A New Paradigm inProteomics

Peptides and proteins have beenassociated with many diseasestates such as cancers, diabetes,neurological and cardiovasculardiseases [1-4]. Despite thelimited success of a handful ofbiomarkers, most diseases lacksensitive and specific biomarkers.One of the most successfulbiomarkers, Prostate SpecificAntigen (PSA) has a fairly highfalse-positive rate and very lowclinical sensitivity (~25%).

Many proteomics researchers areinvolved in developing integrat-ed suites of algorithms, statisti-cal methods, and computerapplications to perform pro-teomics profiling directly frommass spectra [5-13]. Thesestrategies have demonstrateddiagnostic sensitivity andspecificity superior to conven-tional single biomarkers. Massprofiling of diseases is a majorparadigm shift in proteomics;the entire pattern containsimportant diagnostic informa-tion. The use of mass spectrome-try as a clinical tool providesmany key advantages overtraditional immunoassays. Thespeed of mass spectroscopy may

enable diagnosis ‘on the fly’.Since the mass spectrometer canaccurately measure proteins andpeptides without the need forantibodies, the developmenttime is drastically reduced.

Spectral profiling is also likelyto play a key role in toxicopro-teomics, where specific profilesmay be used to signal toxicevents. Detection of suchsignatures in animal or earlyhuman clinical trials could helppharmaceutical companiesdetect adverse drug effects,improving the efficiency ofclinical trials and reducing toxic effects.

A Complete BiomarkerDiscovery Platform

PerkinElmer has joined forceswith industry innovatorsVivascience and PredictiveDiagnostics to offer a completesolution for biomarker discoveryin drug discovery and diagnostics.The BioXPRESSION™ BiomarkerPlatform combines three power-ful technologies: high through-put, automated, carrier-proteinbiomarker enrichment with novelmembrane chromatography,exceptional high resolutiondetection using prOTOF™

A Comprehensive BiomarkerDiscovery and Analysis Platform: Application to Alzheimer’s Disease

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AuthorsScott Kuzdzal1, Mary Lopez1, AlvydasMikulskis1, Eva Golenko1, JosephDiCesare1, Eric Denoyer1, WaynePatton1, Richard Ediger1, Lisa Sapp1,Tillmann Ziegert1, Suzanne Ackloo5,Michael R. Wall2, David P. Mannion2,Guy della Cioppa2, Gershon Wolfe2,David Bennett4, and Simon Melov3.

1. PerkinElmer Life and Analytical Sciences, Boston, MA, and Shelton, CT, USA

2. Predictive Diagnostics Inc., Vacavilla, CA, USA

3. Buck Institute for Age Research, Novato, CA, USA

4. Rush Alzheimer’s Disease Center, Chicago, IL, USA

5. Predictive Diagnostics,Vacavilla, CA, USA

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MALDI O-TOF mass spectrometryand powerful, dedicated, biomark-er analysis capability with BAMF™

Biomarker Amplification Filteralgorithms. The introduction ofthis new platform gives researchersthe ability to screen thousands ofsamples and obtain the highestconfidence results for populationsegmentation, pre-clinical trialsand disease profiling.

Reproducible SampleComplexity Reduction

Reproducible sample complexityreduction is an essential first stepin biomarker discovery experi-ments. The large dynamic range ofserum/plasma necessitates theeffective removal of abundantserum proteins to allow analysis ofthe lower concentration analytes.

ProXPRESSION™ ProteinEnrichment Kits enable efficient,reproducible, microscale fractiona-tion for complex protein mixturesusing patented membrane absorber(MA) chromatography technology(Vivascience AG) combined withPerkinElmer’s proprietary buffersand elution chemistries.

Vivascience’s MA technology, incontrast to traditional resins andbeads, features a rigid format thatmakes it highly reproducible androbust. The technology also hassignificant ease-of-use advantageswhen incorporated into thePerkinElmer biomarker screeningsolution.

ProXPRESSION Acidic andBasic Protein Enrichment Kits

The ProXPRESSION Basic ProteinEnrichment Kit and Acidic ProteinEnrichment Kit are designed tofractionate a protein sample so thatthe recovered fraction is enrichedin proteins having either a basic oran acidic pH. Reagents, spin

columns, and protocols are provid-ed to prepare the sample forfurther analysis through clarifica-tion (to remove debris from thesample), charge fractionation, andsalt removal.

In one example, human brain(neocortex) tissue samples werefractionated based on charge andseparated on 2-D gels. An exampleof the enrichment is shown inFigure 1. Many protein spots notvisible in the total protein gel areidentifiable in a gel containing thebasic protein fraction.

Fractionation of acidic and basicproteins resulted in significantcomplexity reduction of proteinpatterns and better resolution ofindividual protein spots as com-pared to the protein patterns fornon-fractionated proteins [14].

ProXPRESSION BiomarkerEnrichment Kits

Analyzing low molecular weightpeptide biomarkers in serum andplasma has been extremely diffi-cult due to the vast number ofcontaminating species (salts,lipids, etc.) present. These contam-inants pose problems for separa-tion methods as well as detection

systems. High concentrations oflipids and salts can suppress theionization of peptide biomarkers inMALDI-TOF MS analysis. 2-D gelscannot effectively separate proteinsbelow 10 kilodaltons, the rangewhere MS has the greatest sensitiv-ity and specificity.

The low molecular weight regionof the human proteome is com-prised of peptides and fragments ofproteins. This ‘fragmentome’ hasattracted much interest because itis a relatively ‘unmined’ region ofthe proteome [15]. These lowermolecular weight species have theability to bind to carrier proteins(such as albumin), extending their half-lives in serum. TheProXPRESSION BiomarkerEnrichment Kits utilize cibacronblue to capture these carrierproteins. Proprietary elutionbuffers are then used to generatehigh fidelity peptide biomarkersignatures.

A key advantage of using a massspectroscopy-based approach inmining this low molecular weightproteome is that the mass spec-trometer can reproducibly recordprotein fragment peaks. If abiomarker for a given disease state

Figure 1. Application of ProXPRESSION Basic Protein Enrichment Kit to human neocortical braintissue. Many protein spots were enriched, as evident in comparison of the 2-D gels prior to (A)and after fractionation (B). Approximate range of proteins: 6-200 kDa, pI 6.0 –10.0.

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is a fragment of a larger protein, it may be extremely difficult orimpossible to produce effectiveantibodies for the conventionalELISA diagnostic tests. Suchantibodies would most likelydemonstrate cross-reactivity to thelarger parent protein as well.

Application of these kits to humanserum/plasma results in high-content, reproducible signatures of the low molecular weightproteome (Figure 2). This methodresults in reproducible and robustprotein signatures. Surface-basedbiomarker enrichment strategieshave been criticized for theirlimited binding capacities andtendency to bind the more abundant serum proteins [16, 17].The ProXPRESSION biomarkerenrichment kits offer a uniqueopportunity to discover biomarkerswithin the low molecular weightproteome.

MALDI Orthogonal-TOFMass Spectrometry:Enhanced BiomarkerFeature Detection

MALDI mass spectrometry hasbecome an important tool forproteomics researchers. MALDIOrthogonal-TOF mass spectrome-try provides superior biomarkerdiscovery performance characteris-tics over traditional axial MALDIsystems [18-22]. Pulsing ionsorthogonally into the time-of-flightdetection region separates ioniza-tion conditions in the samplingchamber from detection. Theinfluence of ionization conditionson mass accuracy is effectivelyeliminated.

The excellent mass accuracy andsensitivity and high resolutionachievable over a wider mass rangeassures high information contentin the MALDI spectra. This trans-

Figure 2. MALDI O-TOF MS spectra of three healthy human serum replicates processed usingProXPRESSION Biomarker Enrichment Kits (1,000–10,000 Da displayed, insert shows zoom ofreproducibility in the 5,000–8,000 Da range).

lates into ease-of-use and higherconfidence in the resulting data.

Wider Mass Range

MALDI O-TOF MS is capable ofcollecting data for a broader massrange (100 Da – 300,000 Da) in a

single acquisition. This reduces thenumber of runs and eliminates theneed for ‘spectral-stitching’ tocombine multiple acquisitions.Figure 3 illustrates the ability toacquire peptides and proteins in a single acquisition.

Figure 3. Wider Mass Range Analyses: Collect from 100 Da to 300 kDa in a single acquisition(700–25,000 Da shown). 1) Angiotensin I, 2) ACTH 18–39, 3) Cytochrome C (equine), 4) Myoglobin (equine) and 5) Trypsinogen.

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Generating DiseaseFingerprints with BAMFTechnology

BAMF Technology generates a set of rules that distinguishes diseasefrom non-disease (or respondersfrom non-responders, etc.). The firststep in generating a disease finger-print involves recording qualityprotein signatures for well-charac-terized disease and non-diseasesamples. The high resolution,sensitivity and mass accuracy ofMALDI O-TOF MS provides veryreproducible, information-richspectra. These protein signatures are uploaded to the BAMFTechnology LINUX cluster processingcenter directly from the prOTOFacquisition computer via a 128-bitencrypted web interface.

BAMF Technology is a proprietarypattern recognition engine and adiscovery platform whose primarypurpose is the illumination ofuncommon features present inensembles of spectra. These featuresare used to develop classificationmodels and screen unknowns. TheBAMF Technology suite consists of several analytical components,including some common computa-tional algorithms, and other, highlyspecialized custom algorithms. Theplatform uses known techniquesand algorithms as well as propri-etary techniques and combinationsof techniques and selected algo-rithms to achieve superior and morereproducible results as compared toknown and existing methods andsystems that are currently available.

The BAMF Technology is a platformtechnology in that it comprises abasic data processing frameworkinto which various known computa-tional algorithms may be plugged.As such, the platform is independ-ent of particular algorithms making

the platform highly adaptable. Theplatform also functions in two modes:a learning mode in which biomarkermodels are determined (also calleda training or discovery mode); andan application mode in which thelearned models are applied toclassify unknown data samples(also called a classification mode).

A key feature of the BAMFTechnology platform is that it usesconsensus at various processes andcomponents to provide a result.BAMF Technology can use manydata sources, including massspectrometry, MRI, X-ray, sono-grams, and raw binary data.

The BAMF Technology platformcomprises three process steps in thelearning mode to train the systemand provide the framework foranalyzing data and providingresults based on specific inputs.These steps include: OutlierRejection (also known as “Pre-Processing”), Feature Discovery,and Model Building. In classifica-tion mode, the BAMF Technologyplatform comprises the processsteps of Outlier Rejection andCategorization.

Pre-Processing

Data is received from a massspectrometer as vector data (m/zalong with an intensity level). ThePre-Processing comprises qualitycontrol steps used to identify andremove spectra represented bysystemic error and to ensure anacceptable level of uniformity of thedata. It does not affect or changeany of the data that is passed on to the next step. Pre-Processingestablishes the uniform set toproceed with in subsequent steps,such as Feature Selection and Model Building.

High Sensitivity & Resolution

Collisional cooling of ions providesvery high sensitivity by capturing alarge cross section of ions emergingfrom the MALDI laser plume.Rapidly reducing the ion energydistribution of the emerging ionbeam allows capture of divergentions which would otherwise be lost.These ions are focused towards theaxis of the beam, reducing ion lossand increasing sensitivity. Resolutionis greater than 10,000 (FWHM) forpeptides and proteins up to 10 kDa.

Mass Accuracy Stability

A single external calibrant can beused to achieve better than 10 ppmmass accuracy over an entiresample plate (up to 384 samples).With this exceptional mass accuracy over an entire experiment,researchers can focus more ondiscovering biomarkers, and spendless time performing calibration or aligning spectra.

BAMF Technology:Advanced InformaticsAnalysis

BAMF (Biomarker AmplificationFilter) Technology (PredictiveDiagnostics, Vacaville, CA) is acomprehensive suite of in silicomachine learning technologies andadvanced informatics tools thatgenerates biomarker fingerprints fordiagnostic and drug discoveryapplications [23]. BAMFTechnology performs qualitycontrol steps, generates and scorespotential biomarkers, builds theoptimal biomarker model, andclassifies the disease state accordingto an optimized computationalmodel. These design elements allowfor a highly reproducible diseasefingerprint that accurately classifiesblinded samples.

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Feature Selection

In the Feature Selection Step,algorithms are run to look forfeatures in the spectral data that areidentified by distinguishing varia-tions from group to group or spectrato spectra. Feature Selectionincludes three steps: Filtering,Human Intervention and Consensus.Feature selection can be bypassed if the operator wants to focus onspecific features, for example, thosethat lie beneath the noise floor.

Model Building

The Feature Selection process willidentify many biomarkers, onlysome of which may be useful asdiagnostic predictors. A model is aparticular combination of biomark-ers. The Model Building process isused to build and test all thepossible models of the identifiedbiomarkers to determine whichbiomarkers in which combinationshave the best diagnostic utility. Thechallenge is that the greater thenumber of features, the greater thenumber of models and the moretime and computer processingintensive the search for useful

Figure 4. BAMF Technologyis a proprietary, integratedpattern recognition engineand discovery platform forsignal processing and imageanalysis to discover,differentiate, and validatebiomarkers.

biomarkers becomes. On the otherhand, if only a sampling of modelsis tested in order to save time andcomputing power, the chances offinding the model with optimalutility decreases.

These various processes in theModel Building step may result inmarkers that are not exactly intu-itive, as they do not representknown elements or significantpeaks in the spectra. However theymay act as helper markers toprovide a more accurate picture ofthe indication for which detectionis desired. Irrespective of theapproach used, all classificationalgorithms that represent the ModelBuilding step undergo a consensusfunction for determination of thefinal selected class of categorizers.The consensus function operates bypolling the best and worst cate-gories to identify overlaps, using a stringent and majority rulesapproach.

The results of the analysis providedby the BAMF Technology platformon an input submitted for testingand quantification after the system

is fully trained includes the outputof a confidence level that is basedon the nature of the results and thecloseness of the results to thecriteria set during the systemtraining. Again, consensus analysisof the output of more than onecategorizer may be used duringModel Building.

Models can be altered at will givingthe user control over what featuressignificantly impact screening.BAMF Technology then goes throughseveral additional proprietary stepsto generate the optimum diseasepredictor features. Once thesebiomarkers are calculated, a model(or disease ‘fingerprint’) is createdfor the disease state.

Classification Using BAMFGenerated DiseaseFingerprints

The procedure for classification ofunknown samples is shown in Figure4. The samples are processed andanalyzed using MALDI O-TOF massspectrometry. The resulting proteinprofiles are then run against themodel to see how well they classify.

• Offers the potential for stand-alone diagnostics.

• Results are highly reproducible.

Alzheimer’s Disease

An estimated 15 million peopleworldwide suffer from Alzheimer’sdisease (AD). It is a progressiveillness that kills nerve cells anddestroys nerve connections in thebrain. The disease is not reversibleand currently there is no cure. The lifespan of an Alzheimer’sdisease victim is generallyreduced, although a person maylive anywhere from 3 to 20 yearsafter diagnosis. The disease ismarked by mental changes result-ing from damage in the braintissue. Because these changescannot be visualized until anautopsy, diagnosis for the diseaseis based on symptoms that patientshave. Symptoms include gradualloss of awareness, memory, andjudgment as well as mood andbehavioral disturbances. Currentproteomics research efforts are

focused on identifying disease-specific protein biomarkers to aid in diagnoses.

Analysis of Alzheimer’sDisease Samples

Serum and brain (cortex) tissuesamples were analyzed using thebiomarker discovery platformdescribed herein. ProXPRESSIONAcidic and Basic Protein EnrichmentKits were used to fractionate thetissue samples prior to peptide massfingerprinting (PMF) experiments.The ProXPRESSION BiomarkerEnrichment Kits were used togenerate disease fingerprints usingBAMF Technology. The experimentaldesign is shown in Figure 5.

Peptide Mass Fingerprintingof Alzheimer’s DiseaseSamples

Serum samples were obtained fromthe Religious Orders Study, a studyfunded by the National Institute onAging in 1993. The study includes

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Benefits of BAMFTechnology

The combination of MALDIOrthogonal-TOF mass spectrometryand the high throughput BAMFTechnology provides the mostcomprehensive analysis of thedisease state. Some of the benefitsinclude:

• Data may be uploaded via asecure, 128-bit encrypted webinterface. This obviates the needfor continuous software updatesand provides a seamless transi-tion from data acquisition toinformatics analysis.

• Delivers up to 100 percentspecificity and sensitivity com-pared to single-biomarker tests.

• Includes many QC/QA pre-processing steps to eliminate poor spectra and ensure qualityresults.

• Terabytes of data can be managedin a single location.

• Provides visualization tools(including contour plots).

Figure 5. Alzheimer’s disease samples were fractionated and analyzed using two complementarystrategies: peptide mass fingerprinting (green) and BAMF disease fingerprinting (red).

Figure 6. Differentially expressed proteinsobtained from basic fractionation of ADbrain lysates.

more than 1,000 older nuns, priests,and brothers without knowndementia from about 40 groupsacross the U.S. All study partici-pants participate in annual cogni-tive and motor testing and all haveagreed to brain donation at the timeof death. The study has greater than95% follow-up of survivors with up to 10 data points.

The sample set consists of twogroups, approximately fifteensamples each of Alzheimer’s humanbrain tissue (neocortex) and con-trols. 335 ‘blinded’ sera were alsoobtained. Brain lysate and serasamples were diluted 10X inbinding buffers. The diluted samples were fractionated usingProXPRESSION Acidic and Basicprotein enrichment kits. 2-D gelswere run as described in reference

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24. Gel images were acquired usinga PerkinElmer ProXPRESS™

Proteomic Imaging System. Samplepreparation and trypsin digestionwere carried out using the Montage®

In Gel Trypsin Digest Kit (Millipore).Spectra were acquired on theprOTOF 2000 MALDI O-TOF andanalyzed with TOFworks™ software(PerkinElmer).

Over 100 statistically significantdifferentially expressed proteinspots were identified by differentialgel electrophoresis (Figure 6). Spotswere ranked based on the followingcriteria: the number of gels present,the P-value, the differential expres-sion value and spot resolution. Thetop-ranked gel spots were excised,digested with the enzyme trypsinand identified using MALDI O-TOFmass spectrometry.

When the 2-D gels images from ADand non-AD basic fractions wereanalyzed, 17 protein spots (13different proteins) were identifiedas being differentially expressed.Table 1 lists the identities of theseproteins and their expression inboth groups. Seven-protein spotswere decreased 2- to 7-fold in ADincluding protein phosphatase 1,phospholipase C, pyruvate kinase,phosphoinositide-3-kinase andfructose biphosphate aldolase.

Generating an Alzheimer’sDisease Fingerprint UsingBAMF Technology

The 335 randomized serum sampleswere processed in triplicate usingProXPRESSION BiomarkerEnrichment Kits. Reference serumand no-serum controls were also

Table 1. Listing of identifications for the highest ranked proteins from basic fractionation.

Spot ID Protein MW (kDa) pI Expression (Normal/AD)

1780 Protein phosphatase 1 110.9 5.5 3.98

2153 Phospholipase C, delta 86.5 6.2 2.00

2324 Pyruvate kinase 58.5 7.2 6.15

2463 Phosphoinositide-3-kinase 54.6 5.8 2.19

3061 Fructose biphosphate aldolase 39.8 6.4 7.26

3049 Aldolase C 39.8 6.4 1.94

3370, 3436, 3383 Glyceraldehyde-3-phosphate 36.2 8.4 0.24, 0.10, 0.42dehydrogenase

3334 GDNF receptor, alpha 3 precursor 46.1 9.2 0.71

2777 Enolase-1 47.4 7.0 0.32

2339 Copine III 61.0 5.7 0.68

387 KARP-1 Binding protein 164.9 6.8 0.43

2446 hCRMP-2 62.7 6.0 0.22

1260 Valosin-containing protein 90.0 5.1 0.58

2584 Protein kinase C 85.0 6.8 0.32

Table 2. Sensitivity and specificity ofthe BAMF Technology generatedAlzheimer’s disease fingerprint.

Sensitivity Specificity

94% 89%

samples and enhance the detectionof low-abundance proteins. Thecombination of reproduciblefractionation methods with preciseand accurate separation, detectionand identification results in successful, statistically significantprotein identifications. Some of the proteins identified in this studyhave been reported in previousAlzheimer’s research studies. Thecytosolic enzyme alpha-enolase hasbeen identified as a target of proteinoxidation and is involved in the

glycolytic pathway in the pathologi-cal events of AD [25]. Other pro-teins identified as differentiallyexpressed in this study have notbeen reported and their possibleroles in neuro-degeneration arebeing explored.

Fractionation of Alzheimer’s disease and control samples using ProXPRESSION BiomarkerEnrichment Kits generated highlyreproducible spectra by MALDI O-TOF MS. These kits allowresearchers to investigate the low

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processed in each experiment.Seventy percent of the 1,050 samplespectra dataset were grouped intothree training sets:

• Group 1 = Alzheimer’s patientsand patients diagnosed with mildcognitive impairment (68 samplestotal).

• Group 2 = only Alzheimer’ssamples (43 samples).

• Group 3 = control population(143 samples).

These samples were analyzed on aprOTOF 2000 mass spectrometer.The protein signatures wereuploaded to the BAMF processingcenter.

For the remaining spectra, 30% ofthe samples from each group (91samples total) were pooled into asingle “blinded testing group”. Thisdataset was run against the diseasemodel generated from the trainingset. The Alzheimer disease modelwas able to discern control samplesfrom Alzheimer’s samples with 94%sensitivity and 89% specificity.Four potential biomarkers with high sensitivity and specificity were discovered.

Summary

PerkinElmer’s powerful, integratedbiomarker discovery platformincludes fractionation/enrichmentkits, automated MultiProbe® IIliquid handling, MALDI O-TOFmass spectrometry and powerfulBAMF informatics software. Thisplatform was used to process morethan 300 human serum samplesfrom Alzheimer’s and normalpatients.

Alzheimer’s disease and controlsamples were fractionated usingProXPRESSION Basic ProteinEnrichment Kits. These kits reducethe complexity of proteomic

Figure 7. BAMF generated contour plots for control and Alzheimer’s disease samples.

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References

1. Gretzer, M.B., and Partin, A.W. PSA markers in prostate cancer detection. Urol. Clin. North Am.2003, 30, pp. 677– 686.

2. Gion, M., Mione, R., Barioli, P., and Dittadi, R. Dynamic use of tumor markers, rationale, clinical applications and pitfalls. Anticancer Res. 1996, 16, pp. 2279–2284.

3. Marian, A.J., Nambi, V. Biomarkers of Cardiac Disease. Expert Rev. Mol. Diagn. Nov 4, 2004, 6, pp. 805–20.

4. Trull, Andrew K., et al., ed. Biomarkers of Disease: An Evidence-Based Approach.Cambridge Press, June 2002.

5. Conrads T.P. and Veenstra T.D. The Utility of Proteomic Patterns for the Diagnosis of Cancer. Current Drug Targets - Immune, Endocrine & MetabolicDisorders. March 2004; Vol. 4, No. 1, pp. 41–50.

6. Radulovic, D., Jelveh, S., Ryu, S., Hamilton, T.G., Foss, E., Mao, Y. and Emili, A. Informatics Platform for Global Proteomic Profiling and Biomarker Discovery Using Liquid Chromatography-Tandem Mass Spectrometry. Mol. Cell. Proteomics. 2004, 3. pp. 984–997.

7. Yasui, Y., Pepe, M., Thompson, M.L., Adam, B.L., Wright, G.L. Jr., Qu, Y., Potter, J.D., Winget, M., Thornquist, M., Feng, Z. A data-analytic strategy for proteinbiomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics. Jul 4, 2003, 3, pp. 449–63.

8. Adam, B.L., Qu, Y., Davis, J.W., Ward, M.D., Clements, M.A., Cazares, L.H., Semmes, O.J., Schellhammer, P.F., Yasui, Y.,

Feng, Z., Wright, G.L. Jr. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. Jul 1, 2002, Vol. 62, No. 13, pp. 3609–14.

9. Kang, X., Xu, Y., Wu, X., Liang, Y., Wang, C., Guo, J., Wang, Y., Chen, M., Wu, D., Wang, Y., Bi, S., Qiu, Y., Lu, P., Cheng, J., Xiao, B., Hu, L., Gao, X., Liu, J., Wang, Y., Song, Y., Zhang, L., Suo, F., Chen, T., Huang, Z., Zhao, Y., Lu, H., Pan, C., Tang, H. Proteomic Fingerprints for Potential Application to Early Diagnosis of Severe Acute Respiratory Syndrome. Clin. Chem. Nov 18, 2004.

10. Adam, B.L., Vlahou, A., Semmes, O.J., Wright, G.L. Jr. Proteomic approaches to biomarker discovery in prostate and bladder cancers [Review]. Proteomics, 2001, 1, pp. 1264–70.

11. Petricoin, E.F. III, Ornstein, D.K.,Paweletz, C.P., Ardekani, A., Hackett, P.S., Hitt, B.A., et al. Serum proteomic patterns for detection of prostate cancer. J.Natl. Cancer. Inst. 2002, 94, pp. 1576–8.

12. Li, J., Zhang, Z., Rosenzweig, J., Wang, Y.Y., Chan, D.W. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin. Chem. 2002, 48, pp. 1296–304.

13. Petricoin, E.F. III, Liotta, L.A. Mass spectrometry-based diagnostics: the upcoming revolution in disease detection [Editorial]. Clin. Chem. 2003, 49, pp. 533–4.

14. Lopez, M.F. et al. Microscale fractionation facilitates detection of differentially expressed

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molecular weight proteome (i.e.fragmentome), a virtually unex-plored treasure trove of biomarkers.These spectra were used to generatea BAMF Alzheimer’s diseasefingerprint. This fingerprint successfully classified Alzheimer’sdisease samples from controls withhigh sensitivity and specificity.

The PerkinElmer biomarker platform enables complementarydiscovery strategies: identificationusing PMF, and rapid proteinprofiling (i.e. fingerprinting) usingBAMF technology. Knowledge of aprotein’s identity is important forlearning more about a diseaseprocesses underlying pathology.The rapid screening capabilities of protein profiling offer incrediblepotential for improving clinicaldiagnostics. This comprehensive,integrated biomarker discoveryplatform, however, is not limited to biomarker discovery for clinicaldiagnostics. The performancecharacteristics described are wellsuited to meet the demands oftoxicoproteomics and drug efficacystudies. The rapid classificationcapabilities of the integratedbioinformatics platform describedherein can be used to screen for anddetect adverse effects of drug trials.Ultimately these tools may result inbetter, safer drugs and more effi-cient patient stratification, fulfillingthe unrealized promises of thegenomics revolution.

For a complete BioXPRESSIONBiomarker Platform product list, see inside back cover.

proteins in Alzheimer’s disease brain samples. Electrophoresis.Aug 25, 2004, 15, pp. 2557–63.

15. Mehta, A.I., Ross, S., Lowenthal,M.S., Fusaro, V., Fishman, D.A., Petricoin, E.F. 3rd, Liotta, L.A. Biomarker amplification by serum carrier protein binding. Dis. Markers. 2003-2004, Vol. 19, No. 1, pp. 1–10.

16. Diamandis, E.P. Proteomic Patterns in Biological Fluids: Do They Represent the Future of Cancer Diagnostics? Clin. Chem. 2003, Vol. 49, No. 8, pp. 1272 –1278.

17. Cottingham, K. Clinical Proteomics: Are We There Yet? Anal. Chem. Nov. 1, 2003, pp. 472A– 476A.

18. Chernushevich, I.V., Ens, W., Standing, K.G., Analytical Chemistry, 1999, 71, p. 452A.

19. Loboda, A.V., Ackloo, S., Chernushevich, I.V. A high-performance matrix-assisted laser desorption/ionization orthogonal time-of-flight mass spectrometer with collisional cooling. Rapid Communicationsin Mass Spectrometry. Vol. 17, No. 22, pp. 2508–2516.

20. Kuzdzal, S.A., DiCesare, J., Ackloo, S., Lopez, M., Loboda, A., Sapp, L. MALDI Orthogonal-TOF: Enhanced Mass Accuracy, Stability and Resolution over a Wider Mass Range. Oral presentation at PittCon 2004, Chicago, IL.

21. Denoyer, E., Kuzdzal, S.A., Lopez, M., Mikulskis, A., DiCesare, J. Advances in Biomarker Discovery by MALDI O-TOF Mass Spectral Profiling. PerkinElmer Application Note, 2004.

22. Kuzdzal, S.A., Lopez, M., Sapp, L., Mikulskis, A., Golekno, E. Advances in Protein Identification by Peptide Mass Fingerprinting with a New Orthogonal MALDI-O-TOF. PerkinElmer Application Note, 2004.

23. Flanagan, N. Novel Proteomics Technologies & Software. Genetic Engineering News.May 1, 2004, Vol. 24, No. 9.

24. Lopez, M.F., Kristal, B.S., Chernokalskaya, E., Lazarev, A., Shestopalov, A.I., Bogdanova, A., Robinson, M. Electrophoresis. 2000, 21, pp. 3427–3440.

25. Castegna, A., Aksenov, M., Thongboonkerd, V., Klein, J.B., Pierce, W.M., Booze, R., Markesbery, W.R., Butterfield, D.A. Proteomic identification of oxidatively modified proteins in Alzheimer’s disease brain. J. Neurochem. Sep 2002, Vol. 82, No. 6, pp. 1524–32.

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BioXPRESSION Biomarker Platform Complete Product Listing

A complete line of reagents and instrumentation from PerkinElmer and its partners supports your laboratory’s proteomics needs atevery step.

Product Description

BAMF™ (Biomarker Amplification Filter) A sophisticated analysis technology from Predictive Diagnostics, Inc. to identify patterns Data Analysis of proteins and peptides over a highly secure Internet connection from high-resolution

mass spec data. Builds multiple models for high-confidence and develops the optimal biomarker set.

MALDIchip™ Target Plates Our disposable MALDI targets simplify your sample throughput needs.

MultiPROBE® II Liquid Handling System Reduce sample preparation errors with our flexible, robotic liquid handling solution.

Nonlinear Dynamics Ltd. A complete suite of image analysis and bioinformatics software, including ProMST™

Software Products mass spectrometry bioinformatics package and Progenesis™ image analysis software.

ProFINDER™ Image Analysis Software Find 50–100% more true spots than with standard software packages. Powerful, comprehensive and easy to use.

prOTOF™ 2000 MALDI O-TOF Our breakthrough MALDI orthogonal time of flight mass spectrometer with collisionalMass Spectrometer cooling is the most stable and accurate single MALDI MS system available. It provides

improved instrument stability, resolution, and mass accuracy across a wide mass range making it the best choice for biomarker discovery and peptide mass fingerprinting.

ProXCISION™ Proteomics Our high precision ProXCISION excises spots from 2D gels and bands from 1D gels Gel Cutting Robot that were identified by the ProXPRESS, ensuring you get the spots you’re after every

time. The ProXCISION’s highly accurate, precise protein picking interfaces with ProFINDER 2D Image Analysis Software for the utmost in accuracy and traceability.

ProXPRESSION™ Protein Enrichment Our fractionation kits are ideal for preparing plasma and serum samples for MALDI MSand Biomarker Enrichment Kits to support biomarker discovery. They provide a complete set of reagents for sample

concentration and desalting and employ Vivascience membrane chromatography technology. The procedure is easy to use, reducing the complexity of sample preparation and increasing processing speed.

ProXPRESS™ 2D Proteomic Our imager provides the most accurate and reproducible detection and quantification Imaging System of low abundance proteins on large 2D and 1D gels of any CCD-based imaging system.

SYPRO® Ruby Stain For gel imaging, Molecular Probes’ stain provides excellent sensitivity.

TOFprep™ MALDI Spotter Our spotter automates the digestion, clean-up, concentration and spotting of samples for MALDI MS analysis.

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©2005 PerkinElmer, Inc. All rights reserved. The PerkinElmer logo and design are registered trademarks of PerkinElmer, Inc. BioXPRESSION, MALDIchip, ProFINDER, ProXCISION,ProXPRESS, ProXPRESSION, prOTOF, TOFprep, TOFworks are trademarks and MultiPROBE is a registered trademark of PerkinElmer, Inc. or its subsidiaries, in the United States and othercountries. Montage is a registered trademark of Millipore Corporation. BAMF is a trademark of Predictive Diagnostics. SYPRO is a registered trademark of Molecular Probes, Inc. All othertrademarks not owned by PerkinElmer, Inc. or its subsidiaries that are depicted herein are the property of their respective owners. PerkinElmer reserves the right to change this document at any time without notice and disclaims liability for editorial, pictorial or typographical errors.

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