Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.
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通过多个内部控制基因的几何平均，对实时定量RT-PCR数据的准确归一化。
DOI：
10.1186/gb-2002-3-7-research0034
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发表时间：
2002-06-18
期刊：
影响因子：
12.3
通讯作者：
中科院分区：
文献类型：
--
作者：
使用实时逆转录 PCR 对不同人体组织中不同丰度和功能类别的 10 个管家基因进行了评估。常规使用单个基因进行标准化会导致很大比例的测试样本出现相对较大的误差。基因表达分析在生物学研究中变得越来越重要，实时逆转录 PCR (RT-PCR) 成为对选定基因进行高通量和准确表达谱分析的首选方法。鉴于该方法的灵敏度、重现性和大动态范围的提高，对用于标准化的适当内部对照基因的要求变得越来越严格。尽管据报道管家基因表达差异很大，但没有系统调查能够正确确定与仅使用一个对照基因的常见做法相关的错误，也没有提出解决该问题的适当方法。我们概述了一种稳健且创新的策略，用于识别给定组织中表达最稳定的控制基因，并确定计算可靠的标准化因子所需的最小基因数量。我们评估了不同人体组织中不同丰度和功能类别的十个管家基因，并证明传统使用单个基因进行标准化会导致很大比例的测试样本出现相对较大的误差。通过分析公开的微阵列数据，多个精心挑选的管家基因的几何平均值被验证为准确的标准化因子。这里提出的标准化策略是准确 RT-PCR 表达谱分析的先决条件，除其他外，它开辟了研究微小表达差异的生物学相关性的可能性。
Using real-time reverse transcription PCR ten housekeeping genes from different abundance and functional classes in various human tissues were evaluated. The conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.
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