Quantitative studies of the structure of proteins in solution by fourier-transform infrared 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.

Disease recognition by infrared and Raman spectroscopy.

Biochemical analysis and mapping of atherosclerotic human artery using FT-IR microspectroscopy

The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.

Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy

When a Fourier transform infrared microspectrometer was first interfaced with the National Synchrotron Light Source in September 1993, there was an instant realization that the performance at the diffraction limit had increased 40–100 times. The synchrotron source transformed the IR microspectrometer into a true IR microprobe, providing high‐quality IR spectra for probe diameters at the diffraction limit. The combination of IR microspectroscopy and synchrotron radiation provides a powerful new tool for molecular spectroscopy. The ability to perform IR microspectroscopy with synchrotron radiation is still under development at Brookhaven National Laboratory, but several initial studies have been completed that demonstrate the broad‐ranging applications of this technology and its potential for materials characterization.

Fourier transform infrared microscopical analysis with synchrotron radiation: The microscope optics and system performance (invited)

Micro-FT-IR spectroscopy is a versatile technique that enables the user to obtain high-quality spectra on small samples This technique is employed to probe the subcellular chemical composition of atherosclerotic arterial walls and create three-dimensional images representing changes in the composition Two different rabbit model systems that produced atherosclerosis, either due to genetic disorder or due to physical injury, are studied The FT-IR spectra are collected from a 20- × 20-μm area of 5-μm-thick arterial section, which is smaller than a single cell The presence of cholesterol esters and other ester-containing compounds is identified from the -O-C=O stretching (1735 cm−1) absorption, and the cis unsaturation in the acyl chains of these compounds is represented by C=C-H stretching (3005 cm−1) absorption The protein content is indicated by the intensities of the amide I (1650 cm−1) band The data clearly show the changes occurring in the chemical composition of an arterial wall, varying with the physiological and morphological changes within the artery and the arteries from different animal models The power of this technique is demonstrated by excellent correlation with the hot stage polarizing microscopy technique, used to characterize the arterial lipids in situ The three-dimensional images of the arterial walls, indicating the concentration of different chemical constituents, are very useful in understanding the mechanism of atherosclerosis development

Infrared Micro-Imaging of Atherosclerotic Arteries:

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Infrared Micro-Imaging of Atherosclerotic Arteries: