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

Abstract

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.

Comparison of liver biopsies before and after direct-acting antiviral therapy for hepatitis C and correlation with clinical outcome

“…( 2 )” was more objectively calculated in these five study patients using a quantitative imaging software algorithm (Visiopharm), as described in the methods, “ Quantification of hepatic steatosis ” section (Fig. 3 E–H) and other studies 22 – 24 . Using this method, two of the patients (patients 2 and 10) had decreased steatosis (4.49- and 6.20-fold, respectively) and three patients (patients 4, 7, and 8) had increased steatosis (7.34-, 2.22-, and 3.26-fold, respectively) post-DAA treatment (Table 2 , Fig.…”

“…An interesting observation was the marked difference in the percentage of hepatic steatosis in the estimation made by the pathologist when compared to that determined using the Visiopharm algorithm. Visiopharm is an advanced imaging software platform that enables more precise quantification of various parameters such as fibrosis and steatosis in histology material 22 , 24 , 32 . Overall, the percentages estimated by the pathologist were much higher than those determined by the imaging software.…”

Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

“…Quantitative digital image analysis methods of lung morphometry have been introduced, but a limitation is their requirement of manual interventions (21)(22)(23)(24)(25)(26)(27)(28). Consequently, these techniques are laborious, time consuming, and subject to researcher intra-and inter-variabilities.…”

“…Ashcroft scoring utilizes a numerical scale from 0 through 8 to grade fibrosis; it is subject to intra-and inter-scorer variability (20). Computerized digital image analysis was developed to utilize a continuous measure of fibrosis in various rodent or human tissues, including lung, liver, heart, and kidney (21)(22)(23)(24)(25)(26)(27)(28). Most computerized digital image analyses evaluate severity of fibrosis by quantifying collagen content stained by picrosirius red or to a lesser extent by Masson trichrome or immunostaining.…”

Pharmacological validation of SSc-ILD mouse model bleomycin-induced by osmotic minipump

“…Hyp determination, being a destructive assay, precludes other histological analyses or alternative readouts that might be considered 30 .…”