Single Small Extracellular Vesicle (sEV) Quantification by Upconversion Nanoparticles.
- Publisher:
- AMER CHEMICAL SOC
- Publication Type:
- Journal Article
- Citation:
- Nano Lett, 2022, 22, (9), pp. 3761-3769
- Issue Date:
- 2022-05-11
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
acs.nanolett.2c00724.pdf | 4.74 MB | Adobe PDF |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author |
Huang, G https://orcid.org/0000-0001-7524-8199 |
|
dc.contributor.author |
Zhu, Y https://orcid.org/0000-0002-8840-176X |
|
dc.contributor.author |
Wen, S https://orcid.org/0000-0002-4670-4658 |
|
dc.contributor.author | Mei, H | |
dc.contributor.author | Liu, Y | |
dc.contributor.author | Wang, D | |
dc.contributor.author | Maddahfar, M | |
dc.contributor.author | Su, QP | |
dc.contributor.author |
Lin, G https://orcid.org/0000-0001-9880-8478 |
|
dc.contributor.author |
Chen, Y https://orcid.org/0000-0002-7886-7144 |
|
dc.contributor.author |
Jin, D https://orcid.org/0000-0003-1046-2666 |
|
dc.date.accessioned | 2023-01-30T04:32:39Z | |
dc.date.available | 2023-01-30T04:32:39Z | |
dc.date.issued | 2022-05-11 | |
dc.identifier.citation | Nano Lett, 2022, 22, (9), pp. 3761-3769 | |
dc.identifier.issn | 1530-6984 | |
dc.identifier.issn | 1530-6992 | |
dc.identifier.uri | http://hdl.handle.net/10453/165589 | |
dc.description.abstract | Cancer-derived small extracellular vesicles (sEVs) are potential circulating biomarkers in liquid biopsies. However, their small sizes, low abundance, and heterogeneity in molecular makeups pose major technical challenges for detecting and characterizing them quantitatively. Here, we demonstrate a single-sEV enumeration platform using lanthanide-doped upconversion nanoparticles (UCNPs). Taking advantage of the unique optical properties of UCNPs and the background-eliminating property of total internal reflection fluorescence (TIRF) imaging technique, a single-sEV assay recorded a limit of detection 1.8 × 106 EVs/mL, which was nearly 3 orders of magnitude lower than the standard enzyme-linked immunosorbent assay (ELISA). Its specificity was validated by the difference between EpCAM-positive and EpCAM-negative sEVs. The accuracy of the UCNP-based single-sEV assay was benchmarked with immunomagnetic-beads flow cytometry, showing a high correlation (R2> 0.99). The platform is suitable for evaluating the heterogeneous antigen expression of sEV and can be easily adapted for biomarker discoveries and disease diagnosis. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | AMER CHEMICAL SOC | |
dc.relation | http://purl.org/au-research/grants/nhmrc/1160635 | |
dc.relation | National Natural Science Foundation of China61729501 | |
dc.relation | http://purl.org/au-research/grants/arc/DE220100846 | |
dc.relation | http://purl.org/au-research/grants/arc/FL210100180 | |
dc.relation.ispartof | Nano Lett | |
dc.relation.isbasedon | 10.1021/acs.nanolett.2c00724 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject.classification | Nanoscience & Nanotechnology | |
dc.subject.mesh | Epithelial Cell Adhesion Molecule | |
dc.subject.mesh | Extracellular Vesicles | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Lanthanoid Series Elements | |
dc.subject.mesh | Nanoparticles | |
dc.subject.mesh | Neoplasms | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Neoplasms | |
dc.subject.mesh | Lanthanoid Series Elements | |
dc.subject.mesh | Nanoparticles | |
dc.subject.mesh | Extracellular Vesicles | |
dc.subject.mesh | Epithelial Cell Adhesion Molecule | |
dc.title | Single Small Extracellular Vesicle (sEV) Quantification by Upconversion Nanoparticles. | |
dc.type | Journal Article | |
utslib.citation.volume | 22 | |
utslib.location.activity | United States | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Science | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering | |
pubs.organisational-group | /University of Technology Sydney/Strength - IBMD - Initiative for Biomedical Devices | |
pubs.organisational-group | /University of Technology Sydney/Centre for Health Technologies (CHT) | |
utslib.copyright.status | closed_access | * |
dc.date.updated | 2023-01-30T04:32:38Z | |
pubs.issue | 9 | |
pubs.publication-status | Published | |
pubs.volume | 22 | |
utslib.citation.issue | 9 |
Abstract:
Cancer-derived small extracellular vesicles (sEVs) are potential circulating biomarkers in liquid biopsies. However, their small sizes, low abundance, and heterogeneity in molecular makeups pose major technical challenges for detecting and characterizing them quantitatively. Here, we demonstrate a single-sEV enumeration platform using lanthanide-doped upconversion nanoparticles (UCNPs). Taking advantage of the unique optical properties of UCNPs and the background-eliminating property of total internal reflection fluorescence (TIRF) imaging technique, a single-sEV assay recorded a limit of detection 1.8 × 106 EVs/mL, which was nearly 3 orders of magnitude lower than the standard enzyme-linked immunosorbent assay (ELISA). Its specificity was validated by the difference between EpCAM-positive and EpCAM-negative sEVs. The accuracy of the UCNP-based single-sEV assay was benchmarked with immunomagnetic-beads flow cytometry, showing a high correlation (R2> 0.99). The platform is suitable for evaluating the heterogeneous antigen expression of sEV and can be easily adapted for biomarker discoveries and disease diagnosis.
Please use this identifier to cite or link to this item:
Download statistics for the last 12 months
Not enough data to produce graph