The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Engineered nanointerfaces for microfluidic isolation and molecular profiling of tumor-specific extracellular vesicles
Open accessAbstract
Extracellular vesicles (EVs) carry RNA, DNA, proteins, and lipids. Specifically, tumor-derived EVs have the potential to be utilized as disease-specific biomarkers. However, a lack of methods to isolate tumor-specific EVs has limited their use in clinical settings. Here we report a sensitive analytical microfluidic platform (EV HB-Chip) that enables tumor-specific EV-RNA isolation within 3 h. Using the EV HB-Chip, we achieve 94% tumor-EV specificity, a limit of detection of 100 EVs per μL, and a 10-fold increase in tumor RNA enrichment in comparison to other methods. Our approach allows for the subsequent release of captured tumor EVs, enabling downstream characterization and functional studies. Processing serum and plasma samples from glioblastoma multiforme (GBM) patients, we can detect the mutant EGFRvIII mRNA. Moreover, using next-generation RNA sequencing, we identify genes specific to GBM as well as transcripts that are hallmarks for the four genetic subtypes of the disease.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
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
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Isolation and Characterization of Exosomes from Cell Culture Supernatants and Biological Fluids
Théry, C., Amigorena, S., Raposo, G., & Clayton, A. (2006) · Current Protocols in Cell Biology · doi:10.1002/0471143030.cb0322s30 · Cited by 5,766
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
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
Self-citationChaotic Mixer for Microchannels
Stroock, A. D., Dertinger, S. K. W., Ajdari, A., Mezić, I., Stone, H. A., & Whitesides, G. M. (2002) · Science · doi:10.1126/science.1066238 · Cited by 3,435
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
Cited by
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-citationRNA delivery by extracellular vesicles in mammalian cells and its applications
O’Brien, K., Breyne, K., Ughetto, S., Laurent, L. C., & Breakefield, X. O. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-0251-y · Cited by 1,811
Self-citationAdvances 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
Self-citationExosomes and Exosome-Inspired Vesicles for Targeted Drug Delivery
Antimisiaris, S. G., Mourtas, S., & Marazioti, A. (2018) · doi:10.20944/preprints201810.0507.v1 · Cited by 605
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
Tian, T., Qiao, S., & Tannous, B. A. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c07981 · Cited by 27
Challenges and Promise for Glioblastoma Treatment through Extracellular Vesicle Inquiry
Liguori, G. L. (2024) · Cells · doi:10.3390/cells13040336 · Cited by 24
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
Monteiro-Reis, S., Carvalho-Maia, C., Bart, G., Vainio, S. J., Pedro, J., Silva, E. R., Sales, G., Henrique, R., & Jerónimo, C. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063267 · Cited by 21
Metadata
- Authors
- Reátegui, E., van der Vos, K. E., Lai, C. P., Zeinali, M., Atai, N. A., Aldikacti, B., Floyd, F. P., H. Khankhel, A., Thapar, V., Hochberg, F. H., Sequist, L. V., Nahed, B. V., S. Carter, B., Toner, M., Balaj, L., T. Ting, D., Breakefield, X. O., & Stott, S. L.
- Venue
- Nature Communications
- Published
- 2018-01-12
- Volume
- 9
- Issue
- 1
- Pages
- 175-175
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2783823932
- Cited by
- 330
- References
- 12
- FWCI
- 11.61
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
