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3D chemical imaging of the brain using quantitative IR spectro-microscopy

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Abstract

Three-dimensional (3D) histology is the next frontier for modern anatomo-pathology. Characterizing abnormal parameters in a tissue is essential to understand the rationale of pathology development. However, there is no analytical technique, in vivo or histological, that is able to discover such abnormal features and provide a 3D distribution at microscopic resolution. Here, we introduce a unique high-throughput infrared (IR) microscopy method that combines automated image correction and subsequent spectral data analysis for 3D-IR image reconstruction. We performed spectral analysis of a complete organ for a small animal model, a mouse brain with an implanted glioma tumor. The 3D-IR image is reconstructed from 370 consecutive tissue sections and corrected using the X-ray tomogram of the organ for an accurate quantitative analysis of the chemical content. A 3D matrix of 89 × 106 IR spectra is generated, allowing us to separate the tumor mass from healthy brain tissues based on various anatomical, chemical, and metabolic parameters. We demonstrate that quantitative metabolic parameters can be extracted from the IR spectra for the characterization of the brain vs. tumor metabolism (assessing the Warburg effect in tumors). Our method can be further exploited by searching for the whole spectral profile, discriminating tumor vs. healthy tissue in a non-supervised manner, which we call ‘spectromics’.

Graphical abstract: 3D chemical imaging of the brain using quantitative IR spectro-microscopy

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Publication details

The article was received on 29 Jul 2017, accepted on 13 Oct 2017 and first published on 17 Oct 2017


Article type: Edge Article
DOI: 10.1039/C7SC03306K
Citation: Chem. Sci., 2018, Advance Article
  • Open access: Creative Commons BY license
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    3D chemical imaging of the brain using quantitative IR spectro-microscopy

    A. Ogunleke, B. Recur, H. Balacey, H. Chen, M. Delugin, Y. Hwu, S. Javerzat and C. Petibois, Chem. Sci., 2018, Advance Article , DOI: 10.1039/C7SC03306K

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