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ava i l ab l e a t www.sc i enced i rec t . com

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Review

Evolution of organelle-associated protein profiling

Wei Yana,b, Ruedi Aebersolda,c, Elaine W. Rainesb,⁎aInstitute for Systems Biology, Seattle, WA 98103, USAbUniversity of Washington, Department of Pathology, Seattle, WA 98104, USAcInstitute of Molecular Systems Biology, ETH Zurich and Faculty of Science, University of Zurich, 8093 Zurich, Switzerland

A R T I C L E D A T A

⁎ Corresponding author.E-mail address: ewraines@u.washington.ed

1874-3919/$ – see front matter © 2008 Elsevidoi:10.1016/j.jprot.2008.11.017

A B S T R A C T

Keywords:

Identification of the protein constituents of cell organelles forms the basis for studies to definethe roles of specific proteins in organelle structure and functions. Over the past decade, the useof mass spectrometry-based proteomics has dissected various organelles and allowed theassociation of many novel proteins with particular organelles. This review chronicles theevolution of organelle proteomics technology, and discusses how many limitations, suchas organelle heterogeneity and purity, can be avoided with recently developed quantitativeprofiling approaches. Although many challenges remain, quantitative profiling of organellesholds the promise to begin to address the complex and dynamic shuttling of proteins amongorganelles that will be critical for application of this advanced technology to disease-basedchanges in organelle function.

© 2008 Elsevier B.V. All rights reserved.

Organelle proteomicsSubtractive organelle proteomicsQuantitative organelle profilingDigital organelle signatureMass spectrometry

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42. Cataloguing proteins from isolated organelles identifies organelle-associated proteins and contaminants . . . . . . . . 73. Subtractive organelle proteomics help distinguish organelle-associated proteins from background proteins . . . . . 84. Quantitative profiling of organelles improves the quality of organelle assignment . . . . . . . . . . . . . . . . . . . . 95. Conclusion and challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1. Introduction

Eukaryotic cells are organized into functionally distinct,membrane-enclosed compartments or organelles, such asthe nucleus, endoplasmic reticulum (ER), Golgi, and mito-chondria. Comprehensive knowledge of the organelle con-stituents, in particular proteins, can provide important

u (E.W. Raines).

er B.V. All rights reserved

information on the structure and function of the cell.Traditionally, microscopy is used to characterize the subcel-lular localization of individual proteins, employing antibodiesor expression of tagged proteins. In the post-genomics era,global analysis of subcellular localization of large fractions ofthe proteome has become possible. Successful studies in yeastemployed fluorescently tagged proteins to characterize their

.

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proteome-wide subcellular localizations [1,2]. However, suchtag-based microscopic analyses cannot decipher more com-plex mammalian systems, primarily due to technical difficul-ties with the generation of genome-wide tagging constructsthat can consistently express mammalian proteins at wildtype levels.

An alternative approach to analyze the association ofproteins with specific organelles is to isolate organelles usingbiochemical methods followed by identification of proteinsin the isolated organelles. Mass spectrometry (MS)-based pro-teomics has proven to be a powerful tool to identify andquantify proteins in various complex protein systems, includ-ing organelles [3]. Data generated using the combinedapproach of biochemical isolation of organelles and MSanalysis over the past decade have established the associationof a number of novel proteins with particular subcellularfractions. However, the difficulties with purification of theorganelle in question to homogeneity as well as protein

Fig. 1 –Development of new strategies for proteomics analysis toPartial separation of 3 organelles (individual organelles displayefractionation are shown schematically on the left. The middle pafractions analyzed by immunoblot analysis of organelle markerspectrometric data are illustrated in the right panel: A. Direct idenB. Organelle fraction identifications after subtraction of proteinsquantitative approaches; C. Identification and quantification of pdistribution pattern among all of the fractions for each protein toorganellemarker proteins. The third approach can be used to profproteins.

identification problems with MS analysis result in a highfalse identification rate. These problems have led to thedevelopment ofmore advancedmethods to determine proteinassociations among subcellular fractions [4–6]. Assisted byquantitative profiles of subcellular distributions and improvedprotein identification with the new generation of massspectrometers [7], the accuracy of protein associations withspecific organelles has been significantly improved. In thisreview, we focus on the evolution of technology for organelleproteomics research, starting from simple cataloging attemptsin the early years to the quantitative organelle profilinganalysis that emerged about two years ago. Example orga-nelles are used to discuss the application of individual meth-odologies, and limitations and advantages of the approachesare provided. Perspectives regarding future directions andchallenges are also considered. For comprehensive profiles ofspecific organelles, readers are referred to previously pub-lished review articles [8–10] and other original publications in

more accurately associate proteins with specific organelles.d with different colors and shapes) by density gradientnel demonstrates a typical distribution pattern of individualproteins. Three different approaches to analyze the masstification of proteins in specific fractions of purified organelle;in background fractions using different qualitative androteins in all fractions, and establishment of a quantitativecreate an organelle signature based on the distribution ofile organelles and predict subcellular localization of unknown

Table 1 –MS-based proteomics analysis has evolved to optimize the assignment of proteins with specific organelles

Organelle Isolation method Proteomicsapproach

Species Tissue/cell type Reference

Cataloguing proteins from isolated organellesTraditional fractionationClathrin-coated vesicle Differential and density

gradient centrifugation1D, LC-MS/MS Rat Brain [41]

Endoplasmic reticulum Density gradientcentrifugation

2D, MS/MS Mouse Liver [42]

Endoplasmic reticulum-Golgiintermediate compartment

Density gradientcentrifugation

1D, LC-MS/MS Human HepG2 liver cell line [43]

Exosome Differentialcentrifugation

1/2D, LC-MS/MS Human Melanoma cell linesSK-MEL-28 andMeWo; urine

[44,45]

Golgi Detergent extraction,anion-exchangechromatography

2D, LC-MS/MS Rat Liver [46]

Detergent partitioning 2D, LC-MS/MS,Edmon sequencing

Rat Liver [47]

Density gradientfractionation, high PHand detergent extraction

2D, LC-MS/MS Rat Liver, epithelial cells [48]

Lipid raft Density gradientfractionation

1D, LC-MS/MS Human Monocytic cell lineTHP-1

[49]

Density gradientfractionation

2D, MS/MS Human Jurkat T cell line [50]

Mitochondria Differential and densitygradient centrifugation

1D, LC-MS/MS Humanandmouse

Heart, Jurkat A3 cellline, and brain, heart,kidney, liver

[39,51,52]

Nuclear pore Differentialcentrifugation

1D, MS/MS Rat andyeast

Liver and N/A [53,54]

Nucleolus Differentialcentrifugation

LC-MS/MS Human Hela cell line [36,55]

Plasma membrane Density gradientcentrifugation

1/2D, LC-MS/MS Rat andmouse

Lung endothelialcells; brain cortex andhippocampal

[56,57]

Spindle pole Differentialcentrifugation

1D, LC-MS/MS Human Hela S3 cell line [58]

Synaptic vesicle and postsynapticdensity fractions

Differential and densitygradient centrifugation

1/2D, LC-MS/MS Rat Brain [59,60]

Biochemical enrichmentLysosome Affinity purification via

mannose-6-phosphatereceptor

2D, LC-MS/MS Humanandmouse

U937 and MCF7 cellline and embryonicfibroblasts

[15,16]

Triton filling and densitygradient fractionation

1D, LC-MS/MS Rat Liver [14]

Mitochondria Differentialcentrifugation and freeflow electrophoresis

2D, MS/MS Yeast [20,21]

Density gradientfractionation and freeflow electrophoresis

LC MS/MS Yeast [22]

Phagosome Latex bead-containingphagosome

2D, LC-MS/MS Mouse J774 macrophage-likecell line; Macrophage

[12,13]

Plasma membrane Isolation of biotinylatedcell surface proteins

2D, LC-MS/MS Human Neuroblastoma, lung,epithelial, colon cells

[18,19]

Postsynaptic protein complex Antibody-based affinitypurification

1D, LC-MS/MS Rat Brain [61]

Spliceosome Gel filtration and affinitypurification

1/2D, LC-MS/MS Yeast [62]

Affinity purification oftagged spliceosome

LC-MS/MS Human Hela cell line [17]

Subtractive organelle proteomicsClathrin-coated vesicle Differential and density

gradient centrifugation2D-DIGE, LC-MS/MS,iTRAQ

Human Hela cell line [28]

Subtractive organelle proteomics

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Table 1 (continued)

Organelle Isolation method Proteomicsapproach

Species Tissue/cell type Reference

Lipid raft Density gradientfractionation

LC-MS/MS, SILAC Human Hela cell line [26]

Nuclear envelope Differentialcentrifugation

LC-MS/MS Rodent Liver [23]

Nuclear extract Differentialcentrifugation

2D, LC-MS/MS Mouse Embryonic stem cellsand embryonic germcells

[63]

Peroxisome Density gradientfractionation

LC-MS/MS, ICAT Yeast [29]

Plasma membrane Fractionation for plasmamembranes and caveolae

1/2D, LC-MS/MS Rat Various tissues, cellline

[56]

Postsynaptic density fractions Differential and densitygradient centrifugation

MS/MS, ICAT Rat Brain [32]

Vacuolar membrane Density centrifugation LC-MS/MS Yeast [64]

Quantitative profiling for organelle signaturesCentrosome Density gradient

fractionationLC-MS/MS, PCP Human Lymphoblastic cell

line KE-37[33]

Endoplasmic reticulum, Golgi Differentialcentrifugation anddensity gradientfractionation

1D, LC-MS/MS,redundant peptide(spectral) counting

Rat Liver [6]

Endoplasmic reticulum, Golgi,mitochondria, plasma membrane

Differentialcentrifugation anddensity gradientfractionation

LC-MS/MS, ICAT,iTRAQ

Arabidopsisthaliana

[4,35]

Endoplasmic reticulum, Golgi,endoplasmic reticulum/Golgi vesicles,mitochondria, nucleus, plasma membrane

Density gradientfractionation

LC-MS/MS, PCP Mouse Liver [5]

Peroxisome Density gradientfractionation

1D, LC-MS/MS, PCP Mouse Kidney [34]

Key to abbreviations used in the table: LC, liquid chromatography; MS/MS, tandem mass spectrometry; 1/2D, one- or two-dimensional gelelectrophoresis; 2D-DIGE, two-dimensional difference gel electrophoresis; iTRAQ, isobaric tags for relative and absolute quantification; SILAC,stable isotope-labeling with amino acids in cell culture; ICAT, isotope-coated affinity tag; PCP, protein correlation profiling.

7J O U R N A L O F P R O T E O M I C S 7 2 ( 2 0 0 9 ) 4 – 1 1

the field that cannot be covered in this review due to spacelimitation.

2. Cataloguingproteins fromisolatedorganellesidentifies organelle-associated proteins andcontaminants

The initial approach used to profile organelle proteins wasto isolate organelles by traditional subcellular fractionationmethods, such as differential centrifugation and densitygradient fractionation, followed by identification of proteincomponents in the target organelles using mass spectrometry(Fig. 1A). Application of this strategy over the past decade hasled to the identification of protein components of numerousorganelles listed in Table 1. These studies have provideda wealth of information on the proteins associated withparticular organelle fractions, including many novel proteins.However, the success of this method depends heavily uponpurification of the target organelles to near homogeneity withsufficient protein recovery for in-depth analysis. It is difficult,if not impossible, to obtain a “pure” organelle from subcellularfractionation approaches. For example, in profiling the Golgi

proteome using density gradient centrifugation and MS analy-sis, only 151 of the 421 total identified proteins were annotatedas either bona fide or novel putative Golgi proteins [11]. Inaddition to the limitations of separation approaches, organellesare frequently heterogeneous in their size and ultrastructuralproperties although they are microscopically defined by theirmorphology. Therefore, data acquired by such high-throughputanalysis need to be further tested by other experimental andbioinformatics approaches to remove false positive contami-nants. Unfortunately, many databases have utilized data fromsuch cataloguing studies, and it is thus critical to determine thebasis of any protein organelle association.

In an effort to improve specificity of organelle isolation andreduce possible contaminants, different biochemical enrich-ment approaches have been reported for purification ofseveral specific organelles (see Table 1, biochemical enrich-ment). For example, to isolate the phagosome, macrophageswere fed latex beads that altered their physical properties andallowed their separation from other organelles [12,13]. Asimilar approach has been reported for isolation of lysosomesfilled with Triton WR1339 [14] that altered the lysosomalcentrifugation properties. A more specific approach to isolateorganelles is to use affinity chromatography targeting

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organelle-specific proteins. Successful application wasreported for purification of a subset of lysosomal proteinscarrying mannose-6-phosphate modifications. Proteins withthe mannose-6-phosphate structure were affinity purified onimmobilized mannose-6-phosphate receptor and then identi-fied by MS [15,16]. Affinity purification can also be achieved byspecifically tagging organelle specific proteins. To profile thespliceosome, Zhou and colleagues tagged pre-mRNA (used toreconstitute the spliceosome) with three hairpins that canbe specifically recognized by a bacteriophage coat proteinMS2. Then the tagged spliceosome, bound with the MS2 fusedwith maltose-binding protein, was subsequently purifiedusing maltose-binding protein affinity chromatography. Theacquired spliceosome was functional in splicing mRNA, and145 spliceosomal proteins were identified from this complex[17]. A tagging strategy was also utilized to isolate plasmamembrane proteins. The plasma membrane proteins wereeffectively fractionated by labeling the lysine residues of theextracellular domain of cell surface proteins with biotin tags.The labeled plasma membrane proteins were affinity purifiedusing streptavidin or avidin chromatography and subse-quently identified by MS [18,19]. In addition to enrichmentapproaches, advanced biochemical separation technology,such as free-flow electrophoresis, has been utilized to purifymitochondria based on their surface charge with improvedproteome coverage [20–22]. Although such biochemicalapproaches increase the purity of isolated organelles in com-parison with the use of simple, density gradient fractionationmethods, complete removal of contaminant proteins is hardto achieve. Therefore, elimination of false-positive identifica-tions again requires further testing, or application of otherproteomic strategies discussed below.

3. Subtractive organelle proteomics helpdistinguish organelle-associated proteins frombackground proteins

Subtractive proteomics strategies were developed to reducethe frequency of false-positive contaminants in organellefractions [23]. To examine proteins in a nuclear envelopefraction by MS, a post-nuclear microsomal fraction was alsoanalyzed in parallel. Proteins identified in the microsomalfractions, presumably containing no nuclear envelope, wereconsidered as background and then subtracted from theproteins in the nuclear envelope fraction. Using this strategy,80 nuclear envelope proteinswere identifiedwith high fidelity.However, this background subtraction scheme utilizes twoprotein lists from separate proteomics analyses, and thus canlead to both false negative and false positive identifications.For example, a false negative result can arise from subtractionof any identification in the background sample, even if itsamount is significantly less than that in the test sample.On theother hand, differences in sample complexities and dynamicranges between the test and background fractions can lead toidentifications in the test but not the background sample andthus generate false positive results. Consequently, suchqualitative-based subtractive proteomics have been mostuseful for samples with similar compositions, and for dataacquired with high performance mass spectrometers.

Some of the limitations of early subtractive proteomicshave been circumvented by the development of quantitativeproteomics approaches that allow comparison of the relativeabundance of proteins from the target and control fraction inthe identical analytical environment following sample com-bination. Foster and colleagues utilized SILAC-based isotopelabeling to analyze lipid rafts [24,25], with heavy isotopes forsamples containing lipid rafts and light isotopes for controlsamples treated with cholesterol-disrupting agents to removelipid rafts [26]. Following cell lysis, sampleswith equal amountsof protein were combined, prepared for lipid raft fractionation,and then analyzed byMS. This quantitative proteomics analysisallowed assignment of proteinswith different heavy/light ratiosinto three protein groups: raft proteins (ratio N7.5), raft-associated proteins (ratio N3.0 but b7.5), or nonspecific proteins(ratio b3.0). A second study to profile clathrin-coated vesiclesused heavy and light iTRAQ reagents to label an untreatedsample and one inwhich the clathrin heavy chain was knockeddown by siRNA [27]. The combined samples were processed,analyzed by MS and the acquired proteins with light/heavyratios N2 were referred to as clathrin coated vesicle-associatedproteins [28]. For both of these studies, the application ofquantified ratios between the sample and control fractions wasthebasis for subtraction of false identifications, anadvance thatlowers error rates. However, the use of arbitrary thresholdsrather than statistical analyses as the basis for assignment oforganelle associations remains a limitation.

A unique attribute of quantitative subtractive proteomicsis that it is less dependent upon absolute purification oforganelle fractions, and thus can even be applied when idealfractionations are not possible. This is illustrated by themethod used to monitor the relative enrichment of proteinsduring purification of yeast peroxisomes [29] in whichsamples before and after a specific affinity purification stepwere differentially labeled with light and heavy ICAT reagent[30], respectively. True peroxisomal proteins, which wereenriched during the affinity purification, were expected tohave relatively high heavy/light ratios, and statistical analysiswas then applied to the quantified proteins. Proteins withratios significantly higher than a statistically determinedthreshold were assigned to the peroxisomal protein group,while all the others were assigned to the background groupand subtracted. To monitor the enrichment of specificorganelles, similar studies targeting zymogen granules usingiTRAQ and postsynaptic density fractions using ICAT havebeen reported [31,32]. An advantage of quantitative subtrac-tive proteomics is that it can be used to profile any organellefractions that utilize enrichment or purification steps. Asshown in Fig. 1 (method B), the target organelle protein(displayed as a blue diamond) in a partially enriched organellesample froma density gradient fractionation is predominantlydistributed in fractions 1–3 among the 11 acquired fractions.Consequently, labeling fractions 1–3 (enriched) and fractions4–11 (depleted) with light and heavy isotope reagents, respec-tively, leads to elevated light/heavy ratios in MS analysis ofproteins associated with the target organelle. Other proteinswith low light/heavy ratios are considered as background andare subtracted from the positive dataset. Application ofthe quantitative subtractive organelle proteomics representsa significant advance and has led to improved accuracy of

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protein organelle profiling, even with fractions of partiallypurified organelles.

4. Quantitative profiling of organelles improvesthe quality of organelle assignment

Quantitative proteomics has also been used to directly moni-tor protein distributions among subcellular fractions. Asillustrated in Fig. 1 (method C), MS analysis of all 11 partiallyseparated fractions can measure the relative abundance ofeach identified protein among different fractions. Applicationof bioinformatics analysis can therefore be used to obtain aquantitative distribution pattern for each protein amongall fractions. Proteins with similar distribution patterns areassumed to be in the same organelle. By examining the rela-tive match of the quantitative distribution of each proteinwith that of the organelle marker proteins, the organelle asso-ciation for each protein can be determined. This is akin to thetraditional protein organelle assignment using subcellularfractionation except that proteins are quantified by MSinstead of by immunoblot.

In the past several years, quantitative protein profilinghas been successfully used to analyze organelles. Profiling thecentrosome was the focus of the first report that measuredthe relative precursor ion intensities of peptides identified byMS in sequential sucrose gradient fractions [33]. The authorsdeveloped amethod termed protein correlation profiling (PCP)to acquire the distribution pattern among fractions for eachprotein. A consensus distribution pattern shared by manyknown centrosomal proteins was derived that constituted adigital organelle signature for the centrosome. This digitalorganelle signature was useful in predicting novel centroso-mal proteins as illustrated by the validation of 19 of the 23novel centrosomal proteins using other subcellular localiza-tion approaches. A similar PCP-based approach successfullyprofiled the mouse peroxisome [34].

The PCP platform was subsequently further expanded as ameans to characterize multiple organelles simultaneously,including the nucleus, ER, Golgi, ER/Golgi vesicles, plasmamembrane, and mitochondria [5]. Digital signatures (orquantitative distribution patterns) for these organelles weresuccessfully acquired from partially separated subcellularfractions. In addition to the PCP, the digital organelle sig-natures can also be acquired from the subcellular fractionsusing other quantitative proteomics methods. An example isan analysis platform termed “localization of organelle pro-teins by isotope tagging” (LOPIT) in which the commercialavailable stable isotope labeling reagents (ICAT and iTRAQ)were used for the quantitation [4,35]. Using LOPIT, digitalsignatures were successfully acquired for organelles ofendoplasmic reticulum, Golgi, plasma membrane, vacuole,and mitochondria on fractions labeled with isotopic reagents.Similarly, organelle signatures for the ER, Golgi and Golgi-derived COPI-coated vesicles (transport vesicles between theGolgi and ER) were determined in an independent study bysimply quantifying redundant peptides for each identifiedprotein combined with significant bioinformatics support [6].Together, these studies demonstrate that multiple organellescan be profiled simultaneously from a partially fractionated

sample utilizing various quantitative proteomics methods,and can allow acquisition of digital signatures for theorganelles based upon statistical analysis.

The quantitative proteomics platforms described abovehave successfully identified quantitative organelle patterns,and significantly improve the quality of organelle assignmentfor each identified protein. Advantages of these quantitativeplatforms include the fact that organelles can be profiledwithout the necessity of complete purification of the targetorganelles, and data from organelle profiles can be moreaccuratelymanagedwith quantitative annotations. In addition,the acquired organelle signatures appear to be more robust, inlarge part because they are defined by consensus distributionpatterns shared by a group of known resident proteins ratherthan by one or two marker protein(s) used in traditionalorganelle analysis. The ability to profile multiple organellesfrom a single fractionation experiment represents a significantadvance, and provides the possibility to analyze the complexityof proteins shuttling among organelles.

5. Conclusion and challenges

Although organelle proteomics has a relatively short historyof about 10 years, it has developed rapidly and has providedimportant information on the constitution and functionalorganization of organelles. While the accuracy of assignedorganelle associations has remained a serious limitation, sig-nificant improvements have been made recently that reducethe false positive rate. The new generation of mass spectro-meters with high mass accuracy and resolving power,particularly the FT-ICR and LTQ-Orbitrap [7], have made amajor contribution to this improvement. Data quality withnew quantitative proteomic approaches is better controlledand documented, even with the similar organelle purificationmethods. Application of this advanced technology to addi-tional organelle analyses has the potential to provide a morecomprehensive and accurate understanding of the subcellularlocalization of the proteome.

However, organelle proteomics must still surmount atleast two major challenges. The first is to develop strategies toprofile dynamic changes in organelle constituents followingdifferent stimuli. Although organelles are composed of residentproteins, many proteins shuttle among different organelles, andtheir redistribution is critical for different organelle functions.Current organelle profiles are oftenmeasured under steady stateconditions, but evaluation of changes in protein distributionunder different conditions could provide important clues toprotein function and the specific impact of molecular perturba-tions, including various disease states. The recent emergence ofquantitative organelle proteomics technologies, especially thoseable to profilemultiple organelles at the same time [4–6], providesthe necessary tools to dissect dynamic states of organelles bycomparing different snapshots of the organelle profiles. Earlyattempts at dynamic profiling are beginning to appear [36,37].However, such approaches are still in their infancy and face thedifficult task of distinguishing proteins dynamically shuttlingamong organelles from the contaminants. It will require accurateand reproducible organelle assignment for all proteins at eachmeasured state before such comparisons are possible.

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A second challenge is to determine the extent to whichthe composition of organelles may vary between differentcells and tissues. Several proteomics studies suggest thatorganelle constituents may be distinct in different tissues [38–40]. As shown in Table 1, the majority of tissue proteomicshave been conducted on liver samples. A recent publicationillustrates the power of dynamic profiling of synaptic proteinsto identify protein changes within 1 h of treatment [65]. Thecontinued expansion of organelle profiling to more diversifiedsamples can be expected in the next few years, and is par-ticularly important, as illustrated in the recent publicationprofiling synaptic proteins [65], to the application of basic cellbiological studies to disease-based pathology.

Acknowledgments

This work was supported in part by Federal funds from theNational Heart, Lung andBlood Institute andNational Institutesof Health, under Contract N01-HV-28179, and HL18645.

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