The ability to diagnose the early onset of disease, rapidly, non-invasively and unequivocally has multiple benefits. These include the early intervention of therapeutic strategies leading to a reduction in morbidity and mortality, and the releasing of economic resources within overburdened health care systems. Some of the routine clinical tests currently in use are known to be unsuitable or unreliable. In addition, these often rely on single disease markers which are inappropriate when multiple factors are involved. Many diseases are a result of metabolic disorders, therefore it is logical to measure metabolism directly. One of the strategies employed by the emergent science of metabolomics is metabolic fingerprinting; which involves rapid, high-throughput global analysis to discriminate between samples of different biological status or origin. This review focuses on a selective number of recent studies where metabolic fingerprinting has been forwarded as a potential tool for disease diagnosis using infrared and Raman spectroscopies.

/pdf/metabolic-fingerprinting-in-disease-diagnosis-biomedical-3o8sqizbfh.pdf

Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy.

The process of histopathology, comprising tissue staining and morphological pattern recognition, has remained largely unchanged for over 140 years. Although it is integral to clinical and research activities, histopathologic recognition remains a time-consuming, subjective process to which only limited statistical confidence can be assigned because of inherent operator variability. Although immunohistochemical approaches allow limited molecular detection, significant challenges remain in using them for quantitative, automated pathology. Vibrational spectroscopic approaches, by contrast, directly provide nonperturbing molecular descriptors, but a practical spectroscopic protocol for histopathology is lacking. Here we couple high-throughput Fourier transform infrared (FTIR) spectroscopic imaging of tissue microarrays with statistical pattern recognition of spectra indicative of endogenous molecular composition and demonstrate histopathologic characterization of prostatic tissue. This automated histologic segmentation is applied to routine archival tissue samples, incorporates well-defined tests of statistical significance and eliminates any requirement for dyes or molecular probes. Finally, we differentiate benign from malignant prostatic epithelium by spectroscopic analyses.

Infrared spectroscopic imaging for histopathologic recognition.

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.

Disease recognition by infrared and Raman spectroscopy.

The sections in this article are


Introduction
Vibrational Spectroscopy – Sensitivity to Structures of Biological Molecules
Nucleic Acids
Proteins
Lipids
Carbohydrates


Cells and Tissues
Cell Biology
Cancer




Experimental Techniques
Histological Samples
IR and Raman Sampling Techniques
Liquid Sampling
Solid Sampling
Macroscopic Sampling
IR Transmission and Reflection
Fiber-Optic Probes
Raman Spectroscopy


Microscopic Sampling
Mid-Infrared Synchrotron Radiation
Mid-Infrared FPA Detection
CCD Detection in Raman






Spectroscopic Sample


Applications
Breast
Cervix


Conclusions
Acknowledgments

Vibrational Spectroscopy in the Detection of Cancer

A method that analyzes mass spectra using a digital computer is disclosed. The method includes entering into a digital computer a data set obtained from mass spectra from a plurality of samples. Each sample is, or is to be assigned to a class within a class set having two or more classes and each class is characterized by a different biological status. A classification model is then formed. The classification model discriminates between the classes in the class set.

Method for analyzing mass spectra

OBJECTIVE To determine whether diagnostic information may be recovered from the infrared spectra of exfoliated cell specimens by using a novel spectral feature extraction method, in conjunction with linear and quadratic discriminant analysis, for spectral classification. STUDY DESIGN Over 800 infrared spectra were included in the study, with corresponding clinical diagnoses based upon cytology and, when available, histology reports. Three sets of classification trials were carried out with the aim of distinguishing the spectra corresponding to normal specimens from CIN 1, 2 and 3. For each of these three cases, the procedure was to: (1) develop a set of provisional classification models using only a "training" subset of the spectra, and (2) test each provisional model by its ability to correctly predict the diagnoses on the basis of the remaining spectra. RESULTS For optimal classification trials, training set classification accuracies were 68% for normal/CIN 1, 73% for normal/CIN 2 and 81% for normal/CIN 3; for the corresponding test sets the classification accuracies were 60%, 60% and 67%, respectively. CONCLUSION The infrared spectra of exfoliated cervical cells carry information regarding the presence or absence of dysplasia, and that information is recoverable--albeit imperfectly at this stage--from the spectra of "real life" cell preparations.

Infrared spectroscopy of exfoliated cervical cell specimens. Proceed with caution.

IR spectroscopy shows the promise of developing into a clinically relevant methodology for both screening and quantitative applications. The principles are straightforward - the IR spectrum of appropriate biological fluids or tissues may be interpreted to reveal diagnostic information either directly through inspection, or indirectly through the use of appropriate classification or quantitation algorithms. Implementing these methods in practice is not as straightforward. This report summarizes our experiences to date in developing methods for the quantitation of several serum analytes from the IR spectra of dried serum films, and for the IR-based detection/grading of abnormalities in exfoliated cervical cell specimens.

Discrimination and quantitation using IR spectra: novel methods for serum analysis and for cervical dysplasia screening

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Infrared spectroscopy of exfoliated cervical cell specimens. Proceed with caution.

Discrimination and quantitation using IR spectra: novel methods for serum analysis and for cervical dysplasia screening