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Within the last decade, microarray technology has evolved from an emerging
technology developed and used by a few laboratories into a well-established
technology used in laboratories all over the world. In fact, the need to
characterize genetic alterations is one of the highest priorities for the future of
medicine and the clinical management of disease. This technology allows the
rapid detection of point mutations, insertions or deletions, loss of heterozygosity,
and gene amplification, which constitute the major nucleic acid
variations associated with human disease. Additional disease-causing changes
may involve DNA methylation and microsatellite instability for which
automatable methods to detect instability in as few as 100 cells at multiple loci
are required. Furthermore, in some instances it may be necessary to detect one
tumor cell among a large number of normal cells, as well as to profile
differentially expressed genes. Eventually, the ultimate goals are to characterize
the entire genome rapidly and inexpensively, ideally using a single cell in order
to survey the whole genome for any nucleic acid variation.
Within the field of proteome research, microarray technology has been
adapted to the protein arena. Although DNA microarrays are quite popular and
in vogue, proteins (not genes) are the targets for drugs; therefore, there is an
increasing need to develop protein chips. Specifically, tools and methods are
needed for the identification and quantification of proteins, and for the study of
protein–protein interactions, enzyme–substrate interactions, and smallmolecule
interactions. Enormous efforts have been undertaken to transfer
standard sandwich immunoassays in miniaturized and parallel formats to
analyze simultaneously the expression of a large number of proteins, e.g.,
serum or tumor biomarkers.
It is becoming clear that microarray technology is capable of fulfilling these
needs. Although some technologies are still confined to research laboratories,
such as those aimed at performing resequencing of known genes and protein
identification, rapid and robust methods are becoming available to address each
of these needs. However, some general goals for diagnostics including
sensitivity, specificity, high throughput, cost effectiveness, and turnaround time
still need improvement. |
