Plant Molecular Biology 48: 75–97, 2002.© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

75

Comprehensive gene expression analysis by transcript profiling

Jonathan Donson∗, Yiwen Fang, Gregg Espiritu-Santo, Weimei Xing, Andres Salazar, SusieMiyamoto, Veronica Armendarez and Wayne VolkmuthCeres, Inc., 3007 Malibu Canyon Road., Malibu, CA 90265, USA (∗author for correspondence; e-mailjdonson@ceres-inc.com)

Key words: cDNA-AFLP, gene expression, microarray, oligonucleotide-based array, SAGE, transcript profiling

Abstract

After the completion of the genomic sequence of Arabidopsis thaliana, it is now a priority to identify all the genes,their patterns of expression and functions. Transcript profiling is playing a substantial role in annotating and deter-mining gene functions, having advanced from one-gene-at-a-time methods to technologies that provide a holisticview of the genome. In this review, comprehensive transcript profiling methodologies are described, including twothat are used extensively by the authors, cDNA-AFLP and cDNA microarraying. Both these technologies illustratethe requirement to integrate molecular biology, automation, LIMS and data analysis. With so much unchartedterritory in the Arabidopsis genome, and the desire to tackle complex biological traits, such integrated systems willprovide a rich source of data for the correlative, functional annotation of genes.

Abbreviations: cDNA-AFLP, cDNA amplified fragment length polymorphism; EST, expressed sequence tag;LIMS, laboratory information management system; MPSS, massively parallel signature sequencing; PCR, poly-merase chain reaction; RDA, representational difference analysis; SAGE, serial analysis of gene expression; SSH,suppression subtractive hybridization

Introduction

The publication of the ‘complete’ nuclear chromoso-mal sequence of Arabidopsis thaliana, following onfrom publication of the plant chloroplast and mito-chondrial genome sequences (Unseld et al., 1997; Satoet al., 1999), has equipped biologists with the oppor-tunity to describe a plant’s basic genetic determinants(Arabidopsis Genome Initiative, 2000). This monu-mental achievement begs the question, ‘How doesthis arrangement of nuclear chromosomal nucleotidesequences provide for the making and survival of aplant?’ Bioinformatic analysis allows the theoreticaldiscrimination of open reading frames and geneticcontrol elements; however, such studies by their na-ture carry inaccuracies (Pavy et al., 1999; Guigóet al., 2000; Cho and Walbot, 2001). Even where ourknowledge of genomic structure allows us to infer the

AFLP� is a registered trademark of Keygene N.V. TheAFLP� technology is covered by patents owned by Keygene N.V.

presence of genes, we can say little about the func-tioning or control of these transcriptional units. While69% of publicly annotated Arabidopsis genes havesome sequence similarity to those with known func-tions in other organisms, only 9% of genes have beencharacterized experimentally (Arabidopsis GenomeInitiative, 2000).

Information on both the physical and functionalannotation of the genome can be gained through tran-script profiling (Hughes et al., 2001; Shoemaker et al.,2001). In recent years, transcript profiling has becomesynonymous with gene expression analysis, largelybecause of the technical difficulties and greater mole-cular complexity of proteomics and metabolomics(Smith, 2000; see other papers in this issue). Suchcorrelations are acceptable in many cases (Celis et al.,2000), even though the term ‘gene expression’ is of-ten used to refer more directly to the compendium ofgene products that ultimately cause cellular responses,and these are more often than not proteins. In some

76

instances protein levels are not reflected by alterationsin mRNA level, and their activities are often con-trolled by post-translational modifications (Gygi et al.,1999). This means one has to take care with both ex-perimental design and interpretation if one wishes toextrapolate transcript levels to those of protein (pro-teomics) or protein activity (metabolomics). Even so,it is generally accepted and there is much experimentalevidence to support the statement that while post-transcriptional events play a role in modulating geneexpression the primary level of control is at transcrip-tion. In this framework, the accessibility of transcriptprofiling in recent years has allowed the establishmentof various high-throughput methodologies of gene ex-pression analysis. These methodologies differ in theirconvenience, expense, number of transcripts assayedand sensitivity (Table 1; Kuhn, 2001). However, asrevealed by the genomic sequencing projects, full au-tomation and data management are essential factors inall comprehensive transcript profiling.

Methods of transcript profiling

Transcript profiling has been going on in one form oranother for over 25 years (Bishop et al., 1974; Al-wine et al., 1977; for review, see Goldberg, 2001).This period has spawned techniques such as northerntransfer hybridization, S1 nuclease analysis and in situhybridization. While these methods are characterizedby good, well-defined sensitivities, they are time-consuming, and therefore best suited for the in-depthanalysis of a small number of genes. By comparison,current high-throughput transcript profiling technolo-gies have relatively poorly defined sensitivities, sothese early methods provide both a valuable meansof confirming and extending results obtained with themore global approaches. Such ‘single-gene’ methodsare part of the lexicon of most molecular biologylabs (Ausubel et al., 2001; Sambrook and Russell,2001), and have been extensively reviewed, and assuch will not be considered further in this article. In-stead, we will confine discussion to high-throughputapproaches that allow for a global view of transcriptlevels, and where possible we will reference workwith plants. These methods can be divided into twoclasses: (1) Direct Analysis, including procedures in-volving nucleotide sequencing and fragment sizing;and (2) Indirect Analysis, involving nucleic acid hy-bridization of mRNA or cDNA fragments. We haveused two methods extensively, cDNA microarraying

and cDNA-amplified restriction fragment polymor-phism (cDNA-AFLP), as representative technologiesfrom each of these groups. All global methods of geneexpression analysis demand good Laboratory Informa-tion Management Systems (LIMS), automation, andpowerful data management and mining systems. Be-cause these aspects have been introduced most widelyin conjunction with cDNA microarraying, we willreview them together with this technology.

Direct analysis – nucleotide sequencing

Large-scale expressed sequence tag (EST) sequencing

Generating sequences from cDNA fragments servestwo purposes, the discovery of new genes and theassessment of their expression levels in the represen-tative tissue (Ewing et al., 1999; Mekhedov et al.,2000; White et al., 2000). The basis of the approachis that the level of an mRNA species in a specific tis-sue is reflected by the frequency of occurrence of itscorresponding EST in a cDNA library. In this respectthe technology is distinct from ratio-based method-ologies, such as microarraying, in being immediatelyquantitative (Bohnert et al., 2001). EST technologiesare attractive because they do not rely on establishedsequence data from the organism under study, andthey also fit well with labs already equipped to carryout high-throughput DNA sequencing (Adams et al.,1991). However, even at a few dollars per sequencethe process can be expensive if one desires to progressbeyond cursory screening of abundant mRNAs to in-depth analysis (Ohlrogge and Benning, 2000). Inaddition to the statistical problems in sampling smallnumbers from a large population (Audic and Claverie,1997), there are also problems of bias in cloning andcDNA synthesis, though this last problem and oth-ers associated with data normalization are not specificto this technology. Auxiliary techniques are availablethat reduce the amount of sequencing. These includesubtraction hybridization (Sargent, 1987) and relatedmethods, representational difference analysis (RDA;Hubank and Schatz, 1994) and suppression subtractivehybridization (SSH; Diatchenko et al., 1996). Nu-merous variants on these technology themes are alsoavailable (Bonaldo et al., 1996; Sagerstrom et al.,1997).

77

Tabl

e1.

Maj

orad

vant

ages

and

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dvan

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sof

tran

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ptpr

ofilin

gte

chno

logi

es.

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E,

seri

alan

alys

isof

gene

expr

essi

on;

MPS

S,m

assi

vely

para

llel

sign

atur

ese

quen

cing

;D

D,

diff

eren

tial

disp

lay;

cDN

A-A

FLP,

cDN

A-a

mpl

ified

frag

men

tle

ngth

poly

mor

phis

man

alys

is;

Olig

o-C

hips

,ol

igon

ucle

otid

e-ba

sed

arra

ys.

Dup

licat

eal

phab

etan

dnu

mer

ical

num

bers

(e.g

.∗ [a

]∗ )in

dica

tead

vant

ages

ordi

sadv

anta

ges

com

mon

tom

etho

dsal

read

yci

tedi

nth

ista

ble.

Cos

tof

tran

scri

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esc

reen

ing

Tech

nolo

gyA

dvan

tage

sD

isad

vant

ages

Part

ial

Com

preh

ensi

ve

5000

–10

000

gene

s>

1000

0ge

nes

Dir

ect

Nuc

leot

ide

sequ

enci

ngE

STse

quen

cing

[1]nu

mer

able

inna

ture

[a]st

atis

tical

lysi

gnifi

cant

cove

rage

can

beex

pens

ive

low

high

[2]no

requ

irem

entf

orex

istin

gse

quen

ceda

ta

[3]co

mpa

tible

with

exis

ting

sequ

enci

ngla

bs

SAG

E∗ [1

]and

[3]∗

∗ [a]∗

[4]le

ssex

pens

ive

than

EST

sequ

enci

ng[b]

requ

ires

com

preh

ensi

vere

fere

nce

data

base

[c]sh

ortt

ags

can

show

redu

ndan

cyin

data

base

MPS

S∗ [1

]and

[4]∗

∗ [b]∗

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nger

tags

than

for

SAG

Eal

low

sm

ore

accu

rate

anno

tatio

n[d]

need

for

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mat

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Fra

gmen

tsiz

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low

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t-up

cost

s[e]

fals

e-po

sitiv

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nsiti

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tivity

prob

lem

s

[f]ex

tens

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band

isol

atio

n+

sequ

enci

ng

cDN

A-A

FLP

∗ [6]∗

∗ [b]a

nd/o

r[f]

∗[7]

spec

ific

prim

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tsov

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me

prob

lem

sw

ithD

D

Indi

rect

Hyb

ridi

zatio

n

high

high

Olig

o-C

hips

[8]hi

ghly

amen

able

toau

tom

atio

n∗ [b

]and

[d]∗

[9]do

esno

treq

uire

clon

ean

dPC

Rfr

agm

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icro

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],[8]

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ial

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brid

izat

ion

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lem

s

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tio-b

ased

:cD

NA

sam

ples

are

proc

esse

din

para

llel

[i]ex

tens

ive

clon

ean

dPC

Rfr

agm

entc

urat

ion

[13]c

ompa

tible

with

labs

havi

ngex

istin

gcl

one

libra

ries

78

Serial analysis of gene expression (SAGE)

By developing SAGE, Velculescu et al. (1995) inge-niously addressed the problem of reducing the costs oftraditional EST sequencing, and further modificationshave allowed this procedure to handle small amountsof tissue (<105 brain cells; Datson et al., 1999) (Pe-ters et al., 1999). Though a similar sequence-basedmethod to EST analysis, SAGE achieves a cost-savingby the concatenation and punctuation of multiple se-quence tags of 10–14 bp, prior to cloning. By the sizeselection of inserts containing 25–50 tags, a compara-ble reduction of cost or increase in depth of analysiscan be achieved over the sequencing of single ESTs.However, this increased efficiency comes at the priceof more extensive sequence reads. Consequently, thistechnology is best applied to organisms whose ge-nomic sequences are known or that have a substantialcDNA sequence database. Because SAGE tags tend tooriginate from the 3′ portion of transcripts, they areless effectively screened against most cDNA libraries,which are generally sequenced only from their 5′ ends.Even with a reference database, because the tags areso short, there can be a redundancy of matches. De-spite these caveats, the technique does not precludea ‘blind’ analysis of gene expression followed by thefurther characterization of tags that show interestingexpression patterns (Matsumura et al., 1999; van denBerg et al., 1999; Chen et al., 2000).

SAGE has been used sparingly in plant research,in contrast to the numerous human and yeast studies(http://www.sagenet.org/). A study in rice seedlingsconsisted of a sample of 10 122 tags for untreatedplants and about 2000 tags of both anaerobicallytreated and untreated plants (Matsumura et al., 1999).Although a small sample, 76.9% of the 10 122 tagsfrom the untreated rice seedlings did not match therice cDNA or EST database at the time, demonstratingthe validity of this method as a means of gene discov-ery. Other on-going SAGE analyses of plants includestudies on soybean root tissues, with or without Rhi-zobium inoculation and Arabidopsis plants transgenicfor tomato Pti4 (Schupp et al., 2001; Mysore et al.,2001).

Massively parallel signature sequencing (MPSS)

As the name suggests, MPSS has effectively tackledthe problems of EST sequencing with regard to speedand depth of analysis, both through refined molecularbiology and, equally importantly, good quality au-tomation and data management (Brenner et al., 2000a,

b). The method, developed at Lynx Therapeutics,Inc., California, is based on the in vitro cloning onmicrobeads of cDNA fragments from a mRNA popu-lation. To achieve this, a collection of oligonucleotidetags is synthesized from a defined set of 4-mers. Thiscollection is so large that when attached to 3′ cDNArestriction fragments, tag-cDNA conjugates are ob-tained with virtually every polynucleotide having aunique tag. These conjugates are then amplified andhybridized to anti-tag sequences specific to separatemicrobeads. As a result, each microbead hybridizesabout 100 000 copies of a single species from theinterrogating cDNA. The cDNA fragments can thenbe sequenced in a flow cell by a method involvingtype IIs restriction endonucleases and the tandem lig-ation of differential adapters, one set of which arefluorescently labeled. A CCD camera with imageprocessing software tracks the florescence of individ-ual beads through the multiple hybridization, ligation,and cleavage steps. Sequences of 16–20 bases areroutinely obtained, which are longer than those fromSAGE analysis, thus helping to resolve redundancyof matches to cDNA databases. With this method, amRNA in high abundance in a population will have itssequence found on a large number of microbeads. Assuch, the technology retains the digital nature of EST-based gene expression profiling, with a throughput thatis vastly superior, allowing for the identification ofhundreds of thousands of mRNAs in the matter of afew days. In describing their method, Brenner et al.(2000a) reported a pilot study in which they obtained1619 000 sequence tags from a human acute mono-cytic leukemia cell line. Statistically, this volume oftags provides the method with an advantage over othergene expression methods, both direct and indirect, inthat it has a high degree of redundancy, making dataanalysis more reliable. In addition, the large samplingallows for the identification of rare mRNAs, thoughthe PCR step is a potential source of transcript profiledistortion.

At the moment, MPSS may appear inaccessibleto the broad scientific community, except throughservice work by Lynx Therapentics, Inc. However,traditional high-throughput nucleotide sequencing andparticularly cDNA microarraying seemed similarlyunavailable when they were first developed. In bothcases, academic and commercial interests promptedthe dissemination of these technologies.

79

Direct analysis - fragment sizing-based methods

Differential display (DD)

The second grouping of direct analysis methods in-volve the discrimination of mRNAs by differentialseparation of representative cDNA fragments on ma-trices; invariably, these methods are PCR-based. Theirfirst application in 1992, through differential display(DD) (Liang and Pardee, 1992) and the related RNAarbitrarily primed PCR (RAP-PCR) (Welsh et al.,1992), gave a unique perspective to gene expressionanalysis by providing a convenient bi-directional viewof the up and down regulation of mRNAs between twoor more samples. Differential display is a four-stepprocess: (1) reverse transcription of mRNA with an-chored oligo dTV primers; (2) amplification of cDNAwith arbitrary primers; (3) resolution of amplifiedcDNA by polyacrylamide gel electrophoresis; and, (4)isolation of resolved fragments from the gel, followedby amplification and sequencing. RAP-PCR differsonly in Step 1, in that arbitrary primers are used. Since1992, there has been a veritable cornucopia of pub-lished variations on this theme of differential display(Matz and Lukyanov, 1998). At the time, these meth-ods provided a far more cost-effective approach toidentifying differentially expressed genes compared toexisting subtractive methods. Consequently, they havebeen used extensively in plant research (Visioli et al.,1997; Sablowski and Meyerowitz, 1998). However,a survey of such studies shows that while differentialdisplay has been an effective means of gene discoveryit has had less value as a means of gene expressionprofiling (Sambrook and Russell, 2001). This stemsfrom the inherent nature of the PCR processes used inthis and associated methods (McClelland et al., 1995).Because arbitrary primers are used at low annealingtemperatures to allow for priming at multiple sites,the amplified products are not solely dependent onthe initial concentration of a particular cDNA, but arealso a function of the quality of match of the primersto the template. Predictably, under such conditionsprofiles are highly influenced by the PCR conditionsas well as the sample quality (Matz and Lukyanov,1998). This problem manifests itself in a high per-centage of false-positive bands (Sun et al., 1994;Sompayrac et al., 1995), lack of sensitivity (Berti-oli et al., 1995), and difficulties with reproducibility(Haag and Raman, 1994; Zhang et al., 1998). The useof longer primers has reduced but not fully alleviatedproblems of false-positive bands (Zhao et al., 1995;

Martin and Pardee, 1999). Similar modifications havebeen employed to address sensitivity (Ikonomov andJacob, 1996) and reproducibility problems (Linskenset al., 1995). However, these changes represent mod-ifications that do not change the inherent problemof arbitrarily primed PCR reactions. In conclusion,differential display methods are relatively cheap andsimple means of screening for differentially expressedgenes, and are particularly good where the availabilityof RNA is limited (Renner et al., 1998; Bosch et al.,2000). However, they are not very accurate in quanti-tatively profiling global levels of gene expression, asillustrated by the number of false-positives generated.

Selective fragment amplification

To counteract the problems associated with differ-ential display, Keygene in the Netherlands devel-oped cDNA-amplified fragment length polymorphismanalysis (cDNA-AFLP) under stringent PCR condi-tions afforded by the ligation of adaptors to restrictionfragments, and the use of specific primer sets (Fig-ure 1) (Vos et al., 1995; Bachem et al., 1996). Avariation of this technology that employs only one re-striction enzyme (TaqI) has also been described (Habuet al., 1997). In addition, there are a number of othersimilar methods to cDNA-AFLP (for review, see Matzand Lukyanov, 1998). In contrast to differential dis-play, these methods allow for a systematic survey ofthe organism’s transcriptome through the use of se-lective fragment amplification. Three of them havebeen developed as automated systems for gene expres-sion analysis (READS, Prashar and Weissman, 1996;GeneCalling, Shimkets et al., 1999; TOGA, Sutcliffeet al., 2000).

GeneCalling has been used to study the effect ontranscript levels of estradiol-induced expression of apair of maize transcription factors, known to acti-vate flavonoid synthesis (Bruce et al., 2000). LikecDNA-AFLP, the GeneCalling method consists ofthree processes: restriction enzyme digestion, adapterligation and PCR amplification; the method claims tohave a sensitivity of detecting greater than 1 in 100000 mRNAs (Shimkets et al., 1999). A particularlyattractive component of the GeneCalling method is theconfirmation of fragment-to-sequence correlations bycompetitive PCR (Shimkets et al., 1999). Bruce et al.(2000) screened about 19 000 fragments for each of sixsamples, which represented about 6000–8000 genesper sample because of fragment-to-cDNA redundancy.In the first instance, they used a fragment’s length

80

Figure 1. The cDNA-AFLP protocol. This procedure consists of six steps: 1, the reverse transcription of mRNA using an oligo-dT primer toproduce cDNA; 2, digestion of double-stranded cDNA with a pair of restriction enzymes (in this case, MseI and TaqI); 3, ligation of adaptersspecific for the two restriction sites; 4, pre-amplification of fragments with primers specific to the two adapter sequences, but with a singlenucleotide extension to reduce mismatching at the selective amplification stage; 5, selective amplification with adapter-specific primers withnucleotide extensions at their 3′ ends (2 nucleotides for the TaqI primer, 3 nucleotides for the MseI primer); and 6, visualization of individualTaqI/MseI fragments on a polyacrylamide gel, as the TaqI primer is end labeled with 33P. See the paper of Vos et al. (1995) for hypotheses onthe nature of the selective amplification. AFLP� is a registered trademark of Keygene N.V. The AFLP� technology is covered by patentsowned by Keygene N.V.

and bordering restriction sites to search a databaseof maize sequences. This provided new informationabout the action of the transcription factors, as theydetected both known and novel gene responses. Theyalso demonstrated that there was a good correlationof profiling results with northern analyses, though thiswas not always the case. This aberrant behavior waspossibly due to genes cross-hybridizing on RNA gelblots that could be differentiated by GeneCalling.

Of the methods using selective fragment amplifica-tion, cDNA-AFLP has been the most popular choice,as it shows both good reproducibility and sensitivity,and a good correlation with northern analysis (Table 2;1 copy per cell; Bachem et al., 1998) (Durrant et al.,2000; Jones et al., 2000). In collaboration, KeygeneN.V., and Ceres, Inc., established a high-throughputadaptation of cDNA-AFLP for A. thaliana by generat-ing a comprehensive reference database (Figure 2A).We selected samples from diverse tissues and treat-ments of plants in order to obtain RNA populationswith as full a representation of the A. thaliana tran-scriptome as possible. By systematically sequencingcDNA-AFLP fragments from these samples we couldlink their mobilities to about two-thirds of the pro-jected A. thaliana transcriptome (Arabidopsis GenomeInitiative, 2000). With the establishment of this highcoverage database, differential expression of genescan now be screened in a high-throughput mannerpurely by querying the database with a cDNA-AFLP

profile (Figure 2B), without the need for further se-quencing (Volkmuth et al., in preparation). Whileone would anticipate that correlative data would beconfined to reactions involving individual primer com-binations, in fact, band intensities for different frag-ments from single cDNAs showed good correlationsacross multiple primer combinations. For the pre-dicted one third of the transcriptome for which wedid not sequence a cDNA-AFLP fragment we are stillable to make sequence predictions that show a 90%accuracy, based on mobility, restriction enzyme sitesand the differentiating nucleotides. In conclusion, thisdatabase and the strong correlation with microarraydata using duplicate samples demonstrated the util-ity of the cDNA-AFLP system for gene expressionprofiling (Volkmuth et al., in preparation). In a sim-ilar manner, Qin et al. (2001) generated a computerprogram (GenEST) to correlate predicted fragmentsgenerated from EST sequences of the nematode Glo-bodera rostochiensis with actual cDNA-AFLP frag-ments. They demonstrated an excellent correlation,though the study lacked resolution (16% coverage ofESTs), and by nature of the ESTs under study involvedonly abundant genes. The throughput and automationof the gel-based cDNA-AFLP system may be furtherenhanced by the use of fluorescent labeling, multi-plexing, and capillary-based electrophoresis (Ito et al.,1994; Buntjer et al., 2001; Cho et al., 2001).

81

Tabl

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83

Figure 2. Establishment (A) and interrogation (B) of the cDNA-AFLP Reference Database for Arabidopsis thaliana. 1, 27 tissues wereharvested to create 11 pooled samples; 2, mRNAs from these pools were analyzed by cDNA-AFLP analysis (+2/+3 differentiating nucleotides,2 enzyme combinations); 3, 4, all cDNA-AFLP bands were eluted from the gels, PCR-amplified and sequenced; 5, the sequence informationfor each fragment was linked to its gel mobility in a database; 6, this database can be queried with AFLP� profiles to link band mobil-ities and intensities (expression data) with the individual band’s sequence (gene). AFLP� is a registered trademark of Keygene N.V. TheAFLP� technology is covered by patents owned by Keygene N.V.

Of the other ‘selective fragment amplification’methods, READS and TOGA generate only one frag-ment for each mRNA species. Arguments can be madein favor of the simplicity of such ‘one-fragment’ sys-tems, but counter arguments can also be put forwardfor the value of redundancy in data sets, as illustratedby the multiple fragments per mRNA species obtainedwith cDNA-AFLP and GeneCalling. Stanssens andZabeau have modified the cDNA-AFLP method sothat only one restriction fragment is monitored foreach cDNA (Breyne and Zabeau, 2001). Using thismodified method, they profiled transcript levels duringthe cell cycle of tobacco, analyzing 18 000 cDNA-AFLP tags, of which 10% exhibited a modulatedbanding pattern.

Indirect analysis

The principles underlying the hybridization of com-plementary nucleotide sequences are embodied in thestructure of duplexed nucleic acids, and have beenexploited experimentally for decades (Gillespie andSpiegelman, 1965). In this time, nucleic acid hy-bridization has been used in a variety of guises inthe quantification of plant RNA levels; in the 1970s,it was used to study sequence complexity (Goldberget al., 1978). Also in that decade, Southern developeda method using a solid support in hybridization studiesof DNA fragments separated by gel electrophoresis(Southern, 1975). This led to a major advance in theanalysis of gene expression with the development ofnorthern-transfer hybridization (Alwine et al., 1977).This technique has been immensely useful over the

84

years, though ironically the approach is less globaland the method less genomic in nature than preced-ing solution-based systems. With the availability ofnucleotide sequences and clones as physical reagents,hybridization-based approaches now allow for the si-multaneous analysis of tens of thousands of genes;quite literally one can globally survey the transcriptionof a plant by hybridization. The interest in this form oftranscript profiling has been spurred by the develop-ment of two parallel microarray-based technologies,one based on spotting cDNA fragments (cDNA mi-croarrays; Schena et al., 1995), the other, the arrayedsynthesis of oligonucleotides (GeneChips; Lockhartet al., 1996). These two methods have been exten-sively reviewed in recent years (Duggan et al., 1999;Lipshutz et al., 1999).

Oligonucleotide-based arrays

GeneChips, oligonucleotide-based arrays produced byAffymetrix, have been made to about 8200 differ-ent Arabidopsis ESTs (Zhu and Wang, 2000; Harmeret al., 2000) and 1500 maize ESTs (Baldwin et al.,1999); a full transcriptome Arabidopsis GeneChipis also under development (Zhu and Wang, 2000;Cho and Walbot, 2001). These arrays are producedby the synthesis of oligonucleotides directly onto asolid matrix using photolithographic masks to deter-mine the correct sequence (Lockhart et al., 1996;Warrington et al., 2000). For the commercially avail-able Arabidopsis GeneChips, 16 ‘probe pairs’ weresynthesized per gene. One set of 16 consisted of‘mismatch oligonucleotides’ that were identical to a‘perfect match’ set except for the 13th nucleotidein each 25-mer. These mismatched oligonucleotidesare used to assess cross-hybridization and local back-ground signals. Affymetrix GeneChips are expensiveto make, not least because of the need to manufac-ture the glass masks. However, digital micromirrorarrays that form virtual masks may provide a cheaperand more accessible alternative (Singh-Gasson et al.,1999).

There are other promising oligonucleotide-basedtechnologies, including those that array 5′-terminallymodified oligonucleotides (Kane et al., 2000;Okamoto et al., 2000), unmodified oligonucleotides(Ten Bosch et al., 2000), and phosphoamidites forthe in situ synthesis of oligonucleotides (Shoemakeret al., 2001). In this latter example, exon arrays wereconstructed spanning 50 slides, containing 1090 40860-mer probes representing 442 785 exons (Shoe-

maker et al., 2001). This system could reliably detecttranscripts at one copy per cell and the results corre-lated closely with comparable cDNA arrays (Hugheset al., 2001). Where they found discrepancies, theseinvolved genes that were members of multi-gene fam-ilies. Shoemaker et al. (2001) also produced tilingarrays consisting of overlapping oligonucleotides cov-ering an entire genomic region. Such arrays are ableto define gene structure because exons of the sametranscript show identical expression patterns across allexperimental treatments. Similar tiling arrays are pro-posed for the annotation of A. thaliana (Cho and Wal-bot, 2001). Although such arrays are high-throughputin nature, full-length cDNAs still have a role to play inannotation because of the reliability and precision oftheir sequences.

Two forms of ink-jet printer (bubble-jet and piezo-electric) have been used for producing oligonucleotidearrays, and these systems promise reduced spot sizes.The delivery of phosphoamidite monomers (Blan-chard et al., 1996; Shoemaker et al., 2001) has ob-vious advantages over the dispensing of synthesizedoligonucleotides (Okamoto et al., 2000; Ansubel etal., 2001), as problems of washing and carry-over aregreatly simplified. In a comparison of the sensitivityof detection of 50-mer amino-linker modified oligonu-cleotides over PCR products about 360 bp in length,no significant difference was found provided appropri-ate design criteria were followed (Kane et al., 2000).Ten Bosch et al. (2000) came to a similar conclusion,but also presented evidence for the differentiation ofoverlapping yeast genes. Using this technology, theyhave synthesized a full transcriptome oligonucleotidearray for yeast.

cDNA-microarrays

The basic idea of spotted nucleic acid arrays for geneexpression analysis is not new and has been used insome form for over twenty years (Kafatos et al., 1979).Recently, cDNA microarraying has caused a revolu-tion in molecular biology, invited by the availability ofgenomic sequences. Its rapid incorporation into plantresearch labs is testament to the immediacy of geneexpression studies and the demand for a more holis-tic approach throughout plant molecular biology. Theencouragement of this global view of biology throughthe marriage of automation to the pertinent protocolshas benefited from the altruistic behavior of its devel-opers, who have actively dispensed their knowledge(http://cmgm.stanford.edu/pbrown/).

85

Figure 3. Schema for the generation and interrogation of cDNA microarrays. 1. PCR fragments are amplified from cDNA clones. 2. PCRfragments are purified, quality control-tested on agarose gels, buffer-adjusted and spotted on to coated glass microscope slides by means ofa computer-controlled X-Y-Z robot. 3. Total or poly(A)+ RNA from both the test and reference sample is fluorescently labeled with eitherCy3 or Cy5 nucleotides by reverse transcription. 4. The fluorescently labeled cDNAs are pooled and allowed to hybridize to the array. Afterhybridization, the array is washed to remove unhybridized molecules. The amount of fluorescent cDNA that hybridizes to each of the spotsis then measured by a fluorescent scanner, through sequential exposures to excitation wavelengths specific to the two Cy dyes. Spectrallycharacteristic emissions from these dyes are captured with a confocal sensor. 5. Emission intensities are extracted and these are linked to thecorresponding clone ID, and through extensive LIMS (Figure 4) to experimental details, array information and hybridization conditions. Thesedata can be normalized (6) and merged to produce a pseudo-colored image (7), or analyzed as part of multiple experiments, by ratio-based datamining tools (8) (Figure 5).

Although DNA microarrays can be used in a va-riety of genomic studies, their major use to date hasbeen in gene expression analysis, particularly in thecase of plant research. Reviews of methodology areavailable (Eisen and Brown, 1999; Hegde et al., 2000,Ausubel et al., 2001), as are two recent books on thesubject (Schena, 1999, 2000). The Internet is also arich source of information on experimentation withmicroarrays, including reviews of equipment, pro-tocols, and suppliers of pre-fabricated arrays (Fereaand Brown, 1999; http://www.gene-chips.com/;http://www.deathstarinc.com/science/biology/chips.html). The basic strategy for the manufacture and

interrogation of cDNA microarrays is described inFigure 3. As microarray experiments are conductedunder conditions where there is a large excess ofimmobilized probe to labeled cDNA, the kinetics ofhybridization are pseudo-first-order. Therefore, the in-tensity of the fluor’s emission is proportional to thelevel of the specific labeled cDNA in the hybridiza-tion solution. A particularly attractive aspect of thesystem is that it is ratio-based, with the two cDNAsamples under analysis being hybridized in parallel.This is important because it removes the variability ofarray fabrication and individual hybridizations fromthe equation. This aspect makes radioactive nylon-

86

or nitrocellulose-based arrays less attractive (Desprezet al., 1998). Indeed, while absolute quantification ofRNA levels can be inferred by incorporating appropri-ate dosing controls, generally it is the relative increaseof a mRNA between different treatments or tissues thatis of interest. For mRNA copy-number calculations,the highly optimized kinetics of olignucleotide arraysoffers the better solution.

Because spots can be arrayed at distances as lowas 150 µm center-to-center, it is self-evident thatrepresentative PCR fragments from the complete tran-scriptome of A. thaliana can be deposited on a singleslide. Full transcriptome arrays hold a huge attrac-tion (DeRisi et al., 1997; Hihara et al., 2001), asone can assay traits without preconceived ideas. Evenso, judiciously selected gene subsets can also be usedeffectively (Aharoni et al., 2000; Girke et al., 2000;Reymond et al., 2000; Schenk et al., 2000; Kawasakiet al., 2001). RDA (Welford et al., 1998; Nelson andDenny, 1999), SSH (Yang et al., 1999) and differentialdisplay-based methods (Display Systems, Vista, CA)have all been used to refine the complexity of probeson microarrays. Indeed, as microarraying becomesa standard laboratory technology emphasis may shiftfrom full transcriptome coverage to systems that allowfor more rapid screening of gene subsets. Then, fulltranscriptome arrays may be used largely as a meansof refining candidate probes for smaller arrays. In thisvein, Genometrix produces slides (VistaArrays) whichcontain 96 miniarrays, each of 256 probes, which canbe used to process samples in parallel (Eggers, 2000).

Automation and laboratory information managementsystems (LIMS)

Although the first published report on cDNA microar-rays in 1995 included 48 Arabidopsis ESTs (Schenaet al., 1995), it is only in the last year that there hasbeen an explosion of plant related microarray stud-ies (Table 2). This suggests that the revolution goingon in plant molecular biology labs has less to dowith microarraying than with the introduction of au-tomation. Up to 1 April 2001, there have been 18published plant microarray papers (17 cDNA microar-ray, 1 GeneChip), of which 12 have involved analysisof A. thaliana. In addition, work has been reportedon rice, maize, strawberry, petunia, ice plant, limabean and the cyanobacterium Synechocystis. We haveused cDNA microarraying to study the Arabidopsistranscriptome (Fang et al., 2000; Okamuro et al.,2000). As of the end of the millennium, these mi-

croarrays allowed profiling of greater than 40% of thetranscriptome (Fang et al., 2000). To achieve this pref-erentially large coverage at the time, we made use ofthe A. thaliana full-length cDNA-sequencing programat Ceres, Inc. From our collection of cDNA clones,a set of about 10,000 was selected and gene-specificprimers were designed close to the 3′ end of eachindividual cDNA. To ensure the quality of the PCRproduct from these clones we instigated a highly auto-mated system that tracks samples, including clones,PCR fragments and tissues, from the start of theprocess to final data analysis (Figure 4). While sucha LIMS system might seem a luxury, it is in fact a sub-stantial time-saver, as it reduces the number of qualitycontrol assays one has to perform with a more manualprocess. Automation, LIMS and effective data man-agement are essential components of comprehensivetranscript profiling (Ermolaeva et al., 1998; Bassettet al., 1999). We made several conscious decisionswith regard to the generation of PCR fragments. Onour robotics we use disposable tips for liquid dis-pensing to avoid cross-contamination of reagents andtemplates. To further ensure the specificity of theprobes on the microarrays we also use gene specificprimers. For anyone running high-throughput roboticprocedures such as microarraying, assays to check forcross contamination are a constant concern, thoughwith planning some of these issues can be addressedas part of data collected from every array hybridiza-tion. Contamination of microarrays can be insidiousif through ill fortune low-expressing gene probes be-come contaminated with DNA complementary to highexpressing genes. Sequencing or any other commonassay method would not readily detect such contami-nations. Consequently, preventative action is the bestpolicy. It is interesting that apart from the work per-formed at Ceres, Inc., most current plant studies do notuse gene-specific primers (Fang et al., 2000; Okamuroet al., 2000). Also, in contrast to the arrays we haveproduced, only Seki et al. (2001) have used full-lengthcDNAs.

There are a large number of robots and spottingpins sold commercially for the printing of arrays (Mel-drum, 2000; Mittal, 2001), as well as the option tobuild your own robot (http://cmgm.stanford.edu/pbrown/), adapt existing robotics (Macas et al.,1998), or resort to a hand-held device (www.vp-scientific.com). Commercial robots invariably comewith good software for designing arrays and trackingclones, or it can be purchased separately (Zhou et al.,2000).

87

Figure 4. Laboratory Information Management System for cDNA microarray analysis. Arrows indicate the flow of materials and processes.Data flow in the facility is automated through the use of multiple robots including automated plate handling, bar codes and specialized software,except at instances where manual input is need. In these cases, stations have been set up with web interfaces to provided easy database input.Data reports are sent to and retrieved from specific groups of tables in the appropriate database by the individual processes and stations (indicatedby common shading).

Microscope slides are the popular choice as an ar-ray support because they are non-porous and show lowautofluorescence. There is now a large selection ofcommercially available coated microscope slides withamine or aldehyde surface chemistries, that have goodhydrophobicity and that enhance the binding of DNA(Celis et al., 2000; Mittal, 2001). Even so, the originaloption, polylysine coated slides, is still widely used(Eisen and Brown, 1999; Fang et al., 2000; Kawasakiet al., 2001). In some instances amino-linked PCRfragments are preferred (Ruan et al., 1998; Aharoniet al., 2000; Maleck et al., 2000; Wang et al., 2000b),on the basis that this enhances the sensitivity of thesystem (Schena et al., 1996; Aharoni et al., 2000;Kawasaki et al., 2001). Sensitivities in the range 1:20000 (Yazaki et al., 2000) to 1:300 000 (Seki et al.,

2001) have been reported in plant studies, thoughwithout any clear correlation with slide chemistry.

Cross-hybridization

During the analysis of experimental data particular at-tention has to be given to whether probes spotted on tothe glass slide cross-hybridize to heterologous cDNAs.A discovery from the genomic sequencing project wasthat 65% of the genes in Arabidopsis are members ofgene families, and the proportion of gene families withmore than two members is considerably higher thanin other sequenced eukaryotes (Arabidopsis GenomeInitiative, 2000; Bevan et al., 2001). Indeed, the evo-lution of Arabidopsis is thought to have involved anumber of large-scale duplications, followed by sub-sequent gene loss (Blanc et al., 2000; Vision et al.,2000). Even greater gene duplication is likely to be

88

found with other plant species. However, duplicationof sequence, does not necessarily mean duplication offunction (Cho and Walbot, 2001), so for comprehen-sive transcript profiling one might still wish to assayall genes. Two plant-based studies have attemptedan assessment of this problem (Girke et al., 2000;McGonigle et al., 2000). In a study by Girke et al.(2000) a FAD2 gene from Arabidopsis was arrayed inthree different forms of identical length and GC con-tent, but with nucleotide similarities to the native geneof 100%, 90% and 80%, respectively. The fragmentof 80% similarity showed no detectable hybridiza-tion. In the same paper, four ferredoxin sequencesand three acyl-ACP-desaturase sequences from otherspecies with more variable clusters of similarity tothe Arabidopsis sequences showed cross hybridizationthresholds between 60–70%. On this basis the au-thors estimated that cross-hybridization occurred withtheir system if related genes have greater than 70–80% sequence identity. Similarly, in a study of themaize glutathione S-transferase gene family, McGo-nigle et al. (2000) found that gene expression databehaved independently for genes below ca. 80% sim-ilarity. Studies on yeast estimate that cross hybridiza-tion becomes significant at or above 75% sequencesimilarity (Spellman et al., 1998). Given that cross-hybridization will occur, solutions include a morejudicial choice of probes, representing 3′-UTR re-gions of mRNAs (Yazaki et al., 2000), or the use ofoligonucleotide-based microarrays that can discrimi-nate between nucleotide sequences of less than 93%similarity (Lipshutz et al., 1999). However, evenif oligonucleotide arrays eventually become the pre-dominate microarray platform, one area where cDNAmicroarrays, with their longer probes, may have an ad-vantage is in experiments with heterologous species;for example, Arabidopsis microarrays might be usedin parallel experiments with Brassica species (Girkeet al., 2000).

Sample preparation

The choice of samples and the quality of labeled cD-NAs are major factors in determining the sensitivityof microarraying. When it comes to sampling of tis-sues, studies on plant microarrays and GeneChipshave illustrated how genes show diurnal and circa-dian responses (Harmer et al., 2000; Schaffer et al.,2001). These phenomena are not new observations(Kreps and Kay, 1997), though the data do providean estimate of the number of similarly affected genes

in Arabidopsis. However, these studies, and an ex-ample of sample-to-sample variation in the paper ofKawasaki et al. (2001), illustrate at a molecular levelthe importance of good experimental and samplingdesign, if one aspires to assay a single variable. In-evitably, selection of tissues must be as sophisticatedas the methods used for transcript profiling, as ulti-mately one wishes to profile transcripts in one or asmall cluster of cells. To date, published plant mi-croarray experiments have involved the analysis ofheterogeneous samples, either whole plants or tissues,each of which consists of multiple cell types, spatiallydistinct from one another. Differences in mRNA lev-els in a small number of cells may be swamped bythe dilution effect of RNA from millions of differentcells. There is no universal answer to this problem,and possible solutions include cell culturing, cell sort-ing, ablation experiments and the judicious use ofmutants (Sheen et al., 1995; McCabe et al., 1997;Liu et al., 1999; Reymond et al., 2000; Bohnert et al.,2001). Laser capture microdissection (LCM) has beenused in animal systems for the procurement of micro-scopic and pure subpopulations of cells from tissues(Emmert-Buck et al., 1996), but its use in plant tissuesmay be complicated by the cell walls. Ultimately, allmodern profiling methods will be at their most power-ful in understanding mechanisms of development anddifferentiation when combined with classical geneticand cytological approaches.

Conclusions from expression profiling are influ-enced by the ability to make comparisons across alarge number of diverse and precisely executed experi-ments. In designing an experiment one must decide onthe most appropriate control samples to use in suchcomparisons. For pairwise analysis this is generallya simple matter, but for experiments with multiplesamples universal controls might be desirable to al-low comparison across different data sets (Eisen andBrown, 1999). Care must also be taken in the choiceof controls spiked into the hybridization solution tomonitor microarray performance and to facilitate nor-malization. The experimental design process extendsto the seemingly obvious repetition of experiments inorder to assess variability, though this is not alwaysfollowed (Lee et al., 2000).

Having sampled your tissue, one needs to decidewhat RNA fraction should be analyzed. Differentialtranscript levels between similar samples, perhapstreated and untreated, can be determined by measuringmRNA levels in total cell RNA. This allows a cor-relation to be drawn between a change in level of a

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particular transcript and the treatment. However, sincemRNA levels do not always correlate with protein lev-els the extrapolation to measuring translation is lesseasily made (Gygi et al., 1999). The correlation ofdifferential transcript levels with the translationally ac-tive versus the translationally inactive pools of mRNAis particularly difficult if the samples are disparatein nature. A solution to this is through the labelingof polysome-associated RNA (translationally activemRNA) as against mRNAs associated with ribonu-cleoprotein particles or monosomes (translationallyinactive mRNA) (Zong et al., 1999). Plant polysomefractions can be readily separated by sucrose gradi-ent centrifugation (Jackson and Larkins, 1976). Thisprocess can be taken a step further in that purificationof membrane-bound polysomes will afford a selectionof mRNAs of proteins destined for membranes and se-cretion (Diehn et al., 2000). Similar strategies mightalso be envisaged for the study of other subcellularlocalization.

cDNA microarray analysis traditionally uses arelatively large amount of mRNA (1–2 µg). Thisis usually labeled by the incorporation of fluores-cently labeled nucleotides into first-strand cDNA or bythe cross linking of N-hydroxysuccinimide-activatedfluorescent dyes to aminoallyl groups incorporatedinto the cDNA (http://cmgm.stanford.edu/pbrown/). Aconsequence of the need to work with more refinedtissues is that these levels of mRNA may not beavailable. In conjunction with LCM, an amplificationmethod based on cyclical rounds of T7 polymerasein vitro transcription has been developed (Luo et al.,1999; Salunga et al., 1999; Wang et al., 2000a). Thismethod makes use of the linear amplification of T7polymerase, and has been shown to produce resultsthat correlate well with original mRNA levels, in con-trast to the exponential nature of PCR-based methods(Lockhart and Winzeler, 2000). Luo et al. (1999) re-port the amplification of mRNA from 500–1000 braincells for microarray analysis. Alternative strategies in-clude increasing the effective concentration of a subsetof the RNA population under study, by reducing itscomplexity. This can be done by generating probesfrom differential display or cDNA-AFLP fragments(Trenkle et al., 1998). Other than the amplificationof the labeled sample, there are also procedures thatallow for kinetically more efficient hybridizations andthe amplification of fluorescent signals (Stears et al.,2000).

Hybridization conditions

Microarray hybridizations are performed for a periodof 4–16 h in either formamide or SSC-based solutions(Eisen and Brown, 1999; Hegde et al., 2000). Volumesare kept to a minimum (ca. 0.033 µl per mm2 of cover-slip) under a floating coverslip. To allow the process-ing of twelve microarrays at one time, Genomic Solu-tions, Michigan, (www.genomicsolutions.com), sellsan automated hybridization and washing station. How-ever, one area that sets current cDNA-microarraysapart from membrane-based arrays is that they arenot generally reusable (Desprez et al., 1998; Bald-win et al., 1999), though a newer slide derivatizationpurports to make this possible (Beier and Hoheisel,1999). Nanogen and Illumina are two companiesthat have experimented with alternative technologiesthat reduce hybridization times and allow for arrayreuse. Nanogen has produced a chip where the probesare independently, electronically activated (Sosnowskiet al., 1997; Edman et al., 1997). Current models haveonly 99 probe sites, making them more suitable forsmall-scale expression profile studies. Even so, theelectronics allow the labeled DNA interrogating thechip to be effectively concentrated, thus speeding hy-bridization times dramatically. Controlled reversal ofthe electric field can also achieve a defined washingstringency. With the independently controlled probes,this raises the prospect of having highly defined hy-bridization and washing conditions, for what is tradi-tionally a passive process controlled by salt concen-trations and temperature. The electronic process alsofacilitates the effective removal of all labeled cDNA,so that the chips can be re-used. Such a system couldallow multiple hybridization experiments to be per-formed extremely quickly. The system is currentlybeing beta tested as a gene expression system.

By contrast, Illumina produces self-assembledbead arrays on bundles of individual, selectivelyetched optical fibers (Ferguson et al., 1996; Walt,2000). Oligonucleotides can be either synthesized di-rectly on to these beads or pre-synthesized moleculescan be attached. The sequences on the beads areregistered by fluorescent tagging of the beads or bydecoding secondary DNA tags by hybridization. Thissystem is reported to assay very small volumes andto be highly sensitive, allowing for potentially fasterhybridizations. In addition, multiple hybridization cy-cles can be performed with the same array (Steemerset al., 2000). Other systems in development for im-proving hybridization kinetics and signal detection

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have been recently reviewed (Steel et al., 2000; Blohmand Guiseppi-Elie, 2001).

The parallel hybridization of two cDNA samples,a major factor in the efficiency of microarraying, isafforded by the dual detection of fluorescent moieties,typically Cy3 and Cy5. The fluorescent signals fromcDNA bound to probes on a microarray are monitoredby scanning systems that are mostly confocal in nature(http://cmgm.stanford.edu/pbrown/) (Schermer, 1999;Basarsky et al., 2000; Mittal, 2001). Some commer-cial models now allow the use of multiple absorptionand emission wavelengths, facilitating third-channelnormalization of hybridizations and more than twosamples to be hybridized to each microarray. Datafrom these machines is typically captured as two 16-bit TIFF images, one for each fluor. Commercial andpublic programs are available for extracting data fromthese image files, and these also include features forspot finding and the flagging of physical artifacts, suchas dust (Bassett et al., 1999; Mittal, 2001).

Data analysis

Transcript profiling produces extremely large datasets, and even relatively simple studies can run intomillions of data points (e.g. Arabidopsis FunctionalConsortium (AFGC; http://afgc.stanford.edu/) (Wis-man and Ohlrogge, 2000). It is self-evident that suchdata sets cannot be organized on simple spreadsheets,but instead requires effective database resources formanagement and analysis (Bassett et al., 1999; Zhouet al., 2000). Molecular biology and computationare inexorably entwined in the science of transcriptprofiling (Figures 4 and 5).

Normalization of extracted data is a requirementfor all gene expression profiling. This is because,irrespective of the method, part of the processing of bi-ological material and data extraction is inevitably doneseparately for each sample. A number of normaliza-tion approaches can be employed, including the use of‘housekeeping genes’, that are hypothesized to showa constant expression between samples, and globalapproaches, where the total level of gene expressionfor a large portion of the total genes under study isconsidered to stay constant (Table 2). Where samplesare similar, global normalization works well. For moredivergent samples, normalization with a designatedsubset of genes may have an advantage.

Because of the nature of cDNA-microarraying themost useful data output is the ratio of transcript levelsbetween two samples. The first task on being faced

with these data is to decide what is ‘significant’. Forplant microarray studies, ratios of 1.5 (McGonigleet al., 2000) to 3.32 (Aharoni et al., 2000) have beenchosen, with provision for signals to be above a certainbackground threshold (Table 2). In some cases thesevalues have been based on experiments where variabil-ity was assessed between duplicate RNA preparations(Wang et al., 2000b; Kawasaki et al., 2001), or in moreadvanced studies, between two duplicate experiments(Schenk et al., 2000; Kawasaki et al., 2001). Hugheset al. (2000) clearly illustrated the importance of suchcontrol experiments by establishing a gene-specificerror model, conducting 63 control experiments com-pared to 300 compendium experiments in yeast. Inthis way, the effect of intrinsically fluctuating mRNAspecies can be considered. This is particularly impor-tant for GeneChip experiments where each array isinterrogated with only a single labeled cDNA popu-lation (Harmer et al., 2000; Zhu and Wang, 2000).Alternative strategies have involved assessments frommultiple repetitions of the same experiment and statis-tical analysis of variance (Aharoni et al., 2000; Sekiet al., 2001).

Correlative analysis

An analysis of ratios provides a valuable means oflooking at pair-wise comparisons (Arimura et al.,2000; Wang et al., 2000b; Seki et al., 2001). How-ever, particularly for associating gene function, greaterinsights can be obtained when one draws correlationsbetween the coordinated responses of genes (Figure 5;Fang et al., 2000) (Okamuro et al., 2000; Malecket al., 2000; Schaffer et al., 2001; Kawasaki et al.,2001). Hughes et al. (2000) present a compellingcase for functionally annotating yeast by creating acompendium of transcript profiles from 300 diversemutations and chemical treatments. The principle isa simple one – ‘guilt by association’, genes whoseexpression profiles cluster together over a range ofexperimental treatments are more likely to be func-tionally linked than those that do not (Chu et al.,1998). As such, the functions of uncharacterized genesmight be defined by their clustering with genes ofknown function over a diverse range of experiments(Chu et al., 1998) (Figure 5; Fang et al., 2000). Ofthe many clustering methods, the pairwise average-linkage cluster analysis of Eisen et al. (1998), withits visually pleasing graphical representation, has beenthe most popular in plant microarray studies (Figure 5;Fang et al., 2000) (Maleck et al., 2000; Schaffer

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et al., 2001; Kawasaki et al., 2001). Even so, its re-placement of actual data with averaged data at eachiteration, the use of similarity measures as opposedto Euclidean distance, and the creation of a hierar-chical format where one does not naturally exist, arefactors to consider when evaluating the results (Tava-zoie et al., 1999). To alleviate some of these problems,data can be first partitioned into preliminary groupingsusing methods such as self-organizing maps (SOMs;Tamayo et al., 1999) and k-means clustering (Tava-zoie et al., 1999). However, these methods requirea decision on how many clusters you wish to define,although there are other methods where this is not nec-essary (Heyer et al., 1999). Needless to say, all thesemethods have prejudices, and the similarity metrics inthemselves are not an indication of the significanceof clusters. We have set up an expression databaselinking correlations in expression data to sequenceand function (Volkmuth, unpublished data; Figure 5;Fang et al., 2000). Recently, a number of commer-cial software packages have become available that leadthe user through expression data analysis with visu-ally pleasing normalization, correlation and clusteringfeatures (Figure 5) (Richmond and Somerville, 2000;Goodman, 2001; Mittal, 2001).

Downstream analysis

Nobody handling large gene expression data setswould question the importance of computationallybased organization and analysis (Figures 4 and 5). Ul-timately, however, because of a number of technicalissues, such as sample tracking, cross-hybridizationand contamination, one should independently confirmimportant results obtained by expression profiling.The favorite method for plant microarray studies hasbeen northern analysis, though as an automated systemreal-time RT-PCR is an attractive alternative (Table 2,Figure 5; Sambrook and Russell, 2001). Plant expres-sion studies have shown good qualitative agreementbetween northern and microarray data (Ruan et al.,1998; Maleck et al., 2000; Wang et al., 2000b).

The value of clustering genes within and betweenexperiments can be enhanced immensely by link-age of the expression database to other informationstockpiles (Ermolaeva et al., 1998; Marcotte et al.,1999; Burks, 1999). Such databases include the Na-tional Center for Biotechnology Information (NCBI;Wheeler et al., 2001) which provides a source ofgene annotations, protein domain databases (Pfam;Bateman et al., 2000) and the Kyoto Encyclopedia

of Genes and Genomes (KEGG; Kanehisa and Goto,2000), a database of metabolic pathways. In addi-tion, transcript profiles are informative for genomeannotation, as they facilitate characterization of genesand help identify promoter elements (Spellman et al.,1998; Harmer et al., 2000). At Ceres, Inc., we are ableto link expression data to our full-length cDNA collec-tion, providing correlations with superior annotation.Part of this collection of full-length cDNA sequenceshas been provided to TIGR in order to help with theirgenomic annotation of Arabidopsis.

In another expression-based genome study, Cohenet al. (2000) showed that genes near each other onyeast chromosomes show correlated expression, sug-gesting a positional influence of chromosome struc-ture on gene expression. As one can see, multifar-ious genomic characteristics can be integrated withtranscriptional profiling. Likewise, this can be ex-tended to include information from proteomics (e.g.protein profiles and protein interaction analysis) andmetabolomics, indeed all genomic platforms, as partof a well-organized data management system. Pheno-typic data are perhaps best integrated with expressiondata by cross-reference to plant insertional mutants,noting also when and where mRNAs are expressed(Azpiroz-Leehan and Feldmann, 1997; Parinov andSundaresan, 2000; Hughes et al., 2000; Petersen et al.,2000). The net result of this data organization shouldbe a multi-dimensional representation of a plant, bothbioinformatically (Vidal, 2001) and visually (Streicheret al., 2000). This is surely the aspiration of plantgenomics, a truly holistic view of biology where dis-ciplines such as genetics, anatomy and biochemistrymerge in one vision and model of a plant. Such aplan will also provide for more insightful reductionistapproaches. In an effort to reach that goal, transcriptprofiling has now joined nucleotide sequencing as atruly global means of plant analysis.

Acknowledgements

We wish to thank our colleagues at Cenes, Inc., andKeygene N.V. The authors are grateful to Dick Flanellfor comments on the text and to Amy Hunt for help inpreparing the manuscript.

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