Lin Shinn-Long, Chang Fang-Lin, Ho Shinn-Ying, Charoenkwan Phasit, Wang Kuan-Wei, Huang Hui-Ling
Department of Anesthesiology, Tri-Service General Hospital and National Defense Medical Center, Taipei, Taiwan.
Department of Anesthesiology, Kang-Ning General Hospital, Taipei, Taiwan.
PLoS One. 2015 Oct 5;10(10):e0139806. doi: 10.1371/journal.pone.0139806. eCollection 2015.
Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization. Recent studies have sought to reveal the mechanism of opioid receptors and neuroinflammation by observing morphological changes of cells in the rat spinal cord. This work proposes a high-content screening (HCS) based computational method, HCS-Morph, for predicting neuroinflammation in morphine tolerance to facilitate the development of tolerance therapy using immunostaining images for astrocytes, microglia, and neurons in the spinal cord. HCS-Morph first extracts numerous HCS-based features of cellular phenotypes. Next, an inheritable bi-objective genetic algorithm is used to identify a minimal set of features by maximizing the prediction accuracy of neuroinflammation. Finally, a mathematic model using a support vector machine with the identified features is established to predict drug-treated images to assess the effects of tolerance therapy. The dataset consists of 15 saline controls (1 μl/h), 15 morphine-tolerant rats (15 μg/h), and 10 rats receiving a co-infusion of morphine (15 μg/h) and gabapentin (15 μg/h, Sigma). The three individual models of astrocytes, microglia, and neurons for predicting neuroinflammation yielded respective Jackknife test accuracies of 96.67%, 90.00%, and 86.67% on the 30 rats, and respective independent test accuracies of 100%, 90%, and 60% on the 10 co-infused rats. The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells. The set of features for predicting neuroinflammation from images of astrocytes comprises mean cell intensity, total cell area, and second-order geometric moment (relating to cell distribution), relevant to cell communication, cell extension, and cell migration, respectively. The present investigation provides the first evidence for the role of gabapentin in the attenuation of morphine tolerance from phenotypic changes of astrocytes and microglia. Based on neuroinflammation prediction, the proposed computer-aided image diagnosis system can greatly facilitate the development of tolerance therapy with anti-inflammatory drugs.
长期吗啡治疗会导致耐受性,从而减弱镇痛效果并阻碍其临床应用。最近的研究试图通过观察大鼠脊髓中细胞的形态变化来揭示阿片受体和神经炎症的机制。这项工作提出了一种基于高内涵筛选(HCS)的计算方法HCS-Morph,用于预测吗啡耐受性中的神经炎症,以便利用脊髓中星形胶质细胞、小胶质细胞和神经元的免疫染色图像来促进耐受性治疗的发展。HCS-Morph首先提取大量基于HCS的细胞表型特征。接下来,使用一种可遗传的双目标遗传算法,通过最大化神经炎症的预测准确性来识别最小特征集。最后,建立一个使用支持向量机并结合已识别特征的数学模型,以预测药物处理后的图像,从而评估耐受性治疗的效果。数据集包括15只生理盐水对照大鼠(1微升/小时)、15只吗啡耐受大鼠(15微克/小时)和10只同时输注吗啡(15微克/小时)和加巴喷丁(15微克/小时,西格玛)的大鼠。用于预测神经炎症的星形胶质细胞、小胶质细胞和神经元的三个单独模型,在30只大鼠上的刀切检验准确率分别为96.67%、90.00%和86.67%,在10只同时输注大鼠上的独立检验准确率分别为100%、90%和60%。实验结果表明,神经炎症活动在星形胶质细胞和小胶质细胞中比在神经元细胞中表现得更明显。从星形胶质细胞图像预测神经炎症的特征集包括平均细胞强度、总细胞面积和二阶几何矩(与细胞分布有关),分别与细胞通讯、细胞伸展和细胞迁移相关。本研究为加巴喷丁通过星形胶质细胞和小胶质细胞的表型变化减轻吗啡耐受性的作用提供了首个证据。基于神经炎症预测,所提出的计算机辅助图像诊断系统可以极大地促进抗炎药物耐受性治疗的发展。