Sato Masanao, Mitra Raka M, Coller John, Wang Dong, Spivey Natalie W, Dewdney Julia, Denoux Carine, Glazebrook Jane, Katagiri Fumiaki
Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, 1500 Gortner Avenue, St Paul, MN 55108, USA.
Plant J. 2007 Feb;49(3):565-77. doi: 10.1111/j.1365-313X.2006.02972.x. Epub 2006 Dec 20.
Studies of the behavior of biological systems often require monitoring of the expression of many genes in a large number of samples. While whole-genome arrays provide high-quality gene-expression profiles, their high cost generally limits the number of samples that can be studied. Although inexpensive small-scale arrays representing genes of interest could be used for many applications, it is challenging to obtain accurate measurements with conventional small-scale microarrays. We have developed a small-scale microarray system that yields highly accurate and reproducible expression measurements. This was achieved by implementing a stable gene-based quantile normalization method for array-to-array normalization, and a probe-printing design that allows use of a statistical model to correct for effects of print tips and uneven hybridization. The array measures expression values in a single sample, rather than ratios between two samples. This allows accurate comparisons among many samples. The array typically yielded correlation coefficients higher than 0.99 between technically duplicated samples. Accuracy was demonstrated by a correlation coefficient of 0.88 between expression ratios determined from this array and an Affymetrix GeneChip, by quantitative RT-PCR, and by spiking known amounts of specific RNAs into the RNA samples used for profiling. The array was used to compare the responses of wild-type, rps2 and ndr1 mutant plants to infection by a Pseudomonas syringae strain expressing avrRpt2. The results suggest that ndr1 affects a defense-signaling pathway(s) in addition to the RPS2-dependent pathway, and indicate that the microarray is a powerful tool for systems analyses of the Arabidopsis disease-signaling network.
对生物系统行为的研究通常需要监测大量样本中许多基因的表达情况。虽然全基因组芯片能提供高质量的基因表达谱，但成本高昂，通常限制了可研究的样本数量。尽管代表感兴趣基因的廉价小规模芯片可用于许多应用，但用传统小规模微阵列获得准确测量结果具有挑战性。我们开发了一种小规模微阵列系统，可产生高度准确且可重复的表达测量结果。这是通过实施一种基于基因的稳定分位数归一化方法进行芯片间归一化，以及一种探针打印设计来实现的，该设计允许使用统计模型校正打印尖端和不均匀杂交的影响。该芯片测量单个样本中的表达值，而非两个样本之间的比率。这使得能够在许多样本之间进行准确比较。该芯片在技术重复样本之间通常产生高于0.99的相关系数。通过该芯片与Affymetrix基因芯片测定的表达比率之间0.88的相关系数、定量逆转录聚合酶链反应以及将已知量的特定RNA掺入用于分析的RNA样本中，证明了其准确性。该芯片用于比较野生型、rps2和ndr1突变体植物对表达avrRpt2的丁香假单胞菌菌株感染的反应。结果表明，ndr1除了影响RPS2依赖的途径外，还影响一种防御信号途径，并且表明该微阵列是用于拟南芥疾病信号网络系统分析的强大工具。
