Cell mechanics are pivotal in regulating cellular activities, diseases progression, and cancer development. However, the understanding of how cellular viscoelastic properties vary in physiological and pathological stimuli remains scarce. Here, we develop a hybrid self-similar hierarchical theory-microrheology approach to accurately and efficiently characterize cellular viscoelasticity. Focusing on two key cell types associated with livers fibrosis—the capillarized liver sinusoidal endothelial cells and activated hepatic stellate cells—we uncover a universal two-stage power-law rheology characterized by two distinct exponents, and . The mechanical profiles derived from both exponents exhibit significant potential for discriminating among diverse cells. This finding suggests a potential common dynamic creep characteristic across biological systems, extending our earlier observations in soft tissues. Using a tailored hierarchical model for cellular mechanical structures, we discern significant variations in the viscoelastic properties and their distribution profiles across different cell types and states from the cytoplasm (elastic stiffness and viscosity ), to a single cytoskeleton fiber (elastic stiffness ), and then to the cell level (transverse expansion stiffness ). Importantly, we construct a logistic-regression-based machine-learning model using the dynamic parameters that outperforms conventional cell-stiffness-based classifiers in assessing cell states, achieving an area under the curve of 97% vs. 78%. Our findings not only advance a robust framework for monitoring intricate cell dynamics but also highlight the crucial role of cellular viscoelasticity in discerning cell states across a spectrum of liver diseases and prognosis, offering new avenues for developing diagnostic and therapeutic strategies based on cellular viscoelasticity.

中文翻译：

---

超越刚度：多尺度粘弹性特征作为评估细胞类型和状态的生物力学标记


细胞力学对于调节细胞活动、疾病进展和癌症发展至关重要。然而，对细胞粘弹性特性在生理和病理刺激下如何变化的了解仍然很少。在这里，我们开发了一种混合自相似分层理论-微流变学方法来准确有效地表征细胞粘弹性。重点关注与肝纤维化相关的两种关键细胞类型——毛细血管化肝窦内皮细胞和活化的肝星状细胞——我们发现了一种通用的两阶段幂律流变学，其特征在于两个不同的指数 和 。从两个指数得出的机械分布表现出区分不同细胞的巨大潜力。这一发现表明生物系统中存在潜在的共同动态蠕变特征，扩展了我们早期对软组织的观察。使用针对细胞机械结构定制的分层模型，我们可以识别不同细胞类型和状态（从细胞质（弹性刚度和粘度）到单个细胞骨架纤维（弹性刚度））的粘弹性特性及其分布曲线的显着变化，然后到细胞水平（横向膨胀刚度）。重要的是，我们使用动态参数构建了一个基于逻辑回归的机器学习模型，该模型在评估细胞状态方面优于传统的基于细胞硬度的分类器，实现了 97% 与 78% 的曲线下面积。 我们的研究结果不仅为监测复杂的细胞动力学提供了一个强大的框架，而且还强调了细胞粘弹性在识别一系列肝脏疾病和预后中的细胞状态中的关键作用，为开发基于细胞粘弹性的诊断和治疗策略提供了新途径。