Abstract
Purpose :
To investigate the mole fraction of trace elements and transition metals in ocular tissue sections by correlative Analytical Electron Microscopy (AEM) and nano-secondary ion mass spectrometry (nano-SIMS) elemental mapping yielding better detection limits and shorter acquisition times as compared to AEM alone.
Methods :
Human and mouse retinal tissue samples were embedded without heavy metal stain for electron microscopy. Neighbouring sections were used for AEM, i.e. energy dispersive x-ray microanalysis (EDX) and electron energy loss spectroscopy (EELS), and for nano-SIMS using Cs+ and O- primary ions, respectively. They were investigated with respect to ultrastructure and elemental composition.
Results :
The ultrastructure of the tissue and the elemental composition of melanosomes and lipofuscin granules of the choroid and RPE, already determined by EDX and EELS microanalyses, can adequately be investigated by nano-SIMS using the secondary ion maps. Melanosomes, 0.5 – 1 μm in diameter, contain pheomelanin and yield sulfur maps and maps of trace elements like calcium, copper and sodium, the latter not detected by EDX microanalysis. Lipofuscin granules show especially high phosphorous content. Transition metal mole fractions of about 0.1 at%, as investigated by EDX microanalysis, can be measured using SIMS elemental maps with a lateral resolution of less than 200 nm with typical acquisition times of 30 minutes for each area of interest.
Conclusions :
Nano-SIMS has excellent detection limits and a lateral resolution of better than 200 nm and we demonstrate the possibilities for chemical mapping, high-sensitivity trace element detection and reduced acquisition times. However, quantification of Nano-SIMS data is not easily possible. Both methods yielded the melanin type in melanosomes and trace elemental content including copper and zinc, which is important for oxidative stress and the onset of age-related macular degeneration research. Combining AEM with Nano-SIMS yields quantification of Nano-SIMS data which have significantly better detection limits as compared to AEM.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.