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Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles
Henry Joutsijoki1, Kirsi Penttinen2, Martti Juhola1
1Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Insights
Machine learning effectively distinguishes genetic cardiac diseases using iPSC-derived cardiomyocyte calcium transient signals. This method shows high accuracy for hypertrophic cardiomyopathy (HCM) and long QT syndrome (LQTS) identification.
Area of Science:
- Cardiology
- Stem Cell Biology
- Computational Biology
Background:
- Induced pluripotent stem cells (iPSC)-derived cardiomyocytes (CMs) model human cardiac diseases.
- Machine learning (ML) applied to Ca2+ transient signals can diagnose genetic cardiac diseases.
- Previous work demonstrated ML's ability to differentiate certain genetic cardiac diseases from controls.
Purpose of the Study:
- To assess ML's effectiveness in distinguishing between specific mutations within hypertrophic cardiomyopathy (HCM) and long QT syndrome (LQTS).
- To differentiate MYBPC3 (HCMM) and TPM1 (HCMT) mutations in HCM.
- To differentiate KCNQ1 (LQT1) and KCNH2 (LQT2) mutations in LQTS.
Main Methods:
- Collected Ca2+ transient signals from 419 HCM and 228 LQTS iPSC-CM samples.
- Preprocessed Ca2+ signals and computed peak waveforms.
- Applied various ML algorithms for classification of disease subtypes.
Main Results:
- Achieved classification accuracy of 89% for distinguishing HCM subtypes (HCMM vs. HCMT).
- Achieved perfect classification accuracy of 100% for distinguishing LQTS subtypes (LQT1 vs. LQT2).
Conclusions:
- The applied ML methods are highly efficient for identifying genetic cardiac diseases.
- This approach offers a promising diagnostic tool for specific HCM and LQTS mutations.
- iPSC-CMs coupled with ML provide a powerful platform for cardiac disease diagnostics.
Background:
Modeling human cardiac diseases with induced pluripotent stem cells not only enables to study disease pathophysiology and develop therapies but also, as we have previously showed, it can offer a tool for disease diagnostics. We previously observed that a few genetic cardiac diseases can be separated from each other and healthy controls by applying machine learning to Ca2+ transient signals measured from iPSC-derived cardiomyocytes (CMs).
Objectives:
For the current research, 419 hypertrophic cardiomyopathy (HCM) transient signals and 228 long QT syndrome (LQTS) transient signals were measured. HCM signals included data recorded from iPSC-CMs carrying either α-tropomyosin, i.e., TPM1 (HCMT) or MYBPC3 or myosin-binding protein C (HCMM) mutation and LQTS signals included data recorded from iPSC-CMs carrying potassium voltage-gated channel subfamily Q member 1 (KCNQ1) mutation (long QT syndrome 1 [LQT1]) or KCNH2 mutation (long QT syndrome 2 [LQT2]). The main objective was to study whether and how effectively HCMM and HCMT can be separated from each other as well as LQT1 from LQT2.
Methods:
After preprocessing those Ca2+ signals where we computed peak waveforms we then classified the two mutations of both disease pairs by using several different machine learning methods.
Results:
We obtained excellent classification accuracies of 89% for HCM and even 100% for LQT at their best.
Conclusion:
The results indicate that the methods applied would be efficient for the identification of these genetic cardiac diseases.
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