3D Quantitative Chemical Imaging of Tissues by Spectromics. Issue 12 (December 2017)

• Record Type: Journal Article
• Authors: Petibois, Cyril
• Is Part Of: Trends in biotechnology. Volume 35:Issue 12(2017)
• Page Start: 1194
• Page End: 1207
• Publication Date: 2017-12
• Subjects: physical chemistry -- spectromics -- 3D microscopy -- histopathology -- molecular analysis -- substructures; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2017.08.002
• Languages: English
• ISSNs: 0167-7799

Abstract

Abstract : Mid-infrared (IR), Raman, and X-ray fluorescence (XRF) spectroscopy methods, as well as mass spectrometry (MS), can be used for 3D chemical imaging. These techniques offer an invaluable opportunity to access chemical features of biological samples in a nonsupervised way. The global chemical information they provide enables the exploitation of a large array of chemical species or parameters, so-called 'spectromics'. Extracting chemical data from spectra is critical for the high-quality chemical analysis of biosamples. Furthermore, these are the only currently available techniques that can quantitatively analyze tissue content (e.g., molecular concentrations) and substructures (e.g., cells or blood vessels). The development of chemical-derived biological metadata appears to be a new way to exploit spectral information with machine learning algorithms. Trends: 3D chemical imaging is achieved by several spectromicroscopic methods. These provide a quantitative analysis of tissue content and substructures with a depth of information that no other histological technique can determine from the same sample. However, they are currently underexploited despite their potential. Standardization of spectral data acquisition and treatments is the next frontier in the development of 3D chemical imaging routines. This methodological effort is required to advance the development of reliable analytical tools for the biosciences and industry. Spectromics is emerging as a new trend for nonsupervised and automated data treatment from 3D spectrum matrices. It has the advantages of being able to use all the chemical information available from spectra and of interpretability, since spectral data can be converted into biological metadata.