Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers

Abstract

Biospectroscopy is employed to derive absorbance spectra representative of biomolecules present in biological samples. The mid-infrared region (λ = 2.5 μm-25 μm) is absorbed to give a biochemical-cell fingerprint (v = 1800-900 cm(-1)). Cellular material produces complex spectra due to the variety of chemical bonds present. The complexity and size of spectral data sets warrant multivariate analysis for data reduction, interpretation, and classification. Various multivariate analyses are available including principal component analysis (PCA), partial least-squares (PLS), linear discriminant analysis (LDA), and evolving fuzzy rule-based classifier (eClass). Interpretation of both visual and numerical results facilitates biomarker identification, cell-type discrimination, and predictive and mechanistic understanding of cellular behavior. Biospectroscopy is a high-throughput nondestructive technology. A comparison of biomarkers/mechanistic knowledge determined from conventional approaches to biospectroscopy coupled with multivariate analysis often provides complementary answers and a novel approach for diagnosis of disease and cell biology.

Raman Micro-Spectroscopy for Rapid Screening of Oral Squamous Cell Carcinoma

“…The order of the principal components (PCs) denotes their importance to the dataset. PC1 describes the highest amount of variation, PC2 the second highest, and so on (Kelly et al, 2011;Martin et al, 2007). A PCA Scatter plot groups similar datasets (spectra) according to the loadings of the PCs and can be used to distinguish different datasets (samples).…”

Multivariate Analysis for Fourier Transform Infrared Spectra of Complex Biological Systems and Processes

“…Indeed, only a suitable combination of biospectroscopy and of multivariate analysis would provide robust and reliable results through the identification of specific biomarkers, an essential prerequisite for unbiased result interpretation [19,20]. …”

“…Furthermore, due to the intrinsic complexity of biological systems, their spectral analysis requires the support of appropriate multivariate analysis approaches able to tackle the study of high-dimensional data, to verify firstly the reproducibility of the results and then to extract the most significant spectral information [18][19][20][21] (see for details paragraph 4).…”

“…This is even more crucial when time-dependent biological processes, such as cell maturation or differentiation, are studied. Indeed, in this case it is fundamental to be able to extract from the spectral data the relevant information of the process you are investigating [18][19][20][21].…”

Mid-infrared spectroscopic assessment of nanotoxicity in Gram-negative vs. Gram-positive bacteria

“…The mid-IR region, known as the "biochemical-cell fingerprint" region, is where the majority of biochemical structures absorb IR energy and vibrate. 28,29 This technique, coupled with multivariate analysis, allows identification of biochemical alterations induced by specific treatments.…”