Title: Proteomics of serum extracellular vesicles identifies a novel COPD biomarker, fibulin-3 from elastic fibres
Authors:
Taro Koba, Yoshito Takeda*, Ryohei Narumi, Takashi Shiromizu, Yosui Nojima, Mari
Ito, Muneyoshi Kuroyama, Yu Futami, Takayuki Takimoto, Takanori Matsuki, Ryuya
Edahiro, Satoshi Nojima, Yoshitomo Hayama, Kiyoharu Fukushima, Haruhiko Hirata,
Shohei Koyama, Kota Iwahori, Izumi Nagatomo, Mayumi Suzuki, Yuya Shirai, Teruaki
Murakami, Kaori Nakanishi, Takeshi Nakatani, Yasuhiko Suga, Kotaro Miyake,
Takayuki Shiroyama, Shohei Koyama, Hiroshi Kida, Takako Sasaki, Koji Ueda, Kenji
Mizuguchi, Jun Adachi, Takeshi Tomonaga, Atsushi Kumanogoh
Data Supplement
Sample collection and approval
All serum samples were collected in Osaka University Hospital and stored at -80ºC until analysis. Approval was obtained from the Osaka University Graduate School of
Medicine Institutional Review Board; all patients gave written informed consent to participate in the study, and they were arrowed to fast on free will. All methods were performed in accordance with relevant guidelines and regulations. Mouse experiments were approved by the Osaka University’s Animal Care and Use Committee, and all of the animal procedures were performed following the Osaka University guidelines on animal care.
Statistical analyses
Statistical analyses were conducted using JMP Pro v. 14.3.0 (SAS Institute, Cary, NC,
USA). Pearson’s chi-square test or Welch’s t-test was used to compare healthy controls with COPD patients. Correlations between two parameters were calculated using Spearman’s rank correlation coefficients. Differences were considered statistically significant at p < 0.05. Ward’s hierarchical cluster analysis was used to divide COPD patients into two groups by up- and down-regulated proteins. Receiver operating characteristic curves were constructed using the SRM (selected reaction monitoring) results. Areas under the curves were calculated to evaluate the diagnostic value of each marker. Multiple logistic regression analysis was applied to calculate the predictive probability of a multimarker for the diagnosis of COPD. Analysis of covariance was applied to confirm significant difference in fibulin-3 regardless of age.
Isolation of extracellular vesicles from human and mouse sera
Extracellular vesicles (EVs) were isolated from less than 20 μL of mouse or human serum by differential ultracentrifugation, as previously described [1] and in accordance with the most recent guidelines (MISEV 2018) [2]. Briefly, serum samples were centrifuged at 2000 × g for 30 min to remove debris. Supernatants were then passed through a 0.22-μm spin filter (Agilent Technologies, Santa Clara, CA, USA) and centrifuged on a cushion made of sucrose/D2O for non-targeted proteomics or without sucrose for targeted proteomics at 100,000 × g for 90 min. In the sucrose cushion method, filtered supernatants were overlaid onto 0.5 mL of 30% sucrose/D2O cushion
(1.21 g/mL) and spun at 100,000 × g in a swing rotor (P56ST, Hitachi Koki, Tokyo,
Japan) for 1 h. The pellet and sucrose fraction were resuspended in phosphate-buffered saline (PBS) and subsequently ultra-centrifuged twice at 100,000 × g for 70 min. The resulting EV pellets were lysed with 100 µL of lysis buffer (50 mM Tris-HCl, pH 9.0, containing 6 M urea and 5% sodium deoxycholate for mouse samples; 50 mM
NH4HCO3 containing 12 mM sodium deoxycholate and 12 mM sodium N-lauroyl sarcosinate for human samples), and incubated at 95 °C for 5 min. The EV lysates were stored at -80 °C until the processing of the EV proteins.
Nanoparticle tracking analysis (NTA)
Analysis of the size distribution and the number of EVs was carried out by using the
NanoSight LM10HS with a blue laser system (NanoSight, Amesbury, UK) as previously described [3]. Briefly, nanoparticle tracking analysis (NTA) was performed on isolated EVs, previously diluted 20-fold with PBS. All the events were recorded in a 60-s video for further analysis using the NTA software. The Brownian motion of each particle was tracked between frames to calculate its size using the Stokes-Einstein equation.
Tunable Resistive Pulse Sensing
EVs isolated by ultracentrifugation were resuspended in a solution containing 100 mM
KCl and 40 mM HEPES. The sizes and numbers of EVs were measured on an Izon qNano system with tunable resistive pulse sensor technology (Izon Science,
Christchurch, New Zealand), as previously reported [4]. The instrument was equipped with an NP200A membrane (Izon Science), which contained a tunable nanopore optimized for the detection of particles in the 100-400 nm size range.
Processing of EV proteins for proteomics
Trypsin digestion of EV proteins was performed by phase transfer surfactant protocol
[5]. Briefly, the EV lysates were reduced with dithiothreitol and alkylated with iodoacetamide as previously reported [6]. Mouse samples were diluted 10-fold with 50 mM Tris-HCl (pH 9.0), while human samples were diluted 5-fold with 50 mM
NH4HCO3. The diluted samples were digested at 37°C overnight with 1 µg trypsin for human samples or a trypsin-to-protein ratio of 1:20 (w/w) for mouse samples
(proteomics grade; Roche, Mannheim, Germany). After digestion, the sample was mixed with an equal volume of ethyl acetate, acidified with 0.5% trifluoroacetic acid, and then vortexed. After centrifugation, the upper organic phase was discarded. The resulting tryptic peptides for non-targeted proteomics were desalted with a pipette-tip column of C18 resin as previously reported [7], while the ones for targeted proteomics were cleaned up with pipette-tip columns of C18 and strong cation exchange resins as previously reported [8].
Isobaric chemical labeling and prefractionation of EV peptides.
The desalted peptides for non-targeted EV proteomics were labeled with the reagents for isobaric-peptides-labeling [iTRAQ and tandem mass tag (TMT) reagents]. The iTRAQ 4plex (AB Sciex, Warrington, UK) or the TMT10plex (Thermo Fisher
Scientific, Bremen, Germany) reagents were used for the mouse or human samples, respectively, according to each manufacturer’s protocol.
In iTRAQ labeling of peptides, each of the dried samples was dissolved in 30 μL of 1.0 M trimethylamine bicarbonate buffer (Sigma-Aldrich, St. Louis, MO, USA) and then incubated with the iTRAQ reagents for 1 hour at room temperature. The samples were combined after termination of the reaction with 100 μL of water. The iTRAQ-labeled sample mixture was separated into 22 fractions using a high-performance liquid chromatography system (Prominence UFLC; Shimadzu
Corporation, Kyoto, Japan) with a ZORBAX 300 Extend-C18 column (Agilent
Technologies), and then the fractions were stored at -80°C until liquid chromatography-mass spectrometry (LC-MS) was performed.
In TMT labeling of peptides, each of the dried samples was dissolved in 10 μL of 0.1 M trimethylamine bicarbonate buffer and then incubated with 4 μL TMT reagents for 1 hour at room temperature. The samples were combined after termination of the reaction with 0.8 μL of 5% hydroxylamine (Wako Pure Chemical, Osaka, Japan). The
TMT-labeled sample mixtures were separated into seven fractions by pipette-tip columns of C18 and strong cation exchange resins as previously reported [8], and then the fractions were stored at -80°C until LC-MS was performed.
Synthetic peptides
Stable synthetic isotope-labeled peptides with C-terminal 15N- and 13C-labeled arginine or lysine residue (isotopic purity > 99%) were purchased from JPT Peptide
Technologies Gmbh (Berlin, Germany) (crude purity).
Liquid chromatography-mass spectrometry in EV proteomics
The samples for non-targeted proteomics were analyzed by hybrid quadrupole-Orbitrap mass spectrometers (Q-Exactive and Q-Exactive Plus, Thermo Fisher Scientific); the
Q-Exactive and Q-Exactive Plus were used for mouse and human samples, respectively.
Meanwhile, the samples for targeted proteomics were analyzed by a triple quadrupole mass spectrometer (TSQ-Vantage, Thermo Fisher Scientific).
The Q-Exactive and Q-Exactive Plus were coupled with UltiMate™ 3000
RSLCnano ultra-high-performance liquid chromatography (UHPLC) System (Thermo
Scientific) while the TSQ-Vantage was coupled with a Paradigm MS2 Nano-LC system (Michrom Bioresources, Auburn, CA, USA). Each of the nano-LC-MS systems was equipped with an HTC-PAL autosampler (CTC Analytics, Zwingen, Switzerland) with a trap column (0.075 × 20 mm, Acclaim PepMap RSLC Nano-Trap Column; Thermo
Fisher Scientific) for sample injection and a nano-LC-MS interface (AMR, Tokyo,
Japan) that ionized peptides using nano-electrospray ionization (ESI) in positive ion mode. Analytical columns, which were in-house columns with a spray needle, were packed with reverse-phase material ReproSil-Pur C18-AQ, 1.9-μm resin (Dr. Maisch,
Ammerbuch-Entringen, Germany) into a self-pulled needle (column lengths were 150 mm, 300 mm, and 100 mm for Q-Exactive, Q-Exactive Plus, and TSQ-Vantage, respectively, while every inner diameter was 75 μm). The mobile phases consisted of buffers A (0.1% formic acid and 2% acetonitrile) and B (0.1% formic acid and 90% acetonitrile). The nano-LC gradient was ramped up from 5–35% buffer B for 95 min for
Q-Exactive, 145 min for Q-Exactive Plus, and 75 min for TSQ-Vantage. The flow rates were set at 280 nL/min for Q-Exactive and Q-Exactive Plus and at 200 nL/min for
TSQ-Vantage.
The MS parameters of Q-Exactive and Q-Exactive Plus were the following: full MS scans were performed using an Orbitrap mass analyzer (scan range, 350–1800 m/z, with a resolution of 70,000 after accumulation of ions to a 3 × 106 target value). The 12 most intense precursor ions were selected and fragmented in the collision cell by higher-energy collisional dissociation with a maximum injection time of 120 ms, normalized collision energy of 30%, and a resolution of 35,000. The MS/MS ion-selection threshold was set to 5 × 104 counts. The isolation widths were 3.0 Da for
Q-Exactive and 1.0 Da for Q-Exactive Plus, respectively.
The MS parameters of TSQ-Vantage were the following: scan width of 0.002 m/z, Q1 and Q3 resolution of 0.7 full widths at half maximum, cycle time of 2.5 s, and a gas pressure of 1.8 mTorr. The setting of the transitions, which means pairs of precursor m/z and product m/z to monitor a target peptide in an SRM analysis, were optimized for
91 target peptides by performing a test run of the synthetic isotope-labeled-peptide mixture as previously reported [9]. The transition list is shown in supplementary table
E4. The collision energy (CE) for each peptide was obtained by the equations CE =
0.034 × precursor m/z - 0.848 for doubly charged precursor ions and CE = 0.022 × precursor m/z + 5.953 for triply charged precursor ions. Before LC-MS measurements, the samples for non-targeted EV proteomics were dissolved in buffer A while the samples for targeted proteomics were dissolved in buffer
A with the isotope-labeled-peptide internal standard mixture.
Data analysis in EV proteomics
The raw files acquired in the mouse EV proteomics were examined using Proteome
Discoverer software (version 1.3, Thermo Fisher Scientific) integrated with a database search algorithm Mascot (version 2.5, Matrix Science, London, UK) against a mouse protein database (UniProt mouse without isoform, version 14.3) with a precursor mass tolerance of 5 ppm, a fragment ion mass tolerance of 0.01 Da, and trypsin specificity allowing for up to 1 missed cleavage. The carbamidomethylation of cysteine and iTRAQ 4plex at lysine and at the peptide N-terminus were set as fixed modifications.
The oxidation of methionine was allowed as a variable modification. Peptides and proteins with a false discovery rate of <1% were considered significant. The quantitative values of each protein were obtained as ratios of the iTRAQ signal intensities of the elastase-treatment sample to those of the PBS-treatment sample. The raw files acquired in the human non-targeted EV proteomics were examined using MaxQuant software (version 1.5.1.2) integrated with the Andromeda search algorithm [10] against the human protein database (UniProt human without isoform, version 15.5) with a precursor mass tolerance of 7 ppm, a fragment ion mass tolerance of 0.01 Da, and trypsin specificity allowing for up to 1 missed cleavage. The carbamidomethylation of cysteine and TMT10plex at lysine and the peptide N-terminus were set as fixed modifications. The oxidation of methionine was allowed as a variable modification. Peptides and proteins were accepted with a false discovery rate of <1%.
The quantitative values of each protein were obtained as ratios of the TMT signal intensities of each sample to those of the reference samples that were prepared by mixing all the samples.
The raw files acquired in targeted proteomics were analyzed using Skyline software [11]. SRM signal peaks corresponding to each target peptides were assigned by comparing with an isotope-labeled-peptide internal standard of each counterpart. The quantitative values of the target peptides were obtained as ratios of the endogenous target peptides to the isotope-labeled-peptide internal standard using one transition per peptide with the highest signal.
Serum proteomics
Human serum samples were applied to Pierce Top 12 Abundant Protein Depletion Spin
Columns (#85164; Thermo Fisher Scientific) according to the manufacturer’s protocol.
The flow-through protein fraction was mixed with a 6-fold volume of ice-cold acetone, followed by incubation at -30°C for 12 h. The proteins included in the mixture were precipitated by centrifugation at 15,000 × g at 4°C for 10 min. The resulting protein pellet was dissolved in 50 µL lysis buffer consisting of 9 M urea and 20 mM
HEPES-NaOH (pH 8.0), and the protein concentration of the solution was determined using Bradford assay. An aliquot (20 µg protein) was subjected to a cycle of dithiothreitol reduction and reductive alkylation of cysteine residues. The solution was diluted to achieve a urea concentration of 2 M with 20 mM HEPES-NaOH (pH 8.0) and subjected to protein hydrolysis with porcine trypsin (mass spectrometry grade; Promega
Co., Madison, WI, USA) at 37°C for 16 h, with an enzyme-to-substrate ratio of 1:20
(wt/wt). The resulting peptide mixture was desalted using the C18 STAGE tip and dried under vacuum. The dried sample was dissolved in a solvent consisting of water, acetonitrile, and formic acid at a volume ratio of 98:2:0.1 and diluted to 250 ng/µL. Of the peptide solution, 2 µL (containing 500 ng peptide) was used for liquid chromatography-tandem mass spectrometry (LC-MS/MS) according to the following specifications and settings: briefly, peptide separation was performed with an
UltiMate™ 3000 RSLCnano liquid chromatograph containing a C18 capillary LC column (Nano HPLC Capillary Column; 75-mm internal diameter, 150-mm length,
3-mm particle size; Nikkyo Technos, Tokyo, Japan). The mobile phases consisted of formic acid, acetonitrile, and water at volume ratios of 0.1:0:100 for mobile phase A and 0.1:90:10 for mobile phase B. The peptides were continuously eluted at a rate of
350 nL/min in gradient mode: the initial proportion of 5% mobile phase B was increased linearly to 35% B over 100 min, followed by an increase to 95% B during the next 5 min. After washing with a non-gradient flow at 95% B for 5 min, the column was equilibrated again with the solvent of 5% B for the next separation. The total elution time was 120 min. For gasification of the protonated peptides, the LC effluent was interfaced with an ESI source in a positive ion mode on a Q-Exactive mass spectrometer. The set parameters included a spray voltage of 1.5 kV and a capillary temperature of
200°C. Protonated peptides in the gas phase were analyzed sequentially for MS/MS in data-dependent scanning mode, consisting of a full-range scan at an m/z range of 350 to
1500 and subsequent product ion scans for each of the ten most intense ions in the full scan mass spectrum.
Label-free relative peptide quantitation was performed by direct comparison of each MS scan profile using Progenesis QI for proteomics (QIP) software (version 2.0;
Nonlinear Dynamics, Durham, NC). The normalized abundance obtained for each peptide was subjected to statistical analysis using one-way analysis-of-variance. In this study, the detected feature was taken to be statistically significant when the P-value for a given peptide was below 0.05. To identify the peptide sequence, the MS/MS data were searched using Mascot software against human protein sequences (20,409 entries) in the
Swiss-Prot database (October 2018) and the amino acid sequences of protein contaminants (48 entries) in The Proteome Machine Organization. The search parameters were: enzyme, semitrypsin; maximum missed cleavage, two; peptide tolerance, ±5 ppm; MS/MS tolerance, ±0.02 Da; mass, monoisotopic mass; fixed modification for carbamidomethyl (cysteine, +57.021 Da), and variable modification for oxidation (methionine, +15.099 Da). The false discovery rate was estimated on a decoy database using the Mascot software. We used a 1% false discovery rate as a cutoff for the export of results from the analysis.
Bioinformatics analysis of proteome with version information
To identify biologically relevant molecular networks and pathways in the proteome, the following tools were used: Ingenuity Pathways Analysis (ver 01.13, Qiagen N.V., Venlo,
Netherlands) for protein localization and enrichment analysis, KeyMolnet software (ver
6.2, KM Data, Tokyo, Japan) for pathway analysis, and STRING (ver 11.0, ELIXIR,
Cambridgeshire, UK) for protein-protein interaction analysis. Disease enrichment analysis was performed using R ver 3.6.0 (https://www.r-project.org/) with the DOSE package (ver 3.10.2) and visualized by enrichplot package (ver 1.4.0).
Immunoblotting
Serum, EVs, cells, and mouse lung tissues were lysed in RIPA Lysis and Extraction Buffer (Ref 89900; Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (Cat A-0014; ITSI Biosciences, LLC, Johnstown, PA, USA). Lysates were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, transferred to polyvinylidene difluoride membranes, and probed with primary antibodies followed by peroxidase-conjugated secondary antibodies. The following primary antibodies were used: mouse anti-human CD63 (1:1000; MEX002-3; Medical
& Biological Laboratories Co., Ltd., Nagoya, Japan), mouse anti-human CD9 (1:1000;
MM2/57; Thermo Fisher Scientific), rabbit anti-human calnexin (1:2000; ab22595,
Abcam, Cambridge, UK), mouse anti-human flotillin-1 (1:1000; 610821, BD
Biosciences, Franklin Lakes, NJ, USA), rabbit anti-human apolipoprotein A1 (1:2000;
GTX112692, GeneTex), rat anti-mouse CD9 (1:1000; KMC8; BD Bioscience), rabbit anti-mouse flotillin-1 (1:1000; ab50671; Abcam), rabbit anti-mouse TSG101 (1:1000; ab125011; Abcam), rabbit anti-mouse beta actin (1:5000; 5125S; Cell Signaling
Technology, Danvers, MA, USA), and rabbit anti-mouse and human fibulin-3 (1:1000; produced in-house) [12]. Samples for CD9, CD63, and fibulin-3 were prepared in non-reducing conditions. Immunoreactive signals were visualized using SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific). ImageJ
(National Institutes of Health, Bethesda, MD, USA) was used for densitometry analyses.
Immunohistochemistry
Commercially available lung tissue array slides (LUD481; US Biomax Inc., Rockville,
MD, USA) were deparaffinized using xylene and alcohol, incubated with 10 mmol/L citrate buffer (pH 6.0) for antigen retrieval, and blocked with 3% bovine serum albumin in PBS at room temperature for 1 h. Slides were incubated with anti-fibulin-3 (1:250)
(GTX111657; GeneTex, Inc., Irvine, CA, USA), anti-TPP-2 (1:100) (ab180177;
Abcam), anti-Fibulin-1 (1:25) (ab211536; Abcam), anti-CD81 (1:100) (ab155760;
Abcam), anti-EMILIN-1 (1:100) (sc-50430; Santa Cruz Biotechnology, Dallas, TX,
USA), anti OIT3 (1:50) (MBS2028378; MyBioSource) and rabbit monoclonal IgG
(ab172730; Abcam) for negative control at 4°C overnight, followed by anti-rabbit horseradish peroxidase-conjugated secondary antibody at room temperature for 30 min.
Sandwich enzyme-linked immunosorbent assay
Fibulin-1 (MBS2881118; MyBioSource, Inc., San Diego, CA, USA) and fibulin-3
(SEF422Hu; Cloud-Clone Corp., Houston, TX, USA) levels in serum were assessed by sandwich enzyme-linked immunosorbent assay, following the manufacturer’s instructions.
Transmission electron microscopy
Samples of EVs (10 μg) were adsorbed onto a formvar/carbon-coated nickel grid for 1 h.
EVs were fixed with 2% paraformaldehyde and then incubated with the mouse anti-human CD9 antibody or rabbit anti-fibulin-3 antibody [12] or rabbit monoclonal
IgG (ab172730; Abcam) for negative control. Immunoreactive EVs were visualized with an anti-mouse IgG antibody preabsorbed with 10 nm gold particles. Samples were negatively stained with 2% aqueous uranyl acetate for 15 min and observed.
Lung specimens were fixed at 4 °C in 2.5 % glutaraldehyde in 0.1 M phosphate buffer
(pH 7.2) and postfixed at room temperature in 2 % osmium tetroxide in the same buffer.
After dehydration in ethanol, the specimens were embedded in Epon 812 resin (TAAB, Berkshire, UK). The ultrathin sections (60 nm) were stained with uranyl acetate and lead citrate, and observed.
Analysis of alveolar areas of mouse lung tissue sections
Image acquisition and evaluation of alveolar area were conducted with a Keyence
BZ-X700 fluorescence microscope with the Hybrid Cell Count software (BZ-X
Analyzer, Keyence, Osaka, Japan). Five random 20× lung images per mouse were used for the analysis.
Preparation of Cas9 and gRNA
Cas9 and Alt-R® S.p. HiFi Cas9 Nuclease were purchased from Integrated DNA
Technologies, Inc. (IDT; Coralville, IA, USA). To design the guide RNA (gRNA) sequence (5'-GGTCAAGTCACAGTACACCG-3'), we used software tools
(http://crispor.tefor.net/ and https://crispr.dbcls.jp/) that predicted unique target sites throughout the mouse genome. Specific CRISPR RNA (crRNA; Alt-R CRISPR-Cas9 crRNA) was purchased from IDT and assembled with trans-activating CRISPER RNA (tracrRNA; Alt-R CRISPR-Cas9 tracrRNA) before use according to the manufacturer’s instructions.
Electroporation into mouse embryos
Pronuclear-stage mouse embryos were prepared by thawing frozen embryos (CLEA
Japan, Inc.) For electroporation, 100–150 embryos thawed for 1 h were placed into a chamber with 40 µL of serum-free media (Opti-MEM, Thermo Fisher Scientific) containing 100 ng/µL Cas9 protein and 200 ng/µL gRNA. They were electroporated with a 5-mm gap electrode (CUY505P5 or CUY520P5; Nepa Gene, Chiba, Japan) in a
NEPA21 Super Electroporator (Nepa Gene). The parameters of the poring pulses for the electroporation were: voltage, 225 V; pulse width, 1 ms; pulse interval, 50 ms; and number of pulses, 4. The first and second transfer pulses were: voltage 20, V; pulse width, 50 ms; pulse interval, 50 ms; and number of pulses, 5. Mouse embryos that developed to the two-cell stage after the introduction of Cas9 and gRNA were transferred into the oviducts of female surrogates anesthetized with sevoflurane.
Genotyping analysis
Genomic DNA was extracted from the tail tip using the KAPA Express Extract DNA
Extraction Kit (Kapa Biosystems, London, UK). For PCR and sequence analysis, we used primers which amplified the targeted region (5’- CACGCTGAACAACCTTGGTA
-3’, 5’- CCTTTGAAAAGACCCAGCAC -3’). PCR was performed in a total volume of
15 μL under the following conditions: 1 cycle at 94°C for 1 min; 30 cycles at 98°C for
10 s, 60°C for 15 s and 68°C for 30 s; and 1 cycle at 72°C for 3 min. The final reaction
mixture contained 200 μM dNTPs, 1.0 mM MgCl2, and 0.4 μM of primer. The PCR products were then directly sequenced using the BigDye Terminator v3.1 cycle sequencing mix and the standard protocol for an Applied Biosystems 3130 DNA
Sequencer (Life Technologies, Thermo Fisher Scientific, Waltham, MA, USA).
References
1. Thery C, Amigorena S, Raposo G, Clayton A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr Protoc Cell Biol 2006: Chapter 3: Unit 3.22. 2. Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin-Smith GK, Ayre DC, Bach JM, Bachurski D, Baharvand H, Balaj L, Baldacchino S, Bauer NN, Baxter AA, Bebawy M, Beckham C, Bedina Zavec A, Benmoussa A, Berardi AC, Bergese P, Bielska E, Blenkiron C, Bobis-Wozowicz S, Boilard E, Boireau W, Bongiovanni A, Borras FE, Bosch S, Boulanger CM, Breakefield X, Breglio AM, Brennan MA, Brigstock DR, Brisson A, Broekman ML, Bromberg JF, Bryl-Gorecka P, Buch S, Buck AH, Burger D, Busatto S, Buschmann D, Bussolati B, Buzas EI, Byrd JB, Camussi G, Carter DR, Caruso S, Chamley LW, Chang YT, Chen C, Chen S, Cheng L, Chin AR, Clayton A, Clerici SP, Cocks A, Cocucci E, Coffey RJ, Cordeiro-da-Silva A, Couch Y, Coumans FA, Coyle B, Crescitelli R, Criado MF, D'Souza-Schorey C, Das S, Datta Chaudhuri A, de Candia P, De Santana EF, De Wever O, Del Portillo HA, Demaret T, Deville S, Devitt A, Dhondt B, Di Vizio D, Dieterich LC, Dolo V, Dominguez Rubio AP, Dominici M, Dourado MR, Driedonks TA, Duarte FV, Duncan HM, Eichenberger RM, Ekstrom K, El Andaloussi S, Elie-Caille C, Erdbrugger U, Falcon-Perez JM, Fatima F, Fish JE, Flores-Bellver M, Forsonits A, Frelet-Barrand A, Fricke F, Fuhrmann G, Gabrielsson S, Gamez-Valero A, Gardiner C, Gartner K, Gaudin R, Gho YS, Giebel B, Gilbert C, Gimona M, Giusti I, Goberdhan DC, Gorgens A, Gorski SM, Greening DW, Gross JC, Gualerzi A, Gupta GN, Gustafson D, Handberg A, Haraszti RA, Harrison P, Hegyesi H, Hendrix A, Hill AF, Hochberg FH, Hoffmann KF, Holder B, Holthofer H, Hosseinkhani B, Hu G, Huang Y, Huber V, Hunt S, Ibrahim AG, Ikezu T, Inal JM, Isin M, Ivanova A, Jackson HK, Jacobsen S, Jay SM, Jayachandran M, Jenster G, Jiang L, Johnson SM, Jones JC, Jong A, Jovanovic-Talisman T, Jung S, Kalluri R, Kano SI, Kaur S, Kawamura Y, Keller ET, Khamari D, Khomyakova E, Khvorova A, Kierulf P, Kim KP, Kislinger T, Klingeborn M, Klinke DJ, 2nd, Kornek M, Kosanovic MM, Kovacs AF, Kramer-Albers EM, Krasemann S, Krause M, Kurochkin IV, Kusuma GD, Kuypers S, Laitinen S, Langevin SM, Languino LR, Lannigan J, Lasser C, Laurent LC, Lavieu G, Lazaro-Ibanez E, Le Lay S, Lee MS, Lee YXF, Lemos DS, Lenassi M, Leszczynska A, Li IT, Liao K, Libregts SF, Ligeti E, Lim R, Lim SK, Line A, Linnemannstons K, Llorente A, Lombard CA, Lorenowicz MJ, Lorincz AM, Lotvall J, Lovett J, Lowry MC, Loyer X, Lu Q, Lukomska B, Lunavat TR, Maas SL, Malhi H, Marcilla A, Mariani J, Mariscal J, Martens-Uzunova ES, Martin-Jaular L, Martinez MC, Martins VR, Mathieu M, Mathivanan S, Maugeri M, McGinnis LK, McVey MJ, Meckes DG, Jr., Meehan KL, Mertens I, Minciacchi VR, Moller A, Moller Jorgensen M, Morales-Kastresana A, Morhayim J, Mullier F, Muraca M, Musante L, Mussack V, Muth DC, Myburgh KH, Najrana T, Nawaz M, Nazarenko I, Nejsum P, Neri C, Neri T, Nieuwland R, Nimrichter L, Nolan JP, Nolte-'t Hoen EN, Noren Hooten N, O'Driscoll L, O'Grady T, O'Loghlen A, Ochiya T, Olivier M, Ortiz A, Ortiz LA, Osteikoetxea X, Ostergaard O, Ostrowski M, Park J, Pegtel DM, Peinado H, Perut F, Pfaffl MW, Phinney DG, Pieters BC, Pink RC, Pisetsky DS, Pogge von Strandmann E, Polakovicova I, Poon IK, Powell BH, Prada I, Pulliam L, Quesenberry P, Radeghieri A, Raffai RL, Raimondo S, Rak J, Ramirez MI, Raposo G, Rayyan MS, Regev-Rudzki N, Ricklefs FL, Robbins PD, Roberts DD, Rodrigues SC, Rohde E, Rome S, Rouschop KM, Rughetti A, Russell AE, Saa P, Sahoo S, Salas-Huenuleo E, Sanchez C, Saugstad JA, Saul MJ, Schiffelers RM, Schneider R, Schoyen TH, Scott A, Shahaj E, Sharma S, Shatnyeva O, Shekari F, Shelke GV, Shetty AK, Shiba K, Siljander PR, Silva AM, Skowronek A, Snyder OL, 2nd, Soares RP, Sodar BW, Soekmadji C, Sotillo J, Stahl PD, Stoorvogel W, Stott SL, Strasser EF, Swift S, Tahara H, Tewari M, Timms K, Tiwari S, Tixeira R, Tkach M, Toh WS, Tomasini R, Torrecilhas AC, Tosar JP, Toxavidis V, Urbanelli L, Vader P, van Balkom BW, van der Grein SG, Van Deun J, van Herwijnen MJ, Van Keuren-Jensen K, van Niel G, van Royen ME, van Wijnen AJ, Vasconcelos MH, Vechetti IJ, Jr., Veit TD, Vella LJ, Velot E, Verweij FJ, Vestad B, Vinas JL, Visnovitz T, Vukman KV, Wahlgren J, Watson DC, Wauben MH, Weaver A, Webber JP, Weber V, Wehman AM, Weiss DJ, Welsh JA, Wendt S, Wheelock AM, Wiener Z, Witte L, Wolfram J, Xagorari A, Xander P, Xu J, Yan X, Yanez-Mo M, Yin H, Yuana Y, Zappulli V, Zarubova J, Zekas V, Zhang JY, Zhao Z, Zheng L, Zheutlin AR, Zickler AM, Zimmermann P, Zivkovic AM, Zocco D, Zuba-Surma EK. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 2018: 7(1): 1535750. 3. Yoshioka Y, Kosaka N, Konishi Y, Ohta H, Okamoto H, Sonoda H, Nonaka R, Yamamoto H, Ishii H, Mori M, Furuta K, Nakajima T, Hayashi H, Sugisaki H, Higashimoto H, Kato T, Takeshita F, Ochiya T. Ultra-sensitive liquid biopsy of circulating extracellular vesicles using ExoScreen. Nat Commun 2014: 5: 3591. 4. de Vrij J, Maas SL, van Nispen M, Sena-Esteves M, Limpens RW, Koster AJ, Leenstra S, Lamfers ML, Broekman ML. Quantification of nanosized extracellular membrane vesicles with scanning ion occlusion sensing. Nanomedicine (Lond) 2013: 8(9): 1443-1458. 5. Masuda T, Tomita M, Ishihama Y. Phase transfer surfactant-aided trypsin digestion for membrane proteome analysis. Journal of proteome research 2008: 7(2): 731-740. 6. Kume H, Muraoka S, Kuga T, Adachi J, Narumi R, Watanabe S, Kuwano M, Kodera Y, Matsushita K, Fukuoka J, Masuda T, Ishihama Y, Matsubara H, Nomura F, Tomonaga T. Discovery of colorectal cancer biomarker candidates by membrane proteomic analysis and subsequent verification using selected reaction monitoring (SRM) and tissue microarray (TMA) analysis. Molecular & cellular proteomics : MCP 2014: 13(6): 1471-1484. 7. Rappsilber J, Mann M, Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2007: 2(8): 1896-1906. 8. Adachi J, Hashiguchi K, Nagano M, Sato M, Sato A, Fukamizu K, Ishihama Y, Tomonaga T. Improved Proteome and Phosphoproteome Analysis on a Cation Exchanger by a Combined Acid and Salt Gradient. Anal Chem 2016: 88(16): 7899-7903. 9. Narumi R, Shimizu Y, Ukai-Tadenuma M, Ode KL, Kanda GN, Shinohara Y, Sato A, Matsumoto K, Ueda HR. Mass spectrometry-based absolute quantification reveals rhythmic variation of mouse circadian clock proteins. Proc Natl Acad Sci U S A 2016: 113(24): E3461-3467. 10. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 2008: 26(12): 1367-1372. 11. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010: 26(7): 966-968. 12. Kobayashi N, Kostka G, Garbe JH, Keene DR, Bachinger HP, Hanisch FG, Markova D, Tsuda T, Timpl R, Chu ML, Sasaki T. A comparative analysis of the fibulin protein family. Biochemical characterization, binding interactions, and tissue localization. J Biol Chem 2007: 282(16): 11805-11816.
Supplementary figure legends
Figure E1. Comparison of depleted serum proteomic profile with that of serum
Extracellular vesicles (EVs).
A. Localization analyses conducted by Ingenuity Pathway Analysis revealed that more
proteins detected in EVs were located in plasma membrane and cytoplasm
compared with those in serum.
B. Proteins detected in serum EVs totaled 406, while 330 proteins were detected in
serum by non-targeted proteomics, and 155 of them were commonly detected.
C. Percentage of serum abundant proteins detected in the top 20 proteins by
non-targeted proteomics. On average, 22.8% of the top 20 proteins identified in EVs
were abundant serum proteins, while 56.7% were in depleted serum. Error bars
indicate mean ± SD.
***, p-value < 0.001
Figure E2. Using Ward’s hierarchical cluster analysis of significantly up- and down-regulated proteins in the discovery study, COPD patients were divided into two groups by several kinds of proteins, indicating that these biomarker candidates might be helpful in endotyping.
Figure E3. A Venn diagram of up-regulated proteins in serum EVs of mice using non-targeted proteomics with iTAQ label (×1.2 or more) and of humans using non-targeted proteomics with TMT label (significant). Fifteen proteins were commonly up-regulated.
Figure E4. Fibulin-1 and -3 levels in serum were assessed by sandwich enzyme-linked immunosorbent assay, and no significant difference was seen. n. s., not significant
Figure E5. The purity of EVs and the existence of fibulin-3 in EVs isolated with
EVSeond, size exclusion chromatography, were confirmed by western blotting. CD9 was positive in EVs as an EV marker protein, while haptoglobin, as a negative control of EVs, was negative.
Figure E6. The immunohistochemistry results of biomarker candidates TPP-2, fibulin-1,
CD81, EMILIN-1, and OIT3. All proteins were expressed in both alveolar epithelial cells (black arrows) and extracellular matrix (black arrowheads) to varying degree.
Scale bar = 100 µm.
Figure E7. Evaluation of fibulin-3 knockout mouse.
A. Immunoblotting results of lung homogenate of wild type and knockout (KO) mice.
Defect of fibulin-3 in KO mouse lungs was confirmed.
B. No significant difference was observed in body weight at 12 and 22 weeks. Error
bars indicate mean ± SD.
C. No significant difference was observed in bronchoalveolar lavage fluid total cell
count at 29 weeks. Error bars indicate mean ± SD. Data are representative of three
independent studies with similar results.
n. s., not significant