HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening

BMC Bioinformatics

Research

Phasit Charoenkwan¹  Hua-Chin Lee²  Hui-Ling Huang²  Shinn-Ying Ho²  Li-Wei Ko²  Eric Hwang³  Robert W Cutler⁴ 

[1] Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, 300, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, 300, Hsinchu, Taiwan;Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, 300, Hsinchu, Taiwan;Program in Physics, School of Pure and Applied Sciences, Edison State College, 33919, Florida, USA;

关键词: Support Vector Machine;    Nocodazole;    Neurite Length;    Support Vector Machine Parameter;    Quadratic Regression Model;   

DOI  :  10.1186/1471-2105-14-S16-S12

来源: Springer

【 摘 要 】

BackgroundHigh-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.ResultsWe 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.ConclusionsFew 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.

【 授权许可】

Unknown   
© Charoenkwan et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.