In brief

A machine learning algorithm predicts arrhythmia by tracking calcium cycling as a measure of electrophysiological function in human stem cell-derived pluripotent cardiomyocyte cell cultures.

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To catch a skipped beat

14 Apr 2023

By analysing heart cells, a new machine learning algorithm could help predict arrhythmia risk before symptoms appear.

The rhythm of life arguably starts with our heartbeat—whose constant pulsing changes tempo in response to our daily activities and emotional state. For people living with cardiac arrhythmias, or irregular heartbeats, this steady beat can change unpredictably with potentially dangerous consequences.

Despite being a leading cause of heart failure, cardiac arrhythmia has remained notoriously difficult to diagnose and prevent. However, a stem-cell breakthrough may offer some hope: skin cells from a patient can be chemically reverted to an embryonic stem cell state before being coaxed into heart cells. This ‘heart in a dish’ allows researchers to model heart physiology in a relatively non-invasive manner, and without the need for human embryos to be harvested.

The next hurdle is matching heart cell (cardiomyocyte) behaviour with disease risk, said Boon Seng Soh, a Principal Investigator at A*STAR’s Institute of Molecular and Cell Biology (IMCB). “While some  in vitro  phenotypes have been linked with arrhythmia, we still lack a robust method for identifying key electrophysiological changes associated with heart conditions,” explained Soh.

Working with a team from the National University of Singapore, Soh and colleagues harnessed the power of machine learning (ML) algorithms to help bridge this gap and analyse large calcium-cycling datasets, a lab test used to assess the heart’s electrical activity.

The team first collected training data from human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) with a special reporter to visualise how calcium moves through the heart cell during contraction. They then input over 3,000 calcium-cycling data points collected from healthy and arrhythmic cardiomyocytes. Going a step further, Jeremy Pang, the leading author of the study, also trained binary classifiers to distinguish between specific subtypes of arrhythmia.

In their study, the scientists found that their ML algorithm could predict the presence (and subtype) of arrhythmia with over 90 percent accuracy. Soh said this predictive power could empower cardiologists to pick up on the condition's early signs in patients long before symptoms manifest. “Using cardiomyocytes generated from human induced pluripotent stem cells, we potentially can predict the types of arrhythmias the person may develop years later.”

Additionally, ML-driven platforms could help accelerate the screening of medications for any potential toxic effects on the heart. Personalised testing of drugs on patient-derived cardiomyocytes could help avoid heart-related side effects.

Soh, the study’s corresponding author, said the team plans to tweak the ML platform to handle data from miniaturised heart tissue culture models. “We intend to adapt the platform to work with 3D-chambered cardiac organoid in vitro models which we have recently developed,” Soh said, adding that they also hope to apply ML to tackle age-related heart failure.

The A*STAR-affiliated researchers contributing to this research are from the Institute of Molecular and Cell Biology (IMCB) and the A*STAR Skin Research Labs (A*SRL).

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Pang J.K.S., Chia S., Zhang J., Szyniarowski P., Stewart C., et al. Characterizing arrhythmia using machine learning analysis of Ca2+ cycling in human cardiomyocytes. Stem Cell Reports 17 (8), 1810-1823 (2022). │article

About the Researcher

Boon Seng Soh obtained a Bachelor of Science with honours degree from the National University of Singapore. As an A*STAR Graduate Scholarship recipient, his PhD studies focused on the optimisation of stem cell cultures and differentiation towards pulmonary stem cells under the co-supervision of Athanasios Mantalaris at Imperial College London and Bing Lim at the A*STAR Genome Institute of Singapore. In 2011, Soh joined the laboratory of Kenneth Chien at Harvard University to work on the biology of multipotent cardiac stem cells, in both murine- and human-based model systems. His research focus is clinically driven, with an emphasis on understanding the underlying molecular and cellular mechanisms in diseases towards developing targeted therapies. His current research interests focus on modelling human cardiac diseases using both 2D and 3D culture systems.

This article was made for A*STAR Research by Wildtype Media Group