Robust quantitative assessment of collagen fibers with picrosirius red stain and linearly polarized light as demonstrated on atherosclerotic plaque samples

Collagen is an important component in maintaining structural integrity and functionality of tissues and is modulated in various biological processes. Its visualization and possible quantification using histopathological stains can be important for understanding disease progression or therapeutic response. Visualization of collagen fiber with the histological stain picrosirius red (PSR) is enhanced with polarized light and quantitative analysis is possible using circular polarizers. However, linear polarizers are more commonly available and easier to optically align. The objective of the present study is to demonstrate a novel image acquisition technique and analysis method using linearly polarized light. The proposed imaging technique is based on image acquisition at multiple slide rotation angles, which are co-registered to form a composite image used for quantitative analysis by pixel intensity or pixel counting. The technique was demonstrated on multiple human coronary samples with varying histopathologies and developed specifically to analyze cap collagen in atherosclerotic plaque. Pixel counting image analysis was found to be reproducible across serial tissue sections and across different users and sufficiently sensitive to detect differences in cap structural integrity that are likely relevant to prediction of rupture risk. The benefit of slide rotation angle under linear polarization to acquire images represents a feasible and practical implementation for expanding the general utility of PSR for quantitative analysis.

Automated summary

This study demonstrates a novel image acquisition technique and analysis method using linearly polarized light for quantitative analysis of collagen content in tissues. The technique showed high reproducibility and sensitivity in analyzing cap collagen in atherosclerotic plaque, suggesting its potential usefulness for predicting rupture risk.