Molecularly Engineered Phenoxazinone-Skeleton Cascade-Activated NIR Probes for Monitoring Fe2+/Viscosity in Ferroptosis-Mediated Parkinson's Disease
- PMID: 41764402
- DOI: 10.1002/advs.202524057
Molecularly Engineered Phenoxazinone-Skeleton Cascade-Activated NIR Probes for Monitoring Fe2+/Viscosity in Ferroptosis-Mediated Parkinson's Disease
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease, in which ferroptosis may be the crucial event leading to dopaminergic neuron death. Accumulated ferrous ions (Fe2+) and increased intracellular viscosity promote of ferroptosis. Precisely monitoring Fe2+/Viscosity, especially in ferroptosis, is crucial for PD theranostic. However, a feasible strategy is lacking. In this study, series of Fe2+/Viscosity cascade-activated near-infrared fluorescence probes (NP1-5) are synthesized. Through optical characterization and theoretical calculations, NP3 is selected as the optimal probe to monitor Fe2 +/Viscosity via redox reactions and twisted intramolecular charge transfer processes. To verify this concept in the context of ferroptosis intervention in PD, an innovative nanoplatform is constructed based on NP3 and quercetin, modified with rabies virus glycoprotein-29 and defined as PQR nanoparticles (PQR NPs). In vitro and in vivo experiments demonstrate that PQR NPs not only alleviate ferroptosis-induced loss of dopaminergic neurons by reducing oxidative stress and neuroinflammation, mitigating α-synuclein aggregation, and restoring mitochondrial function, but also could monitor the elevated Fe2 +/Viscosity in ferroptosis of PD models. Present study developed a facile tool for monitoring Fe2+/Viscosity in ferroptosis. The findings have strong application potential in theranostics of PD and other ferroptosis related diseases.
Keywords: Parkinson's disease; cascade‐activated probes; ferroptosis; ferrous ion; quercetin; viscosity.
© 2026 The Author(s). Advanced Science published by Wiley‐VCH GmbH.
References
-
- T. Kurth and R. Brinks, “Projecting Parkinson's Disease Burden,” Bmj 388 (2025): r350, https://doi.org/10.1136/bmj.r350.
-
- L. Xu, Z. Wang, and Q. Li, “Global Trends and Projections of Parkinson's Disease Incidence: a 30‐year Analysis Using GBD 2021 Data,” Journal of Neurology 272 (2025): 286, https://doi.org/10.1007/s00415‐025‐13030‐2.
-
- M. Hodaie, J. S. Neimat, and A. M. Lozano, “The Dopaminergic Nigrostriatal Systemand Parkinson's Disease,” Neurosurgery 60 (2007): 17–30, https://doi.org/10.1227/01.NEU.0000249209.11967.CB.
-
- B. Garcia Santa Cruz, A. Husch, and F. Hertel, “Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: General overview, main challenges, and future directions,” Frontiers in Aging Neuroscience 15 (2023): 1216163, https://doi.org/10.3389/fnagi.2023.1216163.
-
- A. A. Vijayakumari, H. H. Fernandez, and B. L. Walter, “MRI‐based multivariate gray matter volumetric distance for predicting motor symptom progression in Parkinson's disease,” Scientific Reports 13 (2023): 17704, https://doi.org/10.1038/s41598‐023‐44322‐0.
Grants and funding
LinkOut - more resources
Miscellaneous