Characterization of Multiple Myeloma Vesicles by Label-Free Relative Quantitation

Proteomics 2013, 13, 3013–3029 3013DOI 10.1002/pmic.201300142

RESEARCH ARTICLE

Characterization of multiple myeloma vesicles by

label-free relative quantitation

Sean W. Harshman1,2∗, Alessandro Canella3∗, Paul D. Ciarlariello2, Alberto Rocci4,Kitty Agarwal5,6, Emily M. Smith3, Tiffany Talabere3, Yvonne A. Efebera3,Craig C. Hofmeister3, Don M. Benson Jr.3, Michael E. Paulaitis6,7,Michael A. Freitas1,2 and Flavia Pichiorri3∗∗

1 Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University, Columbus, OH,USA

2 Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA3 Department of Internal Medicine, Division of Hematology, The Ohio State University, Columbus, OH, USA4 Myeloma Unit, Division of Hematology, Azienda Ospedaliera Citta` della Salute e della Scienza di Torino,University of Turin, Torino, Italy

5 Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA6 Nanoscale Science and Engineering Center, The Ohio State University, Columbus, OH, USA7 Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, USA

Multiple myeloma (MM) is a hematological malignancy caused by a microenviromentallyaided persistence of plasma cells in the bone marrow. The role that extracellular vesicles(EVs), microvesicles and exosomes, released by MM cells have in cell-to-cell communicationand signaling in the bone marrow is currently unknown. This paper describes the proteomiccontent of EVs derived from MM.1S and U266 MM cell lines. First, we compared the proteinidentifications between the vesicles and cellular lysates of each cell line finding a large overlapin protein identifications. Next, we applied label-free spectral count quantitation to determineproteins with differential abundance between the groups. Finally, we used bioinformatics tocategorize proteins with significantly different abundances into functional groups. The resultsillustrate the first use of label-free spectral counting applied to determine relative proteinabundances in EVs.

Keywords:

Cell biology / Exosomes / Label free / LC-MS/MS / Microvesicles

Received: April 9, 2013Revised: June 19, 2013

Accepted: July 13, 2013

� Additional supporting information may be found in the online version of this article atthe publisher’s web-site

1 Introduction

Multiple myeloma (MM) is the second most common hema-tological malignancy accounting for more than 10 000 deathsannually [1]. Recent improvements in antineoplastic drugs

Correspondence: Dr. Michael A. Freitas, The Ohio State UniversityMedical Center, 460 West 12th Avenue, Columbus, OH 43210, USAE-mail: [email protected]: +1-614-688-8675

Abbreviations: BST-2, bone marrow stromal cell antigen 2; cryo-

TEM, cryo-transmission electron microscopy; EV, extracellularvesicles; FDR, false discovery rate; IgG� LC, IgG kappa light chain;MM, multiple myeloma; NCL, nucleolin

including proteasome inhibitors and immune modulatingdrugs have improved overall patient outcomes [2]. There isa tight relationship between MM plasma cells and the bonemarrow stromal cells, and this stroma has a pivotal role inthe regulation of MM cell growth and survival, as well as sol-uble factors and adhesion molecules [3]. Although importantsoluble factors and adhesion molecules, such as TNF-� andCD49d have been identified, small lipid membrane boundvesicles are hypothesized to also play a role in cell–cell sig-naling [3–5]. Microvesicles or exosomes (called extracellular

∗These authors contributed equally to this work.∗∗Additional corresponding author: Dr. Flavia Pichiorri,E-mail: [email protected]

C© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

3014 S. W. Harshman et al. Proteomics 2013, 13, 3013–3029

vesicles (EVs)), released by almost all cell types, are smallstructures based on a lipid bilayer and are recognized as im-portant in facilitating intercellular communication withoutcell-to-cell contact [4,5]. Recently, several studies have focusedon the role of circulating EVs in cancer biology. These vesiclesincrease tumor survival and expansion by carrying bioactivemRNA, miRNA, and proteins into the extracellular space al-lowing for functional manipulation of the surrounding tumormicroenvironment [4, 5].

MS-based proteomics is a powerful tool used to charac-terize the protein content of EVs [6–31]. In this article, weused shotgun proteomics to identify proteins contained invesicles derived from two distinct MM cell lines. We fur-ther applied label-free spectral count relative quantitation toassess the differences in protein abundances [32, 33]. Thisapproach revealed proteins of variable abundance acrossthese MM cell-derived vesicles. Our results establish afoundation for further functional studies of MM biologythrough the identification of proteins associated with vesicletargets.

2 Materials and methods

2.1 Cell line tissue culture

MM.1S and U266 cell lines were obtained from AmericanType Culture Collection (ATCC Manassas, VA, USA) andcultured using modified conditions originally described byGoldman-Leikin et al. and Nilsson et al. [34,35]. Briefly, cellswere maintained at 0.4 × 106 cells/mL by incubation at 37�Cwith 5% CO2 in RPMI-1640 media supplemented with 10%FBS, 2 mM glutamine (GlutaMAX), 50 U/mL penicillin-G, and 50 �g/mL streptomycin (Life Technologies, GrandIsland, NY, USA). To eliminate artifacts from serum-derivedvesicles, 48 h before analysis, 100–200 × 106 cells were pel-leted at 300 × g for 10 min and resuspended in serum freemedia at 1–1.5 × 106 cells/mL.

2.2 Vesicle isolation

The method used for isolation of cell line derived vesicleswas previously described by Thery et al. [36]. In short, serum-starved cells and media were centrifuged at 300 × g for10 min at 4�C. Supernatant was collected and centrifugedagain at 2000 × g for 20 min at 4�C. The cell pellets werefrozen and stored at −80�C for later use. Supernatant washarvested and vacuum ultracentrifuged at 10 000 × g for30 min at 4�C to remove residual cell debris. Supernatantwas collected and ultracentrifuged at 100 000 × g for 70 minat 4�C with vacuum. The resulting supernatant was discarded,pellets from multiple tubes were resuspended in 1 mL of PBS,pooled into a single tube, and ultracentrifuged at 100 000 ×g as described previously. Supernatant was eliminated andpellets of vesicles were frozen and stored at −80�C.

2.3 Flow cytometry

MM.1S and U266 cell lines were analyzed for annexin V andpropidium iodide staining. Following serum starvation, cellswere washed 1× with PBS and annexin V and propidiumiodide staining solution (Clonetech Laboratories, MountainView, CA, USA) was added. Samples were allowed to standfor 15 min in the dark. Cells were washed 1× with PBSand immediately analyzed. All analyses were completed ona Beckman Coulter CXP flow cytometer (Beckman Coulter,Brea, CA, USA).

2.4 Cryo-transmission electron microscopy

(cryo-TEM)

Vesicles derived from MM1.S and U266 cells were preparedfor cryo-TEM within a controlled environment (22�C and 95%relative humidity) of an automated vitrification device (FEIVitrobot Mark IV, FEI, Hillsboro, OR, USA). To prepare vit-rified specimens, 4 �L suspensions of EVs were applied toglow discharged lacey carbon-coated copper grids (400 mesh,Pacific Grid-Tech, San Francisco, CA, USA) and flash-frozenin liquid ethane. The vitrified samples were stored underliquid nitrogen before transferring to a Gatan Cryo holder(Model 626.DH) and visualized in a FEI Tecnai G2 F20 STtransmission electron microscope (FEI). The microscope wasoperated at 200 kV and under low dose mode to minimize ra-diation damage to the samples. Images were captured usinga 4 k × 4 k Gatan Ultrascan CCD camera at a magnificationof 38 000×.

2.5 Dynamic light scattering (DLS)

Number distributions of EV hydrodynamic diameters werederived from DLS measurements using a Nano ZetasizerZen3600 (Malvern Instruments Ltd., Worcestershire, UnitedKingdom). Samples were diluted to the required count rateof 50–300 kilocounts per second and equilibrated at 25�C. Allmeasurements were made in triplicate. The Stokes–Einsteinrelation was used to calculate particle diameters from mea-sured translational diffusion coefficients.

2.6 Preparation of samples for MS

The preparation of both the cell-derived vesicles and theglobal lysates was done following a modified method previ-ously developed in our lab [32]. Briefly, three biological repli-cates of vesicle isolations or 48 h serum starved cell pellets(100 000 cells) were resuspended in 50 mM ammonium bi-carbonate (Sigma Aldrich, St. Louis, MO) supplemented with0.5% Rapigest SF surfactant (Waters, Milford, MA). A total of800 ng of sequencing grade modified trypsin (Promega, Madi-son, WI, USA) was added to each sample and incubatedovernight (>16 h) at 37�C. The reaction was suspended and


Proteomics 2013, 13, 3013–3029 3015

Rapigest was precipitated through the addition of 98% formicacid (Acros Organics, Geel, Belguim) to approximately 30%v/v. Samples were returned to 37�C for 30 min. Solutions werecentrifuged 3× at 21 000 × g removing the supernatant follow-ing each centrifugation. Peptides were speedvac’d to drynessand resuspended in 20 �L of 2% ACN with 0.1% formic acid(aq). Final peptide concentrations were measured by 280 nmabsorbance using a NanoDrop ND-1000 (NanoDrop, Wilm-ington, DE, USA) spectrometer.

2.7 LC-MS/MS

A total of 1–2 �g of peptides were loaded for RP-HPLC sep-aration on a Dionex Ultimate 3000 capillary/nano HPLC(Dionex, Sunnyvale, CA, USA) and mass analyzed by aThermoFisher LTQ Orbitrap XL mass spectrometer (Ther-moFisher, Waltham, MA, USA). The LTQ Orbitrap XL wasfitted with a micro/nanospray ionization source (MichromBioresources Inc., Auburn, CA, USA). HPLC separationswere carried at a flow rate of 2 �L/min on a 0.2 mm ×150 mm C18 column (5 �m, 300 A, Michrom BioresourcesInc., Auburn, CA, USA). Mobile phases were HPLC wa-ter (J.T. Baker, Center Valley, PA, USA) and ACN (EMDMillipore, Billerica, MA, USA) each supplemented with 0.1%v/v formic acid. The 300-min HPLC gradient was as follows.Starting at 2% mobile phase B, the gradient was increasedlinearly to 5% at 12 min, 15% at 40 min, 30% at 170 min,55% at 240 min, 85% at 265 min, and 90% at 270 min. Thecolumn was held at 90% for 5 min, followed by equilibra-tion at 2% for 24 min. The heated capillary temperature andelectrospray voltage on the LTQ Orbitrap XL were 175�C and2.0 kV, respectively. Top five data-dependent mode was uti-lized in positive ion mode with dynamic exclusion of repeatcount = 3, repeat duration = 30.00, exclusion list size =500, exclusion duration = 350 s, and exclusion mass width of±1.50 m/z. Protein identifications were obtained using theMassMatrix search engine (v 2.4.2) and the UniprotKB com-plete H. sapiens proteome (as of 18Sep12) [37–40]. Search pa-rameters included three trypsin missed cleavages, precursorion tolerance of ±10 ppm and a product ion tolerance of ±0.8Da. Cytoskeletal, epidermal, and cuticle keratin identifica-tions were recognized as contaminant proteins and removedfrom the analysis (listed in Supporting Information Data4–8). The false discovery rate (FDR) was estimated using thereversed sequences of the target database. The parsing of pro-tein identifications and spectral counts was conducted fromeach data file and combined using an in-house python appli-cation [41]. For combined protein lists, the protein matcheswere retained based on an FDR threshold of 5% and twounique peptide matches or a max decoy cutoff of 2 for eachset of protein identifications.

2.8 Label-free relative quantitation

Relative quantitation of the LC-MS/MS data was performedusing the label-free approach described by Liu et al. and

Colinge et al. [42, 43]. The spectral counts used in the analy-sis did not include modified, semitryptic, or shared peptides,including those from multiple protein isoforms. Protein listswere generated as follows. Search results were combined intoa single harmonized table. This table contains the protein ID,the number of spectral counts, the number of peptides, se-quence coverage, and protein scores. Proteins were groupedbased on common peptide sets. Each protein group is repre-sented by the protein ID with the highest number of spectralcounts. Spectral count quantitation was performed using onlythe top protein matches that had two or more distinct peptidesequences in at least one sample and protein scores above thedecoy match discriminant score threshold. The decoy matchdiscrimanty score was determined by taking the protein scorefor the third decoy match or the decoy score that exceeds thetarget-decoy FDR of 5%. Spectral count quantitation was per-formed on the proteins that had a minimum of five total spec-tral counts across all samples. These criteria are very conser-vative and may reduce the apparent limit of detection becauserare protein matches with low counts are removed from thequantitative analysis. The spectral count data and their esti-mated FDRs are provided in Supporting Information Data5–8. Significance analysis of relative protein abundance fromspectral count data was determined by using the edgeR bio-conductor package [44]. Peptide spectral count distributionswere modeled using a Poisson/negative binominal distribu-tion and normalized to the respective spectral count librarysize [44–46]. Differences in protein abundance were evaluatedbased upon an exact text for the overdispersed data [46]. Falsediscovery was controlled by applying a Benjamini–Hochbergmultitest correlation (� = 0.05) to final p-values [47]. The cpmwere calculated as the base 2 log of the normalized averagecounts across a row divided by one million.

2.9 Computational annotations, clustering, and

bioinformatics

Venn diagrams were created using the BioVenn web applica-tion (http://www.cmbi.ru.nl/cdd/biovenn/) [48]. Clusteringanalysis and visualization was performed using open sourcesoftware Cluster 3.0 and Java Tree View (ver. 1.1.6r2). Bioin-formatic annotations of gene ontology for identified proteinswere searched against the PANTHER Classification System(http://www.pantherdb.org/) [49, 50].

2.10 Immunoblotting

Cell and vesicles were lysed using a modified RIPA buffer(50 mM Tris pH 7.5, 150 mM NaCl, 10% glycerol, 1.0% NP-40,0.1% SDS, and protease and phosphatase inhibitors). Proteinconcentrations of the lysates were determined by Bradford as-say (Bio-Rad, Richmond, CA, USA). Equivalent amounts ofglobal lysates and vesicle lysates were run in a 4–15% Tris-HClSDS-PAGE TGX gel (Bio-Rad), transferred to nitrocellulose


http://www.cmbi.ru.nl/cdd/biovenn/

http://www.pantherdb.org/


Figure 1. Cryo-transmission electron mi-croscopy (cryo-TEM) images of the (A)MM.1S cell-derived extracellular vesiclesand (B) U266 cell-derived extracellularvesicles, indicated by the black. The cryo-TEM carbon support grids (white ar-rows) are also seen in these images.(C) Number distributions of MM.1S andU266 extracellular vesicle diameters de-rived from dynamic light scattering (DLS)measurements.

and blotted for CD9, CD44, actin and nucleolin (NCL) (SantaCruz Biotechnology, Santa Cruz, CA, USA), glyceraldehyde3-phosphate dehydrogenase (GAPDH, Cell Signaling Tech-nology, Boston, MA, USA), IgG kappa light chain (IgG� LC),MHC class I, and bone marrow stromal cell antigen 2 (BST-2)(, Abcam, Cambridge, MA, USA). Chemiluminescent detec-tion was performed using anti-mouse and anti-rabbit IgG-HRP (GE Healthcare, Piscataway, NJ, USA) and either ECLWestern Blotting Detection Reagents (GE Healthcare) or Su-perSignal West Femto Kit (Pierce Biotechnology, Rockford,IL, USA). HeLa (CD9) and ARH77 (IgG� LC) global lysateswere used as positive controls for the immunoblots.

3 Results

3.1 Size distribution and structural characteristics

of MM-derived vesicles

The MM cell-derived vesicles were imaged by cryo-TEM to ob-tain morphological characteristics. Figure 1A and 1B showsrepresentative cryo-TEM images of vesicles derived from theMM.1S and U266 cell lines. The images depict vesicles thatare spherical in shape with a single lipid bilayer, and hydro-dynamic diameters that range roughly from 50 to 200 nm.Several vesicles are observed to contain daughter vesicles


Proteomics 2013, 13, 3013–3029 3017

(Fig. 1A, top) or internal, electron dense material (Fig. 1B,top). DLS was performed to assess the size distributionsof the enriched vesicles. The DLS analysis of the U266-derived vesicles shows a monomodal distribution of vesiclediameters ranging from 80 to 200 nm (average diameter of138 nm), while the MM.1S vesicle diameters are somewhatlarger, ranging from 100 to 200 nm (average diameter of177 nm) with a small population of even larger vesicles withdiameters between 240 and 260 nm (Fig. 1C). Collectively,these results indicate that our MM cell-derived vesicle prepa-rations yield EVs with similar size distributions and similarspherical morphologies for the two cell lines [4].

It has been shown that apoptotic cells release organelle-containing vesicles as part of their death program [51]. It ispossible therefore that vesicle isolations may contain apop-totic bodies. To address the possibility of apoptotic body con-tamination in our vesicle preparations, annexin V/popidiumiodide flow cytometry was conducted on serum-starved cellsprior to vesicle isolation. Supporting Information Data 1shows that more than 98% of cells are negative for annexinV/popidium iodide staining. Confirming that the majorityof the vesicles are derived from cells that are nonapoptotic.Taken together, the data indicate that vesicles obtained fromnonapoptotic MM.1S and U266 MM cell lines have diametersbetween 50 and 200 nm.

3.2 Proteomics of MM cell line derived vesicles

Vesicles isolated from the MM.1S and U266 cell lines andthe corresponding global cell lysates were characterized byLC-MS/MS proteomic analysis. An overview of the proteomicworkflow used is provided in Supporting Information Data2. The LC-MS/MS base peak chromatograms for the vesiclesand cell lysates (Supporting Information Data 3 & 4) showhigh similarity. A database search of the LC-MS/MS fromthree experimental replicates (starting from cells grown fromseparate cultures) for each of the vesicles yielded 311 and 272protein identifications for the MM.1S and U266 cell lines,respectively (Supporting Information Data 5 & 6). The LC-MS/MS analysis of the global MM.1S and U266 cell lysatesyielded 279 and 353 protein identifications (Supporting In-formation Data 7 & 8). Venn diagrams are provided in Fig. 2Aand 2B to show the overlap in protein identifications betweenthe cell-derived vesicles and their global lysates. While there isa high number of overlapping protein IDs, unique proteinswere observed in the cell line derived vesicles (24%, 72 forMM.1S, and 15%, 49 for U266) and the lysates (18%, 55 forMM.1S, and 35%, 111 for U266).

Recent literature on protein composition of vesicles ob-tained from many cell lines reported that several proteinsare similar irrespective of cell of origin [52–54]. However,vesicles may also harbor proteins unique to the cell of ori-gin. To determine if this hypothesis holds true for our dataset, we compared the protein IDs in MM.1S and U266 vesi-cles. The Venn diagram in Fig. 2C shows 32 (10%) proteins

unique to the MM.1S vesicles and 13 (4%) proteins unique toU266 with 324 common proteins. Additionally, comparisonof the MM.1S and U266 vesicle identifications to the down-loadable ExoCarta database of EV identified proteins yield alarge number of previously identified proteins (83% MM.1Sand 77% U266, Supporting Information Data 5 & 6) [30, 31].These results are consistent with recent proteomic analysesof EVs [30, 31, 53, 54].

3.3 Label-free comparison of cellular proteins

and vesicles

The LC-MS/MS data show a high similarity in protein IDsbetween the cell-derived vesicles and their corresponding cel-lular lysates. Hierarchical cluster analysis reveals significantdifferences in the relative protein spectral counts betweenvesicle and cellular proteins. Cluster heat maps are providedalong with ontological classification of the protein molecularfunctions and biological processes (Fig. 3). Label-free spec-tral count quantitation was then used to determine the sig-nificance of relative differences in protein abundance [32,33].Relative quantitation determined 298 and 366 proteins withsignificant differences in protein spectral counts betweenthe MM.1S and U266 vesicles and their corresponding cel-lular lysates (p < 0.05) (Supporting Information Data 9).Classification of significant proteins by molecular functionsand biological processes determined, using the PANTHERgene ontological search software, that vesicles have signifi-cantly different protein abundance than their cell of origin(Fig. 3).

Hierarchical clustering of the MM.1S and U266 vesiclesshows tight grouping by cell of origin (Fig. 4A). Label-freerelative quantitation determined 125 proteins with signifi-cantly different abundance between the MM.1S and U266vesicles (p < 0.05) (Table 1 and Supporting InformationData 9). The smear plot (constructed by plotting the log foldchange versus logcpm is provided in Fig. 4C). The PAN-THER gene ontological annotations for biological processand molecular function for those proteins with statisticallydifferent abundance are provided in Fig. 4D. These data sug-gest that the protein abundances in vesicles can distinguishbetween the cells of origin. Additionally, the results showthat the measurement of these differences in relative abun-dance more closely reflects biologic function than protein IDalone.

The increase in the relative abundance of specific proteinsin the vesicles could be attributed to higher protein expressionin a given cell type rather than specific packaging of proteinsinto the vesicles. To determine if the differences in vesicularprotein compositions are driven by cell type versus packaging,we plotted the fold change (log fold change) of each proteinin the MM.1S versus U266 vesicles versus its correspondingchange in the cellular protein. Figure 5A shows proteins withno significant change in vesicular abundance or cellular ex-pression in the same direction (i.e. both increased/decreased


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71


Proteomics 2013, 13, 3013–3029 3019

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51


3020 S. W. Harshman et al. Proteomics 2013, 13, 3013–3029Ta

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0004

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3194

9|S10

AB

_HU

MA

NPr

ote

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0153

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PY

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Test

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ific

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Pro

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MA

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rote

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113

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2.56

199

8.99

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1

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0111

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_HU

MA

NG

TPa

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4092

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HC

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MA

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sG

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MD

H1

PE

=1

SV

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812

192

33

−1.7

8222

10.0

5221

0.00

130

−1


Proteomics 2013, 13, 3013–3029 3021Ta

ble

1.

Co

nti

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Sta

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Rep

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Rep

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Rep

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Rep

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Rep

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Rep

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Log

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mp

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t.ad

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sp|Q

9Y69

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C4_

HU

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NC

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rid

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lar

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2333

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1

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2034

4540

3359

0.88

978

12.4

2375

0.00

143

1

sp|P

5220

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GD

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MA

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ph

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1672

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4972

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HU

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8.67

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343

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HU

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HU

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1718

2011

50

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4210

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351

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827

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1

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HU

MA

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ge.


Proteomics 2013, 13, 3013–3029 3023

Figure 2. Venn diagram renderings ofoverlapping and unique protein identifi-cations. (A) MM.1S vesicles (EV) versusglobal cell lysate (CL). (B) U266 vesicles(EV) versus global CL. (C) MM.1S vesicles(EV) versus U266 vesicles (EV). Data showmany overlapping protein identificationswhile also harboring unique IDs in eachcomparison.

in the vesicles and global lysates). As expected, these datapoints cluster about a line with a slope of 1 indicating vesicleabundance was driven predominantly by expression in theparent cell type. Conversely, Fig. 5B highlights the proteinswith opposing significant differences between the vesiclesand cellular lysates (i.e. increased abundance or expressionin one sample type while the other remains unchanged ordecreases). These data primarily cluster about the 0 intercept.However, there are several proteins located in the upper leftand lower right quadrant that are significantly enriched inthe vesicle samples independent of changes in the cellularprotein abundance (Fig. 5C). For the complete list of proteinswith independent changes in abundance, see Supporting In-formation Data 10. These results illustrate the power of thelabel-free approach in establishing the patterns of abundanceof vesicular proteins relative to cellular expression revealedin the global lysate expression.

3.4 Validation of LC-MS/MS protein identifications

and relative quantitation

To validate proteomic identifications and relative quantita-tion, we conducted immunoblots on the cell-derived vesicles(Fig. 6A) and both vesicles and parent cell lysates (Fig. 6B).Based on the LC-MS/MS data (Table 1 and Supporting In-formation Data 9) and antibody availability, several proteinswere identified for validation by immunoblot. First, CD9 wasselected, as it was not identified in the U266-derived vesi-cles while showing enrichment in the MM.1S-released vesi-cles. Conversely, IgG� LC was selected due to LC-MS/MSidentification in the U266-derived vesicles while remainingundetected in the MM.1S vesicles. Next, NCL was identi-fied for validation because the MS data showed enrichmentin the MM.1S vesicles compared to the U266 vesicles. Fi-nally, GAPDH was chosen as it was identified in both the



Figure 3. Clustering of LC-MS/MS spectral counts and pie chart illustrations of the PANTHER gene ontology annotations for molecularfunction and biological process for those proteins with significantly different abundances between the cell line vesicles when comparedto the parent cell lysate. (A) Clustering of spectral count data for the MM.1S vesicles and parent cell lysate. (B) Enlarged clustering ofspectral count data for those proteins selected for validation from MM.1S cell line. (C) PANTHER gene ontological annotations for themolecular function and biological process for proteins with statistically different abundances from the MM.1S cell line. (D) Clustering ofspectral count data for the U266 vesicles and parent cell lysate. (E) Enlarged clustering of spectral count data for those proteins selectedfor validation from the U266 cell line. (F) PANTHER gene ontological annotations for the molecular function and biological process forproteins with statistically different abundances from the U266 cell line. Results suggest variable abundance of specific proteins, which canconfer different potential biological processes and molecular functions.


Proteomics 2013, 13, 3013–3029 3025

Figure 4. EdgeR label-free analysis for differentially expressed proteins from the MM.1S- and U266-derived vesicles. (A) Hierarchal cluster-ing of LC-MS/MS spectral count data. (B) Enlarged clustering of spectral count data for those proteins selected for validation. (C) Smear plotof the log fold change (logFC) by logcpm for the vesicle data from each cell line. (D) Pie chart illustrations of the PANTHER gene ontologyannotations for molecular function and biological process for those proteins differentially expressed between the MM-derived vesicles.Data suggest that vesicular protein abundance distinguishes between the cells of origin allowing for differential functional potential.

MM.1S- and U266-derived vesicles. The immunoblot shownin Fig. 6A confirms the presence and relative abundancesof CD9, IgG� LC, NCL, and GAPDH in the vesicles derivedfrom the respective cell lines as described above.

Further validation of the LC-MS/MS-derived relative abun-dances was confirmed through a second immunoblot con-taining both vesicle and cell lysates (Fig. 6B). First as above,CD9 was shown by MS to be only identified in the vesiclesof the MM.1S cell line. The blot in Fig. 6B further con-firms the MS data and Fig. 6A for the identification of CD9.Additionally, the LC-MS/MS data shows that CD44, MHCclass I, and BST-2 were enriched in the vesicles of both celllines when compared to the cell lysates. The immunoblotshown in Fig. 6B confirms the MS data shown in Table 1 andSupporting Information Data 9 for CD9, CD44, MHC class

I, and BST-2 proteins. Both GAPDH and actin were blot-ted as housekeeping genes. However, due to challenges inprotein loading associated with vesicles, the common house-keeping genes are not completely adequate for normaliza-tion proposes in this subcellular fraction. Regardless, the im-munoblots confirm the relative changes we observe in theproteomics experiments while also exposing challenges innormalization of protein abundances between vesicles andcells.

4 Discussion

Cellular communication through soluble factors and cell-to-cell adhesion molecules has long been established for many



Figure 5. Smear plot of log fold change bylog fold change for the vesicles and global celllysates. (A) Proteins with no significant change inabundance between vesicles and global lysateschanges in the same direction. (B) Proteins withsignificant abundance differences in opposite di-rections between the vesicle and global lysatesamples. (C) Overlay of (A) and (B). Those pro-teins with the greatest independent differencesin abundance are labeled with Uniprot accessionnumbers.

hematological cell types including MM [55]. Until recently,the role of vesicular communication between cells has gonerelatively unstudied. Limitations in sample management, pu-rity, and preparation yield make vesicle study challenging.The monomodal size distributions of the vesicles isolatedfrom both MM cell lines suggest a single, monodisperse pop-ulation of vesicles in each case. The MM.1S vesicles are some-what larger (average diameter of 177 nm) and have a slightlybroader size distribution (standard deviation of 6.2 nm) com-pared to the U266 vesicles: average diameter of 138 nm anda standard deviation of 5.6 nm. These average diameters andsize distributions correspond approximately to the exosomesubpopulation of cell-secreted vesicles [4]. The similarity ofthe proteomic identifications between the vesicle populationsalso suggests similar vesicle populations for the two MM celllines.

Proteomic analysis of cell-derived vesicles has become theprimary tool for vesicular protein characterization. Mainly inthe last decade, vesicles from many in vitro and in vivo ori-gins have been analyzed by various MS methods [6–31]. Ourstudy represents an advance in vesicular proteomics throughthe use of label-free relative quantitation to characterize MMcell-derived vesicles and global lysates. We identified 583 to-tal vesicular proteins from the MM.1S and U266 vesicles.Although the LC-MS/MS data identified a number of com-mon EV proteins, such as antigen presenting molecules(MHC class I and II), adhesion molecules (tetraspaninsand integrins), membrane transport and fusion molecules(annexins, flotillin, and Rab proteins), cytoskeletal proteins(actin, tubulin, and moesin), and many others such as pyru-vate kinase, GAPDH, 14–3–3 proteins, HSP70, HSP90, elon-gation factor 1�, and the histones H2B, H2A, and H4, we also


Proteomics 2013, 13, 3013–3029 3027

Figure 6. Validation of protein iden-tifications and spectral count relativequantitation from the proteomic anal-ysis of the MM.1S- and U266-derivedvesicles and global cell lysates by im-munoblot. (A) Immunoblot of vesicle-identified proteins. Blot was probed forCD9, IgG� LC, nucleolin, and GAPDH.(B) Immunoblot for comparison of pro-tein relative abundance between vesi-cles and cell lysates. Blots were probedfor CD9, CD44, MHC class I, BST-2,GAPDH, and actin. Data confirms theLC-MS/MS protein identifications andrelative abundances observed in thedata sets. HeLa (CD9) and ARH77 (IgG�

LC) global lysates were used as positivecontrols.

identified 32 and 13 proteins unique to the vesicles derivedfrom the MM.1S and U266 cell lines, respectively [52–54].These results support the hypothesis that EVs have commonprotein profiles in large part, but with small sets of uniqueproteins corresponding to the parent cells of origin [52–54].Furthermore, the exclusive presence of BST-2 in the EV com-partment of MM cells strongly supports the specificity of ouranalysis.

While there are only a small number of different identifi-cations between the MM.1S and U266 vesicles, the relativeabundances of proteins in the MM cell-derived vesicles aremore divergent. The label-free relative quantitation of theMM.1S and vesicle data sets shows 125 proteins with statisti-cally different protein abundance. These proteins correspondto an array of functions both biologically and molecularly. Forexample, the RNA-binding protein NCL was shown to havehigher abundance in the MM.1S vesicles. NCL is a highlyconserved multifunctional protein, abundantly expressed inthe nucleolus of normal cells [56]. It has long been knownas a protein critical for ribosomal RNA biogenesis [56]. Inthe cytoplasm, NCL functions to regulate mRNA translationand stability of several tumor progression genes, includingBCL2, thereby inhibiting apoptosis of cancer cells. NCL is anintegral component of the DROSHA-DGCR8 microprocessorcomplex and recently we have shown that NCL promotes thematuration of a specific set of miRNAs that are implicatedin the pathogenesis of several human cancers, such as miR-21, miR-103, miR-221, and miR-222, whose overexpressionis often associated with greater aggressiveness and resistanceto antineoplastic therapies [57–60]. The presence of NCL andother RNA-binding proteins in MM EVs may allow furtherstudies that will focus on the understanding their role in RNAtransfer in cancer cells.

Alignment of vesicular and global cell lysate protein iden-tifications shows a low number of unique identifications be-tween the samples. Similar to the vesicle-to-vesicle compar-isons, more divergent protein abundance was found with theapplication of the label-free edgeR analysis to the data sets.These data show 298 (MM.1S) and 268 (U266) proteins with

variable abundance. For example, MM.1S vesicles show anincreased abundance of HLA class II histocompatibility anti-gens when compared to the MM.1S global cell lysate. Theseresults are in line with previous studies of B-cell-derived ex-osomes [61, 62]. Raposo et al. [61] showed that the vesicularMHC class II complexes can stimulate T cells in vitro. MHCclass I has been identified as classical vesicle marker in theserum of cancer patients. The mechanisms of tumor cell re-sistance to immune effector functions are diverse and can beboth intrinsic and reactive. A central immune escape route isthe partial or complete downregulation of this complex at thecell surface, thereby limiting or avoiding recognition by cyto-toxic CD8+ T effector cells and the induction of apoptosis [63,64]. Based on these observations, it is reasonable to hypothe-size that the specific shedding of MHC class I can be a com-mon characteristic of MM cells to avoid the immune systemresponse and support their growth, although further studiesin MM patients will be required to support this observation.

Finally, we are the first to apply a label-free approach toidentify variably abundance among proteins in the vesiclesand their parent cell. Our study reveals that only a smallnumber of unique proteins are packaged into EVs [52–54].Our study also reveals a more divergent protein abundancein the vesicles of MM cell lines.

This work was supported by the Ohio State University Peloto-nia Fellowship Program (A.R.), by Ohio State University Pela-tonia Idea grant (F.P., D.B.), in part from NIH grants (R01CA107106, P01 CA124570 and RC2 AG036559), NSF (EEC-0425626 and EEC-019790) and funded in part by MultipleMyeloma Opportunities for Research & Education (MMORE)and in part from Sidney Kimmel Foundation Scholar Award(F.P.). The cryo-TEM data were obtained at the TEM facility atthe Liquid Crystal Institute, Kent State University, supported bythe Ohio Research Scholars Program Research Cluster on Sur-faces in Advanced Materials. The authors thank Dr. Min Gao fortechnical support provided for the TEM experiments.

The authors have declared no conflict of interest.



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Characterization of Multiple Myeloma Vesicles by Label-Free Relative Quantitation