The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Mapping Subpopulations of Cancer Cell-Derived Extracellular Vesicles and Particles by Nano-Flow Cytometry
Abstract
The elusive complexity of membranous extracellular vesicle (EV) and membrane-less extracellular particle (EP) populations released from various cellular sources contains clues as to their biological functions and diagnostic utility. In this study, we employed optimized multicolor nano-flow cytometry, structured illumination (SIM), and atomic force microscopy (AFM) to bridge sensitive detection at the single EV/EP level and high-throughput analysis of cancer cell secretomes. We applied these approaches to particles released from intact cells driven by several different transforming mechanisms or to cells under therapeutic stress imposed by pharmacological inhibition of their oncogenic drivers, such as epidermal growth factor receptor (EGFR). We demonstrate a highly heterogeneous distribution of biologically relevant elements of the EV/EP cargo, including oncoproteins (EGFR), clotting factors (tissue factor), pro-metastatic integrins (ITGA6, ITGA4), tetraspanins (CD63), and genomic DNA across the entire particulate secretome of cancer cells. We observed that targeting EGFR activity with irreversible kinase inhibitors (dacomitinib) triggers emission of DNA containing EP/EV subpopulations, including particles (chromatimeres) harboring both EGFR and DNase-resistant chromatin. While nano-flow cytometry enables quantification of these changes across the entire particular secretome, SIM reveals individual molecular topography of EV/EP subsets and AFM exposes some of their physical properties, including the presence of nanofilaments and other substructures. We describe differential uptake rates of distinct EV subsets, resulting in preferential internalization of exosome-like small EVs by cancer cells to the exclusion of larger EVs. Thus, our study illustrates the potential of nano-flow cytometry coupled with high-resolution microscopy to explore the cancer-related EV/EP landscape.
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Théry, C., Witwer, K. W., Aikawa, E., Alcaraz, M. J., Anderson, J. D., Andriantsitohaina, R., Antoniou, A., Arab, T., Archer, F., Atkin‐Smith, G. K., et al. (2018) · Journal of Extracellular Vesicles · doi:10.1080/20013078.2018.1535750 · Cited by 11,099
Self-citationShedding light on the cell biology of extracellular vesicles
van Niel, G., D'Angelo, G., & Raposo, G. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.125 · Cited by 8,369
Tumour exosome integrins determine organotropic metastasis
Hoshino, A., Costa-Silva, B., Shen, T.-L., Rodrigues, G., Hashimoto, A., Tesic Mark, M., Molina, H., Kohsaka, S., Di Giannatale, A., Ceder, S., et al. (2015) · Nature · doi:10.1038/nature15756 · Cited by 5,101
Kowal, J., Arras, G., Colombo, M., Jouve, M., Morath, J. P., Primdal-Bengtson, B., Dingli, F., Loew, D., Tkach, M., & Théry, C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521230113 · Cited by 3,426
Reassessment of exosome composition
Jeppesen, D. K., Fenix, A. M., Franklin, J. L., Higginbotham, J. N., Zhang, Q., Zimmerman, L. J., Liebler, D. C., Ping, J., Liu, Q., Evans, R., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.029 · Cited by 2,975
Routes and mechanisms of extracellular vesicle uptake
Mulcahy, L. A., Pink, R. C., & Carter, D. R. F. (2014) · Journal of Extracellular Vesicles · doi:10.3402/jev.v3.24641 · Cited by 2,832
Molecular anatomy of a trafficking organelle
Takamori, S., Holt, M., Stenius, K., Lemke, E. A., Grønborg, M., Riedel, D., Urlaub, H., Schenck, S., Brügger, B., Ringler, P., et al. (2006) · Cell · doi:10.1016/j.cell.2006.10.030 · Cited by 2,373
Christianson, H. C., Svensson, K. J., van Kuppevelt, T. H., Li, J.-P., & Belting, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1304266110 · Cited by 903
Immune evasion mediated by PD-L1 on glioblastoma-derived extracellular vesicles
Ricklefs, F. L., Alayo, Q., Krenzlin, H., Mahmoud, A. B., Speranza, M. C., Nakashima, H., Hayes, J. L., Lee, K., Balaj, L., Passaro, C., et al. (2018) · Science Advances · doi:10.1126/sciadv.aar2766 · Cited by 610
Amphiregulin exosomes increase cancer cell invasion
Higginbotham, J. N., Demory Beckler, M., Gephart, J. D., Franklin, J. L., Bogatcheva, G., Kremers, G.-J., Piston, D. W., Ayers, G. D., McConnell, R. E., Tyska, M. J., et al. (2011) · Current Biology · doi:10.1016/j.cub.2011.03.043 · Cited by 360
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Exosome-based liquid biopsies in cancer: opportunities and challenges
Yu, W., Hurley, J., Roberts, D., Chakrabortty, S. K., Enderle, D., Noerholm, M., Breakefield, X. O., & Skog, J. K. (2021) · Annals of Oncology · doi:10.1016/j.annonc.2021.01.074 · Cited by 870
Extracellular Vesicles and Nanoparticles: Emerging Complexities
Jeppesen, D. K., Zhang, Q., Franklin, J. L., & Coffey, R. J. (2023) · Trends in Cell Biology · doi:10.1016/j.tcb.2023.01.002 · Cited by 705
Technologies and Standardization in Research on Extracellular Vesicles
Gandham, S., Su, X., Wood, J., Nocera, A. L., Alli, S. C., Milane, L., Zimmerman, A., Amiji, M., & Ivanov, A. R. (2020) · Trends in Biotechnology · doi:10.1016/j.tibtech.2020.05.012 · Cited by 494
Engineered Extracellular Vesicles for Cancer Therapy
Zhang, X., Zhang, H., Gu, J., Zhang, J., Shi, H., Qian, H., Wang, D., Xu, W., Pan, J., & Santos, H. A. (2021) · Advanced Materials · doi:10.1002/adma.202005709 · Cited by 428
Jia, Y., Yu, L., Ma, T., Xu, W., Qian, H., Sun, Y., & Shi, H. (2022) · Theranostics · doi:10.7150/thno.74305 · Cited by 391
Extracellular vesicles: A bright star of nanomedicine
Wu, P., Zhang, B., Ocansey, D. K. W., Xu, W., & Qian, H. (2021) · Biomaterials · doi:10.1016/j.biomaterials.2020.120467 · Cited by 368
Visan, K. S., Lobb, R. J., Ham, S., Lima, L. G., Palma, C., Edna, C. P. Z., Wu, L., Gowda, H., Datta, K. K., Hartel, G., et al. (2022) · Journal of Extracellular Vesicles · doi:10.1002/jev2.12266 · Cited by 308
Di Bella, M. A. (2022) · Biology · doi:10.3390/biology11060804 · Cited by 304
Analysis of extracellular vesicles as emerging theranostic nanoplatforms
Xing, Y., Cheng, Z., Wang, R., Lv, C., James, T. D., & Yu, F. (2020) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213506 · Cited by 67
Metadata
- Authors
- Choi, D., Montermini, L., Jeong, H., Sharma, S., Meehan, B., & Rak, J.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-08-30
- Volume
- 13
- Issue
- 9
- Pages
- 10499-10511
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2970230025
- Cited by
- 221
- References
- 10
- FWCI
- 6.87
- Type
- journal article
- Language
- en
- Open access
- closed
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