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细胞、细胞核和焦点黏附形态的自动化定量分析。

An automated quantitative analysis of cell, nucleus and focal adhesion morphology.

机构信息

Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.

出版信息

PLoS One. 2018 Mar 30;13(3):e0195201. doi: 10.1371/journal.pone.0195201. eCollection 2018.

Abstract

Adherent cells sense the physical properties of their environment via focal adhesions. Improved understanding of how cells sense and response to their physical surroundings is aided by quantitative evaluation of focal adhesion size, number, orientation, and distribution in conjunction with the morphology of single cells and the corresponding nuclei. We developed a fast, user-friendly and automated image analysis algorithm capable of capturing and characterizing these individual components with a high level of accuracy. We demonstrate the robustness and applicability of the algorithm by quantifying morphological changes in response to a variety of environmental changes as well as manipulations of cellular components of mechanotransductions. Finally, as a proof-of-concept we use our algorithm to quantify the effect of Rho-associated kinase inhibitor Y-27632 on focal adhesion maturation. We show that a decrease in cell contractility leads to a decrease in focal adhesion size and aspect ratio.

摘要

黏附细胞通过黏着斑感知其环境的物理特性。通过定量评估黏着斑的大小、数量、方向和分布,结合单个细胞的形态及其相应的细胞核,有助于更好地理解细胞如何感知和响应其物理环境。我们开发了一种快速、用户友好且自动化的图像分析算法,能够以高精度捕获和描述这些单个成分。我们通过定量分析对各种环境变化以及对机械转导细胞成分的操作的响应来验证算法的稳健性和适用性。最后,作为概念验证,我们使用我们的算法来量化 Rho 相关激酶抑制剂 Y-27632 对黏着斑成熟的影响。我们表明,细胞收缩力的降低导致黏着斑的大小和纵横比降低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a76a/5877879/f2f52b5965a6/pone.0195201.g001.jpg

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