A novel method to detect unlabeled inorganic nanoparticles and submicron particles in tissue by sedimentation field-flow fractionation
Open Access
- 3 December 2008
- journal article
- Published by Springer Nature in Particle and Fibre Toxicology
- Vol. 5 (1) , 18
Abstract
A novel methodology to detect unlabeled inorganic nanoparticles was experimentally demonstrated using a mixture of nano-sized (70 nm) and submicron (250 nm) silicon dioxide particles added to mammalian tissue. The size and concentration of environmentally relevant inorganic particles in a tissue sample can be determined by a procedure consisting of matrix digestion, particle recovery by centrifugation, size separation by sedimentation field-flow fractionation (SdFFF), and detection by light scattering. Background Laboratory nanoparticles that have been labeled by fluorescence, radioactivity, or rare elements have provided important information regarding nanoparticle uptake and translocation, but most nanomaterials that are commercially produced for industrial and consumer applications do not contain a specific label. Methods Both nitric acid digestion and enzyme digestion were tested with liver and lung tissue as well as with cultured cells. Tissue processing with a mixture of protease enzymes is preferred because it is applicable to a wide range of particle compositions. Samples were visualized via fluorescence microscopy and transmission electron microscopy to validate the SdFFF results. We describe in detail the tissue preparation procedures and discuss method sensitivity compared to reported levels of nanoparticles in vivo. Conclusion Tissue digestion and SdFFF complement existing techniques by precisely identifying unlabeled metal oxide nanoparticles and unambiguously distinguishing nanoparticles (diameter<100 nm) from both soluble compounds and from larger particles of the same nominal elemental composition. This is an exciting capability that can facilitate epidemiological and toxicological research on natural and manufactured nanomaterials.Keywords
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