A high-performance, small-scale microarray for expression profiling of many samples in Arabidopsis-pathogen studies

Masanao Sato, Raka M. Mitra, John Coller, Dong Wang, Natalie W. Spivey, Julia Dewdney, Carine Denoux, Jane Glazebrook, Fumiaki Katagiri

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)565-577
Number of pages13
JournalPlant Journal
Issue number3
StatePublished - Feb 2007


  • Calibration probe
  • Expression profiling
  • Stable genes-based quantile normalization
  • Statistical model
  • Systems analysis

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