The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Rapid magnetic isolation of extracellular vesicles via lipid-based nanoprobes
Open accessAbstract
Extracellular vesicles (EVs) can mediate intercellular communication by transferring cargo proteins and nucleic acids between cells. The pathophysiological roles and clinical value of EVs are under intense investigation, yet most studies are limited by technical challenges in the isolation of nanoscale EVs (nEVs). Here, we report a lipid-nanoprobe system that enables spontaneous labelling of nEVs for subsequent magnetic enrichment in 15 minutes, with isolation efficiency and cargo composition similar to what can be achieved by the much slower and bulkier method of ultracentrifugation. We also show that this approach allows for downstream analyses of nucleic acids and proteins, enabling the identification of EGFR and KRAS mutations following nEV isolation from the blood plasma of non-small-cell lung-cancer patients. The efficiency and versatility of the lipid-nanoprobe approach opens up opportunities in point-of-care cancer diagnostics. Nanoscale extracellular vesicles can be efficiently isolated in about 15 minutes, for downstream analyses of nucleic acids and proteins, via spontaneous labelling through a lipid nanoprobe and subsequent magnetic capture of the labelled vesicles.
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Cites
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., Harris, P. L., Haserlat, S. M., Supko, J. G., Haluska, F. G., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040938 · Cited by 11,486
Extracellular vesicles: Exosomes, microvesicles, and friends
Raposo, G., & Stoorvogel, W. (2013) · Journal of Cell Biology · doi:10.1083/jcb.201211138 · Cited by 8,099
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
Glypican-1 identifies cancer exosomes and detects early pancreatic cancer
Melo, S. A., Luecke, L. B., Kahlert, C., Fernandez, A. F., Gammon, S. T., Kaye, J., LeBleu, V. S., Mittendorf, E. A., Weitz, J., Rahbari, N., et al. (2015) · Nature · doi:10.1038/nature14581 · Cited by 2,880
Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver
Costa-Silva, B., Aiello, N. M., Ocean, A. J., Singh, S., Zhang, H., Thakur, B. K., Becker, A., Hoshino, A., Mark, M. T., Molina, H., et al. (2015) · Nature Cell Biology · doi:10.1038/ncb3169 · Cited by 2,692
An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage
Newman, A. M., Bratman, S. V., To, J., Wynne, J. F., Eclov, N. C. W., Modlin, L. A., Liu, C. L., Neal, J. W., Wakelee, H. A., Merritt, R. E., et al. (2014) · Nature Medicine · doi:10.1038/nm.3519 · Cited by 2,220
Systemically Injected Exosomes Targeted to EGFR Deliver Antitumor MicroRNA to Breast Cancer Cells
Ohno, S.-I., Takanashi, M., Sudo, K., Ueda, S., Ishikawa, A., Matsuyama, N., Fujita, K., Mizutani, T., Ohgi, T., Ochiya, T., et al. (2013) · Molecular Therapy · doi:10.1038/mt.2012.180 · Cited by 1,666
Cited by
Advances in therapeutic applications of extracellular vesicles
Wiklander, O. P. B., Brennan, M. Á., Lötvall, J., Breakefield, X. O., & EL Andaloussi, S. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aav8521 · Cited by 1,039
Exosomes as a new frontier of cancer liquid biopsy
Yu, D., Li, Y., Wang, M., Gu, J., Xu, W., Cai, H., Fang, X., & Zhang, X. (2022) · Molecular Cancer · doi:10.1186/s12943-022-01509-9 · Cited by 855
Cheng, G., Li, W., Ha, L., Han, X., Hao, S., Wan, Y., Wang, Z., Dong, F., Zou, X., Mao, Y., et al. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b03584 · Cited by 371
Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes
Ko, J., Bhagwat, N., Yee, S. S., Ortiz, N., Sahmoud, A., Black, T., Aiello, N. M., McKenzie, L., O’Hara, M., Redlinger, C., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b05503 · Cited by 249
Chen, C., Sun, M., Wang, J., Su, L., Lin, J., & Yan, X. (2021) · Journal of Extracellular Vesicles · doi:10.1002/jev2.12163 · Cited by 163
Yang, L., Patel, K. D., Rathnam, C., Thangam, R., Hou, Y., Kang, H., & Lee, K. (2022) · Small · doi:10.1002/smll.202104783 · Cited by 81
Liu, Y., Xia, Y., Smollar, J., Mao, W., & Wan, Y. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188539 · Cited by 23
Self-citationIn vitro affinity maturation of antibody against membrane-bound GPCR molecules
Wang, J., An, L., Zhao, Y., Zhang, C., Li, S., Ye, C., Jing, S., & Hang, H. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-10030-x · Cited by 11
Metadata
- Authors
- Wan, Y., Cheng, G., Liu, X., Hao, S.-J., Nisic, M., Zhu, C.-D., Xia, Y.-Q., Li, W.-Q., Wang, Z.-G., Zhang, W.-L., Rice, S. J., Sebastian, A., Albert, I., Belani, C. P., & Zheng, S.-Y.
- Venue
- Nature Biomedical Engineering
- Published
- 2017-04-10
- Volume
- 1
- Issue
- 4
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W2607346246
- Cited by
- 279
- References
- 12
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- green
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