Stiffness estimation and classification of biological cells using constriction microchannel: poroelastic model and machine learning

IF 2.3 4区 工程技术 Q2 INSTRUMENTS & INSTRUMENTATION Microfluidics and Nanofluidics Pub Date : 2024-02-24 DOI:10.1007/s10404-024-02710-6
S. A. Haider, G. Kumar, T. Goyal, A. Raj
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Abstract

Mathematical and computational models linking cell mechanical properties with deformation are crucial for understanding cellular behavior. While various techniques measure the stiffness and viscosity of cells, recent experiments suggest that cells exhibit poroelastic behavior, characterized by solid mesh networks immersed in cytosol liquid (Moeendarbary et al. in Nat Mater 12:253–261, 2013. https://doi.org/10.1038/nmat3517). Despite this, a mathematical model relating poroelastic cell deformation and Young's modulus of solid networks has not been reported. This study presents the first poroelasticity-based mathematical model for relating cell deformation with Young’s modulus of solid mesh networks. The model is validated by utilizing the experimental data of the cell’s squeezing behavior through a constriction microchannel. The predicted Young’s modulus for HeLa, MCF-10A, and MDA MB-231 cell lines are 153.64 ± 60.3 kPa, 97.84 ± 41.7 kPa, and 67.9 ± 48.8 kPa, respectively, which matches well with the conventional measurements. Additionally, two artificial neural network (ANN) models were developed which predicted Young's modulus and viscosity for these cell lines based on migration and deformation characteristics through constriction microchannel, achieving high accuracy (R ~ 0.974 and R ~ 0.999, respectively). Further, a linear Support Vector Machine (SVM) model classified cell lines based on initial diameter and elongation in the constriction microchannel measured from static images. The combined analytical and computational approach proposed here offers direct quantitative estimates of cell mechanical properties and cell classification based on their squeezing behavior through constriction microchannel.

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利用收缩微通道对生物细胞进行刚度估算和分类:孔弹性模型和机器学习
摘要 将细胞机械特性与变形联系起来的数学和计算模型对于理解细胞行为至关重要。虽然有多种技术可以测量细胞的硬度和粘度,但最近的实验表明,细胞表现出孔弹性行为,其特征是浸泡在细胞液中的固体网状网络(Moeendarbary 等人,发表于 Nat Mater 12:253-261, 2013. https://doi.org/10.1038/nmat3517)。尽管如此,有关孔弹性细胞变形和固体网络杨氏模量的数学模型尚未见报道。本研究首次提出了基于孔弹性的数学模型,将细胞变形与固体网格网络的杨氏模量联系起来。该模型利用细胞通过收缩微通道的挤压行为的实验数据进行了验证。预测的 HeLa、MCF-10A 和 MDA MB-231 细胞系的杨氏模量分别为 153.64 ± 60.3 kPa、97.84 ± 41.7 kPa 和 67.9 ± 48.8 kPa,与传统测量结果十分吻合。此外,还建立了两个人工神经网络(ANN)模型,根据通过收缩微通道的迁移和变形特征预测这些细胞系的杨氏模量和粘度,准确度很高(R ~ 0.974 和 R ~ 0.999)。此外,线性支持向量机(SVM)模型根据静态图像测量的收缩微通道初始直径和伸长率对细胞系进行了分类。本文提出的分析与计算相结合的方法可直接定量估计细胞的机械特性,并根据细胞在收缩微通道中的挤压行为对细胞进行分类。
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来源期刊
Microfluidics and Nanofluidics
Microfluidics and Nanofluidics 工程技术-纳米科技
CiteScore
4.80
自引率
3.60%
发文量
97
审稿时长
2 months
期刊介绍: Microfluidics and Nanofluidics is an international peer-reviewed journal that aims to publish papers in all aspects of microfluidics, nanofluidics and lab-on-a-chip science and technology. The objectives of the journal are to (1) provide an overview of the current state of the research and development in microfluidics, nanofluidics and lab-on-a-chip devices, (2) improve the fundamental understanding of microfluidic and nanofluidic phenomena, and (3) discuss applications of microfluidics, nanofluidics and lab-on-a-chip devices. Topics covered in this journal include: 1.000 Fundamental principles of micro- and nanoscale phenomena like, flow, mass transport and reactions 3.000 Theoretical models and numerical simulation with experimental and/or analytical proof 4.000 Novel measurement & characterization technologies 5.000 Devices (actuators and sensors) 6.000 New unit-operations for dedicated microfluidic platforms 7.000 Lab-on-a-Chip applications 8.000 Microfabrication technologies and materials Please note, Microfluidics and Nanofluidics does not publish manuscripts studying pure microscale heat transfer since there are many journals that cover this field of research (Journal of Heat Transfer, Journal of Heat and Mass Transfer, Journal of Heat and Fluid Flow, etc.).
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