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Article

Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization

1
IREC Imaging Platform (2IP), Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
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Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
3
Pole of Pneumology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
4
Mechanisms of Inherited Kidney Diseases Group, University of Zurich, 8057 Zurich, Switzerland
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Pole of Nephrology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
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Louvain Centre for Toxicology and Applied Pharmacology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
*
Authors to whom correspondence should be addressed.
Biomolecules 2020, 10(11), 1585; https://doi.org/10.3390/biom10111585
Received: 19 October 2020 / Revised: 19 November 2020 / Accepted: 20 November 2020 / Published: 22 November 2020
(This article belongs to the Special Issue Digital Pathology)
Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods are operator-dependent, organ-specific, and/or need advanced equipment. Therefore, we developed a robust, minimally operator-dependent, and tissue-transposable digital method for fibrosis quantification. The proposed method involves a novel algorithm for more specific and more sensitive detection of collagen fibers stained by picrosirius red (PSR), a computer-assisted segmentation of histological structures, and a new automated morphological classification of fibers according to their compactness. The new algorithm proved more accurate than classical filtering using principal color component (red-green-blue; RGB) for PSR detection. We applied this new method on established mouse models of liver, lung, and kidney fibrosis and demonstrated its validity by evidencing topological collagen accumulation in relevant histological compartments. Our data also showed an overall accumulation of compact fibers concomitant with worsening fibrosis and evidenced topological changes in fiber compactness proper to each model. In conclusion, we describe here a robust digital method for fibrosis analysis allowing accurate quantification, pattern recognition, and multi-organ comparisons useful to understand fibrosis dynamics. View Full-Text
Keywords: fibrosis; picrosirius red; digital analysis; collagen proportionate area; whole section; region-of-interest; fibrosis pattern fibrosis; picrosirius red; digital analysis; collagen proportionate area; whole section; region-of-interest; fibrosis pattern
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MDPI and ACS Style

Courtoy, G.E.; Leclercq, I.; Froidure, A.; Schiano, G.; Morelle, J.; Devuyst, O.; Huaux, F.; Bouzin, C. Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization. Biomolecules 2020, 10, 1585. https://doi.org/10.3390/biom10111585

AMA Style

Courtoy GE, Leclercq I, Froidure A, Schiano G, Morelle J, Devuyst O, Huaux F, Bouzin C. Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization. Biomolecules. 2020; 10(11):1585. https://doi.org/10.3390/biom10111585

Chicago/Turabian Style

Courtoy, Guillaume E.; Leclercq, Isabelle; Froidure, Antoine; Schiano, Guglielmo; Morelle, Johann; Devuyst, Olivier; Huaux, François; Bouzin, Caroline. 2020. "Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization" Biomolecules 10, no. 11: 1585. https://doi.org/10.3390/biom10111585

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