{"title":"Stiffness estimation and classification of biological cells using constriction microchannel: poroelastic model and machine learning","authors":"S. A. Haider, G. Kumar, T. Goyal, A. Raj","doi":"10.1007/s10404-024-02710-6","DOIUrl":null,"url":null,"abstract":"<div><p>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 (<i>R</i> ~ 0.974 and <i>R</i> ~ 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.</p></div>","PeriodicalId":706,"journal":{"name":"Microfluidics and Nanofluidics","volume":"28 3","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microfluidics and Nanofluidics","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10404-024-02710-6","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0
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.
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.).