The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Isolation of biologically-active exosomes from human plasma
Open accessAbstract
Effects of exosomes present in human plasma on immune cells have not been examined in detail. Immunological studies with plasma-derived exosomes require their isolation by procedures involving ultracentrifugation. These procedures were largely developed using supernatants of cultured cells. To test biologic activities of plasma-derived exosomes, methods are necessary that ensure adequate recovery of exosome fractions free of contaminating larger vesicles, cell fragments and protein/nucleic acid aggregates. Here, an optimized method for exosome isolation from human plasma/serum specimens of normal controls (NC) or cancer patients and its advantages and pitfalls are described. To remove undesirable plasma-contaminating components, ultrafiltration of differentially-centrifuged plasma/serum followed by size-exclusion chromatography prior to ultracentrifugation facilitated the removal of contaminants. Plasma or serum was equally acceptable as a source of exosomes based on the recovered protein levels (in μg protein/mL plasma) and TEM image quality. Centrifugation on sucrose density gradients led to large exosome losses. Fresh plasma was the best source of morphologically-intact exosomes, while the use of frozen/thawed plasma decreased exosome purity but not their biologic activity. Treatments of frozen plasma with DNAse, RNAse or hyaluronidase did not improve exosome purity and are not recommended. Cancer patients’ plasma consistently yielded more isolated exosomes than did NCs’ plasma. Cancer patients’ exosomes also mediated higher immune suppression as evidenced by decreased CD69 expression on responder CD4+ T effector cells. Thus, the described procedure yields biologically-active, morphologically-intact exosomes that have reasonably good purity without large protein losses and can be used for immunological, biomarker and other studies.
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
Extracellular vesicles: Exosomes, microvesicles, and friends
Raposo, G., & Stoorvogel, W. (2013) · Journal of Cell Biology · doi:10.1083/jcb.201211138 · Cited by 8,099
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
B lymphocytes secrete antigen-presenting vesicles.
Raposo, G., Nijman, H. W., Stoorvogel, W., Liejendekker, R., Harding, C. V., Melief, C. J., & Geuze, H. J. (1996) · The Journal of experimental medicine · doi:10.1084/jem.183.3.1161 · Cited by 3,546
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
Exosomes: biogenesis, biologic function and clinical potential
Zhang, Y., Liu, Y., Liu, H., & Tang, W. H. (2019) · Cell & Bioscience · doi:10.1186/s13578-019-0282-2 · Cited by 2,119
Using exosomes, naturally-equipped nanocarriers, for drug delivery
Batrakova, E. V., & Kim, M. S. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.07.030 · Cited by 1,041
Mustapic, M., Eitan, E., Werner, J. K., Berkowitz, S. T., Lazaropoulos, M. P., Tran, J., Goetzl, E. J., & Kapogiannis, D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00278 · Cited by 441
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
Metadata
- Authors
- Muller, L., Hong, C.-S., Stolz, D. B., Watkins, S. C., & Whiteside, T. L.
- Venue
- Journal of Immunological Methods
- Publisher
- Elsevier BV
- Published
- 2014-09
- Volume
- 411
- Pages
- 55-65
- ISSN
- 0022-1759 (ISSN-L)
- OpenAlex
- W2078894965
- Cited by
- 489
- References
- 3
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Isolation of biologically-active exosomes from human plasma. https://app.cochat.ai/papers/10.1016/j.jim.2014.06.007
- The paper
- Muller, L., Hong, C.-S., Stolz, D. B., Watkins, S. C., & Whiteside, T. L. (2014). Isolation of biologically-active exosomes from human plasma. Journal of Immunological Methods. https://doi.org/10.1016/j.jim.2014.06.007
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.