Saberigarakani Alireza, Patel Riya P, Almasian Milad, Zhang Xinyuan, Brewer Jonathan, Hassan Sohail S, Chai Jichen, Lee Juhyun, Fei Baowei, Yuan Jie, Carroll Kelli, Ding Yichen
Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
Department of Biology, The University of Texas at Dallas, Richardson, TX 75080, USA.
Cell Rep Methods. 2025 Aug 18;5(8):101113. doi: 10.1016/j.crmeth.2025.101113. Epub 2025 Jul 23.
Novel insights into cardiac contractile dysfunction at the cellular level could deepen understanding of arrhythmia and heart injury, which are leading causes of morbidity and mortality worldwide. We present a comprehensive experimental and computational framework combining light-field microscopy and single-cell tracking to investigate real-time volumetric data in live zebrafish hearts, which share structural and electrical similarities to the human heart. Our system acquires 200 vol/s with lateral resolution of up to 5.02 ± 0.54 μm and axial resolution of 9.02 ± 1.11 μm across the whole depth using an expectation-maximization-smoothed deconvolution algorithm. We apply a deep-learning approach to quantify cell displacement and velocity in blood flow and myocardial motion and to perform real-time volumetric tracking from end-systole to end-diastole within a virtual reality environment. This capability delivers high-speed and high-resolution imaging of cardiac contractility at single-cell resolution over multiple cycles, supporting in-depth investigation of intercellular interactions in health and disease.
对细胞水平心脏收缩功能障碍的新见解可能会加深对心律失常和心脏损伤的理解,而心律失常和心脏损伤是全球发病和死亡的主要原因。我们提出了一个综合的实验和计算框架,结合光场显微镜和单细胞跟踪技术,以研究活斑马鱼心脏中的实时体积数据,斑马鱼心脏在结构和电生理方面与人类心脏相似。我们的系统使用期望最大化平滑反卷积算法,以每秒200个体积的速度采集数据,在整个深度范围内横向分辨率高达5.02±0.54μm,轴向分辨率为9.02±1.11μm。我们应用深度学习方法来量化血流和心肌运动中的细胞位移和速度,并在虚拟现实环境中从收缩末期到舒张末期进行实时体积跟踪。这种能力可在多个周期内以单细胞分辨率提供心脏收缩性的高速高分辨率成像,支持对健康和疾病状态下细胞间相互作用的深入研究。