Rule Rebecca A, Pozhitkov Alex E, Noble Peter A
Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
J Microbiol Methods. 2009 Feb;76(2):188-95. doi: 10.1016/j.mimet.2008.10.011. Epub 2008 Oct 30.
Nonspecific target binding (i.e., cross-hybridization) is a major challenge for interpreting oligonucleotide microarray results because it is difficult to determine what portion of the signal is due to binding of complementary (specific) targets to a probe versus that due to binding of nonspecific targets. Solving this challenge would be a major accomplishment in microarray research potentially allowing quantification of targets in biological samples. Marcelino et al. recently described a new approach that reportedly solves this challenge by iteratively deconvoluting 'true' specific signal from raw signal, and quantifying ribosomal (rRNA) sequences in artificial and natural communities (i.e., "Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data", Proc. Natl. Acad. Sci. 103, 13629-13634). We evaluated their approach using high-density oligonucleotide microarrays and Latin-square designed experiments consisting of 6 and 8 rRNA targets in 16 different artificial mixtures. Our results show that contrary to the claims in the article, the hidden correlations in the microarray data are insufficient for accurate quantification of nucleic acid targets in complex artificial target mixtures.
非特异性靶标结合（即交叉杂交）是解释寡核苷酸微阵列结果的一项重大挑战，因为很难确定信号的哪一部分是由于互补（特异性）靶标与探针的结合，哪一部分是由于非特异性靶标的结合。解决这一挑战将是微阵列研究的一项重大成就，有可能实现对生物样品中靶标的定量分析。马塞利诺等人最近描述了一种新方法，据报道该方法通过从原始信号中迭代解卷积出“真正的”特异性信号，并对人工群落和自然群落中的核糖体（rRNA）序列进行定量分析，从而解决了这一挑战（即“通过揭示寡核苷酸微阵列数据中的隐藏相关性来准确量化相似序列中的低丰度靶标”，《美国国家科学院院刊》103卷，第13629 - 13634页）。我们使用高密度寡核苷酸微阵列以及由16种不同人工混合物中的6个和8个rRNA靶标组成的拉丁方设计实验对他们的方法进行了评估。我们的结果表明，与文章中的说法相反，微阵列数据中的隐藏相关性不足以对复杂人工靶标混合物中的核酸靶标进行准确量化。
