Understanding the dynamics of cardiomyocyte ploidy is of paramount importance for deciphering the cellular mechanisms propelling cardiac development,1  regeneration,2  and disease.3  Cardiomyocytes possess diverse ploidy levels that can vary significantly among the cell population. Both polyploidization and ploidy reduction are well documented in liver hepatocytes in response to injury and repair, but these processes are poorly understood in the context of the heart. To address this gap in scientific understanding, a novel approach using single-cell, flow cytometry–based imaging and software-driven machine learning algorithms has been employed.

The existing technical limitations associated with traditional histologic or microscopic examination of selected cell populations include potential subjectivity and low-throughput data collection. These methods often rely on small sample sizes and can introduce bias, affecting the accuracy and precision of experimental findings. In addition, precise and accurate nuclear ploidy counts for cardiomyocytes in mass have not been easy to obtain.

In the present study, the Amnis ImageStream X Mark II imaging flow cytometer (Cytek Biosciences), IDEAS software analysis, and machine learning were used to revisit ploidy in cardiomyocytes. Amnis ImageStream flow cytometry enables photomicroscopy of individual cells and fluorescence image acquisition (Fig. 1), leading to high-throughput data collection from thousands of cells in a single experimental session. Photomicrographs were processed using IDEAS Software's machine learning module to obtain target cellular data. Configured to perform population-based analysis of thousands of cells, this software extracted key features from the images, allowing for precise measurements of nuclear ploidy in each cardiomyocyte. This approach greatly improves analysis compared to traditional methods by expanding the population size, avoiding subjective bias, and enhancing accuracy.

Fig. 1

Images captured using the Cytek Amnis ImageStream X Mark II imaging flow cytometer of enzymatically isolated, single-cell adult mouse cardiomyocytes. Images were analyzed using IDEAS software to determine nuclear count and ploidy level, using a custom-scripted machine learning analysis module. From left to right: mononucleated cells, binucleated cells, and multinucleated cells.

Fig. 1

Images captured using the Cytek Amnis ImageStream X Mark II imaging flow cytometer of enzymatically isolated, single-cell adult mouse cardiomyocytes. Images were analyzed using IDEAS software to determine nuclear count and ploidy level, using a custom-scripted machine learning analysis module. From left to right: mononucleated cells, binucleated cells, and multinucleated cells.

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With this trinity of tools, the groundwork has been laid for precise quantification of nuclear polyploidy in cardiomyocytes. This technique offers swift and reliable processing of data sets consisting of single-cell images and empowers population-based studies of polyploidy, paving the way for advanced explorations of ploidy dynamics in myocardial biology. This methodology will be used to reevaluate ploidy response to myocardial injury, repair, and regeneration.

Open Access: © 2023 The Author(s). Published by The Texas Heart Institute®. This is an Open Access article under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC, https://creativecommons.org/licenses/by-nc/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, and the use is noncommercial.

Author Contributions: J. Mitchell performed experiments, built algorithms, analyzed data, and wrote the summary. M. Sussman designed experiments, supervised workflow, and revised the summary.

Conflict of Interest Disclosure: None.

Funding/Support: National Institutes of Health (No. R01 HL067245).

Section Editor: Emerson Perin, MD, PhD.

Meeting Presentation: Presented at the 3rd Annual International Symposium on Cardiovascular Regenerative Medicine; May 12-13, 2023; Houston, TX.

Acknowledgments: The Sussman Lab at San Diego State University.

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