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Bessel-beam hyperspectral CARS microscopy with sparse sampling: Enabling high-content high-throughput label-free quantitative chemical imaging

Masia, Francesco, Pope, Iestyn, Watson, Peter, Langbein, Wolfgang and Borri, Paola 2018. Bessel-beam hyperspectral CARS microscopy with sparse sampling: Enabling high-content high-throughput label-free quantitative chemical imaging. Analytical Chemistry 90 (6) , pp. 3775-3785. 10.1021/acs.analchem.7b04039

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Abstract

Microscopy-based high-content and high-throughput analysis of cellular systems plays a central role in drug discovery. However, for contrast and specificity, the majority of assays require a fluorescent readout which always comes with the risk of alteration of the true biological conditions. In this work, we demonstrate a label-free imaging platform which combines chemically specific hyperspectral coherent anti-Stokes Raman scattering microscopy with sparse sampling and Bessel beam illumination. This enabled us to screen multiwell plates at high speed, while retaining the high-content chemical analysis of hyperspectral imaging. To demonstrate the practical applicability of the method we addressed a critical side effect in drug screens, namely, drug-induced lipid storage within hepatic tissue. We screened 15 combinations of drugs and neutral lipids added to human HepG2 liver cells and developed a high-content quantitative data analysis pipeline which extracted the spectra and spatial distributions of lipid and protein components. We then used their combination to train a support vector machine discriminative algorithm. Classification of the drug responses in terms of phospholipidosis versus steatosis was achieved in a completely label-free assay.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Physics and Astronomy
Publisher: American Chemical Society
ISSN: 0003-2700
Funders: Biotechnology and Biological Sciences Research Council
Date of First Compliant Deposit: 8 March 2018
Date of Acceptance: 5 March 2018
Last Modified: 29 Jan 2019 22:32
URI: http://orca.cf.ac.uk/id/eprint/109746

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