Copy Number Variation Analysis by Ligation-Dependent PCR Based on Magnetic Nanoparticles and Chemiluminescence
Copy Number Variation Analysis by Ligation-Dependent PCR Based on Magnetic Nanoparticles and Chemiluminescence
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基于磁性纳米颗粒和化学发光的连接依赖性 PCR 拷贝数变异分析
DOI：
10.7150/thno.10117
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发表时间：
2015-01-01
期刊：
影响因子：
12.4
通讯作者：
He, Nongyue
中科院分区：
文献类型：
--
作者：
Liu, Ming;Hu, Ping;He, Nongyue
本文采用磁分离和化学发光相结合的检测技术，建立了一种新的拷贝数变异（CNV）分析系统。首先将氨基修饰探针固定在羧化磁性纳米颗粒（MNPs）上，然后与生物素- dutp产物杂交，然后用连接依赖性聚合酶链反应（PCR）扩增。经链霉亲和素修饰碱性磷酸酶（STV-AP）键合和磁分离后，检测CL信号。结果表明，PCR产物的定量可以通过CL信号值来反映。在最佳条件下，对氯离子体系进行了定量表征，氯离子强度与目标浓度的对数呈线性相关。为了验证该方法，检测了人类基因组中六个基因的拷贝数。为了比较检测的准确性，我们采用了多重连接相关探针扩增（MLPA）和MNPs-CL检测。总体而言，MLPA分析有两个差异，而MNPs-CL检测只有一个差异。研究表明，MNPs-CL系统具有简单、灵敏、特异等特点，适合于CNV分析，是一种有用的分析工具。此外，该系统还有待进一步完善，其在各种疾病基因组变异检测中的应用正在进一步研究中。
A novel system for copy number variation (CNV) analysis was developed in the present study using a combination of magnetic separation and chemiluminescence (CL) detection technique. The amino-modified probes were firstly immobilized onto carboxylated magnetic nanoparticles (MNPs) and then hybridized with biotin-dUTP products, followed by amplification with ligation-dependent polymerase chain reaction (PCR). After streptavidin-modified alkaline phosphatase (STV-AP) bonding and magnetic separation, the CL signals were then detected. Results showed that the quantification of PCR products could be reflected by CL signal values. Under optimum conditions, the CL system was characterized for quantitative analysis and the CL intensity exhibited a linear correlation with logarithm of the target concentration. To validate the methodology, copy numbers of six genes from the human genome were detected. To compare the detection accuracy, multiplex ligation-dependent probe amplification (MLPA) and MNPs-CL detection were performed. Overall, there were two discrepancies by MLPA analysis, while only one by MNPs-CL detection. This research demonstrated that the novel MNPs-CL system is a useful analytical tool which shows simple, sensitive, and specific characters which are suitable for CNV analysis. Moreover, this system should be improved further and its application in the genome variation detection of various diseases is currently under further investigation.