Han Junlei, Xu Xingyuan, Li Jianhua, Zhang Haiyan, Su Weiguang, Wang Ke, Li Huimin, Xu Zhipeng, Chen Jun, Cai Xinxia, Sun Yu, Wang Li
School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
Shandong Institute of Mechanical Design and Research, Jinan 250353, China.
ACS Nano. 2025 Jul 8;19(26):24052-24066. doi: 10.1021/acsnano.5c06655. Epub 2025 Jun 26.
Heart-on-a-chip (HOC) platforms play a pivotal role in cardiac research, yet existing models suffer from mechanical mismatch between the soft myocardial tissue, sensors, and substrate, leading to impaired myocardial function and compromised data capture. Here, we introduce a mechanically matched HOC to address these challenges by mimicking the Young's modulus of the myocardial bilayer, including the elastic epicardium (30-70 kPa) and soft extracellular matrix (28-37 kPa). A process based on liquid-gas phase transition-induced porosification was developed, which introduces porosity into polydimethylsiloxane through controlled tetradecane phase transition, allowing for a tunable reduction in Young's modulus. This platform demonstrated excellent durability, withstanding over 1,000,000 stretch cycles, and allowed continuous electromechanical monitoring of cardiomyocyte behavior for 11 days. The mechanically matched platform promoted significant upregulation of key genes linked to cell adhesion, contraction, and electrical propagation (e.g., ITGA1, CACNA1C, SCN5A, and KCNH2) and enhanced excitation-contraction coupling by 128% compared to mismatched models. Additionally, the integration of machine learning into the HOC further improved drug classification accuracy, demonstrating the potential for advancing pharmacological evaluation.
芯片上的心脏(HOC)平台在心脏研究中起着关键作用,但现有的模型存在软心肌组织、传感器和基底之间的机械不匹配问题,导致心肌功能受损和数据采集受到影响。在此,我们引入一种机械匹配的HOC,通过模拟心肌双层的杨氏模量来应对这些挑战,包括弹性心外膜(30 - 70千帕)和柔软的细胞外基质(28 - 37千帕)。开发了一种基于液 - 气相转变诱导孔隙化的工艺,该工艺通过可控的十四烷相变将孔隙引入聚二甲基硅氧烷,从而实现杨氏模量的可调降低。该平台展示了出色的耐久性,能够承受超过100万次拉伸循环,并允许对心肌细胞行为进行连续11天的机电监测。与不匹配的模型相比,机械匹配的平台促进了与细胞黏附、收缩和电传导相关的关键基因(如ITGA1、CACNA1C、SCN5A和KCNH2)的显著上调,并将兴奋 - 收缩偶联增强了128%。此外,将机器学习集成到HOC中进一步提高了药物分类准确性,展示了推进药理学评估的潜力。