Spectrally separated dual-label upconversion luminescence lateral flow assay for cancer-specific STn-glycosylation in CA125 and CA15-3
: Ekman Miikka, Salminen Teppo, Raiko Kirsti, Soukka Tero, Gidwani Kamlesh, Martiskainen Iida
Publisher: Springer Nature
: 2024
: Analytical and Bioanalytical Chemistry
: Analytical and bioanalytical chemistry
: Anal Bioanal Chem
: 416
: 13
: 3251
: 3260
: 1618-2642
: 1618-2650
DOI: https://doi.org/10.1007/s00216-024-05275-z(external)
: https://link.springer.com/article/10.1007/s00216-024-05275-z(external)
: https://research.utu.fi/converis/portal/detail/Publication/387617668(external)
Multiplexed lateral flow assays (LFAs) offer efficient on-site testing by simultaneously detecting multiple biomarkers from a single sample, reducing costs. In cancer diagnostics, where biomarkers can lack specificity, multiparameter detection provides more information at the point-of-care. Our research focuses on epithelial ovarian cancer (EOC), where STn-glycosylated forms of CA125 and CA15-3 antigens can better discriminate cancer from benign conditions. We have developed a dual-label LFA that detects both CA125-STn and CA15-3-STn within a single anti-STn antibody test line. This utilizes spectral separation of green (540 nm) and blue (450 nm) emitting erbium (NaYF4:Yb3+, Er3+)- and thulium (NaYF4: Yb3+, Tm3+)-doped upconverting nanoparticle (UCNP) reporters conjugated with antibodies against the protein epitopes in CA125 or CA15-3. This technology allows the simultaneous detection of different antigen variants from a single test line. The developed proof-of-concept dual-label LFA was able to distinguish between the ascites fluid samples from diagnosed ovarian cancer patients (n = 10) and liver cirrhosis ascites fluid samples (n = 3) used as a negative control. The analytical sensitivity of CA125-STn for the dual-label LFA was 1.8 U/ml in buffer and 3.6 U/ml in ascites fluid matrix. Here we demonstrate a novel approach of spectrally separated measurement of STn-glycosylated forms of two different cancer-associated protein biomarkers by using UCNP reporter technology.
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