BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs
<italic>Goal:</italic> Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We d...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10490213/ |
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| author | Youngbin Kim Kunlun Wang Roberta I. Lock Trevor R. Nash Sharon Fleischer Bryan Z. Wang Barry M. Fine Gordana Vunjak-Novakovic |
| author_facet | Youngbin Kim Kunlun Wang Roberta I. Lock Trevor R. Nash Sharon Fleischer Bryan Z. Wang Barry M. Fine Gordana Vunjak-Novakovic |
| author_sort | Youngbin Kim |
| collection | DOAJ |
| description | <italic>Goal:</italic> Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. <italic>Methods:</italic> We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). <italic>Results:</italic> Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. <italic>Conclusions:</italic> We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs. |
| format | Article |
| id | doaj-art-0364c818667741609d6dace91158f06a |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-0364c818667741609d6dace91158f06a2025-01-30T00:03:41ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01523824910.1109/OJEMB.2024.337746110490213BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and DrugsYoungbin Kim0https://orcid.org/0000-0003-1919-1575Kunlun Wang1https://orcid.org/0009-0009-3486-9752Roberta I. Lock2Trevor R. Nash3https://orcid.org/0000-0003-4555-7577Sharon Fleischer4https://orcid.org/0000-0002-9852-6697Bryan Z. Wang5https://orcid.org/0000-0002-0682-3222Barry M. Fine6https://orcid.org/0000-0001-6298-2888Gordana Vunjak-Novakovic7https://orcid.org/0000-0002-9382-1574Department of Biomedical Engineering, Columbia University, New York, NY, USADepartment of Biomedical Engineering, Columbia University, New York, NY, USADepartment of Biomedical Engineering, Columbia University, New York, NY, USADepartment of Biomedical Engineering, Columbia University, New York, NY, USADepartment of Biomedical Engineering, Columbia University, New York, NY, USADepartment of Biomedical Engineering, Columbia University, New York, NY, USADepartment of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY, USADepartment of Biomedical Engineering, Columbia University, New York, NY, USA<italic>Goal:</italic> Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. <italic>Methods:</italic> We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). <italic>Results:</italic> Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. <italic>Conclusions:</italic> We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.https://ieeexplore.ieee.org/document/10490213/Calcium handlingcardiac analysiscontractile functiondrug responsemachine learning (ML) |
| spellingShingle | Youngbin Kim Kunlun Wang Roberta I. Lock Trevor R. Nash Sharon Fleischer Bryan Z. Wang Barry M. Fine Gordana Vunjak-Novakovic BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs IEEE Open Journal of Engineering in Medicine and Biology Calcium handling cardiac analysis contractile function drug response machine learning (ML) |
| title | BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs |
| title_full | BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs |
| title_fullStr | BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs |
| title_full_unstemmed | BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs |
| title_short | BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs |
| title_sort | beatprofiler multimodal in vitro analysis of cardiac function enables machine learning classification of diseases and drugs |
| topic | Calcium handling cardiac analysis contractile function drug response machine learning (ML) |
| url | https://ieeexplore.ieee.org/document/10490213/ |
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