PDF(Pubmed)
Abstract：
Goal: 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. Methods: 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). Results: 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. Conclusions: 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.
摘要：
目标：收缩反应和钙处理是了解心脏功能和生理学的核心，然而，现有的量化这些指标的分析方法通常很耗时，容易出错,或需要专用设备/许可证。我们开发了BeatProfiler,一套用于量化收缩功能的心脏分析工具，钙处理，和多个体外心脏模型的力生成，并应用下游机器学习方法进行深度表型和分类。方法：我们首先验证BeatProfiler的准确性，鲁棒性，并通过对具有固定数据集的现有工具进行基准测试来提高速度。我们进一步证实了其强有力地表征疾病和剂量依赖性药物反应的能力。然后，我们证明了通过我们的自动采集管道获取的数据可以进一步用于机器学习（ML）分析，以表型为限制性心肌病的疾病模型和描述心脏活性药物功能反应。为了准确地在这些生物信号之间进行分类，我们应用基于特征的ML和深度学习模型（时间卷积双向长短期记忆模型或TCN-BiLSTM）。结果：针对现有工具的基准测试显示，通过提高低信号数据的灵敏度，BeatProfiler比现有工具更好地检测和分析收缩和钙信号。减少误报，分析速度提高7到50倍。在通过公开方法(PM)准确检测到的信号中，BeatProfiler提取的特征与PM具有很高的相关性，证实其可靠且与PM一致。BeatProfiler提取的特征以98%的准确度将限制性心肌病心肌细胞从等基因健康对照中分类，并将relax90确定为与先前发现一致的顶级特征。我们还表明，我们的TCN-BiLSTM模型能够以96％的准确率对无药物对照和4种具有不同作用机制的心脏药物进行分类。我们进一步将Grad-CAM应用于基于卷积的模型，以识别这些药物在钙信号中的扰动特征区域。结论：我们预计BeatProfiler的功能将有助于通过快速表型分析推进心脏生物学的体外研究，揭示心脏健康和疾病的潜在机制，并能够对心脏病和对药物的反应进行客观分类。
