High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.
High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.
复制标题
暴露于CUO纳米颗粒的人H4神经瘤细胞的高含量图像分析。
DOI：
10.1186/1472-6750-7-66
复制
发表时间：
2007-10-09
影响因子：
3.5
通讯作者：
Wong, Stephen T C
中科院分区：
文献类型：
--
作者：
Li, Fuhai;Zhou, Xiaobo;Zhu, Jinmin;Ma, Jinwen;Huang, Xudong;Wong, Stephen T C
基于高内容筛选(High Content Screen，HCS)的图像分析正成为一种重要而广泛使用的研究工具。利用这一技术，可以从高含量的细胞图像中提取丰富的细胞信息。在这项研究中，一个自动化的，可靠的和室内开发的定量细胞图像分析系统已经被用来量化金属氧化物纳米颗粒对人神经胶质瘤细胞H4的毒性反应。该系统已被证明是我们研究中必不可少的工具。用IN cell Analyzer 1000采集暴露于不同浓度CuO纳米颗粒的H4神经胶质瘤细胞的细胞图像。开发了一种全自动细胞图像分析系统，用于对细胞存活率进行图像分析。采用多重自适应阈值方法将核团图像中的像素分为三类：亮核、暗核和背景。在我们的图像分析方法的发展过程中，我们取得了以下成果：(1)对细胞图像进行适当尺度的高斯滤波，以产生每个细胞核内部的局部强度极大值；(2)建立了一种新的基于梯度向量场的局部强度极大值检测方法；(3)提出了一种基于统计模型的分裂方法来克服分割不足的问题。计算结果表明，所提出的图像分析系统能够正确检测和分割95.9%的原子核。所提出的自动图像分析系统可以有效地分割暴露于CuO纳米颗粒的人H4神经胶质瘤细胞的图像。计算结果证实了我们的生物学发现，即人H4神经胶质瘤细胞对CuO纳米颗粒的损伤具有剂量依赖性的毒性反应。
High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study. The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system. The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.