Ex vivo and in vivo diagnosis of C6 glioblastoma development by Raman spectroscopy coupled to a microprobe

The potential of Raman spectroscopy for ex vivo and in vivo classification of normal and glioblastoma brain tumor development was investigated. High-quality spectra of normal and tumor tissues were obtained using a portable Raman spectrometer coupled to a microprobe with a signal integration time of 5 s. Ex vivo results demonstrated that by using the biochemical information contained in the spectra, we were able to distinguish between normal brain features (white and gray matter), invasion, and tumor tissues with a classification accuracy of 100%. Differences between these features resulted from variations in their lipid signal contributions, which probably reflect differences in the level of myelinization. This finding supports the ability of in vivo Raman spectroscopy to delineate tumor margins during surgery. After implanting C6 cells in rat brain, we monitored, in vivo, the development of glioblastoma tumor from days 0 to 20 post-implantation (PI). The classification exhibited a clear separation of the data into two clusters: one cluster was associated with normal brain tissues (cortex), and the second was related to data measured from tumor evolution. The second cluster could be divided into two subclusters, one associated with tumor tissue from 4 to 13 days PI and the second related to tumor tissue from 15 to 20 days PI. Histological analysis reveals that the differences between these two subclusters are: the presence of a massive infiltration zone in the brain tissue from 4 to 13 days PI, and; a maturation of the tumor characterized by the appearance of edematous and necrotic zones, as well as a diminution in the proliferative and invasive area, from 15 days. This work demonstrates the potential of Raman spectroscopy to provide diagnostic information for the early detection of tumors in vivo.     

Detection of collagens in brain tumors based on FTIR imaging and chemometrics

Fourier transform infrared (FTIR) imaging has been used as a molecular histopathology tool on brain tissue sections after intracranial implantation and development of glioma tumors. Healthy brain tissue (contralateral lobe) as well as solid and diffuse tumor tissues were compared for their collagen contents. IR spectra were extracted from IR images for determining the secondary structure of protein contents and compared to pure product spectra of collagens (types I, III, IV, V, and VI). Multivariate statistical analyses of variance and correspondence factorial analysis were performed to differentiate healthy and tumor brain tissues as well as their classification according to their secondary structure profiles. Secondary structure profiles revealed that no collagen was present in healthy tissues; they are also significantly different from solid and diffuse tumors (p < 0.05). Solid and diffuse tumors could be discriminated with respect to the secondary structure profile of fibrillar and non-fibrillar collagens, respectively. We can thus propose to develop FTIR imaging for histopathology examination of tumors on the basis of collagen contents.     

Differentiation between normal and tumor vasculature of animal and human glioma by FTIR imaging

Malignant gliomas are very aggressive tumors, highly angiogenic and invading heterogeneously the surrounding brain parenchyma, making their resection very difficult. To overcome the limits of current diagnostic imaging techniques used for gliomas, we proposed using FTIR imaging, with a spatial resolution from 6 to 10 μm, to provide molecular information for their histological examination, based on discrimination between normal and tumor vasculature. Differentiation between normal and tumor blood vessel spectra by hierarchical cluster analysis was performed on tissue sections obtained from xenografted brain tumors of Rag-gamma mice 28 days after intracranial implantation of glioma cells, as well as for human brain tumors obtained in clinics. Classical pathological examination and immunohistochemistry were performed in parallel to the FTIR spectral imaging of brain tissues. First on the animal model, classification of FTIR spectra of blood vessels could be performed using spectral intervals based on fatty acyl (3050-2800 cm(-1)) and carbohydrate (1180-950 cm(-1)) absorptions, with the formation of two clusters corresponding to healthy and tumor parts of the tissue sections. Further data treatments on these two spectral intervals provided interpretable information about the molecular contents involved in the differentiation between normal and tumor blood vessels, the latter presenting a higher level of fatty acyl chain unsaturation and an unexpected loss of absorption from osidic residues. This classification method was further successfully tested on human glioma tissue sections. These findings demonstrate that FTIR imaging could highlight discriminant molecular markers to distinguish between normal and tumor vasculature, and help to delimitate areas of corresponding tissue.     

Methodology for fiber-optic Raman mapping and FTIR imaging of metastases in mouse brains

The objectives of this study were to optimize the preparation of pristine brain tissue to obtain reference information, to optimize the conditions for introducing a fiber-optic probe to acquire Raman maps, and to transfer previous results obtained from human brain tumors to an animal model. Brain metastases of malignant melanomas were induced by injecting tumor cells into the carotid artery of mice. The procedure mimicked hematogenous tumor spread in one brain hemisphere while the other hemisphere remained tumor free. Three series of sections were prepared consecutively from whole mouse brains: dried, thin sections for FTIR imaging, hematoxylin and eosin-stained thin sections for histopathological assessment, and pristine, 2-mm thick sections for Raman mapping. FTIR images were recorded using a spectrometer with a multi-channel detector. Raman maps were collected serially using a spectrometer coupled to a fiber-optic probe. The FTIR images and the Raman maps were segmented by cluster analysis. The color-coded cluster memberships coincided well with the morphology of mouse brains in stained tissue sections. More details in less time were resolved in FTIR images with a nominal resolution of 25 microm than in Raman maps collected with a laser focus 60 microm in diameter. The spectral contributions of melanin in tumor cells were resonance enhanced in Raman spectra on excitation at 785 nm which enabled their sensitive detection in Raman maps. Possible reasons why metastatic cells of malignant melanomas were not identified in FTIR images are discussed.     

Investigation of skin and skin lesions by NIR-FT-Raman spectroscopy

There is a vast demand for in vivo methods for the detection of skin cancer, one of the most dangerous skin lesions. Use of near infrared Fourier transform (NIR-FT)-Raman spectroscopy virtually eliminates the fluorescence of the normal cell constituents and provides a signal to noise ratio, r _SN, large enough to successfully evaluate the spectra using chemometric methods. A novel fiber optic probe for NIR-FT-Raman spectroscopy was used, which allows sterilization and the prevention of hazards due to laser radiation and makes in vivo measurements possible. The Raman spectra of normal skin are dominated by the connective tissue, mainly collagen type I. The Raman spectra of skin with inflammatory diseases show an increased lipid and water content. Kaposi sarcomas show typical features of tumors mainly in the amide III and the protein backbone range. A clear separation of Raman spectra of normal skin from those of benign and malignant neoplasms can be achieved by cluster analysis. However, the unequivocal diagnosis of skin cancer needs investigation of a larger number of more defined skin samples, taking into consideration the concurrent appearance of different skin symptoms like coloring and inflammation. Received: 25 July 1997 / Revised: 16 September 1997 / Accepted: 20 September 1997     

Near infrared Raman spectra of human brain lipids

Human brain tissue, in particular white matter, contains high lipid content. These brain lipids can be divided into three principal classes: neutral lipids including the steroid cholesterol, phospholipids and sphingolipids. Major lipids in normal human brain tissue are phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, phosphatidic acid, sphingomyelin, galactocerebrosides, gangliosides, sulfatides and cholesterol. Minor lipids are cholesterolester and triacylglycerides. During transformation from normal brain tissue to tumors, composition and concentration of lipids change in a specific way. Therefore, analysis of lipids might be used as a diagnostic parameter to distinguish normal tissue from tumors and to determine the tumor type and tumor grade. Raman spectroscopy has been suggested as an analytical tool to detect these changes even under intra-operative conditions. We recorded Raman spectra of the 12 major and minor brain lipids with 785 nm excitation in order to identify their spectral fingerprints for qualitative and quantitative analyses.     

Raman mapping and FTIR imaging of lung tissue: congenital cystic adenomatoid malformation

Congenital cystic adenomatoid malformations (CCAMs) are benign masses of non-functional lung tissue developing from an overgrowth of the terminal bronchioles with subsequent suppressing of alveolar growth. For the first time CCAMs are studied by Raman mapping and Fourier transform infrared (FTIR) imaging. Both vibrational spectroscopic methods are able to analyze the biochemical composition of tissues and their pathological changes at the molecular level. Cryosections were prepared on calcium fluoride substrates from CCAMs and from normal lung tissue of two infant patients who underwent surgery. Raman maps were collected at a step size of 100 microm in order to assess the whole tissue section and at a smaller step size of 10 microm in order to resolve details in selected areas. FTIR images were collected in the macroscopic and microscopic modes. Data sets were segmented by cluster analysis and the mean spectra of each cluster were compared. At low lateral resolution a lower red blood cell content and higher lipid content were found in CCAMs than in normal lung tissue. At higher lateral resolution, accumulations of lipids and glycogen were identified in CCAMs. The lipid aggregates contain a high concentration of phosphatidylcholine. It is discussed how the combined application of Raman mapping and FTIR imaging might improve the differential diagnosis of lung malformations and how both modalities might be applied to other bioanalytical and biomedical problems in the future.     

FTIR and Raman microspectroscopy of normal,benign, and malignant formalin-fixed ovarian tissues

Ovarian cancer is the sixth most common cancer among women worldwide, and mortality rates from this cancer are higher than for other gynecological cancers. This is attributed to a lack of reliable screening methods and the inadequacy of treatment modalities for the advanced stages of the disease. FTIR and Raman spectroscopic studies of formalin-fixed normal, benign, and malignant ovarian tissues have been undertaken in order to investigate and attempt to understand the underlying biochemical changes associated with the disease, and to explore the feasibility of discriminating between these different tissue types. Raman spectra of normal tissues indicate the dominance of proteins and lower contents of DNA and lipids compared to malignant tissues. Among the pathological tissues studied, spectra from benign tissues seem to contain more proteins and less DNA and lipids compared to malignant tissue spectra. FTIR studies corroborate these findings. FTIR and Raman spectra of both normal and benign tissues showed more similarities than those of malignant tissues. Cluster analysis of first-derivative Raman spectra in the 700–1700 cm⁻¹ range gave two clear groups, one corresponding to malignant and the other to normal+benign tissues. At a lower heterogeneity level, the normal+benign cluster gave three nonoverlapping subclusters, one corresponding to normal and two for benign tissues. Cluster analysis of second-derivative FTIR spectra in the combined spectral regions of 1540–1680 and 1720–1780 cm⁻¹ resulted into two clear clusters corresponding to malignant and normal+benign tissues. The cluster corresponding to normal+benign tissues produced nonoverlapping subclusters for normal and benign tissues at a lower heterogeneity level. The findings of this study demonstrate the feasibility of Raman and FTIR microspectroscopic discrimination of formalin-fixed normal, benign, and malignant ovarian tissues.     

Characterization of lipid extracts from brain tissue and tumors using Raman spectroscopy and mass spectrometry

Brain tissue is characterized by high lipid content. The amount of lipids decreases, and its composition changes in the most frequent primary brain tumor, the glioma. Scope of the current paper was to extract quantitatively lipids from porcine and human brain tissue as well as from five human gliomas using a modified protocol according to Folch. The lipid extracts were studied by Raman spectroscopy with 785 nm excitation and by mass spectrometry with electron impact ionization. Porcine and human brain tissues have similar water and lipid content and show similar Raman and mass spectra. In contrast, gliomas are characterized by increased water content and decreased lipid content. Elevated phosphatidylcholine to cholesterol ratios in lipid extracts of gliomas were indicated by Raman bands of the choline group and cholesterol. Due to its higher sensitivity, mass spectrometry detected increased levels of cholesterol ester relative to cholesterol in lipid extracts of gliomas. For comparison, thin tissue sections were prepared from the glioma specimens before lipid extraction; infrared spectroscopic images were recorded and analyzed by a supervised classification model. This study demonstrates how to improve the analysis of brain tumors and to complement the diagnosis of brain pathologies using a multimodal approach.     

Raman spectroscopic imaging for in vivo detection of cerebral brain metastases

We report for the first time a proof-of-concept experiment employing Raman spectroscopy to detect intracerebral tumors in vivo by brain surface mapping. Raman spectroscopy is a non-destructive biophotonic method which probes molecular vibrations. It provides a specific fingerprint of the biochemical composition and structure of tissue without using any labels. Here, the Raman system was coupled to a fiber-optic probe. Metastatic brain tumors were induced by injection of murine melanoma cells into the carotid artery of mice, which led to subcortical and cortical tumor growth within 14 days. Before data acquisition, the cortex was exposed by creating a bony window covered by a calcium fluoride window. Spectral contributions were assigned to proteins, lipids, blood, water, bone, and melanin. Based on the spectral information, Raman images enabled the localization of cortical and subcortical tumor cell aggregates with accuracy of roughly 250 μm. This study demonstrates the prospects of Raman spectroscopy as an intravital tool to detect cerebral pathologies and opens the field for biophotonic imaging of the living brain. Future investigations aim to reduce the exposure time from minutes to seconds and improve the lateral resolution.     

The Effects of ex vivo Handling Procedures on the Near-Infrared Raman Spectra of Normal Mammalian Tissues

Recent studies in the literature have investigated the feasibility of tissue diagnostics based on Raman spectroscopy. The majority of these compare the ex vivo spectra of normal and diseased tissue. Due to the time lapse between tissue excision and spectroscopic examination, samples must be frozen or otherwise preserved to maintain their native biochemical states. In order to establish optimum procedures for ex vivo Raman spectroscopy of tissue, the effects of tissue drying, formalin fixing, snap freezing, tissue freezing in optimal cutting temperature (OCT) medium and extended post-thaw durations were studied to determine if any of these handling procedures introduced spectral artifacts. Experiments on representative tissues indicated that tissue heating due to the excitation light did not change the spectra significantly. With minor exceptions, OCT and formalin did not contaminate tissue spectra, so that samples stored for histological examination could also be studied with Raman spectroscopy. Tissue dehydration caused disruption of the protein vibrational modes, which caused spectral artifacts. It is concluded that ex vivo tissue samples should be frozen in OCT. Prior to spectral analysis, the tissue should then be acclimatized at room temperature in phosphate-buffered saline (PBS) and immersed in PBS during spectroscopic examination.     

OPTICAL PENETRATION IN HUMAN INTRACRANIAL TUMORS

Abstract— The optical penetration depth has been measured in human intracranial tumors. The penetration depth in highly malignant gliomas is typically twice as large as the penetration depth in the surrounding normal brain tissue. Typical values for different wavelengths are: 1 mm for blue/green, 2.5 mm for red and 6 mm for near infrared. The penetration depth in a metastasis (from lung) is about the same as in the surrounding tissue.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.     

Raman endoscopy for in vivo differentiation between benign and malignant ulcers in the stomach

The aim of this study was to evaluate the clinical utility of an image-guided Raman endoscopy technique for in vivo differential diagnosis of benign and malignant ulcerous lesions in the stomach. A rapid-acquisition image-guided Raman endoscopy system with 785 nm laser excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 s during clinical gastroscopic examinations. A total of 1102 in vivo Raman spectra were acquired from 71 gastric patients, in which 924 Raman spectra were from normal tissue, 111 Raman spectra were from benign ulcers whereas 67 Raman spectra were from ulcerated adenocarcinoma. There were distinctive spectral differences in Raman spectra among normal mucosa, benign ulcers and malignant ulcers, particularly in the spectral ranges of 800-900, 1000-1100, 1245-1335, 1440-1450 and 1500-1800 cm(-1), which primarily contain signals related to proteins, DNA, lipids and blood. The malignant ulcerous lesions showed Raman signals to be mainly associated with abnormal nuclear activity and decrease in lipids as compared to benign ulcers. Partial least squares-discriminant analysis (PLS-DA) was employed to generate multi-class diagnostic algorithms for classification of Raman spectra of different gastric tissue types. The PLS-DA algorithms together with leave-one tissue site-out, cross validation technique yielded diagnostic sensitivities of 90.8%, 84.7%, 82.1%, and specificities of 93.8%, 94.5%, 95.3%, respectively, for classification of normal mucosa, benign and malignant ulcerous lesions in the stomach. This work demonstrates that image-guided Raman endoscopy technique associated with PLS-DA diagnostic algorithms has for the first time promising clinical potential for rapid, in vivo diagnosis and detection of malignant ulcerous gastric lesions at the molecular level.     

In vivo fluorescence spectroscopy of nonmelanoma skin cancer

In vivo and ex vivo tissue autofluorescence (endogenous fluorescence) have been employed to investigate the presence of markers that could be used to detect tissue abnormalities and/or malignancies. We present a study of the autofluorescence of normal skin and tumor in vivo, conducted on 18 patients diagnosed with nonmelanoma skin cancers (NMSC). We observed that both in basal cell carcinomas (BCC) and squamous cell carcinomas (SCC) the endogenous fluorescence due to tryptophan residues was more intense in tumor than in normal tissue, probably due to epidermal thickening and/or hyperproliferation. Conversely, the fluorescence intensity associated with dermal collagen crosslinks was generally lower in tumors than in the surrounding normal tissue, probably because of degradation or erosion of the connective tissue due to enzymes released by the tumor. The decrease of collagen fluorescence in the connective tissue adjacent to the tumor loci was validated by fluorescence imaging on fresh-frozen tissue sections obtained from 33 NMSC excised specimens. Our results suggest that endogenous fluorescence of NMSC, excited in the UV region of the spectrum, has characteristic features that are different from normal tissue and may be exploited for noninvasive diagnostics and for the detection of tumor margins.     

Analysis of human brain tissue, brain tumors and tumor cells by infrared spectroscopic mapping

This study uses infrared (IR) spectroscopic, point detection, mapping procedures to examine tissue samples from normal brain specimens and from astrocytic gliomas, the most frequent human brain tumors. Model systems were derived from cultured glioma cell lines. IR spectra of normal tissue sections distinguished white matter from gray matter by increased spectral contributions from lipids and cholesterol. Qualitatively the same differences were found in IR spectra of low and high grade glioma tissue sections pointing to a significant reduction of brain lipids with increasing malignancy. Whereas spectral contributions of proteins and lipids were similar in IR spectra of glioma cells and tissues, nucleic acid bands were more intense for cells suggesting higher proliferative activities. For statistical analyses of IR spectroscopic maps from 71 samples, a parameter for the lipid to protein ratio was introduced involving the CH(2) symmetric stretch band with lipids as main contributors and the amide I band of proteins. As this parameter correlated with the grade of gliomas obtained from standard histopathological examination, it was applied to classify brain tissue sections based on IR spectroscopic mapping.     

Regional cerebral blood flow measurement in brain tumors

The regional cerebral blood flow (CBF) was determined on seventeen patients with brain tumors. Ring type single photon emission CT (SPECT) was used following intravenous injection of 133Xe. Case materials included eleven meningiomas and six malignant gliomas. Evaluation was performed with emphasis on the following points; Correlation of the flow data within tumors to the angiographic tumor stains, Influence of tumors on the cerebral blood flow of the normal brain tissue, Correlation between degree of peripheral edema and the flow data of the affected hemispheres. There was significant correlation between flow data within tumors and angiographic tumor stains in meningiomas. Influence of tumors on cerebral blood flow of the normal tissue was greater in meningiomas than in gliomas. There was negative correlation between the degree of peripheral edema and the flow data of the affected hemisphere. It has been concluded that the measurement of CBF in brain tumors is a valuable method in evaluation of brain tumors.     

Crisp and soft multivariate methods visualize individual cell nuclei in Raman images of liver tissue sections

Raman and infrared spectroscopy have been recognized to be promising tools in clinical diagnostics because they provide molecular contrast without external stains. Here, vertex component analysis (VCA) was applied to Raman and Fourier transform infrared (FTIR) images of liver tissue sections and the results were compared with K-means cluster analysis, fuzzy C-means cluster analysis and principal component analysis. The main components of VCA from three Raman images were assigned to the central vein, periportal vein, cell nuclei, liver parenchyma and bile duct. After resonant Mie scattering correction, VCA of FTIR images identified veins, liver parenchyma, cracks, but no cell nuclei. The advantages of VCA in the context of tissue characterization by vibrational spectroscopic imaging are that the tissue architecture is visualized and the spectral information is reconstructed. Composite images were constructed that revealed a high molecular contrast and that can be interpreted in a similar way like hematoxylin and eosin stained tissue sections.     

Spatial distribution of heme species in erythrocytes infected with Plasmodium falciparum by use of resonance Raman imaging and multivariate analysis

The multivariate algorithm hierarchical cluster analysis is applied to sets of resonance Raman spectra collected from human erythrocytes infected with the malaria parasite Plasmodium falciparum. The images obtained yield information about the distribution of hemoglobin and hemozoin (or malaria pigment) within the parasitized cells and about their molecular structure. This method has the advantage of conveying more information than other imaging approaches based on resonance Raman spectroscopy, and it is a promising tool to study the hemozoin formation process and its interaction with antimalarial drugs within unstained, well-preserved parasites.     

In and ex vivo breast disease study by Raman spectroscopy

In this work, Raman spectra in the 900?C1,800?cm^?1 wavenumber region of in vivo and ex vivo breast tissues of both healthy mice (normal) and mice with induced mammary gland tumors (abnormal) were measured. In the case of the in vivo tissues, the Raman spectra were collected for both transcutaneous (with skin) and skin-removed tissues. To identify the spectral differences between normal and cancer breast tissue, the paired t-test was carried out for each wavenumber using the whole spectral range from both groups. Quadratic discriminate analysis based on principal component analysis (PCA) was also used to determine and evaluate differences in the Raman spectra for the various samples as a basis for diagnostic purposes. The differences in the Raman spectra of the samples were due to biochemical changes at the molecular, cellular and tissue levels. The sensitivity and specificity of the classification scheme based on the differences in the Raman spectra obtained by PCA were evaluated using the receiver operating characteristic (ROC) curve. The in vivo transcutaneous normal and abnormal tissues were correctly classified based on their measured Raman spectra with a discriminant proportion of 73%, while the in vivo skin-removed normal and abnormal tissues were correctly classified again based on their measured Raman spectra with a discriminant proportion of 86%. This result reveals a strong influence due to the skin of the breast, which decreased the specificity by 11%. Finally, the results from ex vivo measurements gave the highest specificity and sensitivity: 96 and 97%, respectively, as well as a largest percentage for correct discrimination: 94%. Now that the important bands have been experimentally determined in this and other works, what remains is for first principles molecular-level simulations to determine whether the changes are simply due to conformational changes, due to aggregation, due to changes in the environment, or complex interactions of all of the above.