Reiner Salzer

Disease recognition by infrared and Raman spectroscopy

Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data-classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber-optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least-squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis.

Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors

This study assessed the diagnostic potential of Raman spectroscopic mapping by evaluating its ability to distinguish between normal brain tissue and the human intracranial tumors gliomas and meningeomas. Seven Raman maps of native specimens were collected ex vivo by a Raman spectrometer with 785 nm excitation coupled to a microscope with a motorized stage. Variations within each Raman map were analyzed by cluster analysis. The dependence of tissue composition on the tissue type in cluster averaged Raman spectra was shown by linear combinations of reference spectra. Normal brain tissue was found to contain higher levels of lipids, intracranial tumors have more hemoglobin and lower lipid to protein ratios, meningeomas contain more collagen with maximum collagen content in normal meninges. One sample was studied without freezing. Whereas tumor regions did not change significantly, spectral changes were observed in the hemoglobin component after snap freezing and thawing to room temperature. The results constitute a basis for subsequent Raman studies to develop classification models for diagnosis of brain tissue.

Analysis of human brain tissue, brain tumors and tumor cells by infrared spectroscopic mapping

This study uses infrared (IR) spectroscopic, point detection, mapping procedures to examine tissue samples from normal brain specimens and from astrocytic gliomas, the most frequent human brain tumors. Model systems were derived from cultured glioma cell lines. IR spectra of normal tissue sections distinguished white matter from gray matter by increased spectral contributions from lipids and cholesterol. Qualitatively the same differences were found in IR spectra of low and high grade glioma tissue sections pointing to a significant reduction of brain lipids with increasing malignancy. Whereas spectral contributions of proteins and lipids were similar in IR spectra of glioma cells and tissues, nucleic acid bands were more intense for cells suggesting higher proliferative activities. For statistical analyses of IR spectroscopic maps from 71 samples, a parameter for the lipid to protein ratio was introduced involving the CH(2) symmetric stretch band with lipids as main contributors and the amide I band of proteins. As this parameter correlated with the grade of gliomas obtained from standard histopathological examination, it was applied to classify brain tissue sections based on IR spectroscopic mapping.

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

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

Identification of Primary Tumors of Brain Metastases by Infrared Spectroscopic Imaging and Linear Discriminant Analysis

This study applies infrared (IR) spectroscopy to distinguish normal brain tissue from brain metastases and to determine the primary tumor of four frequent brain metastases such as lung cancer, colorectal cancer, breast cancer, and renal cell carcinoma. Standard methods sometimes fail to identify the origin of brain metastases. As metastatic cells contain the molecular information of the primary tissue cells and IR spectroscopy probes the molecular fingerprint of cells, IR spectroscopy based methods constitute a new approach to determine the primary tumor of a brain metastasis. IR spectroscopic images were recorded by a FTIR spectrometer equipped with a macro sample chamber and coupled to a focal plane array detector. Unsupervised cluster analysis of IR images revealed variances within each sample and between samples of the same tissue type. Cluster averaged IR spectra of tissue classes with known diagnoses were selected to develop a metric with eight variables. These data trained a supervised classification model based on linear discriminant analysis that was used to identify the origin of 20 cryosections including one brain metastasis with an unknown primary tumor.

Identification of tumor tissue by FTIR spectroscopy in combination with positron emission tomography

A method is described for identifying tumor tissue by means of FTIR microspectroscopy and positron emission tomography (PET). Thin tissue sections of human squamous carcinoma from hypopharynx (FaDu) and human colon adenocarcinoma (HT-29) grown in nude mice were investigated. FTIR spectroscopic maps of the thin tissue sections were generated and evaluated by Fuzzy C-Means (FCM) clustering and principal component analysis (PCA). The processed data were reassembled into images and compared to stained tissue samples and to PET. Tumor tissue could successfully be identified by this FTIR microspectroscopic method, while it was not possible to accomplish this with PET alone. On the other hand, PET permitted the non-invasive screening for suspicious tissue inside the body, which could not be achieved by FTIR.

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

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

Natural and artificial ion channels for biosensing platforms

The single-molecule selectivity and specificity of the binding process together with the expected intrinsic gain factor obtained when utilizing flow through a channel have attracted the attention of analytical chemists for two decades. Sensitive and selective ion channel biosensors for high-throughput screening are having an increasing impact on modern medical care, drug screening, environmental monitoring, food safety, and biowarefare control. Even virus antigens can be detected by ion channel biosensors. The study of ion channels and other transmembrane proteins is expected to lead to the development of new medications and therapies for a wide range of illnesses. From the first attempts to use membrane proteins as the receptive part of a sensor, ion channels have been engineered as chemical sensors. Several other types of peptidic or nonpeptidic channels have been investigated. Various gating mechanisms have been implemented in their pores. Three technical problems had to be solved to achieve practical biosensors based on ion channels: the fabrication of stable lipid bilayer membranes, the incorporation of a receptor into such a structure, and the marriage of the modified membrane to a transducer. The current status of these three areas of research, together with typical applications of ion-channel biosensors, are discussed in this review.

Rapid and Label-Free Classification of Human Glioma Cells by Infrared Spectroscopic Imaging

The discrimination of cell types is a crucial task in cell biology. Available techniques, based on an irreversible treatment of the cells, do not allow a sensitive label‐free characterization under in situ conditions. Infrared spectroscopic imaging is a new and useful tool for studying individual cells. It has established itself as a powerful method to probe the molecular composition and to indicate the biochemistry of cells. Monolayers of cultivated U343, T1115 and T508 human glioma cells were characterized using infrared spectroscopic imaging. A classification algorithm based on linear discriminant analysis was developed to distinguish different cells without labeling. The classification is based upon spectral features which mainly arise from proteins, nucleic acids, and cholesterol. An accuracy of 91% and 84% was obtained for cells of U343 and T1115, respectively. Cells of the T508 cell line exhibit some misclassifications resulting in a lower accuracy rate of 73%. As the results demonstrate, the potential of infrared spectroscopic imaging method to assess the overall molecular composition of cells in a non‐destructive manner opens the possibility to characterize cells on a molecular level without labels or an irreversible treatment.

Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues

Abstract We use partial class memberships in soft classification to model uncertain labeling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually expressed as fractions of the confusion matrix, like sensitivity, specificity, negative and positive predictive values. We extend this concept to soft classification and discuss the bias and variance properties of the extended performance measures. Ambiguity in reference labels translates to differences between best-case, expected and worst-case performance. We show a second set of measures comparing expected and ideal performance which is closely related to regression performance, namely the root mean squared error RMSE and the mean absolute error MAE. All calculations apply to classical crisp as well as to soft classification (partial class memberships as well as one-class classifiers). The proposed performance measures allow to test classifiers with actual borderline cases. In addition, hardening of e.g. posterior probabilities into class labels is not necessary, avoiding the corresponding information loss and increase in variance. We implemented the proposed performance measures in R package “softclassval” which is available from CRAN and at softclassval.r-forge.r-project.org . Our reasoning as well as the importance of partial memberships for chemometric classification is illustrated by a real-word application: astrocytoma brain tumor tissue grading (80 patients, 37,000 spectra) for finding surgical excision borders. As borderline cases are the actual target of the analytical technique, samples which are diagnosed to be borderline cases must be included in the validation.