High-throughput AI quantification of cardiomyocyte hypertrophy in rodent models

This article and associated images are based on a poster originally authored by Katarina Rydén Markinhuhta, Linda Hägerstrand, Margareta Behrendt, Weike Bao, Qing-Dong Wang, Alison Rowles and Pernilla Eliasson and presented at ELRIG Drug Discovery 2025 in affiliation with AstraZeneca.

This poster is being hosted on this website in its raw form, without modifications. It has not undergone peer review but has been reviewed to meet AZoNetwork's editorial quality standards. The information contained is for informational purposes only and should not be considered validated by independent peer assessment. 

Introduction

Traditional assessment of cardiac hypertrophy in rodent models relies on stereological analysis at the cellular level, which provides high precision but is limited by complexity and low throughput.¹

Macroscopic measures, such as heart weight to tibia length ratio (HW/TL) and left ventricular (LV) mass, are commonly used to measure cardiac hypertrophy but lack specificity, as increases in weight may reflect non-hypertrophic changes within the myocardium.

To overcome the limitations of conventional hypertrophic methods, the research team developed an AI-based image analysis algorithm for accurate and high-throughput quantification of cardiomyocyte hypertrophy using histological sections.

Study design and results

Experimental overview. Cardiomyocyte membranes in rodent heart tissue were visualized by laminin immunohistochemistry and scanned as virtual whole-slide images.

A deep learning algorithm, implemented in Visiopharm and trained on manually annotated reference images, was used to automatically identify cardiomyocytes with centrally located nuclei, ensuring selection of true cross-sectional profiles and minimizing sectioning artifacts. The average cardiomyocyte cross-sectional area (CM area) was used as a quantitative measure of hypertrophy.

Results. The method was validated in two established rodent models of hypertrophy: the Transverse Aortic Constriction (TAC) mouse model and the Zucker Diabetic Sprague-Dawley Fatty (ZSF1) rat model. In both, AI-based morphometric analysis detected hypertrophy with sensitivity equal to or greater than conventional metrics such as the HW/TL ratio and LV mass, which was cautiously reviewed by a pathologist.

Image analysis of CM area correlates significantly with HW/TL ratio and LV mass in hypertrophic disease models

Hypertrophy measurements in TAC mouse model

Image Credit: Image courtesy of Katarina Rydén Markinhuhta et al., in partnership with ELRIG (UK) Ltd.

Hypertrophy measurements in ZSF1 rat model

Image Credit: Image courtesy of Katarina Rydén Markinhuhta et al., in partnership with ELRIG (UK) Ltd.

Cardiomyocytes, with cross-sectional profile, visualized with laminin immunohistochemistry and automated identification using algorithm. Image Credit: Image courtesy of Katarina Rydén Markinhuhta et al., in partnership with ELRIG (UK) Ltd.

Conclusions

The automated, AI-based image analysis of cardiomyocyte cross-sectional area presents a robust, specific, and scalable strategy for quantifying pathological cardiac hypertrophy in rodent models, surpassing conventional methods.

This platform enables detailed, high-throughput cell-level assessment of pathological cardiac hypertrophy, providing a useful tool for identifying new pharmacological treatments for cardiac hypertrophy.

References

1. Mühlfeld, C., Nyengaard, J.R., and Mayhew, T.M. (2010). A review of state-of-the-art stereology for better quantitative 3D morphology in cardiac research. 19(2), pp.65–82. https://doi.org/10.1016/j.carpath.2008.10.015.
2. Bensley, J.G., et al. (2016). Three-dimensional direct measurement of cardiomyocyte volume, nuclearity, and ploidy in thick histological sections. Scientific Reports, 6(1). https://doi.org/10.1038/srep23756.

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Last Updated: Nov 7, 2025