通过优化双曲通道动态特征，实现卵巢癌细胞无标签高通量精准分型。
Robust Image-Driven Phenotyping of Ovarian Tumor Cells using Optimized Dynamic Features in Hyperbolic Channels
- 构建93维特征空间，用结构一致性和统计稳定性筛选出抗流变干扰的鲁棒特征。
- 特征噪声中流速相关方差从69.9%降至9.3%，子类型判别准确率显著提升。
- 适合微流控高通量单细胞表型分析，尤其对肿瘤异质性研究有重要价值。
在微流控器件中进行无标签、基于图像的细胞机械表型分析，可实现单细胞高通量检测。然而，复杂微通道（如双曲几何结构）虽能揭示连续拉伸应力下的瞬态形变动态，但其产生的高维特征空间极易受流体动力学伪影影响。流量波动常扭曲判别边界，使特征分布与流体条件相关而非内在生物学特性。为此，我们提出一种稳定性导向的分析框架，将流变噪声与真实机械生物信号解耦。通过追踪健康与恶性卵巢细胞的形态动力学、运动学及胞内光密度轨迹，构建了93维特征空间。基于结构一致性与统计持续性的交叉流筛选策略，提取出鲁棒描述符，形成任务适配子集（二分类20特征；癌症亚型分类25特征）。方差归因分析表明，流条件相关伪影被有效消除；在亚型分类任务中，主成分的流相关方差由69.9%降至9.3%。此外发现，宏观二分类依赖整体运动状态转换，而克隆亚型识别需依赖局部胞内光密度异质性。这些优化子集在多种机器学习架构和受限采样条件下保持诊断保真度。该框架为连续动态表型分析建立了鲁棒、流无关的基础。
原文摘要 · Abstract (English)
Label-free, image-based cellular mechanophenotyping in microfluidic devices provides a high-throughput method for single-cell profiling. However, while complex microchannels (e.g., hyperbolic geometries) reveal transient deformation dynamics under continuous extensional stress, the resulting high-dimensional feature spaces are highly susceptible to hydrodynamic artifacts. Flow rate variations often distort discriminative boundaries, linking feature distributions to fluid conditions rather than intrinsic biology. To overcome this, we introduce a stability-guided analytical framework that decouples flow-induced noise from authentic mechanobiological signatures. We tracked the morphodynamic, kinematic, and intracellular optical-density trajectories of healthy and malignant ovarian cells to build a 93-dimensional feature space. Using a cross-flow screening strategy based on structural consistency and statistical persistence, we isolated robust descriptors, creating task-adapted subsets (20 features for binary classification; 25 for cancer subtyping). Variance-attribution analysis confirmed the neutralization of flow-conditioned artifacts; notably, flow-associated variance in the primary principal component fell from 69.9% to 9.3% in the subtyping task. We also found that macroscopic binary discrimination depends on bulk kinematic transitions, while clonal subtyping requires localized intracellular optical heterogeneity. These optimized subsets maintained diagnostic fidelity across multiple machine learning architectures and restricted sampling conditions. This framework establishes a robust, flow-independent foundation for continuous dynamic phenotyping.
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