Analyzing valve interstitial cell mechanics and geometry with spatial statistics

JOURNAL OF BIOMECHANICS

卷:93

Article

Lejeune, Emma^1,2  Sacks, Michael S.^1,2 

[1] Univ Texas Austin, James T Willerson Ctr Cardiovasc Modeling & Simul, Oden Inst Computat Engn & Sci, Austin, TX 78712 USA

[2] Univ Texas Austin, Dept Biomed Engn, Austin, TX 78712 USA

关键词: Atomic force microscopy;    Cell mechanics;    Cell modeling;   

DOI  :  10.1016/j.jbiomech.2019.06.028

来源: Elsevier

【 摘 要 】

Understanding cell geometric and mechanical properties is crucial to understanding how cells sense and respond to their local environment. Moreover, changes to cell mechanical properties under varied micro environmental conditions can both influence and indicate fundamental changes to cell behavior. Atomic Force Microscopy (AFM) is a well established, powerful tool to capture geometric and mechanical properties of cells. We have previously demonstrated substantial functional and behavioral differences between aortic and pulmonary valve interstitial cells (VIC) using AFM and subsequent models of VIC mechanical response. In the present work, we extend these studies by demonstrating that to best interpret the spatially distributed AFM data, the use of spatial statistics is required. Spatial statistics includes formal techniques to analyze spatially distributed data, and has been used successfully in the analysis of geographic data. Thus, spatially mapped AFM studies of cell geometry and mechanics are analogous to more traditional forms of geospatial data. We are able to compare the spatial autocorrelation of stiffness in aortic and pulmonary valve interstitial cells, and more accurately capture cell geometry from height recordings. Specifically, we showed that pulmonary valve interstitial cells display higher levels of spatial autocorrelation of stiffness than aortic valve interstitial cells. This suggests that aortic VICs form different stress fiber structures than their pulmonary counterparts, in addition to being more highly expressed and stiffer on average. Thus, the addition of spatial statistics can contribute to our fundamental understanding of the differences between cell types. Moving forward, we anticipate that this work will be meaningful to enhance direct analysis of experimental data and for constructing high fidelity computational of VICs and other cell models. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free