Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the pathological accumulation of amyloid-β (Aβ) plaques, which serve as crucial biomarkers for disease diagnosis and therapeutic evaluation. While fluorescence imaging has emerged as a powerful technique for Aβ detection, current probes face limitations in clinical application due to insufficient photostability and short blood half-life, resulting in compromised signal-to-noise ratios (SNRs) and imaging resolution. Herein, two bright quinoxalinone-based fluorescent probes (QNO-AD-PEGs) were presented, which incorporate hydrophilic poly(ethylene glycol) (PEG) chains for enhanced biocompatibility and an Aβ-specific N,N-dimethylaminophenyl recognition unit. QNO-AD-PEG1 demonstrated exceptional binding affinity for Aβ42 aggregates (Kd = 42 nM) and a remarkable 49-fold fluorescence enhancement upon target engagement, with a quantum yield (ΦAβ) of 11.45%. In vivo imaging revealed that QNO-AD-PEG1 effectively crossed the blood-brain barrier (BBB) and exhibited a prolonged half-life (315 min). Notably, the probe successfully visualized age-dependent Aβ plaque progression in AD mouse models. This study presents a significant breakthrough in molecular imaging for neurodegenerative diseases, offering a versatile tool for both fundamental AD research and potential clinical applications. © 2025 American Chemical Society.
| Original language | English |
|---|---|
| Pages (from-to) | 12347-12355 |
| Journal | Analytical Chemistry |
| Volume | 97 |
| Issue number | 23 |
| Online published | 16 May 2025 |
| DOIs | |
| Publication status | Published - 17 Jun 2025 |
Funding
This work was financially supported by the National Natural Science Foundation of China (Nos. U21A20308, 22207079) and the Foundation from Science and Technology Major Project of Tibetan Autonomous Region of China (XZ202201ZD0001G). The authors also thank Yanhong Liu and Jing Li from the Comprehensive Training Platform of Specialized Laboratory, College of Chemistry, Sichuan University, for sample analysis.
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