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The iron, copper and zinc concentration in A# plaques in the APP/PS1 mouse model of Alzheimer’s disease correlates with metal levels in the surrounding neuropil Simon A James, Quentin Isaac Churches, Martin D de Jonge, Ian E Birchall, Victor Streltsov, Gawain McColl, Paul A Adlard, and Dominic J Hare ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.6b00362 • Publication Date (Web): 13 Dec 2016 Downloaded from http://pubs.acs.org on December 15, 2016

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ACS Chemical Neuroscience

The iron, copper and zinc concentration in Aβ plaques in the APP/PS1 mouse model of Alzheimer’s disease correlates with metal levels in the surrounding neuropil

Simon A. James1,2*†, Quentin I. Churches3†, Martin D. de Jonge2, Ian E. Birchall1, Victor Streltsov3, Gawain McColl1, Paul A. Adlard1, Dominic J. Hare1,4*

1

The Florey Institute of Neuroscience and Mental Health, The University of Melbourne,

Parkville, Victoria, 3025, Australia 2

Australian Synchrotron, Clayton, Victoria, 3168, Australia

3

Biomedical Manufacturing, CSIRO Manufacturing, Clayton South, Victoria 3169, Australia

4

Elemental Bio-imaging Facility, University of Technology Sydney, Broadway, New South

Wales, 2007, Australia

† These authors contributed equally.

* Corresponding authors: Dr Simon James. Mailing address: The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, Victoria, 3052, Australia. Ph. +61 3 9035 6420. Email. [email protected] Dr Dominic Hare. Mailing address: University of Technology Sydney, PO Box 123, Broadway, New South Wales, 2007, Australia. Ph. +61 3 9035 9549. Email. [email protected]

Keywords: Aβ plaques, iron, copper, zinc, XFM mapping

ACS Paragon Plus Environment

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Abstract

The metal ions of iron, copper and zinc have long been associated with the aggregation of βamyloid (Aβ) plaques in Alzheimer’s disease; an interaction that has been suggested to promote increased oxidative stress and neuronal dysfunction. We examined plaque metal load in the hippocampus of APP/PS1 mice using X-ray fluorescence microscopy to assess how the anatomical location of Aβ plaques was influenced by the metal content of surrounding tissue. Immunohistochemical staining of Aβ plaques colocalized with areas of increased X-ray scattering power in unstained tissue sections, allowing direct X-ray basedassessment of plaque metal levels in sections subjected to minimal chemical fixation. We identified and mapped 48 individual plaques in four subregions of the hippocampus from four biological replicates. Iron, Cu and Zn areal concentrations (ng cm-2) were increased in plaques compared to the surrounding neuropil. However, this elevation in metal load reflected the local metal make-up of the surrounding neuropil, where different brain regions are enriched for different metal ions. After correcting for tissue density, only Zn levels remained elevated in plaques. This study suggests that the in vivo binding of Zn to plaques is not simply due to increased protein deposition.

Introduction

Since the observation that Zn2+ ions rapidly induce the formation of β-amyloid (Aβ) aggregates in vitro1 there has been much attention paid to the potential role of transition metals, namely iron (Fe), copper (Cu) and zinc (Zn), in the formation of the senile plaques present in the Alzheimer’s disease (AD) brain. The hypothesized mechanism of metalmediated Aβ toxicity has expanded to implicate all three metals in both oligomerization and aggregation of Aβ and the formation of harmful hydroxyl radicals,2 giving rise to the oxidative stress model of AD.3 This has also directed therapeutic attempts to directly target metal ions as a means to ameliorate aberrant metal-Aβ interactions.4 However, uncertainty remains as to whether metal-Aβ biochemistry is a cause or effect of the disease,5 with evidence suggesting that in vivo Aβ oligomerization can be inhibited independent of effects on metal ions.6


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Directly assessing metal-Aβ pathology requires an analytical approach with minimal risk of disturbing the relatively weak intermolecular bonds between these ligand pairs.7 Such an approach requires a means to identify plaques and quantify metal content in minimallytreated specimens. A method that minimally disturbs the endogenous chemical environment or subjects the sample to histochemical or fluorescent labelling is required so as not to perturb regional biometal distribution.8 Previous reports have provided insight into the association of metals with Aβ plaques in both a murine model9,10 and human AD tissue.11 Given the need to identify the plaques prior to imaging so as to increase experimental efficiently both studies applied an exogenous label to visually identify Aβ aggregates prior to elemental analysis, with the latter example using formalin-fixed, paraffin-embedded tissue sections. Though only a relatively small number of plaques were analyzed these studies described contrasting associations of Fe, Cu and Zn with plaques. Questions remained as to the degree of disruption (if any) that direct pre-treatments to the tissue section prior to chemical imaging may have caused to endogenous metal abundance/distribution.

Using the improved rate of data acquisition available with the 384-element Maia detector system, installed at the Australian Synchrotron X-ray fluorescence microscopy (XFM) beamline, we devised a method for identifying Aβ plaques in situ without the need for immunolabelling or staining with dyes specific to fibrillary structures. In animals with a high plaque load the Fe, Cu and Zn content was mapped in plaques and in the context of surrounding brain regions. We found that the degree to which different metal ions associated with plaques is both element-specific and dependent on the metal content of the surrounding neuropil.

Results and Discussion

Regions of elevated Compton scatter collocate with Aβ plaques

The ability to detect and localize plaques in brain sections of APP/PS1 animals via Compton inelastic scattering was confirmed using formalin-fixed, paraffin embedded brain tissue


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serially sectioned at 5 μm thickness at the levels of the hippocampus and surrounding frontal cortical tissue. The first section was stained immunohistochemical staining of Aβ plaques using an in-house produced 1E8 antibody for Aβ (Fig. 1a,b), and the immediately adjacent section was mounted on a Si3N4 window for XFM analysis. Compton scattering allowed depiction of Aβ plaques (Fig. 1c), as the degree of scatter is proportional to tissue density12 and plaques are approximately twice as protein-dense as surrounding tissue.13 A section from the frozen sample was sectioned at 40 µm and the same stereotaxic level, mounted on a Si3N4 window and mapped using XFM. Following XFM, the samples were floated off the Si3N4 window in water, remounted on a standard microscope slide and immunohistochemically stained for Aβ (using the same protocol as described above) to confirm that increased Compton scattering marked plaque deposition. A composite image of Compton scatter and Aβ staining from the same tissue section post XFM analysis shows that areas of high Compton scattering corresponded to Aβ plaques (Fig. 1d).

Stratified distributions of Fe, Cu and Zn in the hippocampal formation

Brightfield microscopy was used to provide fiducial markers for image analysis such as the granular layer of the dentate gyrus (Fig. 2a), from which the hippocampal formation and all associated substructures were imaged by XFM. Maps of Compton scatter from unstained sections showed clear anatomical boundaries corresponding to known neuroanatomy (Fig. 2b). Using anatomical (Fig. 2c)14 and metal reference atlases,15,16 masks for regions of interest (ROIs) according to subcategories of the CA1 hippocampal formation and dentate gyrus (DG) were manually annotated. Regions identified were the stratum oriens (CA1so); pyramidal layer (CA1sp); stratum radiatum (CA1sr); lacunosum moleculare (CA1slm); molecular layer (DG-mo); granular cell layer (DG-sg); polymorph layer (DG-po) and the lateral nucleus of the thalamus (TH-LN). Mean spatial metal concentrations from the Fe, Cu and Zn maps (Fig. 2d-f; n = 4; as ng cm-2) for each region are shown in Fig. 2g-i. XFM maps of Compton inelastic scatter, Fe, Cu and Zn for each scanned section are shown as Supporting Figures S1-4.

Areal distributions and density of metals in Aβ plaques


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A total of 49 plaques were identified in sections from four animals mapped by XFM (Supporting Figure S5). Plaques were located in CA1-sr, CA1-slm, DG-mo and DG-po regions, as well as a single plaque in the lateral nucleus of the thalamus, which was not included in the analysis. The metal content of Aβ plaques was slightly above the mean concentration of the surrounding neuropil for Fe in the CA1-sr, CA1-slm and DG-po; and for Cu in the CA1-sr only (p < 0.05; Fig. 3a-b; d-e). For Zn, metal levels were slightly higher in plaques in the CA1sr region compared to surrounding tissue, and markedly higher (p < 0.01-0.001) in the CA1slm and DG-mo. In the DG-po, where Zn levels are natively higher than adjacent regions, the metal load of Aβ plaques did not differ from the neuropil Zn levels (Fig. 3c,f).

Iron levels within regions of plaque deposition did not markedly differ from one another, with plaques in these regions showing an equivalent increase of approximately 3% above the surrounding tissue. For both Cu and Zn, metal load on Aβ plaques correlated with the metal content of the neuropil, with plaques located in regions with higher metal levels showing a proportionally higher metal load compared to regions where metal content of the neuropil was lower.

A report by Leskovjan and colleagues13 found that, upon normalizing for total protein content (measured using Fourier transform infrared spectroscopy), Fe and Cu content in the plaques is actually lower than the surrounding neuropil in the APP/PS1 mouse. As Compton inelastic scatter is proportional to electron number,17,18 and by extension in this sample type density, we normalized both plaque and neuropil metal content to background-corrected (according to the ̅ + 3 method described above to compensate for the combined Compton scatter originating from the atmosphere and Si3N4 window) Compton scatter to examine if plaque metal load was an effect of the dense proteinaceous component of the inclusions. In agreement with Leskovjan et al, we found that Fe content in plaques was significantly lower than the neuropil. The larger error in the Cu measurement showed a nonsignificant trend of lower metal levels in plaques, but the significant increase in Zn load of Aβ plaques in the CA1-sr, CA1-slm and DG-mo remained (Fig. 4).

We examined the areal density of metals in cross-sections of representative plaques from the four regions in which they were identified (Fig. 5). Zinc was consistently elevated in CA1-


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sr, CA1-slm and DG-mo plaques, with a more modest increase across the plaque in the DGmo, where Zn levels in the neuropil were higher than adjacent regions.

Discussion of results

To better characterize plaque-metal interactions in the brain, we employed a multi-stage analytical approach. Initially we identified Aβ plaques in the hippocampus of APP/PS1 mice and assessed the associated total metal content. This murine model of cerebral amyloidosis, which expresses both mutant human amyloid precursor protein (APP) and presenilin 1, recapitulates several specific age-related AD-like pathologies.19 The increased levels of extracellular Aβ peptides in these animals gives rise to extracellular plaques in animals aged 6-months and older, which continue to accumulate as the animal ages.20 Autometallography (e.g. silver amplification of catalytic metals) has suggested plaques form within Zn-rich cortical layers.21 The APP/PS1 mouse shows evidence of altered metal homeostasis in line with plaque formation, including age-dependent increases in total Fe,22 the effects of which can be attenuated by treatment with the Fe chelator deferoxamine.23 These mice also show altered expression and distribution of Zn transporters.24

In general, the data presented here shows that Fe, Cu and Zn association with Aβ plaques was higher than the basal metal levels within the brain regions assessed. Iron showed the least deviation from the mean value for the 48 plaques assessed through the hippocampal regions, whereas Zn and Cu showed a higher degree of variation, in line with the surrounding neuropil metal levels. Our data generally agrees with that reported by Leskovjan et al,13 with plaques in the APP/PS1 mice exhibiting a higher Zn load than Fe and Cu, compared to the surrounding tissue; and that normalization to total mass content (largely protein) abrogated a significant increase of Fe and Cu in plaques. Although we acknowledge there are limitations outlined by the authors in using this double-transgenic mouse as a model for AD, we did observe similarities in intra-plaque metal distributions that have previously been reported, albeit with extensive sample pre-treatment;11 namely a slight Cu ‘halo’ (Fig. 5e-h) effect around the plaque rim, as well as a more concentrated amount of Zn within the plaque core. Our results build on the time-course study reported by Leskovjan et al,9 who found that only 56-week old APP/PS1 mice exhibited increased metal


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load in plaques, compared to mice aged 24 and 40 weeks. We show here that metal-plaque association appears to be consistent with the 56-week old time point when extended to 18 months of age, when APP/PS1 mice exhibit a severe AD phenotype.19

The DG-mo contained the highest concentration of Cu, with plaques in this region also showing the highest relative metal load. This finding is particularly intriguing, considering the recently reported role of the Aβ peptide as a high affinity Cu2+ binder via a histidine binding motif 25 and as a synaptic Cu2+ scavenger26 in a region where synaptic density is particularly high.27 Given the relatively higher Cu content of the DG-mo the Cu halo is consistent with biochemical characterization of elevated levels of exchangeable copper in this animal model28 as the plaques will readily bind any available copper. The observed ‘halo’ effect in Cu maps also mirrors previous reports of a lipid-rich ‘ring’ surrounding the Aβ plaque core in both transgenic models and human AD sections.29,30 Copper forms complexes with low-density lipoproteins (LDL) in a dose-dependent manner, where a greater number of complexes are formed in a Cu-rich environment, and that this complex promotes lipid peroxidation.31 As LDLs and their associated receptor proteins play an important role in Aβ deposition,32,33 the potential colocalization of Cu and lipids in Aβ plaques may provide some insight into the role of Cu-catalyzed oxidative stress as a result of plaque formation. While we believe that minimal chemical exposure has mitigated potential artecfact arising from Cu mobilization, further experiments mapping metal distribution in brains that had no potential chemical disturbance (i.e. analysis of unfixed, non-cyroprotected tissue) are required to confirm this.

Zinc similarly showed a relationship between neuropil metal concentration and Aβ plaque association, with the highest areal density (albeit not significantly different within plaques) corresponding to inclusions within the DG-po, where regional zinc levels were greater than all adjacent sublayers of the hippocampus. Zinc binds to Aβ in a similar manner to Cu2+ via histidine residues at the N-terminus, with a proposed additional weaker site involving aspartic acid, valine, asparagine and lysine within the middle structure of the peptide.34 Although Cu2+ has a 10,000 times greater affinity to Aβ than Zn2+, the presence of as little as 0.01 mol equivalent of Zn2+ in a Cu2+/Zn2+ mixture induces the formation of tightly-packed fibrils.35 In vitro studies suggest that the mechanism of Cu2+ and Zn2+ binding to the full


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length Aβ1-40 peptide that may induce aggregation are chemically distinct, with Cu2+ participating in a reversible reaction that forms a stable complex, whereas Zn2+ forms an initially weak Zn2+-Aβ complex that transitions to more stable complexes over time.36 Assuming that this complex is retained throughout Aβ oligomerization and aggregation, this may explain why only Zn displays a higher areal concentration relative to total tissue density. Iron binding to Aβ is less well defined, though it is proposed that a similar binding site utilizing N-terminus histidine residues, as well as glutamic and aspartic acid.37

These data show only a correlation between metal levels in the neuropil and plaque metal load; to demonstrate that metal levels within surrounding tissue has a direct effect on plaque-metal association experiments would need to be performed in animals that exhibit both Aβ deposition and impaired metal metabolism. For instance, ZnT3 (zinc transporter-3) null mice have markedly less Zn within the hippocampal formation compared to wild type,38 and when crossed with animals overexpressing mutant human APP these animals had a significantly lower density of Aβ plaques compared to mice over-expressing both APP and ZnT3.39 XFM analysis of the spatial distribution of Zn with respect to plaque density would provide additional evidence as to whether the metal content of the tissue in which a plaque is located has a direct influence on the degree to which Zn is associated with aggregated Aβ proteins.

Conclusions

The data presented here suggest that for Cu and Zn, both metals with well-established binding sites to Aβ, the degree of association with plaques is more dependent on metal concentrations in the surrounding neuropil than for Fe, which has a lower affinity to the Aβ peptide. Only Zn demonstrates a higher apparent affinity for Aβ plaques when areal concentrations are normalized to tissue density. These data provide useful information in further elucidation of the mechanisms by which metal ions, specifically Cu and Zn, promote Aβ oligomerization and aggregation. It remains to be determined if the changes in metal ion distribution plays a causal role in plaque formation and disease progression, or is a downstream effect of plaque formation. Further, this work demonstrates a practical sample preparation method to minimally disrupt protein-metal bonds in proteinaceous inclusions,


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which will be of technical value in other studies examining diseases where aggregated proteins are a pathological feature.

Methods:

Animals

All procedures involving transgenic mice conformed to the Australian National Health and Medical Research Council Code of Practice and were approved by the University of Melbourne ethics committee. Eighteen-month old APP/PS1 [B6C3-Tg(APPswe,PSEN1 dE9)85Dbo/J] mice were anesthetized with sodium pentobarbitone (80 mg kg−1; Lethobarb; Virbac) and transcardially perfused with ice-cold 0.1 M phosphate buffered saline (PBS; pH 7.4; Sigma) prior to the removal of the brain.

Immunohistochemistry

Staining of Aβ plaques was performed according to a previously reported method.40 Sections were cut at 5 µm thickness and dewaxed in xylene and decreasing concentrations of ethanol, then washed for 5 min in running water. Following washing, sections were treated with 90% formic acid for 5 mins and blocked for endogenous peroxidase activity in 5% H2O2. Sections were then treated with 0.2% casein in a Tris buffer before being incubated for one hour with a 1:2,000 dilution of an in-house produced 1E8 monoclonal primary antibody that recognizes Aβ17-24.41 Antibody staining was visually enhanced using a commercial labelled streptavidin-biotin kit (Dako) and hydrogen peroxidediaminobenzidine (DAB). Sections were briefly counterstained with hematoxylin, then mounted, coverslipped and photographed.

Sample preparation for XFM

Two preparations were used for XFM mapping experiments. For experiments examining the relationship between plaque deposition and inelastic (Compton) scattering, one brain was fixed in 4% paraformaldehyde and then dehydrated in increasing concentrations of ethanol


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(80-100% v/v), 100% xylene and then infiltrated with paraffin. The brain was then embedded in paraffin and sectioned at 5 µm on a standard microtome. These sections were floated onto a 10 x 10 mm silicon nitride (Si3N4) window with a 16 mm2 membrane (Silson) and dried under a stream of argon. For unstained sections, brains (n = 4) were removed and briefly fixed in 4% paraformaldehyde and cryoprotected in 30% sucrose in 0.1 M PBS42 prior to sectioning at -20 ˚C. We have previously found that the metal content of deep brain structures (specifically the basal ganglia) are unaffected by this brief cryoprotection method.43 It should be noted that the impact of this cryoprotection method on hippocampal metal distributions are not known. Regardless, the method described here does not subject the tissue to any additional chemical treatment after cryosectioning. Sections of 40 µm thickness were mounted on Si3N4 windows and air-dried in a dust-free environment. Following XFM analysis (see below), sections were floated off the window in water, mounted on a standard microscope slide and stained for Aβ as outlined above.

XFM analysis

Samples mounted on Si3N4 windows were affixed to a Perspex frame and placed in the path of a 12.73 keV X-ray beam, focused to ~2 µm spot (FWHM) in the sample plane, at the XFM beamline of the Australian Synchrotron. This incident energy was chosen in order to separate elastic and Compton scatter from elemental fluorescence.

Samples were scanned through the focused beam while X-ray fluorescence was binned at 1 µm intervals. X-ray emission was collected using the 384-element Maia detector system positioned in the back-scatter geometry and full fluorescence spectra were obtained at each pixel. Each 1 µm2 pixel corresponded to an effective dwell time of ~15 ms. The resulting elemental maps ranged up to 800,000 pixels in size and required 3-4 hrs of data collection. Measurements of single element foils (Mn and Pt, Micromatter Canada) under the same experimental conditions were used to establish elemental quantitation.44 The variation between repeated reference measurements that bracketed measurement of the samples was below 1%. Quantitative deconvolution of elemental fluorescence was performed using GeoPIXE v7.245 which applies a first-order ‘matrix’ correction to estimate the re-absorption of the elemental fluorescence by the tissue. The magnitude of this correction depends on


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the energy of the X-ray fluorescence along with the modelled composition and densitythickness of the tissue section.46 Assuming a specimen of the type described in our previous XFM study47 the impact of assumptions inherent to the analysis on the determined, quantitative, areal densities for Fe, Cu and Zn will be less than ~1%.46

Data analysis

Elemental maps were processed in Fiji (http://fiji.sc/Fiji)48 for visualization. Images were thresholded to exclude the pixels containing the upper ~1 % of areal densities as this group represents non-tissue debris. Maps of Compton scatter were used to define the spatial extent of the sample and areas devoid of tissue (i.e. holes in the section). These latter areas were used as a proxy sample blank (i.e. Si3N4 window only) to establish the limit of detection (̅ + 3) for each element (FeLOD = 250 ng cm-2; CuLOD = 1 ng cm-2; ZnLOD = 10 ng cm-2).

As 99% of biological material is composed of H, C, N or O the observed differences in scattering power within a tissue section (evidenced by the high contrast in the Compton maps) arise from a differential distribution of mass. The specimens were cut to a uniform 40 µm thickness, thus observed differences in scattering power should reflect differences in the mass distribution (density) of the sample prior to desiccation. Residual water content of the tissue could also contribute to detected scatter, although Aβ plaques are comparatively water-deplete relative to surrounding tissue.49 Intra-tissue differences in scattering power provided a useful proxy for identifying anatomical structures and regions of interest were manually defined using the maps of Compton scatter. This approach confirmed that changes in tissue density are in line with the physiologically distinct domains identified in the Allen Reference Atlas.14 These regions were used to define regions of interest (ROIs) for further analysis.

Similarly, the increased density of Aβ plaques suggested that punctate areas of increased Compton scattering present in the APP/PS1 hippocampus provided a means for identifying plaques without the need for exogenous probes or histological stains. Immunohistochemical staining of Aβ17-24, as described in the Results section, confirmed that scattering power was an effective means for identifying Aβ deposits.


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Analysis of extracted elemental concentrations and construction of all statistical plots was performed in Prism v6.0h (GraphPad). Statistical comparisons were made using a Student’s t-test with Holm-Sidak post hoc correction for multiple comparisons, with significance defined as p < 0.05.

Acknowledgements

SAJ is an NHMRC-ARC Dementia Fellow (1103882). PAA is an ARC Future Fellow (FT120100030). DJH performed this work supported in part by a Ramaciotti Foundation Establishment Grant. We gratefully acknowledge the support of the Victorian Government's Operational Infrastructure Support Program. Parts of this research were undertaken on the X-ray Fluorescence Microscopy beamline at the Australian Synchrotron, Victoria, Australia.

Associated Content

Supporting information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.XXX/acschemneur-XXXX. XFM maps (Figure S1-4) and plaque masks (Figure S5)


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Figure 1: A) 1E8 (anti-Aβ17-24) labeled, DAB-enhanced and hematoxylin counterstained photomicrograph of Aβ plaques in the hippocampal formation of an APP/PS1 mouse. B) Zoomed area marked with red box in (A). C) XFM Compton inelastic scattering map neighboring section of region shown in (B). D) Composite image of Compton scattering (red; including area in C) and post-XFM histology (green) from a single, separate section showing colocalization of high intensity Compton scatter and 1E8-labeled plaques. Scale bars = 50 µm.


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Figure 2: A) Brightfield microscopy image with hippocampal formation marked by black box. B) Compton inelastic scattering map used to delineate dentate gyrus and CA1 field of the hippocampus structures from (C) the Allen Reference Atlas of the mouse brain.14 Note THLN encompasses both the lateral posterior (LP) and lateral dorsal (LD) nuclei of the thalamus (TH). D-F) Representative Fe, Cu and Zn maps showing distribution of metals through substructures of the hippocampal formation. G-I) Mean areal metal concentrations per anatomical region (error bars = standard error of the mean). n = 4 for all regions except CA1so and TH-LN (n = 3). Scale bars = 50 µm.


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Figure 3: A-C) Mean ± standard deviation of Fe, Cu and Zn in individual plaques according to anatomical location. Dotted line denotes mean areal metal concentration, dashed line


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denotes ± 95% confidence interval. D-F) Mean plaque metal content versus surrounding neuropil for Fe, Cu and Zn. Boxes = interquartile range; + = mean; line = median; error bars = minimum to maximum values. * p < 0.05; ** p < 0.01; *** p < 0.001. For neuropil n = 4; for plaques nCA1-sr = 5; nCA1-slm = 6; nDG-mo = 31; nDG-po = 6.


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Figure 4: A) Fe, B) Cu and C) Zn content of plaques and neuropil, normalized to equivalent background-corrected Compton inelastic scattering. Boxes = interquartile range; + = mean; line = median; error bars = minimum to maximum values. * p < 0.05; ** p < 0.01; *** p < 0.001. For neuropil n = 4; for plaques nCA1-sr = 5; nCA1-slm = 6; nDG-mo = 31; nDG-po = 6.


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Figure 5: A-D) Compton scatter, Fe, Cu and Zn maps of hippocampal formation of APP/PS1 mouse. Scale bar = 50 µm. E-H) Zoomed images of plaques (median filtered with a 2 pixel radius) and plot of smoothed (2nd order smoothing, nearest neighbor) Fe, Cu and Zn concentrations in a cross-section across the central diameter of representative plaques from CA1-sr, CA1-slm, DG-mo and DG-po regions. A clear ‘halo’-like distribution was observed for Cu, with highest Zn density present within the plaque core. Scale bar = 10 µm.


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For Table of Contents Use Only The iron, copper and zinc concentration in Aβ plaques in the APP/PS1 mouse model of Alzheimer’s disease correlates with metal levels in the surrounding neuropil

Simon A. James1,2*†, Quentin I. Churches3†, MarƟn D. de Jonge2, Ian E. Birchall1, Victor Streltsov3, Gawain McColl1, Paul A. Adlard1, Dominic J. Hare1,4*

1

The Florey Institute of Neuroscience and Mental Health, The University of Melbourne,

Parkville, Victoria, 3025, Australia 2

Australian Synchrotron, Clayton, Victoria, 3168, Australia

3

Biomedical Manufacturing, CSIRO Manufacturing, Clayton South, Victoria 3169, Australia

4

Elemental Bio-imaging Facility, University of Technology Sydney, Broadway, New South

Wales, 2007, Australia


24

PS1

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