Center for Biomedical Engineering, Brown University, Providence, RI, USA.
School of Engineering, Brown University, Providence, RI, USA.
Nat Protoc. 2018 Dec;13(12):3042-3064. doi: 10.1038/s41596-018-0077-7.
Understanding the biological implications of cellular mechanotransduction, especially in the context of pathogenesis, requires the accurate resolution of material deformation and strain fields surrounding the cells. This is particularly challenging for cells displaying branched, 3D architectures. Here, we provide a modular approach for 3D image segmentation and strain mapping of topologically complex structures. We describe how to use our approach, using neural cells and networks as an example. In addition to describing how to implement the computational analysis, we provide details of a cell culture protocol that can be used to generate neural networks for analysis and experimentation. This protocol allows for transformation of matrix-induced strains, and their full resolution across single cells or networks in three dimensions. The protocol also provides analyses to compute both the locally varying cytoskeletal strains and the average strain experienced by cells. An additional module allows spatial correlation of these strain maps with cytoskeletal features, including neurite disruptions such as neuronal blebs. Image processing and strain mapping take ≥3 h, with the exact time required being dependent on use case, software familiarity, and file size.
理解细胞力学转导的生物学意义，特别是在发病机制方面，需要准确解析细胞周围的材料变形和应变场。对于呈现分支、3D 结构的细胞来说，这尤其具有挑战性。在这里，我们提供了一种用于 3D 图像分割和拓扑复杂结构应变映射的模块化方法。我们将描述如何使用我们的方法，以神经细胞和网络为例。除了描述如何实现计算分析之外，我们还提供了细胞培养方案的详细信息，该方案可用于生成用于分析和实验的神经网络。该方案允许对基质诱导的应变及其在单个细胞或整个网络中的三维全分辨率进行转换。该方案还提供了计算细胞骨架应变和细胞所经历的平均应变的分析。另一个模块允许将这些应变图与细胞骨架特征（包括神经元泡等神经突中断）进行空间相关。图像处理和应变映射需要 ≥3 小时，具体时间取决于用例、软件熟悉程度和文件大小。
