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.     

Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-??m step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.     

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.     

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.     

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.     

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.     

Characterization and analysis of mycobacteria and Gram-negative bacteria and co-culture mixtures by Raman microspectroscopy, FTIR, and atomic force microscopy

The molecular composition of mycobacteria and Gram-negative bacteria cell walls is structurally different. In this work, Raman microspectroscopy was applied to discriminate mycobacteria and Gram-negative bacteria by assessing specific characteristic spectral features. Analysis of Raman spectra indicated that mycobacteria and Gram-negative bacteria exhibit different spectral patterns under our experimental conditions due to their different biochemical components. Fourier transform infrared (FTIR) spectroscopy, as a supplementary vibrational spectroscopy, was also applied to analyze the biochemical composition of the representative bacterial strains. As for co-cultured bacterial mixtures, the distribution of individual cell types was obtained by quantitative analysis of Raman and FTIR spectral images and the spectral contribution from each cell type was distinguished by direct classical least squares analysis. Coupled atomic force microscopy (AFM) and Raman microspectroscopy realized simultaneous measurements of topography and spectral images for the same sampled surface. This work demonstrated the feasibility of utilizing a combined Raman microspectroscopy, FTIR, and AFM techniques to effectively characterize spectroscopic fingerprints from bacterial Gram types and mixtures.

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.     

Confocal Raman microspectral imaging (CRMI) of murine stem cell colonies

Confocal Raman micro-spectral imaging (CRMI) is a relatively novel technique for the construction of label-free images of biological entities, such as cells or tissue sections. This method utilizes thousands of spatially resolved Raman spectra, and sophisticated image analysis algorithms, to construct images which are based strictly on the inherent biochemical abundance contrast afforded by Raman microscopy. Here, we apply this methodology to monitor the very early communication processes that occur in a growing stem cell colony, yielding information on biochemical composition during development processes of the early stages of embryogenesis.     

Multivariate image analysis of a set of FTIR microspectroscopy images of aged bovine muscle tissue combining image and design information

In this paper we present an algorithm for analysing sets of FTIR microscopic images of tissue sections. The proposed approach allows one to investigate sets of many FTIR tissue images both with respect to sample information (variation from image to image) and spatial variations of tissues (variation within the image). The algorithm is applied to FTIR microscopy images of beef loin muscles containing myofibre and connective tissue regions. The FTIR microscopy images are taken of sub-samples from five different beef loin muscles that were aged for four different lengths of time. The images were investigated regarding variation due to the ageing length and due to the homogeneity of the connective tissue regions. The presented algorithm consists of the following main elements: (1) pre-processing of the spectra to overcome large quality differences in FTIR spectra and differences due to scatter effects, (2) identification of connective tissue regions in every image, (3) labelling of every connective tissue spectrum with respect to its location in the connective tissue region, and (4) analysis of variations in the FTIR microscopic images in regard to ageing time and pixel position of the spectra in the connective tissue region. Important spectral parameters characterising collagen and proteoglycan structure were determined. Figure Effective optical path length estimated by EMSC     

Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues

Hyperspectral images can provide useful biochemical information about tissue samples. Often, Fourier transform infrared (FTIR) images have been used to distinguish different tissue elements and changes caused by pathological causes. The spectral variation between tissue types and pathological states is very small and multivariate analysis methods are required to describe adequately these subtle changes.     

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.     

Fourier transform infrared imaging analysis in discrimination studies of St. John's wort (Hypericum perforatum)

In the present study, Fourier transform infrared (FTIR) imaging and data analysis methods were combined to study morphological and molecular patterns of St. John's wort (Hypericum perforatum) in detail. For interpretation, FTIR imaging results were correlated with histological information gained from light microscopy (LM). Additionally, we tested several evaluation processes and optimized the methodology for use of complex FTIR microscopic images to monitor molecular patterns. It is demonstrated that the combination of the used spectroscopic method with LM enables a more distinct picture, concerning morphology and distribution of active ingredients, to be gained. We were able to obtain high-quality FTIR microscopic imaging results and to distinguish different tissue types with their chemical ingredients.     

Reflective Light Properties of Tissue Layers in Beech (Fagus sylvatica L.) Leaves

Abstract— Reflective light properties of various tissue layers (adaxial epidermis, palisade and spongy parenchyma and abaxial epidermis) of beech (Fagus sylvatica L.) leaves were measured using paradermal (parallel to epidermis) and transverse sections in the visible and infrared spectral bands by a high-resolution reflectance cytophotometer. Results showed an increment of reflectances of rest leaves following successive removal of paradermal tissue layers (adaxial epidermis, palisade parenchyma, abaxial epidermis). Reflectances of palisade parenchyma with spongy parenchyma and abaxial epidermis increased linearly in the infrared and green spectral bands with increasing chloroplast count. The enhancement of paradermal reflectances with successive removal of paradermal tissue layers was due to various optical effects of tissue/cell layers, whereby removal of paradermal tissue layers containing light absorbers, e.g. chloroplasts/chlorophyll-protein complexes, is of importance. The parabolic relationships found between lateral reflectances and distances of various tissue/cell layers from adaxial epidermis indicated lateral reflectances of the mesophyll to be governed mainly by selective chlorophyll absorb-ances of light according to Lambert-Beer's law.     

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.     

Blind image analysis for the compositional and structural characterization of plant cell walls

A new image analysis strategy is introduced to determine the composition and the structural characteristics of plant cell walls by combining Raman microspectroscopy and unsupervised data mining methods. The proposed method consists of three main steps: spectral preprocessing, spatial clustering of the image and finally estimation of spectral profiles of pure components and their weights. Point spectra of Raman maps of cell walls were preprocessed to remove noise and fluorescence contributions and compressed with PCA. Processed spectra were then subjected to k-means clustering to identify spatial segregations in the images. Cell wall images were reconstructed with cluster identities and each cluster was represented by the average spectrum of all the pixels in the cluster. Pure components spectra were estimated by spectral entropy minimization criteria with simulated annealing optimization. Two pure spectral estimates that represent lignin and carbohydrates were recovered and their spatial distributions were calculated. Our approach partitioned the cell walls into many sublayers, based on their composition, thus enabling composition analysis at subcellular levels. It also overcame the well known problem that native lignin spectra in lignocellulosics have high spectral overlap with contributions from cellulose and hemicelluloses, thus opening up new avenues for microanalyses of monolignol composition of native lignin and carbohydrates without chemical or mechanical extraction of the cell wall materials.     

Assessment of the ageing of triterpenoid paint varnishes using fluorescence,Raman and FTIR spectroscopy

The assessment of the influence of natural and artificial ageing on the spectrofluorescence of triterpenoid varnishes dammar and mastic is the focus of this work. Both Fourier transform infrared (FTIR) microscopy using attenuated total reflectance and Raman spectroscopy have been employed for complementary molecular analysis of samples. Synchronous fluorescence spectroscopy, excitation emission spectroscopy, and statistical analysis of data have been used to monitor changes in the optical properties of varnish samples. Assessment of naturally and artificially aged samples using excitation emission spectroscopy suggests that extensive exposure to visible light does not lead to easily appreciable differences in the fluorescence of mastic and dammar; cluster analysis has been used to assess changes, which occur with artificial ageing under visible light, indicating that differences in the fluorescence spectra of aged triterpenoids may be insufficient for their discrimination. The results highlight significant differences between the initial fluorescence of films of dammar and mastic and the fluorescence, which develops with ageing and oxidation, and specific markers, which change with ageing in FTIR and Raman spectra, have been identified.     

High contrast images of uterine tissue derived using Raman microspectroscopy with the empty modelling approach of multivariate curve resolution-alternating least squares

Approaches that allow one to rapidly understand tissue structure and functionality in situ remain to be developed. Such techniques are required in many instances, including where there is a need to remove with a high degree of confidence positive tumour margins during surgical excision. As biological tissue has little contrast, gold standard confirmation of surgical margins is conventionally undertaken by histopathological diagnosis of tissue architecture via optical microscopy. Vibrational spectroscopy techniques, when coupled to sophisticated computational analyses, are capable of constructing bio-molecular contrast images of unstained tissue. To assess the relative applicability of a range of candidate algorithms to distinguish the in situ bio-molecular structures of a complex tissue, the empty modelling approach of multivariate curve resolution-alternating least squares (MCR-ALS) was compared to hierarchical cluster analysis (HCA) or principal component analysis (PCA). Such chemometric analyses were applied to Raman images of benign (tumour-adjacent) endometrium, stage I and stage II endometrioid cancer. Re-constructed images from the in situ bio-molecular tissue architectures highlighted features associated with glandular epithelium, stroma, glandular lumen and myometrium. Of the tested chemometric analyses, MCR-ALS provided the best bio-molecular contrast images, superior to those derived following HCA or PCA, with clear and defined margins of histological features. Iteratively-resolved spectra identified wavenumbers responsible for the contrast image. Wavenumbers 1234 cm(-1) (Amide III), 1390 cm(-1) (CH(3) bend), 1675 cm(-1) (Amide I/lipid), 1275 cm(-1) (Amide III), 918 cm(-1) (proline) and 936 cm(-1) (proline, valine and proteins) were responsible for generating the majority of the contrast within MCR-ALS-generated images. Applications of sophisticated computational analyses coupled with vibrational spectroscopy techniques have the potential to lend novel functionality insights into bio-molecular structures in vivo.     

Simultaneous Raman and infrared spectroscopy: a novel combination for studying bacterial infections at the single cell level

Sepsis is a life-threatening clinical condition responsible for approximately 11 million deaths worldwide. Rapid and accurate identification of pathogenic bacteria and its antimicrobial susceptibility play a critical role in reducing the morbidity and mortality rates related to sepsis. Raman and infrared spectroscopies have great potential to be used as diagnostic tools for rapid and culture-free detection of bacterial infections. Despite numerous reports using both methods to analyse bacterial samples, there is to date no study collecting both Raman and infrared signatures from clinical samples simultaneously due to instrument incompatibilities. Here, we report for the first time the use of an emerging technology that provides infrared signatures via optical photothermal infrared (O-PTIR) spectroscopy and Raman spectra simultaneously. We use this approach to analyse 12 bacterial clinical isolates including six isolates of Gram-negative and six Gram-positive bacteria commonly associated with bloodstream infection in humans. To benchmark the single cell spectra obtained by O-PTIR spectroscopy, infrared signatures were also collected from bulk samples via both FTIR and O-PTIR spectroscopies. Our findings showed significant similarity and high reproducibility in the infrared signatures obtained by all three approaches, including similar discrimination patterns when subjected to clustering algorithms. Principal component analysis (PCA) showed that O-PTIR and Raman data acquired simultaneously from bulk bacterial isolates displayed different clustering patterns due to the ability of both methods to probe metabolites produced by bacteria. By contrast, signatures of microbial pigments were identified in Raman spectra, providing complementary and orthogonal information compared to infrared, which may be advantageous as it has been demonstrated that certain pigments play an important role in bacterial virulence. We found that infrared spectroscopy showed higher sensitivity than Raman for the analysis of individual cells. Despite the different patterns obtained by using Raman and infrared spectral data as input for clustering algorithms, our findings showed high data reproducibility in both approaches as the biological replicates from each bacterial strain clustered together. Overall, we show that Raman and infrared spectroscopy offer both advantages and disadvantages and, therefore, having both techniques combined in one single technology is a powerful tool with promising applications in clinical microbiology.

O-PTIR was used for simultaneous collection of infrared and Raman spectra from clinical pathogens associated with bloodstream infections.     

Vibrational spectroscopic (FTIR and FT Raman) studies, first order hyperpolarizabilities and HOMO, LUMO analysis of p-toluenesulfonyl isocyanate using ab initio HF and DFT methods

The Fourier transform infrared (FTIR) and FT Raman spectra of p-toluenesulfonyl isocyanate (p-tosyl isocyanate) have been measured. The molecular geometry, vibrational frequencies, infrared intensities, Raman activities and atomic charges have been calculated by using ab initio HF and density functional theory calculation (B3LYP) with 6-311+G(d,p) basis set. Complete vibrational assignment and analysis of the fundamental modes of the compound were carried out using the observed FTIR and FT Raman data. The thermodynamic functions of the title compound were also performed with the aid of HF/6-311+G(d,p) and B3LYP/6-311+G(d,p) levels of theory. Simulated FTIR and FT Raman spectra for p-tosyl isocyanate showed good agreement with the observed spectra. The calculated HOMO and LUMO energies show that charge transfer occurs within the molecule. The dipole moment (μ), polarizability (α) and the hyperpolarizability (β) values of the investigated molecule have been computed using HF and B3LYP methods.