Label-free imaging and spectroscopic analysis of intracellular bacterial infections.

Staphylococcus aureus is one of the most frequent human pathogens that can also act as a facultative intracellular pathogen causing infections that are extremely difficult to treat. Only little is known about the pathogen's intracellular adaptation strategies to escape the host's response. Here, we present an advanced Raman-based imaging approach providing high quality false-color images to specifically identify intracellular S. aureus and to localize them exactly in three dimensions within endothelial cells. At the same time unprecedented insights into the metabolic characteristics of the pathogen are provided in a label-free and nondestructive manner. The spectral information reveals that the intracellular bacteria are in the exponential growth phase with a reduced replication rate and biochemically different from extracellular bacteria proving their adaptation to the host's conditions. This powerful biophotonic analysis tool paves the way for further mechanistic studies of difficult-to-investigate infection processes.

Tumor margin identification and prediction of the primary tumor from brain metastases using FTIR imaging and support vector machines.

Infrared spectroscopy enables the identification of tissue types based on their inherent vibrational fingerprint without staining in a nondestructive way. Here, Fourier transform infrared microscopic images were collected from 22 brain metastasis tissue sections of bladder carcinoma, lung carcinoma, mamma carcinoma, colon carcinoma, prostate carcinoma and renal cell carcinoma. The scope of this study was to distinguish the infrared spectra of carcinoma from normal tissue and necrosis and to use the infrared spectra of carcinoma to determine the primary tumor of brain metastasis. Data processing follows procedures that have previously been developed for the analysis of Raman images of these samples and includes the unmixing algorithm N-FINDR, segmentation by k-means clustering, and classification by support vector machines (SVMs). Upon comparison with the subsequent hematoxylin and eosin stained tissue sections of training specimens, correct classification rates of the first level SVM were 98.8% for brain tissue, 98.4% for necrosis and 94.4% for carcinoma. The primary tumors were correctly predicted with an overall rate of 98.7% for FTIR images of the training dataset by a second level SVM. Finally, the two level discrimination models were applied to four independent specimens for validation. Although the classification rates are slightly reduced compared to the training specimens, the majority of the infrared spectra of the independent specimens were assigned to the correct primary tumor. The results demonstrate the capability of FTIR imaging to complement histopathological tools for brain tissue diagnosis.

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.

Advances in optical biopsy--correlation of malignancy and cell density of primary brain tumors using Raman microspectroscopic imaging.

Raman spectroscopy is a promising tool towards biopsy under vision as it provides label-free image contrast based on intrinsic vibrational spectroscopic fingerprints of the specimen. The current study applied the spectral unmixing algorithm vertex component analysis (VCA) to probe cell density and cell nuclei in Raman images of primary brain tumor tissue sections. Six Raman images were collected at 785 nm excitation that consisted of 61 by 61 spectra at a step size of 2 micrometers. After data acquisition the samples were stained with hematoxylin and eosin for comparison. VCA abundance plots coincided well with histopathological findings. Raman spectra of high grade tumor cells were found to contain more intense spectral contributions of nucleic acids than those of low grade tumor cells. Similarly, VCA endmember signatures of Raman images from high grade gliomas showed increased nucleic acid bands. Further abundance plots and endmember spectra were assigned to tissue containing proteins and lipids, and cholesterol microcrystals. Since no sample preparation is required, an important advantage of the Raman imaging methodology is that all tissue components can be observed - even those that may be lost in sample staining steps. The results demonstrate how morphology and chemical composition obtained by Raman imaging correlate with histopathology and provide complementary, diagnostically relevant information at the cellular level.

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.

Plasmonic modes of extreme subwavelength nanocavities.

We study the physics of a new type of subwavelength nanocavities. They are based on U-shaped metal-insulator-metal waveguides supporting the excitation of surface plasmon polaritons. The nanocavity arrays are excited by plane waves at either a normal or oblique incidence. Because of their finite length, discrete modes emerge within the nanocavity. We show that the excitation symmetry with respect to the cavity ends permits the observation of even and odd modes. Our investigations include near- and far-field simulations and predict a strong spectral far-field response of the comparably small nanoresonators. The strong near-field enhancement observed in the cavity at resonance might be suitable to increase the efficiency of nonlinear optical effects and quantum analogies and might facilitate the development of optical elements, such as active plasmonic devices.

Characterization of atherosclerotic plaque depositions by Raman and FTIR imaging.

Spectroscopy-based imaging techniques can provide useful biochemical information about tissue samples. Here, we employ Raman and Fourier transform infrared (IR) imaging to characterize composition and constitution of atherosclerotic plaques of rabbits, fed with a high cholesterol diet. The results were compared with conventional light microscopy after staining with hematoxylin eosin, and elastica van Gieson. The spectral unmixing algorithm vertex component analysis was applied for data analysis and image reconstruction. IR microscopy allowed for differentiation between lipids and proteins in plaques of full aortic cross sections. Raman microscopy further discriminated cholesterol esters, cholesterol and triglycerides. FTIR and Raman images were recorded at a resolution near 20 micrometer per pixel for a large field of view. High resolution Raman images at 1 micrometer per pixel revealed structural details at selected regions of interest. The intima-media and the lipid-protein ratio were determined in five specimens for quantitation. These results correlate well with histopathology. The described method is a promising tool for easy and fast molecular imaging of atherosclerosis.

Interpreting CARS images of tissue within the C-H-stretching region.

Single band coherent anti-Stokes Raman scattering (CARS) microscopy is one of the fastest implementation of nonlinear vibrational imaging allowing for video-rate image acquisition of tissue. This is due to the large Raman signal in the C-H-stretching region. However, the chemical specificity of such images is conventionally assumed to be low. Nonetheless, CARS imaging within the C-H-stretching region enables detection of single cells and nuclei, which allows for histopathologic grading of tissue. Relevant information such as nucleus to cytoplasm ratio, cell density, nucleus size and shape is extracted from CARS images by innovative image processing procedures. In this contribution CARS image contrast within the C-H-stretching region is interpreted by direct comparison with Raman imaging and correlated to the tissue composition justifying the use of CARS imaging in this wavenumber region for biomedical applications.

Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis.

Contemporary brain tumor research focuses on two challenges: First, tumor typing and grading by analyzing excised tissue is of utmost importance for choosing a therapy. Second, for prognostication the tumor has to be removed as completely as possible. Nowadays, histopathology of excised tissue using haematoxylin-eosine staining is the gold standard for the definitive diagnosis of surgical pathology specimens. However, it is neither applicable in vivo, nor does it allow for precise tumor typing in those cases when only nonrepresentative specimens are procured. Infrared and Raman spectroscopy allow for very precise cancer analysis due to their molecular specificity, while nonlinear microscopy is a suitable tool for rapid imaging of large tissue sections. Here, unstained samples from the brain of a domestic pig have been investigated by a multimodal nonlinear imaging approach combining coherent anti-Stokes Raman scattering, second harmonic generation, and two photon excited fluorescence microscopy. Furthermore, a brain tumor specimen was additionally analyzed by linear Raman and Fourier transform infrared imaging for a detailed assessment of the tissue types that is required for classification and to validate the multimodal imaging approach. Hence label-free vibrational microspectroscopic imaging is a promising tool for fast and precise in vivo diagnostics of brain tumors.