Abstract
Many cardiac pathologies involve changes in tissue structure. Conventional analysis of structural features is extremely time-consuming and subject to observer bias. The possibility to determine spatial interrelations between these features is often not fully exploited. We developed a staining protocol and an ImageJ-based tool (JavaCyte) for automated histological analysis of cardiac structure, including quantification of cardiomyocyte size, overall and endomysial fibrosis, spatial patterns of endomysial fibrosis, fibroblast density, capillary density and capillary size. This automated analysis was compared to manual quantification in several well-characterized goat models of atrial fibrillation (AF). In addition, we tested inter-observer variability in atrial biopsies from the CATCH-ME consortium atrial tissue bank, with patients stratified by their cardiovascular risk profile for structural remodeling. We were able to reproduce previous manually derived histological findings in goat models for AF and AV block (AVB) using JavaCyte. Furthermore, strong correlation was found between manual and automated observations for myocyte count (r = 0.94, p < 0.001), myocyte diameter (r = 0.97, p < 0.001), endomysial fibrosis (r = 0.98, p < 0.001) and capillary count (r = 0.95, p < 0.001) in human biopsies. No significant variation between observers was observed (ICC = 0.89, p < 0.001). We developed and validated an open-source tool for high-throughput, automated histological analysis of cardiac tissue properties. JavaCyte was as accurate as manual measurements, with less inter-observer variability and faster throughput.
Highlights
Many cardiac pathologies involve changes in tissue structure
We previously demonstrated that 6 months of atrial fibrillation (AF) does not lead to an increase of overall fibrosis but does result in endomysial fibrosis in the epicardial layer of the atria, impairing transverse p ropagation[6]
In the context of fibrosis, the density of fibroblasts is relevant[9], both because they are responsible for excess extracellular matrix ECM formation, and because fibroblasts may couple to myocytes electrically, thereby affecting conduction through electrotonic interactions[10,11]
Summary
Many cardiac pathologies involve changes in tissue structure. Conventional analysis of structural features is extremely time-consuming and subject to observer bias. We developed a staining protocol and an ImageJ-based tool (JavaCyte) for automated histological analysis of cardiac structure, including quantification of cardiomyocyte size, overall and endomysial fibrosis, spatial patterns of endomysial fibrosis, fibroblast density, capillary density and capillary size. This automated analysis was compared to manual quantification in several well-characterized goat models of atrial fibrillation (AF). Assessment of fibrosis is commonly performed in histological sections by quantifying the relative area occupied by fibrous tissue in Sirius Red or Masson’s Trichrome staining, irrespective of the particular type of fibrosis This usually involves setting a subjective color threshold by the operator to distinguish fibrous tissue from myocytes. Manually measuring myocyte dimensions in histological or immunohistochemical staining is time-intensive and prone to observer bias
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