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Molecular Pathology via Infrared and Raman Spectral Imaging1)
Abstract
During the last 15years, vibrational spectroscopic methods have been developed, which can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome, and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for the detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors, and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction, or gene arrays. Because of the inherent sensitivity toward changes in the biomolecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that obtained by any one of the conventional techniques.
... The methods of SHP, including data acquisition and data pre- processing, have been described in detail in the litera- ture. 2,20,21 A block diagram of the required steps will be dis- cussed in section 2.7. All spectroscopic studies reported here were carried out on 'low emissivity' (low-e) slides (Kevley Technologies, Chesterfield, OH) that are totally reflective toward infrared radiation, but are nearly totally transparent to visible light; thus, the same sample can be used both for infra- red data acquisition and, after appropriate staining, for classi- cal histopathology. ...
... The wavelength-dependent intensity distortions can further be reduced by regional (vector-) normalization. Normalization by region 21 has the added advantage that the protein amide I and amide II bands can be de-emphasized with respect to the low frequency (1000-1400 cm −1 ) region. ‡ Infrared spectral data were acquired from pixels 6.25 μm on edge using a PerkinElmer (Shelton, CT, USA) model Spectrum-One/Spotlight 400 imaging infrared micro-spectrometer, resulting in 25 600 pixel spectra for each square millimeter of tissue. ...
We report results on a statistical analysis of an infrared spectral dataset comprising a total of 388 lung biopsies from 374 patients. The method of correlating classical and spectral results and analyzing the resulting data has been referred to as spectral histopathology (SHP) in the past. Here, we show that standard bio-statistical procedures, such as strict separation of training and blinded test sets, result in a balanced accuracy of better than 95% for the distinction of normal, necrotic and cancerous tissues, and better than 90% balanced accuracy for the classification of small cell, squamous cell and adenocarcinomas. Preliminary results indicate that further sub-classification of adenocarcinomas should be feasible with similar accuracy once sufficiently large datasets have been collected.
... Generally, FTIR-and Raman-SHP are proven, highly accurate label-free methods for tissue classification [6,24,[27][28][29]. Nowadays, vibrational spectra of a thin tissue slice are routinely recorded microscopically with high spatial resolution. ...
Infrared spectral histopathology is a well-established method for label-free tissue classification. Flexible fibre-optic probes allow a remote sensing for in-vivo tissue annotation. The performance of infrared spectral analysis of colorectal tissue with millimetre spatial resolution for cancer lesion identification and grading within a surgical setting was assessed. Colorectal tissue was removed during routine therapeutic surgery. By consecutive spectroscopy, luminal positions within and outside the cancer lesion were analysed using fibre-coupled probes for attenuated total reflection (ATR) measurements using a diamond-prism or cone, respectively. Subsequent routine histopathology provided the gold standard for diagnosis and grading. For spectral data analysis, two feature selection algorithms were applied. Results from linear discriminant analysis and ensemble random forest classifiers based on leave-one-third-out cross-validation and test-set validation with independent data are presented. The spectral discrimination of tumour versus normal tissue under the cross-validation scheme was achieved with an accuracy of 90. ±. 5% (sensitivity of 89. ±. 7%, specificity of 90. ±. 7%), whereas respective test-set validation led to an accuracy of 80% (sensitivity of 83% and specificity of 78%). Low versus high tumour grading was assessed under cross-validation with an accuracy of 81. ±. 8% (sensitivity of 80. ±. 16%, and specificity of 81. ±. 14%). Thus, fibre-optic infrared spectroscopic tissue analysis has the potential of supporting clinical decisions by providing immediate tissue and grading information during surgery.
Background
The increasing number of cancer cases requires new imaging approaches for intraoperative tumor characterization.Objective
Utilization of new optical/photonic methods in combination with artificial intelligence (AI) approaches to address urgent challenges in clinical pathology in terms of intraoperative computational spectral histopathology.Methods
Multimodal nonlinear imaging by combining the spectroscopic methods coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), fluorescence lifetime imaging microscopy (FLIM), and second harmonic generation (SHG).ResultsBy using multimodal spectroscopic imaging, tissue morphochemistry, i.e., its morphology and molecular structure can be visualized in a label-free manner. The multimodal images can be automatically analyzed using AI-based image analysis approaches. For clinical application in terms of frozen section diagnostics or in vivo use, the presented multimodal imaging approach can be translated into a compact microscope or endoscopic probe concepts.Conclusions
The synergistic combination of spectroscopic imaging modalities in combination with automated data analysis has great potential for fast and precise tumor diagnostics e.g., in terms of precise surgical guidance in laser or robotic surgery. Overall, intraoperative multimodal spectroscopic imaging may represent an innovative advancement for tumor diagnostics in the future, directly leading to improved patient care and significant cost savings.
During the last 15years, vibrational spectroscopic methods have been developed, which can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome, and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for the detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors, and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction, or gene arrays. Because of the inherent sensitivity toward changes in the biomolecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that obtained by any one of the conventional techniques.
This chapter presents a review of the methodology for analyzing large spectral data sets typically available from vibrational microspectral measurements. Focusing on supervised methods, it highlights that the spectral variance is correlated with prior knowledge of the outcome. In unsupervised methods, no input data except the spectral hypercube is provided to the algorithm, and the class membership is calculated from the variance of the data set. Several factor methods have been developed to decompose data sets into a bilinear model of variables. Methods and algorithms are derived in the chapter that allow for supervised classification, for example, to allow the construction of computer-based self-learning diagnostic algorithms. The correlation of spectral changes with continuously varying parameters is also introduced. This method is commonly employed in “two-dimensional” FTIR spectroscopy. In analogy to 2D-NMR spectroscopy, 2D-infrared (IR) spectroscopy is based on the cross-correlation function between variations in the time-dependent spectral intensities.
We report results of a study utilizing a novel tissue classification method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples on a tissue microarray. The spectral diagnostic method allows reproducible and objective classification of unstained tissue sections. This is accomplished by acquiring infrared data sets containing thousands of spectra, each collected from tissue pixels ∼6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis that reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology is presented, suggesting that spectral histopathology can achieve levels of diagnostic accuracy that is comparable to that of multipanel immunohistochemistry.Laboratory Investigation advance online publication, 9 February 2015; doi:10.1038/labinvest.2015.1.
Results of a study comparing infrared imaging data sets collected on different instruments or instrument platforms are reported, along with detailed methods developed to permit such comparisons. It was found that different instrument platforms, although employing different detector technologies and pixel sizes, produce highly similar and reproducible spectral results. However, differences in the absolute intensity values of the reflectance data sets were observed that were cause by heterogeneity of the sample sub-strate in terms of reflectivity and planarity.
The natural dye, haematoxylin obtained from the logwood, haematoxylon campechianum is the most important and most used dye in histology, histochemistry, histopathology and in cytology. It is especially used in histopathology and cytology for the diagnosis of malignant and non malignant lesions. It can be used as a primary stain and as a counter stain where it will differentiate acidophilic materials from basophilic materials and stain non cellular substances such as fibrin, crystals and pigments in various shades depending on the nature of the mordant used and the second stain. Haematoxylin has been used extensively in the demonstration of certain parasites, lipids, carbohydrates, nucleic acids, metals, connective tissue fibers and in immunohistochemistry. Haematoxylin has also been used in the demonstration of several intracellular substances such as mitochondria, chromosomes, chromatin, nucleoli, centrioles, nuclear membrane, ground cytoplasm, cross striations of muscle fibres and chromatin granules in several staining techniques. Haematoxylin is therefore an indispensable dye in histochemistry and histopathology.
An account is given on the state of the art in the interpretation of infrared microscopy of cells and tissues. Focus is on the subtle changes in the DNA/RNA spectral regions between normal, precancerous, and cancerous samples. This discussion aims at establishing the limits of infrared spectroscopy as a tool to diagnose cellular abnormalities that may be related to cancer.
We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry.
Whole slide imaging (WSI), or "virtual" microscopy, involves the scanning (digitization) of glass slides to produce "digital slides". WSI has been advocated for diagnostic, educational and research purposes. When used for remote frozen section diagnosis, WSI requires a thorough implementation period coupled with trained support personnel. Adoption of WSI for rendering pathologic diagnoses on a routine basis has been shown to be successful in only a few "niche" applications. Wider adoption will most likely require full integration with the laboratory information system, continuous automated scanning, high-bandwidth connectivity, massive storage capacity, and more intuitive user interfaces. Nevertheless, WSI has been reported to enhance specific pathology practices, such as scanning slides received in consultation or of legal cases, of slides to be used for patient care conferences, for quality assurance purposes, to retain records of slides to be sent out or destroyed by ancillary testing, and for performing digital image analysis. In addition to technical issues, regulatory and validation requirements related to WSI have yet to be adequately addressed. Although limited validation studies have been published using WSI there are currently no standard guidelines for validating WSI for diagnostic use in the clinical laboratory. This review addresses the current status of WSI in pathology related to regulation and validation, the provision of remote and routine pathologic diagnoses, educational uses, implementation issues, and the cost-benefit analysis of adopting WSI in routine clinical practice.
Chemometrics in Analytical Spectroscopy provides students and practising analysts with a tutorial guide to the use and application of the more commonly encountered techniques used in processing and interpreting analytical spectroscopic data. In detail the book covers the basic elements of univariate and multivariate data analysis, the acquisition of digital data and signal enhancement by filtering and smoothing, feature selection and extraction, pattern recognition, exploratory data analysis by clustering, and common algorithms in use for multivariate calibration techniques. An appendix is included which serves as an introduction or refresher in matrix algebra. The extensive use of worked examples throughout gives Chemometrics in Analytical Spectroscopy special relevance in teaching and introducing chemometrics to undergraduates and post-graduates undertaking analytical science courses. It assumes only a very moderate level of mathematics, making the material far more accessible than other publications on chemometrics. The book is also ideal for analysts with little specialist background in statistics or mathematical methods, who wish to appreciate the wealth of material published in chemometrics.
Raman microspectroscopy-based, label-free imaging methods for human cells at sub-micrometre spatial resolution are presented. Since no dyes or labels are used in this imaging modality, the pixel-to-pixel spectral variations are small and multivariate methods of analysis need to be employed to convert the hyperspectral datasets to spectral images. Thus, the main emphasis of this paper is the introduction and comparison of a number of multivariate image reconstruction methods. The resulting Raman spectral imaging methodology directly utilizes the spectral contrast provided by small (bio)chemical compositional changes over the spatial dimension of the sample to construct images that can rival fluorescence images in terms of spatial information, yet without the use of any external dye or label.
Spectral cytopathology (SCP) is a robust and reproducible diagnostic technique that employs infrared spectroscopy and multivariate statistical methods, such as principal component analysis to interrogate unstained cellular samples and discriminate changes on the biochemical level. In the past decade, SCP has taken considerable strides in its application for disease diagnosis. Cultured cell lines have proven to be useful model systems to provide detailed biological information to this field; however, the effects of sample fixation and storage of cultured cells are still not entirely understood in SCP. Conventional cytopathology utilizes fixation and staining methods that have been established and widely accepted for nearly a century and are focused on maintaining the morphology of a cell. Conversely, SCP practices must implement fixation protocols that preserve the sample's biochemical composition and maintain its spectral integrity so not to introduce spectral changes that may mask variance significant to disease. It is not only necessary to evaluate the effects on fixed exfoliated cells but also fixed cultured cells because although they are similar systems, they exhibit distinct differences. We report efforts to study the effects of fixation methodologies commonly used in traditional cytopathology and SCP including both fixed and unfixed routines applied to cultured HeLa cells, an adherent cervical cancer cell line. Data suggest parallel results to findings in Part 1 of this series for exfoliated cells, where the exposure time in fixative and duration of sample storage via desiccation contribute to minor spectral changes only. The results presented here reinforce observations from Part 1 indicating that changes induced by disease are much greater than changes observed as a result of alternate fixation methodologies. Principal component analysis of HeLa cells fixed via the same conditions and protocols as exfoliated cells (Part 1) yield nearly identical results. More importantly, the overall conclusion is that it is necessary that all samples subjected to comparative analysis should be prepared identically because although changes are minute, they are present. F or the past decade, infrared (IR) microspectroscopy has climbed its way to being considered a competitive alternative to conventional cytopathology practices. Traditional cytopathology includes the inspection of stained cells, visually measuring predetermined parameters, such as nucleus-to-cytoplasm (N/C) ratio, staining patterns, morphology of nuclear membrane, etc., and assigning a diagnosis based on these parameters. 1,2 IR microspectroscopy is at the forefront of new methods being developed because it is a label free and reproducible method that evaluates a physical measurement, the biochemical composition, of each unstained cell; the term " spectral cytopathology (SCP) " has been coined to describe the combination of microscopic infrared data acquisition and analysis of the spectral data via multivariate methods. 3−5 After IR acquisition, samples can then be subjected to traditional staining protocols and evaluated via conventional cytopathol-ogy means to compare results from both techniques. Since the early successes of SCP, many groups have begun investigating cultured cell lines to provide additional information regarding disease diagnosis and biological information. 6−8 Cultured cells serve several purposes ranging from distinguishing between different cell lines to their behavior in understanding disease and evaluation of drug effects and uptake. 8−10 Cultured cell lines offer a microscopic model system to explore and probe mechanisms, pathways, drug interactions, etc. Most importantly, diseased cells can be potentially biopsied from a patient's organ and propagated in cell culture conditions to be investigated thoroughly. 8,11 Often fixation procedures are applied to preserve cells for extended periods; however, the spectral effects of fixation on cultured cells are not entirely understood. 12 Previous reports claim fixation protocols introduce large spectral changes and obstruct proper analyses, speculating fixation methods as obstacles to be avoided. 13−15 This is the second paper in a series aimed at addressing the effects of fixation and storage conditions on spectral data of cellular samples. In the first paper, we described the influence of these factors to exfoliated oral (buccal) mucosa cells and demonstrated that exceedingly small variances occurred upon various fixation methods that were negligible in comparison to
This chapter emphasizes results from all vibrational techniques, in particular those from vibrational circular dichroism (VCD) measurements. It reviews the basic results for peptides, proteins, nucleic acids, and lipids, and also explores some results that demonstrate how imaginative application of these techniques can reveal information that is not available from any other techniques. Owing to their formidable size, low solubility, and structural complexity, proteins were realized early on to be poor subjects for initial studies using vibrational spectroscopy. A plethora of vibrational spectroscopic data exists for various small linear and cyclic peptides. The chapter reviews the basic principles, including the transition dipole coupling model to explain the conformational sensitivity of IR spectroscopy; furthermore, it contains the most comprehensive compilation of amino acid side group vibrations in peptides and proteins, and a thorough discussion of the amide I and II manifolds.
Die Raman-Mikrospektroskopie hat sich in den letzten Jahren aufgrund ihrer Labelfreiheit und ihrer hohen molekularen Selektivität als extrem leistungsstarke Analysenmethode für die biomedizinische Diagnostik etabliert. So gelingt es mittels der Raman-Mikrospektroskopie Mikroorganismen wie bakterielle Krankheiterreger auf Einzelzellebene schnell zu identifizieren und charakterisieren. Lineare Raman-Spektroskopie und nichtlineare Raman-Techniken wie die CARS-Mikroskopie besitzen großes Potenzial zur objektiven Beurteilung von Zellen oder Gewebe zur Frühdiagnose von Krankheiten wie Krebs.
Raman microspectroscopy has established itself in the last years as an extremely capable analytical method for biomedical diagnosis because it is labelfree and provides high molecular selectivity. That way Raman microspectroscopy allows for a fast identification and characterization of microorganisms like e.g. pathogens on a single cell level. Linear Raman spectroscopy and non-linear Raman techniques like CARS microscopy have great potential for an objective evaluation of cells or tissue for an early diagnosis of diseases like e.g. cancer.
The analysis of hyperspectral data sets requires the determination of
certain basis spectra called 'end-members.' Once these spectra are
found, the image cube can be 'unmixed' into the fractional abundance of
each material in each pixel. There exist several techniques for
accomplishing the determination of the end-members, most of which
involve the intervention of a trained geologist. Often these-end-members
are assumed to be present in the image, in the form of pure, or unmixed,
pixels. In this paper a method based upon the geometry of convex sets is
proposed to find a unique set of purest pixels in an image. The
technique is based on the fact that in N spectral dimensions, the
N-volume contained by a simplex formed of the purest pixels is larger
than any other volume formed from any other combination of pixels. The
algorithm works by 'inflating' a simplex inside the data, beginning with
a random set of pixels. For each pixel and each end-member, the
end-member is replaced with the spectrum of the pixel and the volume is
recalculated. If it increases, the spectrum of the new pixel replaces
that end-member. This procedure is repeated until no more replacements
are done. This algorithm successfully derives end-members in a synthetic
data set, and appears robust with less than perfect data. Spectral
end-members have been extracted for the AVIRIS Cuprite data set which
closely match reference spectra, and resulting abundance maps match
published mineral maps.
Background: Barrett’s oesophagus is the columnar-lined metaplasia that occurs in response to severe gastro-oesophageal reflux and accounts for the dramatic rise in adenocarcinoma at the gastro-oesophageal junction. Diagnostic methods: Endoscopic recognition and pathological diagnosis of the condition is fraught with erroneous interpretation of the pre-malignant degeneration of dysplasia. Screening and surveillance programmes have yet to impact on the disease. Photodiagnosis by spectroscopy and imaging is under intense investigation. The methods can be divided into two groups of morphological (elastic scattering, optical coherence tomography) and molecular and biochemical (Raman and fluorescence). The major diagnostic problem remains the differentiation between inflammation and dysplasia. Raman spectroscopy does offer molecular-specific diagnosis and fibre-optic probes are being developed. The future appears to be multi-modal imaging combined with spectroscopy. Results: Photodynamic therapy is a realistic option for the eradication of dysplastic Barrett’s oesophagus. A recently reported randomised trial has demonstrated a significant improvement in the eradication of dysplasia and prevention of oesophageal cancer. Conclusions: Optical diagnosis and optical eradication have a bright future for the management of Barrett’s oesophagus.
Vibrational spectroscopic imaging techniques are new tools for visualizing chemical components in tissue without staining. The spectroscopic signature can be used as a molecular fingerprint of pathological tissues. Fourier transform infrared imaging which is more common than Raman imaging so far has already been applied to identify the primary tumor of brain metastases. The current study introduces a two level discrimination model for Raman microspectroscopic images to distinguish normal brain, necrosis and tumor tissue, and subsequently to determine the primary tumor. 22 Specimens of normal brain tissue and brain metastasis of bladder carcinoma, lung carcinoma, mamma carcinoma, colon carcinoma, prostate carcinoma and renal cell carcinoma were snap frozen, and thin tissue sections were prepared. Raman microscopic images were collected with 785 nm laser excitation at 10 μm step size. Cluster analysis, vertex component analysis and principal component analysis were applied for data preprocessing. Then, data of 17 specimens were used to train the discrimination model based on support vector machines with radial basis functions kernel. The training data were discriminated with accuracy better than 99%. Finally, the discrimination model correctly predicted independent specimens. The results were superior to discrimination by partial least squares discriminant analysis and support vector machines with linear basis function kernel that were applied for comparison.
Infrared spectra of the normal connective, the normal epithelial, and the malignant epithelial tissues of cervix from seven patients have been measured as a function of pressure. Extremely high quality spectra of these tissue samples have been obtained. Consequently, structural differences at the molecular level among these three types of cervical tissues have been extracted from their pressure-tuning infrared spectra in the regions of the symmetric and antisymmetric stretching modes of phosphodiester groups, the C-O stretching mode, the CH2 bending mode, and the amide I mode. Significant differences in many features between the infrared spectra of the normal and the malignant cervical tissues and cells suggest that the infrared spectra of exfoliated cells and the biopsy of cervical tissues may be used in rapid evaluation of cervical cancer or in screening of large-volume normal cervical specimens. The infrared spectrum of the normal connective tissue of cervix in the frequency region 950 to 1100 cm−1 is similar to that of the malignant cervical tissue and cells. Therefore, if only this region of the spectrum is examined, the normal connective tissue will be misinterpreted as malignant tissue. However, the normal connective tissue can be differentiated unambiguously from the malignant tissue or the normal epithelial tissue by the infrared spectra in the frequency region 1200 to 1500 cm−1, where several well-defined sharp bands are unique for the normal connective tissue.
Background: Barrett’s oesophagus is the columnar-lined metaplasia that occurs in response to severe gastro-oesophageal reflux and accounts for the dramatic rise in adenocarcinoma at the gastro-oesophageal junction. Diagnostic methods: Endoscopic recognition and pathological diagnosis of the condition is fraught with erroneous interpretation of the pre-malignant degeneration of dysplasia. Screening and surveillance programmes have yet to impact on the disease. Photodiagnosis by spectroscopy and imaging is under intense investigation. The methods can be divided into two groups of morphological (elastic scattering, optical coherence tomography) and molecular and biochemical (Raman and fluorescence). The major diagnostic problem remains the differentiation between inflammation and dysplasia. Raman spectroscopy does offer molecular-specific diagnosis and fibre-optic probes are being developed. The future appears to be multi-modal imaging combined with spectroscopy. Results: Photodynamic therapy is a realistic option for the eradication of dysplastic Barrett’s oesophagus. A recently reported randomised trial has demonstrated a significant improvement in the eradication of dysplasia and prevention of oesophageal cancer. Conclusions: Optical diagnosis and optical eradication have a bright future for the management of Barrett’s oesophagus.
The object of this investigation has been to determine the feasibility of using chemically labelled antibodies as reagents for the detection and orientation of antigenic material in mammalian tissue. Such a method requires the retention of specificity by the antibody-molecule during and after the necessary chemical manipulation, a stable chemical linkage between the antibody and its label, and a label that can be detected when present in minute quantities. A further important requirement demands the separation of the labelled-antibody solution from unconjugated tracer-material. In addition a method of this sort would obviously be more useful for many studies if it were possible to determine not only those organs, but those cells which contain the antigen in question. Accordingly we investigated the possibility of employing materials for labelling that could be detected by optical rather than by analytic or radiographic methods.
This paper presents a short review on the improvements in data processing for spectral cytopathology, the diagnostic method developed for large scale diagnostic analysis of spectral data of individual dried and fixed cells. This review is followed by the analysis of the confounding effects introduced by utilizing reflecting "low-emissivity" (low-e) slides as sample substrates in infrared micro-spectroscopy of biological samples such as individual dried cells or tissue sections. The artifact introduced by these substrates, referred to as the "standing electromagnetic wave" artifact, indeed, distorts the spectra noticeably, as postulated recently by several research groups. An analysis of the standing wave effect reveals that careful data pre-processing can reduce the spurious effects to a level where they are not creating a major problem for spectral cytopathology and spectral histopathology.
During the past years, many studies have shown that infrared spectral histopathology (SHP) can distinguish different tissue types and disease types independently of morphological criteria. In this manuscript, we report a comparison of immunohistochemical (IHC), histopathological and spectral histopathological results for colon cancer tissue sections. A supervised algorithm, based on the "random forest" methodology, was trained using classical histopathology, and used to automatically identify colon tissue types, and areas of colon adenocarcinoma. The SHP images subsequently were compared to IHC-based images. This comparison revealed excellent agreement between the methods, and demonstrated that label-free SHP detects compositional changes in tissue that are the basis of the sensitivity of IHC. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
The first study interpreting B-lymphocyte activation in normal lymph nodes using vibrational micro-spectral imaging is reported. Lymphocyte activation indicates the presence and response against a pathogen, regardless of the inciting pathogen's etiology, whether a benign, reactive or malignant process. Understanding the biochemical makeup of lymphocyte activation during early stages of disease and immune response may offer significant aid in determining a tumor's origin without the presence of malignant metastatic cells but within lymph nodes that are reactive and displaying regions of hyperplasia. Infrared and Raman data scrutinized via unsupervised multivariate methods may provide a physical and reproducible method to determine the biochemical components and variances therein of activated lymph nodes with distinguishing characteristics depending on the malignancy present in the region or elsewhere in the body. The results reported here provide a proof-of-concept study that reveal a potential to screen lymph nodes for disease without the presence of metastatic cells. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
Non-invasive optical imaging techniques, such as fluorescence imaging (FI) or bioluminescence imaging (BLI) have emerged as important tools in biomedical research. As demonstrated in different animal disease models, they enable visualization of physiological and pathophysiological processes at the cellular and molecular level in vivo with high specificity. Optical techniques are easy to use, fast, and affordable. Furthermore, they are characterized by their high sensitivity. In FI, very low amounts of the imaging agent (nano- to femtomol or even less) can be detected. Due to the absorption and scattering of light in tissue, optical techniques exhibit a comparably low spatial resolution in the millimeter range and a depth limit of a few centimeters. However, non-invasive imaging of biological processes in small animals and in outer or inner surfaces as well as during surgery even in humans is feasible. Currently two agents for fluorescence imaging are clinically approved, namely indocyanine green (ICG) and 5-aminolevulinic acid (5-ALA). In the past years, a number of new optical imaging agents for FI and reporter systems for BLI have been developed and successfully tested in animal models. Some of the FI agents might promise the application in clinical oncology. In this chapter, we describe the basic principles of non-invasive optical imaging techniques, give examples for the visualization of biological processes in animal models of cancer, and discuss potential clinical applications in oncology.
Laser tweezers Raman spectroscopy (LTRS), a technique that integrates optical tweezers with confocal Raman spectroscopy, is a variation of micro-Raman spectroscopy that enables the manipulation and biochemical analysis of single biological particles in suspension. This article provides an overview of the LTRS method, with an emphasis on highlighting recent advances over the past several years in the development of the technology and several new biological and biomedical applications that have been demonstrated. A perspective on the future developments of this powerful cytometric technology will also be presented. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
Raman and infrared spectroscopy have been recognized to be promising tools in clinical diagnostics because they provide molecular contrast without external stains. Here, vertex component analysis (VCA) was applied to Raman and Fourier transform infrared (FTIR) images of liver tissue sections and the results were compared with K-means cluster analysis, fuzzy C-means cluster analysis and principal component analysis. The main components of VCA from three Raman images were assigned to the central vein, periportal vein, cell nuclei, liver parenchyma and bile duct. After resonant Mie scattering correction, VCA of FTIR images identified veins, liver parenchyma, cracks, but no cell nuclei. The advantages of VCA in the context of tissue characterization by vibrational spectroscopic imaging are that the tissue architecture is visualized and the spectral information is reconstructed. Composite images were constructed that revealed a high molecular contrast and that can be interpreted in a similar way like hematoxylin and eosin stained tissue sections.
Raman spectroscopy, in combination with optical microscopy provides a new non-invasive method to asses and image cellular processes. Based on the spectral signatures of a cell's components, it is possible to image cellular organelles such as the nucleus, chromatin, mitochondria or lipid bodies, at the resolution of conventional microscopy. Several multivariate algorithms, for example hierarchical cluster analysis or orthogonal subspace projection, may be used to reconstruct an image of a cell. The noninvasive character of the technique, as well as the associated chemical information, may offer advantages over other imaging techniques such as fluorescence microscopy. Currently of particular interest are uptake and intracellular fate of various pharmaceutical nanocarriers, which are widely used for drug delivery purposes, including intracellular drug and gene delivery. We have imaged the uptake and distribution patterns of several carrier systems over time. In order to distinguish the species of interest from their cellular environment spectroscopically, the carrier particles or the drug load itself may be labeled with deuterium. Here, we introduce the concept of Raman imaging in combination with vertex component data analysis to follow the uptake of nanocarriers based on phospholipids as well as biodegradable polymers.
Intracranial tumors are neoplasias of brain tissue or other tissue inside the skull. Cryosections of the three most frequent primary intracranial tumors—gliomas, meningeomas and schwannomas—were prepared on calcium fluoride windows and studied by both Raman spectroscopy and infrared (IR) spectroscopy. Spectroscopic maps were recorded by sequential acquisition of spectra in a raster pattern. Cluster analyses on selected wavenumber regions were applied for evaluation of these data sets. Raman spectroscopic contributions of proteins including collagen and hemoglobin were identified in cluster centroids, as were nucleic acids and lipids including cholesterol, cholesterol ester (CE) and phosphatidylcholine (PC). Moreover, hydroxyapatite and tricalciumphosphate could be identified as markers for calcification. The spatial distributions of these spectral properties were visualized in pseudocolor Raman and IR maps representing the cluster memberships. The prospects of both methods are discussed. Copyright © 2006 John Wiley & Sons, Ltd.
The contribution summarizes how infrared and Raman spectroscopy can contribute to a better diagnosis of the most frequent primary and secondary brain tumors. The methods are compared with other biophotonic techniques applied in neuro-oncology. First results are presented for gliomas and brain metastases. Finally the methods are transferred to an animal model.
Keywords:
brain tissue;
brain tumors;
infrared (IR) and Raman spectroscopy;
vibrational spectroscopy
Spectral cytology, the diagnosis of disease based on objective physical measurements on individual cells and subsequent computer‐based algorithmic interpretation, promises to provide faster and more reliable results than classical cytology. The measurements described in this review are based on well‐established vibrational microspectroscopic techniques, which provide a snapshot of the biochemical composition of a cell, or parts thereof. The spectral data are subsequently diagnosed by unsupervised and supervised methods of multivariate analysis.
More than any other recent attempts to improve on cytology, these spectral methods exhibit exquisite sensitivity toward small changes in cellular conditions. In fact, these cellular conditions, for example, fixation or proliferation state, have to be controlled very carefully to eliminate spurious effects. This chapter provides the ground work for later applications of this methodology in medical diagnostics. Furthermore, the application of novel research tools, such as confocal Raman microscopy, followed by cluster analysis of the hyperspectral data sets, and their relevance to research in cellular biology are described.
This chapter outlines the use of Raman spectroscopy for discrimination of pathologies in the bladder and esophagus based upon the biochemical signature accompanying the disease process. This has great strength in that objective molecular-specific analysis becomes possible for diagnosis and understanding of carcinogenesis processes. The importance of the gold-standard histopathology, sample handling procedures and spectrometer standardization are outlined. Methods of sampling in vivo and in vitro are discussed and discrimination methodologies are demonstrated. Finally exploitation of the inherent biochemical signature found within the tissues is explored and an attempt is made to provide relative concentrations of significant biochemical components.
Keywords:
esophagus;
bladder;
Raman;
biochemistry;
standardization;
histopathology;
discrimination;
cancer;
dysplasia
This chapter introduces the field of vibrational spectroscopic imaging and discusses the advances made to date in terms of biomedical diagnosis. The reader is introduced to the spectral signatures of typical cellular components, and the need for an objective method for cancer diagnosis is discussed. Besides an overview of typical instrumentation and components, this chapter provides a working application, namely the diagnosis of metastatic cancer in lymph nodes. Through this application methods of sample preparation, data acquisition and data preprocessing and analysis are discussed.
Keywords:
spectral diagnosis;
imaging;
lymph nodes;
cancer;
infrared microspectroscopy;
IR-MSP;
HCA;
ANN
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. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
The in vitro differentiation of embryonic stem (ES) cells into specific phenotypes plays an important role in the development of stem cell therapy, tissue engineering and regenerative medicine. Currently, there are no biological assays able to characterise and monitor in situ and in real-time ES cells during the differentiation process. We applied Raman micro-spectroscopy to compare undifferentiated murine ES cells with murine ES cells in the differentiation process. The most significant differences between undifferentiated and differentiated ES cells (16 days differentiation via formation of embryoid body) were related to the nucleic acids. The decrease in the magnitude of RNA 813 cm−1 Raman peaks (25%) in the differentiated ES cells in comparison to undifferentiated ES cells, suggests that part of the RNA in the ES cells is used for the synthesis of specific proteins in the early stages of differentiation. In the same time, the DNA 786 cm−1 Raman peaks were lower by 50%, indicating that the differentiated cells are more in the G1 phase than S, G2 or M phases of the cell cycle. This result suggests that the proliferation rate of differentiated cells is reduced following development of a mature phenotype. This study shows the feasibility of using Raman micro-spectroscopy to monitor in situ and in real-time the differentiation of ES cells by using the intensity of Raman peak of nucleic acids as differentiation markers.
This paper explores different phenomena that cause distortions of infrared absorption spectra by mixing of reflective and absorptive band shape components of infrared spectra, and the resulting distortion of observed band shapes. In the context of this paper, we refer to the line shape of the variations of the refractive index in spectral regions of an absorption maximum (i.e., in regions of "anomalous dispersion") as "dispersive" or "reflective" line shape contributions, in analogy to previous spectroscopic literature. These distortions usually result in asymmetric bands with a negative intensity contribution at the high wavenumber of the band, accompanied by a shift toward lower wavenumber, and confounded band intensities. In extreme cases of band distortions caused by the "resonance Mie" (RMie) mechanism, spectral peaks may be split into doublets of peaks, change from positive to negative peaks, or appear as derivative-shaped features.
Nonlinear optical (NLO) imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) show great potential for biomedical imaging. In order to facilitate the diagnostic process based on NLO imaging, there is need for an automated calculation of quantitative values such as cell density, nucleus-to-cytoplasm ratio, average nuclear size. Extraction of these parameters is helpful for the histological assessment in general and specifically e.g. for the determination of tumor grades. This requires an accurate image segmentation and detection of locations and boundaries of cells and nuclei. Here we present an image processing approach for the detection of nuclei and cells in co-registered TPEF and CARS images. The algorithm developed utilizes the gray-scale information for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. The approach reported is capable for an automated segmentation of cells and nuclei in multimodal TPEF-CARS images of human brain tumor samples. The results are important for the development of NLO microscopy into a clinically relevant diagnostic tool. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
In this paper we describe the advantages of collecting infrared microspectral data in imaging mode opposed to point mode. Imaging data are processed using the PapMap algorithm, which co-adds pixel spectra that have been scrutinized for R-Mie scattering effects as well as other constraints. The signal-to-noise quality of PapMap spectra will be compared to point spectra for oral mucosa cells deposited onto low-e slides. Also the effects of software atmospheric correction will be discussed. Combined with the PapMap algorithm, data collection in imaging mode proves to be a superior method for spectral cytopathology. (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
IntroductionMethods
Results and DiscussionConclusions
AcknowledgmentsReferences
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-μm 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.
KeywordsChemometrics–Raman spectroscopy–Image processing–Hyperspectral data
A tutorial article is presented for the use of linear and nonlinear Raman microspectroscopies in biomedical diagnostics. Coherent anti-Stokes Raman scattering (CARS) is the most frequently applied nonlinear variant of Raman spectroscopy. The basic concepts of Raman and CARS are introduced first, and subsequent biomedical applications of Raman and CARS are described. Raman microspectroscopy is applied to both in-vivo and in-vitro tissue diagnostics, and the characterization and identification of individual mammalian cells. These applications benefit from the fact that Raman spectra provide specific information on the chemical composition and molecular structure in a label-free and nondestructive manner. Combining the chemical specificity of Raman spectroscopy with the spatial resolution of an optical microscope allows recording hyperspectral images with molecular contrast. We also elaborate on interfacing Raman spectroscopic tools with other technologies such as optical tweezing, microfluidics and fiber optic probes. Thereby, we aim at presenting a guide into one exciting branch of modern biophotonics research.
The 1602 cm(-1) Raman signature, which we call the "Raman spectroscopic signature of life" in yeasts, is a marker Raman band for cell metabolic activity. Despite the established fact that its intensity sensitively reflects the metabolic status of the cell, its molecular origin remained unclear. In this work, we propose ergosterol as the major contributor of the 1602 cm(-1) Raman signature. The theoretical isotope shift calculation for ergosterol agreed with previous observations. Furthermore, experiments showed that the Raman spectrum of ergosterol corresponds very well with the depleting spectral component in yeast that behaves together with the 1602 cm(-1) signature when the cells are under stress. This work implies that the 1602 cm(-1) Raman signature could serve as an intrinsic ergosterol marker in yeasts for the study of sterol metabolism in vivo and in a label-free manner, which could not be done by any other techniques at the current stage. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
We have optimized an imaging methodology capable of monitoring individual live HeLa cells using non-synchrotron FTIR in an aqueous environment. This methodology, in combination with MATLAB based pre-processing techniques, allows fast and efficient collection of data with high signal-to-noise ratio in comparison with previous methods using point mode data collection, which required manual operation and more collection time. Also, presented are early results that illustrate interpretable spectral differences from live cells treated with chemotherapeutic drugs, demonstrating the potential of this methodology to develop more desirable modes of treatment for patients in their diagnoses and treatments for disease.
One of the key enabling features of coherent Raman scattering (CRS) techniques is the dramatically improved imaging speed over conventional vibrational imaging methods. It is this enhanced imaging acquisition rate that has guided the field of vibrational microscopy into the territory of real-time imaging of live tissues. In this feature article, we review several aspects of fast vibrational imaging and discuss new applications made possible by the improved CRS imaging capabilities. In addition, we reflect on the current limitations of CRS microscopy and look ahead at several new developments towards real-time, hyperspectral vibrational imaging of biological tissues. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
During the last 11 years, 5 molecular subtypes of breast carcinoma (luminal A, luminal B, Her2-positive, basal-like, and normal breast-like) have been characterized and intensively studied. As genomic research evolves, further subtypes of breast cancers into new "molecular entities" are expected to occur. For example, a new and rare breast cancer subtype, known as claudin-low, has been recently found in human carcinomas and in breast cancer cell lines. There is no doubt that global gene expression analyses using high-throughput biotechnologies have drastically improved our understanding of breast cancer as a heterogeneous disease. The main question is, however, whether new molecular techniques such as gene expression profiling (or signature) should be regarded as the gold standard for identifying breast cancer subtypes. A critical review of the literature clearly shows major problems with current molecular techniques and classification including poor definitions, lack of reproducibility, and lack of quality control. Therefore, the current molecular approaches cannot be incorporated into routine clinical practice and treatment decision making as they are immature or even can be misleading. This review particularly focuses on the "basal-like" breast cancer subtype that represents one of the most popular breast cancer "entities". It critically shows major problems and misconceptions with and about this subtype and challenges the common claim that it represents a "distinct entity". It concludes that the term "basal-like" is misleading and states that there is no evidence that expression of basal-type cytokeratins in a given breast cancer, regardless of other established prognostic factors, does have any impact on clinical outcome.
FTIR absorption micro-spectroscopy is a widely used, powerful technique for analysing biological materials. In principle it is a straightforward linear absorption spectroscopy, but it can be affected by artefacts that complicate the interpretation of the data. In this article, artefacts produced by the electric-field standing-wave (EFSW) in micro-reflection-absorption (transflection) spectroscopy are investigated. An EFSW is present at reflective metallic surfaces due to the interference of incident and reflected light. The period of this standing wave is dependent on the wavelength of the radiation and can produce non-linear changes in absorbance with increasing sample thickness (non-Beer-Lambert like behaviour). A protein micro-structure was produced as a simple experimental model for a biological cell and used to evaluate the differences between FTIR spectra collected in transmission and transflection. By varying the thickness of the protein samples, the relationship between the absorbance and sample thickness in transflection was determined, and shown to be consistent with optical interference due to the EFSW coupled with internal reflection from the sample top surface. FTIR spectral image data from MCF 7 breast adenocarcinoma cells was then analysed to determine the severity of the EFSW artefact in data from a real sample. The results from these measurements confirmed that the EFSW artefact has a profound effect on transflection spectra, and in this case the main spectral variations were related to the sample thickness rather than any biochemical differences.
Given a set of mixed spectral (multispectral or hy- perspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: 1) the endmembers are the vertices of a simplex and 2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Conventional histopathology is rapidly shifting towards digital integration. Will microscopes (and pathologists) soon be obsolete? Or are we dealing with just another image modality that leaves the core of tissue diagnosis intact? This article provides an overview of current digital pathology applications and research with emphasis on whole slide imaging (WSI). Static or interactive digital pathology work stations already can be used for many purposes, e.g. telepathology expert consultations, frozen section diagnosis in remote areas, cytology screening, quality assurance, diagnostic validations for clinical trials, quantitation of hormone receptor or HER2 studies in breast cancer, or three-dimensional visualization of anatomical structures, among others. Changes of workflow in histology laboratories are beginning to enable digital image acquisition and WSI in a routine setting. WSI plays an increasing role in pathology education, glass slide boxes in medical schools are being replaced by digital slide collections; digital slide seminars and virtual microscopy are used for postgraduate and continuing medical education in pathology. Research and efforts to validate WSI systems for diagnostic settings are ongoing.
A method is presented for acquiring high-spatial-resolution spectral maps, in particular for Raman micro-spectroscopy (RMS), by selectively sampling the spatial features of interest and interpolating the results. This method achieves up to 30 times reduction in the sampling time compared to raster-scanning, the resulting images have excellent correlation with conventional histopathological staining, and are achieved with sufficient spectral signal-to-noise ratio to identify individual tissue structures. The benefits of this selective sampling method are not limited to tissue imaging however; it is expected that the method may be applied to other techniques which employ point-by-point mapping of large substrates.
Spectral cytopathology (SCP) is a novel approach for disease diagnosis that utilizes infrared spectroscopy to interrogate the biochemical components of cellular samples and multivariate statistical methods, such as principal component analysis, to analyze and diagnose spectra. SCP has taken vast strides in its application for disease diagnosis over the past decade; however, fixation-induced changes and sample handling methods are still not systematically understood. Conversely, fixation and staining methods in conventional cytopathology, typically involving protocols to maintain the morphology of cells, have been documented and widely accepted for nearly a century. For SCP, fixation procedures must preserve the biochemical composition of samples so that spectral changes significant to disease diagnosis are not masked. We report efforts to study the effects of fixation protocols commonly used in traditional cytopathology and SCP, including fixed and unfixed methods applied to exfoliated oral (buccal) mucosa cells. Data suggest that the length of time in fixative and duration of sample storage via desiccation contribute to minor spectral changes where spectra are nearly superimposable. These findings illustrate that changes influenced by fixation are negligible in comparison to changes induced by disease.
We observe the redox state changes with respiration of cytochromes b and c in mitochondria in a living Saccharomyces cerevisiae cell as well as in isolated mitochondria with the very use of Raman microspectroscopy. The possibility of monitoring the respiration activity of mitochondria in vivo and in vitro by Raman microspectroscopic quantification of the cytochrome redox states is suggested. It will lead to a new means to assess mitochondrial respiration activity in vivo and in vitro without using any labelling or genetic manipulation.
The non-destructive and label-free monitoring of extracellular matrix (ECM) remodeling and degradation processes is a great challenge. Raman spectroscopy is a non-contact method that offers the possibility to analyze ECM in situ without the need for tissue processing. Here, we employed Raman spectroscopy for the detection of heart valve ECM, focusing on collagen fibers. We screened the leaflets of porcine aortic valves either directly after dissection or after treatment with collagenase. By comparing the fingerprint region of the Raman spectra of control and treated tissues (400-1800 cm(-1)), we detected no significant differences based on Raman shifts; however, we found that increasing collagen degradation translated into decreasing Raman signal intensities. After these proof-of-principal experiments, we compared Raman spectra of native and cryopreserved valve tissues and revealed that the signal intensities of the frozen samples were significantly lower compared to those of native tissues, similar to the data seen in the enzymatically-degraded tissues. In conclusion, our data demonstrate that Raman microscopy is a promising, non-destructive and non-contact tool to probe ECM state in situ.
Surface Enhanced Raman Spectroscopy or SERS has witnessed many successes over the past 3 decades, owing particularly to its simplicity of use as well as its highly-multiplexing capability. This article provides an overview of SERS and its applicability in the field of bio-medicine. We will preview recent developments in SERS substrate designs, and the various sensing technologies that are based on the SERS phenomenon. An overview of the clinical applications of SERS is also included. Finally, we provide an opinion on the future trends of this unique spectroscopic technique.