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结合图像恢复和牵引力显微镜技术研究细胞外基质依赖性角蛋白丝网络可塑性。

Combining Image Restoration and Traction Force Microscopy to Study Extracellular Matrix-Dependent Keratin Filament Network Plasticity.

作者信息

Yoon Sungjun, Windoffer Reinhard, Kozyrina Aleksandra N, Piskova Teodora, Di Russo Jacopo, Leube Rudolf E

机构信息

Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.

Interdisciplinary Centre for Clinical Research, RWTH Aachen University, Aachen, Germany.

出版信息

Front Cell Dev Biol. 2022 May 11;10:901038. doi: 10.3389/fcell.2022.901038. eCollection 2022.

Abstract

Keratin intermediate filaments are dynamic cytoskeletal components that are responsible for tuning the mechanical properties of epithelial tissues. Although it is known that keratin filaments (KFs) are able to sense and respond to changes in the physicochemical properties of the local niche, a direct correlation of the dynamic three-dimensional network structure at the single filament level with the microenvironment has not been possible. Using conventional approaches, we find that keratin flow rates are dependent on extracellular matrix (ECM) composition but are unable to resolve KF network organization at the single filament level in relation to force patterns. We therefore developed a novel method that combines a machine learning-based image restoration technique and traction force microscopy to decipher the fine details of KF network properties in living cells grown on defined ECM patterns. Our approach utilizes Content-Aware Image Restoration (CARE) to enhance the temporal resolution of confocal fluorescence microscopy by at least five fold while preserving the spatial resolution required for accurate extraction of KF network structure at the single KF/KF bundle level. The restored images are used to segment the KF network, allowing numerical analyses of its local properties. We show that these tools can be used to study the impact of ECM composition and local mechanical perturbations on KF network properties and corresponding traction force patterns in size-controlled keratinocyte assemblies. We were thus able to detect increased curvature but not length of KFs on laminin-322 versus fibronectin. Photoablation of single cells in microprinted circular quadruplets revealed surprisingly little but still significant changes in KF segment length and curvature that were paralleled by an overall reduction in traction forces without affecting global network orientation in the modified cell groups irrespective of the ECM coating. Single cell analyses furthermore revealed differential responses to the photoablation that were less pronounced on laminin-332 than on fibronectin. The obtained results illustrate the feasibility of combining multiple techniques for multimodal monitoring and thereby provide, for the first time, a direct comparison between the changes in KF network organization at the single filament level and local force distribution in defined paradigms.

摘要

角蛋白中间丝是动态的细胞骨架成分,负责调节上皮组织的机械性能。尽管已知角蛋白丝(KFs)能够感知并响应局部微环境理化性质的变化,但在单丝水平上,动态三维网络结构与微环境之间的直接关联尚无法实现。使用传统方法,我们发现角蛋白流速取决于细胞外基质(ECM)组成,但无法解析单丝水平上与力模式相关的KF网络组织。因此,我们开发了一种新方法,将基于机器学习的图像恢复技术与牵引力显微镜相结合,以解读在特定ECM模式上生长的活细胞中KF网络特性的精细细节。我们的方法利用内容感知图像恢复(CARE)将共聚焦荧光显微镜的时间分辨率提高至少五倍,同时保留在单KF/KF束水平上准确提取KF网络结构所需的空间分辨率。恢复后的图像用于分割KF网络,以便对其局部特性进行数值分析。我们表明,这些工具可用于研究ECM组成和局部机械扰动对大小可控的角质形成细胞聚集体中KF网络特性和相应牵引力模式的影响。因此,我们能够检测到与纤连蛋白相比,层粘连蛋白-322上的KFs曲率增加但长度未增加。微打印圆形四联组中单个细胞的光消融显示,KF片段长度和曲率的变化出人意料地小但仍然显著,同时牵引力总体降低,而不影响修饰细胞组中的全局网络方向,无论ECM涂层如何。单细胞分析还揭示了对光消融的不同反应,在层粘连蛋白-332上不如在纤连蛋白上明显。获得的结果说明了结合多种技术进行多模态监测的可行性,从而首次在单丝水平上KF网络组织的变化与特定范式中的局部分布之间进行了直接比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ad8/9131083/0678ed0f1b6c/fcell-10-901038-g001.jpg

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