Dynamic Image Analysis of Human Embryonic Stem Cells to Monitor Pluripotency

描述（由申请人提供）：人类胚胎干细胞（hESC）具有多能性，因此在细胞治疗方面具有巨大潜力。用于 hESC 生产的非破坏性、非侵入性质量评估和保证的分析可靠方法将对 hESC 的细胞治疗、药物筛选和其他用途大有裨益。我们发现 hESC 集落纹理和边界特征为确定集落的多能性水平提供了有用的特征（Mangoubi 等人，提交给 IEEE Trans. Biomed. Eng.）。多能 hESC 的细胞特征包括核高动力学和缺乏染色质浓缩。我们建议开发基于图像的纹理和边界分析分析算法，该算法将测量 1) 整个 hESC 集落纹理和边界的动力学以及 2) 细胞核纹理的动力学。该方法通过测量多能细胞分化为神经元谱系时的单细胞核动力学、染色质动力学和异染色质形成，用作 hESC 生产的质量保证工具。我们的目标是应用并继续开发我们的新信号和图像处理方法，以应用于 hESC 生物学。这些基于图像处理的方法将专门用于 1) 评估多细胞集落和单细胞核的多能性，2) 预测集落命运的时间历史，3) 预测细胞生产的动态。干细胞无损评估方法的开发需要在以下领域超越最先进的分析算法：参数和非参数分类、高效的变分分割和曲线演化方法、创新的边界清晰度和扩散性分析、非高斯子空间学习和检测方法以及多分辨率分层动态模型。我们的目标是开发这些分析工具来定量测量非晶细胞和亚细胞结构，尽管这些创新将有利于从功能磁共振成像到组织工程的医学成像领域。我们的目标包括：1) 开发数学图像处理方法，用于定量区分多能干细胞和分化的个体干细胞和集落的纹理和边界，2) 将这些方法扩展到动态图像，以测量干细胞纹理和边界的动态变化，3) 开发时空动态和控制模型，预测和帮助维持 hESC 的质量。

DESCRIPTION (provided by applicant): Human embryonic stem cells (hESC) have great potential for cellular therapy because they are pluripotent. Analytical reliable methodologies for the non-destructive non-invasive quality evaluation and assurance for hESC production would be of great benefit to cellular therapy, drug screening and other uses of hESC. We have found that hESC colony texture and border characteristics provide useful features for determining the colony's level of pluripotency (Mangoubi et al., submitted to IEEE Trans. Biomed. Eng.). Cellular characteristics of pluripotent hESC include nuclear hyperdynamics and lack of chromatin condensation. We propose to develop image based texture and border analysis analytical algorithms that would measure 1) the kinetics of whole hESC colony texture and borders and 2) the kinetics of cell nuclei texture. The methodology is to be used as a quality assurance tool for hESC production by measuring the kinetics of single cell nuclei, chromatin dynamics and heterochromatin formation as pluripotent cells differentiate into neuronal lineages. Our objective is to apply and continue to develop our new signal and image processing methodologies for application to hESC biology. These image processing based methods will be specifically used to 1) evaluate the pluripotency of multicell colonies and single cell nuclei, 2) predict the time history of colony fate, and 3) predict the dynamics of cell production. Development of the stem cell non-destructive evaluation methodology would require beyond state of the art analytical algorithms in the following areas: Parametric and non-Parametric classification, efficient variational segmentation and curve evolution methods, innovative border crispness and diffusivity analysis, non-Gaussian subspace learning and detection methods, and multi-resolution hierarchical dynamic models. Our objective is to develop these analytical tools for quantitative measurement of amorphous cellular and subcellular structures, though these innovations will benefit areas of medical imaging ranging from fMRI to tissue engineering. Our aims include: 1) The development of mathematical image processing methods for quantitatively distinguishing the texture and border of pluripotent from differentiated individual stem cells and colonies, 2) the extension of these methods to kinetic images for measuring dynamic changes in stem cell textures and borders, and 3) the development of spatio-temporal dynamic and control models that predict and help maintain the quality of hESC.