04 June 2020
Nanoparticles can signal onset of Alzheimer’s
Published online 29 November 2017
Scientists create non-toxic, magnetic nanoparticles to detect early signs of Alzheimer’s disease.
Magnetic resonance imaging can usually capture the vicious plaque build-ups, but it’s only able to do so at a very late stage of Alzheimer’s disease, probably when it’s already too late to reverse or ease any of the disease’s effects.
The main challenge in diagnostics lies in finding a contrast agent that can safely cross into the brain. Nanoparticles are good candidates. But the semipermeable membrane known as blood-brain barrier, which protects the brain from foreign agents that can disturb its neural function, keeps these nanoparticles out.
It’s what prompted Xuefei Huang, a researcher at the Michigan State University, US, to explore ways to modify nanoparticles to try and make them cross the barrier. Huang and his colleagues began to sift through potential molecules that could be used to modify magnetic nanoparticles.
The team, that included a researcher from Benha University, Egypt, narrowed down their search to two molecules: an animal protein and sialic acid, which is an organic acid widely found in the brain and human milk.
After coating the nanoparticles with the animal protein and sialic acid, the scientists exposed them to an artificial blood-brain-barrier. The nanoparticles successfully passed through the barrier.
The modified nanoparticles were then injected into mice, genetically engineered to produce human-like beta amyloid plaques in their brains. The nanoparticles selectively bound to the plaques in the mouse brains, and were able to track the build-up of plaques in the brain.
Besides diagnosing Alzheimer’s disease at an early stage, the non-invasive method could help scientists understand the disease better, possibly providing leads for new therapies, says Huang.
- Nasr, S. H. et al. Detection of β-amyloid by sialic acid coated bovine serum albumin magnetic nanoparticles in a mouse model of Alzheimer’s disease. Small. https://doi.org/10.1002/smll.201701828 (2017)