Although microarrays are relatively inexpensive and easy to use, they suffer from one obvious drawback: the sequences of the mRNA samples to be analyzed must be known in advance and represented by a corresponding probe on the array. With the development of improved sequencing technologies, investigators increasingly use RNA-seq, discussed earlier, as a more direct approach for cataloging the RNAs produced by a cell. For example, this approach can readily detect alternative RNA splicing, RNA editing, and the many noncoding RNAs produced from a complex genome.
DNA microarrays and RNA-seq analysis have been used to examine everything from the changes in gene expression that make strawberries ripen to the gene expression “signatures” of different types of human cancer cells; or from changes time (number of PCR cycles)
Figure 8–64 DNA microarrays are used to analyze the production of mRNA from mRNA from thousands of different mRNAs in a single experiment. In this example, sample 1 sample 2 mRNA is collected from two different cell samples—for example, cells treated with a hormone and untreated cells of the same type—to allow for a direct comparison of the specific genes expressed under both conditions.
convert to cDNA, convert to cDNA,
The mRNAs are converted to cDNAs that are labeled with a red fluorescent labeled with red labeled with green dye for one sample and a green fluorescent dye for the other. The labeled samples are mixed and then allowed to hybridize to the microarray. Each microscopic spot on the microarray is a 50-nucleotide DNA molecule of defined sequence made by chemical synthesis and spotted on the array. The DNA sequence represented by each spot is different, and the hundreds of thousands of such spots are designed to span the sequence of the genome. The DNA sequence of each spot is kept track of by computer. After incubation, the array is washed and the fluorescence scanned. Only a small proportion of the microarray, representing 676 genes, is shown. Red spots indicate that the gene in sample 1 is expressed at a higher level than the corresponding gene in sample 2, and the green spots indicate the opposite. Yellow spots reveal genes that are expressed at about equal levels in both cell samples. The intensity of the fluorescence provides an estimate of how much RNA is present from a gene. Dark spots indicate little or no expression of the gene whose probe is located at that position in the array.
that occur as cells progress through the cell cycle to those made in response to sudden shifts in temperature. Indeed, because these approaches allow the simultaneous monitoring of large numbers of RNAs, they can detect subtle changes in a cell, changes that might not be manifested in its outward appearance or behavior.
Comprehensive studies of gene expression also provide information that is useful for predicting gene function. Earlier in this chapter, we discussed how identifying a protein’s interaction partners can yield clues about that protein’s function. A similar principle holds true for genes: information about a gene’s function can be deduced by identifying genes that share its expression pattern. Using an approach called cluster analysis, one can identify sets of genes that are coordinately regulated. Genes that are turned on or turned off together under different circumstances are likely to work in concert in the cell: they may encode proteins that are part of the same multiprotein machine, or proteins that are involved in a complex coordinated activity, such as DNA replication or RNA splicing. Characterizing a gene whose function is unknown by grouping it with known genes that share its transcriptional behavior is sometimes called “guilt by association.” Cluster analyses have been used to analyze the gene expression profiles that underlie many interesting biological processes, including wound healing in humans (Figure 8–65).