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Free Neuropathology 3:19 (2022)


From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives

Gilbert Georg Klamminger1,2,3, Katrin B. M. Frauenknecht2,3, Michel Mittelbronn2,3,4,5,6,7, Felix B. Kleine Borgmann2,3,1,5

1 Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
2 National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
3 Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
4 Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
5 Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
6 Department of Life Sciences and Medicine (DLSM), University of Luxembourg, Esch-sur-Alzette, Luxembourg
7 Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, Esch-sur-Alzette, Luxembourg

Corresponding author:
Felix B. Kleine-Borgmann · Department of Oncology (DONC) · 84, Val Fleuri; L-1526 Luxembourg · Luxembourg

Submitted: 31 May 2022

Accepted: 17 July 2022

Copyedit and Layout by: Jerry Lou

Published: 05 August 2022


Keywords: Raman spectroscopy, Neurooncology, Neurodegeneration, Neurosurgery, Neuropathology, Machine learning


In recent years, Raman spectroscopy has been more and more frequently applied to address research questions in neuroscience. As a non-destructive technique based on inelastic scattering of photons, it can be used for a wide spectrum of applications including neurooncological tumor diagnostics or analysis of misfolded protein aggregates involved in neurodegenerative diseases. Progress in the technical development of this method allows for an increasingly detailed analysis of biological samples and may therefore open new fields of applications. The goal of our review is to provide an introduction into Raman scattering, its practical usage and also commonly associated pitfalls. Furthermore, intraoperative assessment of tumor recurrence using Raman based histology images as well as the search for non-invasive ways of diagnosis in neurodegenerative diseases are discussed. Some of the applications mentioned here may serve as a basis and possibly set the course for a future use of the technique in clinical practice. Covering a broad range of content, this overview can serve not only as a quick and accessible reference tool but also provide more in-depth information on a specific subtopic of interest.


As one special method of various vibrational spectroscopic techniques, Raman spectroscopy (RS) has been an integral part in neuroscience research for some time now, be it in neuro-oncology for tumor classification1 or for the biochemical description of various protein aggregates in neurodegenerative diseases2. Currently it is making its way towards a clinical implementation3. Looking at the numerous advantages of RS, the reasons for an increased use in research are obvious: it enables fast and user-friendly (easy to apply) analysis for the purpose of tissue identification (e.g., identification of different brain regions in three mice strains4) by observed changes in the vibrational level of the underlying biochemical and molecular composition. Compared to other advanced molecular techniques, reproducible results can be obtained with few requirements regarding sample preparation. The insensitivity to water molecules predestines the technology for its use in a biomedical context.

To date, the vast majority of studies using Raman spectroscopy examine unprocessed native, or frozen tissue/cells - few publications make use of formalin-fixed or paraffin-embedded (FFPE) tissue because Raman measurements remain challenging due to the strong contribution of paraffin wax to spectral intensity, thin specimens, and a disruption of the molecular integrity, which is related to the preceding fixation process. The long-term archivability and the large number of available samples, however, suggest use of RS FFPE tissue in pathology is desirable, e.g., for the analysis of tumor heterogeneity, or identification of very small tumor fragments, which could escape diagnostic high throughput of histology samples. The following review and perspective paper is divided into three parts: a) the basics of RS and the most common forms of its application in medical research are presented, b) the use of RS in selected neuroscience disciplines is accentuated with the aim to present different research questions – but even more importantly – the most interesting findings discovered with the help of RS, c) a future outlook for potential application of RS in research but also in the daily clinical work is provided. At this point, the minireview by Payne et al.5 needs to be mentioned; it describes in a clear way not only applications of RS in neuroscience, but also sets a special focus on the technical aspects and benefits of advanced spectroscopy-based techniques depending on the particular use case.

By contrast, the following work places a special emphasis on topics that will inevitably become relevant to the practicing spectroscopist at some point, such as varying tissue sample requirements in different clinical settings (surgery department/pathology department) or common data processing methods, to name a few. Whenever it serves expedient the attentive reader shall be referred to additional more in-depth reading.

Search for relevant literature

A literature search (the search terms “Raman”, “Raman spectroscopy” were each combined alternately with the terms “brain”, “neuro”, “neuroscience”, “brain tumor”, “tumor”, “neurooncology”, “glioma”, “neurodegeneration”, “neurodegenerative disease”, “Alzheimer’s disease”, “Parkinson’s disease”, “Huntington”, “amyotrophic lateral sclerosis”, “prion disease“, ”multiple sclerosis”, “myelin”, “demyelination”, “stroke” “brain ischemia”, “brain injury”, “muscular diseases”, “brain infections”, “meningitis”, “psychiatry”) was performed, and online databases PubMed Central® and Google Scholar® were browsed for relevant reviews and original articles; other types of literature, such as congress papers, letters, comments e.g., were excluded. After search results were identified, they were hand-screened for eligibility (inclusion criteria: employment of RS on brain/peripheral nervous/muscle tissue, RS on extracellular components/cells of the nervous/muscular system, or RS in relation to neurological/oncological/psychological disorders; exclusion criteria: use of vibrational spectroscopic techniques other than RS) based on title/abstract. Within the responsibility of the authors, the final selection of literature was conducted based on the article full text. Finally, associated bibliographies of selected publications were searched for additional relevant sources that semantically met the search criteria. Only English language literature was considered – even though Japanese research groups describe an employment of Raman spectroscopy in rat brains, and human brains / brain tumors as early as the 90s6–9. Although references to historical developments are pointed out whenever a contemplation of the historical context seemed valuable special focus is set on literature of the years 2021 and 2022, reflecting ongoing research projects/groups - such as spectroscopical examination of microglial changes due to SARS-Cov-2 exposure10 - using RS in neuroscience.

Principle of Raman scattering and general spectrometer set up

The Raman effect is the process of inelastic scattering of photons; this effect was first described in 1928 by C.V. Raman, who examined the characteristics of scattered photons when applying a light source on different liquids11,12. For his discovery, the Indian physicist won the Nobel prize in 193013, but despite the discovery of the Raman effect in the first half of the 21st century, it took until late 1960s before it was first used in a biomedical context14–17.

The interaction of incident light with a molecule leads to changes in the vibrational state, so that the molecule falls into an excited virtual vibrational state. When returning to the ground state, the largest amount of the incident photons is elastically scattered, which means that the energy of the scattered photon is the same as that of the incident photon (=Rayleigh scattering). Only a minor part of the scattered light experiences a change in its energy compared to the incident light; in fact when the molecule ends up on a different state in comparison to the ground state, the photon is inelastically scattered. Depending on the interaction between the molecule and the photon, inelastically scattered light can have a higher energy (anti-Stokes effect) or a lower energy (Stokes effect) than the incident light, whereas in practical application mainly Stokes scatter is attributed to a resulting Raman signal, due to its higher intensity.18 See Figure 1 for a visualization of the vibrational states transitions.

Figure 1. Occurring optical phenomena when irradiating a biological sample with a photon source (laser).
Left: Vibrational states (v0, v1, v2) involved in Rayleigh and Raman scattering. In case of elastic scattering (Raleigh scattering), incoming photons temporarily change the vibrational state of a molecule - after this excitation, the molecule returns back to the initial vibrational state (v0). In the case of Stokes Raman scattering, a molecule gains energy due to the excitation process and finally ends up in a higher vibrational state (it rises from v0 to v1) – the scattered photon has lower energy than the incident light. In Anti-Stokes scattering the molecule ends up on a lower vibrational state after excitation compared to the ground state (it falls from v1 to v0) – therefore, the scattered photon gains energy.
Right: In contrast, the phenomenon of fluorescence occurs when a molecule absorbs light and thus is temporarily transferred to a higher electronic state (v’0, v’1, v’2).

In order to be Raman-active as a molecule, i.e., to emit inelastic Raman scattering, a change in polarizability is required - this already shows a difference to a related and often confused spectroscopic technique, infrared spectroscopy, in which an absorbed photon leads to a change in the dipole moment19. Another phenomenon, also based on absorption and often observable as a disruptive factor in Raman measurements due to its stronger signal is fluorescence; here the molecule, excited by energy of absorbed photons, leaves the ground electronic state and is transferred to a higher electronic state - as soon as it returns to the ground state, energy is re-emitted as fluorescence light20.

The interaction of photons with their target molecules resulting in an inelastic Raman scattering with a distinct energy difference reflects specific chemical bonds and constitutions. This spectral fingerprint can indicate the identity of the target molecule. A spectrum can therefore be defined as a representation of the intensity values (based on the degree of change in polarizability) and the differing frequencies (Raman shift) in a function18,20. The x-axis displays the Raman shift in the unit wavenumber cm-1, thereby the wavenumber is reciprocal to the wavelength and thus directly proportional to photon energy19. The conventional experimental application of the process using the pure Raman effect is so the called Spontaneous Raman Scattering (SpRS). Additionally, there are several derivative methods allowing, for example, scattering with enhanced signal intensity or reduced background noise, thus lending themselves to different applications such as Raman Imaging (e.g., by coherent Raman spectroscopy). Table 1 gives an overview of the technical background and advantages of commonly used variants of RS in neuroscience. For a more detailed insight into the theoretical aspects of RS the interested reader may refer to Cialla-May et al.21, who provides a comprehensive overview in the book “Micro-Raman Spectroscopy: Theory and Application” by Popp et al.22. Additionally, Hu et al.23, Shi et al.24 and Evans et al.25 give a good overview about stimulated Raman spectroscopy (SRS) and coherent anti-Stokes Raman scattering (CARS); Zheng et al.26 wrote an instructive review about surface-enhanced Raman scattering (SERS).

Table 1. Summary of commonly used Raman techniques, their physical background and the associated advantages and disadvantages.

The exact structure of a Raman spectrometer differs depending on the manufacturer and the technology used. Only general components and their function are discussed below; additional components such as an additional laser or a special Raman substrate are commonly required in spectrometer setups of advanced Raman techniques (Table 1). With a focus lens, emitted photons of a laser source are focused on the sample, and after interaction with the sample both the elastic and the inelastic scattered photons are collected by a collecting lens. The reflected and elastically scattered light is then separated from the remaining light, typically by a dichroic mirror. A prism or diffraction grating spatially separates the light according to wavelength, leading it to a detection system - a photo paper was employed in the classical setup - either simultaneously on a charge-coupled device (CCD) or through a monochromator on a photomultiplier tube (PMT) (Figure 2).

Figure 2. Schematic and simplified representation of a Raman spectrometer set up.

As excitation source, typically lasers, is used where the manner of photon generation as well as the wavelength differ. Commonly employed excitation wavelengths within the biomedical field are 532nm, 785nm, 830nm, or 1064nm - for practical application specific effects on the tissue type of interest as well as potentially induced background signals must be considered individually and adapted according to the experimental setup27. Most employed lasers nowadays are diode lasers; with the advantage of portability and favorable energy efficiency, they have replaced the gas-based lasers (helium neon laser, argon-ion laser) that were often used in the past. The type of proton emission can be divided into continuous-wave lasers and pulsed lasers; the former being more common in SpRS and the latter being necessary in SRS18,28. It is necessary to bundle photons both in the suitable focus on the sample (focus lens) and to collect the scattered photons (collecting lens) after interaction with the sample. Next, Raleigh scattered photons are filtered by a dichroic mirror and separated according to their wavelength using a diffraction grating; depending on the sampling aperture (exit slits/pinholes) within the setup, a certain number of photons are detected in a final step by the sensitive detection system. While the classical “scanning spectrometer” employs a rotatable grid concentrating the photons on a narrow exit slit and a photomultiplier tube behind detecting RS, modern set ups usually use a CCD detector. This multichannel way of photon detection (a multichannel array chip consisting of several pixels) allows for simultaneous registration and display of all photons, i.e., the whole Raman spectra18,28,29. Regular wavelength calibration (process of transferring pixel hits on the CCD detector to distinct displayed wavenumbers) is recommended to receive reproducible spectra over the entire duration of the experiment28.

Raman spectra can be employed in various ways. In addition to the possibility of using them as raw spectra primarily for the identification of biochemical components of a sample, methods called Raman microscopy/imaging use the assignment of colors to Raman bands (only a limited number of wavenumbers is acquired or analyzed)30 over a scanned sample to generate contrast. When extended to focusing through the depth of the sample, three-dimensional Raman images can be built16. Raman microscopy/imaging techniques31,32 and computational image generation algorithms have been advanced to generate Raman images of various brain pathologies, e.g., gliomas, stroke and demyelination25 or to image metabolism in the brain33–35. Using this approach of data visualization, it is possible to obtain a similar look to traditional H&E-stained slides on unstained specimen, which enables histopathological diagnosis36. In Raman mapping, the whole Raman spectrum for each point of the desired area of the specimen is acquired (either point by point or with an excitation laser forming a line on the sample and measuring simultaneously); using computational analysis afterwards, a visualization of differences in the spectral properties of data points is achieved30.

Peak assignment

Raman peaks may occur at first sight in various forms with different characteristics. In addition to certain single peaks that appear narrow and can be assigned to exactly one corresponding functional group, an additive effect of several adjacent Raman active molecules in the sample can also result in broad peaks. Furthermore, the presence of several contributing components, and thus neighbor dependent changes in the vibrational mode in one specimen, may affect the actual peak in comparison to an isolated measurement20. The application of RS in the biomedical context often pays special attention of the regions within the wavenumbers 400-2000cm-1 and 2700-3500cm-1. These regions, often referred to as "biological fingerprint regions" in the literature, are characterized by a high proportion of Raman peaks arriving from functional groups of a typical biological specimen28. An introduction to the use of RS for identification of different molecular functional groups can be found in Pezzotti et al.37 (RS and cell biology) , Czamara et al.38 (RS and lipids), Rygula et al.39 (RS and proteins) and Wiercigroch et al.40 (RS and carbohydrates).

By using RS on biomolecules such as proteins, it is not only possible to identify molecular functional groups i.e., differentiate between different amino acids/proteins, but also spatial confirmations can be detected since the Raman signal is influenced by aromatic/non-aromatic side chains and the backbone of a protein. Distinct vibrations result in certain amide bands (Amide band A, B, I-VII)41; for example carbonyl stretching modes, N–H bending or C–N stretching results in the widely used Amid I (1600-1690 cm−1), Amid II (1480-1580 cm-1) and Amid III peaks (1230-1300 cm−1). They allow further examination of the peptide secondary structure. In larger unordered protein measurements a precise peak attribution may not be possible due its large number of contributors18,39,42–44.

Lipids are ubiquitous in biological specimen, as they form the membranes of cells and organelles. Depending on the literature, spectral properties resulting mainly from the hydrocarbon chain and partly from the polar head group can be assigned to the regions 1050-1200cm-1 (C-C stretching), 1250-1300cm-1 and 1400-1500cm-1 (CH2, CH3 group activity) or also to the regions below 600cm-1 and between 1000-1150 cm-1 (opposite motion of carbon atoms of the hydrocarbon chain). Consistently, an area within the high wavenumber region 2700-3500cm-1 (sometimes solely the range between 2800-3100cm-1 is considered in the literature) is reported and attributed in a large part to stretching of C-H groups. In-depth analyzes of peak intensity and distribution in the high wavenumber region allow conclusions to be drawn about the saturation status of fatty acids and the aliphatic/aromatic components of steroids18,38,45,46. An interesting contribution at this point may come from Krafft et al.45, who in 2005 measured and characterized twelve brain lipids and further related occurring peaks to their functional groups and Pezzotti et al.47, who employed RS to visualize single (phospho-)lipids in neuronal cells.

Carbohydrates and underlying C-C and C-H structures give rise to bonds in various areas within the Raman spectrum18. For a long time, minor attention was paid to the investigation of carbohydrates. Although specific peak assignment is possible, in comparison to protein and lipids it remains less specific40.

About 30 Raman peaks of nucleotides, distributed over several areas within the spectrum, are mostly attributed due to purine/pyrimidine ring modes and phosphate groups (especially peaks next to 800cm-1 and 1100cm-1). They are useful for characterization of inter alia DNA, tRNA, and nucleic acid-protein complexes18,48.

Spectroscopic examination not only allows for examination of these specific functional groups enumerated above, but also to display their interactions, such as protein-protein / protein-lipid interaction. Their changes in spectral property under different conditions can also be measured17. On that note, Lee et al.49 have even managed to use SRS as a tool in neurophysiology when examining the spectral properties of neuronal membrane potential.

Although specific Raman peaks have been described for various molecules50–56, one should be cautious when actually assigning peaks to one's own sample. While peaks may be characteristic for a certain biochemical compound, they can also arise from different sources; viz they are not specific. In order to correctly assign peaks / detect them within a spectrum, it is essential to reduce potential confounders within the sample or the experimental set-up pre/post-experimentally. A potential way to assign distinct peaks with high evidence is direct observation: Targeted manipulation of a sample can help to confirm the source of a peak.

The vibrational spectroscopic experimental setup

RS is a fast, non-destructive, user friendly, and easy to apply on tool providing molecular information with minimal sample preparation requirements in a reproducible manner. However, a routine use of RS-based tools in neuroscience has not yet been established. Regardless of the numerous advantages certain limitations have to be considered not only pre-experimentally, but also during implementation of an experiment and afterwards when visualizing and processing the obtained data.

The occurrence of the physically related phenomenon of (auto-)fluorescence (photons of the pump beam are absorbed by molecules of the sample which are raised to another energy level - when returning to the basic energy level a photon is emitted, see also Figure 1) is regularly observed and the expected intensity in this case is well above the intensity of the Raman signals. To reduce wavelength- dependent autofluorescence, a distinct wavelength of the excitation source can be selected, or SERS can be used57,58. Although in contrast to other sophisticated laboratory techniques (e.g., genetic/epigenetic testing) there are less requirements for a correctly prepared Raman sample. A few things need to be considered in order to avoid the occurrence of spectral background noise and spectral contamination: Samples must be placed on a robust Raman substrate so that the selected measuring point and the focus remain stable. Depending on the experimental question as well as the expected background noise and the costs, various Raman substrates are available. In addition to gold or aluminum-coated glass slides (as a function of the excitation wavelength glass alone exhibits a strong and broad fluorescence background signal in the “biological fingerprint region”), special slides (low-e slides, CaF2 slides, quartz slides) can be considered28. These are characterized by a low spectral background or single peak attribution. Fullwood et al.59 and Kerr et al.60 examined the effect of substrate choice for spectral histopathology in more detail. It has been shown that CaF2 slides (exclusive peak at 321cm-1 or 322cm-1 respectively, depending on the literature)61 have the least influence on the spectral background in comparison to low-E slides and Spectrosil slides. The single background peak can either be ignored due to its irrelevant occurrence out of the important range of biological components within the Raman spectrum, or can be subtracted via computational analysis afterwards. As a low-cost alternative aluminum foil can be used, which itself does not generate any significant background noise62–64.

Furthermore, the sample condition (most commonly native/frozen or formalin-fixed) needs to be considered pre-experimentally. Although fresh tissue samples allow for a straightforward attribution of Raman peaks to underlying biochemical components, they must be processed and analyzed within a certain time window and cannot be stored for a longer period of time. When working with fresh tissue, dehydration and associated denaturation of functional biochemical groups need to be prevented e.g., by keeping the specimen hydrated19,65. As an alternative, Raman measurements of frozen biological samples allow longer storage and at the same time still give an insight in the biochemical composition of the biological sample. Nevertheless, it should be noted that a reduction in certain peak intensities and significant alteration of Raman signal in comparison to native tissue were described when using frozen sections66,67.

The handling of formalin-fixed, methanol-fixed, or FFPE samples is routine during the pathological workflow; even though samples allow long archivability and are broadly available, this way of fixation damages the biological Raman spectrum to a certain degree since the tissue undergoes an aggressive chemical procedure68–72. Both formalin and methanol fixation reproducibly alter spectral tissue properties and affect Raman bands assigned to lipids, proteins, and nucleic acids73. Despite formalin-induced biochemical changes such as formation of cross-links in the structure of the amino acids, spectroscopic assessment and classification of formalin-fixed biological tissue is possible66; in contrast, methanol-fixation was reported to potentially hamper the detection of tissue malignancy72,74.

The prominent spectrum of bound paraffin wax is reflected in certain points at 1063, 1133, 1296 and 1441cm-1, which make a manual or digital dewaxing process necessary and require a careful interpretation of the obtained spectra75. Several conditions (aggressive chemical processing, required choice of special substrate and the fineness of the tissue) hamper spectroscopic examination when employing RS on FFPE tissue in the pathology department, although spatial orientation on the sample and proper identification of certain areas are a potential advantage.

In the literature different approaches used RS on processed tissue; in any case they all face similar difficulties. Huang et al.68 described the effects of formalin fixation on RS of cancerous human bronchial tissue, whereas Draux et al.71 described the influence of formalin and air drying on single cancer cells and attributed spectral changes to affection of nucleic acids and proteins. Even though not only a loss of the original chemical composition but also potential contamination due to the process of formalin-fixation in murine brain tissue was determined by Hackett et al.76, several studies proposed formalin fixation as a sufficient and favorable method for subsequent spectroscopic diagnostic77,78. As a proof of concept, Stefanakis et al.79 demonstrated the feasibility of vibrational spectroscopy on formalin-fixed malignant brain tissue. Employing vibrational spectroscopy on FFPE tissue, an effect on the lipid content due to the dewaxing process was reported; nevertheless, Raman bands related to cellular and extracellular proteins were successfully measured80. Gaifulina and colleagues81 examined large intestine FFPE tissue from rats and analyzed biochemical signals obtained with label-free RS in the processed tissue. Other groups examined FFPE tissue of rectal cancer to predict radiotherapy response82, to map/analyze cervical tissue83,84, or employed RS on healthy and malignant breast85–88/ovarian89/prostatic90 tissue in various fixation states. For a good overview on the influence of tissue processing on biological Raman spectra the reader may refer to the work from Faoláin et al.66.

During spectroscopic examination, background noise due to a nearby photon source (e.g., room light) should be considered and reduced by performing the Raman measurement in a darkened area or with dimmed operating room light91–94. Additional methods of spectra quality control during intraoperative measurement have also been proposed95,96. By ensuring that the laser settings (wavelength and power, duration of acquisition) are optimized for the examined sample, the best signal-to-noise ratio can be determined, and thermal tissue decomposition can be prevented. This form of sample destruction can be detected by a burned area where the former focus area of the laser is located, as well as by the presence of an additional carbon band at approx. 1500cm-1 in the Raman spectrum28.

Data processing and computational analysis

After the measurement, the large amount of data97 should be sorted and stored in a structured manner (data annotation) to address the research question properly. It is good practice to start the data processing with an initial visualization of the data. In this way clear deviations from an expected result such as strong contamination or cosmic ray artifacts (randomly occurring electromagnetic radiation) and hot pixels (overresponse of a pixel on the CCD detector to an incoming photon) can be recognized and corrected28,98,99. For a more detailed reading on potential anomalies and artifacts that may occur, see Bowie et al.100.

During data preprocessing, a baseline correction can be applied to the data to minimalize residual background signal and autofluorescence101,102; a common way to model and subtract the background noise to obtain the intrinsic sample spectrum103,104. Additionally, a common way to further reduce the noise in the data is a smoothing technique, such as Savitzky-Golay filtering28,105,106. Both of the above-mentioned methods must not be used without proper caution as there is always the risk of producing artifacts, as well as equalizing significant data points. In order to correct confounders that result from the experiment setup itself (e.g., slightly different dryness or thickness of the specimens) data normalization methods, such as min-max normalization or z-normalization, usually precede the actual data analysis107. Specialized spectroscopy software are commercially available and enable even the inexperienced spectroscopist to use the acquired data in a structured and comprehensive manner108.

Due to the large amount of data, several data reduction methods are used for quick explorative purposes, above all PCA (principal component analysis) is widely employed. This unsupervised clustering technique can be used to determine principal components in a big data set, which explains a significant part of the variance and reduces noise41,109.<

In the last step of computational analysis, classification algorithms and machine learning techniques110,111 are commonly used to classify the spectral data either according to pre-experimental defined groups (supervised clustering) or according to new groups based on similarities in spectral properties (unsupervised clustering)112.

A widely used technique in unsupervised clustering is hierarchical cluster analysis (HCA), in which the data is transferred to a higher-dimensional space, cluster in a certain proximity to one another based on similar properties. A number of cluster variables can be specified individually, which forms the selected number of similar clusters103. Unsupervised clustering is beneficial for exploratory research questions since no prior knowledge of possible group properties is required28.

Common methods used for supervised clustering are trees/random forest classifications (several decision trees in a row) or support vector machines (search for a hyperplane to distinguish between classes)91. The groups determined a priori are referred to as "classes" and the gold standard histopathology often serves as ground truth. In general, the algorithm is trained with a training data set and tested with an external validation data set afterwards. To avoid overfitting (capability of good differentiation only on the specific training data set) a validation of performance e.g., k-fold cross validation or holdout validation is performed, and metrices of algorithm performance (e.g., sensitivity, specificity, f1-score, accuracy, AUROC/AUPR value) are calculated afterwards based on its output113. Ralbovsky and colleagues provided an overview of machine learning algorithms and their functions in Raman based cancer detection112.

RS in Neurooncology

With a growing number of publications in the last years (Zhang et al.114 and Banerjee et al.115 described a change in spectroscopic properties of glioma cells in comparison to astrocytes already in the mid-2000s), the neuro-oncological field is one of the largest areas of research on RS, in which the therapeutical balancing act between maximum resection of normal-brain-resembling tumorous residues and minimal surgical disruption of healthy brain functions proves particularly difficult.

On the subject of RS in (neuro)oncology reviews by Auner et al.20 and Hollon et al.116 give a comprehensive introduction to the respective topic; for further reading on implications and current progress of RS in oncology see also Santos et al.117.

At first sight, use of this spectroscopic technique mainly apply to two main research focuses: on the one hand a spectroscopic detection of malignancy118,119 which in a next steps allows precise, accurate diagnosis of the tumor entity intraoperatively without having to wait for further traditional tissue processing (pathological diagnosis on frozen sections)120,121, and on the other hand real time surgery guidance i.e., live feedback intraoperatively122,123 aiming for maximal tumor resection124–126. Both topics merge and evolve at a certain point; this may result in new research questions, e.g., when aiming to determine tumor infiltration zone / resection margin or when aiming for detection of tumor genetics on various states of tumor tissue. Moreover, also basic research questions in oncology can be addressed with this vibrational spectroscopic technique e.g., monitoring lipotoxicity in glioblastoma cells127, observing cell response of U251 glioblastoma cells after induced apoptosis128, examining the glycosylation pattern of proteins in medulloblastoma129, or observation of redox state of mitochondrial cytochromes130, just to name a few. Most research groups use SpRS20 as an easy to apply, label free method. More advanced Raman techniques in neurooncology131 are used predominantly in animal models132–134 – where Surface enhanced resonant Raman spectroscopy (SERRS) detection of tumor margins135 has shown prognostic benefits136, or CARS was employed for detection of different human brain tumors in a mouse model137.

RS for detection of tumor group, genetic alteration and histomorphology

RS can distinguish between grey and white matter and (partly) other brain regions such as cerebellum, striatum, basal forebrain - both macroscopically and on cellular resolution4,138–146,147. Interestingly, one analysis of the mouse brain using SERS revealed a different spectral fingerprint and thus also different biochemical composition between left and right hemisphere148. Spectroscopically feasible discrimination between glioma tissue and brain tissue was reported in several studies3,149–153 as well as between dura mater and meningioma, which was demonstrated to be based in part on peaks corresponding to collagen and on the higher lipid content within tumorous tissue154–156. Beside these binary classification models, several studies showed the potential of RS aiming for a multiclass classification to differentiate various tumor entities within one classifier119,157–166 or to determine the primary site of metastasis167,168.

Using Raman mapping/imaging for brain tumor visualization116,169, even special morphological features of tumors (e.g., necrosis in glioblastoma, cell density or individual cell nuclei) could be identified170–172. Even though areas of tumor necrosis are typically characterized by an increased presence of proteins such as phenylalanine (around 1032cm-1, among others) as well as cholesterol esters (1739cm-1)171,173, one group proposed two distinct spectral properties within the necrosis of glioblastoma cells: “highly necrotic”, showing an increase in plasma proteins and “peri-necrotic”, exhibiting a higher lipid content174. The histopathological heterogeneity of tumor tissue samples was addressed in fresh and frozen brain sections, although possible confusion between different tumor components (i.e., tumor hemorrhage and necrosis) is described36,173. The genomic heterogeneity in glioblastoma has also been successfully addressed175. Other approaches make use of an alternative advanced Raman technique named Stimulated Raman histology176–179 (SRH), where distinct wavenumbers are used for image acquisition and virtual H&E-like images are generated after computational processing. With this approach in combination with deep convolutional neural networks, amongst others Hollon et al. assessed (pediatric180) brain tumors intraoperatively1,181,182. In the scope of this imaging approach, also a traditional pathological diagnosis based on digital Raman histology slides seems feasible183–185.

RS could be used to identify brain edema186, tumor recurrence187 or tumor margins188–194 but also tumor infiltration zones.195,196 In general, infiltrative glioma cells showed significant spectral differences in the regions of phenylalanine and Amide III (around 1030cm-1 and 1230-1300cm−1), as well as the region assigned to C-C stretching lipids and nucleotides (around 1050-1100cm-1) – just to list a few wavenumbers of interest exemplarily197. Ji et al.196 report the cellularity within a sample as well as the density of axons and the ratio of lipid and protein contents as the basis for the difference in spectral properties. Even single tumor cells198 were detectable using RS, something alternative imaging methods struggle with. RS was also applied to observe glioblastoma tumor evolution199, to determine the molecular subtype of glioblastoma200, and to give insight in glioma biochemistry201.

RS was shown to be superior in differentiation of brain tumor and glioblastoma in comparison to 5-ALA-induced fluorescence202,203, and capable to detect IDH mutations in gliomas – inter alia changes in the spectral protein profile are consistently reported in case of IDH mutation204–206. It also showed diagnostic value in tumor discrimination when measuring small extracellular vesicles207, or potential when tracking/detecting metabolic changes208–210 in brain tumors/cancer cells, as well as drug delivery mechanisms211 and post-therapeutic changes212 in glioblastoma cells.

Spectroscopic classification of different grades of brain tumors is possible213. Zhou et al.214 distinguished between different WHO grades of gliomas using Raman bands of tryptophane (around 1588cm−1, among others) and carotenoids (1008cm−1, 1157cm−1, 1521cm−1, 2320cm−1, and 2667cm−1) as well as the peak intensity ratio between proteins and lipids in the high wavenumber region (2934cm−1/2885cm−1). The group of Morais et al.215 and Lilo et al.216 differentiated between different grades of meningiomas. Zhang et al.217 associated an intensity ratio in the high wavenumber region with different meningioma grades. While gliomas/neuroepithelial tumors and meningiomas have been described218 and morpho-chemically analyzed219,220 extensively221, some work also exist on neuroblastomas. One group differentiated between different neural crest-derived tumors in fresh and frozen tissue222,223, and Ricciardi et al.224 used RS to examine changes in the biochemistry of neuroblastoma cells after exposure to radiation. Medulloblastomas225, biopsies of the pituitary gland209,226, seeds of retinoblastomas227, and carcinoma metastases228 have been spectroscopically studied as well.

Early, intraoperative, and neuropathological diagnostics using RS

Perioperative ex vivo tissue assessments allow for direct and early treatment decision, e.g., when examining smear brain tumor samples94 or discriminating between primary CNS (central nervous system) lymphoma and glioblastoma based on biopsies229. RS can also be applied intraoperatively (in vivo) - recently even in dogs230 - using a hand-held probe for tumor classification231–237, where a real-time auditory feedback mechanism has been proposed to guide the neurosurgeon238. Transcranial RS, leaving the skull intact, has been proposed and demonstrated in a mouse model239.

Using optical spectroscopy applied on FFPE tissue, Devpura et al.240 and Gajjar et al.159 examined a possible application of RS to various brain tumors already in 2012/2013. Shortly after, Fulwood et al.241 distinguished between glioblastoma, metastases and normal brain using immersion RS on FFPE samples. Livermore et al. 204 have been able to carry out the above-mentioned analysis of the IDH mutation detection in glioblastoma tumors also on FFPE tissue. Different histological areas can be distinguished in glioblastoma in FFPE tissue, with a sound separability between the peritumoral area and the area of necrosis242.

To enable early and non-invasive cancer diagnosis, some approaches aim for identification of meningioma243 and glioma244 patients based on serum samples and resulting spectroscopic behavior. Using RS as an additive technique, Le Reste et al.245 combine spectroscopic data and transcriptomic data for machine learning analyses on glioblastoma subtypes and related clinical outcomes.

RS in Neurodegenerative Diseases

Misfolded proteins and aggregates in various diseases246–248, e.g., Alzheimer's (tau and amyloid), Parkinson's (alpha-synuclein), Huntington's (polyglutamine), are in general accessible to vibrational spectroscopic techniques249. Usage of these techniques ranges from tracking and characterization of misfolded proteins41, to potential new diagnostic methods250,251, especially in biofluids252–254. Studies on the pathological hallmarks of neurodegenerative diseases used a variety of RS techniques; most frequently employed techniques are SERS, TERS (Tip-enhanced Raman spectroscopy), as well as DUVRR255,256 (deep UV resonance Raman), where a wavelength in the range of UV (200nm) is used as excitation source which results in an increased intensity. Another common technique named ROA (Raman optical activity) makes use of the principle that a chiral molecule scatters left and right handed polarized photons at different intensities and so is particularly useful to analyze protein aggregates257,258. Furthermore, also IR (infrared)-spectroscopy and related/modified vibrational methods are common, and a combination of techniques could lead to an increased diagnostic ability and gain of knowledge2,259–262. Several ways of increasing the detectability of a sample via RS have gained popularity in the neurodegenerative field. Bringing in a labelled isotope into the backbone of a peptide shifts certain amid bands and enables a demarcation from the existing amide bands emanating from the unlabeled proteins in the sample, although an overlap of Raman peaks of interests may occur263,264. Another similar approach integrates external probes such as unnatural amino acids with vibrational potential into the sample, which can afterwards be traced by specific Raman peaks, often in the range between 1900-2900cm-1 where the interference with other peaks of the specimen is minor264–266. For further reading, Devitt et al.2 provides a detailed insight into the use of RS in the field of neurodegenerative diseases.

Around 20 years ago conventional RS was already capable of distinguishing between AD brain tissue and healthy control brain tissue (in 2022 machine learning algorithms are useful to do the same267) and to determine the presence of amyloid-beta-sheets in senile plaques268–270. Shortly after, Raman signals of the hippocampus of AD rats were proposed to aid diagnosis of AD271. Kurouski et al.44 give an overview of the application of RS in the course of plaque formation and structure; Wilkosz et al.41 provide a comprehensive list of wavenumbers associated with protein aggregation. Detailed examinations of the (secondary)-structure of beta-amyloid in various experimental set ups have been carried out using DUVRR272–275 or ROA44. Cunha et al.276 used a combination of Raman techniques for amyloid plaque characterization. SERS has been used to identify tau protein and (soluble) amyloid beta277,278, and to detect amyloid-beta1-40 monomers and amyloid-beta1-40 fibrils in solution279 as well as in brain tissue280. Aβ40 and Aβ42281 were shown to be distinguishable. TERS was used to characterize natural Aβ1-42 fibrils and identify toxic oligomeric forms282,283.

RS was capable of visualizing amyloid in AD brains post mortem and of displaying neuritic plaques and neurofibrillary tangles284 – even though the latter findings were questioned and measurement of lipofuscin granulates instead of plaques was proposed285 Raman imaging also determined the presence of hemoproteins in senile plaques286 and allowed for reconstruction of the evolution process of different types of amyloid beta plaques287. Based on RS measurements, AD-associated astrogliosis288 and lipid deposits in vicinity of fibrillary plaques were identified and further morphologically described289.

Beside the identification of amyloid beta290–292, for example in the surrounding of neuronal spines293, Raman imaging294,295 has been used to compare the concentration of Aβ in hippocampal regions and eye lens tissue296 and to determine cholesterol- and sphingomyelin-rich structures surrounding amyloid plaques, thought to represent dystrophic neurites297. Another research group used CARS to determine a higher content of lipid, collagen and amyloid fibers in Alzheimer-affected brain samples298.

Searching for biomarkers as an early diagnostic tool in AD299–302, human tears303, saliva,304 cerebrospinal fluid305 (different states of amyloid beta confirmations could be detected in cerebrospinal fluid already in 2008306), retinal imaging307 and blood samples308–318 have been evaluated for a potential diagnosis of AD using spectral differences arriving from platelets319 or the concentration of the neurotransmitters Glutamate (GLU) and γ-aminobutyric acid (GABA)320. In the course of this approach, it has been shown that cortical cataract may not be a sufficient predictor of AD296. The detection of neurotransmitters using RS has been shown and further analyzed, by Ardini et al.321, Lee et al322, Moody et al.323–325 (i.e. RS for detection of neurotransmitters through the skull), Cao et al.326 / Zhou et al.327 (neurotransmitter detection in serum), Ciubuc et al.328 (RS for dopamine detection and analysis), Silwal et al.329 (dopamine and dopamine transporter interaction), Manciu et al.330 (dopamine – serotonin interaction) and Shi et al.331 (quantification of norepinephrine).

In addition, RS is also suitable to examine the interaction of beta-amyloid with metal ions332–337. Interestingly, detection of tau335–338 and insulin342–345 has so far been studied to a lesser extent; ozone exposure as a known risk factor has been found to lead to spectroscopically measurable changes of the hippocampus in a rat model346.

In Parkinson’s Disease (PD), a main focus of the application of RS is the characterization of the secondary structure of alpha-synuclein338,347–349 as well as the identification of alpha-synuclein aggregations, feasible not only in the brain but also in the gut350. Mensch et al.351 used ROA to examine the spectral properties of α-synuclein during transition to its secondary structure. Another group spectroscopically characterized the striatal extracellular matrix in a PD mouse model352. Since early loss of dopaminergic neurons is an early change in patients with PD, different approaches aim for detection of dopamine353–355, e.g., in striatum of mice356, or in blood samples of patients with antipsychotic drug-induced Parkinsonism357. Other efforts to establish early diagnostic tests for PD, such as examination of erythrocytes and blood coagulation in PD patients358, were carried out e.g., by Carlomagno et al.359 using saliva of PD patients and Schipper et al.360 who combined RS and NIRS (near infrared spectroscopy) to distinguish between blood samples of PD patients and a control group through different spectroscopic properties correlated with oxidative stress. Mammadova et al.361 used RS in a PD mouse model to detect pathological retinal changes as a method to distinguish between healthy and diseased samples.

Analyzing peripheral nervous tissue in ALS mice and autopsies of patients suffering from ALS, Tian et al.362 showed that Raman imaging was capable of visualizing and detecting early pathological changes. Different approaches distinguish between altered lipids and proteoglycans in spinal cord tissue of ALS mice and healthy controls363, or test the prognostic value of SERS in ALS patients364. In addition to the many approaches to diagnose AD and PD patients by RS, others focus on ALS as well. For diagnostic purposes, Zhang et al.365 used SERS on plasma samples to distinguish between ALS patients and a healthy control group; Morasso et al.366 proposed vibrational spectroscopy and extracellular vesicles as a potential biomarker and another research group spectroscopically examined saliva from ALS, PD, and AD patients, showing differences in the spectral properties of each group367.

In the context of Huntington Disease (HD), RS has been used for quantification and visualization of aggregated polyglutamine368 and for the assessment of its structure369,370. Huefner et al.371 found significant changes in the spectra related to disease progression, as well as differences corresponding to genotype and gender in serum samples of HD patients and healthy controls. In another approach, membrane composition of HD-affected and control peripheral fibroblasts were separatable using RS, suggesting that cell membrane damage may serve as future diagnostic biomarker372.

RS has also been used for research on Prion Diseases373–378; one research group employed the method to examine the diagnostic value when analyzing blood samples of sheep to detect the altering from of PrPC to PrPSc379.

Spectroscopic examination of myelin composition in the CNS and in peripheral nerve tissue

RS proves useful to gain a deeper understanding of the molecular myelin composition; Pezzotti et al.380 examined the physical chemistry of cocultured neuronal and Schwann cells. In addition, RS may be advantageous to detect pathological processes of demyelinating diseases in the CNS or in peripheral nerve tissue. Carmona et al.381 studied the spectroscopic hallmarks of lipid chains in myelin membranes as well as the secondary structure of associated proteolipid proteins (PLP). Some publications report the possibility of detecting myelin in vivo using Raman microscopy382,383; Huang et al.384 described different compositions of myelin structures, whereas Wang et al.385 used CARS microscopy to detect not only myelin but also axons, the node of Ranvier, and the Schmidt-Lanterman incisure. Fu et al.386 visualized fiber tracts in mice brain by imaging the myelin along the axons. In 2021 Lucas et al.387 used CARS to determine myelination deficits in a fragile-X-syndrome mouse model. Out of pure academic interest the publication of Poulen et al.388, in which Raman scattering on spinal cord myelin distinguishes between three different species (human, mouse, lemur), shall be mentioned at this point.

Few Raman experiments deal with Multiple sclerosis (MS)389; the process of myelin degradation can be addressed with RS not only quantitatively390 but also qualitatively. To tackle alterations in the biochemical compositions in human brains postmortem, Poon et al.391–393 measured various pathologic features and showed that even normal appearing white matter next to MS lesions included spectroscopically measurable changes. Imitola et al.394 correlate the presence of microglia (on a side note: even the activation of microglia is traceable using RS395) and axonal injury/demyelination using CARS microscopy. Fu et al.396 applied the same method to examine different time points of experimental autoimmune encephalomyelitis in mice and Gasecka et al.397 used CARS to detect induced autoimmune demyelination in spinal cord of mice. Another approach was carried out by the team of Alba-Arbalat et al.398; they detected spectral changes of defined molecules in the retina (even an in vivo use of RS applied on human retina is in line with laser safety regulations399) - associated not only with different phases of MS, but also age-related in healthy patients.

Raman-based research of myelin composition and pathology is not limited to MS, it also extends to the study of demyelination and its biochemical changes in peripheral nerve tissue400 - even pathological401 and age related402 changes. Using different Raman techniques the remyelination process in the spinal cord of rats after iatrogenic induced demyelination403, as well as remyelination in rat sciatic nerve404, and biochemical changes during nerve injury405,406 can be tracked. Another approach used CARS imaging to interpret the interaction of different macrophages (resident and recruited) after Wallerian degeneration407.

Upcoming novel fields for RS - from stroke to muscular diseases to psychiatry

RS has been applied in combination with infrared spectroscopy and atomic force microscopy to characterize different types of thrombi in ischemic stroke408 or to characterize atherosclerotic plaques409,410. Changes in fibrin concentration in a blood clot after zonal thrombolysis with urokinase411, or the metabolic regulation of artery tone412 were examined. Other research groups investigated spectroscopic changes in the hippocampus due to cerebral ischemia-reperfusion413, or spectroscopic changes in the amount of Cu+ and Cu2+ ions in brain ischemia414. Russo et al.415 used Raman traceable cytochrome c to investigate effects of insulin on the hippocampus after transient ischemic brain conditions, Yamazoe et al.416 used a self-developed Raman approach to detect areas of an ischemic core area. The group of Caine et al.417 used a combination of imaging techniques, amongst others Raman imaging, to track biochemical changes in the peri-infarct zone after induced stroke in a mouse model. As an alternative way of infarction diagnostic, Fan et al.418 proposed tear RS in combination with machine learning tools as a non-invasive technique.

In context of brain hemorrhages, Raman imaging has been used to detect microvessels and induced hemorrhage419, as well as to track the oxygen flow in brain vessels420. Furthermore, RS was employed as a method in rat brains with striatal hemorrhages to evaluate the biochemical composition after rehabilitation treatment421. Employing SERS, the subarachnoid hemorrhage biomarker glial fibrillary acid protein can be detected422. SERS can also be used to assess complications post subarachnoid hemorrhage, like vasospasm and hydrocephalus423.

In tissue conditions of brain or spine injury, RS was applied to tissue of rat models424–426 and on retinae of mice after traumatic brain injury427. Biochemical changes in affected areas arising from hem or divergent levels of cholesterol were discovered428 and compared to MRI scans429. RS was capable of detecting injured motor cortex areas where certain spectroscopic properties were associated with cell death430. Employment of SERS-based methods allow for detection of neuron-specific enolase (NSE), N-acetylasparate or S-100β in blood samples as biomarkers for brain injury431–435; aiming for intraoperative assessment of molecular changes - one group developed a device for intracranial spectroscopy within brain injury436. Changes in the biochemical and cellular composition of rat brain after gamma radiation have been addressed by Kočović et al.437. For a further reading the reader may refer to Stevens et al.438, who has recently reviewed the current deployments of Raman spectroscopy in traumatic brain injury in a detailed way.

Even muscular diseases are accessible to RS: Niedieker et al.439 used CARS imaging to visualize morphological hallmarks such as glycogen storage and internalized nuclei in various muscular diseases; Alix et al.440 reported different spectral properties of mitochondrial and non-mitochondrial muscular diseases; and Gautam et al.441 showed the differences in the spectra of Raman measurements from muscles of Drosophila with certain mutations affecting the muscular system in comparison to healthy controls. SpRS was used for in vivo identification of Duchenne muscular dystrophy (DMD) affected muscles in a mouse model and human muscles affected with the same disease with ex vivo measurements showing similar Raman peaks442. Hentschel et al.443 evaluated the use of fibroblasts together with application of CARS and other methods to study the etiology of neuromuscular diseases. Blood sample testing for the diagnosis of DMD was proposed and successfully performed in a mouse model444; the comparison of spectral properties of the erythrocyte membrane in DMD patients and healthy controls demonstrated biochemical differences due to protein anomaly445.

One of the potential domains of RS in the area of infectious diseases of the brain and meninges is the diagnostic detection of pathogens. It has already been capable of identifying viral strains446, changes in bacterial metabolism447, or differentiate/detect different types of bacteria related to meningitis448,449. Although the diagnoses of tuberculous meningitis450 or Neisseria meningitis451 as well as possible differentiation of blood cell types452 using RS on CSF samples is reported, reliable detection of bacterial meningitis in CSF was not yet sufficiently sensible; therefore, a combination of techniques was suggested453. Another approach employs RS in neuroimmunology as a tool to monitor apoptotic changes in hippocampal progenitor cells454.

RS has also been applied in psychiatric disorders; e.g. to visualize the drug mechanism of a serotonin reuptake inhibitor in mouse brain455 and to identify blood serum samples based on alterations in phospholipids and proteins of patients with affective disorders456–458. Recently, Chaichi et al.459 measured changes in brain lipidome spectroscopically in post-traumatic stress disorder (PTSD) rats, but also the vibrational spectroscopic properties within myalgic encephalomyelitis have been subjected to further analysis460,461.

Conclusions and outlook

All studies and literature cited in this review focused on preclinical/clinical use of RS with the intention to provide the interested reader a general overview rather than a detailed account of each particular topic. Before jumping into action and establishing RS as an additional research method in one’s own laboratory, taking a look on the methodological reviews by Butler et al.28 (including concrete information about the general experimental setup and requirements for biological tissue), and Guo et al.462 (analysis of Raman data, machine learning algorithms) may prove useful.

Upcoming applications of RS potentially aim for in vivo prediction of progression risk463 or employment of vibrational spectroscopy for detection of epileptogenic brain regions464. Advanced Raman techniques such as Spatially offset Raman Spectroscopy (SORS)465 may potentially permit live insight into tissue biochemistry of deeper brain structures. Alternatively, a future establishment of intraoperative Raman imaging (in particular it may even be performed in vivo466) will potentially allow fast detection of both histomorphological features and tumor genetics; therefore producing an integrated diagnosis467 at an early stage of the diagnostic workflow468. Extensive clinical studies aiming for approval of RS in neuroscience by regulatory authorities are still missing, even though a clinical need and a patient benefit has been demonstrated by a broad range of groups and laboratories. To translate promising results into clinical practice, several challenges should be considered. When vibrational spectroscopy is tested as a diagnostic method in a multicenter approach, experimental workflows of spectroscopic examination need to be standardized and facilitated; consensus within the spectroscopic community on a collaborative experimental setup and procedure prevents potential invariances due to different sample preparation protocols and hidden artifacts91. To maximize spectral output and enhance spectral intensity in a clinical setting, handheld probes / spectrometers with optimized design and in vivo parameters as well as a preferably low signal-to-noise and high signal-to-background ratio are currently under investigation by a growing number of companies stepping up their efforts in the interface of research and clinical implementation.117,469

Since the use of RS on FFPE tissue allows direct comparison with the diagnostic gold standard of histology, RS is expected to expand its applications in neuropathological diagnostics in the future. Upcoming studies will not only challenge the current use of RS on unstained FFPE tissue (is reliable diagnosis also achievable on H&E stained samples?) but also discuss a potential use of various Raman substrates in a cost-oriented manner470. To reduce the cost factor (id est expensive substrates such as CaF2 or low-E slides) future employment of RS on glass slides seems worthwhile; therefore, occurring autofluorescence during measurement needs to be addressed. Within that approach, the use of a certain excitation wavelength or the detection of only a small spectral wavenumber range have been proposed60,471. In this sense, Ibrahim et al.472 aimed to use glass as Raman substrates by employing a digital processing method.

In the field of neurodegenerative diseases, a major and highly anticipated impact of RS could be the early and non-surgical diagnosis of disorders in a reproducible manner. Despite promising results, this application area is only beginning to develop. To maximize diagnostic reliability, a deeper understanding of Raman features and their corresponding biochemical origin in biofluids is key. Within the huge amount of obtained data, it remains necessary to address patient dependent spectral variation as well as variations related to a concrete experimental set up. Close cooperation between different research groups and ensured data share470 potentially accelerate the development towards clinical implementation.

An exemplary success story of clinical translation was reported in the field of dermatology, where RS had already been established as a diagnostic method for early detection of skin cancer; a handheld device was commercially produced in Canada463,473,474. To speed up translation from research labs to commercialization and clinical use, several networks have been founded, e.g., International Society for Clinical Spectroscopy (ClirSpec, clirspec.org) and Raman4Clinics (raman4clinics.eu), all aiming for exchange of expertise and creation of research collaboration117.

To conclude, it highly likely that RS will continue to evolve as a method in the intersection of applied biophysics and medicine – and potentially make its way deeper into the field of life science, such as detection of plastic in zebrafish brain homogenates as a result of exposure to nanoplastic475 and even more clinical applications. Where the journey will finally lead remains to be seen in the next years.


Figure 1 and Figure 2 were created with BioRender.com. MM thanks the FNR for funding support (FNR PEARL P16/BM/11192868 grant).


Luxembourg National Research Fond, FNR (FNR PEARL P16/BM/11192868 grant to M.M.)

Conflicts of Interest

We have no conflicts of interest to disclose.


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