Machine Learning-Enhanced Ultrasensitive Immuno-CRISPR Array Facilitates Early Diagnosis of Alzheimer's Disease by Detecting Multiple Plasma Biomarkers.

Early and accurate diagnosis of Alzheimer's disease (AD) remains a significant challenge due to the multifactorial and dynamic nature of its pathology. Although plasma-based biomarkers such as amyloid-β (Aβ) and phosphorylated tau (p-tau) have shown promise as diagnostic indicators, current single-biomarker detection techniques lack the requisite sensitivity and specificity for early-stage diagnosis. Here, we present the development of an ultrasensitive CRISPR-based multi-protein detection array (UCMDA) capable of concurrently detecting six core AD biomarkers, including Aβ40, Aβ42, p-tau181, p-tau217, p-tau231, and p-tau396,404. By integrating antibody pair-based multiplex recombinase polymerase amplification (RPA) with spatially encoded CRISPR-Cas12a detection, the UCMDA achieves a detection limit of 1 fg/mL, which is 10 000-fold more sensitive than conventional ELISA. Clinical validation in a cohort of 155 plasma samples demonstrated that logistic regression (LR)-based integration of the six biomarkers significantly enhanced diagnostic performance, with the multi-biomarker model substantially outperforming single-biomarker approaches in diagnosing AD-MCI and AD. This platform offers a scalable, cost-effective, and minimally invasive strategy for early detection and disease monitoring. This work highlights the potential of CRISPR-based multiplex protein detection technologies combined with machine learning-assisted analysis to enhance the precision of diagnosing neurodegenerative disorders.