University of Bordeaux, Inserm U1029 LAMC, Allée Geoffroy Saint-Hilaire Bat. B2, F33600 Pessac, France; Academia Sinica, Institute of Physics, 128 Sec. 2, Academia Road, Nankang, Taipei 11529, Taiwan, Republic of China.
Trends Biotechnol. 2017 Dec;35(12):1194-1207. doi: 10.1016/j.tibtech.2017.08.002. Epub 2017 Sep 8.
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.
中红外(IR)、拉曼(Raman)和 X 射线荧光(XRF)光谱方法以及质谱(MS)可用于 3D 化学成像。这些技术为以非监督方式获取生物样本的化学特征提供了宝贵的机会。它们提供的全局化学信息使人们能够利用大量的化学物质或参数,即所谓的“代谢组学”。从光谱中提取化学数据对于生物样本的高质量化学分析至关重要。此外,这些是目前唯一可定量分析组织含量(例如分子浓度)和亚结构(例如细胞或血管)的技术。利用机器学习算法开发化学衍生的生物元数据似乎是利用光谱信息的一种新方法。