Unsupervised unmixing of Raman microspectroscopic images for morphochemical analysis of non-dried brain tumor specimens

Raman microspectroscopic imaging provides molecular contrast in a label-free manner with subcellular spatial resolution. These properties might complement clinical tools for diagnosis of tissue and cells in the future. Eight Raman spectroscopic images were collected with 785 nm excitation from five non-dried brain specimens immersed in aqueous buffer. The specimens were assigned to molecular and granular layers of cerebellum, cerebrum with and without scattered tumor cells of astrocytoma WHO grade III, ependymoma WHO grade II, astrocytoma WHO grade III, and glioblastoma multiforme WHO grade IV with subnecrotic and necrotic regions. In contrast with dried tissue section, these samples were not affected by drying effects such as crystallization of lipids or denaturation of proteins and nucleic acids. The combined data sets were processed by use of the hyperspectral unmixing algorithms N-FINDR and VCA. Both unsupervised approaches calculated seven endmembers that reveal the abundance plots and spectral signatures of cholesterol, cholesterol ester, nucleic acids, carotene, proteins, lipids, and buffer. The endmembers were correlated with Raman spectra of reference materials. The focus of the single mode laser near 1 μm and the step size of 2 μm were sufficiently small to resolve morphological details, for example cholesterol ester islets and cell nuclei. The results are compared for both unmixing algorithms and with previously reported supervised spectral decomposition techniques.     

Suitability of infrared spectroscopic imaging as an intraoperative tool in cerebral glioma surgery

Infrared spectroscopic imaging is a promising intraoperative tool which enables rapid, on-site diagnosis of brain tumors during neurosurgery. A classification model was recently developed using infrared spectroscopic images from thin tissue sections to grade malignant gliomas, the most frequent class of primary brain tumor. In this study the model was applied to 54 specimens from six patients with inhomogeneous gliomas composed of regions with different tumor density and morphology. The resection was controlled using neuronavigation which transfers the findings obtained by preoperative magnetic resonance imaging (MRI) into the operating field. For comparison, all specimens were independently evaluated by histopathology after hematoxylin and eosin staining. The infrared-derived grading agreed with histopathology and MRI findings for almost all specimens. With regard to histopathological assessment, sensitivities of 100% (22/22) and 93.1% (27/29) and specificities of 96.9% (31/32) and 88.0% (22/25) were achieved, depending on whether the classification was based on the predominant or maximal tumor grade, respectively, in the specimen. Altogether, in 98% (53/54) of all specimens the decision to continue or not continue tumor resection could have been made according to the infrared spectroscopic classification. This retrospective study clearly demonstrates that infrared spectroscopic imaging may help to define tumor margins intraoperatively and to detect high-grade tumor residues for achieving more radical tumor resection. MRT-guided tumor resection (left) is combined with infrared spectroscopy-based tissue classification (middle, right). With regard to histopathological assessment, sensitivities of 100% and 93.1% and specificities of 96.9% and 88.0% were achieved for 54 specimens. (TIF 47.1 KB)     

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.     

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.     

Delimitation of squamous cell cervical carcinoma using infrared microspectroscopic imaging

Infrared (IR) spectroscopic imaging coupled with microscopy has been used to investigate thin sections of cervix uteri encompassing normal tissue, precancerous structures, and squamous cell carcinoma. Methods for unsupervised distinction of tissue types based on IR spectroscopy were developed. One-hundred and twenty-two images of cervical tissue were recorded by an FTIR spectrometer with a 64×64 focal plane array detector. The 499,712 IR spectra obtained were grouped by an approach which used fuzzy C-means clustering followed by hierarchical cluster analysis. The resulting false color maps were correlated with the morphological characteristics of an adjacent section of hematoxylin and eosin-stained tissue. In the first step, cervical stroma, epithelium, inflammation, blood vessels, and mucus could be distinguished in IR images by analysis of the spectral fingerprint region (950–1480 cm⁻¹). In the second step, analysis in the spectral window 1420–1480 cm⁻¹ enables, for the first time, IR spectroscopic distinction between the basal layer, dysplastic lesions and squamous cell carcinoma within a particular sample. The joint application of IR microspectroscopic imaging and multivariate spectral processing combines diffraction-limited lateral optical resolution on the single cell level with highly specific and sensitive spectral classification on the molecular level. Compared with previous reports our approach constitutes a significant progress in the development of optical molecular spectroscopic techniques toward an additional diagnostic tool for the early histopathological characterization of cervical cancer.     

Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) of the oral cavity and oropharynx represents more than 95% of all malignant neoplasms in the oral cavity. Histomorphological evaluation of this cancer type is invasive and remains a time consuming and subjective technique. Therefore, novel approaches for histological recognition are necessary to identify malignancy at an early stage. Fourier transform infrared (FTIR) imaging has become an essential tool for the detection and characterization of the molecular components of biological processes, such as those responsible for the dynamic properties of tumor progression. FTIR imaging is a modern analytical technique enabling molecular imaging of a complex biological sample and is based on the absorption of IR radiation by vibrational transitions in covalent bonds. One major advantage of this technique is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification, and separation steps. With this imaging technique, it is possible to obtain unique images of the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules with high spatial resolution. Analysis and visualization of FTIR imaging datasets are challenging and the use of chemometric tools is crucial in order to take advantage of the full measurement. Therefore, methodologies for this task based on the novel developed algorithm for multivariate image analysis (MIA) are often necessary. In the present study, FTIR imaging and data analysis methods were combined to optimize the tissue measurement mode after deparaffinization and subsequent data evaluation (univariate analysis and MIAs). We demonstrate that it is possible to collect excellent IR spectra from formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) of OSCC tissue sections employing an optimised analytical protocol. The correlation of FTIR imaging to the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The different algorithms used for MIAs of FTIR imaging data dramatically increased the information content of the IR images from squamous cell tissue sections. These findings indicate that intra-operative and surgical specimens of squamous cell carcinoma tissue can be characterized by FTIR imaging.     

5-Aminolevulinic Acid-induced Protoporphyrin IX Levels in Tissue of Human Malignant Brain Tumors

Protoporphyrin IX (PpIX) produced from exogenous, orally administered 5-aminolevulinic acid (ALA) displays high tumor-selective uptake and is being successfully employed for fluorescence-guided resection (FGR) of human malignant gliomas. Furthermore, the phototoxicity of PpIX can be utilized for photodynamic therapy (PDT) of brain tumors, which has been shown previously. Here, the absolute PpIX concentration in human brain tissue was investigated following oral ALA administration (20 mg kg⁻¹ b.w.). An extraction procedure was used to quantify PpIX in macroscopic tissue samples, weighing 0.013–0.214 g, obtained during FGR. The PpIX concentration was significantly higher in vital grade IV tumors (5.8 ± 4.8 μm , mean ± SD, range 0–28.2 μm , n = 8) as compared with grade III tumors (0.2 ± 0.4 μm , mean ± SD, range 0–0.9 μm , n = 4). There was also a large heterogeneity within grade IV tumors with PpIX displaying significantly lower levels in infiltration zones and necrotic regions as compared with vital tumor parts. The average PpIX concentration in vital grade IV tumor parts was in the range previously shown sufficient for PDT-induced tissue damage following irradiation. However, the feasibility of PDT for grade III brain tumors and for grade IV brain tumors displaying mainly necrotic tissue areas without solid tumor parts needs to be further investigated.     

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.     

Differentiation of individual human mesenchymal stem cells probed by FTIR microscopic imaging

Objective of this study is the novel application of Fourier transform infrared (FTIR) microscopic imaging to identify the differentiation state of individual human mesenchymal stem cells with or without osteogenic stimulation. IR spectra of several hundred single cells with lateral resolution of 5-10 microm were recorded using a FTIR imaging spectrometer coupled to a microscope with a focal plane array detector. A classification model based on linear discriminant analysis was trained to distinguish four cell types by their IR spectroscopic fingerprint. Without stimulation two cell types dominated, showing low or high levels of glycogen accumulation at the cell periphery. After stimulation, the protein composition in the cells changed and some cells started expressing calcium phosphate salts such as octacalciumphosphate, a precursor of the bone constituent hydroxyapatite. Few cells were identified which remained in their non-stimulated state. This study demonstrated for the first time that FTIR microscopic imaging can probe stem cell differentiation at the single cell level rapidly, non-destructively and with minimal preparation.     

Fluorescence imaging and point measurements of tissue: applications to the demarcation of malignant tumors and atherosclerotic lesions from normal tissue

The possibilities of using laser-induced fluorescence for tissue diagnostics are discussed. The tissue types investigated are malignant tumors and atherosclerotic lesions. Studies with natural autofluorescence as well as with fluorescent tumor markers are included in this paper. Fluorescence emission and decay data are presented for some tissue chromophores contributing to tissue autofluorescence. Optical spectroscopic characteristics of fluorescent malignant tumor markers are analyzed and instrumental designs for clinical applications are discussed. Images recorded with a multicolor fluorescence imaging system developed in Lund are presented.     

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.     

Hyperspectral unmixing of Raman micro-images for assessment of morphological and chemical parameters in non-dried brain tumor specimens

Hyperspectral unmixing is an unsupervised algorithm to calculate a bilinear model of spectral endmembers and abundances of components from Raman images. Thirty-nine Raman images were collected from six glioma brain tumor specimens. The tumor grades ranged from astrocytoma WHO II to glioblastoma multiforme WHO IV. The abundance plots of the cell nuclei were processed by an image segmentation procedure to determine the average nuclei size, the number of nuclei, and the fraction of nuclei area. The latter two morphological parameters correlated with the malignancy. A combination of spectral unmixing and non-negativity constrained linear least squares fitting is introduced to assess chemical parameters. First, endmembers of the most abundant and most dissimilar components were defined that represent all data sets. Second, the content of the obtained components’ proteins, nucleic acids, lipids, and lipid to protein ratios were determined in all Raman images. Except for the protein content, all chemical parameters correlated with the malignancy. We conclude that the morphological and chemical information offer new ways to develop Raman-based classification approaches that can complement diagnosis of brain tumors. The role of non-linear Raman modalities to speed-up image acquisition is discussed.

mTHPC-mediated photodynamic diagnosis of malignant brain tumors.

Radical tumor resection is the basis for the prolonged survival of patients suffering from malignant brain tumors such as glioblastoma multiforme. We have carried out a phase-II study involving 22 patients with malignant brain tumors to assess the feasibility and the effectiveness of the combination of intraoperative photodynamic diagnosis and fluorescence-guided resection (FGR) mediated by the second-generation photosensitizer meta-tetrahydroxyphenylchlorin (mTHPC). In addition, intraoperative photodynamic therapy (PDT) was performed. Several commercially available fluorescence diagnostic systems were investigated for their applicability in clinical practice. We have adapted and optimized a diagnostic system that includes a surgical microscope, an excitation light source (filtered to 370-440 nm), a video camera detection system and a spectrometer for clear identification of the mTHPC fluorescence emission at 652 nm. Especially in regions of faint fluorescence, it turned out to be essential to maximize the spectral information by optimizing and matching the spectral properties of all components, such as excitation source, camera and color filters. To sum up, on the basis of 138 tissue samples derived from 22 tumor specimens, we have been able to achieve a sensitivity of 87.9% and a specificity of 95.7%. This study demonstrates that mTHPC-mediated intraoperative FGR followed by PDT is a highly promising concept in improving the radicality of tumor resection combined with a therapeutic approach.     

Mass spectrometry imaging as a tool for surgical decision‐making

Despite significant advances in image‐guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image‐guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characterize the tissue at stake is histopathology. Although extremely valuable information is derived from this gold standard approach, it is limited to very few samples during surgery and is not practically used for the delineation of tumor margins. The development and implementation of faster, comprehensive, and complementary approaches for tissue characterization are required to support surgical decision‐making – an incremental and iterative process with tumor removed in multiple and often minute biopsies. The development of atmospheric pressure ionization sources makes it possible to analyze tissue specimens with little to no sample preparation. Here, we highlight the value of desorption electrospray ionization as one of many available approaches for the analysis of surgical tissue. Twelve surgical samples resected from a patient during surgery were analyzed and diagnosed as glioblastoma tumor or necrotic tissue by standard histopathology, and mass spectrometry results were further correlated to histopathology for critical validation of the approach. The use of a robust statistical approach reiterated results from the qualitative detection of potential biomarkers of these tissue types. The correlation of the mass spectrometry and histopathology results to MRI brings significant insight into tumor presentation that could not only serve to guide tumor resection, but that is also worthy of more detailed studies on our understanding of tumor presentation on MRI. Copyright © 2013 John Wiley & Sons, Ltd.     

ALUMINUM SULFONATED PHTHALOCYANINE DISTRIBUTION IN RODENT TUMORS OF THE COLON, BRAIN and PANCREAS

Abstract The most widely discussed aspect of photodynamic therapy (PDT) is the preferential uptake and retention of sensitisers by malignant tissues. The sensitiser usually used is hematoporphyrin derivative (HPD) but this compound is not an ideal photosensitiser for this purpose and we have therefore studied an aluminum sulfonated phthalocyanine (AlSPc) as a possible alternative. Here we have studied the uptake and retention of this compound in a rat colon cancer, a hamster pancreatic cancer and a mouse glioma, using an alkali extraction technique to estimate tissue AlSPc and comparing the results with those from the corresponding normal tissues. All of the tumors studied reached accumulation peaks at 24-48 h after intravenous administration of AlSPc compared with peaks at 1-3 h in the normal tissues. The tumors outside the central nervous system (CNS) reached peak tumor : normal tissue ratios of 2-3 : 1 and the tumor within the CNS, the malignant glioma, reached a far higher ratio of 28 : 1. These ratios are similar to those reported by others using HPD.     

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.     

Distinction of malignant melanoma and epidermis using IR micro-spectroscopy and statistical methods

Infrared spectroscopy is widely perceived as a future technology for cancer detection and grading. Malignant melanoma, an aggressive skin cancer, is accessible to non-invasive IR radiation based surface probes for its identification and grading. The present work examines the differences in the IR spectra of melanoma tissues and the surrounding epidermis in skin biopsies with the objective of identifying diagnostic parameters and suitable computational/statistical methods of analysis. Melanoma could be differentiated from the epidermis in biopsies of 55 patients, using parameters derived from absorbance bands originating from molecular vibrations of nucleic acids and/or their bases. Additionally, absorbances from tyrosine and phosphate that are abnormally elevated in malignant melanoma could be used as markers. Two-dimensional plots of these parameters in tandem with advanced statistical methods successfully demonstrate the potential of IR spectroscopy to distinguish between epidermal and melanoma regions with a high classification success. The work underlines the importance of developing data analysis methods in FTIR based diagnosis using melanoma as a model system.     

An integrated experimental and analytical approach to the chemical state imaging of iron in brain gliomas using X-ray absorption near edge structure spectroscopy

X-ray absorption near-edge structure spectroscopy is used for human neoplastic tissues in order to investigate distributions and chemical states of iron. The specimens used in this study were obtained intraoperatively from brain gliomas of different types and various grades of malignancy and from a control subject. An integrated experimental and analytical approach toward topographic and quantitative analysis in thin freeze-dried cryo-sections is presented. The full XANES spectra at the Fe absorption K edge show the presence of both chemical forms of Fe in the analyzed points of the tissues. The main goal of the work is the chemical state imaging of Fe in tissue areas. Topographic analysis of Fe speciation in the tissues investigated with the use of the XANES technique indicates the presence of microstructures where Fe²⁺ is dominant as well as those with a high abundance of the oxidized form of Fe. The quantitative analysis shows that for all cases the content of the oxidized form of Fe is significantly higher in comparison with Fe²⁺. The highest level of Fe³⁺ is found in the control sample, and the lowest one for the glioma of the highest grade of malignancy. The content of either Fe²⁺ or Fe³⁺ is increased in low grade gliomas in comparison to high-grade malignant tumors.