An Iron Overload and an H-Ferritin Deficient Mouse Model were used to examine the impact of disrupted iron metabolism on the brain. Brain sections were imaged and compared using Synchrotron μXRF spectroscopy. Quantitative measurement of the relative metal ion concentrations for iron, copper and zinc were made across selected regions of interest in the brain. It was generally found that metal ion concentrations of iron and zinc decreased in specific regions in the Iron Overload condition compared with the control, with copper increasing in only one region. Few regions differed in metal ion concentration between the H-Ferritin Deficient Model and the control. The three conditions exhibited similar / identical results for metal ion concentrations in many brain regions, indicating the validity of the method used for comparison between samples. It is clear that there exists a complex relationship between these trace metal

Collingwood, J. F.

Finnegan, M. E.

Norman, D. G.

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INTRODUCTION REFERENCES 1 Thompson, K., et al (2003) Mouse Brains Deficient in H-Ferritin Have Normal Iron Concentration but a Protein Profile of Iron Deficiency and Increased  Evidence of Oxidative Stress. Journal of Neuroscience Research, 71, pp.46-63 2 Becker, J.S., et al (2009) Bioimaging of Metals by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). Mass Spectrometry Re-views, 29, pp.156-175 3 Connor, J.R., et al (2001) Is Hemochromatosis a Risk Factor for Alzheimer’s Disease? Journal of Alzheimer’s Disease, 3, pp.471-477 4 Pinero, D.J., Connor, J.R. (2000) Iron in the Brain: An Important Contributor in Normal and Diseased States. Neuroscientist, 6, pp.435-453 5 Punshon, T., et al (2005) Application of Synchrotron X-Ray Microbeam Spec-troscopy to the Determination of Metal Distribution and Speciation in Biological Tissues. Spectrosc Lett, 38, pp.343-363 6 Dexter, D.T., et al (1991) Alterations in the Levels of Iron, Ferritin and Other Trace Metals in Parkinson’s Disease and Other Neurodegenerative Diseases  Affecting the Basal Ganglia. Brain, 114, pp.1953-1975 7 Allen Mouse Brain Atlas [Internet]. Seattle (WA): Allen Institute for Brain Sci-ence. ©2009. Available from: http://mouse.brain-map.org. 8 Solé, V.A., et al (2007) A multiplatform code for the analysis of energy-dispersive X-ray fluorescence spectra. Spectrochim, 62, pp.63-68. ‘Trace metals in medicine’ have become of increasing interest in recent years especially with the study of        neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s (PD). Certain metals protect the brain against disease but the balance of these metals is precise1. Abnormal metal deposits have been found in     patients with AD and PD, particularly with iron elevation2. To investigate the relationship between iron and neurodegenerative diseases, mouse models have been developed to explore causes of iron accumulation in the brain. The H-Ferritin Deficient Model developed by Thompson et al (2003) causes a deficiency in the iron storage protein Ferritin. Furthermore, as haemochromatosis has been linked to AD3, an Iron Overload         condition was created through mouse diet manipulation. A method for comparing metal ion concentrations          between these models and a control group was needed, as disease-related changes in various metal ion concentration in specific brain regions have been linked to neurodegenerative diseases4. Traditional      methods of mapping free metals in the brain involves staining, however this doesn’t allow for simultaneous multi-element analysis or metal quantification5. This study used Synchrotron Microfocus X-ray Fluorescence Spectroscopy (µXRF) which allowed semi-quantitative mapping of brain metals at a high resolution. The whole brain was mapped with regions being selected based on identifiable metal ion differences post-imaging. This ‘backwards’ approach allowed for a thorough and objective data analysis. Results were then placed in context: for example, in PD, iron decreases in Globus Pallidus, copper decreases and zinc            increases in the Substantia Nigra6, and iron appears largely unaffected in the H-Ferritin Deficient condition1. ACKNOWLEDGEMENTS This research was undertaken as part of the Undergraduate Research   Scholarship Scheme which provided a bursary for the project. The mouse brains were received from Prof. J. Connor’s Group at Penn State       University. The mouse brains were sectioned and stained by               M.E. Finnegan and imaged using the Diamond Light Source Synchrotron by Dr J.F. Collingwood and                 M.E. Finnegan.  D.G. Norman, M.E. Finnegan, J.F. Collingwood School of Engineering, University of Warwick, UK    Analysing the Impact of Iron Dysmetabolism on  Regional Metal Ion Distribution in the Brain SUMMARY An Iron Overload and an H-Ferritin Deficient Mouse Model were used to examine the    impact of disrupted iron metabolism on the brain.  Brain sections were imaged and compared using Synchrotron µXRF spectroscopy. Quantitative measurement of the relative metal ion concentrations for iron, copper and zinc were made across selected regions of interest in the brain.  It was generally found that metal ion concentrations of iron and zinc decreased in specific regions in the Iron Overload condition compared with the control, with copper increasing in only one region. Few regions differed in metal ion concentration between the H-Ferritin Deficient Model and the control.  The three conditions exhibited similar / identical results for metal ion concentrations in many brain regions, indicating the validity of the method used for comparison between samples. It is clear that there exists a complex relationship between these trace metals. Further work is needed to test the significance of the findings in a larger sample size. CONCLUSIONS and FURTHER WORK 1) We observed strong regional variations in brain metal ion concentrations (Fig 5); some of these are consistent with evidence in the current research literature. 2) The concentration range within a given region varies as a function of metal element and brain region as shown by the histograms (Figs 1A-C, 2A-C and 3A-E). 3) Disrupting the iron metabolism affects zinc and copper as well as iron across different regions, indicating a       relationship between these metal ions. 4) For the Iron Overload model, iron decreases in the Dentate Gyrus Crest, Subiculum and also the Substantia Ni-gra Reticular, compared to the control. 5) It was found that iron concentration doesn’t change in the H-Ferritin Deficient condition which agrees with the   literature1, but zinc does increase in the Substantia Nigra Reticular which is similar to PD6. 6) The narrow distribution of intensities and similarity of concentration measured in the majority of brain regions and from the slide background (Figs 1-3) indicate that the method for sample comparison is valid. 7) A larger sample size is required to test for statistically significant differences between the three groups.  The regions outlined in the template were   colour filled for clarity of region boundary after scaling  Templates were imported into ImageJ, (open source image processing software) where they were scaled to            correspond to the original   data sets for direct overlay Template boundaries were traced using Im-ageJ’s ROI selec-tion tool. These regions of interest were then saved The saved ROIs were used to    measure       intensity of each element by brain region Mean, Standard   Deviation and      Histogram data for each region were measured using    ImageJ and          recorded in Excel The .tiff files were imported into PowerPoint where they were scaled to 400% whilst maintaining their aspect ratio Each .tiff image  was        imported into PyMca and saved as colour-intensity (temperature) maps which represented the metal     concentration distribution across the section The metal ion    distribution maps were scaled and     rotated to fit the boundary       template exactly Boundaries were drawn around each identifiable brain region on the different maps. By varying the intensity scales, different regions became visible for template creation and were added to the template. This cumulative process led to the development of the final template with each relevant region outlined To quality-check the templates, they were overlaid onto the light-microscope images to check for cracks or folds in the original       sections. Adjustments were made to ensure no faulty areas were included  Radar plots were drawn with the standard deviation acting as ‘error’ bars for each        absolute mean measurement taken for each region and  condition. Notable differences are recorded in RESULTS Each region was cross-referenced with the relevant level in the    Allen Mouse Brain Atlas Mice Condi-tions 1) H-Ferritin   Deficient   2) Iron Over-load  Brains were removed and frozen at 7 months by Prof.J.Connor’s Group at Penn State University. Frozen brains were sectioned coronally at 30µm thickness onto ultraclean quartz slides and cover-slipped with kapton Images of the slides were taken under the light-microscope for later reference Post-Synchrotron measurement: the data were        converted into PyMca software8         compatible      format The experimentally    measured fluorescent spectral peaks were batch-fitted with the theoretical position of the individual elements   Data were            normalised to the incoming beam    intensity used      during fluorescence mapping and then exported as .tiff files .Tiff images for each element of interest       contained    pixels with    absolute        intensity values The sections were taken to the Diamond Light Source Synchrotron, beamline I18, where they were imaged using Synchrotron µXRF  Each section was imaged at 10keV with a 60µm spot size. Exposure rate was 1 second per point in each The µXRF Spec-trum at each point was collected: this    contained intensity     information for each of the elements present Pre-Project URSS Project Data Analysis Validation Allen Atlas1 A boundary         template was drawn around the           rectangular scanned area and another around the tissue section A centre line was drawn     using the   copper maps as           reference Sections were stained  us-ing haematoxylin to high-light brain region bounda-ries (seen by changes in cell density) for validation of the templates made from the metal ion maps Key: Red stages— sample preparation and imaging. Completed before the URSS Project commenced. Yellow and Green stages were completed during the URSS Project: Yellow: data processing and templating, Green: template validation and metal ion analysis.  RESULTS IRON COPPER ZINC RESULTS OVERVIEW Fig 5: Metal ion map showing  the metal distribution throughout all regions for Iron (red), Copper (green) and Zinc (blue) A C B A B C D E A B C Fig 6: A stained tissue section showing the different cell densities and types across brain regions    Metal Ion Maps      Histograms Fig 3 3B 3A 3C 3D 3E Metal Ion Maps            Histograms Fig 1 1A 1B 1C Histograms     Metal Ion Maps Fig 2 Fig 6 Fig 5 Fig 4 METHODOLOGY Tight match between experimental and theoretical data Experimental Spectra Theoretical Spectra Unfitted Data Fitted Data Fig 4: Allen Mouse Brain Atlas level    corresponding to mapped regions7 2A 2B 2C 

Analysing the impact of iron dysmetabolism on regional metal ion distribution in the brain

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Analysing the impact of iron dysmetabolism on regional metal ion distribution in the brain

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