Dynamic Image Analysis of Human Embryonic Stem Cells to Monitor Pluripotency

描述(申请人提供)：人类胚胎干细胞(HESC)具有多潜能，因此在细胞治疗方面具有巨大的潜力。为hESC生产的无损、无创的质量评价和保证提供可靠的分析方法，将对hESC的细胞治疗、药物筛选和其他用途有很大的帮助。我们发现，hESC菌落纹理和边界特征为确定菌落的多能性水平提供了有用的特征(Mangoubi等人，提交给IEEE Trans。生物医学。英文)。多能的hESC的细胞特征包括核的高动力学和缺乏染色质凝集。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。该方法将被用作hESC生产的质量保证工具，通过测量单细胞核的动力学、染色质动力学和随着多能细胞分化为神经元谱系而形成的异染色质。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，将其应用于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发展需要以下领域的最新分析算法：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用于定量测量无定形细胞和亚细胞结构的分析工具，尽管这些创新将使从功能磁共振成像到组织工程的医学成像领域受益。我们的目标包括：1)发展数学图像处理方法，从分化的单个干细胞和集落中定量区分多能性细胞的纹理和边界，2)将这些方法扩展到动态图像，用于测量干细胞纹理和边界的动态变化，以及3)发展时空动态和控制模型，以预测和帮助维持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.