Abstract
Exosomes are extracellular vesicles derived from the endosomal compartment that are potentially involved in intercellular communication. Here, we found that frequently used biomarkers of exosomes are heterogeneous, and do not exhibit universal utility across different cell types. To uncover ubiquitous and abundant proteins, we used an unbiased and quantitative proteomic approach based on super-stable isotope labeling with amino acids in cell culture (super-SILAC), coupled to high-resolution mass spectrometry. In total, 1,212 proteins were quantified in the proteome of exosomes, irrespective of the cellular source or isolation method. A cohort of 22 proteins was universally enriched. Fifteen proteins were consistently depleted in the proteome of exosomes compared to cells. Among the enriched proteins, we identified biogenesis-related proteins, GTPases and membrane proteins, such as CD47 and ITGB1. The cohort of depleted proteins in exosomes was predominantly composed of nuclear proteins. We identified syntenin-1 as a consistently abundant protein in exosomes from different cellular origins. Syntenin-1 is also present in exosomes across different species and biofluids, highlighting its potential use as a putative universal biomarker of exosomes. Our study provides a comprehensive quantitative atlas of core proteins ubiquitous to exosomes that can serve as a resource for the scientific community.
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Data availability
The data supporting the findings of this study are available within the manuscript, Supplementary Information or source data. The raw MS files and the search/identification files obtained with MaxQuant have been deposited to the ProteomeXchange Consortium through the PRIDE partner repository57 under the accession code PXD020260. Source data are provided with this paper.
References
Colombo, M., Raposo, G. & Thery, C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu. Rev. Cell Dev. Biol. 30, 255–289 (2014).
Kalluri, R. The biology and function of exosomes in cancer. J. Clin. Invest. 126, 1208–1215 (2016).
Mathieu, M., Martin-Jaular, L., Lavieu, G. & Thery, C. Specificities of secretion and uptake of exosomes and other extracellular vesicles for cell-to-cell communication. Nat. Cell Biol. 21, 9–17 (2019).
Kalluri, R. & LeBleu, V. S. The biology, function, and biomedical applications of exosomes. Science 367, eaau6977 (2020).
Kahlert, C. et al. Identification of double-stranded genomic DNA spanning all chromosomes with mutated KRAS and p53 DNA in the serum exosomes of patients with pancreatic cancer. J. Biol. Chem. 289, 3869–3875 (2014).
Valadi, H. et al. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 9, 654–659 (2007).
Hinger, S. A. et al. Diverse long RNAs are differentially sorted into extracellular vesicles secreted by colorectal cancer cells. Cell Rep. 25, 715–725 (2018).
Hurwitz, S. N. et al. Proteomic profiling of NCI-60 extracellular vesicles uncovers common protein cargo and cancer type-specific biomarkers. Oncotarget 7, 86999–87015 (2016).
Kowal, J. et al. Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proc. Natl Acad. Sci. USA 113, E968–E977 (2016).
Haraszti, R. A. et al. High-resolution proteomic and lipidomic analysis of exosomes and microvesicles from different cell sources. J. Extracell. Vesicles 5, 32570 (2016).
Altadill, T. et al. Enabling metabolomics based biomarker discovery studies using molecular phenotyping of exosome-like vesicles. PLoS ONE 11, e0151339 (2016).
Alexander, M. et al. Exosome-delivered microRNAs modulate the inflammatory response to endotoxin. Nat. Commun. 6, 7321 (2015).
Tkach, M. et al. Qualitative differences in T-cell activation by dendritic cell-derived extracellular vesicle subtypes. EMBO J. 36, 3012–3028 (2017).
Muller, L. et al. Human tumor-derived exosomes (TEX) regulate Treg functions via cell surface signaling rather than uptake mechanisms. Oncoimmunology 6, e1261243 (2017).
Melo, S. A. et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523, 177–182 (2015).
Wang, S., Kojima, K., Mobley, J. A. & West, A. B. Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease. EBioMedicine 45, 351–361 (2019).
Chen, G. et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature 560, 382–386 (2018).
Kamerkar, S. et al. Exosomes facilitate therapeutic targeting of oncogenic KRAS in pancreatic cancer. Nature 546, 498–503 (2017).
Usman, W. M. et al. Efficient RNA drug delivery using red blood cell extracellular vesicles. Nat. Commun. 9, 2359 (2018).
Zitvogel, L. et al. Eradication of established murine tumors using a novel cell-free vaccine: dendritic cell-derived exosomes. Nat. Med. 4, 594–600 (1998).
Mendt, M. et al. Generation and testing of clinical-grade exosomes for pancreatic cancer. JCI Insight 3, e99263 (2018).
Kugeratski, F. G. & Kalluri, R. Exosomes as mediators of immune regulation and immunotherapy in cancer. FEBS J. 88, 10–35 (2020).
Kugeratski, F. G., McAndrews, K. M. & Kalluri, R. Multifunctional applications of engineered extracellular vesicles in the treatment of cancer. Endocrinology 162, bqaa250 (2021).
Jeppesen, D. K. et al. Reassessment of exosome composition. Cell 177, 428–445 (2019).
Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J. R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010).
Tyanova, S. et al. Proteomic maps of breast cancer subtypes. Nat. Commun. 7, 10259 (2016).
Ong, S. E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteom. 1, 376–386 (2002).
Geiger, T. et al. Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat. Protoc. 6, 147–157 (2011).
Thery, C. et al. 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 7, 1535750 (2018).
Konoshenko, M. Y., Lekchnov, E. A., Vlassov, A. V. & Laktionov, P. P. Isolation of extracellular vesicles: general methodologies and latest trends. BioMed Res. Int. 2018, 8545347 (2018).
Takov, K., Yellon, D. M. & Davidson, S. M. Comparison of small extracellular vesicles isolated from plasma by ultracentrifugation or size-exclusion chromatography: yield, purity and functional potential. J. Extracell. Vesicles 8, 1560809 (2019).
Pisitkun, T., Shen, R. F. & Knepper, M. A. Identification and proteomic profiling of exosomes in human urine. Proc. Natl Acad. Sci. USA 101, 13368–13373 (2004).
Gonzales, P. A. et al. Large-scale proteomics and phosphoproteomics of urinary exosomes. J. Am. Soc. Nephrol. 20, 363–379 (2009).
Moon, P. G. et al. Proteomic analysis of urinary exosomes from patients of early IgA nephropathy and thin basement membrane nephropathy. Proteomics 11, 2459–2475 (2011).
Raj, D. A., Fiume, I., Capasso, G. & Pocsfalvi, G. A multiplex quantitative proteomics strategy for protein biomarker studies in urinary exosomes. Kidney Int. 81, 1263–1272 (2012).
Admyre, C. et al. Exosomes with immune modulatory features are present in human breast milk. J. Immunol. 179, 1969–1978 (2007).
Gonzalez-Begne, M. et al. Proteomic analysis of human parotid gland exosomes by multidimensional protein identification technology (MudPIT). J. Proteome Res. 8, 1304–1314 (2009).
Baietti, M. F. et al. Syndecan-syntenin-ALIX regulates the biogenesis of exosomes. Nat. Cell Biol. 14, 677–685 (2012).
Doyotte, A., Russell, M. R., Hopkins, C. R. & Woodman, P. G. Depletion of TSG101 forms a mammalian “class E” compartment: a multicisternal early endosome with multiple sorting defects. J. Cell Sci. 118, 3003–3017 (2005).
Razi, M. & Futter, C. E. Distinct roles for Tsg101 and Hrs in multivesicular body formation and inward vesiculation. Mol. Biol. Cell 17, 3469–3483 (2006).
Blanc, L. & Vidal, M. New insights into the function of Rab GTPases in the context of exosomal secretion. Small GTPases 9, 95–106 (2018).
Ostrowski, M. et al. Rab27a and Rab27b control different steps of the exosome secretion pathway. Nat. Cell Biol. 12, 19–30 (2010).
Jaiswal, S. et al. CD47 is upregulated on circulating hematopoietic stem cells and leukemia cells to avoid phagocytosis. Cell 138, 271–285 (2009).
Singh, A. et al. Exosome-mediated transfer of αvβ3 integrin from tumorigenic to nontumorigenic cells promotes a migratory phenotype. Mol. Cancer Res. 14, 1136–1146 (2016).
Hoshino, A. et al. Tumour exosome integrins determine organotropic metastasis. Nature 527, 329–335 (2015).
Escrevente, C. et al. Sialoglycoproteins and N-glycans from secreted exosomes of ovarian carcinoma cells. PLoS ONE 8, e78631 (2013).
Capello, M. et al. Exosomes harbor B cell targets in pancreatic adenocarcinoma and exert decoy function against complement-mediated cytotoxicity. Nat. Commun. 10, 254 (2019).
Sasaki, T., Brakebusch, C., Engel, J. & Timpl, R. Mac-2 binding protein is a cell-adhesive protein of the extracellular matrix which self-assembles into ring-like structures and binds beta1 integrins, collagens and fibronectin. EMBO J. 17, 1606–1613 (1998).
Stros, M. HMGB proteins: interactions with DNA and chromatin. Biochim. Biophys. Acta 1799, 101–113 (2010).
Sims, G. P., Rowe, D. C., Rietdijk, S. T., Herbst, R. & Coyle, A. J. HMGB1 and RAGE in inflammation and cancer. Annu. Rev. Immunol. 28, 367–388 (2010).
Becker, L. M. et al. Epigenetic reprogramming of cancer-associated fibroblasts deregulates glucose metabolism and facilitates progression of breast cancer. Cell Rep. 31, 107701 (2020).
Ouyang, H. et al. Immortal human pancreatic duct epithelial cell lines with near normal genotype and phenotype. Am. J. Pathol. 157, 1623–1631 (2000).
Hwang, R. F. et al. Cancer-associated stromal fibroblasts promote pancreatic tumor progression. Cancer Res. 68, 918–926 (2008).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
Vizcaino, J. A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013).
Acknowledgements
We thank K. Dunner Jr. from the High-Resolution Electron Microscopy Facility at the MD Anderson Cancer Center (CCSG grant NIH P30CA016672) for assistance with transmission electron microscopy experiments; the staff at the Cytogenetics and Cell Authentication Core at MD Anderson Cancer Center for STR testing; the staff at the Advanced Technology MS Proteomic facility at the CRUK Beatson Institute (CRUK A17196); L.A. Snowden for technical support; K. Earl for assistance with SEC methodology; A. Haltom for generating tetraspanin knockdown lines; and M. Kirtley and L.J. Neilson for administrative support. The R.K. laboratory is supported by research funds from The University of Texas MD Anderson Cancer Center. The exosome-related research in the R.K. laboratory is funded by NCI R01 CA213233 and NCI R01 CA231465. F.G.K. funding as an Odyssey Fellow is supported by the Odyssey Program and Theodore N. Law for Scientific Achievement at The University of Texas MD Anderson Cancer Center. S.Z. funding is supported by Cancer Research UK (A29800).
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Contributions
F.G.K. and R.K. conceptualized the study. F.G.K., K.M.M. and R.K. developed methodology. K.H., S.L. and S.Z. performed the acquisition of MS data. F.G.K. performed experiments and data analysis. X.Z. performed isolation of urine-derived exosomes. R.F.H. and S.Z. provided resources. F.G.K. prepared the figures. F.G.K. and R.K. wrote the manuscript. R.K. supervised the work.
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The MD Anderson Cancer Center and R.K. are stock equity holders in Codiak Biosciences Inc. R.K. is a consultant and scientific adviser for Codiak Biosciences Inc. The other authors declare no competing interests.
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Peer review information Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Flow cytometry-based evaluation of cell death in the 14 cell lines upon serum-starvation.
a, Plots show Annexin V and 7-AAD co-staining in serum-starved cells and in the positive control spiked with apoptotic cells. The double positive population is shown in the top right quadrant. b, Quantifications of apoptotic cells (Annexin V + and 7-AAD + ) in the parental cells upon serum-starvation for exosome production. Bar graph shows mean ± s.e.m, individual data points refer to three biological replicates.
Extended Data Fig. 2 Characterization of size distribution and morphology of exosomes from the 14 cell lines.
a, Representative profiles of size distribution of exosomes determined by nanoparticle tracking analysis (NTA), mode of the size distribution is indicated in the figure. b, Quantification of the mode from NTA analyses. Bar graph shows mean ± s.e.m, individual data points refer to three biological replicates for each cell line, dashed line at 100 nm. c, Transmission electron microscopy of exosomes from the 14 cell lines, scale bar = 100 nm, results from one-two replicates per cell type. d, Gating strategy used for flow cytometric analysis of beads-bound exosomes throughout the manuscript.
Extended Data Fig. 3 Evaluation of specificity of FACS-based beads assay using cells silenced for tetraspanins.
a, Expression of CD9, CD63 and CD81 in shRNA-silenced cells compared to shRNA control cells. Results normalized to 18 S. Bar graphs show mean ± s.e.m. of fold-change, individual data points from three biological replicates. Statistical analysis was performed on the delta CT values by two-tailed unpaired t-test, and the exact p-values are shown. Statistical significance defined as p < 0.05. b, Quantification of the mode of exosomes from NTA analyses. Bar graph shows mean ± s.e.m, individual data points refer to three biological replicates. c, Representative profiles of size distribution of exosomes form U87 cells shRNA control, shRNA CD9, shRNA CD63 and shRNA CD81, determined by NTA. d, Representative histograms show the profile of the CD9, CD63 and CD81 in comparison to isotype control-stained beads for shRNA control, shRNA CD9, shRNA CD63 and shRNA CD81 exosomes. Bar graphs show mean ± s.e.m of MFI (geometric mean of fluorescence intensity), individual data points from three biological replicates. Statistical significance was determined by two-tailed unpaired t-test, and the exact p-values are shown. Statistical significance defined as p < 0.05.
Extended Data Fig. 4 Expression levels of CD9, CD63 and CD81 in parental cells.
Expression of each tetraspanin was assessed using two distinct primer pairs (1 and 2), and using 18 S for normalization. Bar graphs show mean ± s.e.m. of 1/delta CT, individual data points from three biological replicates.
Extended Data Fig. 5 Distribution of SILAC ratios of MS samples from the 14 cell lines-derived exosomes.
Histograms show distribution of Log2 ratios toward the Super-SILAC standard. Results from three biological replicates per cell line are shown.
Extended Data Fig. 6 Intensity-based ranking of candidates to exosomal biomarkers.
The 3,759 unambiguously quantified proteins in exosomes from the 14 cell lines are ranked based on their summed light and heavy intensity. The 28 candidates to exosomal biomarkers are highlighted in the scatter plots and listed in the tables in their respective positions in the intensity ranking. Proteins in the range of 5% most intense are highlighted in red.
Extended Data Fig. 7 Validation of DG and SEC isolation methods.
a, Western blot of Flotillin-1 in the 12 fractions (F1-F12) of the OptiPrep gradient. Exosomes from HEK293T, MDAMB231 and PANC-1. Blots from three biological replicates are shown. b, NTA analysis shows the nanoparticle concentration in the recovered SEC fractions (F7-F25). Bar graph shows mean ± s.e.m. of nanoparticles per ml, individual data points from three biological replicates. c, Western blotting of Flotillin-1 in exosome-rich and exosome-depleted pooled SEC fractions. Blots from three biological replicates.
Extended Data Fig. 8 Distribution of SILAC ratios of MS samples from different isolation methods.
Histograms show distribution of log2 ratios toward the Super-SILAC standard of HEK293T, MDAMB231 and PANC-1 samples. Results from three biological replicates for the three isolation methods are shown [density gradient (DG) left, size-exclusion chromatography (SEC) middle, and ultracentrifugation (UC) right].
Extended Data Fig. 9 A cohort of 93 ubiquitous exosomal proteins identified by MS.
a, Venn diagram shows overlap of proteins quantified by Super-SILAC in all 14 cell lines with proteins quantified by the 3 isolation methods in our study. b, Venn diagram shows overlap between proteins quantified in all 14 cell lines and validated by the 3 isolation methods with the human proteins annotated in ExoCarta. c, Heatmap shows the protein levels of the 93 newly identified exosomal proteins in our datasets. Protein abundance is expressed as Log2 SILAC ratios (Exosomes/Super-SILAC standard). d, STRING-based PPI network of the 93 newly identified exosomal proteins. e, Enriched Gene Ontology Cellular Compartment (GOCC), Molecular Function (GOMF) and Biological Process (GOBP) in the cohort of 93 newly identified exosomal proteins. Results from three biological replicates.
Extended Data Fig. 10 Analysis of ubiquitous and abundant exosomal proteins using different approaches.
a, Western blot analysis of ITGB1, LGALS3BP, SLC3A2, Alix, CD47 and TSG101 in UC-isolated exosomes from HEK293T cells. Results from one biological replicate. b, Western blot analysis of ITGB1 in exosomes from the OptiPrep fractions of HEK293T, MDAMB231 and PANC-1. Results from one biological replicate. c, Western blot analysis of ITGB1 in exosomes from the exosome-rich and exosome-depleted SEC fractions, from HEK293T, MDAMB231 and PANC-1. Representative results from three biological replicates. d, Representative histograms show CD47 and ITGB1 levels in comparison to isotype control-stained beads in HEK293T, MDAMB231 and PANC-1-derived exosomes. Bar graph shows mean ± s.e.m. of percentage of positive beads, individual data points from three biological replicates. e, Representative histograms show the profile of the CD47 and ITGB1 levels in comparison to isotype control-stained exosomes from human plasma. Bar graph shows MFI (geometric mean of fluorescent intensity) ± s.e.m, and individual data points refer to ten individuals. Statistical significance was determined using two-tailed unpaired Mann Whitney U-test, and the exact p-values are shown. Significance defined as p < 0.05. f, Gating strategy used for the FACS-based single particle analysis of exosomes. g, Representative histograms show Syntenin-1 and CD63 positivity in non-permeabilized and permeabilized exosomes, in comparison to isotype control stained beads-bound exosomes.
Supplementary information
Supplementary Information
Supplementary Figs. 1–6: (1) The gating strategy used for the analysis of the apoptosis assay in the 14 cell lines. (2) Nanoparticle tracking analysis (NTA) profiles. Representative profile of size distribution of MVs and exosomes from HEK293T, MDA-MB-231 and PANC-1 cells (a). Representative profile of the size distribution of exosomes derived from murine cell lines (b). Profile of size distribution of exosomes derived from the serum of four C57BL/6J mice (c). Representative profile of the size distribution of isolated from fetal bovine serum, horse serum and goat serum (d). Profile of the size distribution of urine-derived exosomes from five individuals (e). (3) Profile of the size distribution and mode of plasma-derived exosomes from individuals 1 to 25 determined by NTA. (4) Profile of the size distribution and mode of plasma-derived exosomes from individuals 26 to 50 determined by NTA. (5) Profile of the size distribution and mode of plasma-derived exosomes from individuals 51 to 75 determined by NTA. (6) Profile of the size distribution and mode of plasma-derived exosomes from individuals 76 to 100 determined by NTA.
Supplementary Tables 1–6
(1) Exclusive proteins quantified in the exosomes derived from the 14 cell lines. (2) Ubiquitous proteins quantified in the exosomes derived from the 14 cell lines. (3) Proteins quantified using the DG, SEC and UC isolation methods in the three cell types analysed. (4) Proposed exosomal markers. (5) Proposed exclusion markers. (6) Information of plasma and urine exosomes.
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Kugeratski, F.G., Hodge, K., Lilla, S. et al. Quantitative proteomics identifies the core proteome of exosomes with syntenin-1 as the highest abundant protein and a putative universal biomarker. Nat Cell Biol 23, 631–641 (2021). https://doi.org/10.1038/s41556-021-00693-y
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DOI: https://doi.org/10.1038/s41556-021-00693-y
