Harmful Algae 19 (2012) 169–180

High resolution microarray assay for rapid taxonomic assessmentof Pseudo-nitzschia spp. (Bacillariophyceae) in the field

Maria W. Smith a, Michelle A. Maier a, Dominic Suciu b, Tawnya D. Peterson a,Tyler Bradstreet a, Jordan Nakayama a, Holly M. Simon a,*a The Center for Coastal Margin Observation & Prediction (CMOP), Oregon Health & Science University, 20000 NW Walker Road, Beaverton, OR 97006, USAb CustomArray Incorporated, 6500 Harbour Heights Pkwy, Suite #201, Mukilteo, WA 98275, USA

A R T I C L E I N F O

Article history:

Received 18 February 2012

Received in revised form 5 July 2012

Accepted 5 July 2012

Available online 14 July 2012

Keywords:

Deployable electrochemical microarray

Pseudo-nitzschia detection

ITS1 oligonucleotide probes

A B S T R A C T

Deployable methods facilitating rapid microbial identification provide opportunities to respond quickly

when pathogenic or toxin-producing organisms threaten water quality. We developed a microarray

assay to streamline identification of microorganisms in the field, focusing on the harmful algal bloom

diatom Pseudo-nitzschia. The assay employed electrochemical signal detection and a simplified protocol,

allowing identification of specific taxa onboard research vessels within 7 h of water collection.

Microarrays targeted the internal transcribed spacer (ITS1) region between the 18S and 28S ribosomal

RNA genes using 307 oligonucleotide probes. The probes, ranging from broadly specific to unique,

represented 118 Pseudo-nitzschia ribotypes from 15 species available at the time of assay development.

Hybridization signals from multiple probes for each target strain were integrated using a novel

algorithm for data analysis. Designated the ‘integrated sumscore data analysis’ (ISDA), the algorithm

used probe specificity metrics for signal integration, with uniqueness corresponding to higher probe

weight values. The integrated signals provided a ‘sumscore’ for each ribotype represented on the array,

indicating its presence or absence in the sample. The algorithm was ‘trained’ by comparison with data

from scanning electron microscopy, and cloning and sequence analysis of Pseudo-nitzschia ITS1 ribotypes

from 7 laboratory cultures and a complex environmental sample. The ISDA provided correct

identification and target sequences for all tested strains. Through design of custom probes (up to

12,000 on a microarray chip), this approach may be used to identify additional microbial taxa of interest

and provides rapid, reliable shipboard assays for basic research or water quality monitoring.

� 2012 Elsevier B.V. All rights reserved.

Contents lists available at SciVerse ScienceDirect

Harmful Algae

jo u rn al h om epag e: ww w.els evier .c o m/lo cat e/ha l

1. Introduction

Characterization and monitoring of harmful algal bloom (HAB)populations has received considerable attention in coastal regionsof the United States (U.S.) in recent years, motivated largely byeconomic and human health impacts. Aided by national researchinitiatives on Oceans and Human Health at NIEHS, NOAA and NSF,significant progress has been made in understanding bloomdynamics of specific HAB organisms through detection of cells andtoxins, and identification of environmental and genetic factorsinfluencing the growth and toxicity of blooms (Erdner et al., 2008).Yet factors controlling bloom dynamics and toxin production byspecies of the diatom genus Pseudo-nitzschia have proven to berelatively difficult to characterize. Blooms of this organism

* Corresponding author at: Oregon Health & Science University, School of

Medicine Department of Science & Engineering, Division of Environmental &

Biomolecular Systems, 20000 NW Walker Road, Beaverton, OR 97006-8921, USA.

Tel.: +1 503 748 1873; fax: +1 503 748 1273.

E-mail address: simonh@ebs.ogi.edu (H.M. Simon).

1568-9883/$ – see front matter � 2012 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.hal.2012.07.003

produce the potent neurotoxin domoic acid (DA) and are foundwidely distributed in coastal waters worldwide including those ofthe U.S., where they represent a significant health and environ-mental hazard (Grant et al., 2010). Although much valuableresearch on the organism has been reported (reviewed in Erdneret al., 2008), lack of distinct pigmentation and morphology,combined with inter- and intraspecific variability in toxinproduction has made defining conditions leading to toxicPseudo-nitzschia blooms particularly challenging. Although oftenpresent at relatively low abundance in waters of the open ocean(Hubbard et al., 2008; Marchetti et al., 2008), these organismsexhibit a rapid growth response and frequently dominatephytoplankton blooms responding to iron enrichment experi-ments (Lundholm et al., 2004; Marchetti et al., 2008). In addition,diverse, co-occurring toxigenic and non-toxigenic Pseudo-

nitzschia species have been observed within oceanic phytoplank-ton assemblages across wide geographical locations (Hasle, 2002;Horner et al., 1997; Hubbard et al., 2008; Trainer et al., 2003). Tostreamline the process of differentiating multiple Pseudo-

nitzschia species co-occurring within potentially toxic blooms,

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180170

we developed a high-resolution microarray assay that identifiesdiverse taxa in a rapid, ship-deployable format.

We used CombiMatrix ElectraSenseTM 4X2K microarrays with2240 oligonucleotide probes and the ElectraSenseTM Reader fordetection of hybridization signals (CustomArray Inc., Mukilteo,WA). This customizable platform was previously validated forpathogen detection and analysis of microbial communities(Bolotin et al., 2009; Lodes et al., 2006, 2007; Postier et al.,2008; Smith et al., 2010) and provides for full probe designflexibility. Building on the work of others, we developed ataxonomic assay to detect and differentiate Pseudo-nitzschia

species based on a fragment of the internal transcribed spacer 1(ITS1) region, located between 18S and 28S rRNA genes of therRNA-encoding cistron. The use of ITS1 ribotypes for phylogeneticanalysis of different species of Pseudo-nitzschia has been validated(Casteleyn et al., 2009; D’Alelio et al., 2009; Hubbard et al., 2008;Lundholm et al., 2006, 2012; Manhart et al., 1995; Marchetti et al.,2008; Moniz and Kaczmarska, 2010; Reid et al., 2008). Similar toother diatoms that have been studied, Pseudo-nitzschia genomescontain several tandem repeats of identical rRNA cistrons (D’Alelioet al., 2009), and very rarely, a few single nucleotide polymor-phisms (SNPs) are observed (Casteleyn et al., 2009; D’Alelio et al.,2009; Reid et al., 2008).

The ElectraSenseTM assay is based on PCR amplification of theITS1 region with specific primers (Hubbard et al., 2008), andsubsequent hybridization of amplicons to DNA oligonucleotidemicroarrays with 307 probes specific for well-characterizedPseudo-nitzschia ITS1 sequences. Detection of microarray hybrid-ization signals was performed electrochemically to facilitatedeployment aboard oceanographic vessels. The total assay timefrom sample collection (water filtration) to data acquisition wasunder 7 h, and the assay was tested during two research cruisesthat took place in spring and late summer of 2009. The utility of theassay was tested further in the laboratory with a large-scalecomparison of samples across years, seasons and locations toevaluate species and strain diversity in Pseudo-nitzschia popula-tions of the northeast Pacific coastal ocean. Results from thiscomprehensive, streamlined assay compare favorably with molec-ular profiling methods used by others (Bano and Hollibaugh, 2002;Diercks et al., 2008; Hubbard et al., 2008; Marchetti et al., 2008;Miller and Scholin, 2000; Orsini et al., 2004; West et al., 2008) todescribe the taxonomic diversity and distribution of Pseudo-

nitzschia species across diverse geographical locations.

2. Materials and methods

2.1. Collection of water samples and environmental data acquisition

Samples from Washington/Oregon coastal waters (44–48.58Nand 123.9–1268W) in the northeast Pacific Ocean were collectedduring seven research cruises between 2007 and 2009. Thefollowing transects in the Pacific Ocean were covered, encom-passing over 300 km of the coastline: La Push (LP), Grays Harbor(GH), Columbia River (CR), and Newport Hydroline (NH). For eachlocation, water was collected at the surface (1–3 m) and at thechlorophyll maximum (4–22 m). Samples were collected using 10-L Niskin bottles mounted to a CTD (conductivity–temperature–depth meter) rosette. Water temperature, salinity, and depth wererecorded with a Sea-Bird 911+ CTD (conductivity–temperature–depth) profiler (Sea-Bird Electronics Inc., Bellevue, WA). Sampleinformation and the collected metadata are deposited togetherwith microarray data in the NCBI’s Gene Expression Omnibus withGEO Series accession number GSE27141 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27141) and are shown inTable S1. All microorganisms present in the water column werecollected by filtering water through Sterivex 0.22 mm filter units

(Millipore, Billerica, MA). The volume of water filtered rangedfrom 1 to 6 l depending on how quickly the filters becameobstructed with biomass. The filters were placed into 2 ml ofRNAlater reagent (Ambion, Austin, TX), frozen, and stored at�80 8C until extraction.

2.2. Laboratory cultures of Pseudo-nitzschia spp.

Water samples for the isolation of Pseudo-nitzschia spp. werecollected from several locations along the northeast Pacific Ocean,including La Push (LP-17) and Queen’s River/La Push (QRLP-a) inSeptember 2008 and May 2009 as described previously (Smithet al., 2010). Pseudo-nitzschia cells were recognized by grossmorphology under light microscopy and single cells or chains wereisolated with a micropipette and transferred to f/2 growth medium(Guillard and Ryther, 1962). The enrichment QRLP-PN2 culturewas isolated in dilution series using a bulb dropper. Specimens inthe cultures were identified with scanning electron microscopy(Hasle and Lundholm, 2005; Kaczmarska et al., 2005; Lundholmet al., 2006). Pseudo-nitzschia spp. were maintained in semi-continuous batch culture under 14:10 light:dark cycle with80 mmol photons m�2 s�1 irradiance at 15 8C. Four additionalestablished cultures were obtained from the University ofCalifornia, Santa Cruz (from K. Hayashi and R. Kudela) collectedfrom Monterey Bay (July 2008 and July 2009) and grown under thesame conditions as the Pacific Northwest (PNW) isolates. Between20 and 100 ml of cultured cells were filtered on 0.2 mmpolycarbonate filters (Millipore, Billerica, MA) and frozen priorto DNA extraction. For each culture, the corresponding ITS1 wasamplified with Pseudo-nitzschia-specific primers and used formicroarray analysis and for construction of clone libraries.

2.3. DNA isolation, PCR amplification and preparation of biotin-

labeled cDNA targets for microarray hybridization

Total DNA was isolated from all microorganisms, bothprokaryotic and eukaryotic, retained on 0.22 mm Sterivex filters.Each Sterivex filter was removed from the plastic housing, cutlongitudinally with a sterile blade, and DNA was isolated using aFastDNA1 SPIN Kit for Soil (MP Biomedicals, Solon, OH) asdescribed by the kit manufacturer. The resulting genomic DNA wasexamined using rapid gel electrophoresis with the FlashGel1

System (Lonza, Allendale, NJ). ITS1 sequences from Pseudo-

nitzschia were amplified from total genomic DNA using PCR withthe following specific primers: PnAIIF, 50-TCTTCATTGTGAATCTGA-30, and PnAIIR, 50-CTTTAGGTCATTTGGTT-30 (Hubbard et al., 2008).PCR was done with 10 ng of total genomic DNA, 0.2 mM of eachprimer, and AmpliTaq Gold 360 Master Mix (Applied Biosystems,Foster City, CA, USA). The reaction consisted of an initialdenaturation at 94 8C for 2 min, followed by 30 cycles of 94 8Cfor 30 s, 50 8C for 30 s, and 72 8C for 2 min. PCR products werevisualized by gel electrophoresis using the FlashGel1 System(Lonza), and purified with the QIAquick PCR purification kit(Qiagen, Valencia, CA). Biotin labeling was performed using LabelITuArray Biotin Kit (Mirus Bio, Madison, WI) as described by themanufacturer. The biotin-labeled DNA targets were purified usingIllustra ProbeQuant G-50 Micro Columns (GE Healthcare, Piscat-away, NJ), and applied for microarray hybridization.

2.4. Cloning and sequencing of the ITS1 amplicons

The ITS1 amplicons were cloned using the TOPO 2.1 TA Cloningkit (Invitrogen, Carlsbad, CA), following the manufacturer’sinstructions. Plasmids from randomly chosen clones were purifiedwith the Fast Plasmid Mini kit (5 Prime, Gaithersburg, MD).Sequencing of both strands using M13 vector primers was

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180 171

performed on an ABI3700XL high throughput sequencer (Molecu-lar & Cell Biology Core, Oregon Health & Science University,Beaverton, OR). Sequencing of 96-well plates of clones generatedfrom samples CR7S-May 2009-751, MB-Bla, and MB-Ala wasperformed by genomic services at Beckman-Coulter (Danvers,MA). The resulting sequences were trimmed, aligned to knownITS1 sequences using BLAST searches against the NCBI database,and clustered using CLC Free Workbench 4.6 (CLC Bio, Cambridge,MA).

2.5. Selection of internal transcribed spacer sequences and

microarray probe design

ITS1 sequences were acquired from NCBI databases based onkey word and literature searches (Hubbard et al., 2008). De-replication of sequences was done with CombiMatrix ProbeWeaver software to retain 118 non-redundant sequences with<100% identity to each other. The sequences were clustered bysimilarity; and clades were selected as separate clusters based on�90% sequence identity within a clade. For a total of 18 clusters; 14contained between 2 and 20 sequences; while 4 others had onlyone sequence each. ITS sequence alignment and clustering was alsodone for diatoms Skeletonema; and Thalassiosira (Armbrust andGalindo, 2001); resulting in 75 ITS1 ribotypes grouped into 19clusters (data not shown). The cluster information was incorpo-rated into the ITS1 annotation labels. Nearly isothermal probeswith Tm of 60–72 8C and 25–35-mer length were designed in half-probe-length steps across each sequence. Probes meeting thefollowing quality metrics were retained: (i) lack of secondarystructure (hairpin formation); (ii) percent G+C (35–85%); and (iii)absence of repeats (Bolotin et al., 2009; Lodes et al., 2006, 2007). Intotal, 7314 probes were selected and analyzed for specificityagainst the database of the 118 clustered ITS1 sequences usingCombiMatrix Genotyper software. The number of cross-hybridiz-ing hits was estimated using the criterion of Tm � 55 8C (Lodes etal., 2006, 2007; Smith et al., 2010). Probes were selected based ontheir uniqueness; discriminating against multiple hits character-istic for broad specificity. However, this was not always possibledue to a relatively high degree of ITS1 sequence conservation; andour desire for probe coverage of all ribotypes. With a target goal of4 probes for each ribotype; 307 probes (with broad specificityprobes hitting multiple targets) were selected for identification of118 Pseudo-nitzschia ribotypes. The same approach was used forSkeletonema; and Thalassiosira; which were to serve as controls forPseudo-nitzschia probe specificity; resulting in selection of 75ribotypes; and producing a set of 174 probes. For each selectedprobe; Genotyper software reported the number of potential hitsacross all ITS1 ribotypes represented on the microarrays; creating aprobe hit profile. These profiles were combined into a 2D ribotypehit map; with probes as rows and ribotypes as columns. In a similarfashion; a cluster hit map was calculated by summarizing allprofiles of the corresponding ribotypes in each cluster (column).The cluster hit map for the three diatoms showed high probespecificity at the genus level: only one of 481 probes was predictedto cross-hybridize to a sequence outside of its target genus(Fig. S1). The relative proportion of unique probes within eachgenus was inversely correlated with conservation of the ITS1sequence; and was very high for Thalassiosira; relatively high forPseudo-nitzschia; and very low for the highly conserved Skeleto-

nema (Fig. S1).

2.6. Microarray hybridization, electrochemical signal detection,

and re-use

The probe sets were synthesized in situ on ElectraSenseTM 4X2Kmicroarrays. Hybridization of ElectraSenseTM microarrays was

performed at 50 8C for 1 h as recommended by the manufacturer(CombiMatrix Corp. and CustomArray Inc.). Biotin-labeled DNAtargets prepared by PCR were used for hybridizations (1–2 mg permicroarray). Electrochemical detection was performed using theElectraSenseTM Detection Kit (CombiMatrix) as described by themanufacturer. The direct numeric quantification of hybridizationsignals for all spots was performed using electrochemicalElectraSenseTM and ElectraSenseTM2 Readers (CombiMatrix). Formicroarray re-use, the labeled targets were chemically denaturedand removed from microarray probes as recommended by themanufacturer. In total, each microarray was used four times.

2.7. Microarray data analysis

Data quality control was performed using ProbeWeaver(CombiMatrix Corp.) and NCI-supported software BRB-ArrayTools(http://linus.nci.nih.gov/pilot/index.html), using distribution anal-ysis of histograms of raw signal intensity data and averagenormalized probe signal intensities. Both types of data showedpronounced separation between the background (negative) andsignal measurements (positive), similar to results described in(Lodes et al., 2006, 2007). Clustering analysis was performed usingCluster and TreeView software (Eisen et al., 1998).

The raw microarray data were normalized by conversion into Z-values (standard values) reflecting their deviation from thepopulation mean. For each probe in a particular hybridizationthe corresponding Z-value was calculated as the differencebetween the probe signal intensity and the mean intensity of allsignals in the hybridization, divided by the standard deviation ofall signals. Next, detection of ribotypes was done using a novelmodification of the standard HybAnalysis software (CombiMatrixCorp. and CustomArray Inc.) (Bolotin et al., 2009; Lodes et al., 2006,2007). The HybAnalysis software detected the presence of apathogen of interest based on a sum of Z-values for multiple probescorresponding to this pathogen (Bolotin et al., 2009; Lodes et al.,2006, 2007). For the modified version, probes were assigned probeweight values (PWV) corresponding to their uniqueness. If a probedid not produce a calculated hit with a given target sequence, thePWV was zero. If the probe produced a calculated hit, thecorresponding PWV was expressed as 1.0 (number of hits to self)divided by the number of calculated hits to all other targets.Finally, for each ribotype, the sum of all PWV was normalized to 1.0(the weighted average approach), to compensate in part for thedifference in probe numbers for targets of different length. ThePWV matrix was calculated once for all ribotypes, and then used asa multiplier to transform the probe Z-values into sumscores. Asumscore value for a ribotype in a given microarray hybridizationwas calculated as the sum of Z-values for each probe, multiplied bythe corresponding PWV as shown:

X307

n¼1

ðPWVn � ZnÞ

A full matrix of sumscores for individual ribotypes (row) in eachmicroarray hybridization/sample (column) was calculated anddeposited as described in Section 2.8. The sumscore thresholdrequired to confidently predict the presence of an individualribotype was determined empirically, as described in Section 3.Since Z-values were negative for targets not present in the samples,corresponding negative sumscores were converted to zeros.

2.8. Data deposition

Microarray data were deposited in NCBI’s Gene ExpressionOmnibus (Edgar et al., 2002) and are accessible through GEO Seriesaccession number GSE27141 (http://www.ncbi.nlm.nih.gov/geo/

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180172

query/acc.cgi?acc=GSE27141). The cloned ITS1 ribotype sequenceswere deposited in GenBank under accession numbers JF308586–JF308627, and JF313257–JF313258.

3. Results

3.1. Probe design for Pseudo-nitzschia ITS1 sequences and

bioinformatics analysis of probe specificity

One hundred and eighteen non-redundant Pseudo-nitzschia

ITS1 sequences were aligned for microarray probe design (Fig. 1),and clustered by similarity based on �90% sequence identity,generating 18 clusters. In most cases ITS1 ribotypes belonging tothe same species grouped together, consistent with the observa-tion that intra-species ITS1 sequence variation is low (Hubbardet al., 2008). Of the species evaluated, only Pseudo-nitzschia

delicatissima and Pseudo-nitzschia pungens sequences were distrib-uted into multiple clusters (3 for the former and 2 for the latter,respectively). The clustered ribotypes were used to design 307oligonucleotide microarray probes (25–35 nt in length) using anapproach developed for pathogen genotyping (Lodes et al., 2006,2007; Smith et al., 2010), with a minimum of 4 probes targetingeach sequence. Probes, comprising ribotype-specific, cluster-specific, and genus-specific sequences, were considered to cross-hybridize to a target ribotype sequence if the correspondinghybridization Tm � 55 8C. Using this criterion, we determined thenumber of ribotypes that could be distinguished by non-identicalprobe hit profiles to be 71 out of the initial 118 (61%). De-replication was subsequently performed to merge ribotypescontaining sequences with identical hit maps into a singleconsensus sequence. Generally, identical hit maps correspondedto 98–99% sequence identity over the full length ITS1, with aminimum of 97% identity.

Cluster hit maps were similarly analyzed for overlappingprofiles (Figs. S1 and S2). In some cases, large insertions anddeletions (indels) (Fig. 1) produced 20–30% difference in the ITS1

Fig. 1. Alignment of 118 non-redundant Pseudo-nitzschia ITS1 target sequences (107–319

shows the degree of sequence conservation expressed as the percentage of identity (fro

sequence. Cluster numbers corresponding to the aligned ribotypes are shown at the le

sequence length, while very high conservation was observed forsequences shared between ribotypes. Such highly identicalsequences of different length formed closely related cluster pairs,i.e., 10 and 46, and 27 and 33 (Fig. S2), and these were merged intosingle clusters (10/46 and 27/33, respectively). A few ITS1sequences separated into different clusters based on relativelyshort hypervariable regions (<25% of the entire ITS length) whilethe majority of the shared sequence was highly conserved. Thiswas observed for cluster pair 3 and 18, which was also merged intoa single cluster (3/18) (Fig. S2). Clusters 3/18 and 10/46 containedribotypes of the same species, P. delicatissima and P. pungens,respectively. The third cluster, 27/33, contained ribotypes corre-sponding to either Pseudo-nitzschia australis or Pseudo-nitzschia

seriata, two closely related, and often confused, species (Hasle,2002).

3.2. Experimental validation of microarray probe specificity

Probe specificity was analyzed experimentally using twocultured isolates from Monterey Bay, California, MB-Ala (P.

pungens) and MB-Bla (P. delicatissima). Aliquots of the correspond-ing ITS1 amplicons were used separately for microarray hybrid-ization and cloning and sequencing (47 clones were sequencedfrom each culture). MB-Ala produced one ITS1 ribotype belongingto P. pungens-A (Hubbard et al., 2008), while a single mutation wasobserved in approximately one-half of the MB-Bla clones,suggesting allelic variation. ITS1 ribotypes identified by sequenceanalysis were used to calculate predicted hit maps for the Pseudo-

nitzschia-specific probes. Fig. 2A and B compares the microarrayhybridization signals from the two cultures with predicted resultsfrom the calculated hit maps. We observed good agreementbetween the calculated and experimental data, indicating that,under the microarray hybridization conditions used, most of theprobes performed according to our calculations. At the same time,deviations from hit map calculations that were observed for probesof broad-specificity highlighted the importance of using multiple

bp in length) used for microarray probe design. The bar graph under the alignment

m 0 to 100%). Positions (bp) are shown above the graph relative to the consensus

ft side of the figure, together with each corresponding Pseudo-nitzschia species.

Fig. 2. Side-by-side comparison of calculated hit profiles and normalized hybridization signals for Pseudo-nitzschia probes (X-axis). Probes are sorted from left to right by (i)

cluster number; (ii) ITS1 GenBank accession number; and (iii) location within the corresponding sequence. Probe hits (Tm � 55 8C) were calculated for the ribotype/s detected

by cloning and sequencing in the 2 laboratory cultures: (A) MB-Ala, (B) MB-Bla, and (C) the environmental sample SEP09-CR7-S-751. The calculated hits are shown as black

bars on upper graphs of each panel. Probe hybridization signals (lower graph of each panel) were normalized to fold-change values over the background (the average of the

lowest 10% of microarray signals). White to black scale indicates magnitude (from low to high, as shown by the scale bar), with a 20-fold saturation threshold.

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180 173

probes for ribotype detection. Furthermore, duplicate hybridiza-tions of 2 cell culture samples, and 1 quadruplicate hybridization ofa cell culture sample produced very similar signal intensity profiles(Fig. S3A), with a mean correlation coefficient of R = 0.94. Arepresentative scatter plot is shown in Figure S3B.

3.3. Design of a novel algorithm for integrated sumscore data analysis

To identify specific Pseudo-nitzschia ribotypes, we designed anovel algorithm to integrate multiple hybridization signals into acombined value. This value, designated a ‘sumscore,’ reflected thepresence or absence of a particular ribotype in a given sample. Themethod, referred to as the ‘integrated sumscore data analysis’(ISDA), builds on an approach described previously for detection ofpathogens that was based on summing the normalized values (Z-values) of multiple probes (Bolotin et al., 2009; Lodes et al., 2006,2007). For this study additional modifications to the signalintegration method were needed to contend with the anticipatedpresence of related, overlapping ITS ribotypes within a singlesample (Hasle, 2002; Horner et al., 1997; Hubbard et al., 2008;Trainer et al., 2003). Probes were given statistical weights, or probeweight values (PWV), that were inversely correlated with thenumber of calculated non-cognate target hits. This reduced theimpact of broad-specificity probes and enhanced the impact of

Table 1Comparison of sequences detected by cloning and sequencing, and ribotypes identified

Culture or sample Cloned ribotype/s

GenBank #

Number

of clones

Number

of SNPs

MB-Ala JF308586 47 0

MB-D5 JF308586 4 0

MB-B3 JF313257 4 0

PNW-MM JF308587 4 0

LP17 PN2 JF308587, JF313258 4 1

MB-Bla JF308587, JF313258 47 1

QRLP-a PN2 JF308599 2 0

JF308587 2 0

SEP09-CR7-S-751 JF308587–JF308598 25 14

JF308599–JF308609 22 13

JF308618, JF308619 6 2

JF308620 6 0

JF308586 5 0

JF308616, JF308617 3 1

JF308610–JF308615 18 11

JF308621, JF308622 5 1

JF308623–JF308626 4 3

JF308627 1 0

a Unless indicated otherwise, the top BLAST hits were 100% identical with 100% covb P. fryxelliana isolates NWFSC 241 and 242 (JN050287, JN050288).c Has partial hits to P. inflatula and P. micropora with up to 84% identity and <50% c

unique probes. The PWV were derived from the 2D ribotype hitmap calculated for 307 Pseudo-nitzschia probes against 71 definedITS1 ribotypes. A sumscore value for a ribotype in a givenmicroarray hybridization was calculated as the sum of Z-values ofeach probe, multiplied by the corresponding PWV (see Section 2 fordetails). The full matrix of sumscores for individual ribotypes (row)in each microarray hybridization/sample (column) is availablethrough GEO submission GSE27141.

3.4. Algorithm ‘training’ for ribotype identification

To evaluate the performance of ribotype identification using theISDA, we applied the method in a blind evaluation to 7 Pseudo-

nitzschia isolates and enrichment cultures from the northeastPacific Ocean and Monterey Bay, California. Six establishedmonoclonal cultures were found by sequence analysis to containa single ribotype each. Two of these (MB-Bla and LP-17 PN2)contained an SNP at approximately 50% frequency (Table 1). Oneenrichment culture (QRLP-a PN2) contained 2 ribotypes corre-sponding to different Pseudo-nitzschia species with no SNPsobserved (Table 1).

The highest sumscore for a given ribotype corresponded inevery case to the cloned ITS1 sequence with the highest identity(98.1–100%) over the entire ITS1 region (Table 1). In many cases,

from microarray hybridization data by top sumscores.

Target ribotype

(top sumscore)

Identity

to target

Top BLAST hita species level

cl10jEF014761 99.3 P. pungens

cl10jEF014761 99.1 P. pungens

cl33jEF014822 99.3 P. australis

cl58jAY257849 99.3 P. delicatissima NWFSC090

cl58jAY257849 99.3 P. delicatissima NWFSC090

cl58jAY257849 99.4–100 P. delicatissima NWFSC090

cl97jEF014877 100 P. decipiens

cl58jAY257849 99.3 P. delicatissima NWFSC090

cl58jAY257849 98.8–100 P. delicatissima NWFSC090

cl97jEF014877 98.9–100 P. decipiens

cl25jEF014859 99.3–100 P. multiseries

cl64jEF014819 98.7 P. subpacifica

cl10jEF014761 99.3 P. pungens

cl73jEF014830 100 P. fraudulenta

None – P. granii

None – P. fryxellianab

None – n/dc

None – n/dc

erage to the query detected in the cultures/sample by cloning and sequencing.

overage.

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180174

there were a number of additional ribotypes (with somewhatlower sequence identities) from the same cluster that also hadrelatively high sumscores. This appeared to be due to overlappingprobe specificity resulting in cross-hybridization with the non-cognate sequence (Fig. S1). Results from 11 microarray hybridiza-tions performed for 7 laboratory cultures produced pair-wisePearson correlation coefficients that were very high for ribotypesfrom the same cluster (R between 0.99 and 1.00), even when theabsolute sumscore values differed 2- to 4-fold. In contrast,correlation was very low (R < 0.5) for sumscore profiles ofribotypes from different clusters. The fact that ribotypes withinthe same cluster did not occur independently suggested that thesamples contained a single, or ‘consensus’ ribotype sequence foreach cluster with possible limited (but undetected by the ISDA)microdiversity at the level of 98–99% sequence identity. Thus, weselected only the ribotype with the top sumscore from each clusterfor further analysis. In the majority of cases, this selectioncorresponded to identification at the species level.

Comparison of results from the ISDA with data from sequencingand microscopy showed correct identification of Pseudo-nitzschia

ribotypes for all cultures (Fig. 3, shown with (+)). However, one P.

delicatissima ribotype from cluster 3/18 produced high sumscoresin all cultures (Fig. 3A), even though it was not identified bysequencing or microscopy (Table 1). This result appeared to be afalse detection artifact, likely caused by the fact that the ITS1 ofribotypes in the 3/18 cluster shared high identities with a numberof sequences from other clusters. Approximately 70% of all probesdesigned for cluster 3/18 produced hits with ribotypes from 3 ormore additional clusters (data not shown). In contrast, this valuewas typically <20% for probes designed for other clusters. This ledus to modify the sumscore calculation to exclude probes with verybroad specificity. However, to minimize probe exclusion forribotypes with low initial probe coverage, we excluded only those

Fig. 3. Sumscore analysis for Pseudo-nitzschia ribotype detection. Heat maps show the sum

from 7 laboratory cultures. White to black scale indicates magnitude (from low to high, as

target microarray ribotypes with the highest sumscores, labeled with cluster number and

probes, including those with broad specificity. (B) The heat map shows sumscores calcula

symbols indicate the target ribotypes that were detected by cloning and sequencing

corresponding microarray targets with top sumscores).

probes with �20 calculated hits to various ribotypes. This resultedin the exclusion of 17 (5.5%) out of 307 probes from the sumscore,ultimately affecting only our ability to detect ITS1 ribotypes fromcluster 3/18.

This modification resulted in reduction of the sumscores for the3/18 ribotype to almost background levels (Fig. 3B). Some othersumscore values were also slightly reduced, however all confirmed(by cloning and sequencing, and microscopy) ribotypes stillretained sumscores �17. This was also true for cultures thatcontained mixtures of different ribotypes, including the QRLP-aPN2 enrichment (Fig. 3B). The sumscores obtained for a 1:1mixture of two Pseudo-nitzschia cultures, MB-B3 and MB-D5, eachcontaining a single ribotype, were also similar to the calculatedsumscores for the individual cultures (Fig. 4A). No false positives ornegatives were observed. For all ribotypes that were not detectedby sequence analysis, the maximum sumscore value was 7.4. Wetherefore selected a sumscore of 10 as the cut-off for detection ofribotypes. Upon hybridization of microarrays with the Pseudo-

nitzschia ITS1 amplicons, sumscore values �10 were detectedexclusively for Pseudo-nitzschia ribotypes (ribotypes of the diatomsSkeletonema and Thalassiosira, for example, did not produce highsumscores) (Fig. 4B). We used scatter plot analysis to comparesumscores calculated for duplicate hybridizations (Fig. S4A and B)and observed high reproducibility, with correlation coefficientsfrom 0.96 to 0.99. In addition, samples collected close together intime and space (i.e., within 1 h in the water column 19 m apart indepth (Fig. S4C), also had highly correlated sumscores (R = 0.95).

3.5. Microarray assay deployment

We established an assay workflow to prepare DNA targets fromenvironmental water samples, hybridize microarrays and analyzecorresponding data onboard research cruises during two field

score values calculated for 10 microarray hybridizations (columns) with amplicons

shown by the scale bar) with the saturation threshold of 30. Rows correspond to the

Pseudo-nitzschia species. (A) The heat map shows the sumscores calculated using all

ted after exclusion of probes that have hits to more than 20 target ribotypes. The ‘‘+’’

(see Table 1 for GenBank accession numbers of the cloned sequences and the

Fig. 4. (A) Sumscore values for hybridizations. Results from experiments with two single-species cultures, P. australis (MB-B3, black bars) and P. pungens (MB-D5, dotted bars),

and a 1:1 mixture of the two (grey bars), are displayed. The in silico calculated sumscore for the mixture of MB-B3 and MD-D5 is shown with hatched bars. The sumscore data

are shown for 5 microarray target clusters containing ribotypes with the top sumscores. ‘‘MB-D5’’ or ‘‘MB-B3’’ below a given cluster indicates the culture in which it was

present. The sumscore cutoff (10) is indicated by the horizontal dashed line. (B) Sumscore values (Y-axis) calculated for all 146 unique ribotypes (X-axis) represented on

microarrays and sorted from left to right by (i) microbial genus: Thalassiosira, Skeletonema and Pseudo-nitzschia; (ii) cluster number; and (iii) ITS1 GenBank accession number.

Sumscores were calculated for 43 hybridizations with amplicons from cultures and environmental samples; negative values were converted to zeros. The sumscore cutoff

(10) is indicated by the horizontal dashed line.

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180 175

campaigns in May, and September, 2009 using the ElectraSenseTM

electrochemical microarray detection system with a deployablepalm-sized reader (CombiMatrix Corp, Mukilteo, WA). The entireassay, beginning with water collection and ending with microarrayhybridization, signal detection and data analysis, requiredapproximately 6.5–7 h. Approximately 100 seawater samplescollected during 7 field campaigns (2007–2009, Table S2) weretested either onboard or in the laboratory using PCR amplificationwith Pseudo-nitzschia-specific primers. For 6 out of 7 fieldcampaigns, �95% of all tested samples produced PCR ampliconswith Pseudo-nitzschia primers. In contrast, ITS1 amplicons wereproduced from fewer than 30% of May 2009 samples (Table S2).Based on genomic DNA yields and amplification of non-diatomITS1, these samples contained substantial amounts of DNA frommicroorganisms other than Pseudo-nitzschia (data not shown).

Fig. 5. Experimental validation of probe specificity. Raw signal intensities (Y-axis) obtained

(X-axis) represented on microarrays. Probes are sorted from left to right by (i) microbial

GenBank accession number, and (iv) location within the corresponding sequence. The panel

When hybridized to microarrays, the amplicons generated fromenvironmental samples with Pseudo-nitzschia-specific primersproduced signals that appeared to be largely specific for thecorresponding Pseudo-nitzschia probes (Fig. 5), although a smallamount of cross-hybridization was observed with probes to thediatoms Skeletonema and Thalassiosira (Fig. 5Al). Similar cross-hybridization signals were not observed with amplicons fromPseudo-nitzschia laboratory cultures (Fig. 5B).

3.6. Validation of the ISDA using a sequenced environmental sample

We cloned and sequenced ITS1 ribotypes from the environ-mental water sample SEP09-CR7-S-751, which was hybridized induplicate to microarrays. The resulting microarray data corre-sponded closely to the calculated hit map generated using

in multiple hybridizations with Pseudo-nitzschia amplicons were plotted for all probes

genus: Pseudo-nitzschia, Skeletonema and Thalassiosira, (ii) cluster number; (iii) ITS1

s show data obtained with 35 environmental samples (A), and 7 laboratory cultures (B).

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180176

sequencing data (Fig. 2C), although some probes with broadspecificity deviated from hit map calculations. Significant ribotypediversity was observed for this sample: sequencing of 95 clonesyielded 8 previously characterized and 2 uncharacterized ribo-types (Table 1, Fig. S5). Microdiversity was pronounced; the 3 mosthighly represented ribotypes contained 11–14 different SNP sites(Table 1). However, individual clones corresponding to eachribotype differed by only 1–2 SNPs (with two exceptions, clonesJF308614–JF308613, and JF308597, containing 4 and 5 SNPs,respectively), and clustered closely together in a phylogenetic tree(Fig. S5). Thus, all of the clones for each identified Pseudo-nitzschia

species could be assigned to a single ribotype, comprisingsequences that were 97–100% identical. This microdiversity couldnot be distinguished by the calculated probe hit profiles (data notshown), and was not resolved by the ISDA. We therefore selected97% sequence identity as the cutoff for distinguishing differentribotypes.

The most abundant ribotype recovered from the samplecorresponded to P. delicatissima NWFSC090 in cluster 58 (Table1, JF308587). This ribotype was represented by 12 subtypescontaining 1–2 SNPs across 12 sites in the ITS1 sequence (Table 1).These data are in contrast with results from previous work findingan invariant sequence type for this organism in diverse locations(Hubbard et al., 2008). Five other ribotypes corresponded to well-described species of Pseudo-nitzschia decipiens, Pseudo-nitzschia

multiseries, Pseudo-nitzschia subpacifica, Pseudo-nitzschia fraudu-

lenta and P. pungens, and were represented by five clusters (97, 25,

Table 2Sumscore dependence upon sequence identity and coverage, evaluated for represen

environmental sample SEP09-CR7-S-751 (second column).

Cloned ribotype

accession #

Species, culture and/or

sample

Microarray

ribotypesa

Target

length

JF308587 P. delicatissima NWFSC090 AY257849 168

MB-Bla, SEP09-CR7-S-75 AY519281 309

JF308586 P. pungens EF014761 142

MB-Ala, EF014779 142

SEP09-CR7-S-751 EF014760 142

EF014774 142

EF014844 142

EF014786 142

EF014790 142

EF014845 142

DQ996018 259

DQ996021 259

AY257846 259

DQ062665 260

DQ166533 259

AY544769 259

JF313257 P. australis EF014822 150

MB-B3 EF014826 150

EF014809 150

AY452523 232

DQ996022 232

AY452527 255

DQ062667 228

JF308618 P. multiseries EF014859 144

SEP09-CR7-S-751 EF014870 144

AY257844 227

DQ996017 227

JF308620 P. subpacifica EF014819 195

SEP09-CR7-S-751 EF014814 195

JF308616 P. fraudulenta SEP09-CR7-S-751 EF014830 202

JF308599 P. decipiens SEP09-CR7-S-751 EF014877 209

a Ribotypes with positive sumscores corresponding to cloned sequences, sorted by tb The length of common overlapping sequence between the cloned sequences and rc Comparison of SNPs and percent identity between the cloned sequences and ribotd Sumscore values calculated for ribotypes from microarray hybridizations with am

64, 73, and 10/46, respectively). All 6 clusters (Fig. S5, shown with(+)), were detected by the ISDA in the SEP09-CR7-S-751 sample(Table 1). The highest sumscores for each cluster corresponded toribotypes that were 98.7–100% identical to cloned sequences overthe full ITS1 sequence length (Table 1).

Additional clones identified by sequence analysis correspondedto two species described only after the design of our probe set wascompleted: Pseudo-nitzschia granii (Marchetti et al., 2008) andPseudo-nitzschia fryxelliana (Lundholm et al., 2012); and two novelribotypes (Fig. S5, Seq8, JF308623–JF308626; and Seq10,JF308627). BLAST searches of public databases indicated theseribotypes had significant identity (83–94%) to multiple ribotypesof Pseudo-nitzschia inflatula and Pseudo-nitzschia micropora withonly partial sequence coverage (49–74%), and large gaps indicatingmultiple indels. Thus, they likely correspond to novel species orstrains within the Pseudo-nitzschia genus. Since these novelribotypes were not included in the original probe design, theywere not detected by the ISDA. Altogether, our results indicatedthat the ISDA accurately identified Pseudo-nitzschia species andstrains from the environmental sample without false positive orfalse negative detection artifacts.

3.7. Dependence of sumscore values on sequence identity and

coverage

In all cases comparing ISDA results and sequencing data, the topsumscore corresponded to the ribotype with the highest identity to

tative cloned sequences (first column) from single-species cell cultures and the

Common

lengthb

Number

of SNPsc

% Identityc Sumscore

cultured

Sumscore

environmentd

168 0–1 99.4–100 21.24 44.66

168 18 89.2 1.84 2.45

142 0 100.0 41.31 33.98

142 1 99.3 41.01 33.76

142 1 99.3 40.50 32.88

142 2 98.6 38.44 31.96

142 2 98.6 37.64 31.38

142 1 99.3 37.64 31.38

142 1 99.3 37.64 31.38

142 2 98.6 37.21 31.24

142 2 98.6 21.57 15.28

142 0 100.0 21.51 15.27

142 3 97.9 10.65 7.28

142 6 95.8 8.15 4.16

142 4 97.18 7.19 3.82

142 6 95.77 6.15 3.07

150 1 99.3 17.88 n/a

150 1 99.3 17.76 n/a

150 7 95.3 14.98 n/a

150 8 94.7 13.28 n/a

150 9 94.0 13.24 n/a

150 0 100.0 5.23 n/a

150 13 91.3 0.32 n/a

144 0 100.0 n/a 34.19

144 1 99.3 n/a 34.18

144 0 100.0 n/a 12.95

144 0 100.0 n/a 12.95

195 2 99.0 n/a 80.71

195 3 98.5 n/a 70.09

202 0 100.0 n/a 12.79

209 0 100.0 n/a 49.71

he sumscore values in descending order.

ibotypes.

ypes.

plicons from the cell cultures, and the environmental sample.

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180 177

the most highly represented sequence recovered by cloning andsequencing (Table 1). Due to relatedness to this highly representedclone, a number of other microarray ribotypes from the samecluster produced positive sumscores as well (Table 2), but only ifthe shared identity was �97% over the entire ITS1 target sequencewas the corresponding sumscore �10 (Table 2). Thus, in addition todetection of different Pseudo-nitzschia species, the ISDA alsoprovided the ITS1 sequences of detected ribotypes at the level of�97% sequence identity. SNPs reducing sequence identity below97% resulted in a dramatic reduction in sumscore values (i.e. P.

delicatissima NWFSC090 and P. pungens in Table 2). A notableexception was observed for cluster 27/33, containing P. australis

and P. seriata ribotypes. Laboratory culture MB-B3 contained P.

australis ribotype JF313257, and the top sumscore detected themost identical (99.3%) P. australis sequence (Table 2). However,several other ribotypes containing up to 9 SNPs producedsumscores above 10. These ribotypes should have scored lower,but sequence analysis showed that the SNPs were located closetogether in a hypervariable 36-nt stretch that was limited in probecoverage, thereby reducing contribution of the SNPs to thesumscore values. This result indicated that sumscore resolutiondepended upon both the number and the distribution of SNPswithin the ITS1 sequence.

Sequence coverage also affected sumscore values. For example,if the ribotype sequence represented on the microarrays was>100 bp longer than the corresponding cloned sequence becauseof insertions due to natural variation, the sumscore decreased,even if the shared sequence was 100% identical (i.e., ribotypeAY452527 corresponding to P. australis, Table 2). Sumscores forsequences with 55–65% coverage and �97% identity were stillabove 10, but their values were 30–50% lower than sumscoresgenerated for sequences with full coverage of the ITS1 region(Table 2).

3.8. Correlation analysis of sumscore profiles from environmental

samples

Our comparison of microarray and sequencing results indicatedthat the ISDA provided reliable species and ITS1 sequenceidentifications for both simple and complex mixtures. We nextused the ISDA to evaluate results from ElectraSenseTM hybridiza-tions of 35 environmental samples collected from Oregon andWashington coastal waters during 7 field campaigns. In total, 42individual ribotypes representing 9 clusters produced at least onesumscore �10.

High intra-cluster sumscores were produced by overlappingprobe-hit profiles of ribotypes sharing �97% identity in thesequenced sample SEP09-CR7-S-751. Thus, each species/clusterwas dominated by a single or consensus ribotype sequence (eventhough related sequences with minor variations were present). Todetermine whether this was generally the case for the environ-mental samples, we performed sumscore correlation analysis, bycalculating pair-wise correlation (Pearson) coefficients for eachribotype against all other detected ribotypes. This analysisshowed very high R values (�0.99) for sumscore profiles of allribotypes within the same microarray cluster (intra-clustercorrelation in Table S3). In contrast, for ribotypes from differentclusters, R values were typically very low, with an average mean of0.12 (inter-cluster correlation in Table S3). These resultssuggested that, like SEP09-CR7-S-751, the other environmentalsamples contained one main (‘consensus’) ribotype for eachcluster with possible limited (but undetected by the ISDA)microdiversity at the level of 98–99% sequence identity. Thecorrelation analysis provided no evidence for the simultaneousoccurrence of divergent (<97% identity) ribotypes from a givencluster in our environmental samples (Table S3).

3.9. Seasonal and yearly variation of Pseudo-nitzschia species and

strains in Washington and Oregon coastal waters

In total, the ISDA detected 9 ribotype clusters corresponding to 8Pseudo-nitzschia species in the environmental sample set. Thesumscores corresponding to these ribotypes are shown as three heatmaps (Fig. 6). Within each location the samples were sortedaccording to the month and year of their collection. The heat mapsshowed that nearly all (32 of 35) environmental samples collected in3 different years across the northern transects of La Push and GraysHarbor (LP and GH), central transect of Columbia River coastalmargin (CR), and southern transect of Newport Hydroline (NH))contained at least 2 different Pseudo-nitzschia species/ribotypesrepresented on microarrays, with an average of 4–6 species detectedin each. Three species, P. delicatissima NWFSC090 (cluster 58), P.

subpacifica (cluster 64), and P. decipiens (cluster 97), were present inthe majority of the environmental samples, whereas three other, P.

pungens (cluster 10/46), P. seriata type 1 (cluster 27/33) and P.

multiseries (cluster 25), appeared mainly in summer and fall (Fig. 6).Pseudo-nitzschia cuspidata (cluster 5) and P. fraudulenta (cluster 73)were only observed in a few late-summer samples. It is unclearwhether P. delicatissima types 1–8 from cluster 3/18 was present inthe samples, since this cluster was largely represented by probes ofbroad specificity (with hits to �20 ribotypes) that were removedfrom analysis. One cluster 3/18 ribotype that was represented bymore specific probes produced sumscores >10 in several of thespring samples, however, they were always lower than thoseobserved for other ribotypes (typically 25–50).

Overall, ribotype diversity was lower in November 2007 andspring (April 08, May 09, shown in bold in Fig. 6) compared tosummer/early fall samples (June through September). Interesting-ly, most May 2009 samples (across all locations) did not producePseudo-nitzschia amplicons, nor did they contain Pseudo-nitzschia

cells when analyzed by flow cytometry (data not shown). PCRamplicons from one May 2009 sample (MAY09-CR30S-567, shownwith asterisk in Fig. 6) did not produce significant sumscores forany of the ribotypes represented on our microarrays, despitestrong signals observed for Pseudo-nitzschia probes of broadspecificity. This suggested the presence of either a ribotype fromthe 3/18 cluster or a novel ribotype(s) not represented on themicroarrays. Another interesting feature of 2009 samples was lowdiversity in September samples from the NH transect, with only 1or 2 of the most common ribotypes observed. In contrast,September 2009 samples from other locations typically contained4–6 different ribotypes.

4. Discussion

4.1. Rapid, field-deployable taxonomic assay for Pseudo-nitzschiaspecies

Identification of noxious species in the field has traditionallybeen a challenging, costly, and time-consuming task (Horner et al.,2005). State-of-the-art methods currently used to identify Pseudo-

nitzschia spp. involves analysis of fine ultrastructural details(Orsini et al., 2004), or epifluorescent microscopy with fluores-cently labeled molecular probes to distinguish species of interest(Miller and Scholin, 2000; Scholin et al., 1997). The taxonomicstructure of the genus Pseudo-nitzschia is not yet fully understood;new species and strains were recently identified in well-studiedlocations, with the current number of species estimated to be over30 (Casteleyn et al., 2009; D’Alelio et al., 2009; Hubbard et al.,2008; Lundholm et al., 2004, 2012; Marchetti et al., 2008; Orsiniet al., 2004; Thessen et al., 2009). Because of this, environmentalmonitoring of Pseudo-nitzschia genetic diversity requires flexiblemolecular assays that can be adapted to include newly discovered

Fig. 6. Pseudo-nitzschia species diversity in the 35 environmental samples collected during 7 field campaigns in 2007–2009. Locations of the sampling sites are shown in the

right panel, grouped into three areas from north to south: (i) La Push (LP) and Grays Harbor (GH); (ii) Columbia River transect (CR); and (iii) Newport Hydroline (NH).

Sumscores corresponding to the top-scoring ribotype from each cluster were used for three heat maps generated separately for the three geographical areas described above.

In total, 9 microarray target clusters corresponding to 8 Pseudo-nitzschia species were detected in at least one environmental sample as having at least one sumscore >10.

White to black scale indicates the sumscore magnitude (from low to high, as shown by the scale bar) with the saturation threshold of 30. Ribotypes (columns) are labeled with

microarray cluster numbers and correspond to the following species: 3, P. delicatissima type 1–8; 58, P. delicatissima NWFSC090; 64, P. subpacifica; 97, P. decipiens; 27, P.

seriata; 25, P. multiseries; 10, P. pungens; 5, P. cuspidata; 73, P. fraudulenta. Rows represent environmental samples, sorted from top to bottom by year (2007–2009) and month.

Spring samples are shown in bold lettering.

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180178

species, and which are in addition rapid, accurate, and ideally,field-deployable.

Our microarray-based assay meets these requirements, usinghundreds of Pseudo-nitzschia ITS1 probes ranging from broad-to-narrow specificity for identification of species, and a ship-deployable work flow that takes less than one working day. Speedand deployment of the assay will allow adaptive samplingstrategies to be applied in the field. The re-usable ElectraSenseTM

format is also relatively inexpensive when compared to fluores-cence microarrays or sequence analysis of large numbers ofsamples, making it attractive as a targeted approach for taxonomicassessment. Such an approach is particularly valuable whencombined with autonomous sample collection and processinginstrumentation such as the MBARI ESP (Preston et al., 2009).

4.2. Integration of hybridization signals from multiple probes for

detection of individual Pseudo-nitzschia ribotypes

With the ISDA, we integrated probe specificity metrics andhybridization signals for each ribotype into a single value, asumscore, corresponding to the presence or absence of a givenribotype. Multiple probes per ribotype and assignment of higherweight values to more unique probes contributed to assayrobustness and selectivity. Training of the ISDA algorithm withITS1 sequence data resulted in a filter application to remove probesof broad specificity (5% of total probes) that produced false positivedetection artifacts.

Further ISDA validation demonstrated that sumscore values>10 for a given ribotype generally correlated with the presence ofthe corresponding full-length Pseudo-nitzschia ITS1 sequence. The

sumscore dropped below this threshold value if the sequence targetwas <97% identical to the microarray ribotype over its full length(Table 2). In Pseudo-nitzschia this corresponds to detection at thespecies level, because isolates with divergence >3% are defined asdifferent species (Hubbard et al., 2008). In addition to detectingabundant targets, in 3 instances the ISDA also detected ribotypespresent in low abundance, including P. fraudulenta, and P. pungens,represented by 3 and 5 clones, respectively (out of 95 total) (Table 1).These results suggest that the microarray assay detection limit wassimilar to that achieved by cloning and sequencing.

The ISDA was reliable for detection of all tested strains exceptfor P. delicatissima types 1–8. We were unable to design uniqueprobes for this group with available sequences, and most of thecorresponding probes of broad-specificity were removed from theanalysis to improve assay confidence. In contrast, P. delicatissima

NWFSC090, for which we were able to design unique probes, wasdetected with high confidence. Generally, P. delicatissima is quitediverse, with variability in its ITS1 region as high as that observedbetween different Pseudo-nitzschia species (Hubbard et al., 2008).Further refinement of our microarray probe set based on recentsequence and taxonomic information (Ribalet et al., 2010; Thessenet al., 2009) will be required for accurate identification of ribotypeswithin this group.

4.3. Diversity of Pseudo-nitzschia in Oregon and Washington

coastal waters

Sequencing of ITS1 amplicons from an environmental sampledemonstrated: (i) pronounced diversity; (ii) predominance of asingle ribotype for each cluster; and (iii) a small proportion of

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180 179

sequences with high (98–99%) sequence identity and 1–2 SNPsdifference from the predominant ribotype (Table 1). This intra-group microdiversity was beyond the ISDA resolution (as indicatedby calculated probe hit profiles), leading to our identification of amain consensus ribotype for each cluster, comprising sequencesthat were �97% identical in multiple samples within our collection.These data are consistent with sequencing results finding that agiven Pseudo-nitzschia species was typically represented by asingle ITS1 genotype and a few closely related sequence variants inwater samples collected from the subarctic and northeast PacificOcean (Hubbard et al., 2008; Marchetti et al., 2008).

Due to dependence upon end-point PCR amplification, andsimilar to the ARISA assay described for Pseudo-nitzschia (Hubbardet al., 2008; Marchetti et al., 2008), results from the ISDA arequalitative, rather than quantitative. However, in addition tospecies and group identification, the ISDA also provides acorresponding ITS1 sequence over the full-length region for targetsthat are closely related to ribotypes represented on microarrays(Table 2).

4.4. Yearly and seasonal dynamics of Pseudo-nitzschia species in

Washington and Oregon coastal waters during 2007–2009

Analysis of the natural diversity of Pseudo-nitzschia populationsin three different locations, encompassing approximately 300 kmof the Washington and Oregon coastline, indicated that 8 out of 15different species represented on microarrays were present in atleast one sample. All 8 species are known to produce domoic acid(Hasle, 2002; Marchetti et al., 2008). Previous studies indicatedthat these same 8 species were distributed widely in PNW andCalifornia coastal waters (for a review see Hasle, 2002), but wereabsent in the northeast subarctic Pacific Ocean (Marchetti et al.,2008). Two other species previously detected in the PNW, P.

australis and Pseudo-nitzschia pseudodelicatissima (Hubbard et al.,2008; Lundholm et al., 2012), were not detected in our samples(despite being represented on the microarrays). Our sequencinganalysis indicated the presence of 4 additional Pseudo-nitzschia

species/strains not represented on the microarrays, including P.

fryxelliana (Lundholm et al., 2012), and P. granii, a widelydistributed species in the northeast subarctic Pacific Ocean(Marchetti et al., 2008) thought to be restricted to cold arcticwaters (El-Sabaawi and Harrison, 2006). In our work P. granii wasfound in a surface sample collected 7 km from the mouth ofColumbia River (water t = 12 8C). The other two novel ribotypes(JF308623–JF308626, and JF308627, Table 1 and Fig. S5) could notbe identified at the species level by our probe set or sequenceanalysis.

Our analysis indicated the presence of 4–7 different Pseudo-

nitzschia species in most samples collected during the summermonths (Fig. 6). Similar richness of Pseudo-nitzschia species wasobserved in the northeast subarctic Pacific Ocean (Marchetti et al.,2008), and in two studies examining coastal waters of the PNWduring 1997–1999 (Stehr et al., 2002) and 2004–2005 (Hubbardet al., 2008). Consistent with previous observations (Stehr et al.,2002), our study identified the same species each year, with thehighest diversity observed in summer, and reduced diversity inspring and late fall. Our data suggest that recurrent Pseudo-

nitzschia blooms in Washington and Oregon coastal waters mayoriginate from the same standing stocks persisting throughoutseasons and years.

Pseudo-nitzschia species diversity was remarkably reduced forcertain locations in 2009 compared to the same locations during2007–2008. For 6 out of 7 field campaigns, �95% of all testedsamples produced PCR amplicons with Pseudo-nitzschia primers(Table S2). In contrast, ITS1 amplicons were produced from fewerthan one-third of the samples collected in May 2009, and from only

2 of the 4 sampling transects (Fig. 6 and data not shown). Thisreduced presence was not observed for any other sampling month.These results may indicate depletion of Pseudo-nitzschia standingstocks during the 2008–2009 winter season. Corresponding waterparameter data for these samples revealed relatively low nitrateconcentrations (Table S1). This may have contributed to the lowPseudo-nitzschia diversity observed in these samples, since diatomblooms on the PNW continental shelf are known to be limitedseasonally by nitrogen availability (Ribalet et al., 2010). InSeptember 2009 samples, Pseudo-nitzschia diversity was moretypical, with 4–6 species in each sample, except for 3 samplescollected along the NH transect which produced signals for only 1–2 species each (Fig. 6, underlined). This reduced diversity mayagain be related to limited nutrient concentrations (in this case,nitrate and phosphate) observed for the corresponding samples(Table S1). Further in-depth study is necessary to identify thebiogeochemical factors affecting Pseudo-nitzschia diversity in theenvironment.

While DNA-based identifications of Pseudo-nitzschia spp. do notyield information about cell toxicity, the microarray assay andISDA may improve on initial screening procedures used to monitorcoastal waters for harmful algal species. Using results from thisstudy for guidance, we will iterate microarray probe sets,incorporating new sequences for the ITS1 region as they becomeavailable. The flexible probe design algorithm will also allow us toinclude additional targets, such as genes for RuBisCO and rRNA(Lundholm et al., 2012). Finally, future work performed with alarger environmental sample set will allow us to link results fromenvironmental metadata analyses and domoic acid assays tomicroarray assay results for a more comprehensive understandingof Pseudo-nitzschia species dynamics in coastal waters of the PNW.

Acknowledgements

We gratefully acknowledge the chief scientists of CMOP cruises,and the captains and crews of the R/V Wecoma, R/V New Horizon,and R/V Barnes for their assistance in this research. R. Kudela (UCSanta Cruz) kindly provided four of the Pseudo-nitzschia culturesfrom Monterey Bay (MB-Bla, MB-Ala, MB-D5, and MB-B3). Theresearch reported in this manuscript was funded by a grant fromthe National Science Foundation (NSF grant OCE 0424602), andsupported in part through a NSF graduate fellowship to MAM. Weare also grateful to the CMOP Cyberinfrastructure team forproviding access to environmental data and J. Needoba for sampleanalysis, including nutrients and chlorophyll.[SS]

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in

the online version, at http://dx.doi.org/10.1016/j.hal.2012.07.003.

References

Armbrust, E.V., Galindo, H.M., 2001. Rapid evolution of a sexual reproduction genein centric diatoms of the genus Thalassiosira. Applied and Environment Micro-biology 67 (8), 3501–3513.

Bano, N., Hollibaugh, J.T., 2002. Phylogenetic composition of bacterioplanktonassemblages from the Arctic Ocean. Applied and Environment Microbiology68 (2), 505–518.

Bolotin, S., Lombos, E., Yeung, R., Eshaghi, A., Blair, J., Drews, S.J., 2009. Verification ofthe Combimatrix influenza detection assay for the detection of influenza Asubtype during the 2007–2008 influenza season in Toronto, Canada. VirologyJournal 6, 37–52.

Casteleyn, G., Adams, N.G., Vanormelingen, P., Debeer, A.-E., Sabbe, K., Vyverman,W., 2009. Natural hybrids in the marine diatom Pseudo-nitzschia pungens(Bacillariophyceae): genetic and morphological evidence. Protist 160 (2),343–354.

D’Alelio, D., Amato, A., Kooistra, W.H.C.F., Procaccini, G., Casotti, R., Montresor, M.,2009. Internal transcribed spacer polymorphism in Pseudo-nitzschia multistriata

M.W. Smith et al. / Harmful Algae 19 (2012) 169–180180

(Bacillariophyceae) in the Gulf of Naples: recent divergence or intraspecifichybridization? Protist 160 (1), 9–20.

Diercks, S., Metfies, K., Schroeder, F., Medlin, L.K., Colijn, F., Evangelista, V., Barsanti,L., Frassanito, A.M., Passarelli, V., Gualtieri, P., 2008. Detection of phytoplanktonwith nucleic acid sensors. In: Algal Toxins: Nature, Occurrence, Effect andDetection, Springer, Netherlands, pp. 285–299.

Edgar, R., Domrachev, M., Lash, A.E., 2002. Gene expression omnibus: NCBI geneexpression and hybridization array data repository. Nucleic Acids Research 30(1), 207–210.

Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D., 1998. Cluster analysis anddisplay of genome-wide expression patterns. Proceedings of the NationalAcademy of Sciences of the United States of America 95 (25), 14863–14868.

El-Sabaawi, R., Harrison, P.J., 2006. Interactive effects of irradiance and temperatureon the photosynthetic physiology of the pennate diatom Pseudo-nitzschia granii(Bacillariophyceae) from the northeast subarctic Pacific. Journal of Phycology42 (4), 778–785.

Erdner, D., Dyble, J., Parsons, M., Stevens, R., Hubbard, K., Wrabel, M., Moore, S.,Lefebvre, K., Anderson, D., Bienfang, P., Bidigare, R., Parker, M., Moeller, P., Brand,L., Trainer, V., 2008. Centers for oceans and human health: a unified approach tothe challenge of harmful algal blooms. Environmental Health 7 (Suppl. 2), S2.

Grant, K.S., Burbacher, T.M., Faustman, E.M., Gratttan, L., 2010. Domoic acid:neurobehavioral consequences of exposure to a prevalent marine biotoxin.Neurotoxicology and Teratology 32 (2), 132–141.

Guillard, R.R.L., Ryther, J.H., 1962. Studies of marine planktonic diatoms. I. Cyclotellanana Hustedt and Detonula confervacea (Cleve) Gran. Canadian Journal ofMicrobiology 8, 229–239.

Hasle, G.R., 2002. Are most of the domoic acid-producing species of the diatomgenus Pseudo-nitzschia cosmopolites? Harmful Algae 1 (2), 137–146.

Hasle, G.R., Lundholm, N., 2005. Pseudo-nitzschia seriata f. obtusa (Bacillariophy-ceae) raised in rank based on morphological, phylogenetic and distributionaldata. Phycologia 44 (6), 608–619.

Horner, R., Garrison, D.L., Plumley, F.J., 1997. Harmful algal blooms and red tideproblems on the U.S. West Coast. Limnology and Oceanography 42 (5), 1076–1088.

Horner, R.A., Postel, J.R., Halsband-Lenk, C., Pierson, J.J., Pohnert, G., Wichard, T.,2005. Winter-spring phytoplankton blooms in Dabob Bay, Washington. Prog-ress in Oceanography 67 (3–4), 286–313.

Hubbard, K.A., Rocap, G., Armbrust, E.V., 2008. Inter- and intraspecific communitystructure within the diatom genus Pseudo-nitzschia (Bacillariophyceae). Journalof Phycology 44, 637–649.

Kaczmarska, I., LeGresley, M.M., Martin, J.L., Ehrman, J., 2005. Diversity of thediatom genus Pseudo-nitzschia Peragallo in the Quoddy Region of the Bay ofFundy, Canada. Harmful Algae 4 (1), 1–19.

Lodes, M.J., Suciu, D., Elliott, M., Stover, A.G., Ross, M., Caraballo, M., Dix, K., Crye, J.,Webby, R.J., Lyon, W.J., Danley, D.L., McShea, A., 2006. Use of semiconductor-based oligonucleotide microarrays for influenza A virus subtype identificationand sequencing. Journal of Clinical Microbiology 44 (4), 1209–1218.

Lodes, M.J., Suciu, D., Wilmoth, J.L., Ross, M., Munro, S., Dix, K., Bernards, K., Stover,A.G., Quintana, M., Iihoshi, N., Lyon, W.J., Danley, D.L., McShea, A., 2007.Identification of upper respiratory tract pathogens using electrochemical de-tection on an oligonucleotide microarray. PLoS One 2 (9), e924.

Lundholm, N., Bates, S.S., Baugh, K.A., Bill, B.D., Connell, L.B., Leger, C., Trainer, V.L.,2012. Cryptic and pseudo-cryptic diversity in diatoms – with descriptions ofPseudo-nitzschia hasleana sp. nov. and P. fryxelliana sp. nov. 1. Journal ofPhycology 48 (2), 436–454.

Lundholm, N., Hansen, P.J., Kotaki, Y., 2004. Effect of pH on growth and domoic acidproduction by potentially toxic diatoms of the genera Pseudo-nitzschia andNitzschia. Marine Ecology Progress Series 273, 1–15.

Lundholm, N., Moestrup, Ø., Kotaki, Y., Hoef-Emden, K., Scholin, C., Miller, P., 2006.Inter- and intraspecific variation of the Pseudo-nitzschia delicatissima complex(Bacillariophyceae) illustrated by rRNA probes, morphological data and phylo-genetic analyses. Journal of Phycology 42 (2), 464–481.

Manhart, J.R., Fryxell, G.A., Villac, M.C., Segura, L.Y., 1995. Pseudo-nitzschia pungensand P. multisenes (Bacillariophyceae): nuclear ribosomal DNAs and speciesdifferences. Journal of Phycology 31 (3), 421–427.

Marchetti, A., Lundholm, N., Kotaki, Y., Hubbard, K., Harrison, P.J., Armbrust, E.V.,2008. Identification and assessment of domoic acid production in oceanicPseudo-nitzschia (bacillariophyceae) from iron-limited waters in the northeastsubarctic Pacific. Journal of Phycology 44, 650–661.

Miller, P.E., Scholin, C.A., 2000. On detection of Pseudo-nitzschia (Bacillariophyceae)species using whole cell hybridization: sample fixation and stability. Journal ofPhycology 36 (1), 238–250.

Moniz, M.B.J., Kaczmarska, I., 2010. Barcoding of diatoms: nuclear encoded ITSrevisited. Protist 161 (1), 7–34.

Orsini, L., Procaccini, G., Sarno, D., Montresor, M., 2004. Multiple rDNA ITS-typeswithin the diatom Pseudo-nitzschia delicatissima (Bacillariophyceae) and theirrelative abundances across a spring bloom in the Gulf of Naples. Marine EcologyProgress Series 271, 87–98.

Postier, B., DiDonato Jr., R., Nevin, K.P., Liu, A., Frank, B., Lovley, D., Methe, B.A., 2008.Benefits of in-situ synthesized microarrays for analysis of gene expression inunderstudied microorganisms. Journal of Microbiological Methods 74 (1), 26–32.

Preston, C.M., Marin 3rd, R., Jensen, S.D., Feldman, J., Birch, J.M., Massion, E.I.,Delong, E.F., Suzuki, M., Wheeler, K., Scholin, C.A., 2009. Near real-time, auton-omous detection of marine bacterioplankton on a coastal mooring in MontereyBay, California, using rRNA-targeted DNA probes. Environmental Microbiology11 (5), 1168–1180.

Reid, C., Kaczmarska, I., Martin, J.L., Moniz, M.B.J., 2008. Morphological, biological,and molecular characteristics of the diatom Pseudo-nitzschia delicatissima fromthe Canadian Maritimes. Botany 86, 763–772.

Ribalet, F., Marchetti, A., Hubbard, K.A., Brown, K., Durkin, C.A., Morales, R., Robert,M., Swalwell, J.E., Tortell, P.D., Armbrust, E.V., 2010. Unveiling a phytoplanktonhotspot at a narrow boundary between coastal and offshore waters. Proceed-ings of the National Academy of Sciences of the United States of America 107(38), 16571–16576.

Scholin, C.A., Miller, P., Buck, K., Chavez, F., Harris, P., Haydock, P., Howard, J.,Cangelosi, G., 1997. Detection and quantification of Pseudo-nitzschia australis incultured and natural populations using LSU rRNA-targeted probes. Limnologyand Oceanography 42 (5), 1265–1272.

Smith, M.W., Herfort, L., Tyrol, K., Suciu, D., Campbell, V., Crump, B.C., Peterson, T.D.,Zuber, P., Baptista, A.M., Simon, H.M., 2010. Seasonal changes in bacterial andarchaeal gene expression patterns across salinity gradients in the ColumbiaRiver Coastal Margin. PLoS One 5 (10), e13312.

Stehr, C.M., Connell, L., Baugh, K.A., Bill, B.D., Adams, N.G., Trainer, V.L., 2002.Morphological, toxicological, and genetic differences among Pseudo-nitzschia(Bacillariophyceae) species in inland embayments and outer coastal waters ofWashington State, USA. Journal of Phycology 38 (1), 55–65.

Thessen, A.E., Bowers, H.A., Stoecker, D.K., 2009. Intra- and interspecies differencesin growth and toxicity of Pseudo-nitzschia while using different nitrogensources. Harmful Algae 8 (5), 792–810.

Trainer, V.L., Eberhart, B.T., Wekell, J.C., Adams, N.G., Hanson, L., Cox, F., Dowell, J.,2003. Paralytic shellfish toxins in Puget Sound, Washington State. The Journal ofShellfish Research 22 (1), 213–223.

West, N.J., Obernosterer, I., Zemb, O., Lebaron, P., 2008. Major differences of bacterialdiversity and activity inside and outside of a natural iron-fertilized phytoplank-ton bloom in the Southern Ocean. Environmental Microbiology 10 (3), 738–756.