High-throughput expression profiling techniques.

Great optimism exists that correlating genomics with function will lead to a better understanding of the detailed workings of the nervous system. The nearly complete sequencing of the genomes of several eukaryotic species and the invention of high-throughput expression profiling techniques now provide the means to rapidly investigate the molecular underpinnings of phenotypic change with reasonable accuracy. This approach is beginning to have an impact in neurobiology and we can expect it to be similarly useful for investigating problems in the chemical senses. Careful thought is necessary, however, in selecting and matching specific techniques and tissues, in confirming differences in mRNA abundance and in interpreting the results. A symposium on Functional Genomics in Neural Systems was held during the AChemS XXIII meeting to illustrate both the potential and the limitations of these techniques for investigating questions of importance to neurobiology in general and to the chemical senses in particular. A brief technical introduction, summarized herein, was followed by two keynote talks. Dr Daniel Geschwind (UCLA) shared his work identifying genes whose expression is correlated with differentiating neurons in developing neural tissues. Moving then to the other end of a nerve cell’s life, Dr Tomas Prolla (University of Wisconsin) described different sets of genes that are affected in the aged brain and how many of the age-related changes can be prevented by restricting the intake of calories. Both speakers incorporated their experiences and perspectives on the technical challenges that are a critical element in the successful use of these new and still improving techniques. Expression profiling techniques allow the simultaneous analysis of the abundance of many thousands of transcripts. By doing so, they provide three advantages. (i) They accelerate the discovery of transcripts whose abundance is correlated with any particular phenotype. (ii) Even more exciting than discoveries about individual transcripts, however, is the ability to observe patterns that emerge from comparing the known functions of all the affected transcripts. (iii) Finally, these techniques can provide hypothesis testing about gene expression patterns underlying phenotypic change on a broad scale. Clearly, expression profiling techniques provide powerful tools. The tools available for expression profiling can be grouped into three types (Table 1). (i) Clone and count methods generate thousands of 9–20 bp sequences representing specific sites near the 3′ ends of poly(A+) RNAs (Velculescu et al., 1995; Brenner et al., 2000). Sequences of this length are sufficient to uniquely identify most transcripts. The abundance of a transcript is measured simply as the number of times its sequence tag is encountered. Overall, this is a highly effective method, but laborious and expensive enough that it is less common than the other two types. (ii) Microarray methods depend upon hybridization of probes derived from RNA samples against DNA or RNA spots bound to a solid substrate (Schena et al., 1995). The commercial production of microarrays, the familiarity of investigators with nucleic acid hybridization methods and the rapidity of data collection have made this the most popular type of expression profiling method. Its limitations include the obvious fact that it is restricted to analyzing only those sequences present on the array and that it is slightly less sensitive than the other methods. The paper by Prolla (Prolla, 2002) that follows describes the details of expression profiling with Affymetrix GeneChip oligonucleotide arrays. Numerous reviews describing the details of, and differences among, microarray methods are available (Duggan et al., 1999; Lipshutz et al., 1999; Hegde et al., 2000; Luo and Geschwind, 2001). (iii) Differential subtraction methods use the power of PCR, adapting its conditions to favor amplification of differentially abundant cDNAs. The first invented was differential display (Liang and Pardee, 1992). More recently devised techniques, such as representational difference analysis (RDA) of cDNA (Hubank and Schatz, 1994) and suppressive subtractive hybridization (Diatchenko et al., 1996), share its advantages and appear to be more robust and accurate. For example, RDA of cDNA rarely yields false positives, is sequence-independent, interrogates the majority of transcripts simultaneously and can be used with very small amounts of tissue. Not that it lacks limitations, however. Analysis of the products is laborintensive and its dynamic range can be limited when many differences are present (though this can be at least partially overcome by repeating the procedure). For all three types of expression profiling methods there exists one major drawback—achieving the number of repetitions required for standard statistical analyses is often impossible or im-