【摘要】 目的 编写一个MATLAB算法，用于自动化测量运动神经元(motor neuron, MN)神经突起，并利用该算法评估有机磷酸酯阻燃剂三(2-氯乙基)磷酸酯[Tris(2-chloroethyl) Phosphate, TCEP]对MN神经突起生长的影响。方法 诱导人胚胎干细胞逐步分化为MN,利用βⅢ-tubulin标记不同条件下的神经突起进行荧光成像，开发图像识别算法用于自动分析神经突起以及污染物处理后的神经突起网络面积变化。在MN分化过程中，利用不同浓度的TCEP处理细胞，TCEP浓度分别为0、25、50和100μmol/L。结果 成功诱导出表达胆碱乙酰化酶的MN。开发的图像识别算法可以对图像进行批量化处理，能够计算神经突起网络所占像素面积，并可以通过优化细丝保留阈值来保留微弱的神经突起，提高测量的精度。基于该算法的量化数据表明，TCEP从50μmol/L开始显著降低神经突起网络面积百分比(P<0.05)。结论 基于人源MN神经突起模型成功开发了图像识别算法，并且该算法能够用于TCEP对神经突起的毒性评估。更多还原

【Abstract】 Objective To develop an MATLAB algorithm for the automated measurement of motor neuron(MN) neurites and apply this algorithm to evaluate the organophosphate flame retardant tris(2-chloroethyl) phosphate(TCEP) on the growth of MN neurites. Methods Human embryonic stem cells were induced to gradually differentiate into MN. After the neurites were labelled with βIII-tubulin for fluorescence image processing, an image process algorithm was developed to automatically analyze the neurite and changes in neurite network area after pollutant treatment. During MN differentiation, the cells were treated with different concentrations of TCEP(0, 25, 50 and 100 μmol/L). Results MNs were successfully induced with the expression of choline acetyltransferase. The developed image recognition algorithm could analyze images in batches, and calculate the pixel area occupied by neural network, and reserve weak neurites by optimizing neurite retention threshold to improve the accuracy of measurement. The quantification data from image process algorithm showed that TCEP significantly decreased the percentage of neurite network area since the dose started from 50 μmol/L(P<0.05). Conclusion An image process algorithm is successfully developed for automated measurement of neurites based on the human MN neurite model. Moreover, this algorithm can be applied to the toxicity assessment of TCEP.更多还原