In Vivo Exposure Pathways of Ambient Magnetite Nanoparticles Revealed by Machine Learning-Aided Single-Particle Mass Spectrometry.
Weican ZhangShiwei HuoShenxi DengKe MinCha HuangHang YangLin LiuLuyao ZhangPeijie ZuoLihong LiuQian LiuGui-Bin JiangPublished in: Nano letters (2024)
Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs in vivo is extremely difficult, and the distribution, translocation, and metabolism of UFPs remain unclear. Here, we report a label-free, machine learning-aided single-particle inductively coupled plasma mass spectrometry (spICP-MS) approach for tracing the exposure pathways of airborne magnetite nanoparticles (MNPs), including external emission sources, and distribution and translocation in vivo using a mouse model. Our results provide quantitative analysis of different metabolic pathways in mice exposed to MNPs, revealing that the spleen serves as the primary site for MNP metabolism (84.4%), followed by the liver (11.4%). The translocation of inhaled UFPs across different organs alters their particle size. This work provides novel insights into the in vivo fate of UFPs as well as a versatile and powerful platform for nanotoxicology and risk assessment.
Keyphrases
- mass spectrometry
- machine learning
- particulate matter
- label free
- risk assessment
- cystic fibrosis
- mouse model
- liquid chromatography
- capillary electrophoresis
- high performance liquid chromatography
- high resolution
- drinking water
- air pollution
- gas chromatography
- artificial intelligence
- multiple sclerosis
- big data
- heavy metals
- ms ms
- type diabetes
- high throughput
- deep learning
- metabolic syndrome