超快光片光镊用于细胞和软组织原位并行生物力学表征

中文摘要 AI 辅助

细胞和组织力学性能的定量表征对于理解疾病进展和组织再生至关重要。光镊（OT）能够直接施加生物学相关的力；然而，光镊一直局限于对细胞进行单轴压痕，从而限制了通量。此外，使用象限光电二极管不足以评估组织生物力学表征所需的大位移。我们提出了光片光镊作为力传感器（LOFT），该方法通过同时多粒子捕获和在亚纳牛力下的并行表征，将通量提高了至少3倍。LOFT通过独特地集成用于扩展捕获的光片照明、用于增强光学梯度力的飞秒脉冲激光器以及用于观察力传递的基于视频的粒子追踪来实现。该平台通过单细胞压痕实验得到验证。然后，我们将LOFT应用于心肌组织，揭示了健康和梗死区域之间显著的生物力学差异；其解释进一步得到了使用相同光源和平台的定量多光子成像的支持。这项工作首次展示了光镊用于完整软组织力学测试，并确立了LOFT作为一种多功能、多用途平台，用于对复杂生物系统进行高通量、微创的原位力学表征。

英文摘要

Quantitative characterization of the mechanical properties of cells and tissues is essential for understanding disease progression and tissue regeneration. Optical tweezers (OT) enable the direct application of biologically relevant forces; however, OT has been limited to single axial indentations of cells, thereby restricting throughput. Furthermore, the use of quadrant photodiodes is insufficient for assessing the large displacements required for biomechanical characterization of tissues. We present light-sheet optical tweezers as a force transducer (LOFT), an approach that improves the throughput by at least 3x through simultaneous multiparticle trapping and parallelized characterization under sub-nN forces. LOFT is achieved by uniquely integrating light-sheet illumination for extended trapping, femtosecond-pulsed lasers to augment the optical gradient force, and videography-based particle tracking for observation of force transduction. The platform is validated through single-cell indentation experiments. We then apply LOFT to myocardial tissue, revealing significant biomechanical differences between healthy and infarcted regions; the interpretation of which is further supported by quantitative multiphoton imaging using the same optical source and platform. This work represents the first demonstration of OT for the mechanical testing of intact soft tissues, and establishes LOFT as a versatile, multifunctional platform for high-throughput, minimally invasive, mechanical characterization of complex biological systems in situ.

发表机构

• Brown University, School of Engineering（布朗大学工程学院）
• Brown University, Institute for Biomedical Engineering and Medicine（布朗大学生物医学工程与医学院）
• Brown University, Center for Digital Health（布朗大学数字健康中心）

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