Inferring relative surface elastic moduli in thin-wall models of single cells
Inferring relative surface elastic moduli in thin-wall models of single cells
复制标题
DOI：
10.1140/epjp/s13360-022-02907-0
复制
发表时间：
2022-08-01
影响因子：
3.4
通讯作者：
Wu，Min
中科院分区：
文献类型：
--
作者：
Deng，Yaqi;Wei，Chaozhen;Wu，Min
在单壁细胞中测量不同位置的细胞壁的力学性能越来越受到人们的关注。我们提出了一种推理方案，该方案通过跟踪材料标记点沿膨胀和松弛的细胞壁轮廓的位置来映射沿细胞壁的相对表面弹性模量分布。开发了一个初步方案，通过计算材料标记点之间的张力和弹性伸长来提供表面弹性模量的阶跃函数推断。我们进行了分析，以考察初级方案对标记点位置的扰动的稳定性，这种扰动可能是由于来自实验的图像采集和处理而发生的。摄动分析表明，当标记点之间的间距较大时，原始格式对噪声更稳定，并已被数值实验所证实，其中我们将原始格式应用于模拟超弹性薄膜变形的合成细胞轮廓，该模拟在标记点位置具有随机噪声。为了提高原始格式在有噪声的情况下弹性模数分布的空间分辨率，我们提出了两种优化方案，将弹性模数的阶跃函数推论转换为光滑曲线推论。第一种优化方案基于来自相同细胞类型的多个细胞样本的标记点位置来推断规范的弹性模量分布。第二种优化方案是一种简化的高性价比方案，它基于单个单元格的标记点位置来推断弹性模量。数值实验表明，第一种优化方案显着提高了对底层正则弹性模量分布的推断精度，当底层弹性模量梯度为非线性时，甚至可以捕捉到一定程度的非线性。第二种经济有效的方案能够一致地预测弹性模数梯度的趋势。
There is a growing interest in measuring the cell wall mechanical property at different locations in single walled cells. We present an inference scheme that maps relative surface elastic modulus distributions along the cell wall based on tracking the location of material marker points along the turgid and relaxed cell wall outline. A primary scheme is developed to provide a step-function inference of surface elastic moduli by computing the tensions and elastic stretches between material marker points. We perform analysis to investigate the stability of the primary scheme against perturbations on the marker-point locations, which may occur due to image acquisition and processing from experiments. The perturbation analysis shows that the primary scheme is more stable to noise when the spacing between the marker points is coarser, and has been confirmed by the numerical experiments where we apply the primary scheme to synthetic cell outlines from simulations of hyper-elastic membrane deformation with random noise on the marker-point locations. To improve the spatial resolution of elastic modulus distribution of the primary scheme with noise, we propose two optimization schemes that convert the step-function inferences of elastic moduli into smooth-curve inferences. The first optimization scheme infers a canonical elastic modulus distribution based on marker-point locations from multiple cell samples of the same cell type. The second optimization scheme is a simplified cost-effective version that infers the elastic moduli based on marker-point locations from a single cell. The numerical experiments show that the first optimization scheme significantly improves the inference precision for the underlying canonical elastic modulus distributions and can even capture some degree of nonlinearity when the underlying elastic modulus gradients are nonlinear. The second cost-effective scheme is capable of predicting the trend of the elastic modulus gradients consistently.