Single-Chain Variable Fragments Targeting Plasma Exosome Amyloid-β Peptide Subtypes for Diagnosis of Alzheimer’s Disease

The diagnostic value of the Aβ1-42/Aβ1-40 ratio in peripheral blood as a biomarker for Alzheimer’s disease (AD) remains a subject of considerable interest. However, this ratio in plasma exosomes is still underestimated. To investigate the potential clinical performances, this study has established a chemiluminescence immunoassay (CLIA) for the specific detection of Aβ1-42 and Aβ1-40 in plasma exosomes using single-chain variable fragments (scFv). Plasma samples from 20 AD patients and 20 non-AD individuals were detected by the established scFv-based CLIA, with the traditional monoclonal antibody (mAb)-based CLIA as the reference method. The results demonstrated that the plasma exosome Aβ1-42/Aβ1-40 ratio in AD patients (0.70 ± 0.35) was significantly lower than that in the non-AD control group (1.82 ± 0.79, p < 0.001). In comparison with the mAb-based assay, the scFv-based CLIA exhibited superior analytical performance, with a lower limit of detection (1 pg/mL vs. 50 pg/mL) and higher specificity (cross-reactivity rate < 5%). When the Aβ1-42/Aβ1-40 ratio of 1.075 was set as the cutoff value, the scFv-based assay achieved a diagnostic sensitivity of 90.0%, which was numerically higher than that of the mAb-based assay (85.0%), with comparable specificity (90.0% vs. 90.0%). Generally, this study has developed a highly sensitive and specific scFv-based method for the detection of plasma exosome Aβ subtypes, which supports that the plasma exosome Aβ1-42/Aβ1-40 ratio is a potential non-invasive biomarker for differentiating clinically diagnosed AD from non-AD controls in this pilot cohort.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2026-11-08
DOI
https://doi.org/10.3390/ijms272110006
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
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article

Single-Chain Variable Fragments Targeting Plasma Exosome Amyloid-β Peptide Subtypes for Diagnosis of Alzheimer’s Disease

Hui Jiang, Zan Wang, Xiaojin Zhou
International Journal of Molecular Sciences
Alzheimer's disease research and treatments
article

Single-Chain Variable Fragments Targeting Plasma Exosome Amyloid-β Peptide Subtypes for Diagnosis of Alzheimer’s Disease

Hui Jiang, Zan Wang, Xiaojin Zhou
article en

Abstract

The diagnostic value of the Aβ1-42/Aβ1-40 ratio in peripheral blood as a biomarker for Alzheimer’s disease (AD) remains a subject of considerable interest. However, this ratio in plasma exosomes is still underestimated. To investigate the potential clinical performances, this study has established a chemiluminescence immunoassay (CLIA) for the specific detection of Aβ1-42 and Aβ1-40 in plasma exosomes using single-chain variable fragments (scFv). Plasma samples from 20 AD patients and 20 non-AD individuals were detected by the established scFv-based CLIA, with the traditional monoclonal antibody (mAb)-based CLIA as the reference method. The results demonstrated that the plasma exosome Aβ1-42/Aβ1-40 ratio in AD patients (0.70 ± 0.35) was significantly lower than that in the non-AD control group (1.82 ± 0.79, p < 0.001). In comparison with the mAb-based assay, the scFv-based CLIA exhibited superior analytical performance, with a lower limit of detection (1 pg/mL vs. 50 pg/mL) and higher specificity (cross-reactivity rate < 5%). When the Aβ1-42/Aβ1-40 ratio of 1.075 was set as the cutoff value, the scFv-based assay achieved a diagnostic sensitivity of 90.0%, which was numerically higher than that of the mAb-based assay (85.0%), with comparable specificity (90.0% vs. 90.0%). Generally, this study has developed a highly sensitive and specific scFv-based method for the detection of plasma exosome Aβ subtypes, which supports that the plasma exosome Aβ1-42/Aβ1-40 ratio is a potential non-invasive biomarker for differentiating clinically diagnosed AD from non-AD controls in this pilot cohort.

International Journal of Molecular SciencesVol. 27(21)
State Key Laboratory of Digital Medical Engineering (CN), Southeast University (CN)
Alzheimer's disease research and treatments
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