A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
High‐Sensitivity Detection of Urinary Extracellular Vesicles With Upconverting Nanoparticle‐Based Lateral Flow Immunoassay
Tekijät: Islam, Md. Khirul; Mahmud, Imran; Ali, Klinton; Salminen, Teppo; Taimen, Pekka; Boström, Peter J.; Leivo, Janne; Lamminmäki, Urpo; Martiskainen, Iida
Kustantaja: Wiley
Kustannuspaikka: HOBOKEN
Julkaisuvuosi: 2025
Journal: Journal of Extracellular Biology
Tietokannassa oleva lehden nimi: Journal of Extracellular Biology
Lehden akronyymi: J EXTRACELL BIOL
Artikkelin numero: e70053
Vuosikerta: 4
Numero: 7
Sivujen määrä: 7
ISSN: 2768-2811
eISSN: 2768-2811
DOI: https://doi.org/10.1002/jex2.70053
Verkko-osoite: https://doi.org/10.1002/jex2.70053
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/499206650
Urinary extracellular vesicles (uEVs) are well-known to express tetraspanin family membrane proteins abundantly on their surface. In this study, we aimed to develop an upconverting nanoparticle (UCNP)-based lateral flow immunoassay (UCNP-LFIA) designed for the rapid and high-sensitivity detection of CD63-positive uEVs for direct urinalysis. The assay utilizes UCNPs reporter to detect low concentrations of EVs. Minimally processed uEV samples from bladder cancer (BlCa) (n = 62), benign prostatic hyperplasia (BPH) (n = 50) and healthy (n = 30) individuals were tested in sandwich UCNP-LFIA format, capturing uEVs with the same anti-CD63 antibody conjugated to UCNP and immobilized on the test zone. After 80 min, the strips were read with an upconversion luminescence reader device. This UCNP-LFIA measured CD63-positive EVs with high sensitivity, exhibiting a limit of detection (LoD) of 4 x 107 EVs/mL. The concentration of CD63-positive EVs in BlCa patients showed a 2.3-fold increase compared to benign conditions (p = 0.007), and a 16-fold increase compared to healthy controls (p = 0.00001). The results demonstrate the potential of UCNP-LFIA platform for sensitive and quantitative detection of uEVs, highlighting its promise as a tool for EV detection at point-of-care diagnostics.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
Research Council of Finland. Open access publishing facilitated by Turun yliopisto, as part of the Wiley - FinELib agreement. This research was supported by the PoDoCo project funded by The Paulo Foundation and Uniogen Oy, Ella and Georg Ehrnrooth Foundation, Päivikki and Sakari Sohlberg Foundation and by the Research Council of Finland's Flagship InFLAMES (337530/357910).
