Some of the most representative Raman peaks for nucleic acids, proteins and lipids in cells.
Raman mapping is a noninvasive, label‐free technique with high chemical specificity and high potential to become a leading method in biological and biomedical applications. As opposed to Raman spectroscopy, which provides discrete chemical information at distinct positions within the sample, Raman mapping provides chemical information coupled with spatial information. The laser spot scans the investigated sample area with a preset step size and acquires Raman spectra pixel by pixel. The Raman spectra are then discriminated from each other by chemometric analysis, and the end result is a false color map, an image of the sample that contains highly precise structural and chemical information. Raman imaging has been successfully used for label‐free investigations at cellular and subcellular level. Cell compartments, cell responses to drugs and different stages of the cell cycle from the stem cell to the completely differentiated cell were successfully distinguished. This technique is also able to differentiate between healthy and cancer cells, indicating great potential for replacing conventional cancer detection tools with Raman detection in the future.
- Raman spectroscopy
- Raman mapping
- Raman imaging
- medical diagnosis
1.1. Basic concept and working principle
Various techniques are being currently used for imaging of cells and tissues. Individually, each technique is able to address some aspects of the system under study. For example, optical microscopy is very often used for cell and tissue analysis; it is a cost‐effective method which gives morphological information, but is unable to provide molecular and structural information. Electron microscopy and atomic force microscopy are high spatial resolution techniques, able to image subcellular compartments; however, they lack chemical specificity. In most cases, fluorescence microscopy, for example, confocal scanning fluorescence microscopy, is used for cellular visualization. Fluorescence microscopy requires fluorescent labels specifically bound to the substrate under study. Subcellular structures can be visualized, but since each fluorescent label is excited by a different wavelength, the number of structures that can be visualized is limited. The need to introduce fluorophores and their limited stability and photo bleaching are drawbacks of using confocal microscopy. Nonetheless, the technique is largely used for cell imaging and imaging of cellular uptake of micro and nanoparticles. There is great need for techniques that provide chemically specific information coupled to spatial information for the visualization of, for example, cellular uptake and localization of biologically active molecules, cellular transport pathways, molecular changes in cancer vs healthy tissues, etc.
Raman mapping (Raman imaging, Raman scanning or Raman micro‐spectroscopy) has recently become an emerging imaging technique in biological and biomedical research and applications. The Raman effect is based on inelastic scattering of photons when electromagnetic waves interact with atoms or molecules. The small fraction of incident photons scattered inelastically have different frequencies compared to the incident photons. The phenomenon is called Raman scattering, and the difference in frequency between the incident photons and scattered photons is the so‐called Raman shift (cm−1). The Raman shift is related to the vibrational levels of each specific molecule, being used as a fingerprint for molecular identification .
In contrast to Raman spectroscopy, which provides discrete chemical information at distinct positions within the sample, Raman mapping provides chemical information coupled with spatial information . Raman mapping is a noninvasive, label‐free technique, with high chemical specificity. In Raman mapping, the laser spot scans the investigated sample area with a preset step size and acquires Raman spectra at every set point. The Raman spectra are then discriminated from each other by chemometric analysis, and the end result is an image of the sample that contains highly precise structural and chemical information. Excitation wavelengths in the visible and near‐infrared range give high spatial resolution (<1 μm), making Raman spectroscopy combined with microscopy an ideal tool for biological samples imaging, and especially for cell and tissue imaging. In this latter case, Raman mapping has important advantages over conventional biological assays: it is a rapid, noninvasive, label‐free technique, which does not damage the cells if using suitable laser wavelengths and power.
1.2. Instrumentation and data analysis
The most important parameters to ensure the success of a Raman imaging measurement on biological samples are the wavelength and power of the laser, the resolution of the images, and the sample preparation and fixation. The intensity of the scattered radiation is proportional to the wavelength at the power of −4 (~
In some cases, for samples that cannot be detected by regular Raman scattering, signal enhancement can be induced. In some situations, it is possible to obtain resonance Raman effects. Such effects take place when the laser excitation wavelength overlaps with the absorption band of the molecules due to electronic transitions, and this can lead to increase the Raman intensity by a factor of 103–105 . The phenomenon is called resonant Raman scattering. Consequently, Raman imaging of a resonant molecule can be significantly improved by choosing an excitation laser wavelength in the absorption band region of the molecule. For example, cytochrome C absorbs light at around 520 nm and shows a strong resonance Raman effect when analyzed using a 532 nm laser. This property can be used to image its intracellular distribution. Other examples of molecules that can benefit from strong Raman resonance effects are the carotenoids, chlorophylls, vitamin B12 and heme proteins [3, 10].
Another way to obtain signal enhancement in Raman spectroscopy is to use surface‐enhanced Raman spectroscopy (SERS) or coherent anti‐Stokes Raman scattering (CARS). In SERS, it is possible to reach high enhancements of the Raman intensity (by a factor of 107 or more) when the molecule of interest is adsorbed onto or in the very close vicinity of plasmonic metallic nanostructures such as silver and gold . This effect significantly lowers the detection limit of molecules. CARS is a nonlinear optical effect in which two lasers, a pump laser and a Stoke laser, are overlapped and strongly focused onto the sample to generate the CARS signal. When the difference in frequency between the pump and the Stoke lasers is tuned to the exact value of a vibrational frequency within the sample, strong enhancements of the CARS signal can occur .
The instrumental resolution is very important for cell and tissue mapping, in order to be able to image cellular and subcellular structures. The lateral resolution is limited by the wavelength of the laser and the numerical aperture of the objective used for the experiment, while the axial resolution is given by the instrument aperture (slit or pinhole) and the refractive index of the immersion medium. Currently available Raman spectrometers can go down to 200 nm for the lateral resolution and 500 nm for the axial resolution .
Cells and tissues could be fixed on specific substrates for usage over longer periods of time. The most important requirements for a substrate suitable for Raman imaging are as follows: (a) transparency in the visible and near‐infrared region of the light spectrum; (b) low background signal to avoid overlapping with the Raman signals from the sample; and (c) suitability for cell culture growth or tissue fixation. Calcium and magnesium fluoride (CaF2 and MgF2) and quartz are the preferred substrates for Raman imaging. Glass and plastic substrates are not recommended because of high background signals [2, 12]. Zinc selenide (ZnSe) has the disadvantage of weak cell adherence . A variety of cell fixation methods has been so far reported can be used: paraformaldehyde, methanol, methanol:acetone, formalin, air‐drying and cytocentrifugation [2, 6, 13]. For live cell imaging, special instrument setups, in which cells are confined in a sterile chamber and kept at 37°C and a 5% CO2 atmosphere to ensure viability [14, 15], have been reported.
After taking the pixel‐by‐pixel Raman spectra, the raw dataset needs to be processed in order to identify the key molecules in the sample and based on their spectral fingerprint, to generate the false color Raman images. Since no label is used, the pixel‐to‐pixel spectral variations are small and multivariate methods of analysis need to be employed to get the Raman images from the dataset. Several approaches are currently used: principal component analysis (PCA), self‐modeling curve resolution (SMCR), K‐means cluster analysis (KMCA), hierarchical cluster analysis (HCA), divisive correlation cluster analysis (DCCA), vertex component analysis (VCA), fuzzy C‐means cluster analysis (FCCA) and linear discriminant analysis (LDA) [6, 16–20].
Here we aim to highlight the recent advances of Raman mapping and provide an overview on its emerging applications, which range from single cell and tissue imaging to medical diagnosis, including cancer detection. Some applications that will be discussed include:
Identification of cell cycle phase 
Cellular responses to drugs 
Medical diagnosis, including cancer detection based on the capacity of Raman mapping to detect molecular changes in cells, tissues or biofluids, that are either the cause or the effect of diseases [7, 33, 34]
Cancer detection based on the ability to discriminate between normal and cancer cells 
2. Raman mapping for cell imaging
Several Raman peaks are used as fingerprints for the intracellular identification of nucleic acids, proteins and lipid‐rich structures. In terms of chemical information, the richest part in a Raman spectrum is the region below 1800 cm−1, also called the fingerprint region. Some representative Raman bands from the fingerprint region, characteristic for nucleic acids, proteins and lipids, are given in Table 1 [6, 37, 38]. The region between 1800 and 2800 cm−1 is the so‐called silent region, since no Raman cellular vibrations arising from functional groups appear in this region, excepting triple‐bond vibrations. Finally, the high‐frequency region above 2800 cm−1 is dominated by C–H stretching vibrations (CH, CH2, CH3).
Stem cells are attractive to be studied in the biomedical field because they have the ability to differentiate into any cell phenotype, and they can proliferate indefinitely . Differentiation of stem cells could be used in stem cell therapy and tissue engineering. The differentiation process of stem cells needs identification of specific markers; currently, this involves the use of immunolabeling or fluorescence. Both methods have the disadvantage of being invasive. In contrast, using Raman‐specific intrinsic fingerprints would be advantageous, being noninvasive, nonlabeling approach, and able to provide accurate and highly specific information. The method would be suitable also for cells that lack specific markers for separation by conventional means  such as cardiomyocytes.
The intracellular distribution and concentration of nucleic acids were used by Ghita et al.  to distinguish between undifferentiated and differentiated stem cells, more precisely from undifferentiated neural stem cells and the glial cells derived from them. They manage to differentiate with 89.4% sensitivity and 96.4% specificity. The spectral fingerprint of the nucleic acid backbone was used to detect DNA‐ and RNA‐rich regions. The Raman spectrum of β‐DNA shows a strong band at 788 cm−1 and a shoulder at 835 cm−1, characteristic to the symmetric O‐P‐O stretching vibrations of the phosphate groups and to the asymmetric O‐P‐O vibrations, respectively. In case of RNA, the symmetric O‐P‐O vibrations appear shifted to 813 cm−1, while the shoulder corresponding to the asymmetric O‐P‐O stretching disappears. Based on the spectral differences between DNA and RNA, it was found that undifferentiated neural cells have higher concentration of nucleic acids compared to glial cells. The Raman band at 813 cm−1 was the indicator of the differentiation status, and this allowed distinguishing between the two cell types. Undifferentiated neural stem cells have high concentration of RNA in the cytoplasm (as high as 4 mg/ml), while below the instrument detection limit (<1 mg/ml) in the glial cells. Based on Raman mapping, it was possible to image RNA‐ and DNA‐rich structures in the stem cells. The RNA‐rich structures, representing the stem cells cytoplasm, were imaged using the 813 cm−1 Raman band, and the DNA‐rich part related to the stem cells nucleus was imaged using the 788 cm−1 Raman band. Similar results were reported by the same research group regarding the differentiation status of embryonic stem cells . The differentiated cells had 75% less RNA, as monitored by the decrease in intensity of the 813 cm−1 peak. Basically, most prominent Raman peaks of embryonic stem cells are the ones of proteins (amide I band at 1660 cm−1, amide III at 1200–1300 cm−1, 1005 cm−1 vibration of phenylalanine and C=C stretching at 938 cm−1) and nucleic acids. Dental follicle mesenchymal stem cells were also imaged using Raman mapping; several components, and especially a high concentration of cytoplasmic RNA, were found to be a good indicator to the undifferentiated status of the cells .
|Raman peak position (cm−1)||Assignments|
|684||Guanine ring breathing|
|729||Adenine ring breathing|
|751||Thymine ring breathing|
|782||Uracil ring breathing|
|785||Cytosine ring breathing|
|788||Symmetric O‐P‐O stretching in β‐strands DNA|
|835||Asymmetric O‐P‐O stretching in β‐strands DNA|
|813||Symmetric O‐P‐O stretching in RNA and α‐strands DNA|
|1095–1098||O‐P‐O vibrations (DNA, RNA)|
|938||Backbone C=C stretching|
|1005||Phenylalanine symmetric ring breathing|
|1033||Phenylalanine in plane C–H vibrations|
|1200–1300||Amide III band (CH, NH deformations)|
|1655–1662||Amide I (C=O stretching)|
Raman mapping was also used inside a bioreactor culture system, where human embryonic stem cells were grown and differentiated into cardiomyocytes . The purpose was to monitor the cardiac differentiation of the embryonic bodies. The Raman maps were compared with immunofluorescence imaging. A positive correlation was found between Raman bands at 1340, 1083, 937, 858, 577 and 482 cm−1 and the expression of the α‐actinin protein in the differentiated cardiomyocytes. Konorov et al.  obtained information on the cell cycle phase of human embryonic stem cells. The 783 cm−1 DNA band from a large number of cell nuclei was used as indicator of the cell cycle phase. The results were corrected for the RNA contribution at 811 cm−1. As such, the authors were able to get information on the state of division of the embryonic stem cells by quantifying the DNA and RNA peaks from the Raman spectra and obtained Raman intensities similar to the fluorescence intensities of flow cytometry.
In another study, Pascut et al.  obtained 97% specificity and 96% sensitivity in differentiating the cardiomyocytes derived from human embryonic stem cells. The main spectral features that allowed the discrimination of cardiomyocytes were attributed to glycogen and myofibrils. The results were correlated with the immunofluorescence staining, and a good correlation was observed. The same authors investigated the potential for developing Raman‐activated cell sorting of individual cells . Hashimoto et al.  got information on osteoblast differentiation and mineralization mechanisms by monitoring fluctuations in the cytochrome C concentration. The above preliminary studies suggest that Raman spectroscopy has a great potential to become a leading method for stem cells investigation.
Raman mapping can be used as a tool to obtain molecular fingerprint information from different subcellular compartments. Based on their distinct chemical features, nucleus and cytoplasm and also other cellular organelles can be imaged. For example, in their study on follicle mesenchymal stem cells, Leopold et al.  were able to image the cell nucleus based on the 785 cm−1 band characteristic for the DNA O‐P‐O vibrations. Lipid characteristic peaks, such as the 1446 cm−1 peak characteristic to CH2 vibrations, made it possible to highlight the smooth endoplasmic reticulum in the Raman images, which is known to be the source of intracellular lipid synthesis. Based on characteristic Raman vibrational peaks of lipids, proteins and nucleic acids, Krafft et al.  were able to reconstruct the main cellular components: the nucleus, the contour of the cell and the organelles. They focused mostly on the 2800–3000 cm−1 region, where CH2 and CH3 vibrations from proteins, lipids and nucleic acids are present. In both studies, the cellular organelle identification was carried out based on the score plots obtained from the principal component analysis. From the score plots, false color Raman maps were generated, highlighting the subcellular compartments. Raman images of subcellular organelles were also reported by Krauß et al. . The authors have also correlated well their results with fluorescence microscopy.
Cytochrome C, protein and lipid‐rich structures were evidenced in different Raman images on HeLa cells  by irradiating the cells with 488, 514, 532 and 633 nm lasers. The HeLa cells gave Raman spectra with peaks at 1000 cm−1 (breathing of phenylalanine), 1451, 2850, 2885, 2935 cm−1 (CH2 deformation, CH2 and CH3 stretching) and 1660 cm−1 (amide I bands mode of peptide bonds). When irradiated with the 532 nm laser, resonant peaks at 753, 1127, 1314 and 1583, characteristic to cytochrome C, were obtained. Raman images highlighting the Cytochrome C, protein β‐sheets and lipids were created using the 753 cm−1 peak (pyrrole ring of cytochrome C), the 1686 cm−1 peak (amide I vibration of peptide bond in protein) and the 2852 cm−1 peak (CH2 stretching vibration of hydrocarbon chain of lipids). Since cytochrome C is essential for the electron transfer in mitochondria, the Raman images of cytochrome C should also point out the distribution of mitochondria in the cell. In addition, Raman spectra from the nuclei showed no resonance, being similar for all excitation wavelengths. Matthäus et al.  reported on obtaining Raman maps pointing out the location of different cellular structures in HeLa cells, with emphasis on mitochondria. They performed hierarchical cluster analysis and found localization of mitochondria in the perinuclear region, which was supported by correlation with fluorescence maps.
In some cases, Raman imaging requires the use of tags. This happens when molecules cannot be detected by regular Raman scattering, either because they are in very low amounts, or because their Raman signal overlaps with other compounds and cannot be distinguished clearly. There are two approaches for using tags for Raman imaging: (a) using surface‐enhanced Raman scattering, which implies binding of tags onto the plasmonic nanoparticles surface that can be further used for intracellular identification of analytes [47–50], and (b) taking advantage of the silent region in the Raman spectra of cells between 1800 and 2800 cm−1. In this region, most of the biologically active molecules show no Raman vibrations, so functional molecules with Raman fingerprint in this region could be useful as tags for detection of molecules, which do not give clearly distinguishable Raman peaks in the intracellular medium. This also has the advantage of avoiding the overlap with any endogeneous molecules. Tags suitable for the silent region detection are alkynes, azides, deuterium and nitrile. Palonpon et al.  utilized alkyne‐tagged EdU (5‐ethynil‐2‐deoxyuridine), for the detection of DNA accumulation and synthesis in cells. EdU is rapidly incorporated in the DNA during the replication process, accumulates in the nucleus and is thus suitable for acquiring information about DNA synthesis in cells.
Silver nanoparticles (AgNPs) prepared by reduction with hydroxylamine according to the Leopold and Lendl method  were used for mapping sub‐membrane hemoglobin in erythrocytes (red blood cells) . Erythrocytes contain cytosolic and sub‐membrane hemoglobin. Although hemoglobin exhibits strong Raman scattering, Raman spectra of erythrocytes generally have mostly contribution from cytosolic hemoglobin, since the sub‐membrane hemoglobin is negligible in amount. To trace this sub‐membrane hemoglobin, the authors used SERS‐active AgNPs that were internalized in the cells and accumulated in the cell membrane. SERS images showed the erythrocytes that come in contact with the AgNPs. Lee et al.  used SERS to detect different human breast cancer cell lines phenotypes and to quantify the proteins on the cell surface. For the purpose of SERS enhancement, silica‐encapsulated hollow gold nanospheres conjugated with specific antibodies were used. The expression of epidermal growth factor (EGF), ErbB2 and insulin‐like growth factor‐1 (IGF‐1) receptors was determined in the MDA‐MB‐468, KPL4 and SK‐BR‐3 cell lines by SERS mapping. Different distributions of growth factors were clearly identified and distinguished from their corresponding SERS mapping images. Taking advantage on the characteristic wave number of the carbonyl group vibration that lies within the silent region of the Raman spectra, Kong et al.  developed osmium carbonyl clusters for cancer cell imaging. The clusters were conjugated with PEG‐coated AuNPs and further functionalized with antibody for epidermal growth factor receptors (anti‐EGFR). AuNPs were used for carbon monoxide Raman signal enhancement, while functionalization with antibody was needed since the EGFR is highly expressed in many cancer cell lines. Both EGFR‐positive and EGFR‐negative cancer cells were used. The nanoparticle conjugates were imaged after cellular uptake based on the CO absorption signal at 2030 cm−1, and the results showed the specificity and efficient targeting of CO‐nanoparticle conjugates to EGFR‐positive cells.
For toxicology studies, it is important to be able to distinguish between healthy and apoptotic cells and also to gain information on the molecular changes associated with apoptosis. Zoladek et al.  used Raman imaging for understanding changes associated with apoptosis in the MDA‐MB‐231 human breast cancer cells. Cells were exposed to the apoptotic drug etoposide, and Raman spectra were recorded 2, 4 and 6 h after exposure. An 1.5‐fold increase in the DNA content was observed after 6 h, and the change was assigned to DNA condensation. The most drastic change was in the lipid profile; a high concentration in membrane phospholipids and unsaturated non‐membrane lipids was observed in apoptotic cells. The Raman images of the lipidic areas of the cells were generated based on the 1005 and 1659 cm−1 peaks ratio. The 1005 cm−1 peak with contribution from phenylalanine is not affected by etoposide exposure, while the 1659 cm−1 C=C stretching vibrations from lipids show strong increase upon etoposide exposure, indicating an increased degree of unsaturation of lipids for the apoptotic cells. Okada et al.  used resonant Raman scattering for imaging the intracellular distribution of cytochrome C and observing dynamic changes of its 750 cm−1 band associated with cell apoptosis.
The cellular uptake of nanoparticles and drugs and the cellular responses to drugs are important aspects to be investigated for molecular biomedical applications. Cellular uptake and localization of polyethylene glycol‐coated gold nanoparticles in human prostate cancer cells (LNCaP Pro 5) were visualized based on their photoluminescence peak (180–1800 cm−1). In the Raman images of LNCaP Pro 5 cells with AuNPs internalized, cell nucleus and nucleoli are visible, as well as spots generated from the photoluminescence peak of AuNPs. The nanoparticles are located at different positions inside the cells, depending on the time elapsed from exposure. Two hours after exposure, the AuNPs are located in the cell membrane, 12 h after they are located in the cytoplasm, and after 24 h, AuNPs are imaged in the perinuclear region .
3. Raman mapping for tissue imaging and medical diagnosis
There is clear indication that Raman spectroscopy could provide insights into drug targeting mechanisms and could be used for detection of metabolic interactions of drugs with cancer cells. In their attempt to detect physiologically relevant cellular responses to drugs, El‐Mashtoly et al.  used Raman imaging to quantify the effect of the epidermal growth factor inhibitor panitumumab on colon cancer cells expressing Kirsten‐ras mutations (oncogenic and wild‐type). It is known that oncogenic K‐ras mutations block the response to anti‐epidermal growth factor therapy such as panitumumab, while cells expressing wild‐type mutations respond to the treatment; all these facts were nicely confirmed by the Raman mapping results. The authors used hierarchical cluster analysis on the 700–1800 cm−1 and 2800–3050 cm−1 regions for the identification of subcellular components such as cellular membrane, cytoplasm, nucleus and lipid droplets. They found that the oncogenic mutated K‐ras cells showed no response to the drug, while the wild‐type mutated cells have strong cellular responses to panitumumab treatment, as demonstrated by Raman intensity changes and wave number shifts. The panitumumab‐induced changes are strongest on the lipid droplets, suggesting that lipid droplets might play a crucial role in anticancer therapy. The results were confirmed by fluorescence spectroscopy. In another work, El‐Mashtoly et al.  were able to image the spatial distribution of the erlotinib, another inhibitor of the epidermal growth factor receptor, in colon adenocarcinoma cells upon 12 h of incubating the cells with 100 μM erlotinib solution. Normally, erlotinib cannot be detected by Raman spectroscopy at 100 μM concentrations, which basically implies that its intracellular level was higher due to most likely concentration of the drug in the cell. The authors used the C=C alkyne vibration from the silent region of the Raman spectrum (2085–2140 cm−1) to image the erlotinib distribution and found that the drug was mostly concentrated at the cell borders.
Cells, tissues and bio‐fluids can be imaged by Raman micro‐spectroscopy. Based on the hypothesis that molecular changes associated with different diseases can be quantified by Raman spectroscopy, the method has been used in medical research and diagnosis during the last years. On one hand
The major drawback of Raman technique that limits its application in the medical field is the low efficiency of the inelastic scattering process. Different strategies have been developed to overcome this difficulty, based on, for example: (a) using nonlinear imaging modes such as CARS; (b) acquiring selective sampling of the analyzed probe (e.g., tissue auto‐fluorescence can be used to determine the characteristics of the tissue sample and to further use the information to prioritize the sampling points for Raman spectroscopy); (c) multimodal integration of Raman with other techniques such as auto‐fluorescence; (d) use of fiber‐optic probes for hand held instruments; and (e) use of plasmonic metallic nanoparticles suitable for surface‐enhanced Raman scattering.
Selective sampling for intraoperative diagnosis during the breast cancer conserving surgery leads to a diagnosis of mammary ductal carcinoma with 95.6% sensitivity and 96.2% specificity . As in the study above, discrimination between healthy and tumor areas was based on increased concentrations of nucleic acids (bands at 788, 1098 cm−1) and decreased levels of collagen and fats (851 and 950 cm−1 bands) in the tumor regions. Tissues from 60 patients were deposited on MgF2 plates; 20 μm tissue sections were sampled and analyzed by Raman micro‐spectroscopy, and adjacent sections of 70 μm were stained with H&E. To reduce acquisition times needed for raster scanning, selective sampling was achieved based on integrated auto‐fluorescence imaging and Raman. This procedure is also known as multimodal spectral histopathology. By comparing the Raman images obtained from the k‐means cluster analysis with the ones obtained from H&E staining, the tumor
Cancer and pre‐cancer cells, erythrocytes and lymphocytes were successfully assigned to colon cancer tissue sections by combining Raman imaging with histopathology (H&E staining) and with immunohistochemistry . Hierarchical cluster analysis was used in the spectral region 700–1800 cm−1 and 2600–3100 cm−1. The tumor protein p53 is normally highly expressed in cancer and pre‐cancer cells because it is a tumor suppresser. The possibility of obtaining Raman imaging of tumor and pre‐tumor cells by highlighting p53 active areas was confirmed. By comparing the obtained false color Raman maps with the images given by the anti‐p53 immunohistochemical stained image, it was found that the sample auto‐fluorescence matches the fluorescence from the anti‐p53 stained tissue, proving that the Raman imaging can be used for assigning the p53 active areas of the tissues. The p53 active areas represent more specifically the cancer cell nuclei.
Using SERS‐active nanoparticles for intraoperative detection of tumor margins is another promising direction of research in Raman imaging. With this purpose in mind, Wang et al
4. Raman mapping in plant and algae research
There is a growing interest in getting a more comprehensive understanding of the chemical composition of various plant tissues. Investigations on structural aspects of plant cell wall components, on the chemistry of plant metabolites and relevant plant molecules, are feasible using Raman mapping. NIR‐FT Raman is suitable for imaging of large plant structures such as leaves, seeds and fruits, while the higher resolution visible lasers allow investigation of smaller plant structures.
For example, using the 633 nm laser, it was possible to image the distribution of cell wall components such as cellulose and lignin in a 55‐year‐old black spruce wood (
Carotenoids are another promising class of compounds that can be analyzed and imaged through Raman mapping. They are organic pigments, conjugated double bond chains found in plants and other photosynthetic organisms, including bacteria and fungi. Carotenoids have important physiological roles, making them important molecules in plant biology, food science and pharmacology. In plants and algae, carotenoids protect from photodamage and absorb energy to be used in photosynthesis, whereas in human body they are potent antioxidants, and some of them are vitamin A precursors. The human body is unable to synthesize carotenoids, so they must be introduced through the diet, from carotenoid‐rich foods (e.g., carrots, tomato, maize, kiwi, cucumber, spinach, broccoli, etc). Information about carotenoid distribution in different plants and plant tissues is limited. Brackmann et al
Roots of different carrot cultivars were screened for their individual carotenoid distribution. The β‐carotene signal at 1520 cm−1 was used for integration. The level of β‐carotene was heterogeneous across root sections of orange, yellow, red and purple carrots. In the secondary phloem, the level of β‐carotene increased gradually from periderm toward the core, but declined fast in cells close to the vascular cambium. Lutein and α‐carotene were deposited in younger cells, while lycopene in red carrots accumulated throughout the whole secondary phloem at the same level . Raman mapping was also applied for studies of
Plant polyacetylenes are another class of compounds that can be identified based on their C=C stretching vibration in the 2100–2300 cm−1 range. Using the Raman peaks at 2258 and 2252 cm−1 characteristic to the most common polyacetylenes falcarinol and falcarindiol, Baranska et al
Algae species are important candidates for industrial lipid and biofuel production. Sharma et al
Apart from mutations, the growth media can also induce generation of different metabolites.
Some algae are able to produce large amounts of carotenoids when irradiated with light under specific conditions (e.g.,
Raman mapping is a powerful technique for label‐free, noninvasive investigations of tissues, cells and microorganisms. The resulted Raman maps contain not only spatial information but also valuable structural and chemical information on the analyzed samples. Raman imaging has been successfully used for investigations at cellular and subcellular level, including identification of nucleus, nucleoli, mitochondria and lipid‐rich structures. Cell responses to drugs and different stages of the cell cycle from the stem cell to the completely differentiated cell were as well distinguished. In addition, Raman mapping has a great potential for becoming a leading method in a wide range of biomedical applications, owing to its high chemical specificity, good resolution and to the fact that it is a noninvasive to tissues and cells. It is possible to achieve accurate detection of healthy and cancer tissues. At the moment, for the purpose of medical diagnosis, the results of Raman imaging need most often to be compared with currently used diagnosis tools (PCR, histopathology and immunohistopathology). But since it has already been proved that Raman images are sensitive indicators for cancer detection, there is strong indication of the possibility to replace the conventional tools with Raman detection in the future.
We thank the Romanian National Authority for Scientific Research, CNCS—UEFISCDI, for funding through project number PN
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