[Hanoi, August 2024] In the quest to find better treatments for heart disease, researchers often rely on their understanding of human cell lines or animal models. However, these methods fall short of accurately mimicking human heart conditions. The new technology using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to recap the native-like heart cell, offers a better way to model human heart conditions in the lab. While there are many ways to quantify hiPSC-CMs, analyzing their sarcomeres—the basic contractile units of heart muscle cells—is crucial for understanding heart function. However, the quantitative analysis of sarcomere structures in hiPSC-CMs has been challenging due to the complexity, microscopic scale and variability of these cellular components. A recent AI-based framework published by a research group at VinUni-Illinois Smart Health Center (VISHC) brings us closer to overcoming this challenge and paving the way for more advanced tasks.

Human induced pluripotent stem cells (hiPSCs) can be created by reprogramming healthy or patient-derived somatic cells and then differentiated into all subtypes of cardiomyocytes (hiPSC-CMs). hiPSC-CMs have a wide range of applications, which highlighted was proven effective for drug screening, modeling diseases, and advancing personalized medicine, … Therefore, it is essential to create an automated and high-throughput framework for measuring the maturation of hiPSC-CM. However, quantitative approaches for tracking hiPSC-CMs development are currently limited by labor-intensive and error-prone traditional methods like manual annotation or Fourier transform analysis.

The sarcomere, the contractile unit of the cardiac myocyte, consists of parallel, cross-striated bundles of thin filament containing actin, tropomyosin, and the troponin complex, along with thick filament primarily composed of myosin and its supporting proteins (Figure 1).

Huyen also mentioned the next steps to improve SarcNet’s performance. “Our latest experimental results indicate significant improvements across all performance metrics with the new model. We are in the final stages and look forward to sharing our new findings soon,” she said. In the long term, the team plans to expand the research by building a benchmark dataset and evaluating the effects of a wide range of drugs on hiPSC-CMs.

This innovative tool will be a stepping stone in VISHC’s commitment to solving critical biomedical challenges with practical and effective solutions.

Editor’s Note: The full text of the paper can be accessed at 2405.17926 (arxiv.org)