Ultrasensitive SERS-LFA for the Detection of Neurofilament Light Chain and Machine Learning-Assisted Alzheimer's Disease Classification

Abstract

Neurofilament light chain (NfL), a cytoskeletal protein released during neuronal injury, is a promising biomarker with elevated levels consistently associated with disease severity and progression in multiple neurological conditions, including Alzheimer's disease (AD).However, its quantitative detection in blood remains challenging due to its ultralow concentration and matrix-associated interferences. Here, we present a competitive dual-mode lateral flow assay (LFA) integrated with surface-enhanced Raman scattering (SERS) for ultrasensitive NfL quantification and clinical classification. The sensing strategy utilizes hybrid plasmonic nanoprobes comprising gold nanorods (GNRs) electrostatically assembled with MXene quantum dots (MQDs), functionalized with Raman reporter 4-aminothiophenol (4-ATP) and the target NfL protein. The GNR@MQD nanohybrid exhibited ~14-fold enhancement in SERS intensity over GNRs alone, with an analytical enhancement factor of 1.7 × 10⁷. This signal amplification arises from synergistic electromagnetic and chemical enhancement mechanisms, facilitated by MQD-mediated nanorod alignment and charge transfer interactions. In spiked plasma, the assay achieved a detection limit of 17.38 pg/mL with excellent linearity (R² = 0.97) and high specificity. To demonstrate clinical utility, spectral data from LFA test zones were acquired for plasma samples of AD patients, mild cognitively impaired patients, and control cohorts. A machine learning pipeline integrating principal component analysis (PCA) and multilayer perceptron (MLP) classification enabled group-wise discrimination with 77.8% accuracy and macro-averaged precision, recall, and F1-score values of 0.83, 0.78, and 0.77, respectively. To our knowledge, this is the first report of a GNR@MQD-based nanohybrid applied in a competitive SERS-LFA for blood-based NfL detection and ML-assisted clinical differentiation. The proposed platform represents a cost effective, portable, and intelligent diagnostic solution for early-stage Alzheimer's disease and broader neurodegenerative screening at the point-of-care.