Topic 17.4

Profiling Gene Expression in Plants

In microarray analysis short oligonucleotides or cDNA sequences representing thousands of gene sequences are spotted on a glass slide. RNA samples isolated from control and experimental plants are labeled using two different fluorescent dyes. Both the control and the experimental sample are hybridized to the microarray and the slide is then scanned using two lasers that will excite one or the other dye. The light emitted from the dye is proportional to the amount of RNA that is bound to the gene-specific oligonucleotide or cDNA sequence and thus provides a relative measure of gene expression. To visualize the relative expression levels from both samples, images are often false colored so that one signal is represented with green and the other signal as a red, when the signals overlap they are yellow. This technique has been used to examine changes in gene expression when plants are grown under red or far-red light to define phytochrome signaling pathways and have shown that multiple transcription factors are early targets of phytochrome control (Tepperman et al. 2001, 2004).

More recently, several informatics tools have been developed to further exploit extensive transcriptomics datasets (e.g., www.bar.utoronto.ca). These tools use algorithms to mine expression profiling data sets to look for common promoter motifs, visualize cell- or tissue-specific patterns of gene expression and visualize complex networks.

With the advent of extremely high-throughput sequencing platforms like Illumina/Solexa and SOLiD, it is now possible to generate hundreds of millions of sequence reads to quantitate gene expression (Wang et al. 2009). These technologies when combined with full-genome sequencing offer unique opportunities to examine light signaling networks in both crop and non-traditional model species where access to microarray technology has been a major obstacle to progress in the past.