High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Li
Fuhai
F
The Center for Biomedical Informatics, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA. robert.fh.li@gmail.com
Zhou
Xiaobo
X
Zhu
Jinmin
J
Ma
Jinwen
J
Huang
Xudong
X
Wong
Stephen T C
ST
D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000378:metabolism / Q000473:pathology / Q000648:ultrastructure; D002451:Cell Compartmentation / Q000187:drug effects / Q000502:physiology; D045744:Cell Line, Tumor; D002467:Cell Nucleus / Q000187:drug effects / Q000473:pathology*; D016000:Cluster Analysis; D003300:Copper / Q000633:toxicity*; D003625:Data Collection / Q000379:methods; D005246:Feedback; D005910:Glioma / Q000378:metabolism / Q000473:pathology / Q000648:ultrastructure; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D053758:Nanoparticles / Q000633:toxicity; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity; D012985:Software Design
D003300:Copper; C030973:cupric oxide