Potential of DNA microarrays for developing parallel detection tools(PDTs) for microorganisms relevant to biodefense and
related research needs
Syed A. Hashshama,b,∗, Lukas M. Wickc, Jean-Marie Rouillarde,Erdogan Gularie, James M. Tiedjeb,d
a Department of Civil and Environmental Engineering, Michigan State University, A 126 Research Complex-Engineering, East Lansing, MI 48824, USAb The Center for Microbial Ecology, Michigan State University, 540 Plant and Soil Science Building, East Lansing, MI 48824, USA
c National Food Safety and Toxicology Center, Michigan State University, 165 Food Safety and Toxicology Building, East Lansing, MI 48824, USAd Department of Crop and Soil Sciences, Michigan State University, East Lansing, MI 48824, USA
e Department of Chemical Engineering, The University of Michigan, 3302 G.G. Brown Building, Ann Arbor, MI 48109, USA
Development of parallel detection tools using microarrays is critically reviewed in view of the need for screening multiple microon a single test. Potential research needs with respect to probe design and specificity, validation, sample concentration, selenrichment and amplification, and data analysis are discussed. Data illustrating selected probe design issues for detecting multipixed microbial systems is presented. Challenges with respect to cost, time, and ease of use compared to other methods are also2004 Elsevier B.V. All rights reserved.
eywords: DNA microarrays; Parallel detection tools; Biodefense
. Background
The National Academy report on countering bioterrorismNRC, 2002) identifies intelligence, detection, surveillance,nd diagnosis as key elements of biodefense. It states thatemphasis should be on defense, simply because prolifera-ion of biological weapons is difficult to control.” It furtheruggests that the science and technology community shoulde focused on defenses against biological weapons and that
he means to do so “include environmental detection of bio-ogical agents together with pre-clinical, clinical, and agricul-ural surveillance and diagnosis.” The total number of knownicroorganisms (bacteria, eukaryotes, and viruses) that are of
nterest to various federal agencies responsible for the safetyf air, water, food, animals, and agricultural products runs
nto the hundreds. Hence, development of parallel detection
tools (PDTs) capable of rapidly and economically identifya broad spectrum, if not all, of the microorganisms releto a given matrix (air, water, soil, food, plant, paper, mantissue, body fluid, etc.) is critical.
Such PDTs will most likely employ an array of distguishing genetic and functional signatures (e.g., virulefactors) collected from all the microorganisms of interesidentify the most likely candidate(s) present in a sampinterest. Subsequent analysis of the organism(s) by exapproaches, such as culturing and sequencing, can provfinal confirmation. There are several advantages in adasuch a two-tiered approach for microbial detection relatebiodefense. Paramount among which is the elimination oinitial guesswork. There may not be enough time to carrytraditional time-consuming tests one by one to identifypotential threat(s). PDTs will also provide better resoluwith respect to the strain in cases where a sufficient nuof genetic sequences exist. It will have a lower cost andof testing in comparison to a battery of individual tests
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 669
high throughput and parallel approach for microbial detec-tion using genetic and functional signatures has the additionaladvantage of being able to detect dangerous elements with-out the need to identify the microorganisms. This capabilitymay be able to address the concerns associated with the in-tentional or unintentional mobility of the virulent elements.It has also been identified as a critical need for biodefense(Enserink, 2001; Nass, 2001; Talbot, 2001).
Numerous tools to detect single microorganisms utiliz-ing antibodies, gene sequences, or functional signatures al-ready exist for many of the problematic pathogens (seewww.aoac.org). Tools to detect those microorganisms thatroutinely impact the safety of commercial products have re-ceived the most attention. For example, hundreds of commer-cial kits are available to detectEscherichia coliO157:H7 andmore are being developed to reduce time and cost. Other mi-crobial pathogens have only a few extremely specialized andtime-consuming (up to a week) methods available for theirdetection. It is evident that any parallel approach developedto screen for hundreds of microorganisms together will ini-tially be less competitive compared to some well-establishedkits developed for a single or a few microorganisms. PDTswill most likely not compete in terms of cost when detec-tion of a single known microorganism is of interest and forwhich individual detection kits are available. At the currentc om-p chesi Ther ncesw en thes for al ger-p ltipleg terizet
ideo pedi sionac ge-n ndBd ,2 t al.,2 ta awaeo on-s ;K es( liconw strato esisi usel oly-m ful for
parallel microbial detection due to loss in specificity (Thechipping forecast, 1999; Kane et al., 2000). They may bemore useful, however, for strain identification of a particularspecies by microarray-based comparison of gene sequences(McCluskey et al., 2002; Schoolnik, 2002; Wells and Bennik,2003). Probe design is the critical first step (after making alist of the microorganisms to detect) in the development ofPDTs. After a DNA chip is designed and fabricated, it mustbe validated for the targets and matrices of interest in thereal world. Validation is accomplished by extraction of theDNA or RNA from the sample, labeling it with a fluores-cent dye (using separate protocols for each) and hybridizingthe labeled DNA or RNA with the probes on the biochip.The hybridized signals are then read, using a laser scanner todetermine quantitative abundance or presence/absence of thetargets. Data analysis is an important component in determin-ing presence/abundance of the targets and spans all aspectsof PDT development, from probe design to signal detection.
Many DNA microarray platforms are available now to de-velop PDTs. Some synthesize the probes in situ, using lightand various photolithography techniques (e.g., Affymetrix(Lockhart et al., 1996); NimbleGen (Hasan et al., 1997);Xeotron (Gao et al., 2001); and Febit (Baum et al., 2003)).Others attach the probe to the substrate, using DNA print-ers after synthesizing it in a DNA synthesizer (e.g., Agilent( .,2p ta 003A c is-s enesf iateda , andu asedt ofa mplec ande onlya mi-c ntingt
cen-t allyd f thet ater(m st bec tingt ap-p ouso existf ,2i alP p-a al.,
ost of hundreds of dollars per test, they will also be less cetitive to multiplex polymerase chain reaction approa
f detection of only a dozen or so organisms is needed.eal advantages of PDTs lie in their application to instahere broad-spectrum detection is a necessity, e.g., whuspect agent is unknown and screening must be done
arge number of microorganisms or when strain level finrinting is necessary for source tracking based on muenetic signatures. They may also be useful to charac
he type of microorganisms present as a background.High throughput DNA microarrays or biochips prov
ne of the best platforms for developing PDTs. Develon the 1990s, and initially used mainly for gene expresnalysis (Schena et al., 1995; Lockhart et al., 1996), mi-roarrays are now routinely employed for comparativeomics (McCluskey et al., 2002; Schoolnik, 2002; Wells aennik, 2003), microbial detection (Chizhikov et al., 2001;el Cerro et al., 2002; Loy et al., 2002; Wilson et al., 2002a002b; Busch et al., 2003; Hashsham et al., 2003; Wu e003), single nucleotide polymorphisms analysis (Huber el., 2002; Iwasaki et al., 2002; Lindroos et al., 2002; Urakt al., 2002), sequencing (Drmanac et al., 1998), and manyther applications. An oligonucleotide DNA microarray cists of short (usually 20- to 70-mer) (Lipshutz et al., 1999ane et al., 2000; Hughes et al., 2001) signature sequenc
called probes) on a solid substrate (usually glass or siafers). These probes are either synthesized on the subr attached mechanically, using DNA printers after synth
n a DNA synthesizer. There are DNA microarrays thatong (100- to 500-mer or longer) probes produced by p
erase chain reaction but they are considered less use
e
Hughes et al., 2001), gel-pad arrays (El Fantroussi et al003), and glass slide arrays (Schena et al., 1995)). Exam-les of pilot-scale efforts exist for most of them (Urakawa el., 2002; Wilson et al., 2002a, 2002b; Hashsham et al., 2).s in other microbial detection methods, the most basiues including selection of the intended targets and gor probe design, sample concentration, platform assocdvantages and limitations, validation and data analysisltimate use must be addressed in applying microarray-b
ools (Fig. 1). Detection limit, specificity, quantificationbundance, dynamic range, reliability, speed, cost, saoncentration, and approaches for target amplificationnrichment must all be optimized for performance. Sincesingle or a few protocols must be used to detect multipleroorganisms, optimizing so many parameters is a dauask.
Concentration of microbes (often termed sample conration) for developing most of these tools is an equaunting task. This is because often a small number o
arget microorganisms present in large quantities of w1–1000 L), food (1–100 g), air (10–100 s of m3), or otheredia (soil, body fluid, manure, tissue, paper, etc.) mu
oncentrated into a few microliters without concentrahe inhibitory materials. Only then can the sample belied to a PDT for hybridization and detection. Numerptimized protocols for sample concentration already
or protozoa (Sturbaum et al., 2002), viruses (Scott et al.002), and some bacteria (Safarikova and Safarik, 2001) us-
ng mainly membrane filtration (United States Environmentrotection Agency, 2001a, 2001b) and immunomagnetic seration processes (Safarikova and Safarik, 2001; Guy et
670 S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683
Fig. 1. Many factors must be considered in developing tools for parallel microbial detection. Sample processing and validation are the rate limitingsteps.
2003; Kuczynska et al., 2003). Inconsistent and poor recoveryof the target organisms and the need to process larger volumesof high turbidity waters are the leading research problems forsample concentration (Robertson and Gjerde, 2000). A com-prehensive approach to enrich and/or concentrate all types ofmicroorganisms in a given matrix would be ideal. Efforts toconcentrate protozoa and viruses together have been madebut recovery of all the three groups of microorganisms isless common because concentration methods to recover bac-teria rely heavily upon species-specific antibodies and me-dia enrichment. For sequence-based detection, amplificationand/or further magnetic bead based enrichment of the targetsequence is also an option (Chen and Griffiths, 2001). Thesuccess of this step depends upon the availability of suitableprimers/probes and the absence of inhibitory materials in thesample matrix. Overcoming technical difficulties as well asimproving speed, size, simplicity, and economics of sampleconcentration and target enrichment are key areas that willbenefit from renewed research interest.
Detection limit and quantification of abundance are alsoimportant issues in developing PDTs. Sequence-based de-tection of a few pathogens in the presence of a large numberof harmless microorganisms in an environmental matrix isa challenging task due to the use of miniaturized platformswith limited quantity of probes. At present the lower detec-t nds NAam buta ncen-
tration and target enrichment do serve as key mechanismsto improve the detection limit (Chen and Griffiths, 2001;Safarikova and Safarik, 2001; Sturbaum et al., 2002; Bopp etal., 2003; Kuczynska et al., 2003). However, concentrationmay also reduce signal intensity due to loss of target material.Signal amplification by improvements in methods for targetlabeling (Yguerabide and Yguerabide, 2001; Bao et al., 2002)and in the synthesis of microarray probe density and fidelityare some of the other areas that may improve detection limit.The overall improvement achievable by an appropriate com-bination of the existing protocols is expected to be insufficientfor the current needs.
Quantification of the detected targets on microarrays hasreceived the least attention because most of the traditionalapplications of this technology depend upon the ratio of thesignals obtained for two samples and absolute quantificationwas seldom needed. For parallel microbial detection, abso-lute quantification of the target microorganisms is a necessaryobjective in many cases. Two approaches to accomplish this,one based on the dissociation curve of the probe and targetduplex (El Fantroussi et al., 2003) and the other using stan-dard mixtures to obtain ratios, are currently possible and havereceived limited attention for developing PDTs.
Some additional complexities that may be consideredspecific to microarray-based methods for parallel microbiald (i)s in-c st ass sta-t tives,
ion limit for a target organism using DNA microarrays ahort oligonucleotides probes is approximately 1% by Dbundance (Denef et al., 2003). This obviously is inferior toany technologies that provide much better sensitivityre not as high throughput as microarrays. Sample co
etection include but are not limited to the following:train-level resolution in probe design, (ii) the ability toorporate sequence information for new threats of intereoon as it becomes available, (iii) data analysis requiringistical knowledge about false positives and false nega
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 671
and (iv) the cost of development for new chip designs whenwarranted.
In the following section, a list of selected microorganismsrelevant to biodefense as indicated by various federal agen-cies is presented along with the information about why theyare important. The sections that follow include a descriptionof gene sequence databases useful in their detection, someof the microarray technologies that are available to developparallel microbial detection tools, and the research needs todevelop PDTs for biodefense.
2. Microorganisms relevant to biodefense
Table 1lists selected examples of microorganisms that areof concern to human health along with the associated con-cerns and matrices. Various federal agencies including theCenters for Disease Control and Prevention (CDC), UnitedStates Environmental Protection Agency (US EPA), UnitedStates Department of Agriculture (USDA), United State De-partment of Health and Human Services (HHS), and Animaland Plant Health Inspection Service (APHIS) have preparedan extensive list of such microorganisms (available throughtheir respective websites) due to their impact on human andanimal health and agriculture. None of these lists shouldbe considered exhaustive because information about harm-
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ful microorganisms is continuously growing. The approachadopted by various agencies to manage the threats from thesemicroorganisms depends upon their mandate, health risk, andeconomic considerations. The CDC groups all microorgan-isms and the associated toxins into three priority areas namedA, B, and C, according to their threat level and uses com-prehensive species-specific monitoring during outbreaks asa management tool. The US EPA relies on indicator organ-isms as the most economical tool indicative of human fecalpollution in drinking water. In the food and agriculture indus-try, management of microbiological threats is accomplishedmainly through the detection of individual microorganismsmost relevant to a given category of food. Some of these or-ganisms or groups are ubiquitous and exist in the environmentdue to human and animal fecal pollution and in reservoirs thatrespond positively and negatively to different environmentalconditions (e.g.,E. coli, enterococci,Cryptosporidium, Vib-rio cholerae, and enteric viruses). Many of them are constanthealth threats and have been traditionally controlled in drink-ing water by monitoring indicator organisms or by using ap-propriate treatment technologies. Other microorganisms donot ordinarily exist in the environment, except in localizedendemic cases, and are more relevant to intentional releasescenarios (e.g.,Yersinia pestis, Bacillus anthracis). It is obvi-ous that areas experiencing endemic presence of a pathogenw
able 1elected examples of microorganisms important to human health
ontaminated water, soil, air Soil and water ofthe tropics
0–5
od, water Soil and water 1850 (425)
ood, water Wildlife, birds 2.1–2.4× 106 (500)ontaminated water Humans 500,000–850,
ter Human and fecalanimal waste
ter Human and fecalanimal waste
ill require extra attention to detect intentional release.
ater Food, waterter Human and
animal fecal waste
672 S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683
In addition to detection of microorganisms at the specieslevel, it is often critical to distinguish the strain(s) present forsource tracking and attribution (e.g., in the case ofBacillusanthracis, E. coli, andBacteroides). Similarly, determiningthe genotypes ofCryptosporidium(Straub et al., 2002) andtheir viability may be more relevant than knowledge of itspresence in a water source using signature gene sequences.PDTs are especially useful for such applications. For exam-ple, a better strain level resolution may be achieved by usinggenes related to virulence and other functions. Viability canbe determined by targeting the ribosomes or the mRNA. How-ever, these steps do add to the chip complexity, validation, anduse protocol.
As mentioned before, the microbiological quality of wateris regulated by the U.S. Environmental Protection Agency. Itmandates monitoring of indicator microorganisms, mainlyE. coli and enterococci, and the use of appropriate treatmenttechnologies. These groups are commonly used as indicatorsof human fecal pollution and some of the members are alsopathogens. Over time it has been shown that none of these in-dicator species, as tested by using many commercially avail-able kits, specifically indicate human fecal contamination.These indicators can also emanate from domestic animals andwildlife, and some may even grow naturally in certain envi-ronments. The need to improve the tests for detecting existingi at arebs )i
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only 16,277 sequences in the year 2000. Because the 16S ri-bosomal RNA gene is used to classify the living world intoa phylogenetic framework and has become a cornerstone forbacterial identification (Cole et al., 2003), it is the most com-mon target gene for developing detection tools (Loy et al.,2003). Besides phylogeny, there are many other advantagesin using the 16S rRNA gene for detection. Its phylogeneticgroup-based amplification is possible due to the extensivedatabase and conserved gene sequence regions. Because asingle cell contains between 10,000 and 60,000 ribosomesunder different growth conditions (Commission on PhysicalSciences, 1999), its detection at the RNA level is also possibleavoiding the PCR and its biases.
There are some disadvantages also with the use of the 16SrRNA gene as a target for detection. Because the molecule isonly 1500 bp long and contains highly conserved regions, itcannot yield signature sequences for closely related strains.Therefore, microorganisms cannot be differentiated at thestrain level using the sequence variations in the 16S rRNAgene. Some further resolution can be gained by using the 23SrRNA gene but the database for this molecule is much smallerand experience in using it as a detection target is limited. Thecopy number of the 16S rRNA genes may also vary from onecopy to as many as 15 copies for various organisms, compli-cating the quantification of abundance (Klappenbach et al.,2 allc rRNAd ctureo
rv thea g usedf rainl envi-r andt po-s um-b st oft re isn nsiveg MBL).W hers( gions(t , un-l
ande basesf er ofs , sci-e inga rma-t at-m sa a-n tibi-
ndicator organisms as well as to explore new species thetter indicators of human fecal pollution (e.g.,Clostridiump.,Eubacterium, Bifidobacteriumsp.,Bacteroidessp., etc.s evident.
. Genetic sequence databases for probe design
Once a list of microorganisms relevant to a niche ialized, the next step in developing PDTs is the selectioene targets to design probes. The objective is to detect
he organisms included in the list at the stated resolutionone outside the list. This later requirement of high spcity is of course as good as the sequence databasesany genes have been used as targets for detecting clos
ated microorganisms including the small subunit ribosoNA (16S rRNA for the prokaryotic microorganisms and 1
RNA for the eukaryotic microorganisms), intragenic spaegion (between the 16S and 23S genes), and genes foence factors and functions specific to a given guild or gf microorganisms. Examples are: shiga-like toxin genesE.oli, nitrite reductase (nirS) in denitrifiers, genes contributino antibiotic resistance in pathogens and commensals,rpoBene responsible for encoding�-subunit of the RNA polyerase, andhsp70 for Cryptosporidiumgenotyping (Straubt al., 2002).
atabase for ribosomal RNA genes, maintained by the Cor Microbial Ecology (CME) at Michigan State Universiurrently contains more than 97,000 sequences compa
.-
001). Large variations in signals strengths with very smhanges in probe sequences are also reported for 16Setection and have been attributed to the secondary struf RNAs.
Alternative gene targets (e.g.,gyrB, rpoB, and genes foirulence or a specific function) do not have some ofbove disadvantages. Hence, they are increasingly bein
or detection and especially for differentiation at the stevel. Because the isolation of these genes from theonment is more complicated than for the 16S rRNA,he driving force to collect the sequence (phylogeneticitioning) is not as strong as for the 16S rRNA, the ner of sequences available for these is limited. For mo
he genes related to virulence or a specific function, theo dedicated public database (except for the compreheene sequence databases, e.g., GenBank, DDBJ, and Ehenever it exists due to the efforts of individual researc
e.g., RISSC: database for 16S–23S RNA gene spacer reGarcia-Martinez et al., 2001), gyrB (Watanabe et al., 2001),he number of sequences is limited and its maintenanceess funded from large centralized source, is poor.
With the extraordinary growth in sequence availabilityase in sequencing, the existence of specialized data
or genes related to a specific function and the numbequences in them is expected to grow. For examplentists at the Center for Microbial Ecology are developn antibiotic resistance (AR) gene database. This info
ion is critical if we consider that the most common treent for anthrax is CiprofloxacinTM for which AR genere known to exist with no technical barriers for their mipulation. Furthermore, the growth and spread of an
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 673
otic resistance genes among pathogens and commensalsis considered a major new challenge in controlling infec-tious disease (Isaacson and Torrence, 2002). Similarly un-der a project sponsored by the US Environmental ProtectionAgency (EPA), researchers at Michigan State University aredeveloping a virulence factor activity relationships (VFAR)database to provide a means of predicting the degree of healthhazard for a microorganism from its distinguishable and viru-lent gene sequences (National Research Council, 2001). Suchdedicated databases are expected to be helpful in developingclasses of virulent genes that may be tracked irrespective oftheir host. PDTs using such sequences may also be help-ful in identifying new pathogens that are present in the en-vironment but have not yet been identified or isolated. Atpresent, databases for genes related to a specific function,when available, are in their infancy. They are being developedat the pace permitted by resources available to individual re-searchers. It is well known that probes developed only a fewyears ago and considered specific to a given microorganismhave become less specific mainly due to the availability ofmore sequences in a database related to the target organism.Hence, probes included on PDTs may need to be updatedperiodically.
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Probe length, array density, flexibility (i.e., ability tochange the probe design during its development and use),stepwise yield of probe synthesis, development cost, and costper chip are some of the key parameters that determine thesuitability of a microarray platform to develop PDTs. Allplatforms can synthesize or print probes suitable for PDTs,which is generally 18- to 30-mer. If needed, some are capa-ble of synthesizing longer probes (e.g., Agilent, NimbleGen,Xeotron). All have the required density to address the needsfor a given niche. Affymetrix has higher probe densities butit is more expensive and less flexible due to the use of masks.NimbleGen is also a high-density array (393,000 probes) butis only available as a service combined with synthesis, hy-bridization, and data analysis. Agilent provides glass slidetechnology commercially at a price that is comparable toother commercial platforms. In-house printed glass slide ar-rays, although more expensive and cumbersome to develop,are the most cost effective during use. Revision of probesis possible but requires careful tracking of the synthesizedprobe stock. Febit and PamGene are more recent systems.Febit combines in situ array synthesis, hybridization, scan-ning, and data analysis in the same equipment. PamGenearrays are known for their fast hybridization protocols, com-pleting the process in less than one hour. Xeotron’s in situsynthesis chip platform is also a recent invention (Gao et al.,2 f thel le inc -t elopP omeD ting.T ignc uchc erich o bei vicesc
5
tar-g ides
. Microarray platforms available for theevelopment of PDTs
Major biochip synthesis platforms available today toelop PDTs include in-house printed glass slide arrays (Wangt al., 2002a, 2002b; Relogio et al., 2002), immobilized gelad system (Liu et al., 2001; Barsky et al., 2002; Urakat al., 2002; El Fantroussi et al., 2003), Affymetrix (Wilsont al., 2002a, 2002b), Agilent, NimbleGen, Illumina, aneotron (Hashsham et al., 2003). This list is not exclusive an
epresents only platform types that either were used inished research related to products resembling PDTs orknown interest in developing PDTs. Other microarray p
orms may be equally useful for this purpose. Each mayome advantages and disadvantages associated withxcellent reviews focusing on the main features of mo
hese technologies are plentiful. Hence, the main featurevant to the development of PDTs are listed inTable 2.
able 2ey aspects of oligonucleotides biochip synthesis technologies for pa
Parameter Affymetrix NimbleGena Agile
. Probe length 20 (30)b 70 (85) 60 (
. Array density 500000 393000 22
. Stepwise yield 90+% 97.5% 93
. Flexibility + +++++ ++
. Development cost ++++++ + ++
. Cost per chip ++++ ++++ ++a Available as a service only.b Most common probe length (current maximum possible length).c Not available at present.
001). It has a probe density of 7963 features, is one oeast expensive for developmental work, and comparabost to other systems during use.Fig. 2shows an in situ synhesized biochip from Xeotron that we are using to devDTs for pathogen detection, source tracking, whole genNA and expression analysis, and community fingerprinhe key feature of this platform is its flexibility. Probe desan change from one chip to another without incurring most. The chip, a microfluidic device using modified genybridization chemistry and detection protocols, can als
ntegrated with other automated sample processing deurrently under development.
. Issues related to the development of PDTs
Preparing a list of microorganisms, selection of geneets, and choice of a suitable microarray platform prov
674 S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683
Fig. 2. A XeoChipTM containing 7963 probes synthesized in situ in microfluidic reactors (a), signals obtained for gene targets using a laser scanner (b). Only10% of the chip area is shown for clarity. (Photo:courtesy of Vincent Denef, Wouter Donckerwolcke, and Yoshinobu Matsamura).
the starting point to address the research issues related to thedevelopment of PDTs. Many of these issues are highlightedin Fig. 1. Some that are relevant to all platforms are describedbelow in more detail including: (i) probe design, specificity,and false signals, (ii) validation, quantification, and detec-tion limit, (iii) sample concentration, (iv) selective target am-plification and enrichment, and (v) data analysis tools forPDTs.
5.1. Probe design, specificity, and false signals
The accuracy of detection of PDTs will obviously dependfor a part on the quality of the oligonucleotide sequences onthe microarray. Three major criteria are considered duringoligonucleotide design. First, the sequences used on the chipshave to be specific to their respective targets to avoid anycross-hybridization. Second, these oligonucleotides should
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 675
not fold to form stable secondary structures that may com-pete with the probe during hybridization. This is especiallytrue when naturally folded sequences (rRNA) are targeted.Finally, these oligonucleotides should have consistent ther-modynamic properties (i.e., melting temperatures) across thechips to behave similarly during hybridization.
Probes targeting the 16S rRNA to detect a single microor-ganism or a group of microorganisms are mostly short, 18–25nucleotides in length. Generally, the longer the probe the lessspecific it is due to chances of cross-hybridization with otherclosely related target sequences and decreased destabilizingeffect of a single mismatch in a long sequence during hy-bridization (Kane et al., 2000; Kaderali and Schliep, 2002).Probe lengths for genes related to a specific function may besomewhat longer, especially if the gene sequence is uniqueand detection of single nucleotide polymorphisms that maybe present in different strains is not an objective (Bekal et al.,2003). Probe design for PDTs can be accomplished by oneor more of the following four approaches: (i) by designingprobes for individual microorganisms or a phylogeneticallyrelated group using software commonly used for 16S rRNAprobe design, (ii) by tiling the target into the maximum num-ber of possible n-mers, (iii) by finding all the unique 18- to30-mers in a set of target gene sequences, using bioinformat-ics approaches (Zhang et al., 2002), and (iv) by first identi-f resta m.
phy-l r, itsa cessf e isA pedp gefe ngd sid-e pendo ene.A e an-ae inu-o p tod peri-m tion.T elatedt ret or al mustb er org
ersi oachi tar-g robesf theu are o
recent origin (Li and Stormo, 2001; Kaderali and Schliep,2002; Pozhitkov and Tautz, 2002; Rahman, 2002; Rouillardet al., 2002; Zhang et al., 2002; Matveeva et al., 2003), nouser-friendly software tools exist to accomplish the task. Thealgorithms, however, are generally open source and availablefrom the respective authors. This approach is most usefulwhen the number of organisms to be targeted is large or alarge number of probes are needed for a small set of microor-ganisms. This approach is more likely to be used for probestargeting genes of a functional category. This is due to the in-sufficient number of sequences for genes related to a specificfunction and their unavailability in a single database simi-lar to RDP-II. Tools for exploring primers for their universalamplification and for detection are not well developed. Cur-rent approaches use mostly manual collection of the relatedsequences for a gene target related to a specific function anduse various alignment tools to identify regions of gene se-quences. This yields probes specific to a single or group ofmicroorganisms. For the hybridization data shown inFig. 2,the design of probes for the detection of antibiotic resistancegenes involved identifying and collecting all the sequences ofAR genes from various databases, and using ARB, GeneDoc(Nicholas et al., 1997), and Primrose for various steps in theprocess of probe design.
The fourth probe design approach is conceptual at presenta roor-g niquec eseu T ist atures g mi-c r ab-s own.T ected.S lencefR wp son-a tifya ed mi-c stab-l 2;R de-t s isa ther ,2 enti thep s be-c lesstT le.I tionsa witha ssi-b mul-
ying unique genes among all the microorganisms of intend then finding the unique signatures for each organis
The first approach has been mostly used for designingogenetic probes targeting the 16S rRNA gene. Howevepplication to other types of genes has been shown suc
ully. The software most commonly used for this purposRB, as evident by a large database of individually develorobes (Loy et al., 2003). Primrose, a user-friendly packa
or PC users has also been recently developed (Ashelfordt al., 2002). The tiling approach simply involves breakiown the target gene into a number of n-mers without conration for conserved or unique regions. It does not den preliminary screening of non-unique regions in the glternatively, the 16S rRNA gene database can also blyzed for all unique short oligonucleotides probes (Zhangt al., 2002). Such a set of unique probes that is contusly updated may be quite useful in keeping PDTs uate because it could be used to theoretically and exentally determine the impact of new sequence informahis approach is extendable to other gene sequences r
o a specific function. A limitation of the existing softwaools is their inability to design probes simultaneously farge number of distantly related targets. Probe designe attempted one by one for each phylogenetic membroup.
Bioinformatics approaches for identifying unique n-mn a set of genes, differ from the above probe design apprn the sense that they do not require identification of theet organisms prior to probe design. Instead, suitable p
or the identified microorganisms can be chosen fromnique signature set. Since most of these approaches
-
f
nd should be treated as such. Every pathogenic micanism is expected to have a set of genes that are uompared to all other microorganisms. Identification of thnique genes, using established tools, such as BLAS
rivial. Whether these unique genes also contain signequences useful for the detection of the correspondinroorganisms is not yet explored. Also, the presence oence of these genes in unknown genotypes is not knhe existence of signature sequences, however, is expimilar approaches have been proposed to develop viru
actor activity relationships for microorganisms (Nationalesearch Council, 2001). Sequencing of important neathogens is not considered a bottleneck within a reable timeframe. Thus, it is theoretically possible to idenset of unique genes that encompasses all the sequencroorganisms, design gene specific probes using well eished software tools (Li and Stormo, 2001; Rahman, 200ouillard et al., 2002), and use them as target genes for
ection. When DNA or RNA from isolates or enrichmentvailable, identification of the pathogen will be similar toesults obtained by Wang et al. with a virus chip (Wang et al.002a, 2002b). However, if the suspected target is pres
n a background of large amounts of community DNA,roblem of detection limit needs to be addressed. This iause, any single gene will generally constitute muchhan 1% by weight of the total community DNA (Cho andiedje, 2002) and therefore, will not be easily detectab
f many such genes with varying sequences and funcre selected as targets for microarrays, amplificationuniversal primer will not be an option. It may be po
le, however, to selectively enrich these targets using
676 S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683
tiple capture probes on magnetic beads (Chen and Griffiths,2001).
The primary requirement of probe design specific to a sin-gle organism is that it must be able to specifically detect thetarget gene in a mixture of other genes without cross reactivityto other sequences from closely related strains. Specificity isgenerally achieved through a combination of theoretical anal-ysis and experimental verification. All the above probe designapproaches, except the tiling strategy, carry out some theoret-ical analysis to yield a list of signature sequences specific tothe intended target. However, theoretical analysis alone doesnot reliably predict the probe behavior during use. This is dueto gaps in knowledge related to the thermodynamics of probehybridization with its target on various substrates and undervarying buffer conditions. The theoretical analysis assumesa certain concentration of targets, determination of which isone of the objectives of a PDT. Because the target concentra-tions and their relative abundance are unknown and numer-ous, it is often treated as incalculable. Hence, experimentalvalidation of probe behavior and optimization of hybridiza-tion conditions is necessary. Optimization is a difficult andcumbersome task even when one or a few microorganismsare targeted. With PDTs, the problem gets amplified signif-
Frg(sr
icantly, since a single hybridization protocol must now besuitable for all probes. One approach, that we have adoptedfor high throughput experimental screening of probes, is theuse of heat maps, displayed inFig. 3. The theoretically de-signed good probes and their mismatch counterparts can beclustered in order to distinguish promising probes from badprobes. Wash temperatures that show the highest discrimi-nation between perfect match and mismatch are also readilyvisible for each probe. Ideally, the theoretical tools shouldbe able to predict this behavior under various environmentaland target mixture scenarios.
In spite of careful probe design and experimental valida-tion, there is always a chance for observing positive signalswhen the intended target is absent (false positive) or missingdetection when the target is present (false negative). Possiblereasons for this error include: (i) more than 97% of the to-tal microbial diversity is still unknown, (ii) knowledge aboutthe thermodynamics of probe hybridization on the supportis poor, and (iii) protocol optimization is economically fea-sible for only a few types of matrices and microorganismmixtures. High false positives and negatives are extremelyundesirable and must be accounted for in validation. Anal-ysis of false positives will require more care for PDTs with
ig. 3. Heat map showing sensitivity and specificity of a set of probes. Promatio (red color) when hybridized with the intended target DNA, but a low sireen color) when hybridized with non-target DNA (probes 1–25). Some proprobe 18–25). Bad probes show a low specificity and/or a low signal intensitpecificity (probes 30–34) or only apparently a high specificity (probes 35–3eferred to the web version of the article.)
ising probes have a relatively high signal intensity (red color) and a high PM/MMgnal intensity (black or only low red color) and a low PM/MM ratio (black orbes only have a small temperature range where they show a good PM/MM ratioy (probes 26–29). Some probes with high intensity have a very low specificity, no6). (For interpretation of the references to color in this figure legend, the reader is
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 677
specialized probe designs to accommodate genetic variationin the target populations so that the range of natural and arti-ficially manipulated variants can be detected. Understandingthe background indigenous populations, is also critical to thereliable detection of the infectious agent. This implies thatflexibility of PDTs to allow revisions and upgrades both inprobe design and data analysis based on the availability ofnew sequences is as critical as the initial development itself.
5.2. Validation, quantification, and detection limit
It is conceivable that probes can be designed for most mi-croorganisms in a given database. Synthesis of a PDT con-taining these probes is also trivial, given the technologicaladvances. Validation, however, to determine the usefulnessof PDTs, is the most expensive and the rate-limiting step(Wilson et al., 2002a, 2002b; Urakawa et al., 2002; Wanget al., 2002a). Ideally, a PDT must be validated for eachof the microorganisms individually and in mixtures of vari-ous targets and background microorganisms expected to bepresent in a given sample. It must also address the issue ofclosely related strains that are not harmful but present in thebackground matrix. The protocol(s) must work for all typesof matrices. Validation, accompanied by careful choice ofprobes, may also enable PDTs that are useful for discoveringn opedg n 140vv sesn ch ana igna-t sentiT alsob un-c dgea
s a 0o orre-s eena flu-o minedb avior,a obec t nec-eet ratioa sions nt int ativeo plec ded.I ctedt e ans mix-
ture of microorganisms (at the cellular, DNA, RNA, labeledDNA, or labeled RNA level) against which all unknown sam-ples must be compared, but the utility and ease of use of suchmixture(s) as standards is yet to be demonstrated. Until then,it will be assumed that the use of single sample hybridizationwill be more common for PDTs.
Considering quantification is important, validation mustbe considered at two levels: validation of the presence or ab-sence and validation of the abundance. The first level onlyindicates whether the target is present above or below anarbitrarily specified “detection limit”. All signal intensitiesbelow the detection limit, including those obtained with notarget present, are labeled as absent or below detection limit.All signal intensities above the detection limit are termed aspresent. The second level of validation provides abundancein addition to the presence and absence. Quantification ofabundance is important because it is not sufficient to knowwhich organism among the known threats is present. It ismuch more informative to know how many (or how much) ofthe known threats are present. Relative abundance, obtainableusing mixtures of standard targets and two-dye experimentssimilar to relative gene expression studies, are obviously lessuseful for biodefense-related applications. Whether microar-rays will be able to provide quantification of abundance of agiven target in single dye experiments is still being explored.
an-t ther e. Ana tionb tiont robeo hy-b f thed ei f ag rioust 3U tiono Thet robei
ltingt st-N 2B a( dy-n ghborm ationo ter isa cal-c e pur-p chipa uch ad e ofa tiona als.
ew threats. Consider, for example, the recently devellass slide arrays containing 70-mer probes for more thairuses (Wang et al., 2002a). Although, it was individuallyalidated for only a handful of viruses, new types of viruot represented on the chip were also detectable. Supproach may be extremely useful in cataloguing the s
ure of background microbial communities or viruses pren the environment on a global scale (Anderson et al., 2003).he more difficult need to address is that validation muste revised with new information and reflect the level ofertainty in the positive signals due to the lack of knowlebout the unknown microorganisms.
Hybridization signals for probes are never obtained ar 1 event (indicating an absence or presence) for the cponding targets. A continuum of signals is obtained betwminimum that is close to zero, given by the backgroundrescence of the array substrate, and a maximum detery the dynamic range of the laser scanner, probe behnd the amount of target. A higher signal for a given prompared to another probe giving a lower signal does nossarily mean a higher abundance for the first target (Note:.g., the signal difference of Probe A and B inFig. 3, targeting
he same gene). This was one of the reasons for using approach in applications of microarrays to gene exprestudies. There, an increase in the ratio of cDNA presewo samples (hybridized on the same array) was indicf an increase in the relative amount of cDNA in one samompared to another. Absolute quantification was not neendividual samples to be hybridized on PDTs are not expeo use ratios because the samples are separated in timpace. It is possible to imagine the use of a standard
d
If validation is carried out at a single temperature, quification of relative abundance is only possible by usingatio approach and standard mixtures as mentioned abovlternative to this approach is to determine the hybridizaehavior of the probe and target as a function of hybridiza
emperature, i.e., obtain a dissociation curve for each pn the platform of interest. For protocols using membraneridization, obtaining such a curve was always a part oetection process (Zheng et al., 1996). A dissociation curv
ndicates what fraction of the total target DNA or RNA oiven microorganism remains bound to the probe at va
emperatures (Liu et al., 2001; El Fantroussi et al., 200).nless this fraction is known for each probe, determinaf abundance for the corresponding targets is not likely.
emperature at which 50% of the total signal for a given ps lost in solution is called the dissociation temperature (Td).
Dissociation temperature is loosely related to the meemperature (Tm), calculated theoretically using the Neareeighbor model (Santalucia, 1998; Rouillard et al., 200).reslauer et al. (Breslauer et al., 1986) and Santaluci
Santalucia, 1998) have extensively studied the thermoamic parameters that are needed for the nearest neiodel. One of the parameters is the total molar concentrf the annealing oligonucleotides. Because this paramen unknown to be determined by the PDT, all theoreticalulations use an assumed constant value for comparativoses. Hence, determination of a dissociation curve on at least during the developmental phase is important. Sissociation curve combined with the dissociation curvmismatch probe could enhance the reliability of detec
nd help minimize false positive and false negative sign
678 S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683
Fig. 4. Non-equilibrium melting curves for two 20-mer probes (A, B) specific to Shiga toxin 1A (perfect match, PM) and their mismatch probes (MM, singlemismatch at position 10). Genomic DNA (gDNA) ofE. coli Sakai andE. coli K12 was labeled with Cy3 and Cy5 respectively and hybridized to a Xeotronchip at 20◦C for 16 h. Melting curves were then generated by washing the chip for 2.5 min in 2◦C steps and scanning after each wash. (�) Sakai gDNA boundto PM; (�) Sakai gDNA bound to MM; (�) K12 gDNA bound to PM; (�) K12 gDNA bound to MM.Td values were inferred from melting curves. Note thedifferent scale of the y-axis in A and B.
Non-equilibrium melting curves are a variation of the equi-librium dissociation curves discussed above and are obtainedby washing an array at increasing temperatures after com-plete hybridization at a low temperature. Examples of non-equilibrium melting curves obtained by hybridizing labeledgenomic DNA of twoE. coli strains (Sakai and K12) toa microarray with 20-mer probes targeting the Shiga toxin1A (stx1A), are shown inFig. 4. The Sakai strain containsthe stx1A gene, whereas the K12 strain does not. As ex-pected, both probes A and B yield the strongest signal forthe Sakai strain bound to the perfect match (PM) probe. Al-though, targeting the same probe and having similarTm val-ues, the two probes vary in signal strength (note the differ-ent scale ony-axis). With the Sakai strain, probe A showsabout five times higher signal than probe B. At lower tem-peratures, even the non-specific signal of probe A obtainedwith the K12 strain is higher than the specific signal of probeB obtained with Sakai strain. In this case, known concen-trations of known genomic DNA were hybridized, makingthe interpretation simple. However, if an unknown sampleof unknown concentration yields a signal like the one ob-tained here from K12 with probe A, interpretation will bemore difficult. If only one temperature point is taken into ac-count, e.g., 32◦C, it is difficult to determine if the signal isfrom a specific target at low concentration or from a non-s ider-a df y oft isP 12s
ctionl edf thea that
is below the detection limit can easily become the dominantorganism in a system. Most of the natural pathogen outbreaksare indicative of this major difference. Moreover, for manypathogens the minimum infectious dose is quite low. Hence,continuously improving the detection limit for all pathogensis critical. At the DNA level, the problem of detection limit fora single gene extracted from a mixed microbial communityis qualitatively similar to the problem of detecting rare mes-senger RNAs in human brain cells. The Human genome hassome 30,000 genes. In a typical cell, expression of only 300 orso genes is dominant. The expression of the remaining genesis rare but presumably many of them do influence the overallfunctioning of the cell, making their expression also impor-tant to measure. The current detection limit of microarraybased approaches for gene targets present in mixed commu-nities is much less than desirable (Denef et al., 2003). Usingnitrite reductase (nirS), naphthalene dioxygenase (nahA), andEscherichia coliO-antigen biosynthesis gene, Cho and Tiedje(Cho and Tiedje, 2002) demonstrated that the lower detectionlimit on glass slide arrays using large PCR amplified productsis 1–10 pg of the model gene (Cho and Tiedje, 2002). Assum-ing a microbial genome of 5 Mb forPseudomonas stutzeri,one copy ofnirSgene inP. stutzeri, and 10 pg of lower detec-tion limit, they highlighted that 50 ng ofP. stutzeriDNA mustbe present in the 1�g of total DNA hybridized to the glasss ismm thisl ofm
etect1 of2 und.I forh ag-n smsi are
pecific target, since signals for the PM probe are consbly higher than for the MM probe. TheTd values obtaine
rom melting curves are able to determine the specificithe signals. Whereas,Td for the MM probe for the Sakatrain is clearly lower than for the PM probe, bothTd forM and MM probe have the same low value for the Ktrain.
The issue of absence, low abundance, or below deteimit for biological agents is different from that facor chemicals. Due to the potential for growth underppropriate environmental conditions, a microorganism
lide. This implies that the DNA of the target microorganust constitute at least 5% of the total DNA. Obviously,
imit must be improved by at least two to three ordersagnitude.As a desirable goal, microarrays must be able to dcell of the target microorganism in a background
000–20,000 other types of microorganisms as backgron addition, the total amount of sample DNA neededybridization must also be reduced by 1–2 orders of mitude. Thus, in addition to detecting many microorgani
n parallel, PDTs need to attain detection limits that
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 679
comparable to existing methods for the detected pathogens.The key areas that have the greatest potential are preferentialenrichment using magnetic beads and indirect and directsignal amplification followed by enhancement in quantityand quality of probes. Recently, Denef et al. (Denef et al.,2003) have improved this detection limit to approximately1% of the total population based on DNA abundancefor glass slide arrays using short oligonucleotides probesby better labeling strategies. While, selected examplesexist that demonstrate ways to achieve better detectionlimits (Yguerabide and Yguerabide, 2001; Bao et al.,2002), the need for further improvement is universallyrecognized.
5.3. Sample concentration
All microbial detection methods need approaches to bringthe target microorganisms to the testing platform. It is appar-ent that all the promises of a PDT depend upon successfultransfer of the targets from a large volume of matrix to the chipin a manner that does not inhibit the hybridization process.Microorganisms suspected to be present in large quantities ofmatrices (1–100 m3 of air, 1–100 L of water, 1–100 g of food,and variable quantities of other media) must be transferred to�L volume of liquid before their detection. Because resultsf mplet s fors y toh isitea lem-a nedm oachf Ford edw byr iont n,a tion.M tlyca orea s,g useo ent.F thisp HVfi na meo e EPAf1( s inp me,fi msa virusp ristics
are all important parameters determining their recovery.Many of these existing membrane filtration approaches canbe evaluated for their ability to concentrate all types ofmicroorganisms in parallel. Portability of the instrument, per-formance, cost and reusability of filter, and recoverability ofthe concentrated organisms are the main issues that must beaddressed. The concentration step for water may be followedby enrichment but for other matrices (food, soil, body fluid,tissue, etc.), direct enrichment by selective media is morecommon.
5.4. Selective target amplification and enrichment
If traditional means of sample concentration fail to at-tain a desired minimum for detection limit, alternative ap-proaches to concentrate the target organisms or the targetgenes are needed. These include amplification of the tar-get gene using universal primers, biological enrichment, i.e.,growth of the target microorganism on selective media, andenrichment using capture probes on magnetic beads. Thefirst approach is most useful for the 16S rRNA genes be-cause of the availability of universal primers. The secondapproach is time consuming and dependent on the recover-ability and selectivity of the enrichment media. The third ap-proach is quite promising but still under development. Mosto soci-a hancet tionh d be-f nc-t rsalp
is ane ecificf d re-g selfi wnc rticleso ithc targetm uper-p ut don ved.S tech,A kerD ypeso ificp iza-tI ca-t andr nt re-so liversc to bed
rom PDTs will only be as good as the concentrated sahat is put upon them, it is critical to ensure that methodample concentration exclude material that is inhibitorybridization or plugs the membrane filters before a requmount of water has passed. For air, which is least probtic, membrane filtration followed by transfer of the retaiaterial to a liquid medium has been the preferred appr
or developing a number of microbial detection devices.rinking water requiring filtration of up to 1000 L of finishater, concentration by membrane filtration followed
ecovery is also relatively simple. Membrane filtratechnologies (microfiltration, ultrafiltration, nanofiltrationd reverse osmosis) are now mature for water purificaicrofiltration and ultrafiltration membranes efficien
oncentrate larger microorganisms likeCryptosporidiumndGiardia but for viruses, charged membranes are mdvantageous (Scott et al., 2002). For surface waterenerally a lower volume of 1–100 L is sufficient becaf the higher concentrations of microorganisms presilters and protocols are constantly being optimized forurpose, yielding 40–80% recoveries (e.g., Envirochekltration system with 1�m pore size from Pall Corporationd Filta-Max system from IDEXX Laboratories Inc.). Sof these approaches have also found acceptance by th
or routine monitoring ofCryptosporidium(EPA Method622 (US EPA, 2001a)) and/orGiardia (EPA Method 1623US EPA, 2001b)), and viruses. However, large variationerformance still exist depending upon the sample volultration flow rate, and the number of microorganisnd particles present. For viruses, the nature of thearticle, water characteristics, and membrane characte
f the enrichment and amplification strategies carry asted biases but they are also indispensable tools to en
he detection limit. Selective media and PCR amplificaave been in use for decades. However, as mentione
ore, amplification of gene families related to a specific fuion may be problematic due to the lack of suitable univerimers.
The use of capture probes coated on magnetic beadsmerging technology used to enrich genes related to a sp
unction or genes that do not possess known conserveions for universal amplification. Magnetic separation it
s not new but its application to molecular biology has groonsiderably in the last decade. Super-paramagnetic paf 1–5�m and colloidal beads of 50–200 nm, coated wapture probes can be used to selectively enrich theolecules and separate them using a magnetic field. Saramagnetic particles are attracted to a magnetic field bot exhibit any residual magnetism once the field is remoeveral companies (e.g., Dynabeads from Dynal Biovibeads from Aviva Biosceinces, and ClinProt from Brualtonics) now produce beads for enrichment of many tf biological molecules, including DNA, RNA, and specroteins. The thermodynamics of capture probe hybrid
ion on beads is also being addressed (Stevens et al., 1999).ntegration of the steps involving concentration, amplifiion, and enrichment with detection to obtain a simpleeasonable protocol and device is the goal of many curreearch projects (Straub and Chandler, 2003). The integrationf many of these strategies to PDTs in a manner that deonsistent performance for the matrices of interest is yetemonstrated.
680 S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683
Fig. 5. Major steps and challenges in probe design and data analysis for PDTs.
5.5. Data analysis tools for PDTs
Numerous tools for data processing from microrarrays ex-ist but were originally developed for gene expression analy-sis, not microbial detection. New modules making the call forthe presence/absence or abundance have to be developed andadded to or used in conjunction with the current data analysistools. Statistical and uncertainty analysis tools must also beincorporated to reflect the areas where knowledge gaps exist.Besides the traditional data analysis of microarrays, a num-ber of additional data analysis issues need to be addressedfor PDTs. They are related to one or more of the follow-ing: probe design approach, performance of probes duringvalidation, limited and constantly growing database, charac-teristics and abundance of the known microorganisms presentas background, hybridization behavior of the PDTs, sampleprocessing and enrichment effects, and analysis of dissocia-tion curves (if used).Fig. 5depicts these issues in the form ofa flowchart, starting from probe design to making a call forthe presence or absence of a given target. Gray boxes indicatefields where further fundamental research and developmentof powerful bioinformatics tools are needed. Data analysis forPDTs is closely related to probe design. High sensitivity andspecificity are fundamental characteristics of good probes.Several factors influencing sensitivity and specificity are welle e de-s cano rch isn ald reli-a illi ce ora falser2
icro-b
analyze all types of temperature dependent probe behaviorwill be extremely useful for developing PDTs. Ideally, melt-ing curves should be predictable based on the probe sequenceand then compared to the experimentally obtained curves.Moreover, if one or several unspecific targets are bound to aprobe in addition to the specific target, it would be desirableto deconvolute the observed combined melting curve into theunderling melting curves from the different targets. These re-sults could then be used in the interpretation of hybridizationpatterns and making the positive or negative calls with anassociated uncertainty. In an iterative approach, these resultsmight be used to refine the melting curve analysis, since cer-tain targets may cross-hybridize with closely related targets.
Another key aspect of data analysis for PDTs is the uncer-tainty associated with signal intensities that are closer to thebackground. Obviously, the signal intensities below an arbi-trary cutoff (most commonly the average background plusthree times the standard deviation for the background) willbe termed as background. To minimize false positive, this ar-bitrary cutoff may be set at a higher level. The signals muchabove the cutoff value are positive signals for the correspond-ing targets. However, signals that are barely above the cutoffvalue pose a problem. These signals may be a result of poorbackground, poor probe, or small abundance of good probe.In a mixed community, if the signals emanating from thet ces-s taintya ilityd ntialc
6t
eenf nce-
stablished and are generally incorporated in the probign strategy. However, hybridization behavior of probesnly partly be predicted by these factors and more reseaeeded (Matveeva et al., 2003). Redundant and hierarchicesign of probes is an essential component to increasebility. Consistently high or low signals for all probes w
ncrease the confidence level for a call about the presenbsence of a certain target. Inconsistent results indicateesults or binding of a previously unknown target (Liu et al.,001; Loy et al., 2002).
Dissociation curves are considered key in parallel mial detection (Urakawa et al., 2002). User-friendly tools to
arget organism happen to be in this region, it will be neary to find alternative approaches to enhance the cerssociated with the detection. It is obvious that probabistribution curves for various targets will be an esseomponent of data analysis.
. Strengths, weaknesses, and rate-limiting steps inhe development and use PDTs
The major strengths of PDTs are: the ability (i) to scror hundreds of pathogens together, (ii) to provide a seque
S.A. Hashsham et al. / Biosensors and Bioelectronics 20 (2004) 668–683 681
based resolution not feasible by using other methods, (iii) toenable high throughput source tracking, (iv) to serve as a toolfor exploring unknown bacterial pathogens and viruses, and(v) to track virulence factors irrespective of their host. It isparticularly advantageous when the suspect agent is unknownand screening must be done for a large number of microor-ganisms. It is also extremely useful when the suspect agentis new and traditional methods are not available to detect it(Wang et al., 2002a, 2002b).
Weaknesses of PDTs are, at present, its high cost, longtime of testing, complexity of protocols, and lack of avail-able systems that are validated for more than a few dozenmicroorganisms. The current cost of one chip, irrespective ofthe platform, runs into hundreds of dollars. It is not surprisingthat any parallel approach developed to screen for hundredsof microorganisms together may be more expansive com-pared to some well-established kits developed for a singleor a few microorganisms. However, the choice of whetherPDTs should serve as the high priced consultant in special-ized cases or as the security screen against most pathogensfor all needs related to air, water, food, and agriculture aroundthe nation is an important one. It affects the direction of PDTdevelopment, PDT cost, and the hopes associated with PDTs.Although, most of the traditional tests for many of the mi-croorganisms may also cost hundreds of dollars if used inp etingt lfillp rablec
oto-c t oft anal-y am-G et (B -s pro-t ationt re ustb
proaches for validation will be extremely useful. Other fac-tors limiting the pace of PDT development and use include:(i) availability of gene databases related to a specific functionand probe design tools for genes related to a specific functionsupported by sequencing of the genes useful in detection, (ii)lack of better predictive rules for probe design based on ther-modynamic considerations, (iii) lack of methods for sampleconcentration and enrichment of seemingly unrelated geneswithout the need to use universal primers, (iv) poor detectionlimits for a single gene in a background of many genomesin various matrices, (v) lack of information about the back-ground microbial populations complicating the detection ofpathogens, (vi) lack of approaches to decrease the testingtime and cost and enhance the ease of application of DNAarrays in a diagnostic setting, and (vii) lack of methods ofdata analysis that include statistical rules and uncertaintiesassociated with PDTs.
To address some of the above issues, more recent methodsare using multiplex PCR followed by microarrays to take ad-vantage of both techniques. One amplifies the signals fromvarious genetic elements of interest, and the other providesthe sequence resolution not achievable by PCR. Still othersare developing RT-PCR capabilities on the microarray itself.The combined strength of RT-PCR and microarrays may in-deed address issues related to the abundance and detectionl mayb ed byo esis,t nal-y an-t esst e onet ainstw ed.
R
A g for
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arallel and may take days to complete, there are compechnologies (e.g., real-time PCR (RT-PCR)) that may fuart of the services provided by current PDTs at a compaost.
At present, most microarray-based hybridization prols take several hours followed by a significant amounime for data analysis that is not as standardized as thesis of RT-PCR curves. Only one microarray platform (Pene) using a unique 5D-PulseTM mixing technique is abl
o complete the hybridization process in less than 1 hvaneuningen et al., 2001). Although, this platform is low denity, the fast kinetics demonstrate that modification inocols and mixing approaches may reduce the hybridizime considerably. The 7-min detection of bacteria (Belgradet al., 1999), using RT-PCR is a perfect example of what me set as a target for speed of detection.
Complexity of protocols refers to the required skill ofersonnel carrying out the test. At present, the overall pro
rom start to finish (e.g., for water) may involve the majof the following steps: sample concentration, DNA (or RNxtraction, target enrichment or amplification, labeling,ridization, signal amplification, scanning, and data anis. Most of the initial steps, which impact the hybridizatesults profoundly, are difficult to automate for complexrices. Air is an exception and considerable success haschieved in developing fully automated and multiplexed
ems for air. Although, PDTs are also amenable to automnd integration with other miniaturized devices, most ofurrent focus is on the development of the chip itself.
As stressed before, validation is the rate-limiting stepeveloping PDTs. Hence, alternative high throughput
imits faced by PDTs. In the long run, PDTs themselvese developed similar to computer chips, as parts to be usther devices integrating sample processing, chip synth
arget amplification, hybridization, scanning, and data asis. If all the known pathogens of interest, could be quitatively detected in parallel within a short time of say lhan 1 h at a total reagent cost of less than $100 (plus thime cost of the equipment), then it sets the standard aghich the performance of all the PDTs must be measur
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