF, ErbB2, and insulin-like growth factor1 (IGF-1) receptors in human breast cancer cell lines by SERS [75]. The narrow spectral features of the SERS signal enables more distinct probes to be measured in a smaller region of the optical spectrum with a single laser and detector than fluorescence spectroscopy, allowing for higher levels of multiplexing and multiparameter analysis. Therefore, the functionalized SERS tags enable the detection of multiple antibodies that is a well-accepted route to improve identification or sorting of


tumor cells. SERS-based cytometry has already been recognized as a powerful complement to conventional fluorescence-based cytometry [76] and was demonstrated to detect leukemia and lymphoma cells [77].

Diagnosis of tissue sections by Raman spectroscopy Histology (the study of the anatomy of tissue) and histopathology (the study of diseased tissue) are performed by examining thin slices (sections) of tissue under a light microscope. The ability to visualize or differentially identify microscopic structures is frequently enhanced through the use of histological stains because biological tissue has little inherent contrast. Hundreds of techniques have been developed to selectively stain cells and cellular components. As Raman microspectroscopic imaging provides molecular contrast in tissues without stains, it offers prospects to complement established techniques. In addition, Raman spectroscopy can also be applied under in vivo conditions using fiber optic probes (see next section). A number of recent papers reported Raman studies for the distinction of two tissue classes (normal and pathological) using single spectra. Examples include the differentiation of neoplastic and normal brain tissues [78], normal and basal cell carcinoma tissues [79], and normal and cancerous nasopharyngeal tissues [80]. Such studies do not exploit the full potential of Raman spectroscopy. From a clinical point of view, normal and pathological tissues are composed of various cell types and two-class models constitute an oversimplification. Furthermore, the distinction of normal versus tumor or non-normal is usually not difficult using standard tools. As described below, Raman spectroscopy is able to give differential diagnosis such as normal tissue types (epithelium, connective tissue, muscle, and nervous tissue), inflammation, necrosis, dysplasia, tumor grades, and tumor types. The collection of single spectra does not allow one to obtain morphological information such as identification of tumor margins. However, the acquisition of Raman images is able to resolve cellular details such as the number and distribution of cell nuclei. A small pixel size in Raman images was reported to be advantageous for developing training data sets, despite lengthy acquisition times, owing to the additional morphological information gained [81]. Such imaging data could facilitate the differentiation of further tissue groups. Larger pixel size and faster imaging may be more feasible for clinical application, e.g., also using fiber optic probes (see next section). The authors demonstrated the potential of rapid Raman imaging for automated histopathological diagnosis by a linear discriminant analysis (LDA) using subsets of Raman imaging data (6,483 from 131,672 spectra). An overall training classification model performance of 97.7 % was obtained. The remainder of the map spectra was then projected onto the

classification model resulting in Raman images that correlated well with contiguous hematoxylin and eosin sections. Another strategy used the combination of autofluorescence for prescreening and selection of regions of interest by a sensitive and low specificity method and subsequent Raman spectroscopic assessment of selected regions as a high specificity method [82]. This approach has been demonstrated for basal cell carcinoma in skin tissue sections and enabled the accurate detection of tumor margins. It was also shown that this technique can diagnose the presence or absence of tumors in unsectioned tissue layers, thus eliminating the need for tissue sectioning. An example of the multiclass identification of Raman images is the characterization of Crohn’s diseases (CD) and ulcerative colitis (UC) that belong to the group of inflammatory bowel diseases [83]. In a first step, SVMs were applied to highlight the tissue morphology. Raman spectra distinguished blood, connective tissue, mucus, epithelium, and other tissues. In a second step, SVMs were trained for a molecular classification of epithelium as normal, CD, or UC in 38 subjects. SVMs were also found to be superior for the lymph node diagnostics in the course of breast cancer using Raman spectroscopy [84]. The SVM performance was compared with traditional chemometric methods such as LDA and partial least square discrimination analysis. The Raman signature has also been used as a molecular fingerprint to determine the primary tumor of brain metastasis [85]. Cluster membership maps of Raman images were correlated with consecutive hematoxylin and eosin stained tissue sections to confirm the annotation of the cluster. Then, the first level of a hierarchical SVM classification was trained to distinguish normal brain, necrosis, carcinoma, and other tissues in Raman images from 22 specimens. The second level discriminated brain metastasis of bladder carcinoma, lung carcinoma, mamma carcinoma, colon carcinoma, prostate carcinoma, and renal cell carcinoma. A similar annotation strategy for cancerous and precancerous lesions in the oral cavity was reported [86]. Reference spectra were used as input in an unsupervised hierarchical cluster analysis in order to determine the spectral characteristics and variance within one individual histopathological structure. This resulted in a database of Raman spectral characteristics of individual structures of oral tissue. This database can be used as input for the development of classification and quantitation algorithms. Raman microspectroscopy was coupled with multivariate analysis to biochemically characterize excised wounds in mice and to accurately identify phases of healing of wounds at four time points corresponding to different phases of wound healing [87]. Spectra were deconvolved using multivariate factor analysis into three factor score spectra that act as spectral signatures for different stages of healing and that were correlated with spectra of pure wound be constituents using non-negative least squares fitting. Factor loadings (weights) of spectra that belonged to

C. Krafft, J. Popp

wounds at different time points provide a quantitative measure of wound healing progress in terms of key parameters such as inflammation and granulation. Slowly healing wounds could be identified as outliers and distinguished from normally healing wounds. A number of papers described Raman images of tissue section with a step size near 2 μm. In combination with hyperspectral unmixing algorithms such as principal component analysis, vertex component analysis and N-FINDR cellular structures were resolved such as microcrystals and cell nuclei. The resulting plots of individual components enabled the morphological assessment of Raman images. In agreement with histopathology, it was found that the number and size of cell nuclei increase with the malignancy grade of primary brain tumors [88, 89]. To assess the chemical parameters, hyperspectral imaging was performed on a pooled data set [90]. The resulting endmember spectra are shown in Fig. 5 and represent individual components of proteins, lipids, cholesterol, cholesterol ester, carotenoids, buffer, and nucleic acids. The color-coding depicts the concentration of each component. High lipid content (green) and few cell nuclei (blue) Fig. 5 Raman spectra of brain tumors obtained by spectral unmixing of Raman images using the N-FINDR algorithm (a). Plots of N-FINDR components for Raman images of brain tissue with regular cell density (b), brain tumor with high cell density (c), and necrotic brain tumor (d). Cholesterol ester (magenta), lipids (green), nucleic acids (blue), proteins (red), cholesterol with and without carotenoids (yellow), and buffer (cyan)

indicate brain tissue of normal cell density (Fig. 5b). High protein content (red) and many cell nuclei are consistent with increased cell density of a highly malignant tumor (Fig. 5c). Cholesterol ester (magenta) and a lower number of cell nuclei point to necrotic portions of a tumor (Fig. 5d). A nonnegativity least square algorithm quantified the Raman spectral contributions of the pure components. It was found that the lipid and cholesterol content decreases and the nucleic acid content increases with the tumor grade, whereas the protein content is almost constant. This result demonstrates that both the spectral information that can also be obtained by fiber optic probes (see “Fiber optic Raman spectroscopy”) and the morphological information that can also be obtained rapidly by CRS (see “Progress in coherent Raman scattering in tissue diagnosis”) enable diagnosis of tissues. Raman and SERS systems were developed and applied for in vivo and real-time monitoring and imaging of tissue that advance the potential for the use of these technologies in the clinic. A work presented noninvasive deep tissue molecular images with the use of Raman spectroscopy [91]. SERS nanoparticles and single-wall carbon nanotubes were used to


demonstrate whole-body Raman imaging, nanoparticle pharmacokinetics, multiplexing, and in vivo tumor targeting using an imaging system adapted for small animal Raman imaging. The same group proposed a combined photoacoustic (PA) and Raman approach [92]. Gold nanorods with an aspect ratio of 3.5 were selected for their highest PA signal and used to image subcutaneous xenografts of ovarian cancer cell lines in living mice. A linear relationship was found between the PA signal and the concentration of infected molecular imaging agent with a calculated limit of detection of 0.4 nM gold nanorods. The same molecular imaging agent could be used for clear visualization of the margin between tumor and normal tissue and tumor debulking via SERS imaging.

Fiber optic Raman spectroscopy Optical tools for in vivo diagnostic are much needed such as to guide surgical interventions, delineate lesion margins, or replace random biopsies of suspicious tissues by targeted biopsies which, in turn, would reduce pathology costs and biopsyassociated risks. Several probe geometries have been designed for diagnostic applications of Raman spectroscopy in cancer research of lung, breast, skin, bladder, brain, cervix, oral cavity, and gastrointestinal tract [93]. Raman probe spectroscopy was employed to characterize the plaque composition of arterial walls on a rabbit model in vivo [94]. A miniaturized filtered probe with one excitation and 12 collection fibers in a 1-mm sleeve was used (Fig. 6a– c). The figure shows in an ex vivo experiment how the probe was inserted into the aorta. High signal to noise ratio Raman spectra were obtained with spectral contributions from collagen of the arterial wall, lipids from plaques, and hemoglobin from surrounding red blood cells (Fig. 6d). The non-

Fig. 6 12-around-1 fiber optic Raman probe inserted into rabbit aorta (a). Front face of 1-mmdiameter Raman probe without (b) and with (c) illumination. Overlaid Raman spectra of artery wall (green), blood (red), and atherosclerotic plaques (black) collected with the probe under in vivo conditions (d)

normalized spectra indicate that lipids give more intense bands than collagenous proteins and red blood cells. This property contributes to the good sensitivity for the detection of lipid droplets in single cells (see Fig. 3) and the differentiation between brain tissue with high lipid content and brain tumors with lower lipid content (see Figs. 5 and 6). The Raman spectra indicated increased plaque content near the heart. Although the probe contained multiple collection fibers, they were registered as a sum spectrum and not used for image reconstruction. A filtered probe with a lens for focusing and efficient signal collection was applied to collect Raman spectra from murine brain tumors [95]. A cranial window was inserted for in vivo data collection. The mouse was placed on a motorized stage to acquire Raman images that visualized the tumor mass, its margins, and even subsurface tumor cells. Recently, fiber Bragg gratings were inscribed in the core of collection fibers to suppress the Rayleigh scattered light instead of using dielectric filters [96]. The advantages include (i) the easier fabrication that can even be integrated during the fiber drawing process and (ii) reduced complexity that gives better probe–probe reproducibility and lower costs. The disadvantage of lower sensitivity due to small single-mode cores that are needed for fiber Bragg gratings can be compensated by the design of multicore single-mode fibers. The working principle was demonstrated for Raman spectroscopy of porcine brain tissue. Key obstacles for the clinical implementation of Raman spectroscopy are autofluorescence and compensation of ambient light. It was shown for Raman probe spectroscopy of liver tissue that excitation with a wavelengthmodulated laser can suppress these confounding effects [97]. The principle is that the Raman bands shift as a function of the excitation wavelength whereas the autofluorescence and ambient light remain unchanged and can be separated, e.g., by principal component analysis.

C. Krafft, J. Popp

A research group in Singapore developed a clinical Raman probe system and applied it successfully to several diseases. The clinical utility of confocal Raman spectroscopy was evaluated over near-infrared autofluorescence (AF) spectroscopy and composite AF/Raman spectroscopy to improve early diagnosis of cervical precancer in vivo at colposcopy [98]. A total of 993 Raman spectra of normal tissue and 247 Raman spectra of dysplasia were acquired from 84 cervical patients. Confocal Raman spectroscopy coupled with principal component analysis and LDA yielded the best diagnostic accuracy of 84.1 % for in vivo discrimination of dysplastic cervix. A similar study by the same group evaluated multimodal image-guided Raman endoscopy for real-time in vivo diagnosis of cancer in the esophagus [99]. Multimodal widefield endoscopic imaging included white light reflectance, narrow band imaging, and autofluorescence guidance. A total of 42 Raman spectra were from normal tissues and 33 spectra from malignant tumors. The data were analyzed by biomolecular modeling utilizing six basis reference spectra from representative biochemicals. The fit coefficients for actin, DNA, histones, triolin, and glycogen were found to be most significant for construction of an LDA diagnostic model giving rise to an accuracy of 96 % for in vivo diagnosis of esophageal cancer. Another group developed a Raman endoscope that was used for multiplexed imaging of tissue sections labeled with SERS tags and for label-free imaging of tissues in human patients [100]. The Raman instrument was characterized with SERS particles on excised human tissue particles, and it has shown high sensitivity and multiplexing capabilities, detecting 326 femtomolar concentrations of SERS nanoparticles and unmixing 10 variations of colocalized SERS nanoparticles. Another feature of the noncontact Raman endscope is that it has been designed for efficient use over a wide range of working distances from 1 to 10 mm. A similar ratiometric approach that was described before for cell distinction [70] was presented for early detection of epithelial cancers [101]. The paper addresses the issue of topical application and quantitation of targeted SERS nanoparticles in hollow organs. Although less toxic than intravenous delivery, the additional washing required to remove unbound nanoparticles cannot necessarily eliminate nonspecific pooling. The ratiometric imaging technique determines the relative concentrations of at least two spectrally unique nanoparticle types where one serves as a nontargeted control. In this way the specific detection of bound targeted nanoparticles is improved by adjusting for any nonspecific accumulation following washing and for variations of working distance between Raman endoscope and tissue. The quantitative ratiometric imaging was reported with nanoparticles at picomolar and subpicomolar concentrations and at varying working distances up to 50 mm. The same group developed a system for Raman molecular imaging with SERS nanoparticles in small animals [102]. The

dedicated Raman imaging instrument enabled rapid, highspatial resolution, spectroscopic imaging of SERS nanoparticles over a wide field of view (greater than 6 cm2) with simplified animal handling. SERS signals could be detected in both superficial and deep tissue locations. Another Raman system based on SERS and a tunable filter was designed for small animal imaging [103]. This system enabled widefield Raman imaging with acquisition times which are orders of magnitude lower than achievable with comparable pointscanned methodologies. Typical in vivo images could be acquired in less than 10 s with suitable SERS reporter molecules. The simultaneous detection of four SERS reporter molecules demonstrated the multiplex capability of widefield Raman imaging in biological in vivo applications. A technology with strong potential for clinical translation used inert gold–silica SERS nanoparticles and a handheld Raman scanner that could guide brain tumor resection in the operating room [104]. SERS image-guided resection in a simulated intraoperative scenario was shown to be more accurate than resection using white light visualization alone. Correlation with histology showed that SERS nanoparticles accurately outlined the extent of the tumors. The handheld Raman probe not only allowed near real-time scanning, but also detected additional microscopic foci of cancer in the resection bed that were not seen on static SERS images and would otherwise have been missed.

Progress in coherent Raman scattering in tissue diagnosis The translation of CRS techniques, namely CARS and SRS, to biological and medical applications is technically demanding. Expensive lasers must be coupled to microscopes. Short pulses of light must be precisely aimed, coordinated, and shaped. Detection devices must be optimized to home in on signals and discard background. Most instruments are custombuilt and can only be operated by a few highly skilled experts. CARS images provide label-free morphological contrast and chemical contrast from the selected vibrational band. As most images are registered for a single anti-Stokes shift, chemical imaging (i.e., plotting the intensity as a function of the coordinate) is the method of choice for display. Singleband CARS microscopy is one of the fastest implementations of nonlinear vibrational imaging allowing for video-rate image acquisition of tissue. It is generally accepted that univariate images (images probing just a single property such as the CARS intensity of a single vibration) do not contain sufficient information for differential diagnosis of tissue and cells. The following strategies have been suggested for improvement. First, CARS imaging is combined with other nonlinear modalities which include SHG and TPEF. Second, CARS microscopy can be combined with Raman microscopy to collect full spectra at selected points of interest. Third, multiplex


CARS images can be collected which requires different instrumentation. Forth, turn-key fiber lasers as excitation sources offer user-friendly operation. Technical progress and examples are described next. As the best contrast of CRS is obtained for CH2 vibrations of fatty acids and lipids, most work has been published for tissues and pathologies with high lipid content such as brain, breast, and atherosclerotic plaques. Data analysis and instrumentation An example of a CARS image is given for a brain tumor section at 2,850 cm−1 (Fig. 7). Positive contrast was obtained for lipid-rich features and negative contrast for lipid-deficient features [105, 106]. The lipid-deficient features correlate with cell nuclei as compared with the hematoxylin and eosin stained tissue section. For comparison, Raman images give positive contrast for cell nuclei as shown in Fig. 5. At higher magnification cellular details are observed in the hematoxylin and eosin section that correlate well with CARS features. This result clearly demonstrates that important pathological information can be derived from CARS images of unstained tissue sections. CARS images can be subjected to image processing routines [107, 108] that enable one to delineate the cell nucleic, their number, their size distributions, and the nuclei to cytoplasm ratio in a much faster way than previously described for Raman imaging [90]. The algorithm utilized

Fig. 7 CARS image of brain metastasis tissue section (a) and photomicrograph of subsequently hematoxylin and eosin (H&E) stained section (c). Magnified views of CARS image (b) and H&E stained section (d) showing cellular details

the grayscale information of CARS or TPEF images for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. A similar CARS strategy was presented to identify breast cancer and differentiate its subtypes [109]. Simply by visualizing cellular and tissue features appearing on CARS images, cancerous lesions could be readily separated from normal tissue and benign proliferative lesions. To further distinguish cancer subtypes, quantitative disease-related features describing the geometry and distribution of cancer cell nuclei were extracted and applied to a computerized classification system. The results showed that in situ carcinoma was successfully distinguished from invasive carcinoma while invasive ductal carcinoma (IDC) and invasive lobular carcinoma were also distinguished from each other. Furthermore, 80 % of intermediate grade IDC and 85 % of high grade IDC were correctly distinguished from each other. Progress is expected from alignment-free, all-spliced fiber lasers for excitation of CARS, SHG, and TPEF [110]. Both pump and Stokes pulses for CARS microscopy are available from one fiber end, intrinsically overlapped in space and time, which drastically simplifies the experimental handling for the user. The complete laser setup was mounted on a homemade laser scanning microscope with small footprint that was designed to offer better transmission of near-infrared radiation and larger field of view [111]. Owing to its simplicity, compactness, maintenance-free operation, and ease-of-use this low-cost laser is an ideal source for biomedical applications outside laser laboratories and in particular inside clinics. The performance was illustrated for squamous cell carcinoma of the head and neck in an animal model of atherosclerosis. The pulse duration of the CARS fiber laser introduced above is 65 ps and gives a high spectral resolution of 1 cm−1. This property in combination with CARS imaging at two wavenumbers near 2,850 and 2,930 cm−1 and a new image analysis approach based on colocalization enabled chemical-selective imaging of lipids and proteins [112]. The principle is that a CARS image near 2,850 cm−1 mainly probes symmetric CH2 vibrations, whereas a CARS image near 2,930 probes antisymmetric CH2 and symmetric CH3 vibrations. As CH2 groups are more abundant in lipids than in proteins, the CARS images show different contrast at both wavenumbers and a qualitative decomposition can be achieved. A quantitative analysis remains challenging because of the quadratic concentration dependence. The SHG/TPEF/multiplex CARS multimodal imaging approach was demonstrated for a labelfree differentiation of elastic fibers, triglycerides, collagen, myelin, cellular cytoplasm, and lipid droplets in arterial tissue. Brain tissue Unstained brain samples of the cerebellum of a domestic pig and a human brain tumor tissue section were investigated by

C. Krafft, J. Popp

multimodal nonlinear imaging combining CARS, SHG, and TPEF [106]. A composite image of all modalities was generated for a cerebellum tissue section by collecting a mosaic of 15×15 individual images of 1.2 mm amounting to 18× 18 mm2 at a resolution of 1.2 μm per pixel. Microscopic inspection revealed white matter, gray matter, arachnoid membrane, blood vessels, Purkinje cells, and granule cell layer that coincided well with a hematoxylin and eosin stained parallel section. The tumor margin was visualized in the CARS image by different intensity profiles for tumor and necrosis (Fig. 7). The brain tumor specimen was additionally analyzed by linear Raman and Fourier transform infrared imaging that gave complementary information. Multiplex CARS was applied to investigate fresh mouse brain tissue [113]. The combination of imaginary part extraction followed by principal component analysis led to color contrast between gray and white matter as well as layers of granule and Purkinje cells. Additional quantitative information was obtained by using a decomposition algorithm. The results agreed with hematoxylin and eosin stained reference slides. SRS microscopy is a CRS variant that has several advantages over CARS including a linear relationship between signal intensity and chemical concentration whereas a quadratic dependence is found in CARS. Furthermore, SRS enables quantitative chemical imaging owing to a nondistorted spectrum almost identical to that of spontaneous Raman spectroscopy. Based on histoarchitectural and biochemical differences, two-color SRS microscopy was used ex vivo to detect and classify tumor-infiltrated mouse and human brain sections and fresh mouse brain specimens [114]. Analogous to the case described above for CARS, the ratio of Raman signals at 2,930 and 2,845 cm−1 reflects different lipid and protein contents. Densely cellular or solid tumor regions in mice had a mean intensity ratio I(2,930)/I(2,845) of 4, whereas cortex and white matter had mean ratios of 1.6 and 0.93, respectively. A linear combination of the two raw images at 2,930 and 2,845 cm−1 was used to compute the distributions of lipid and protein and provided structural and chemical contrast in a composite image. Finally, this study applied SRS microscopy in vivo in mice during surgery to reveal tumor margins that were undetectable under standard operative conditions. Atherosclerosis Another multiplex CARS study imaged intact atheromatous lesions and identified distinct chemical profiles of atherosclerotic lipids [115]. Whole aortas from atherosclerotic mice were en face examined by multiplex CARS and four distinctive morphologies of lipids were classified as intra-/extracellular lipid droplets and needle-/plate-shaped lipid crystals. The chemical profiles of atherosclerotic lipids were found to depend on morphologies. It was demonstrated that that needle-/ plate-shaped lipid crystals in advanced plaques had undergone

a phase shift to the solid state with increased protein content implying that lipid modification had occurred beforehand. The plaque depositions in arterial walls of a rabbit model that was studied in vivo by Raman probe spectroscopy as described earlier (“Fiber optic Raman spectroscopy”) was also subjected to CARS microscopy in vitro [94]. The inherent confocality of CARS was utilized to collect three-dimensional CARS images of inner arterial walls by acquiring consecutive scans at 1-μm step size about 50 μm into the tissue. The samples were immersed in buffer during the measurements to prevent denaturation due to drying. CARS microscopy images from normal aorta were recorded in resonance with the CH stretching vibrations of proteins near 2,930 cm−1. They showed a well-defined orientation of the tissue matrix with a fibrillary structure. CARS images from aorta with severe plaque depositions were recorded in resonance with the CH stretching vibrations of lipids around 2,850 cm−1. They visualized a rough surface and loss of fibrillary structure. Instead, a more crystalline and droplet-like arrangement at several positions became apparent. SHG and CARS microscopy was employed for imaging thin cross sections of atherosclerotic arterial tissues [116] that were characterized before by FTIR imaging [117]. The multimodal nonlinear modalities demonstrated that both cholesterol deposition in the lumen and collagen in the normal arterial wall can be imaged and discriminated using SHG and CARS microscopy. Simultaneous detection of both forward and backward scattered SHG signals allows one to distinguish collagen fibers from cholesterol. Different values of fiber mean size, distribution, and anisotropy were calculated for lumen and atherosclerotic lesions. Skin Multimodal imaging of 140 unstained cross sections of 32 individuals was performed [118]. The epidermis, dermis, and subcutis were distinguished in all three applied modalities, but are unveiled best in multimodal images because they give complementary information to some extent. While the subcutis is dominated by the CARS signal due to fat-rich adipocytes, structural proteins such as keratin, myosin, tubulin, and collagen of the dermis are predominantly detected by SHG. No SHG signal is detected in the epidermis, whereas CARS and SHG show equal contributions. Additionally, hair follicles, sebaceous and sweat glands, and blood vessels show characteristic morphochemistry.

Discussion Linear and nonlinear Raman spectroscopies have been applied to a huge variety of biomedical diagnostics and many reports


were published as proof of concept or in relation to a particular problem for the first time. Furthermore, a large amount of literature also exists on SERS that could not be fully covered in this contribution. However, there is still a significant lack of translation and implementation into clinical practice. Such a development requires an active interaction and exchange of knowledge between a wide range of disciplines: laser instrumentation, microscopy, spectroscopy, fiber optics, the life, chemical, biological, and physical sciences. While each community has well-established networks, there exists no joint overarching forum to communicate future needs, plant conceptual ideas, or present practical solutions. A collaborative network called Raman4clinics will be established as a new forum on a European level to work towards the progress of the emerging field of Raman-based applications for clinical diagnostics of body fluids, bacteria, cells, and tissues. Interdisciplinary cooperation will be fostered between scientists within biophotonics, chemometricians, and physicians/clinicians. More national and international research initiatives are required to advance the conversion of scientific findings that were reported here into clinically relevant, practicable, and economically feasible diagnostic methods and systems. Another recently launched network with a similar scope is called CLIRSPEC. A frequently asked question is why the development of Raman-based techniques took such a long time to achieve given their importance and performance. Answers are speculative. Major fundamental devices such as lasers, optics, filters, spectrographs, and detectors were developed more than 10 years ago. The main progress occurred in recent times in data processing that gave robust correlations between spectral information and clinical findings. It is unlikely that a single technique will be successful in fulfilling the unmeet clinical needs. More likely, the future perspective of the field will be a combination of techniques involving Raman, SERS, CARS, SRS, and also other optical modalities with the goal to optimize sensitivity, specificity, speed, and costs. Finally, broader dissemination of new methods can be stimulated by early adopters that deserve more formal recognition [119]. Although it is less attractive to be second, they have a crucial role in validating and defining the limits of new methods. In general, science progresses only when others reproduce and build on previous accomplishments and claims. If a new Raman-based method is capable of substantially improving the chances of obtaining new biomedical and diagnostically relevant insights, a researcher may be sufficiently motivated to try it. An important prerequisite is precise documentation of experimental methods and sampling protocols. Even if this is fulfilled, adopting a method before other research groups have documented that it works as advertised still carries risks. Limitations or serious problems with methods may not emerge until after publication. Then, early adopters have to expend considerable effort polishing a

method or developing workarounds. It is important that information about a problem that seriously affects a method is widely circulated to the community. This can save others from unnecessary grief. However, publications about problems so fundamental that there is no solution are rare in a vibrant field. Potential users of new methods must know the limitations before they can make informed decisions regarding the suitability of a method for their work. Some authors are refreshingly forthright about limitations of their new methods. They are also encouraged to discuss potential problems and pitfalls. In summary, the first manuscript describing a method or tool is only an early step on the road to establishing a method. The next steps taken by others are not as glamorous, but they are no less essential and can provide substantial rewards to those brave enough to take them. The role of early adopters should be appreciated in the validation and promulgation of methods that are published in ABC and elsewhere. A growing number of scientists are confident that histopathology and cytopathology will soon take advantage of label-free microscopy and endoscopy. The debate is over which techniques will win out. Linear and nonlinear Raman spectroscopies are among the hottest candidates. Acknowledgments Financial supports of the EU, the Thüringer Kultusministerium, the Thüringer Aufbaubank, the Federal Ministry of Education and Research, Germany (BMBF), the German Science Foundation (DFG), the Fonds der Chemischen Industrie, and the Carl-Zeiss Foundation are greatly acknowledged.

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