Open AccessAssessment of gene expression in many samples using vertical arrays
Author(s) -
Rosa Ana Risques,
Gaëlle Rondeau,
Martin Judex,
Michael McClelland,
John Welsh
Publication year - 2008
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkn263
Subject(s) - biology , dna microarray , computational biology , gene , gene expression , complementary dna , rna , genetics , gene expression profiling , sample (material) , microarray , expression (computer science) , measure (data warehouse) , data mining , computer science , chemistry , chromatography , programming language
Microarrays and high-throughput sequencing meth- ods can be used to measure the expression of thousands of genes in a biological sample in a few days, whereas PCR-based methods can be used to measure the expression of a few genes in thousands of samples in about the same amount of time. These methods become more costly as the number of biological samples increases or as the number of genes of interest increases, respectively, and these factors constrain experimental design. To address these issues, we introduced 'vertical arrays' in which RNA from each biological sample is converted into multiple, overlapping cDNA subsets and spotted on glass slides. These vertical arrays can be queried with single gene probes to assess the expression behavior in thousands of biological samples in a single hybridization reaction. The spotted subsets are less complex than the original RNA from which they derive, which improves signal-to-noise ratios. Here, we demonstrate the quantitative capabilities of vertical arrays, including the sensitivity and accuracy of the method and the number of subsets needed to achieve this accuracy for most expressed genes.