Chem. Senses 27: 289-291,
2002
© Oxford University Press 2002
SYMPOSIUM: Proceedings of a Symposium on Functional Genomics in Neural Systems |
High-throughput Expression Profiling Techniques
Department of Physiology, University of Kentucky, Lexington, KY 40536-0298, USA
Correspondence to be sent to: Timothy S. McClintock, Department of Physiology, University of Kentucky, 800 Rose St, Lexington, KY 40536-0298, USA. e-mail: mcclint{at}uky.edu
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 trascripts 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 labor-intensive 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 impractical. Empirically defined
criterion levels often correctly identify differentially abundant transcripts,
but are increasingly regarded as unsatisfactory for the obvious reason that
the absence of variance estimation leaves in doubt the risk of type I and type
II errors. The paper by Prolla (Prolla,
2002) discusses several important statistical issues in the use of
microarrays. Validation of expression profiling data by independent techniques
such as Northern blot, RNase protection assay, RNA dot blot, etc. is an
increasingly common solution to the statistical problem. For example, the
paper that follows by Dougherty and Geschwind
(Dougherty and Geschwind, 2002)
describes the use of custom cDNA microarrays to screen the results of an RDA
of cDNA experiment.
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In some situations, choosing an expression profiling technique is easy
since any would suffice. However, in most cases it is appropriate to compare
carefully the advantages and disadvantages of each technique with the
properties of the tissue source and the goal of the experiment. RDA of cDNA is
a good choice in situations requiring a sequence-independent approach with
high accuracy for detecting differential expression. Microarrays are the best
choice when multiple comparisons are desired, when rapid analysis is critical,
or when analyzing a subset of transcripts is sufficient. Some investigators
have used both types of techniques, either in parallel or in series
(Geschwind et al.,
2001; Reick et al.,
2001). The paper by Dougherty and Geschwind
(Dougherty and Geschwind, 2002)
nicely describes the benefits deriving from the complementary advantages of
the two techniques. One general caveat is that all of the expression profiling
techniques use genes as their basic unit. Little has yet been done to adapt
them to analyze the phenotypic diversity caused by alternative splicing of
mRNAs, although differential subtraction methods already have the capacity to
detect some splice variants. Quantifying splice variants is important because
they may represent a majority of the mRNA diversity in mammalian cells
(Lander et al., 2001).
Exons, rather than genes, might therefore be the most appropriate fundamental
units on microarrays. In addition, a particular concern with expression
profiling experiments in neural tissues is cellular heterogeneity. Many
significant neurobiological questions involve phenotypic responses in a
minority of the cells in the affected tissue. Given that many of the
interesting molecular differences often belong to relatively rare transcripts,
expression profiling techniques are often insufficiently sensitive to fully
investigate these questions. To overcome this problem, investigators have, or
will, combine expression profiling techniques with surgical microdissection,
antisense RNA amplification (Eberwine
et al., 1992), laser capture microdissection
(Ohyama et al.,
2000), production of transgenic animals expressing
phenotype-specific markers and isolation of specific cell types by flow
cytometry.
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Accepted December 7, 2001
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