Ultrasensitive Saliva-Based Detection of Early Alzheimer´?s Disease Biomarkers via Nanoparticle-Enhanced Evanescent Scattering Microscopy

Year: 2026

Authors: Dallari C., Ladurner G., Kendrisic M., Manuela F.F., Manzl C., Manzl M., Ponticelli L., Perego L., Goretti F., Credi C., Prokesch M., Woehrer A., Baumann B., Leitgeb R., Pavone F.S.

Autors Affiliation: Univ Florence, European Lab Nonlinear Spect LENS, I-50019 Sesto Fiorentino, Italy; Natl Inst Opt, Natl Res Council INO CNR, I-50019 Sesto Fiorentino, Italy; Med Univ Vienna, Ctr Med Phys & Biomed Engn, A-1090 Vienna, Austria; Med Univ Innsbruck, Inst Neuropathol & Mol Neuropathol, A-6020 Innsbruck, Austria; Scantox Neuro GmbH, A-8074 Grambach, Austria; Med Univ Innsbruck, Inst Biomed Phys, A-6020 Innsbruck, Austria; Univ Florence, Dept Phys, I-50019 Sesto Fiorentino, Italy.

Abstract: Non-invasive biomarkers for early Alzheimer’s disease (AD) screening remain a critical unmet need. Current cerebrospinal fluid (CSF) assays, while highly informative, are invasive and unsuitable for large-scale or repeat testing, whereas blood-based biomarkers, despite recent diagnostic advances, still face challenges related to assay standardization, analytical complexity, and sophisticated instrumentation requirements. Saliva represents an attractive alternative matrix due to its accessibility and minimal burden on patients; however, the extremely low abundance and instability of amyloid-beta (A beta) peptides have thus far limited the development of reliable salivary diagnostics. We developed and validated a nanoparticle-enhanced total internal reflection scattering (TIRS) microscopy platform for ultrasensitive, real-time quantification of salivary A beta proteins. Metallic nanoparticles functionalized with anti-A beta antibodies were used to amplify scattering signals and enable robust detection at sub-picogram concentrations. The assay was evaluated in two established AD mouse models, APP(sl) and 5xFAD, in comparison with wild-type controls (n = 33 and n = 34, respectively). Since the validation of A beta levels in saliva is not feasible with current state-of-the-art technologies, we validated the findings by measuring A beta levels in the cortex and hippocampus via immunohistochemistry and ELISA. The TIRS assay demonstrated high analytical sensitivity and specificity for A beta detection in saliva. In both APP(sl) and 5xFAD models, salivary A beta(4)(2) concentrations were significantly elevated in transgenic mice and showed strong correlations with brain amyloid deposition. Logistic regression and support vector machine (SVM) classifiers were applied to quantify diagnostic performance and threshold-based discrimination based on salivary A beta(4)(2), identified as the most discriminative A beta form in descriptive analyses. In APP(sl) mice, logistic regression and SVM models achieved 92% classification accuracy with balanced sensitivity and specificity. These findings establish nanoparticle-enhanced TIRS as a rapid, accurate, and non-invasive tool for salivary A beta quantification. By overcoming historical limitations of saliva-based biomarker detection, this technology provides a foundation for future translational development, including validation in human cohorts and optimization toward scalable and point-of-care diagnostic implementations.

Journal/Review: ACS SENSORS

Volume: 11 (3)      Pages from: 2784  to: 2795

More Information: The authors would like to acknowledge the Centro di competenza-RISE funded by FAS Regione Toscana; I-PHOQS project financed by the EU next generation PNRR action (CUP B53C22001750006); SENSOR project (Agyr 2024-Airalzh Foundation); ERC Proof of Concept grant 101069344 OPTIMEYEZ; FFG grant 900435; OPTO-19FLUIDIC (CUP B53D23015530006) PRIN project financed by the EU next generation PNRR action; DoptoScreen project (Fondo di Beneficenza Intesa San Paolo 2019, B/2019/0289); Advance Lightsheet Microscopy Italian Mode of Euro-bioimaging ERIC.
KeyWords: saliva-based assay; gold nanoparticles; evanescent wave; total internal reflection scattering (TIRS); ultrasensitive detection
DOI: 10.1021/acssensors.5c04842