標題: HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening
作者: Charoenkwan, Phasit
Hwang, Eric
Cutler, Robert W.
Lee, Hua-Chin
Ko, Li-Wei
Huang, Hui-Ling
Ho, Shinn-Ying
生物科技學系
生物資訊及系統生物研究所
分子醫學與生物工程研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
Institute of Molecular Medicine and Bioengineering
公開日期: 22-十月-2013
摘要: Background: High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. Results: We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e. g., neurite length, and generic feature descriptors (GFDs), e. g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons. Conclusions: Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.
URI: http://dx.doi.org/10.1186/1471-2105-14-S16-S12
http://hdl.handle.net/11536/23473
ISSN: 1471-2105
DOI: 10.1186/1471-2105-14-S16-S12
期刊: BMC BIOINFORMATICS
Volume: 14
Issue: 
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