肿瘤异质性是导致治疗失败的主要因素。能够根据癌细胞的功能对癌细胞进行分类的预测工具可以大大增强治疗效果并延长患者的寿命。此处利用细胞生物力学和癌细胞功能之间的联系，通过颗粒摄取的机械测量对细胞进行分类。机器学习 (ML) 用于根据单细胞摄取不同尺寸颗粒的模式对细胞进行分类。研究了三对人类癌细胞亚群，它们的耐药性或恶性程度各不相同。让细胞与尺寸范围为 0.04 至 3.36 μm 的荧光标记聚苯乙烯颗粒相互作用，并使用流式细胞术分析其摄取模式。机器学习算法准确分类癌细胞亚型，准确率超过 95%。摄取数据对于形态相似的细胞亚群特别有利。此外，发现摄取数据可以作为一种“标准化”形式，可以减少重复实验中的变化。

Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their functions, may substantially enhance therapy and extend patient life span. The connection between cell biomechanics and cancer cell functions is used here to classify cells through mechanical measurements, via particle uptake. Machine learning (ML) was used to classify cells based on single-cell patterns of uptake of particles with diverse sizes. Three pairs of human cancer cell subpopulations, varied in their level of drug resistance or malignancy, were studied. Cells were allowed to interact with fluorescently labeled polystyrene particles ranging in size from 0.04 to 3.36 μm and analyzed for their uptake patterns using flow cytometry. ML algorithms accurately classified cancer cell subtypes with accuracy rates exceeding 95%. The uptake data were especially advantageous for morphologically similar cell subpopulations. Moreover, the uptake data were found to serve as a form of "normalization" that could reduce variation in repeated experiments.