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Abstract Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca2+transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca2+transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca2+transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca2+transient signals. By applying this pipeline to our Ca2+transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca2+analysis.
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CalTrack: High-Throughput Automated Calcium Transient Analysis in Cardiomyocytes
Rationale: Calcium transient analysis is central to understanding inherited and acquired cardiac physiology and disease. Although the development of novel calcium reporters enables assays of CRISPR/Cas-9 genome-edited induced pluripotent stem cell–derived cardiomyocytes and primary adult cardiomyocytes, existing calcium-detection technologies are often proprietary and require specialist equipment, whereas open-source workflows necessitate considerable user expertise and manual input. Objective: We aimed to develop an easy to use open-source, adaptable, and automated analysis pipeline for measuring cellular calcium transients, from image stack to data output, inclusive of cellular identification, background subtraction, photobleaching correction, calcium transient averaging, calcium transient fitting, data collation, and aberrant behavior recognition. Methods and Results: We developed CalTrack, a MatLab-based algorithm, to monitor fluorescent calcium transients in living cardiomyocytes, including isolated single cells or those contained in 3-dimensional tissues or organoids, and to analyze data acquired using photomultiplier tubes or employing line scans. CalTrack uses masks to segment cells allowing multiple cardiomyocyte transients to be measured from a single field of view. After automatically correcting for photobleaching, CalTrack averages and fits a string of transients and provides parameters that measure time to peak, time of decay, tau, peak fluorescence/baseline fluorescence (F max /F 0 ), and others. We demonstrate the utility of CalTrack in primary and induced pluripotent stem cell–derived cell lines in response to pharmacological compounds and in phenotyping cells carrying hypertrophic cardiomyopathy variants. Conclusions: CalTrack, an open-source tool that runs on a local computer, provides automated high-throughput analysis of calcium transients in response to development, genetic or pharmacological manipulations, and pathological conditions. We expect that CalTrack analyses will accelerate insights into physiological and abnormal calcium homeostasis that influence diverse aspects of cardiomyocyte biology.
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- Award ID(s):
- 1647837
- PAR ID:
- 10331855
- Date Published:
- Journal Name:
- Circulation Research
- Volume:
- 129
- Issue:
- 2
- ISSN:
- 0009-7330
- Page Range / eLocation ID:
- 326 to 341
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1161/CIRCRESAHA.121.318868
