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Monitoring of Biochemical Changes through the C6 Gliomas Progression and Invasion by Fourier Transform Infrared (FTIR) Imaging Abdelilah Beljebbar,*,† Sylvain Dukic,† Nadia Amharref,† Salima Bellefqih,‡ and Michel Manfait† Unite´ Me´DIAN, UMR CNRS 6237 - MEDyC, Universite´ de Reims Champagne-Ardenne, IFR 53, UFR de Pharmacie, 51 rue Cognacq-Jay, 51096 Reims CEDEX, France, and Laboratoire Pol Bouin, Reims, CHU Reims, Hoˆpital Maison Blanche, 45 Rue Cognacq Jay 51092 Reims Cedex, France We have investigated the spatial distribution of molecular changes associated with C6 glioma progression using Fourier transform infrared (FT-IR) microspectro-imaging in order to determine spectroscopic markers for early diagnosis of tumor growth. Our results showed that at day 7 after tumor implantation, FTIR investigations displayed a very small abnormal zone associated with the proliferation of C6 cells in the caudate putamen. From this day, rats developed solid and well-circumscribed tumors and invasive areas. The volume of peritumoral areas increased rapidly until day 19. The maturation of the tumor was accompanied by a diminution in its proliferative and invasive area. The presence of necrotic areas was visible from day 15. A non-negative least-squares algorithm was used to quantify spatial distribution of molecular changes in tissues (lipids, nucleic acids, and proteins) associated with glioma progression. Compared to those in normal brain, statistical tests on fit coefficients showed that the concentrations of sphingomyelin (SMY), nucleic acids, phosphatidylserine (PS), and galactocerebroside (GalC) were significantly affected during C6 glioma development. These constituents can be used as spectroscopic markers for C6 glioma progression. Indeed, the concentration of DNA decreased significantly from tumor to invasion, to normal brain tissues, the necrotic area has higher concentrations of the Galc than other areas. The PS content was significantly higher in the peritumoral zone and decreased in the tumor zones matter. The glioblastoma multiforme (GBM) is the most malignant glioma variant with a median survival of only 9-12 months after diagnosis.1 Traditional treatment options for this disease include surgery, radiation therapy, and chemotherapy.2,3 While therapies for high-grade gliomas are helpful, at present, these treatments cannot cure these tumors. The two major reasons are that tumor * To whom correspondence should be addressed. Phone: (33) 3 2691-8376. Fax: (33) 3 2691-8282. E-mail:
[email protected]. † Universite´ de Reims Champagne-Ardenne. ‡ Hoˆpital Maison Blanche. (1) Shinojima, N.; Tada, K.; Shiraishi, S.; Kamiryo, T.; Kochi, M.; Nakamura, H.; Makino, K.; Saya, H.; Hirano, H.; Kuratsu, J.; Oka, K.; Ishimaru, Y.; Ushio, Y. Cancer Res. 2003, 63, 6962–6970. (2) Kaye, A. H.; Hill, I. S. Ann Acad Med Singapore 1993, 22, 470–481. (3) Woodburn, K. W.; Hill, J. S.; Stylli, S.; Kaye, A. H.; Reiss, J. A.; Phillips, D. R. Br. J. Cancer 1994, 70, 398–400. 10.1021/ac901464v CCC: $40.75 2009 American Chemical Society Published on Web 10/13/2009
cells infiltrate into surrounding brain and thus cannot be completely removed by surgery and that most glioma cells are at least partially resistant to radiation and chemotherapy.4-6 In fact, tumor invasion are frequently not well-circumscribed, and thus these tumor sites are left in the brain to proliferate. Therefore, model systems to study glioblastoma growth and invasion may help in the early detection of tissue abnormality and definition of tumor margins and in developing methods for the discovery of novel strategies to block their development and progression. The C6/ wistar glioma model resembles GBM and has been useful for a variety of studies related to brain tumor biology including studies on tumor growth,7,8 invasion,7,9,10 and evaluation of the therapeutic efficacy of cancer treatments.4,11 Fourier transform infrared microspectroscopy (FTIR) is an analytical chemical technique used to study molecular structure and structural interactions in biological systems in a nondestructive manner. It measures absorption of vibrating molecules that have resulted from the energy transitions of vibrating dipoles. An infrared spectrum of a biological sample is composed of characteristic absorption bands originating from all infrared-active vibrational modes of biomolecules present in the tissue, such as proteins, lipids, and nucleic acids.12 Each of these biomolecules absorbs infrared light at certain frequencies over the entire infrared light spectrum. The transformation of tissue from normal to cancer is characterized by molecular changes in tissue composition.13 The identification and quantification of these specific molecular changes within tissues can provide diagnostic information for aiding in early detection of tumors. Indeed, FT-IRM imaging combined a high spatially resolved morphological and biochemical information complementary to histopathology. This (4) Laws, E. R.; Shaffrey, M. E., Jr. Int. J. Neurosci. 1999, 17, 413–420. (5) Holland, E. C. Nature 2001, 2, 120–129. (6) Grobben, B.; De Deyn, P. P.; Slegers, H. Cell Tissue Res. 2002, 310, 257– 270. (7) Nagano, N.; Sasaki, H.; Aoyagi, M.; Hirakawa, K. Acta Neuropathol. 1993, 86, 117–125. (8) San-Galli, F.; Vrignaud, P.; Robert, J.; Coindre, J. M.; Cohadon, F. J. Neurooncol. 1989, 7, 299–304. (9) Bernstein, J. J.; Laws, E. R.; Levine, K. V.; Wood, L. R.; Tadvalkar, G.; Goldberg, W. J. Neurosurgery 1991, 28, 652–658. (10) Chicoine, M. R.; Silbergeld, D. L. J. Neurosurgery 1995, 83, 665–671. (11) Barth, R. F. J. Neurooncol. 1998, 36, 91–102. (12) Parker, F. S. Application of Infrared Spectroscopy in Biochemistry; Biology and Medicine Plenum: New York, 1971. (13) Wang, T. D.; Triadafilopoulos, G.; Crawford, J. M.; Dixon, L. R.; Bhandari, T.; Sahbaie, P.; Friedland, S.; Soetikno, R.; Contag, C. H. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 15864–15869.
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method does not require any sample preparation.14 FTIR has been largely used in medical diagnostics to identify neoplasia in breast,15 cervix,16 colon,17 lung,18 stomach,19 and glioma.20,21 Our present focus is the direct monitoring and interpretation of molecular changes associated C6 glioma growth and invasion by micro-FTIR imaging to better understand the tissue transformation during carcinogenesis. In the current study, micro-FTIR maps were recorded from normal brain tissue and on glioma growth from day 7 to day 21 after injection of C6 glioma cell suspension in brain parenchyma. Multivariate statistical analysis (clustering analysis (CA), and non-negative least-squares algorithm (NNLS)) were used to (i) identify and quantify the molecular changes associated with the development of the glioma tumor (ii) definition of the tumor and peritumoral margins, (iii) grading of malignancy and prognosis based on the presence of necrosis. MATERIALS AND METHODS Sample Preparation. All animal procedures adhered to the “Principles of laboratory animal care” (NIH publication no. 85-23, revised 1985). Male Wistar rats weighing 273 ± 28 g (mean ± SD) were purchased from Harland (Paris, France). The glioma tumors were obtained by a C6 glioma cell suspension injection in brain parenchyma as described elsewhere.6 Eight groups of rats were prepared. One group was used as control. All remaining rats were anesthetized with isoflurane, placed into a stereotactic head holder (Phymep, France), and a small burr hole was drilled into the right side of the skull (anterior 1 mm; lateral 3 mm; lateral depth 4 mm, according to the bregma) and a tumor cell suspension (5 × 106 cells in 10 µL) was injected with a syringe over 2 min. All animals with implanted C6 cells developed tumors with reproducible localization and size around the site of injection. These groups were sacrificed after 5, 7, 9, 12, 15, 18, 21 days postimplantation. After brain excision, tissue samples were snap-frozen by immersion in methyl-butane, cooled down in liquid nitrogen, and stored at -80 °C. Two adjacent sections were cut from each sample using a cryomicrotome. One section, 10 µm thick, was placed onto infrared transparent calcium fluoride (CaF2) slides for infrared imaging. The second section, 7 µm thick, was placed on a microscope glass slide and stained with hematoxylin and eosin (H&E) for histopathological image. Infrared Reference Spectra. All of the reference lipids used in this study were known to be present in the brain determined by TLC technique.22 Reference FTIR spectra were obtained on the following specific pure chemical compounds purchased from Sigma-Aldrich (Saint Quentin Fallavier, France): cholesterol (99%), (14) Bates, J. B. Science 1976, 19, 31–37. (15) Ci, Y.; Gao, T.; Feng, J.; Guo, Z. Appl. Spectrosc. 1999, 53, 312–315. (16) Wong, P.; Wong, R.; Caputo, T.; Godwin, T.; Rigas, B. Proc. Natl. Acad. Sci. U. S. A. 1991, 88, 10988–10992. (17) Rigas, B.; Morgello, S.; Goldman, I.; Wong, P. Proc. Natl. Acad. Sci. U. S. A. 1990, 87, 8140–8144. (18) Yano, K.; Ohoshima, S.; Gotou, Y.; Kumaido, K.; Moriguchi, T.; Katayama, H. Anal. Biochem. 2000, 287, 218–225. (19) Li, Q.; Sun, X.; Xu, Y.; Yang, L.; Zhang, Y.; Weng, S.; Shi, J.; Wu, J. Clin. Chem. 2005, 51, 346–350. (20) Amharref, N.; Beljebbar, A.; Dukic, S.; Venteo, L.; Schneider, L.; Pluot, M.; Vistelle, R.; Manfait, M. Biochim. Biophys. Acta 2006, 1758, 892–899. (21) Beljebbar, A.; Amharref, N.; Leı`veI`ques, A.; Dukic, S.; Venteo, L.; Schneider, L.; Pluot, M.; Manfait, M. Anal. Chem. 2008, 80, 8406–8015. (22) Cherayil, G. D.; Scaria, K. S. J. Lipid Res. 1970, 11, 378–381.
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cholesteryl oleate (3β-Hydroxy-5-cholestene 3-oleate, > 98%), phosphatidylcholine (1,2-diacyl-sn-glycero-3-phosphocholine, 99%, from egg yolk), phosphatidylethanolamine (1,2-dihexadecanoylsn-glycero-3phosphoethanolamine, 99%), phosphatidylserin (1,2Diacyl-sn-glycero-3-phospho-L-serine, 99%, from bovine brain), sphingomyelin (N-acyl-D-sphingosine-1-phosphocholine, 99%, from bovine brain), galactocerebroside (ceramide β- D-galactoside, 99%, from bovine brain), linoleic acid (cis-9,cis-12-Octadecadienoic acid, > 99%), oleic acid (cis-9-Octadecenoic acid, 99%), and DNA from calf thymus. Each lipid was dissolved in a mix of methanol (HPLC grade, CARLOERBA-SDS, Peypin, France) chloroform (HPLC grade, CARLOERBA-SDS, Peypin, France) (v/v). DNA was dissolved in phosphate buffered saline at pH 7.4. These references components were placed onto a CaF2 slide and dried in air before infrared measurement. FT-IRM Microspectrometer. Spectra were collected using an FTIR imaging system (SPOTLIGHT, Perkin-Elmer, France) coupled to a FTIR spectrometer (Spectrum 300, Perkin-Elmer, France). This system is equipped with a liquid N2 cooled Mercury-Cadmium-Telluride MCT line detector comprised of 16 pixel elements. The microscope was equipped with a movable, software-controlled x, y stage. In this study, FTIR images were collected from selected sites with a spatial resolution of 25 µm/pixel, in transmission mode, in the 4000-720 cm-1 range, with a final spectral resolution of 4 cm-1, and 16 scans per pixel. Data acquisition was carried out by means of the Spotlight software package supplied by PerkinElmer. Data Analysis. To allow meaningful comparisons, all FT-IRM data were uniformly preprocessed. After atmospheric correction (removes the peaks corresponding to water vapor and carbon dioxide in the atmosphere that otherwise appear in spectra like noise), data were cut to fingerprint region (900-1800 cm-1), converted to their first derivative, and smoothed using a seven point Savitzky-Golay algorithm in order to minimize the influence of background scatter in the spectra.23 The resulting spectra were then normalized using a Standard Normal Variate (SNV) procedure.24 We used a combination of first derivative and normalization to remove both additive baseline and multiplicative effects.25 Both standard normal variate (SNV) transform and multiplicative scatter correction (MSC) are designed to remove additive baseline and multiplicative signal effects26 resulting in a spectrum with zero mean and a variance equal to one. The difference between the two methods is that SNV is applied to an individual spectrum, whereas MSC uses a reference spectrum such as mean spectrum of the training set. These two techniques can also be used in combination with first derivatives from the spectral data to remove additive baseline effect. All data measured on normal brain and tumor development (from 7 to 21 days growth) were pooled in one data set, processed at the same time and the results were displayed as pseudocolor maps with the same color scale. In this way, we can easily determine all their common and (23) Savitzky, A.; Golay, M. J. E. Anal. Chem. 1964, 36, 1627–1639. (24) Barnes, R. J.; Dhanoa, M. S.; Lister, S. J. Appl. Spectrosc. 1989, 43, 772– 777. (25) Swieringa, H.; de Weijer, A. P.; van Wijk, R. J.; Buydens, L. M. C. Chemom. Intell. lab. Syst. 1999, 49, 1–28. (26) Helland, I. S.; Naes, T.; Isaksson, T. Chemom. Intell. Lab. Syst. 1995, 29, 233–241.
discriminating features by comparing their infrared maps. A multivariate statistical analysis (principal component analysis (PCA) and K-means (KM)) was performed on this data set. PCA was performed on the data set to remove redundancies at different locations in the spectra by finding the independent sources of variation in all spectra, and to reduce the number of variables describing the data set. Thirty PCA scores, accounting for 99% of the captured variance, served as input for the unsupervised classification methods. K-means clustering was performed on these principal component scores. Pseudocolor maps based on cluster analysis were then constructed by assigning a color to each spectral cluster. The cluster spectra were calculated by averaging absorbance spectra associated to each group and used for the interpretation of the chemical or biochemical differences between clusters. Investigation of Orthogonal Relationship between Reference Spectra. Our reference set contained spectra of individual pure components that are known to be present in the brain in measurable quantities (cholesterol, cholesteryl ester, oleic, and linoleic acids, sphingomyelin (SMY), galactocerebroside (GalC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), DNA, and proteins). Before applying NNLS, two tests were performed to verify the independency of the reference set. In fact, the presence of high degree of colinearity between some of these components selected will tend to skew the fit. A test of independence was performed on a reference set by fitting each spectrum of this set by a linear combination of all other remaining reference spectra. This test allowed the identification of the components that had high or low orthogonal relationship. For spectra that formed an independent set, a very poor fit was obtained, judged by the large infrared features in the fit residuals (obtained by subtracting the fit result from the measured spectrum). In order to minimize error due to nonorthogonality of the reference spectra, spectra that could be fitted for more than 95% by the other reference spectra were excluded from the reference. Only components with high orthogonal relationship were selected for fitting. To validate this reference data set, two lipid mixtures were prepared and then analyzed blindly. The measured spectra were then fitted with all reference data to identify and quantify each individual compound present in the mixtures. Non-Negative Least-Squares Fitting Procedure (NNLS). It is well-known that in the linear region of Beer’s law, the absorbance at a specific wavenumber is the sum of the absorbance of all sample components that absorb at that wavenumber. The NNLS method was used to identify individual biochemical changes in tissues during carcinogenesis by using spectral data of pure molecules. Each spectrum measured on tissues were fitted with a set of reference spectra, offset (second-order polynomial) and slope to minimize the Mie scattering effect.27 In fact, the linearity of the Beer-Lambert law is limited by the scattering of light due to particulates in the sample such as DNA and RNA (Mie scattering). These scattering effects produce a broad, undulating background onto which the absorption features are superimposed. The presence of these baseline variations complicated attempts to extract the contribution of pure-component spectra. First(27) Mohlenhoff, B.; Romeo, M.; Diem, M.; Wood, B. R. Biophys. J. 2005, 88, 3635–3640.
derivative processing was used to counter the influence of low frequency effects or dispersion artifacts such as Mie scattering. The fit coefficients were normalized to one (expressed as a percentage) and maps representing the molecular distribution and/or contribution of each individual brain constituent from normal and tumor tissues were built. Chi-squared analysis was used to estimate the goodness of the fit as well as the error associated with model fitting. The colorbar represents the molecular absorbance percentage of each constituent. Indeed, the successful application of the NNLS technique requires the careful elimination of random noise from the spectra as this may affect the fit results. An estimate was made on the influence of noise on the quantitative fit results. For this purpose, noise was added to the FTIR spectra to artificially decrease the signal-to-noise ratio by a factor of 3 and a new set of fit coefficients was calculated. For each of the spectrum, this procedure was repeated 100 times. The standard deviation thus obtained for each of the fit coefficients was used as an estimate for the error in the fit coefficients. Statistical testing of significance was performed on these fit coefficients to identify those brain constituents that significantly change during tumor progression. Because the foregoing data do not conform to normal distributed statistical criteria, nonparametric Wilcoxon test was applied on these fit coefficients to compare the difference between paired brain structures (normal/ invasion or invasion/tumor) during tumor progression.28 The analysis on FT-IRM data was performed with Matlab (Version 7.02, MathWorks, Inc., Matick, U.S.). RESULTS AND DISCUSSION FTIR Characterization of Normal Brain Tissues and C6 Glioma Progression Using Cluster Analysis. An aspect of progression which may be unique to glial tumors is their infiltrative pattern. The C6 gliomas model used in this study exhibit morphological characteristics of human GBM such as necroses with palisading cells. These tumors formed in Wistar rats showed significant degrees of invasion characterized by a diffuse infiltrating border in which individual cells invade the surrounding brain tissue.29 Infiltration of surrounding brain tissue limits or even precludes surgical resection. Therefore, it is critical to accurately map the growth patterns of malignant gliomas in the brain to aid in the development of therapies that can effectively arrest recurrence and progression. In this study, we have investigated the C6 glioma development by micro-FTIR-imaging. Multivariate statistical analysis was performed on a data set containing all FT-IR measurements from normal brain and tumor obtained at different growth times postimplantation (from day 7 to day 21). All animals with implanted C6 cells developed tumors with reproducible localization and size around the site of injection. The rat brains analyzed before 5 days after tumor implantation did not show any visible changes in the brain tissue (data not shown). Indeed, all animals died beyond 25 days postimplantation and therefore no data are available beyond this time point. Histological and FTIR analysis revealed that the tumor size is dependent on the period elapsed after injection of glioma cells. Figure 1 displays FTIR pseudocolor maps of brain tissue and their histopathological images. Ten clusters describing both normal (28) Wilcoxon, F. Biometrics Bull. 1945, 1, 80–83. (29) Bernstein, J. J.; Goldberg, W. J.; Laws, E. R.; Conger, D.; Morreale, V.; Wood, L. Neurosurgery 1990, 26, 622–628.
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Figure 1. Photomicrographs (H&E staining) of normal brain (A) and glioma progression (C, E, G, I, K, M) from, respectively, at 7th, 9th, 12th, 15th, 19th, and 21th days postimplantation. All data measured on several normal brain tissue and tumor evolution were pooled in one data set, processed at the same time to extract all features describing both normal and tumor tissues. Data were cut into the fingerprint region (900-1800 cm-1), and cluster analysis was carried out on the first derivative spectra (to enhance the resolution of superimposed bands). K-means was calculated the cluster-membership of spectra by assigning each color to one class. Pseudocolor FTIR maps of the corresponding histological images, based on 10-means cluster analysis, were measured on normal brain (map B) and tumor progression (maps D, F, H, J, L, and N), respectively.
brain and cancer features were extracted and pseudo FTIR maps were constructed with the same color scale. Different clusters in the FTIR images were correlated with features of the histopathological images. White color represents the area where no tissue was present. In the pseudocolor map obtained from normal brain tissue (Figure 1B), three clusters were sufficient to describe all normal brain features such as white matter (corpus callosum (CC)) associated to cluster 2, two layers associated with gray matter cortex (clusters 1, 3). Comparison between Figure 1A and B shows that the FT-IR image provides complementary information on the cortex when compared to standard histopathology. Figure 1D exhibits an infrared map obtained from tumor brain tissue 7 days after tumor cell injection. This pseudocolor map displays some normal structures associated to white matter and gray matter. One particular structure (cluster 7) was located in the caudate putamen (CP) corresponding to C6 cells injection site. This feature was associated to C6 cell growth. Histological interpretations, however showed small abnormalities in rats sacrificed seven days after implantation of C6 cells (Figure 1C). Indeed, this abnormality is characterized by a diffuse structure within the brain parenchyma. The tumor cells appear scattered in the form of cells or grouped in clusters in the vicinity of the blood vessels. A previous study investigated the proliferative potential of growing 9 L tumors using Ki-67 immunohistochemical staining.30 They reported that the correlation between the tumor size and the mean number of Ki-67 positive cells was evident from day 7 of tumor development. Grobben et al. have demonstrated that at 4-7 postimplantation, individual cells were observed in the corpus callosum.6 At day 9, the viable tumor started to be visible in the implantation site, destroys the corpus callosum and grew into all the cortical layers (Figure 1F). The tumor area was encoded by cluster 6. On the other hand, cluster 7 was detected around the tumor. This cluster was associated to the proliferative and invasive character of glioma tumors. In fact, in our previous study, immunohistochemical Ki-67 and MT1-MMP staining were used to visualize the proliferative and invasive activities of glioma and were clearly correlated with the cluster that encoded the surrounding tumor area.31 Histopathological staining confirms this FTIR result (Figure 1E), In fact, a large focus of invasion (6 mm) were well separated from the surrounding brain tissue. Tumor was hypercellular with cellular and nuclear pleomorphism and mitotic figures observed. From day 12, the tumor is fairly large and deeply situated within the cortex with massive infiltration into the brain tissue (Figure 1H, J, L). Most of cerebral cortex is destroyed by tumor tissue. At, day 15, we observe the appearance of clusters 8 and 9 associated to the formation of necrosis and perinecrosis (Figure 1J). Histological image displayed oedematous zones as well as zones of necrosis with a pseudopalissading cells (>8 mm) (Figure 1I). Around necrotic zones, tumor shows a large cell density with an increase of the proliferation of endothelial cells and hemorrhagic zones. This necrotic zone increased until day 21 postimplantation (Figure 1L and N). Cluster 9 observed in the border of the necrotic zone seems to correlate with the pseudopalisading formation. Cluster 8 correlates to the center of the necrosis (full (30) Coons, S. W.; Johnson, P. C. J. Neuropathol. Exp. Neurol. 1993, 52, 609– 618. (31) Amharref, N.; Beljebbar, A.; Dukic, S.; Venteo, L.; Schneider, L.; Pluot, M.; Manfait, M. Biochim. Biophys. Acta 2007, 1768, 2605–2615.
Figure 2. Representative cluster mean FTIR spectra extracted from pseudocolor maps. Cluster averaged spectra were obtained by meaning absorbance spectra associated to each group. Ten models describing normal (panel A) and glioma brain development (panel B). Each cluster averaged spectrum assigned to one class was plotted with the same color than in pseudocolor map.
necrosis) of the tumor. The presence of necrosis is important for grading tumors and is often linked to a poorer clinical prognosis.32 Indeed, the most characteristic finding of glioblastoma is the necrotic foci surrounded by tumor cells.33 Pallisading cells delineate the foci of necrosis and lymphocytic infiltration, with the occasional formation of edema fluid.34 At day 19, we observed the transformation of the structure of the tumor (Figure 1L). In fact, cluster 6 was replaced partially by cluster 10. On the other hand, 21 days after tumor injection, the tumor was completely described by cluster 10 that occupied almost the entire hemisphere as visualized on the tissue of the section (Figure 1N). Indeed, the maturation of the tumor was accompanied by a diminution in its proliferative and invasive area associated to cluster 7. A previous study reported that the absence of the correlation between the tumor size and the mean number of Ki67 positive cells after 21 days could be due to considerable tumor necrosis beyond that time point.30 Figure 2 shows class average spectra of the normal brain (panel A) and tumor tissues (panel B). These spectra are dominated by two absorbance bands at 1654 and 1546 cm-1 known as the amide I and II, respectively. The band at 1740 cm-1 arises from the stretching mode of CdO groups of lipids. The absorption band (32) Barker, F. G.; Davis, R. L.; Chang, S. M.; Prados, M. D. Cancer 1996, 77, 1161–1166. (33) Kleihues, P.; Burger, P. C.; Scheithauer, B. W. Histological Typing of Tumors of the Central Nervous System; Springer-Verlag: Berlin. 1995; pp 11-14. (34) Auer, R.; Del Maestro, R. F.; Anderson, R. Can. J. Neurol. Sci. 1981, 8, 325–331.
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at 1396 is attributed to COO- symmetric stretching vibrations of fatty acids and amino acids. The strongest bands, at 1238 cm-1 and 1082 cm-1 are due to the antisymmetric and symmetric phosphate stretching mode PO2- of nucleic acids and phospholipids respectively. The relatively weak band at 1170 cm-1, in the normal tissue is due to stretching mode of CsO groups of proteins. The band at 972 cm-1 is attributed to the dianionic PO2 2- monoester of nucleic acids (DNA) and phospholipids such as PC and SMY. The bands at 1466 cm-1 and 1454 cm-1 were attributed, respectively, to CH2 scissoring and antisymmetric deformation of CH3 group (cluster 2, Figure 2A). In white matter, the intensity of the CH2 scissoring (1466 cm-1) is much greater than the intensity of the CH3 asymmetric bending (1454 cm-1) due to its high lipid content. However, in gray matter, these two bands exhibit a similar intensity (cluster 1 and 3, Figure 2A). In fact, gray matter contains predominantly neuronal cell bodies. On the other hand, white matter contains the myelinated axons of the neurons. Therefore in white matter, absorption from CH2 groups of the lipid acyl chains dominates the band at 1466 cm-1. In gray matter, absorptions in this spectral region arise predominantly from protein side chains. In a typical protein, side chains contain approximately equal proportions of CH2 and CH3 groups. The comparison between normal tissue (panel A) and tumor counterpart (panel B) shows a decreased and even disappearance of the band at 1466 cm-1 and 1740 cm-1 associated to lipids and phospholipids in the malignant brain tissues. Indeed, malignant tissues displayed the appearance of the band at 1070 cm-1 attributed to the CsC stretching and a decrease of the intensity of the band at 1082 due to phospholipids diminution compared to white matter spectrum (cluster 2). The variations of the spectral characteristics of the FTIR spectra between the normal and malignant tissues provided a basis for clinical application. Tests of Noncollinearity of the Reference Database. NNLS have been shown to improve analysis precision, accuracy, reliability, and applicability for infrared spectral analyses relative to the more conventional univariate methods of data analysis. This multivariate method derives its power from the simultaneous use of multiple intensities (i.e., multiple variables) in each spectrum.35 One drawback with NNLS is that if there is collinearity between some of the reference spectra, then there can be a bias in the estimation of concentrations, by using this model. Figure 3 displayed the comparison between cluster mean FTIR spectra extracted from pseudocolor maps cluster 2 (white matter), cluster 6 (tumor), and cluster 7 (invasion) and spectra collected from the most individual brain biomolecules used in the fits. The spectra measured on pure brain constituents exhibit exactly the same vibrational frequencies as in the tissues meaning that there are no changes in band frequency or intensity due to molecular environment and/or molecular interaction. Indeed, each of these constituent produces distinctive bands in the infrared spectrum. Several authors have used pure reference spectra to quantify individual constituents present in the tissues such as coronary (35) Mourant, J. R.; Yamada, Y. R.; Carpenter, S.; Dominique, L. R.; Freyer, J. P. Biophys. J. 2003, 85, 1938–1947.
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Figure 3. Comparison between cluster mean FTIR spectra extracted from pseudocolor maps cluster 2 (white matter), cluster 6 (tumor), and cluster 7 (invasion) and spectra collected from the most individual brain biomolecules used in the fits.
atherosclerosis36 breast tissue,37 porcine brain,38,39 and skin.40 Wang et al. showed a very accurate fit despite the presence number of common spectral features or molecular interactions and variations that may induce broadening of individual peaks.13 Rubin et al. have reported that there is a similarity between the spectra of pure elastin and spectra collected from human aortic tissues.41 Before applying the NNLS procedure, a test of noncolinearity was performed on the model set of lipid reference spectra, which were chosen to represent the main kinds of lipids in the brain tissue (cholesterol, cholesteryl ester, oleic, and linoleic acids, SMY, GalC, PC, PE, PS).42 In fact, each spectrum of this reference set was fitted with all remaining reference spectra. As a result, a very poor fit was obtained for each spectrum, as judged by the large (36) Ro ¨mer, T. J.; Brennan, J. F.; Fitzmaurice, M.; Feldstein, M. L.; Deinum, G.; Myles, J. L.; Kramer, J. R.; Lees, R. S.; Feld, M. S. Circulation 1998, 10, 878–885. (37) Haka, A. S.; Shafer-Peltier, K. E.; Fitzmaurice, M.; Crowe, J.; Dasari, R. R.; Feld, M. S. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 12371–12376. (38) Koljenovic, S.; Bakker Schut, T. C.; Wolthuis, R.; Vincent, A. J.; HendriksHagevi, G.; Santos, L.; Kros, J. M.; Puppels, G. J. Anal. Chem. 2007, 79, 557–564. (39) Dreissig, I.; Machill, S.; Salzer, R.; Krafft, C. Spectrochim. Acta, Part A 2009, 71, 2069–2075. (40) Caspers, P. J.; Lucassen, G. W.; Carter, E. A.; Bruining, H. A.; Puppels, G. J. J. Invest. Dermatol. 2001, 116, 434–442. (41) Rubin, S.; Bonnier, F.; Sandt, C.; Venteo, L.; Pluot, M.; Baehrel, B.; Manfait, M.; Sockalingum, G. D. Biopolymers 2008, 89, 160–169. (42) Olsson, N. U.; Harding, A. J.; Harper, C.; Salem, N. J. Chromatogr., B 1996, 681, 213–218.
Figure 4. Graphs representing the evolution of SMY (A), DNA (B), GalC (C), and PS (D), that significantly changes through the C6 glioma progression from 7th to 21th days postimplantation compared to normal tissues (white matter and gray matter). These graphs were selected after nonparametric statistical analysis (Wilcoxon test) on the fit contribution of the all individual constituents in brain tissues. The area of the circles in the graphs was correlated the cluster size and plotted with the same color than in pseudocolor maps.
infrared features in the fit residuals (obtained by subtracting the fit result from the measured pure lipid spectrum). This result demonstrates that our reference spectra form an orthogonal set and can be used to estimate precisely the relative absorbance percentage fractions of each lipid in normal and glioma brain tissues. We have then studied the performance of the method as a function of noise level. For this purpose, noise was added to the FTIR spectra to artificially decrease the signal-to-noise ratio by a factor of 3 and a new set of fit coefficients was calculated. For each of the spectrum, this procedure was repeated 100 times. The standard deviation thus obtained for each of the fit coefficients was used as an estimate for the error in the fit coefficients (less than 5%). To validate this reference data set, two lipid mixtures were then prepared, in blind, by using different reference lipids. The measured spectra were then fitted with all reference spectra (nine lipid spectra) to identify and quantify each individual compound present in the mixtures. As result of this test, we have successfully identified and quantified all lipids used in the two mixtures prepared blindly (cholesterol, PE, PC, GalC, and SMY) with an estimated error of about 5% (data not shown). This result shows that (i) even if these lipids share some common features, each of them has unique absorption spectral patterns (considered as a fingerprint) and (ii) the power of multivariate methods used through the entire infrared light spectrum instead of the use of an isolated spectral feature. Quantification of Molecular Changes in Brain Constituents (Lipids, Proteins, And Nucleic Acids) Associated to Tumor Growth and Invasion. We have monitored the changes in brains during the development of the glioma tumor. Statistical
tests on all fit contribution showed that significant changes were associated to SMY, DNA, PS, and GalC during C6 glioma progression compared to normal tissues (Figure 4). The area of the circles in the graphs was correlated to size of each group. At 7 days postimplantation, the SMY content was similar to those displayed in white matter and gray matter (p < 0.001) (Figure 4A). This SMY concentration increased at day 9 and became constant until day 19 and then decreased rapidly. This result can explain the difference between spectra associated to clusters 6 and 10 that superimposed to pure SMY spectrum. This brain constituent can be used as spectroscopic marker for early diagnosis of tumor from ninth day postimplantation. Folch et al. showed that the SMY concentration increases in brain during growth and development in rats.43 There is evidence that SMY not only inhibits tumor progression in the gut, but also induces apoptosis in other tumor cells in vitro (e.g., in breast cancer MCF 7 cells). However, recently it has been reported that SMY isolated from vesicles released from the surface of tumor cells promoted malignant growth, metastases, invasiveness, motility of endothelial cells, and angiogenesis.44 Ledwozyw et al. have demonstrated that glioma tumors contain a greater amount of SMY as compared to normal cortex tissue.45 Figure 4B showed a higher DNA content in the tumor (cluster 6 and 10). This DNA contribution was decreased significantly from (43) Folch, Pi. J. In Biochemistry of the Developing Nervous System; Waelsch, H., Ed.; 121 Academic Press: New York, 1955. (44) Kim, C. W.; Lee, H. M.; Lee, T. H.; Kang, C.; Kleinman, H. K.; Gho, Y. S. Cancer Res. 2002, 62, 6312–6317. (45) Ledwozyw, A.; Lutnicki, K. Acta Physiol. Hung. 1992, 79, 381–387.
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Figure 5. Biochemical distribution of the brain constituents that changes through the evolution of C6 brain glioma (SMY, DNA, GalC, PS) compared to normal tissue by using NNLS procedure. To determine the molecular composition of tissues, the relative absorbance percentage of the various components were rescaled to add up to 100%. The spatial and biochemical information obtained from these maps can be used to identify which biochemical markers could be more potential indicators of such variations between normal and tumor development.
invasive regions (cluster 7) to normal brain structures (white and gray matters). This brain constituent can be used for early detection of tissue abnormality observed at day 7 and discriminates between normal, invasive, and tumor tissues (p < 0.001). Figure 4C displayed the changes in GalC during the tumor development. This component was higher in the necrotic part of the tissue (appeared from day 15) with the increase of the necrotic area from days 15 to 21. On the other hand, no or small amount of GalC was found in the peritumoral and tumor parts of the tissues. GalC is the main glycolipid component and the cell membrane of the myelinating cell contains a high percentage of this compound.45 This suggests that GalC may play an important role in the successive layering of cell membranes, a process unique to myelination. O’Brien et al. reported that white matter exhibits higher GalC than gray matter.46 The GalC content decreases in the vital tumor region because the oligodendroglia (myelinforming cells) die and myelination ceases very early. On the other hand, the necrotic areas have a higher concentration of this cerebroside, which can thus be used as a spectroscopic marker of necrosis and, in GBM, of tumor grade and necrosis.47 The PS content was significantly higher in the peritumoral and decrease in the tumor zones matter (Figure 4D) (p < 0.001). Indeed, PS was observed in low concentration in the necrosis. A statistically significant difference in the PS content was found between normal/invasive and invasive/tumor zones (p < 0.001). Martin et al. have demonstrated that cells undergoing apoptosis redistribute PS from the inner leaflet of the plasma membrane lipid bilayer to the outer leaflet.48 The externalization of PS is a general feature of apoptosis occurring before membrane bleb formation and DNA degradation.49 Blankenberg et al. reported that an endogenous human protein with a high affinity for membrane-bound PS can be used in vitro to detect apoptosis before other morphologic or nuclear changes associated with programmed cell death.50 The decreased concentration of phospholipids in tumors may be due to increased phospholipid degradation, which can result in the modification of composition, structure, and stability of the membranes and thus to membrane dysfunction. The NNLS image reconstruction allowed the identification of individual intrinsic spatial distribution of individual molecular changes. We have mapped, point by point, the distribution of SMY, DNA, PS, and GalC constituents that significantly changes during C6 glioma development (Figure 5). The distribution of PS allowed the (i) identification of abnormally observed at day 7 postimplantation and (ii) delimitation of the tumor and invasive zones. The GalC permitted monitoring the evolution of the necrosis from days 15 to 21. The DNA and SMY contents were higher in the tumor and lower in the invasive and normal brain structures. These quantitative and qualitative changes in brain chemical constituents (46) O’Brien, J. S.; Sampson, E. L. J. Lipid Res. 1965, 6, 537–544. (47) Suzuki, K. Chemistry and metabolism of brain lipids. In Basic Neurochemistry; Albers, R. W.; ed.; Little, Brown, and Company: Boston; MA, 1972; pp 207-227. (48) Martin, S. J.; Reutelingsperger, C. P.; McGahon, A. J.; Rader, J. A.; van Schie RCA, A.; LaFace, D. M.; Green, D. R. J. Exp. Med. 1995, 182, 1545– 1556. (49) Zwaal, R. F.; Schroit, A. Blood 1997, 89, 1121–1132. (50) Blankenberg, F. G.; Katsikis, P. D.; Tait, J. F.; Davis, R. E.; Naumovski, L.; Ohtsuki, K.; Kopiwoda, S.; Abrams, M. J.; Darkes, M.; Robbins, R. C.; Maecker, H. T.; Strauss, H. W. Proc. Natl. Acad. Sci. U.S.A. 1998, 95, 6349–6354.
between normal and malignant tissue would be important for early and accurate diagnosis of cancer. This study demonstrated that FT-IRM imaging, with high spatially resolved morphological and biochemical information can be used as a diagnostic tool, complementary to histopathology in order to understand the molecular changes associated with tissue alteration. Several studies have used thin layer chromatography to quantify total lipids in normal brain (white and gray matters) but the extraction of lipids from invasion, necrotic, and perinecrotic areas of the tissue is difficult.22 This technique is time-consuming and requires sample preparation (destructive method) and extraction. Indeed, tumor tissues are mostly heterogeneous in nature, and this heterogeneity further depends on the stage of disease and its aggressiveness. For example the cortex is composed with two different layers with different lipid composition. With TLC, it is not possible to separate between these layers. In this study, we have mapped, point by point, the distribution of all individual brain constituents in tissues in order to take in account the heterogeneity of these biological tissues. Vibrational spectroscopic imaging approaches have been previously used for the evaluation of quantitative and qualitative molecular changes during tissue transformation. Previous studies have reported on the characterization of brain tissue using Raman spectroscopy. Mizuno et al. published Raman spectra of different anatomical and functional structures of rat brain.51,52 Koljenovi et al. have investigated the discrimination between vital and necrotic brain tumor tissue, and the detection of meningioma in dura mater based on the Raman spectroscopy.53,54 Krafft et al. have demonstrated that lipid content in tissues can be used as markers to discriminate between healthy and tumor tissues.55 So far, only few studies have investigated the C6 glioma model that resembles GBM by these spectroscopic techniques.20,31,56 Indeed in a recent work, we have studied the distribution of all individual brain constituents between normal and C6 glioma tumor tissues obtained 20 days after tumor cell injection.21 This animal model is very interesting especially for preclinical studies to test novel therapeutic approaches. During transformation from normal brain to tumoral, composition, and concentration of lipids change in a specific way.57 Bambery et al. have investigated deparaffined brain tissues to discriminate between normal and C6 glioma tissues by FTIR spectroscopy.56 The tissues analyzed were deparaffined with a chemical solvent such as xylene and alcohol before FTIR analysis. This treatment does not only remove paraffin but also all lipids in the tissue sections; treatment with water or ethanol removes glycogen. They demonstrated that glioma tissues have a characteristic chemical signature, different from normal tissues (51) Mizuno, A.; Hayashi, T.; Tashibu, K.; Maraishi, S.; Kawauchi, K.; Ozaki, Y. Neurosci. Lett. 1992, 141, 47–52. (52) Mizuno, A.; Kitajima, H.; Kawauchi, K.; Muraishi, S.; Ozaki, Y. J. Raman Spectrosc. 1994, 25, 25–29. (53) Koljenovic’, S.; Choo-Smith, L. P.; Bakker Schut, T. C.; Kros, J. M.; van den Berge, H. J.; Puppels, G. J. Lab. Invest. 2002, 82, 1265–1277. (54) Koljenovic’, S.; Bakker Schut, T. C.; Vincent, A.; Kros, J. M.; Puppels, G. J. Anal. Chem. 2005, 77, 7958–7965. (55) Krafft, C.; Neudert, L.; Simat, T.; Salzer, R. Spectrochim. Acta. Part A 2005, 61, 1529–1535. (56) Bambery, K. R.; Schu ¨ ltke, E.; Wood, B. R.; Rigley MacDonald, S. T.; Ataelmannan, K.; Griebel, R. W.; Juurlink, B. H.; McNaughton, D. A. Biochim. Biophys. Acta 2006, 1758, 900–907. Campanella, R. J. Neurosurg. Sci. 1992, 36, 11–25. (57) Krafft, C.; Sobottka, S. B.; Geiger, K. D.; Schackert, G.; Salzer, R. Anal. Bioanal. Chem. 2007, 387, 1669–1677.
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based on protein and nucleic acids instead of the absence of lipids. In our point of view, by removing lipid from tissues we lose some important information especially for early diagnosis of tissue transformation. Peak ratios are commonly used to discriminate normal from tumor tissues. Krafft et al. have evaluated the usefulness of the lipid-to-protein ratio (2850/1655 cm-1) as a spectroscopic marker to discriminate between normal and tumor tissue, as well as between low- and high-grade glioma tissues.57 Rather than attemping to find and use only an isolated spectral feature in the analysis of spectral data, multivariate methods derive their power from the simultaneous use of multiple intensities (i.e., multiple variables) in each spectrum. FTIR spectroscopy provides information on broad classes of molecules, such as lipids, proteins, carbohydrates, and make up the complex medium of cell and tissues. The spectrum from cells and tissues is an integration of these individual signals from all biomolecules.58 Since each molecular species is associated with specific frequencies, it may thus be possible to identify and quantify these biomolecules individually within the spectrum. Several authors have used NNLS methods for the quantification of individual constituents after tissue alteration. The models developed were used to explain the features present in the tissue/cellular spectra by using spectral data of pure molecules. The quantification of several biomolecules seen during tissue transformation can be used to classify disease states with high sensitivity, specificity, and accuracy. Wang et al. have investigated formalin-fixed Barrett’s esophagus for predicting the underlying histopathology, the early detection and rapid staging of many diseases.13 Their model seems to be very accurate in spite of the presence of a number of common spectral features or molecular interactions and variations that can broaden individual peaks. Krafft et al. have developed a supervised classification model based on the LDA algorithm to IR images of three specimens from one patient.57 This multivariate method was used to develop a chemical/morphological model to quantify chemical composition of coronary atherosclerosis,36 (58) Diem, M.; Romeo, M.; Boydston-White, S.; Miljkoviæ, M.; Mattha¨us, C. Analyst 2004, 129, 880–885.
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breast tissue,37 porcine brain,38 and molecular concentration profiles in the skin.40 CONCLUSION This study demonstrates the potential of FT-IR combined to multivariate statistical analysis to successfully monitor the changes in the molecular composition associated to C6 glioma progression. Cluster analysis allowed investigation of C6 glioma progression (from day 7 to day 21). Different clusters in the FTIR images were correlated with features of the histopathological images such as white and gray matters, tumor, peritumor, and necrosis. In fact, tumor cells appear seven days postimplantation, destroy the corpus callosum and grew into all the cortical layers. Necrosis was visible from 15 days after tumor injection and increased until 21 days. NNLS method was used to accurately quantify the contributions of biochemical changes associated to the development of tumor. Statistical analysis on the fit contribution of the all individual brain constituents during the C6 glioma progression showed that significant changes were associated to SMY, DNA, PS, and GalC. We have mapped, point by point, the distribution of these brain constituents. The distribution of PS allowed the (i) identification of abnormally observed at 7 days postimplantation and (ii) delimitation of the tumor and invasive zones. The GalC permitted monitoring the evolution of the necrosis from days 15 to 21. The DNA and SMY contents were higher in the tumor and lower in the invasive and normal brain structures. These constituents can be used as spectroscopic markers for early detection of tissue abnormality and discrimination among normal, invasion, tumor, and necrosis. ACKNOWLEDGMENT This work was supported by La Ligue de la Marne contre le Cancer, France. We thank Dr. G.Sockalingum for providing assistance in the preparation of the manuscript and Prof. J. M. Millot for statistical analysis. Received for review July 3, 2009. Accepted October 1, 2009. AC901464V
