Masia, Francesco ![]() ![]() ![]() ![]() ![]() ![]() |
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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 |
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Date Type: | Publication |
Status: | Published |
Schools: | Physics and Astronomy Biosciences |
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: | 15 Sep 2023 16:18 |
URI: | https://orca.cardiff.ac.uk/id/eprint/109746 |
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