Skip to main content
NCBI home page
Chemical & Biomedical Imaging logoLink to Chemical & Biomedical Imaging
. 2025 Oct 9;4(3):314–325. doi: 10.1021/cbmi.5c00059

Integrating Microscopy Methods to Study Gene and Protein Expression alongside Metal Ion Distribution and Speciation: A Case Study of Iron within Pyramidal Neurons from Distinct Hippocampal CA1 Subregions

Gaewyn Ellison †,‡, Rhiannon E Boseley §, Meg Willans †, Sarah Williams †, Evelyn S Innes †, Paige Barnard †, Julia Koehn ‡, Somayra S A Mamsa ‡,∥, Paul Quinn ⊥, Daryl L Howard #, Simon A James #, Mark J Hackett †,‡,*
PMCID: PMC13014334  PMID: 41889472

Abstract

Understanding the role of metal ions in normal and abnormal cell function continues to emerge as a critical research area in the biological and biochemical sciences. This is especially true in the context of brain health and neurodegenerative diseases, as the brain is especially enriched in metal ions. A range of microscopy and bioanalytical techniques are available to assist in characterizing and observing changes to the brain metallome. As is the case in many other scientific fields, the integration of multiple analytical methods often yields a more complete chemical picture and deeper biological understanding. Herein, we present a case study applying 4 different analytical methods to provide spatially resolved characterization of chemical and biochemical parameters relating to the iron (Fe) metallome within a specific brain region, cornu ammonis sector 1 (CA1) of the hippocampus. The CA1 hippocampal sector was chosen for investigation due to its known endogenous enrichment in Fe and its selective vulnerability to neurodegeneration. The 4 analytical techniques applied were X-ray fluorescence microscopy (to quantify Fe distribution); X-ray absorption near-edge structure (XANES) spectroscopy to reveal information on Fe oxidation state and coordination environment; immuno-fluorescence to reveal relative abundance of Fe storage proteins (heavy chain ferritin and mitochondrial ferritin); and spatial transcriptomics to reveal gene expression pathways relevant to Fe homeostasis. Collectively, the results highlight that although pyramidal neurons in lateral and medial regions of the hippocampal CA1 sector are morphologically similar, key differences in the Fe metallome are evident. The observed differences within the hippocampal CA1 sector potentially indicate a higher oxidative environment and higher metabolic turnover in medial CA1 neurons relative to lateral CA1 neurons, which may account for the heightened vulnerability to neurodegeneration that is observed in the medial CA1 sector.

Keywords: metallomics, memory, neuorimaging, neurodegeneration, brain, CNS

graphic file with name im5c00059_0010.jpg

graphic file with name im5c00059_0008.jpg

Introduction

Metals ions are critical to cell function, serving vital roles as enzyme cofactors, structural elements of proteins, key components of the electron transport chain, and are involved in a multitude of cell signaling pathways. The above is especially true in the brain, which is highly enriched in the transition metal ions of iron (Fe), copper (Cu), and zinc (Zn). It is well established that Fe, Cu, and Zn are required for healthy brain development and function; however, imbalances in the amount, distribution or chemical form of these metals (collectively termed “metallomics” or “metallome”) has been linked to brain diseases and disorders such as Alzheimer’s disease, − Parkinson’s disease, − multiple sclerosis, amyotrophic lateral sclerosis, , and epilepsy (to name a few). −

Tremendous research efforts have been made characterizing changes in the brain metallome that occur during neurodegenerative diseases and neurological disorders, as well as elucidating the biochemical pathways involved. There are now numerous suites of microscopy modalities available to neuroscientists, each capable of revealing different components of the brain metallome, and which have significant potential to increase our understanding of the role of metal ions in brain health and disease. − This paper, herein, aims to present a case study that brings together four of these techniques to reveal a more wholistic picture of the Fe metallome within a specific brain region (the cornus ammonis sector 1 of the hippocampus, “CA1”). The four techniques applied in this case study are spatial transcriptomics to study gene expression of Fe containing proteins and proteins involved in Fe regulation; immuno-fluorescence microscopy to directly image Fe protein distribution (mitochondrial Ferritin and heavy chain Ferritin); X-ray fluorescence microscopy (XFM) to reveal Fe distribution and abundance; and X-ray absorption near-edge structure (XANES) spectroscopy to reveal Fe speciation. The CA1 sector of the hippocampus has been chosen due to its known Fe enrichment and known vulnerability to neurodegeneration (described below).

The hippocampus is a key brain region for spatial learning and memory and is often one of the first brain regions impacted by neurodegenerative diseases, such as Alzheimer’s disease. One of the major neuron layers within the hippocampus, the cornu ammonis (CA layer), is formed by pyramidal neurons. The CA pyramidal layer can be classified/segmented into 4 morphologically and functionally distinct subfields: CA1, CA2, CA3, and “CA4” (hilus) (Schematic ). , Neurons of the CA1 layer display specific vulnerability to neurodegeneration, and often a lateral to medial trend of neurodegeneration is observed across the CA1 region (medial CA1 neurons are more vulnerable to neurodegeneration than lateral CA1 neurons). , Intriguingly, despite being morphologically similar, CA1 neurons have the highest Fe content relative to CA2, CA3, and CA4 neurons (in mice and rats), − and a lateral to medial trend in Fe content is also observed (medial CA1 neurons have more Fe than lateral neurons).

1. Schematic of Major Hippocampal Neuronal Sectors (Right Panel has Sectors Overlaid on a DAPI Stained Image) (DAPI Used to Stain Nuclei and Reveal Cell Layout in Tissue).

1

Open in a new tab

Analysis of neuron circuitry, electrophysiological responses, and, more recently, gene expression highlights that each hippocampal subfield can be further segregated into additional subregions. , In particular, the CA1 subfield is frequently segmented into CA1a, CA1b, and CA1c subregions, where CA1a is the medial extremity of the CA1 subfield, CA1c is a lateral location bordering the CA2 subfield, and CA1b is in between CA1a and CA1c (Schematic ). Our past work investigating Fe content within the CA1 layer has revealed that pyramidal neurons display a pronounced lateral to medial trend of increasing Fe content–pyramidal neurons in the medial CA1 subfield (i.e., CA1a subregion) contain more Fe than neurons in the lateral CA1 subfield (i.e., CA1c) Given the parallel between Fe content of the CA1 layer and patterns of neurodegeneration, a current research focus of our group is to characterize the Fe metallome within the CA1 region, in order to help understand how it might render this population of cells vulnerable to neurodegeneration.

X-ray fluorescence microscopy has now been established as a routine analytical method for quantitative elemental mapping at the cellular (and sometimes subcellular) level in brain tissue sections. ,,− Although the general requirement of a synchrotron light source can be prohibitive, X-ray brightness, detector sensitivity, and electronics at modern synchrotron facilities enable sufficiently fast data collection such that numerous biological replicates can be completed in a realistic time frame. , More recently, XANES spectroscopy has emerged as a valuable tool to characterize metal ion speciation in situ within ex vivo tissue sections, − cultured cells, or intact organisms (e.g., Caenorhabditis elegans). − The XANES spectrum is sensitive to metal ion oxidation state, coordination geometry, and type of coordination ligand and therefore reveals an informative spectral “fingerprint” of the chemical form (speciation) of a metal ion. Common advantages of both XFM and XANES spectroscopy is that chemical fixation or addition of endogenous stains, dyes, or fluorophores is not required, enabling metal ions to be studied as close as possible to the in vivo state. ,

While XFM and XANES spectroscopy continue to find increasing use by the metallomics community, more conventional methods such as immuno-fluorescent or immunohistochemical detection of metallo-proteins or metal-transport/regulation proteins are still highly utilized. Immuno-fluorescence can offer great insight into tissue metallomics through detection of metal containing proteins such as Fe containing enzymes (e.g., heme-oxygenase), Fe transport proteins (e.g., transferrin), or Fe storage proteins (e.g., ferritins). A more recent development in the context of studying metallo-enzymes, metal-transport proteins, and metal-storage proteins has been the emergence of spatial transcriptomics. , Spatial transcriptomics can enable quantification of gene expression relevant to specific metal-relevant proteins within highly localized tissue regions.

We have recently demonstrated the ability to combine XFM elemental mapping with spatial transcriptomics to compare RNA expression of metallo-proteins and metal regulating proteins within different sectors of the hippocampus (CA1, CA3, and dentate gyrus) and surrounding brain structures (corpus callosum, cortex, subventricular zone). Not surprisingly, our previous study revealed that increased RNA expression for specific metallo-proteins and metal regulating proteins was associated with metal enrichment in specific brain regions. In this current study, we extend from our previous work and combine spatial transcriptomics to study gene expression pathways relevant to the Fe metallome alongside immuno-fluorescent detection of Fe storage proteins, XFM elemental mapping of Fe distribution, and XANES spectroscopic characterization of Fe speciation. These four techniques have been applied to characterize differences in the Fe metallome between lateral and medial hippocampal CA1 pyramidal neurons.

Methods

Experimental Design

Due to timing and scheduling nature of animal work and subsequent tissue generation, the availability and demand of spatial transcriptomics infrastructure, and the scheduling of merit-based access to synchrotron facilities, this study primarily analyzed two cohorts of animals (cohort 1 and cohort 2). All mice across both cohorts were wild-type C57Bl6/J mice, and all tissue analysis was at the same anatomical location (coronal sections, Bregma −1.5 mm). Mice in cohort 1 were 7-month-old C57Bl6/J mice (n = 5), and tissue sections were analyzed with spatial transcriptomics (n = 5 biological replicates) and XFM elemental mapping (n = 4 biological replicates). Mice in cohort 2 were 3-month-old C57Bl6/J mice (n = 5), and tissue sections were analyzed with XFM elemental mapping (n = 5), XANES spectroscopy (n = 3), and immuno-fluorescence (n = 5). Performing XFM elemental mapping on both cohorts confirmed that the lateral-to-medial trend in Fe concentration across the CA1 sector of the hippocampus was consistent in both experimental cohorts despite the age differences. In addition to animals in cohort 1 and 2, tissue from one additional animal (6 month old C57Bl6/J mouse) was analyzed with submicron XFM to reveal subcellular Fe distribution in one neuron in the lateral region of the CA1 pyramidal neuron layer, and one neuron in the medial region of the CA1 pyramidal neuron layer.

Animal Model and Sample Preparation

All animal procedures were approved by the Animal Ethics Committee at Curtin University under approval number ARE2022–5 (cohort 1) and ARE2023–3 (cohort 2). The additional mouse analyzed with submicron XRF was used in a study approved by the Animal Ethics Committee of The University of Western Australia under approval number 2021/ET000020. All mice were maintained on standard chow diet with access to water ad libitum and housed with a 12 h day/night light cycle. Prior to tissue collection mice were anaesthetized using isoflurane, and euthanised by either exsanguination followed by cardiac removal (cohort one) or cervical dislocation (cohort two). Brains were rapidly collected (<3 min post death) and cut midsagittally into hemispheres, flash-frozen in liquid nitrogen-cooled isopentane, and stored at −80 °C until tissue sectioning. Tissue sections (10-μm-thick) were cut at an anatomical location within Bregma −1.5 mm to −2.0 mm, using a cryo-microtome at −16 °C. Due to the variation of Fe content in the CA1 region that occurs laterally to medially across the brain and also anterior to posterior, it is important to minimize anatomic variation within the regions of interest as much as possible (hence the narrow Bregma range analyzed). The 10-μm-thick sections were mounted directly onto 1-μm-thick silicon nitride membranes for XFM and XANES spectroscopic analysis at the Australian Synchrotron (XFM beamline) and mounted onto superfrost plus slides for immuno-fluorescence and spatial transcriptomic analysis. An additional tissue section was cut at 5-μm-thick and mounted onto a silicon-nitride membrane for submicron XFM elemental mapping at the Diamond Light Source U.K. (beamline I14). Tissue sections were stored at −80 °C until required for analysis. For XANES spectroscopic analysis at the Australian Synchrotron, tissue sections were shipped on dry ice and again stored at −80 °C at the facility until analysis (within 7 days of sample shipping). XANES spectroscopic analysis was under a liquid-nitrogen cryostream (estimated to be 120 K, −153 °C). For XFM analysis at both Australian Synchrotron and Diamond Light Source, samples were thawed from −80 °C storage and air-dried at room temperature, and then shipped to the facility, where analysis of the air-dried tissue was performed at room temperature. In the case of spatial transcriptomics, tissue sections were stored at −80 °C for 7 days before being thawed and immediately immersion fixed in 10% formalin.

XFM Elemental Mapping at the Australian Synchrotron (XFM Beamline)

Elemental mapping was undertaken using X-ray fluorescence microscopy (XFM), at the XFM beamline of the Australian Synchrotron (Australian Nuclear Science and Technology Organization). Elemental maps (with a focus on Fe) were collected using a monochromatic incident beam of 15.8 keV, focused to a 2 μm (2-σ) spot with a Kirkpatrick–Baez mirror pair, with data collected using a pixel size of 1 μm and a dwell time of 1 ms (cohort one) or a pixel size of 5 μm and a dwell time of 5 ms (cohort two). The full X-ray emission spectrum from the sample was collected using a 3-element Vortex Si-drift detector at 90° to the incident beam (sample at 45° to incident beam), with elemental foils (Micromatter, Canada), scanned in the same geometry as the samples and used as references for elemental quantification, as per past studies. ,, Elemental maps were reconstructed from the emission spectra using GeoPIXE v7.6j (Commonwealth Scientific and Industrial Research Organization, Australia), which implements a linear transformation matrix for spectral deconvolution. Data were exported as TIFF files of quantitative per-pixel elemental area density (ng cm–2), and the images were then processed in ImageJ (v1.52). Fe content was quantified from regions of interest that were manually selected for the lateral and medial CA1 pyramidal neuron layer.

Submicron XFM Elemental Mapping Data Collection and Analysis (Diamond Light Source, I14 Beamline)

Elemental mapping at submicron resolution (100 nm pixel size), specifically focusing on Fe mapping, was undertaken for one representative lateral and medial CA1 neuron, at beamline I14 at the Diamond Light Source (U.K.). Elemental maps (specifically Fe) were collected by using a monochromatic incident beam of 8.0 keV, focused to a 50 nm spot with a reflective Kirkpatrick–Baez mirror pair. Data were collected using a pixel size of 100 nm and a dwell time of 100 ms. The full X-ray emission spectrum from the sample was collected using a Rayspec 4-element Si-drift detector. PyMCA software was used to fit the full emission spectrum to yield the elemental maps.

XANES Spectroscopic Mapping Data Collection and Analysis (Australian Synchrotron, XFM Beamline)

XANES spectroscopic maps at the Fe K-edge were collected at the X-ray Fluorescence Microscopy beamline at the Australian Synchrotron. XANES spectroscopic maps were compiled from a series of two-dimenisonal (2D) maps collected at successive incident energies across the Fe K-edge (data collected from high to low energy), as previously described. , Specifically, Fe K-edge XANES spectroscopic maps were collected from 7080–7200 eV, and a step size of 0.5 eV was used across the white line features (7105–7140 eV). At each incident energy, data were collected with a 10 ms dwell time and 5 μm pixel steps (the microfocused X-ray beam was focused to a spot size of 2 μm (2-σ) using a Kirkpatrick–Baez mirror pair). The XANES spectra were calibrated with the first inflection point of an elemental Fe foil set to 7112 eV. The X-ray fluorescence emission from the sample was recorded using a 3-element Vortex Si-drift detector orientated 90° to the incident X-ray source (sample mounted at 45° to incident X-ray source). The Fe K-edge XANES spectral maps were reconstructed from recorded Fe Kα emission across the XANES stack with GeoPIXE v6.6j (CSIRO, Australia), which were then exported as TIFF stacks. The TIFF stacks of Fe XANES spectral maps were subsequently loaded into ImageJ (v1.52), where average XANES spectra were calculated from regions of interest manually drawn to encompass either the lateral or medial CA1 pyramidal layer. Fe K-edge XANES spectra were exported from ImageJ and then imported into EXAFSPAK, where background correction and normalization to a uniform edge-jump of 1 was performed. First derivatives of the μXANES spectra (generated with a 13 point Savitzky-Golay smoothing function).

In order to further investigate Fe speciation within the hippocampus, specifically Fe speciation in the Fe enriched CA1 pyramidal neurons compared to the surrounding CA1 tissue (CA1 stratum radiatum), a principal component analysis of the XANES spectroscopic stack was undertaken. The CA1 stratum radiatum contains primary dendrites of the CA1 neurons along with other cell types such as astrocytes. In contrast, the CA1 pyramidal layer contains mainly the neuron bodies (soma) of CA1 pyramidal neurons. Principal component analysis of the XANES spectroscopic stack was undertaken using MANTiS spectromicroscopy software (Mantis 2.3.02.). The XANES spectroscopic Z-stack was exported from ImageJ and imported into MANTiS where principal component analysis was applied across the full spectrum for data reduction and subsequent visual identification of spectroscopic sources of variation between the CA1 pyramidal neuron layer and surrounding CA1 stratum radiatum tissue.

Spatial Transcriptomics

The spatial transcriptomic data in this study are a subset of a larger data set that we have previously published. For this study, data were generated as previously described using the NanoString GeoMx Digital Spatial Profiler (DSP) (NanoString). Specifically, targets were exposed by heat-induced target retrieval (100 ◦C for 15 min) using Tris-EDTA buffer (eBiosciences 00–4956–58) and proteinase K digestion (Thermo Fisher AM2548, 0.1 ug/mL final concentration, 37 °C for 15 min). Target detection was performed with the whole transcriptome atlas from NanoString. The nuclear stain CYTO83 was used for ROI selection to identify the hippocampal pyramidal layers and to specifically define the lateral and medial CA1 regions.

As described previously, all initial data processing of spatial transcriptomics data was performed using the NanoString GeoMx DSP Data Center analysis platform. Quality control was performed to remove reads below the limit of quantitation (LOQ) and remaining data above the LOQ were normalized through upper quartile (Q3) normalization, and background correction was performed using negative probe subtraction. Pathway analysis was performed within the NanoString GeoMx DSP Data Center analysis platform using reactome version 78 (NCBI_08122021). Pathway inclusion was defined within the following constraints: 20% minimum pathway coverage, 5% minimum pathway size, 5 minimum genes in the pathway, max pathway size of 5000 genes, and 10,000 permutations. Statistical comparison was undertaken with a Student’s paired t-test at the 95% confidence interval, with p-values adjusted through Benjamini-Hochberg correction to control for the false discovery rate (FDR). Both uncorrected and corrected p-values are reported in this study.

Immuno-Fluorescence Analysis of Mitochrondrial Ferritin and Heavy Chain Ferritin Immuno-Reactivity

Tissue sections on superfrost plus slides were fixed for 10 min in 4% paraformaldehyde (PFA). PFA was removed by washing in 0.2% Tween-20 in Tris-buffered saline (TBST), and nonspecific binding was blocked for 1 h using 10% goat serum and 1% BSA in TBST. Primary rabbit IgG antibodies against mitochondrial ferritin (Abcam cat. no. ab66111, RRID:AB_3665352) and heavy chain ferritin (Abcam cat. no. ab183781, RRID:AB_2940987) were applied at preoptimized concentrations (10 and 0.294 μg/mL, respectively). Slides were incubated at 4 °C overnight, washed in TBST, then incubated for 2 h at RT in antirabbit IgG, Alexafluor 488-conjugated secondary antibody (Abcam Cat# ab150061, RRID:AB_2571722). Samples were then washed and mounted using ProLong Gold Antifade Mountant with DAPI (Life Technologies). All antibodies were supplied by Abcam, Melbourne, Australia. Imaging was performed using a ZEISS Axio Scan.Z1 instrument at the Curtin MRI Microscopy and Histology facility. ZEN version 3.2 was used to optimize, standardize, and analyze images.

Statistical Analyses

Statistical analyses for spatial transcriptomic data are described above. Statistical analysis for quantitative XFM data and immuno-fluorescence data was performed in Graphpad Prism (v10), using a Student’s paired t-test to test for significant differences in Fe content, mitochondrial ferritin immuno-reactivity, or heavy chain ferritin immuno-reactivity between lateral and medial CA1 regions. Principal component analysis of Fe K-edge XANES data was undertaken in Graphpad Prism (v10).

Results and Discussion

XFM Elemental Mapping Confirms a Lateral to Medial Trend of Increasing Fe Content in CA1 Pyramidal Neurons (Experimental Cohorts One and Two)

Ourselves and others have previously characterized the Fe content and distribution across the rodent hippocampus (mice and rats), revealing that the CA1 pyramidal neuron layer is specifically enriched in Fe. ,− We have also previously shown that there is a pronounced lateral to medial trend of increasing Fe concentration across the CA1 pyramidal neuron layer, i.e., neurons in the medial CA1 subfield (CA1a) contain more Fe than neurons located in lateral portions of the CA1 subfield (i.e., CA1c). We have replicated these past findings across both experimental cohorts used in this study (Figure ), as can be seen by a representative tricolor elemental map (Fe, Cu, Zn) of the hippocampus (Figure A,B) that visually highlights the Fe enrichment in the CA1 subfield and the increasing trend of Fe content in medial relative to lateral regions. In general, the tricolor maps provide good anatomical demarcation of the hippocampus, owing to the Zn enrichment within the mossy fibers of CA3 (stratum lucidum) and polymorph dentate gyrus (PoDG) layers (shown by wide and thin white arrows in Figure A,B for CA3 and DG, respectively); and Cu enrichment in lateral ventricles (shown by short white arrow in Figure A,B). Statistical analysis (Figure C) confirms a significant increase in medial Fe content relative to lateral Fe content across the CA1 pyramidal neuron layer in cohort one (mice aged 3 months, p = 0.0085) and in cohort two (mice aged 7 months, p = 0.025). On average, Fe was found to be 49% and 51% higher in the medial relative to the lateral CA1 pyramidal layer, in experimental cohorts one (3-month-old mice) and two (7-month-old mice), respectively (Figure C). A detailed subcellular investigation of Fe distribution, with appropriate statistical analysis, has not yet been undertaken; however, to demonstrate proof-of-principle, Fe distribution was mapped at 100 nm spatial resolution in one representative lateral and medial CA1 neuron (Figure D). As can be seen in Figure D, both lateral and medial CA1 neurons were observed to contain multiple Fe enriched subcellular deposits. The identity of these deposits remains unknown; however, possibilities could include Fe enriched lysosomes, , lipofuscin, , or ferritin aggregates. It is interesting to note that a greater abundance of Fe enriched deposits is observed in the medial CA1 neuron, which may account for the greater Fe content of medial CA1 neurons relative to that of lateral CA1 neurons. The fraction of the total cell Fe contained within the Fe-rich deposits was measured at 5% and 13% for the lateral and medial neurons, respectively, which agrees with the visual increase in Fe-rich deposits seen in the medial neuron compared to the lateral neuron. Clearly further detailed investigation is now needed to characterize the subcellular origin of Fe in CA1 neurons.

1.

1

Open in a new tab

XFM elemental mapping of Fe content across lateral and medial CA1 pyramidal neuron layer. (A, B) Representative tricolor (Fe-magenta, Cu-yellow, Zn-cyan) maps in mice aged 3 months old (A) and 7 months old (B), showing Fe localization within CA1 pyramidal layer, with increased Fe visible in medial (M) CA1 neurons compared to lateral (L) CA1 neurons. (C) Quantification of Fe in lateral and medial CA1 neuron layer indicates a significant increase in Fe content in the medial relative to lateral region, in both experimental cohorts analyzed (mice aged 3 months old, and mice aged 7 months old). (D) High spatial resolution (submicron) elemental mapping on a single cell from the lateral and medial CA1 neuronal layer, reveals presence of numerous Fe enriched subcellular deposits. The Fe enriched deposits appear more abundant in the medial cell relative to the lateral cell. K and Ca elemental maps are used to locate cell body. In all maps, some small amounts of trace elemental contamination can be seen, identified by localized hotspots (indicated by white asterisks). Units in A, B = ng cm–2. Scale bar in A, B = 500 μm. Units in D are fluorescence counts. Scale bar in D = 2 μm. Statistics: **p < 0.01, *p < 0.05 White dashed region in B show locations used for extraction of μXANES spectra in Figure .

Fe K-Edge μXANES Spectra Indicate Increased Ferric Iron in Medial Relative to Lateral CA1 Neurons (Experimental Cohort One)

Given the pronounced lateral to medial trend of increasing Fe content observed across the CA1 pyramidal neuron layer, we used μXANES spectroscopy to investigate if there is a lateral to medial trend of differential Fe speciation across the CA1 pyramidal neuron layer (Figure ). In the triplicate animals analyzed with XANES spectroscopy (3-month-old mice, experimental cohort one), a subtle but significant shift of the Fe K-edge to higher energies and also a subtle increase in white line intensity was observed in average spectra of medial CA1 neurons relative to lateral CA1 neurons (Figure A). The edge shift was also notable when first derivatives of the XANES spectra were examined (Figure B). Inspection of the first derivative intensity reveals that the first inflection point (superimposed 1s–4s and 1s–4p transitions) , occurs at 7123.2 eV in spectra from lateral CA1 neurons, and 7124.5 eV in spectra from medial CA1 neurons. This shift of the Fe K-edge to higher energies in medial CA1 neurons suggests an increased abundance of ferric (Fe3+) compared to ferrous (Fe2+) Fe in the medial relative to lateral CA1 neurons.

2.

2

Open in a new tab

μXANES spectroscopic analysis of Fe speciation in lateral and medial CA1 neurons. (A) Representative average μXANES spectra from lateral and medial CA1 neurons, showing the edge shift to higher energy and increased white line intensity in spectra from medial CA1 neurons (black arrows), indicative of increased Fe oxidation state (i.e., increased ferric to ferrous ratio). The regions of interest used to calculate the average spectrum are shown in Figure A. (B) first derivatives of the μXANES spectra (generated with a 13 point Savitzky-Golay smoothing function) also reveal the edge shift to higher energy associated spectra from medial CA1 neurons. (C, D) Principal component analysis of (C) scores and (D) loadings, differentiate between μXANES spectra from lateral and medial CA1 neurons (n = 3 triplicate animals).

Principal component analysis was used to visualize the reproducibility of the edge shift between XANES spectra of medial relative to lateral CA1 neurons (the edge shift was observed in all spectra from the three biological replicates studied). As can be seen in Figure C, the principal component (PC) scores plot of PC1 (38.00% data variance) vs PC2 (37.27% data variance) visually discriminates between XANES spectra collected from medial CA1 neurons and lateral CA1 neurons, for the three biological replicates. Data variance described by PC3, PC4, and PC5 were 12.37, 9.59, and 2.77%, respectively. No other combination of principle components provided separation between the lateral and medial data sets. The separation is primarily observed along PC2, with spectra from lateral CA1 neurons displaying positive scores, and spectra from medial CA1 neurons displaying negative scores (Figure C). The PC loadings plot (Figure D) shows strong positive PC2 loadings at energies corresponding to the position of the first inflection point (7120–7125 eV), and strong negative PC2 loadings at energies corresponding to the white line peak (7130–7136 eV). This PC loading pattern is reflective of edge-intensity variation that results from the edge-energy shifts, which is indicative of a data set containing two groups separated by differences in the oxidation state, specifically higher oxidation state in medial CA1 neurons.

The observed K-edge shift to higher energies in medial relative to lateral CA1 neurons, together with the results of PCA analysis, indicates an increased presence of ferric (Fe3+) relative to ferrous (Fe2+) Fe in medial CA1 neurons. One possible explanation is increased Fe storage in the form of ferritin within medial CA1 neurons. However, our past studies using Perl’s staining, which is highly sensitive to ferric Fe within the ferrihydrite core of ferritin, did not show differences in Perls staining between lateral to medial CA1 neurons. , Likewise, as will be presented below, immuno-fluorescence did not reveal differences in ferritin immuno-reactivity between lateral and medial CA1 neurons. Higher Fe storage, in the form of ferritin, in medial relative to lateral CA1 neurons, therefore, seems an unlikely explanation for the observed relative increase in ferric Fe content.

Another possible explanation for the relative increase in the ferric Fe content of medial CA1 neurons could be higher metabolic activity within the regions. This is plausible given that medial CA1 neurons are known to be more excitatory with increased abundance of glutamate receptors when compared to lateral CA1 neurons. − Increased abundance of Fe–S clusters and cytochromes such as Cytochrome c Oxidase and Cytochrome P450 that would support the higher metabolic demands could therefore account for the observed relative increase in ferric Fe within medial CA1 neurons.

Alternatively, higher metabolic turnover within CA1 medial neurons could result in greater flux of Fe through lysosomes, which over time become damaged and accumulate nondegradable cellular material, which often contains Fe. , The increased ferric Fe abundance in medial CA1 neurons could potentially reflect a difference in lysosomal handling of Fe between medial and lateral CA1 neurons; however, clearly more work is required on this topic.

Spatial Transcriptomics Revealed Differential Expression of Fe-Relevant Gene Pathways between Lateral and Medial CA1 Sectors (Experimental Cohort Two)

The emergence of spatial transcriptomics has opened the door to a wealth of new opportunities to study similarities and differences between populations and subpopulations of cells. Apart from morphological differences, gene expression data clearly discriminate between neurons of the CA1, CA2, CA3, and CA4 subfields. , Further, gene expression data also reveal differences in gene expression across subregions of the CA1 subfield. ,, These findings are further confirmed in this current study with principal component analysis of gene expression data differentiating the medial CA1 pyramidal neuron layer (CA1a) from the lateral CA1 pyramidal neuron layer (CA1c), as shown in Figure . With the knowledge that gene expression is different between lateral and medial subregions of the CA1 subfield, a key aim of this current study was to then determine if differences in gene expression relating to the Fe metallome were present, which is described below.

3.

3

Open in a new tab

Principal component (PC) analysis of spatial transcriptomic data, showing separation between lateral (blue circles) and medial (red circles) regions of the CA1 pyramidal neuron layer, based on PC 1 and PC2 scores plot.

We have previously used spatial transcriptomics to characterize hippocampal gene expression of Fe transport proteins, Fe storage proteins, Fe containing enzymes and Fe responsive proteins (iron responsive elements, IREs). Our past findings revealed significant upregulation of gene expression related to the above-mentioned categories of Fe-relevant proteins in the CA1 sector of the hippocampus when compared to other hippocampal regions (CA3 neurons, dentate gyrus) and other regions surrounding the hippocampus (corpus callosum, cortex, subventricular zone). In this current study, we have undertaken a pathway analysis of the gene expression data, specifically to determine differences in the expression of Fe related pathways between lateral and medial CA1 regions (Table ). Interestingly, several key pathways were found to be significantly upregulated in the medial CA1 neuron subregion relative to the lateral CA1 neuron subregion, specifically pathways related to lysosome biogenesis, iron uptake and transport, transferrin endocytosis and recycling, mitochondrial biogenesis, and the citric acid cycle. Collectively, this differential pathway expression data supports the μXANES spectroscopic data, potentially indicating higher metabolic activity that requires greater Fe content/turnover and therefore an increased oxidative environment in medial CA1 neurons.

1. Comparison of Gene Expression Pathway Analysis between Lateral and Medial CA1 Pyramidal Neurons (Negative Enrichment Values Indicate Enrichment in Medial Relative to Lateral Regions).

pathway coverage enrichment normalized enrichment p value corrected p value
lysosome biogenesis 78.8 –0.47 –2.85 0.00022 0.0066
iron uptake and transport 65.6 –0.37 –2.65 0.00022 0.0066
transferrin endocytosis and recycling 67.7 –0.39 –2.14 0.0017 0.021
mitochondrial biogenesis 85.7 –0.34 –1.98 0.0050 0.043
citric acid cycle (TCA cycle) 90.5 –0.38 –1.99 0.0060 0.049
Open in a new tab

Immuno-Fluorescence Reveals That Levels of Fe Storage Proteins are Not Significantly Different between Lateral and Medial CA1 Regions (Experimental Cohort One)

The last component of this case study was to use immuno-fluorescence to characterize the relative abundance of two Fe storage proteins (heavy chain ferritin and mitochondrial ferritin) between lateral and medial CA1 regions (Figures and ). Interestingly, there was no significant difference in the levels of heavy chain ferritin and mitochondrial ferritin between lateral and medial CA1 neurons, p = 0.809 and p = 0.178, respectively (Figures and ). In general, mitochondrial ferritin immuno-reactivity appeared to be ubiquitously expressed over the hippocampus, which is not unexpected. Although heavy chain ferritin also displayed widespread immuno-reactivity, it is interesting to note that visually, there appeared to be less heavy chain ferritin within the CA1 pyramidal layer and the CA1 stratum radiatum layer, compared to the surrounding brain tissue, such as the corpus callosum white matter (Figure ). The regions of strongest heavy chain ferritin immuno-reactivity were localized in distinct hotspots, predominantly in the corpus callosum white matter that surrounds the hippocampus (narrow white arrows in Figure B,C), although some hotspots were observed in the CA1 stratum radiatum layer (thick white arrows, Figure C). As a costaining experiment has not been undertaken, the cellular identity of the heavy chain ferritin hotspots is not known; however, given their size and abundance in the corpus callosum white matter, it is likely they localize to oligodendrocytes (cells known to store large amounts of ferritin).

4.

4

Open in a new tab

Immuno-fluorescence of mitochondrial ferritin immuno-reactivity (green), (A) with DAPI (blue) and (B) without DAPI. (C) Zoomed in view of white boxes in panel A, showing immuno-reactivity in lateral and medial regions of the CA1 pyramidal layer. Although immuno-reactivity was generally ubiquitous, localized regions of increased immuno-reactivity could be seen in some cells (white arrows), but (D) no significant differences were observed between the lateral and medial CA1 neuronal layer. DAPI stain in panel A was used to stain nuclei and reveal cell layout in tissue. Scale bar in A, B = 500 μm, and in C,D = 50 μm.

5.

5

Open in a new tab

Immuno-fluorescence of heavy chain ferritin immuno-reactivity (green), (A) with DAPI (blue) and (B) without DAPI. (C) Zoomed in view of white boxes in panel A, showing immuno-reactivity in lateral and medial regions of the CA1 pyramidal layer. Although immuno-reactivity was generally ubiquitous, localized regions of increased immuno-reactivity could be seen in some cells outside of the CA1 pyramidal cell layer (white arrows). (D) No significant differences in immuno-reactivity toward heavy chain ferritin were observed between the lateral and medial CA1 pyramidal neuronal layer. DAPI stain in panel A was used to stain nuclei and reveal cell layout in tissue. Scale bar in A, B = 500 μm, and in C,D = 50 μm.

Although the primary focus of this study is investigation of differences in Fe speciation between lateral and medial CA1 neurons, it is interesting to note that immuno-fluorescence indicates that ferritin is not likely to cause the lateral to medial Fe gradient across the CA1 pyramidal neurons, nor is it likely to be the main source of Fe within the CA1 pyramidal neurons. To investigate this further, a comparison of the Fe K-edge XANES spectra from the medial CA1 pyramidal neuron layer and adjacent CA1 stratum radiatum layer is presented (Figure A), along with principal component score images (Figure B), and principal component loadings (Figure C), for a representative Fe K-edge XANES image stack. The first 4 principal component score images also showed visual differentiation between the CA1 pyramidal neuron layer and the adjacent CA1 stratum radiatum layer. The XANES spectrum of the CA1 stratum radiatum layer (Figure A) is not consistent with ferritin, as it displays reduced white line intensity and substantial broadening at lower energies relative to the spectrum from the CA1 pyramidal neurons. As the CA1 stratum radiatum layer primarily contains the dendrites that project from the soma of the CA1 neurons, collectively, the Fe K-edge XANES spectra and the immuno-fluorescence data suggest that ferritin is not the major source of Fe in either CA1 neuron bodies or their dendrites, which is consistent with the literature that suggests neurons have relatively low ferritin Fe storage capacity, compared to other cell types. − Unfortunately, XANES spectra were not collected from other surrounding tissue regions, such as the corpus callosum white matter, in this study. It would be of interest in future work to undertake immuno-fluorescence analysis of ferritin distribution along with XANES spectroscopic imaging of larger fields of view to confirm that regions of brain tissue displaying high immuno-reactivity to ferritin, such as the corpus callosum white matter, also yield XANES spectra reflecting a high ferritin content.

6.

6

Open in a new tab

Comparison of Fe speciation in the CA1 pyramidal neuron layer and striatum radiatum layer. (A) Representative K-edge XANES spectra generated from regions of interest shown by red arrow and red dashed ROI (CA1 pyramidal neuron layer) and purple arrow and purple dashed ROI (CA1 stratum radiatum layer) in panel B. In general, Fe K-edge XANES spectra from the CA1 stratum radiatum display reduced white line intensity and a broadening at lower energies (black arrows) compared to spectra from the CA1 pyramidal neuron layer. (B) PC Scores plots for the first 4 PCs, generated from the XANES spectra stacks. (C) Corresponding PC loadings plots. Scale bar B = 50 μm.

In terms of differences in ferritin immuno-reactivity within the CA1 pyramidal neuron layer, on average, there was no difference between the lateral or medial locations. It is possible that there are differences in the extent of Fe loading of ferritin, despite uniform protein expression. Although Perl’s staining was not undertaken in this study, our previous work did not reveal differences in Perl’s staining between lateral and medial CA1 neurons. , Given that Perl’s stain is highly sensitive to the ferrihydrite core of ferritin, our past work indicates that differences in Fe loading of ferritin is not likely to be the cause of the findings of this current study. This finding indicates that net Fe storage is not different across lateral and medial regions of the CA1 sector, and rather, it may suggest that a higher metabolic rate and protein turnover accounts for the differences in total Fe content, Fe oxidation state, and gene expression pathways that are observed. Although the pyramidal neurons share similar morphological appearance across the lateral and medial regions of the CA1 sector, they are known to be physiologically different. , Specifically, medial CA1 neurons are known to be more excitatory than lateral CA1 neurons, with differences in expression of neurotransmitter receptors also observed. Further studies are, however, clearly needed to determine the relationship between the Fe metallome and metabolic or excitatory differences across the CA1 sector.

Conclusions

This study has revealed that the morphologically similar, but physiologically different, pyramidal neurons in the lateral CA1 sector compared to the medial CA1 sector of the hippocampus display key differences in their Fe metallome. In addition to medial CA1 neurons containing more Fe, they appear to contain a great proportion of ferric (Fe3+) relative to neurons in the lateral CA1 sector. Spatial transcriptomics has also revealed that the higher Fe content of medial CA1 neurons associates with increased expression of gene pathways relating to Fe uptake and transport, transferrin endocytosis and recycling, mitochondrial biogenesis, lysosome biogenesis, and the citric acid cycle (TCA cycle). Interestingly, immuno-fluorescence data did not indicate differences between lateral or medial CA1 neurons in the levels of Fe storage proteins, mitochondrial ferritin, or heavy chain ferritin. Collectively, these data indicate that medial CA1 neurons may possess a higher oxidative and increased metabolic environment relative to neurons in the lateral CA1 sector, which is in agreement with the known higher excitatory nature of medial neurons relative to lateral CA1 neurons. Further studies are now required to investigate this in detail, which may potentially reveal pathways through which therapeutic manipulation of the hippocampal Fe metallome may minimize neurodegeneration that occurs in this vulnerable region. Lastly, although this study was performed on two separate cohorts of mice, it highlights the value of integrating multiple analytical methods in the field of metallomics, providing a platform for future studies to integrate direct elemental quantification with measures of metal speciation, alongside gene expression and protein abundance.

Supplementary Material

im5c00059_si_001.xlsx (318.8KB, xlsx)
im5c00059_si_002.xlsx (946.6KB, xlsx)

Acknowledgments

MJH gratefully acknowledges support from the Australian Research Council (ARC Future Fellowship FT190100017). Parts of this research were undertaken at the X-ray fluorescence microscopy beamline at the Australian Synchrotron, part of ANSTO. We gratefully acknowledge travel funding provided from ANSTO to access the Australian Synchrotron. This work was performed in part at the Melbourne Center for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF). This work was carried out with the support of Diamond Light Source, instrument I14 (proposal 34101). SSAM gratefully acknowledges a Westpac Future Leaders PhD scholarship, University Postgraduate Award from the University of Western Australia, and a Research Training Program Scholarship from the Australian Government. MW gratefully acknowledges an AINSE-PGRA.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/cbmi.5c00059.

  • Gene expression pathway analysis results, from spatial transcriptomics (WT CA1 Lat vs Med pathway analysis) (XLSX)

  • Gene expression comparison with statistical analysis, from spatial transcriptomics (WT CA1 Lat vs Med) (XLSX)

The authors declare no competing financial interest.

Published as part of Chemical & Biomedical Imaging special issue “Bioimaging of Metals.”

References

  1. Chang C. J.. Searching for harmony in transition-metal signaling. Nat. Chem. Biol. 2015;11(10):744–747. doi: 10.1038/nchembio.1913. [DOI] [PubMed] [Google Scholar]
  2. Que E. L., Domaille D. W., Chang C. J.. Metals in neurobiology: probing their chemistry and biology with molecular imaging. Chem. Rev. 2008;108(5):1517–1549. doi: 10.1021/cr078203u. [DOI] [PubMed] [Google Scholar]
  3. Adlard P. A., Bush A. I.. Metals and Alzheimer’s disease. J. Alzheimer’s Dis. 2006;10(2, 3):145–163. doi: 10.3233/JAD-2006-102-303. [DOI] [PubMed] [Google Scholar]
  4. Duce J. A., Bush A. I.. Biological metals and Alzheimer’s disease: implications for therapeutics and diagnostics. Prog. Neurobiol. 2010;92(1):1–18. doi: 10.1016/j.pneurobio.2010.04.003. [DOI] [PubMed] [Google Scholar]
  5. Huang X., Moir R. D., Tanzi R. E., Bush A. I., Rogers J. T.. Redox-Active Metals, Oxidative Stress, and Alzheimer’s Disease Pathology. Ann. N. Y. Acad. Sci. 2004;1012(1):153–163. doi: 10.1196/annals.1306.012. [DOI] [PubMed] [Google Scholar]
  6. Leskovjan A. C., Kretlow A., Lanzirotti A., Barrea R., Vogt S., Miller L. M.. Increased brain iron coincides with early plaque formation in a mouse model of Alzheimer’s disease. NeuroImage. 2011;55(1):32–38. doi: 10.1016/j.neuroimage.2010.11.073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Genoud S., Jones M. W., Trist B. G., Deng J., Chen S., Hare D. J., Double K. L.. Simultaneous structural and elemental nano-imaging of human brain tissue. Chem. Sci. 2020;11(33):8919–8927. doi: 10.1039/D0SC02844D. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brooks J., Everett J., Lermyte F., Tjhin V. T., Sadler P. J., Telling N., Collingwood J. F.. Analysis of neuronal iron deposits in Parkinson’s disease brain tissue by synchrotron x-ray spectromicroscopy. J. Trace Elem. Med. Biol. 2020;62:126555. doi: 10.1016/j.jtemb.2020.126555. [DOI] [PubMed] [Google Scholar]
  9. Carmona A., Cloetens P., Devès G., Bohic S., Ortega R.. Nano-imaging of trace metals by synchrotron X-ray fluorescence into dopaminergic single cells and neurite-like processes. J. Anal. At. Spectrom. 2008;23(8):1083–1088. doi: 10.1039/b802242a. [DOI] [Google Scholar]
  10. Chwiej J., Adamek D., Szczerbowska-Boruchowska M., Krygowska-Wajs A., Wojcik S., Falkenberg G., Manka A., Lankosz M.. Investigations of differences in iron oxidation state inside single neurons from substantia nigra of Parkinson’s disease and control patients using the micro-XANES technique. JBIC, J. Biol. Inorg. Chem. 2007;12(2):204–211. doi: 10.1007/s00775-006-0179-5. [DOI] [PubMed] [Google Scholar]
  11. Chwiej J., Fik-Mazgaj K., Szczerbowska-Boruchowska M., Lankosz M., Ostachowicz J., Adamek D., Simionovici A., Bohic S.. Classification of Nerve Cells from Substantia Nigra of Patients with Parkinson’s Disease and Amyotrophic Lateral Sclerosis with the Use of X-ray Fluorescence Microscopy and Multivariate Methods. Anal. Chem. 2005;77(9):2895–2900. doi: 10.1021/ac048173k. [DOI] [PubMed] [Google Scholar]
  12. Dučić T., Barski E., Salome M., Koch J. C., Bähr M., Lingor P.. X-ray fluorescence analysis of iron and manganese distribution in primary dopaminergic neurons. J. Neurochem. 2013;124(2):250–261. doi: 10.1111/jnc.12073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Friedrich I., Reimann K., Jankuhn S., Kirilina E., Stieler J., Sonntag M., Meijer J., Weiskopf N., Reinert T., Arendt T., Morawski M.. Cell specific quantitative iron mapping on brain slices by immuno-μPIXE in healthy elderly and Parkinson’s disease. Acta Neuropathol. Commun. 2021;9(1):47. doi: 10.1186/s40478-021-01145-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Lemelle L., Simionovici A., Colin P., Knott G., Bohic S., Cloetens P., Schneider B. L.. Nano-imaging trace elements at organelle levels in substantia nigra overexpressing α-synuclein to model Parkinson’s disease. Commun. Biol. 2020;3(1):364. doi: 10.1038/s42003-020-1084-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gh Popescu B. F., George M. J., Bergmann U., Garachtchenko A. V., Kelly M. E., McCrea R. P. E., Lüning K., Devon R. M., George G. N., Hanson A. D.. et al. Mapping metals in Parkinson’s and normal brain using rapid-scanning x-ray fluorescence. Phys. Med. Biol. 2009;54(3):651. doi: 10.1088/0031-9155/54/3/012. [DOI] [PubMed] [Google Scholar]
  16. Davies K. M., Bohic S., Carmona A., Ortega R., Cottam V., Hare D. J., Finberg J. P., Reyes S., Halliday G. M., Mercer J. F., Double K. L.. Copper pathology in vulnerable brain regions in Parkinson’s disease. Neurobiol. Aging. 2014;35(4):858–866. doi: 10.1016/j.neurobiolaging.2013.09.034. [DOI] [PubMed] [Google Scholar]
  17. Hilton J. B. W., Kysenius K., Liddell J. R., Mercer S. W., Hare D. J., Buncic G., Paul B., Wang Y., Murray S. S., Kilpatrick T. J.. et al. Evidence for decreased copper associated with demyelination in the corpus callosum of cuprizone-treated mice. Metallomics. 2024;16(1):mfad072. doi: 10.1093/mtomcs/mfad072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Tomik B., Chwiej J., Szczerbowska-Boruchowska M., Lankosz M., Wójcik S., Adamek D., Falkenberg G., Bohic S., Simionovici A., Stegowski Z., Szczudlik A.. Implementation of X-ray Fluorescence Microscopy for Investigation of Elemental Abnormalities in Amyotrophic Lateral Sclerosis. Neurochem. Res. 2006;31(3):321–331. doi: 10.1007/s11064-005-9030-6. [DOI] [PubMed] [Google Scholar]
  19. Chwiej J., Dulinska J., Janeczko K., Appel K., Setkowicz Z.. Variations in elemental compositions of rat hippocampal formation between acute and latent phases of pilocarpine-induced epilepsy: an X-ray fluorescence microscopy study. JBIC, J. Biol. Inorg. Chem. 2012;17(5):731–739. doi: 10.1007/s00775-012-0892-1. [DOI] [PubMed] [Google Scholar]
  20. Chwiej J., Kutorasinska J., Janeczko K., Gzielo-Jurek K., Uram L., Appel K., Simon R., Setkowicz Z.. Progress of elemental anomalies of hippocampal formation in the pilocarpine model of temporal lobe epilepsy: an X-ray fluorescence microscopy study. Anal. Bioanal. Chem. 2012;404(10):3071–3080. doi: 10.1007/s00216-012-6425-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Chwiej J., Winiarski W., Ciarach M., Janeczko K., Lankosz M., Rickers K., Setkowicz Z.. The role of trace elements in the pathogenesis and progress of pilocarpine-induced epileptic seizures. JBIC, J. Biol. Inorg. Chem. 2008;13(8):1267–1274. doi: 10.1007/s00775-008-0411-6. [DOI] [PubMed] [Google Scholar]
  22. Ackerman C. M., Lee S., Chang C. J.. Analytical Methods for Imaging Metals in Biology: From Transition Metal Metabolism to Transition Metal Signaling. Anal. Chem. 2017;89(1):22–41. doi: 10.1021/acs.analchem.6b04631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hare D. J., New E. J., de Jonge M. D., McColl G.. Imaging metals in biology: balancing sensitivity, selectivity and spatial resolution. Chem. Soc. Rev. 2015;44(17):5941–5958. doi: 10.1039/C5CS00055F. [DOI] [PubMed] [Google Scholar]
  24. Paul B., Hare D. J., Bishop D. P., Paton C., Cole N., Niedwiecki M. M., Andreozzi E., Vais A., Billings J. L., Bray L.. et al. Visualising mouse neuroanatomy and function by metal distribution using laser ablation-inductively coupled plasma-mass spectrometry imaging. Chem. Sci. 2015;6(10):5383–5393. doi: 10.1039/C5SC02231B. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Quintana C., Wu T.-D., Delatour B., Dhenain M., Guerquin-Kern Jl., Croisy A.. Morphological and chemical studies of pathological human and mice brain at the subcellular level: Correlation between light, electron, and nanosims microscopies. Microsc. Res. Tech. 2007;70(4):281–295. doi: 10.1002/jemt.20403. [DOI] [PubMed] [Google Scholar]
  26. Mamsa S. S. A., Ellison G., Koehn J., Inder-Smith K., Evans C. W., Graham R. M., Howard D. L., Hackett M. J.. Correlative analysis of metallomic gene expression and metal ion content within the mouse hippocampus. Metallomics. 2025;17(4):mfaf009. doi: 10.1093/mtomcs/mfaf009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Masurkar A. V., Tian C., Warren R., Reyes I., Lowes D. C., Brann D. H., Siegelbaum S. A.. Postsynaptic integrative properties of dorsal CA1 pyramidal neuron subpopulations. J. Neurophysiol. 2020;123(3):980–992. doi: 10.1152/jn.00397.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Igarashi K. M., Ito H. T., Moser E. I., Moser M.-B.. Functional diversity along the transverse axis of hippocampal area CA1. FEBS Lett. 2014;588(15):2470–2476. doi: 10.1016/j.febslet.2014.06.004. [DOI] [PubMed] [Google Scholar]
  29. Grain B. J., Westerkam W. D., Harrison A. H., Nadler J. V.. Selective neuronal death after transient forebrain ischemia in the mongolian gerbil: A silver impregnation study. Neuroscience. 1988;27(2):387–402. doi: 10.1016/0306-4522(88)90276-X. [DOI] [PubMed] [Google Scholar]
  30. Woodhams P. L., Celio M. R., Ulfig N., Witter M. P.. Morphological and functional correlates of borders in the entorhinal cortex and hippocampus. Hippocampus. 1993;3:303–311. doi: 10.1002/hipo.1993.4500030733. [DOI] [PubMed] [Google Scholar]
  31. Hackett M. J., Hollings A., Caine S., Bewer B. E., Alaverdashvili M., Takechi R., Mamo J. C. L., Jones M. W. M., de Jonge M. D., Paterson P. G.. et al. Elemental characterisation of the pyramidal neuron layer within the rat and mouse hippocampus. Metallomics. 2019;11:151–165. doi: 10.1039/C8MT00230D. [DOI] [PubMed] [Google Scholar]
  32. Ellison G., Duong L., Hollings A., Howard D., Jackaman C., Hackett M. J.. Characterising murine hippocampal iron homeostasis, in relation to markers of brain inflammation and metabolism, during ageing. Metallomics. 2022;14(10):mfac064. doi: 10.1093/mtomcs/mfac064. [DOI] [PubMed] [Google Scholar]
  33. Robison G., Zakharova T., Fu S., Jiang W., Fulper R., Barrea R., Zheng W., Pushkar Y.. X-ray fluorescence imaging of the hippocampal formation after manganese exposure. Metallomics. 2013;5(11):1554–1565. doi: 10.1039/c3mt00133d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Carmona A., Chen S., Domart F., Choquet D., Ortega R.. Imaging the structural organization of chemical elements in growth cones of developing hippocampal neurons. Metallomics. 2022;14(1):mfab073. doi: 10.1093/mtomcs/mfab073. [DOI] [PubMed] [Google Scholar]
  35. Porcaro F., Roudeau S., Carmona A., Ortega R.. Advances in element speciation analysis of biomedical samples using synchrotron-based techniques. TrAC, Trends Anal. Chem. 2018;104:22–41. doi: 10.1016/j.trac.2017.09.016. [DOI] [Google Scholar]
  36. Roudeau S., Carmona A., Perrin L., Ortega R.. Correlative organelle fluorescence microscopy and synchrotron X-ray chemical element imaging in single cells. Anal. Bioanal. Chem. 2014;406(27):6979–6991. doi: 10.1007/s00216-014-8004-4. [DOI] [PubMed] [Google Scholar]
  37. Pushkar Y., Robison G., Sullivan B., Fu S. X., Kohne M., Jiang W., Rohr S., Lai B., Marcus M. A., Zakharova T., Zheng W.. Aging results in copper accumulations in glial fibrillary acidic protein-positive cells in the subventricular zone. Aging cell. 2013;12(5):823–832. doi: 10.1111/acel.12112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sullivan B., Robison G., Pushkar Y., Young J. K., Manaye K. F.. Copper accumulation in rodent brain astrocytes: A species difference. J. Trace Elem. Med. Biol. 2017;39:6–13. doi: 10.1016/j.jtemb.2016.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Chwiej J., Palczynska M., Skoczen A., Janeczko K., Cieslak J., Simon R., Setkowicz Z.. Elemental changes of hippocampal formation occurring during postnatal brain development. J. Trace Elem. Med. Biol. 2018;49:1–7. doi: 10.1016/j.jtemb.2018.04.030. [DOI] [PubMed] [Google Scholar]
  40. Quinn P. D., Alianelli L., Gomez-Gonzalez M., Mahoney D., Cacho-Nerin F., Peach A., Parker J. E.. The Hard X-ray Nanoprobe beamline at Diamond Light Source. J. Synchrotron Radiat. 2021;28(3):1006–1013. doi: 10.1107/s1600577521002502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Howard D. L., de Jonge M. D., Afshar N., Ryan C. G., Kirkham R., Reinhardt J., Kewish C. M., McKinlay J., Walsh A., Divitcos J.. et al. The XFM beamline at the Australian Synchrotron. J. Synchrotron Radiat. 2020;27(5):1447–1458. doi: 10.1107/S1600577520010152. [DOI] [PubMed] [Google Scholar]
  42. Everett J., Collingwood J. F., Tjendana-Tjhin V., Brooks J., Lermyte F., Plascencia-Villa G., Hands-Portman I., Dobson J., Perry G., Telling N. D.. Nanoscale synchrotron X-ray speciation of iron and calcium compounds in amyloid plaque cores from Alzheimer’s disease subjects. Nanoscale. 2018;10(25):11782–11796. doi: 10.1039/C7NR06794A. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hackett M. J., Ellison G., Hollings A., Colbourne F., de Jonge M. D., Howard D. L.. “A spectroscopic picture paints 1000 words” mapping iron speciation in brain tissue with “full spectrum per pixel” X-ray absorption near-edge structure spectroscopy. Clin. Spectrosc. 2021;3:100017. doi: 10.1016/j.clispe.2021.100017. [DOI] [Google Scholar]
  44. Hollings A. L., Lam V., Takechi R., Mamo J. C., Reinhardt J., de Jonge M. D., Kappen P., Hackett M. J.. Revealing differences in the chemical form of zinc in brain tissue using K-edge X-ray absorption near-edge structure spectroscopy. Metallomics. 2020;12(12):2134–2144. doi: 10.1039/d0mt00198h. [DOI] [PubMed] [Google Scholar]
  45. Carmona A., Roudeau S., Perrin L., Carcenac C., Vantelon D., Savasta M., Ortega R.. Mapping Chemical Elements and Iron Oxidation States in the Substantia Nigra of 6-Hydroxydopamine Lesioned Rats Using Correlative Immunohistochemistry With Proton and Synchrotron Micro-Analysis. Front. Neurosci. 2019;13:1014. doi: 10.3389/fnins.2019.01014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wang H., Wang M., Wang B., Li M., Chen H., Yu X., Zhao Y., Feng W., Chai Z.. The distribution profile and oxidation states of biometals in APP transgenic mouse brain: dyshomeostasis with age and as a function of the development of Alzheimer’s disease. Metallomics. 2012;4(3):289–296. doi: 10.1039/c2mt00104g. [DOI] [PubMed] [Google Scholar]
  47. Thomas S. A., Mishra B., Myneni S. C. B.. High Energy Resolution-X-ray Absorption Near Edge Structure Spectroscopy Reveals Zn Ligation in Whole Cell Bacteria. J. Phys. Chem. Lett. 2019;10(10):2585–2592. doi: 10.1021/acs.jpclett.9b01186. [DOI] [PubMed] [Google Scholar]
  48. James S. A., Burke R., Howard D., Spiers K., Paterson D., Murphy S., Ramm G., Kirkham R., Ryan C., De Jonge M.. Visualising coordination chemistry: fluorescence X-ray absorption near edge structure tomography. Chem. Commun. 2016;52(79):11834–11837. doi: 10.1039/C6CC06747F. [DOI] [PubMed] [Google Scholar]
  49. James S. A., Hare D. J., Jenkins N. L., de Jonge M. D., Bush A. I., McColl G.. φXANES: In vivo imaging of metal-protein coordination environments. Sci. Rep. 2016;6:20350. doi: 10.1038/srep20350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. James S. A., Roberts B. R., Hare D. J., de Jonge M. D., Birchall I. E., Jenkins N. L., Cherny R. A., Bush A. I., McColl G.. Direct in vivo imaging of ferrous iron dyshomeostasis in ageing Caenorhabditis elegans . Chem. Sci. 2015;6(5):2952–2962. doi: 10.1039/C5SC00233H. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Hackett M. J., McQuillan J. A., El-Assaad F., Aitken J. B., Levina A., Cohen D. D., Siegele R., Carter E. A., Grau G. E., Hunt N. H., Lay P. A.. Chemical alterations to murine brain tissue induced by formalin fixation: implications for biospectroscopic imaging and mapping studies of disease pathogenesis. Analyst. 2011;136(14):2941–2952. doi: 10.1039/c0an00269k. [DOI] [PubMed] [Google Scholar]
  52. Pushie M. J. J., Hollings A., Reinhardt J., Webb S. M., Lam V., Takechi R., Mamo J., Paterson P., Kelly M. E., George G. N.. et al. Sample preparation with sucrose cryoprotection dramatically alters Zn distribution in the rodent hippocampus, as revealed by elemental mapping. J. Anal. At. Spectrom. 2020;35(11):2498–2508. doi: 10.1039/D0JA00323A. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Williams C. G., Lee H. J., Asatsuma T., Vento-Tormo R., Haque A.. An introduction to spatial transcriptomics for biomedical research. Genome Med. 2022;14(1):68. doi: 10.1186/s13073-022-01075-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Mamsa S. S. A., Ellison G., Koehn J., Inder-Smith K., Evans C. W., Graham R. M., Howard D. L., Hackett M. J.. Correlative analysis of metallomic gene expression and metal ion content within the mouse hippocampus. Metallomics. 2025;17(4):mfaf009. doi: 10.1093/mtomcs/mfaf009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Boseley R. E., Howard D. L., Hackett M. J., Lewis S. W.. The transfer and persistence of metals in latent fingermarks. Analyst. 2022;147(3):387–397. doi: 10.1039/D1AN01951A. [DOI] [PubMed] [Google Scholar]
  56. Ryan C. G., Siddons D., Kirkham R., Li Z., de Jonge M., Paterson D., Kuczewski A., Howard D., Dunn P., Falkenberg G.. et al. MAIA X-ray fluorescence imaging: capturing detail in complex natural samples. J. Phys.: Conf. Ser. 2014;499(1):012002. doi: 10.1088/1742-6596/499/1/012002. [DOI] [Google Scholar]
  57. George, G. N. ; Pickering, I. J. . EXAFSPAK: A suite of computer programs for analysis of X-ray absorption spectra 1995. http://ssrl.slac..stanford.edu/exafspak.html.
  58. Lerotic M., Mak R., Wirick S., Meirer F., Jacobsen C.. MANTiS: a program for the analysis of X-ray spectromicroscopy data. J. Synchrotron Radiat. 2014;21(5):1206–1212. doi: 10.1107/S1600577514013964. [DOI] [PubMed] [Google Scholar]
  59. Kurz T., Terman A., Gustafsson B., Brunk U. T.. Lysosomes in iron metabolism, ageing and apoptosis. Histochem. Cell Biol. 2008;129(4):389–406. doi: 10.1007/s00418-008-0394-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Rizzollo F., More S., Vangheluwe P., Agostinis P.. The lysosome as a master regulator of iron metabolism. Trends Biochem. Sci. 2021;46(12):960–975. doi: 10.1016/j.tibs.2021.07.003. [DOI] [PubMed] [Google Scholar]
  61. Höhn A., Jung T., Grimm S., Grune T.. Lipofuscin-bound iron is a major intracellular source of oxidants: Role in senescent cells. Free Radical Biol. Med. 2010;48(8):1100–1108. doi: 10.1016/j.freeradbiomed.2010.01.030. [DOI] [PubMed] [Google Scholar]
  62. Brunk U. T.. On the origin of lipofuscin; the iron content of residual bodies, and the relation of these organelles to the lysosomal vacuome. A study on cultured human glial cells. Adv. Exp. Med. Biol. 1989;266:313–320. doi: 10.1007/978-1-4899-5339-1_22. [DOI] [PubMed] [Google Scholar]
  63. Vidal R., Ghetti B., Takao M., Brefel-Courbon C., Uro-Coste E., Glazier B. S., Siani V., Benson M. D., Calvas P., Miravalle L.. et al. Intracellular Ferritin Accumulation in Neural and Extraneural Tissue Characterizes a Neurodegenerative Disease Associated with a Mutation in the Ferritin Light Polypeptide Gene. J. Neuropathol. Exp. Neurol. 2004;63(4):363–380. doi: 10.1093/jnen/63.4.363. [DOI] [PubMed] [Google Scholar]
  64. Berry A. J., O’Neill H. S. C., Jayasuriya K. D., Campbell S. J., Foran G. J.. XANES calibrations for the oxidation state of iron in a silicate glass. Am. Mineral. 2003;88(7):967–977. doi: 10.2138/am-2003-0704. [DOI] [Google Scholar]
  65. Grossman R., Paden C. M., Fry P. A., Rhodes R. S., Biegon A.. Persistent region-dependent neuroinflammation, NMDA receptor loss and atrophy in an animal model of penetrating brain injury. Future Neurol. 2012;7(3):329–339. doi: 10.2217/fnl.12.25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zamanillo D., Sprengel R., Hvalby Ø., Jensen V., Burnashev N., Rozov A., Kaiser K. M., Köster H. J., Borchardt T., Worley P.. et al. Importance of AMPA receptors for hippocampal synaptic plasticity but not for spatial learning. Science. 1999;284(5421):1805–1811. doi: 10.1126/science.284.5421.1805. [DOI] [PubMed] [Google Scholar]
  67. Oermann E., Warskulat U., Heller-Stilb B., Häussinger D., Zilles K.. Taurine-transporter gene knockout-induced changes in GABAA, kainate and AMPA but not NMDA receptor binding in mouse brain. Anat. Embryol. 2005;210(5):363–372. doi: 10.1007/s00429-005-0024-6. [DOI] [PubMed] [Google Scholar]
  68. Kurz T., Eaton J. W., Brunk U. T.. The role of lysosomes in iron metabolism and recycling. Int. J. Biochem. Cell Biol. 2011;43(12):1686–1697. doi: 10.1016/j.biocel.2011.08.016. [DOI] [PubMed] [Google Scholar]
  69. Cembrowski M. S., Bachman J. L., Wang L., Sugino K., Shields B. C., Spruston N.. Spatial gene-expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons. Neuron. 2016;89(2):351–368. doi: 10.1016/j.neuron.2015.12.013. [DOI] [PubMed] [Google Scholar]
  70. Lein E. S., Callaway E. M., Albright T. D., Gage F. H.. Redefining the boundaries of the hippocampal CA2 subfield in the mouse using gene expression and 3-dimensional reconstruction. J. Comp. Neurol. 2005;485(1):1–10. doi: 10.1002/cne.20426. [DOI] [PubMed] [Google Scholar]
  71. Dong H.-W., Swanson L. W., Chen L., Fanselow M. S., Toga A. W.. Genomic–anatomic evidence for distinct functional domains in hippocampal field CA1. Proc. Natl. Acad. Sci. U.S.A. 2009;106(28):11794–11799. doi: 10.1073/pnas.0812608106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Leonardo E., Richardson-Jones J., Sibille E., Kottman A., Hen R.. Molecular heterogeneity along the dorsal–ventral axis of the murine hippocampal CA1 field: a microarray analysis of gene expression. Neuroscience. 2006;137(1):177–186. doi: 10.1016/j.neuroscience.2005.08.082. [DOI] [PubMed] [Google Scholar]
  73. Todorich B., Zhang X., Connor J. R.. H-ferritin is the major source of iron for oligodendrocytes. Glia. 2011;59(6):927–935. doi: 10.1002/glia.21164. [DOI] [PubMed] [Google Scholar]
  74. Connor J. R., Menzies S., Martin S. S., Mufson E.. Cellular distribution of transferrin, ferritin, and iron in normal and aged human brains. J. Neurosci. Res. 1990;27(4):595–611. doi: 10.1002/jnr.490270421. [DOI] [PubMed] [Google Scholar]
  75. Reinert A., Morawski M., Seeger J., Arendt T., Reinert T.. Iron concentrations in neurons and glial cells with estimates on ferritin concentrations. BMC Neurosci. 2019;20(1):26. doi: 10.1186/s12868-019-0507-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Ellison G., Hollings A. L., Hackett M. J.. A review of the “metallome” within neurons and glia, as revealed by elemental mapping of brain tissue. BBA Adv. 2022;2:100038. doi: 10.1016/j.bbadva.2021.100038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Palomero-Gallagher N., Kedo O., Mohlberg H., Zilles K., Amunts K.. Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus. Brain Struct. Funct. 2020;225(3):881–907. doi: 10.1007/s00429-019-02022-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

im5c00059_si_001.xlsx (318.8KB, xlsx)
im5c00059_si_002.xlsx (946.6KB, xlsx)

Articles from Chemical & Biomedical Imaging are provided here courtesy of American Chemical Society

RESOURCES