Marcelino Luisa A, Backman Vadim, Donaldson Andres, Steadman Claudia, Thompson Janelle R, Preheim Sarah Pacocha, Lien Cynthia, Lim Eelin, Veneziano Daniele, Polz Martin F
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Proc Natl Acad Sci U S A. 2006 Sep 12;103(37):13629-34. doi: 10.1073/pnas.0601476103. Epub 2006 Sep 1.
Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe-target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe-target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (<0.05% of total bacteria), despite the high potential for cross-hybridization by hundreds of different coexisting rRNAs, suggesting this methodology should be expandable to any microarray platform and system requiring accurate identification of low-abundant targets amid pools of similar sequences.
微阵列技术已能够确定单个生物体中数千个基因如何表达以协调其功能。然而，将其应用于自然群落或工程群落时，由于理论和技术上的限制，不同生物体相互作用以产生复杂特性的研究受到了阻碍。在这里，我们描述了一种通用方法，用于在含有同源靶标复杂混合物的系统中准确识别低丰度靶标。我们将非特异性探针 - 靶标相互作用（交叉杂交）的分析预测器与一种优化算法相结合，该算法从受虚假贡献（交叉杂交、噪声、背景和不等的特异性杂交响应）影响的原始信号中迭代解卷积出真实的探针 - 靶标信号。该方法能够以前所未有的特异性和准确性对人工群落和自然群落中的核糖体RNA（rRNA）序列进行定量。将rRNA添加到人工群落和自然群落中的对照实验证明了在不同浓度范围内识别的准确性和定量行为。最后，我们展示了这种方法在准确检测自然群落中低丰度靶标的能力。尽管存在数百种不同共存rRNA交叉杂交的高可能性，但我们仍准确鉴定了沿海海洋样本中天然浓度（占总细菌的<0.05%）的弧菌类群，这表明该方法应可扩展到任何需要在相似序列库中准确识别低丰度靶标的微阵列平台和系统。
