In Vivo and In Vitro Monitoring of Amyloid Aggregation via BSA@FGQDs Multimodal Probe
Abstract
Early detection of peptide aggregate intermediates is quite challenging because of their variable and complex nature as well as due to lack of reliable sensors for diagnosis. Herein, we report the detection of monomers and oligomers using specified fluorescence and a magnetic resonance imaging (MRI) multimodal probe based on bovine-serum-albumin-capped fluorine functionalized graphene quantum dots (BSA@FGQDs). This probe enables in vitro fluorescence-based monitoring of human islet amyloid polypeptide (hIAPP), insulin, and amyloid β(1-42) (Aβ42) monomers and oligomers during the fibrillogenesis dynamic. Up to 90% fluorescence quenching of BSA@FGQDs probe upon addition of amyloid monomers/oligomers was observed due to static quenching and nonradiative energy transfer. Moreover, the BSA@FGQDs probe shows 10 times higher signals in detecting amyloid intermediates and fibrils than that of conventional thioflavin dye. A negative Δ G° value (-36.21 kJ/mol) indicates spontaneous interaction of probe with the peptide. These interactions are hydrogen bonding and hydrophobic as proved by thermodynamic parameters. Visual binding clues of BSA@FGQDs with different morphological states of amyloid protein was achieved through electron microscopy. Furthermore, intravenous and intracranial injection of BSA@FGQDs probe in Alzheimer model mice brain enabled in vivo detection of amyloid plaques in live mice brain by 19F MRI through contrast enhancement. Our proposed probe not only effectively…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.