Burst and principal components analyses of MEA data for 16 chemicals describe at least three effects classes
Burst and principal components analyses of MEA data for 16 chemicals describe at least three effects classes
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DOI：
10.1016/j.neuro.2013.11.008
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发表时间：
2014-01-01
期刊：
影响因子：
3.4
通讯作者：
Shafer, Timothy J.
中科院分区：
文献类型：
--
作者：
Mack, Cina M.;Lin, Bryant J.;Shafer, Timothy J.
微电极阵列（MEA）可用于检测药物和化学品引起的神经元网络功能的变化，并已用于神经毒性筛选。作为概念验证，目前的研究评估了使用主成分分析（PCA）和使用支持向量机（SVM）的化学品类别预测的分析“指纹”的效用，以根据16种化学品的MEA数据对化学效应进行分类。在原代皮层培养中，荷包牡丹碱（BIC）、林丹（LND）、黑索今（RDX）和印防己毒素（PTX）可增加自发放电率;尼古丁（NIC）、对乙酰氨基酚（ACE）和草甘膦（GLY）不改变自发放电率;蝇蕈醇（MUS）、维拉帕米（VER）、氟虫腈（FIP）、氟西汀（FLU）、毒死蜱（CPO）、软骨藻酸（DA）、溴氰菊酯（DELT）和邻苯二甲酸二甲酯（DELT）可降低自发放电率。对平均放电率、爆发参数和同步数据进行PCA，浓度高于每种化学品的平均放电率EC 50。前三个主成分占67.5%，19.7%和6.9%的数据变异性，并用于识别视觉上通过空间接近的化学类别之间的分离。在PCA中，GABA（A）拮抗剂BIC、LND和RDX与其他化学品明显分离。对于SVM预测模型，实验被分类为活性增加、减少或无变化的三个化学类别，在具有10倍交叉验证的径向核下，平均准确度为83.8%。通过PCA分离不同的化学品类别和小数据集的SVM中的高预测精度表明MEA数据可用于使用这些或其他相关方法将化学品分离成效应类别。爱思唯尔公司出版
Microelectrode arrays (MEAs) can be used to detect drug and chemical induced changes in neuronal network function and have been used for neurotoxicity screening. As a proof-of-concept, the current study assessed the utility of analytical "fingerprinting" using principal components analysis (PCA) and chemical class prediction using support vector machines (SVMs) to classify chemical effects based on MEA data from 16 chemicals. Spontaneous firing rate in primary cortical cultures was increased by bicuculline (BIC), lindane (LND), RDX and picrotoxin (PTX); not changed by nicotine (NIC), acetaminophen (ACE), and glyphosate (GLY); and decreased by muscimol (MUS), verapamil (VER), fipronil (FIP), fluoxetine (FLU), chlorpyrifos oxon (CPO), domoic acid (DA), deltamethrin (DELT) and dimethyl phthalate (DMP). PCA was performed on mean firing rate, bursting parameters and synchrony data for concentrations above each chemical's EC50 for mean firing rate. The first three principal components accounted for 67.5, 19.7, and 6.9% of the data variability and were used to identify separation between chemical classes visually through spatial proximity. In the PCA, there was clear separation of GABA(A) antagonists BIC, LND, and RDX from other chemicals. For the SVM prediction model, the experiments were classified into the three chemical classes of increasing, decreasing or no change in activity with a mean accuracy of 83.8% under a radial kernel with 10-fold cross-validation. The separation of different chemical classes through PCA and high prediction accuracy in SVM of a small dataset indicates that MEA data may be useful for separating chemicals into effects classes using these or other related approaches. Published by Elsevier Inc.