Quantitative Chemical Imaging and Unsupervised Analysis Using Hyperspectral Coherent Anti-Stokes Raman Scattering Microscopy

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• 作者：Francesco Masia ; Adam Glen ; Phil Stephens ; Paola Borri ; Wolfgang Langbein
• 刊名：Analytical Chemistry
• 出版年：2013
• 出版时间：November 19, 2013
• 年：2013
• 卷：85
• 期：22
• 页码：10820-10828
• 全文大小：555K
• 年卷期：v.85,no.22(November 19, 2013)
• ISSN：1520-6882

文摘

In this work, we report a method to acquire and analyze hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy images of organic materials and biological samples resulting in an unbiased quantitative chemical analysis. The method employs singular value decomposition on the square root of the CARS intensity, providing an automatic determination of the components above noise, which are retained. Complex CARS susceptibility spectra, which are linear in the chemical composition, are retrieved from the CARS intensity spectra using the causality of the susceptibility by two methods, and their performance is evaluated by comparison with Raman spectra. We use non-negative matrix factorization applied to the imaginary part and the nonresonant real part of the susceptibility with an additional concentration constraint to obtain absolute susceptibility spectra of independently varying chemical components and their absolute concentration. We demonstrate the ability of the method to provide quantitative chemical analysis on known lipid mixtures. We then show the relevance of the method by imaging lipid-rich stem-cell-derived mouse adipocytes as well as differentiated embryonic stem cells with a low density of lipids. We retrieve and visualize the most significant chemical components with spectra given by water, lipid, and proteins segmenting the image into the cell surrounding, lipid droplets, cytosol, and the nucleus, and we reveal the chemical structure of the cells, with details visualized by the projection of the chemical contrast into a few relevant channels.