Regional iron distribution and soluble ferroprotein profiles in the healthy human brain

Progress in Neurobiology xxx (xxxx) xxxx

Contents lists available at ScienceDirect

Progress in Neurobiology journal homepage: www.elsevier.com/locate/pneurobio

Regional iron distribution and soluble ferroprotein profiles in the healthy human brain Erin J. McAlluma,b,*, Dominic J. Harea,b, Irene Volitakisa,b, Catriona A. McLeanb,c, Ashley I. Busha,b, David I. Finkelsteinb, Blaine R. Robertsb,** a

Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health and The University of Melbourne, Parkville, Victoria, Australia The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia c Department of Anatomical Pathology, The Alfred Hospital, Melbourne, Victoria, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Iron Ferroprotein Aging Inductively coupled plasma-mass spectrometry

Iron is essential for brain development and health where its redox properties are used for a number of neurological processes. However, iron is also a major driver of oxidative stress if not properly controlled. Brain iron distribution is highly compartmentalised and regulated by a number of proteins and small biomolecules. Here, we examine heterogeneity in regional iron levels in 10 anatomical structures from seven post-mortem human brains with no apparent neuropathology. Putamen contained the highest levels, and most case-to-case variability, of iron compared with the other regions examined. Partitioning of iron between cytosolic and membranebound iron was generally consistent in each region, with a slightly higher proportion (55 %) in the ‘insoluble’ phase. We expand on this using the Allen Human Brain Atlas to examine patterns between iron levels and transcriptomic expression of iron regulatory proteins and using quantitative size exclusion chromatographyinductively coupled plasma-mass spectrometry to assess regional differences in the molecular masses to which cytosolic iron predominantly binds. Approximately 60 % was associated with ferritin, equating to approximately 25 % of total tissue iron essentially in storage. This study is the first of its kind in human brain tissue, providing a valuable resource and new insight for iron biologists and neuroscientists, alike.

1. Introduction Iron is the most abundant transition metal in the human brain and is an essential cofactor for energy production, myelination, and neurotransmitter metabolism (Hare et al., 2013a). Iron redox cycling at physiological conditions is used by various enzymes to facilitate electron transfer, and disrupted iron metabolism is a major driver of oxidative stress, particularly in neurological disorders (Dixon and Stockwell, 2014; Hare et al., 2013a; Stockwell et al., 2017). Although accounting for only 2 % of total body mass, the human brain accounts for approximately 20 % of the body’s basal oxygen consumption and

energy production, and therefore has a high demand for iron (Bélanger et al., 2011). The mitochondrial respiratory chain is dependent on iron redox activity, and concurrent synthesis of haem and Fe/S clusters further increase iron demand (Lill et al., 2012). The brain has a highlycompartmentalised distribution of iron (Paul et al., 2015), with higher levels found in deep grey matter structures, such as the basal ganglia (Li et al., 2014). Within cells, iron transport to mitochondria and active iron-dependent enzymes must be tightly regulated, as labile iron is prone to react with by-products of mitochondrial respiration to form damaging hydroxyl radicals. After crossing the blood-brain barrier and entering the central

Abbreviations: ACCx, anterior cingulate cortex; ACO1, aconitase 1; cbm, cerebellum; CP, ceruloplasmin; ECx, entorhinal cortex; EPAS1, endothelial PAS domaincontaining protein 1; FBXL5, F-box/LRR-repeat protein 5; FTH1, ferritin heavy chain; FTL, ferritin light chain; FTMT, ferritin mitochondrial; FXN, frataxin; GM, grey matter; HAMP, hepcidin antimicrobial protein; HIF1A, hypoxia-inducible factor 1α; ICP-MS, inductively coupled plasma-mass spectrometry; IRE, iron responsive element; IREB2, iron responsive element binding protein 2; IRP, iron regulatory protein; MRI, magnetic resonance imaging; OCx, occipital cortex; PCBP1, poly(RC) binding protein 1; RES, relative enrichment score; SEC, size exclusion chromatography; SC, spinal cord; SLC11A2, solute carrier family 11 member 2; SLC40A1, solute carrier family 40 member 1; TF, transferrin; TFRC, transferrin receptor; Thal, thalamus; UTR, untranslated region; WM, white matter ⁎ Corresponding author at: Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health and The University of Melbourne, Parkville, Victoria, Australia. ⁎⁎ Corresponding author. E-mail addresses: erin.mcallum@florey.edu.au (E.J. McAllum), blaine.roberts@florey.edu.au (B.R. Roberts). https://doi.org/10.1016/j.pneurobio.2019.101744 Received 30 July 2019; Received in revised form 11 December 2019; Accepted 18 December 2019 0301-0082/ © 2019 Published by Elsevier Ltd.

Please cite this article as: Erin J. McAllum, et al., Progress in Neurobiology, https://doi.org/10.1016/j.pneurobio.2019.101744

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

nervous system, iron is distributed through the interstitum and delivered to neurons and glia, where it is involved in an array of neurochemical functions (Crichton et al., 2011; Moos et al., 2007). Conservative extrapolation from stable isotope tracing experiments in rodents suggests the half-life of iron in the human brain is greater than 10 years (Chen et al., 2014). Brain iron accumulation is a feature of normal ageing (Ward et al., 2014), with lifetime trajectories seemingly dependent on anatomical region (Acosta-Cabronero et al., 2016; Li et al., 2014; Ramos et al., 2014). Magnetic resonance imaging (MRI) using a range of phase, relaxation, and susceptibility mapping approaches supports most estimations of regional distribution of iron in the living brain, and post-mortem validation using single-origin tissue has confirmed compartmentalised and highly variable concentrations in specific anatomical structures (Langkammer et al., 2010). However, in vivo imaging is unable to differentiate between different species of iron and iron-protein ligands. In addition, diamagnetic myelin also contributes to brain susceptibility, further complicating interpretation of MRI data by interfering with assumed iron susceptibility measures. Moller et al. provide an excellent recent review on this topic and outline the necessary developments in this field, including the need for ex vivo validation (Möller et al., 2019). As it stands, investigating the mechanisms behind heterogenous brain iron distribution requires a more direct approach, where post-mortem tissue is needed for further interrogation of iron at the molecular level. Here, we examined 10 neuroanatomical structures taken from multiple donors, and confirmed the heterogeneity of iron distribution in the human brain. By combining this with open access transcription and genetic databases of regional protein expression, we provide new insight into the regulation of iron in the human brain. We have previously shown that iron (like copper and zinc) had a markedly different ‘ferroprotein’ profile in cultured astrocytes and neurons, partially representing the disparate functions and respective complements of ironbinding proteins that were present at distinct concentrations (Hare et al., 2013b). In this study, we advance this approach and report a profile of the variable distribution of iron according to molecular size of soluble ferroproteins in frozen post-mortem human brain.

Table 1 Subject demographics and collected anatomical regions. Sex

Age

PMIa

Regions collected

Male Female Female Male Female Female Male

57.0 63.4 71.3 73.6 81.2 82.7 90.8

48 30.5 25 49 25 28.5 32.5

All All Thalb, Cbmc, SCd, GMe, WMf Putg, Pons, ACCxh, ECxi, OCxj All All All

a

post-mortem interval; b thalamus; c cerebellum; d spinal cord; e grey matter; f white matter; g putamen; h anterior cingulate cortex; I entorhinal cortex; j occipital cortex.

20−30 mg was used to extract soluble iron-binding proteins for size exclusion chromatography (SEC)-ICP-MS. Complete avoidance of blood contamination from the microvasculature is not possible in human brain tissue, though samples were carefully inspected to avoid major sources of blood contamination. Proteins were extracted by first homogenising tissue in equal volumes (w/v) of Tris-buffered saline (TBS; 50 mM Tris, 150 mM NaCl; pH 8.0) containing EDTA-free protease inhibitors (Roche, Australia) using a sterile polypropylene handheld tissue pestle. Homogenates were then centrifuged at 16,000 g for 20 min at 4 °C and the supernatant was decanted using an air-displacement pipette into a clean 1.5 mL polypropylene vial (Techno Plas, Australia). Protein concentration was estimated according to 280 nm UV absorbance with a micro-volume spectrophotometer (NanoDrop, Thermo Fisher, Australia). A 20 μL aliquot was taken for total iron quantification, and protein concentration was used to determine injection volumes for each sample corresponding to 80 μg of total protein for SEC-ICP-MS. The remaining insoluble pellet contained membranebound proteins, organelles and other insoluble biomolecules, and was retained for total iron assay by ICP-MS. Fresh frozen tissue and the insoluble fraction was prepared for ICPMS detection of total iron levels by first lyophilising samples and then allowing to digest in equivalent (w/v) 65 % HNO3 (Suprapur® grade, Merck, Australia) overnight at room temperature. Samples were then heated to 90 °C in a heating block for 20 min, after which the same volume of 32 % H2O2 (BDH Aristar® Ultra grade, VWR Analytical, USA) was added and the sample heated to 70 °C for 15 min. Digests were diluted in 1 % HNO3 (Suprapur® grade in Milli-Q® H2O; Merck) prior to analysis by ICP-MS using our previously-validated method for analysis of brain tissue (Genoud et al., 2017; Hare et al., 2014a; Roberts et al., 2016). Preparation blanks were prepared in an identical manner.

2. Materials and methods 2.1. Human ethics statement This study was approved by The University of Melbourne Health Sciences, Human Ethics Subcommittee (ID1136882) and fully complies with the current Australian Government National Health and Medical Research Council’s National Statement on Ethical Conduct in Human Research.

2.4. Total iron analysis

2.2. Post-mortem human brain tissue

Digested tissue, pellets and aliquots of supernatant were all assayed for total iron using an 7700x Series ICP-MS (Agilent Technologies, Australia) with the operating parameters given in Table S1. A MiraMist nebuliser (Burgener Research Inc, Canada) and standard Scott-type double-pass quartz spray chamber (Glass Expansion, Australia) were used for all measurements and Grade 5.0 helium (Coregas, Australia) was used as a collision gas. Quantification of total iron (as m/z 56) was performed by external calibration using a certified multi-element standard (Merck) diluted in 1 % HNO3 and online addition of yttrium (m/z 89; Accustandard, USA) as an internal standard via a Teflon Tpiece. All measurements were made using spectrum mode with 100 sweeps and four replicate measurements. The limits of detection and quantitation for iron were 0.075 μg L−1 and 0.25 μg L−1, respectively, and the concentration of iron is reported as μg⋅ g−1 of the original tissue wet weight.

Samples of unfixed and frozen post-mortem tissue taken from the putamen (Put), pons, anterior cingulate cortex (ACCx), entorhinal cortex (ECx), occipital cortex (OCx), thalamus (Thal), cerebellum (Cbm), cervical spinal cord (SC), and cortical grey and white matter from parietal cortex (GM and WM, respectively; Fig. S1) were obtained from the Victorian Brain Bank. Tissue was collected by an experience neuropathologist from a total of seven individual cases (three male, four female; age 74.3 ± 11.7 years; post-mortem interval 34.1 ± 10.2 h) that appeared free of any neuropathological condition at autopsy, and six cases were used for analysis of each brain region (Table 1). 2.3. Sample preparation Approximately 10−20 mg of tissue was taken using a sterilised PTFE-coated razor blade for quantitative iron analysis by inductively coupled plasma-mass spectrometry (ICP-MS), and the remaining ca. 2

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

2.5. Size exclusion chromatography-inductively coupled plasma-mass spectrometry

Table 2 Iron regulatory proteins.

Soluble ferroprotein profiles for each sample were produced as time resolved mass chromatograms of m/z 56 using the ICP-MS described above. A visual tutorial of the quantitative SEC-ICP-MS method used can be viewed in Lothian et al. (Lothian and Roberts, 2016). Briefly, a 1260 liquid chromatography system (Agilent Technologies) equipped with a variable wavelength detector and Peltier-cooled autosampler (4 °C) was used for all experiments. Injections of 80 μg total extracted proteins were resolved by molecular size using a Bio SEC-3 (4.6 mm × 300 mm, 150 Å pore diameter, 3 μm particle size; Agilent Technologies) size exclusion column with a 200 mM NH4NO3 (pH adjusted to 7.4; Merck) mobile phase containing 10 μg L−1 cesium and antimony (both Choice Analytical, Australia) as online internal standards (m/z 133 and 121) at a flow rate of 0.4 mL min−1. Eluent from the column passed through the variable wavelength detector where absorbance at 280 nm was monitored, and into the ICP-MS via a length of PEEK tubing to the Miramist® nebuliser. The total run time for each separation was 15.5 min (1.25 column volumes). The operating parameters for the ICP-MS instrument when used in time-resolved analysis mode for SEC-ICP-MS are given in Table S1. Iron was measured on mass as m/z 56. External calibration to determine the total iron amount in each resolved peak was performed using injections of standard solutions containing horse spleen ferritin equivalent to 1, 2, 6, 20 and 60 ng of iron. Limits of detection and quantitation were calculated as described previously using the integrated area under the curve, and were 2.1 ng and 6.9 ng, respectively (Hare et al., 2016b). A human transferrin (Merck) standard containing approximately 25 ng of iron was also prepared to indicate retention volume of an additional highabundance ferroprotein. Total iron levels for calculating injection volumes of standard proteins were determined using the ICP-MS method described above. The mass calibration curve for the Bio SEC-3 column used is shown in Figure S2 and was constructed using detection of absorbance at 280 nm for injections of ferritin, catalase, conalbumin, ovalbumin, carbonic anhydrase, and ribonuclease A using a variable wavelength detector.

Gene

Protein

Allen Brain Atlas probe ID

TFRC SLC11A2 TF SLC40A1 HAMP CP FTL FTH1 FTMT ACO1 IREB2

Transferrin receptor Divalent metal transporter-1 Transferrin Ferroportin Hepcidin antimicrobial peptide Ceruloplasmin Ferritin, light polypeptide Ferritin, heavy polypeptide Ferritin, mitochondrial Aconitase-1 Iron responsive element binding protein-2 F-box/LRR-repeat protein 5 Hypoxia inducible factor 1, alpha subunit Endothelial PAS domain protein 1 (hypoxia inducible factor 2, alpha subunit) Frataxin Poly(rC) binding protein 1

CUST_14970_PI416261804 A_24_P381494 A_23_P212500 A_32_P151454 CUST_14747_PI416261804 A_23_P40817 A_23_P50498 A_24_P919330 A_32_P160896 A_23_P9415 A_24_P188005

FBXL5 HIF1A EPAS1

FXN PCBP1

CUST_7155_PI416261804 A_23_P48637 A_23_P210210

CUST_16731_PI416261804 A_24_P103025

two-stage step-up method described by Benjamini, Krieger and Yekutieli. Significance threshold was set at FDR (or q) value < 0.05. For direct comparisons between white and grey matter, the groups were compared using a Student’s unpaired t-test with a significance level α = 0.05. 3. Results and discussion 3.1. Heterogeneity of iron concentrations in the brain Quantitative analysis of iron in total tissue from various brain regions confirmed a heterogeneous and compartmentalised distribution, depending on anatomical location. This was most evident in the putamen (Fig. 1; Table 3). This is consistent with previous studies that have also shown putamen, along with other regions of the basal ganglia, have a high iron content when compared with brain regions outside the deep grey matter structure (Andrasi et al., 2000; Ramos et al., 2014). Iron content for the other measured regions is also consistent with published reports (Rajan et al., 1997; Ramos et al., 2014). Spinal cord tissue has not been routinely included in regional metal determinations of multiple brain regions, though the iron content reported here for cervical SC is in agreement with levels reported for lumbar SC tissue (Ince et al., 1994).

2.6. Extracting mRNA data from the Allen Brain Atlas mRNA transcript data was downloaded from the open access, online Allen Human Brain Atlas (human.brain-map.org) (Hawrylycz et al., 2012). For each gene investigated, a single mRNA probe was selected, and the probes used are listed in Table 2. Z-score data for each probe was extracted for only the same brain regions that we analysed as part of our set of tissues. 2.7. Statistical analysis All statistical analyses were performed using Prism 7 (GraphPad, USA). For total, insoluble and soluble iron content, and for iron content of different soluble ferroprotein peaks, the mean for each brain region was compared to the overall mean (i.e. mean iron levels in all measured brain regions) using one-way ANOVA. Multiple comparisons were corrected for by controlling the false discovery rate using two-stage step-up method of Benjamini, Krieger and Yekutieli (Benjamini et al., 2006). A relative enrichment score (RES) for iron associated with each peak was calculated by determining the iron in each peak as a percentage of total soluble iron (Fetotal) relative to the overall mean for every sample (Femean) (Eq. 1).

RES =

Fetotal −1 Femean

Fig. 1. Comparison of total iron levels between different brain regions. Dotted line indicates mean for all regions and shaded area indicates SD. For all data, mean ± SD are shown. Iron in the putamen was significantly higher than basal levels (one-way ANOVA with FDR correction; **** p < 0.0001, n=6 per region).

(1)

For each peak, the mean RES for each brain region was compared to the overall mean RES using one-way ANOVA. Multiple comparisons were corrected for by controlling the false discovery rate (FDR) using 3

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

2012; Gong et al., 2015; Hallgren and Sourander, 1958). Here, we found no significant correlation between age and iron content for any of the brain regions analysed (Fig. S3). This is consistent with studies that examine a similar, older age range for all regions examined apart from the putamen, which is a sub-region of the basal ganglia and has been shown to continue to accumulate iron with age. Ramos et al. (Ramos et al., 2014) used a comparable method (atomic absorption spectroscopy) to examine regional relationships between iron levels and age, and reported a similar r of 0.15 (compared to our r of 0.17) using 42 putamen samples across a similar age range. Importantly, while Ramos et al. described the basal ganglia structures (including putamen) as having the most significant correlation, they reported no measures of statistical robustness. We conducted a power analysis on the total iron levels measured in putamen. To show significance in the measured correlation of r = 0.17 between total iron and age in the putamen, at 80 % power we would need 44 individual cases. Thus, the small number of human cases examined here, spanning a 33.8-year age range, is substantially underpowered for detecting correlation between age and brain iron levels in putamen that previous MRI imaging studies have shown in larger cohorts (Acosta-Cabronero et al., 2016; Bilgic et al., 2012; Buijs et al., 2017; Li et al., 2014).The post-mortem interval was, on average, around half that used in other reports of iron levels in normal aged brain regions (Langkammer et al., 2010, 2012), and showed no apparent influence on region iron concentration.

Table 3 ICP-MS of total iron from different brain regions and percentage distribution between soluble and insoluble fractions. All data is mean ± SD. Region

Put Pons ACCx ECx OCx Thal Cbm SC Overall

Total iron concentration (μg⋅ g−1 wet weight)

125 ± 60.2 22.6 ± 12.5 25.9 ± 7.8 31.8 ± 10.0 45.5 ± 8.8 40.4 ± 21.5 41.5 ± 12.0 15.7 ± 8.0 43.5 ± 39.5

Iron content (% of total) Insoluble

Soluble

57.9 47.5 62.5 63.3 54.2 58.9 54.9 46.1 55.6

42.1 52.5 37.5 36.7 45.8 41.1 45.1 53.9 44.4

± ± ± ± ± ± ± ± ±

2.1 13.3 6.9 5.2 6.8 6.6 6.9 9.6 9.4

± ± ± ± ± ± ± ± ±

2.1 13.3 6.9 5.2 6.8 6.6 6.9 9.6 9.4

Ramos et al. (Ramos et al., 2014) extensively profiled iron content in various regions from a large number of post-mortem brains using atomic absorption spectrometry. The iron concentrations presented here cannot be directly compared, as Ramos et al. expressed their results in μg g−1 tissue dry weight. Potential confounding effects of differential water content preclude conversion to directly comparable units, though we were able to compare relative differences between brain regions and the overall mean. When compared to Ramos et al., the effect size reflecting regional variation was consistent. Interestingly, while iron content was heterogeneous across brain regions, compartmentalisation of iron between cytosolic and membranous fractions was not. When calculated as a partitioned percentage of total iron, soluble and insoluble iron in each brain region did not significantly differ from the overall mean for all measured regions (Table 3). With the exception of the pons and SC, the slight majority of iron was associated with the insoluble fraction. Insoluble fractions contain iron-rich myelinated fibres (Moos et al., 2007) and mitochondria (Levi and Rovida, 2009), which represents the primary source of iron not soluble in an aqueous buffer. Given that both pons and SC are myelin-rich regions, it was somewhat surprising that we observed a greater proportion of iron in the soluble fraction. However, the data for these two regions has a much greater spread than the other brain regions analysed. This may indicate that these regions have a more variable distribution of iron concentration, possibly due the presence of variable numbers of iron-rich oligodendrocytes, necessary for producing and maintaining myelin fibres (Möller et al., 2019). The trajectory of iron retention in the brain generally follows an exponential saturation function, with most brain regions rapidly increasing during the first two decades of life, then slowing as the brain reaches maturity (Pirpamer et al., 2016). In vivo imaging of iron by MRI, atomic spectroscopy and histochemical staining have all conclusively shown that certain areas of the basal ganglia disproportionately accumulate iron into old age, relative to other brain regions (Acosta-Cabronero et al., 2016; Aquino et al., 2009; Bilgic et al.,

3.2. Relationship between iron levels and iron-regulatory protein expression Iron is both an essential biological co-factor and a potential driver of oxidative stress, so the different iron requirements of each brain region are highly coordinated by a network of iron-regulatory proteins. To examine the relationship between this network and iron levels in the brain regions we examined, we extracted mRNA expression data for a panel of well-characterised iron regulatory proteins (Table 2) from the online, open-source Allen Human Brain Atlas (Hawrylycz et al., 2012). Expression of mRNA transcripts in each brain region are displayed as zscores as per the Allen Human Brain Atlas output (Fig. 2) (Allen Institute for Brain Science, 2013). In general, the expression of mRNA corresponding to major iron regulatory proteins was similar across brain regions, as indicated by the majority of z-scores falling between -1 and 1. That is, the majority of mRNA transcripts in the brain regions examined are expressed at a level within one standard deviation of the brain-wide average for that transcript. The cerebellum is an obvious exception to this general trend, with a wider range of z-scores, and six of the 16 mRNA transcripts being expressed at levels more than one standard deviation from the overall mean. Cerebellum had comparatively low expression of FTL, FTH1 and EPAS1 mRNA, and high expression of IREB2, FBXL5 and HIF1A mRNA. The only other transcripts to be expressed at levels more than one standard deviation from the overall mean were CP, FTL and EPAS1 in putamen, and TRFC in pons.

Fig. 2. Relative mRNA expression (as z-scores) of key iron proteins extracted from the Allen Human Brain Atlas (Hawrylycz et al., 2012), for the different brain regions used in this study. 4

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

TFRC and SLC11A2 mRNA transcripts and high iron levels observed in putamen in our analysis. On the other hand, binding of IRPs to IREs in the 5’ UTR of mRNAs (SLC40A1, FTL, FTH1 and EPAS1), inhibits their translation (Kühn, 2015). This system is able to rapidly respond to abnormal fluctuations in iron levels, maintaining homeostasis and minimising potential detrimental effects. In general, extrapolating mRNA transcript data to protein expression must be done cautiously. The ability to predict protein expression from mRNA levels has been shown to be low for the human brain (Bauernfeind and Babbitt, 2017). The Allen Human Brain Atlas consists of mRNA expression data for six individual brains reported as a z-scores (calculated independently for each probe, across all donors and regions) (Allen Institute for Brain Science, 2013). Although brain iron uptake across the blood-brain barrier is heavily regulated in the adult brain (McCarthy and Kosman, 2015), a recent stable-isotope tracing study in rats showed that dietary iron intake influences brain iron levels (Chen et al., 2013), while magnetite nanoparticles from air pollution have been detected in olfactory tissue (Maher et al., 2016), effectively bypassing the bloodbrain barrier. Thus, dietary and environmental influences cannot be discounted in our sample set. Additionally, the mean age of the Allen Human Brain Atlas donors was 42.5 ± 13.4 years, compared to the 74.0 ± 12.7 mean age of brains used here to directly assess iron levels. Brain iron content and expression of iron regulatory proteins (particularly transferrin and ferritin) increases with age (Ward et al., 2014), and with an estimated half-life in the brain of over 10 years (Chen et al., 2014) periods of high iron intake may introduce an additional source of variation (Hare et al., 2015a, 2018), recently shown using stable isotope tracing studies in adult rats (Chen et al., 2013). To our knowledge the impact of differential iron uptake on regional compartmentalisation and subsequent protein expression has not been investigated. While there are certainly limitations in the interpretation of these data (and in interpretation of mRNA data more generally), resources such as the Allen Human Brain Atlas provide incredibly valuable and powerful tools for neuroscientists, when access to human brain material is limited.

Fig. 3. (a) Quantitative size exclusion chromatograms (n = 6; see Fig. S3, ESI) of soluble iron-binding proteins in each analysed brain region. (b) Segmentation of a representative chromatogram (occipital cortex; dark line) indicating the five resolvable iron binding protein peaks used, with iron traces of horse-spleen ferritin (30 ng iron; dashed red line), human transferrin (25 ng iron; dashed blue line), human haemoglobin (30 ng iron; dashed green line) standards.

Relative amounts of TFRC and SLC11A2 mRNA, encoding the transferrin receptor-1 and divalent metal transporter-1 proteins, respectively, which play a key role in iron import, were relatively low in putamen. This is somewhat surprising, as the putamen contained significantly higher iron concentrations than the other regions. The high expression levels of mRNA corresponding to ferritin, the primary iron storage protein, suggests that the putamen may maintain a readily accessible pool of iron. This is consistent with in vivo field-dependent R2 increase (FDRI) imaging by MRI, which has shown iron levels in the putamen are associated with characteristic relaxation rate of ironbound ferritin (Bartzokis et al., 1997). Conversely, relative amounts of TFRC and SLC11A2 were highest in pons, despite it having the lowest iron concentration of the brain regions we examined (excluding spinal cord). Interpreting mRNA transcript levels as an indication of protein expression must be done cautiously, as regulation of expression of many proteins involved in iron metabolism is known to occur post-translationally by the iron regulatory protein (IRP)/iron responsive element (IRE) system (Kühn, 2015). There are two known IRPs in humans – aconitase 1 (ACO1) and iron responsive element binding protein 2 (IREB2). The mRNA binding properties of these proteins are determined by cellular iron levels, and binding of IRPs to IREs in untranslated regions (UTR) of mRNA dictates the translation and expression of several proteins crucial for iron metabolism. In low iron, ACO1 and IREB2 bind to IREs in mRNA, whereas in high iron, ACO1 forms an iron-sulfur cluster and acquires aconitase activity, and IREB2 is degraded (Kühn, 2015). mRNAs with IREs in their 3’ UTR (TFRC and SLC11A2), are stabilised by the binding of IRPs (i.e. low iron), facilitating translation. In the absence of bound IRPs (i.e. high iron), these mRNAs are unstable and degraded (Kühn, 2015). This is consistent with the low levels of

3.3. Regional soluble ferroprotein distributions We examined regional iron-protein interactions in soluble protein extracts from each anatomical region using SEC-ICP-MS (Fig. 3a; see Fig. S4, for individual mean ± 95 % CI of n = 6 chromatograms per region) calibrated as described in Lothian et al. (Lothian and Roberts, 2016). Integration of the baseline-corrected chromatograms identified five consistently-resolvable peaks (Fig. 3b; referred to hereafter as peaks 1–5) with approximated molecular weight ranges and mass at peak apex given in Table 4 (see Fig. S2 for molecular weight calibration curve). Some measurements for peak 2, 4 and 5 were at detection limits and below the limits of quantitation, so we have restricted comparative quantitative analysis to peaks 1 and 3. Ferritin, transferrin and haemoglobin standards were also injected to confirm peak apex elution volume and mass of three major iron-binding proteins. Size exclusion is a low resolution separation technique that uses an aqueous isocratic mobile phase at physiological pH that is directly compatible with standard plasma operating conditions (Bishop et al., 2018), and was chosen to preserve ferrous and ferric iron-protein ligands. Protein-metal binding in a complex sample is dynamic, with competitive binding of Table 4 Molecular mass range and mass at peak apex for peaks 1–5.

5

Peak

Ve/V0

Mass range (kDa)

Mass at peak apex (kDa)

1 2 3 4 5

1.04 1.20 1.36 1.60 1.73

461 – 162 158 – 64 63 – 11 11 – 5 4–1

382 129 42 8 3

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

peak was region-dependent. The area under the curve was integrated for each peak to determine the total amount of associated iron (Fig. 4ae; Table 5). Compared to the overall mean for each peak, there was a significantly larger amount of iron associated with peak 1, 2 and 3 in putamen, and peak 3 in pons, and a significantly lower amount of iron associated with peak 2 and 4 in spinal cord (p < 0.05-0.0001; one-way ANOVA). To further investigate this apparent regional heterogeneity of iron between iron-binding proteins, we calculated a relative enrichment score (see Experimental) to describe the proportion of soluble iron in each region that is associated with each peak (Fig. 4f). This showed there was a relatively higher proportion of iron associated with peak 1 in putamen and peak 3 in pons and spinal cord; and a lower proportion of iron associated with peak 1 in pons and spinal cord. This indicates a substantial proportion of the iron in putamen that accounts for the measured difference in soluble species is associated with proteins that elute in the mass range of peak 1, which includes ferritin. The five individual peaks represent the total soluble iron-binding proteome; therefore we would expect many low-abundance ferroproteins to co-elute and be obscured by peak tailing of peaks 1 and 3. The range of iron and the high amount of iron associated mainly with peaks 1 and 3 make it difficult to detect ferroproteins of lower abundance with this method. This high dynamic range is evident, even for the peaks that we can detect, with the most abundant peak (peak 1) being two orders of magnitude higher than the least abundant peaks. This may also explain the lack of a significant peak associated with the molecular weight of transferrin, despite it being the main iron transport protein in the brain (Haacke et al., 2005). Transferrin is expressed at levels approximately one tenth of ferritin, with each molecule of transferrin binding just two iron ions compared with the capacity of ferritin to bind up to 4500 (Haacke et al., 2005). To fully understand iron-transferrin binding in different brain regions, transferrin saturation would need to be investigated directly (Hare et al., 2015b). This analysis detected no peak consistent with a low molecular weight, labile iron pool. The labile iron pool describes the chelatable and redox-active portion of cellular iron content, is transient in nature and is thought to be a direct contributor to oxidative stress (Kakhlon and Cabantchik, 2002). The existence of a labile iron pool in normal physiology is a contentious topic in iron biochemistry, though it is

metal ions with varying affinities forming part of the biochemical machinery that delivers iron to specific proteins (Foster et al., 2014). This chemical environment is particularly susceptible to mis-metallation events occurring ex vivo (Cvetkovic et al., 2010), and we used the closest possible approximation of cytosolic pH in the post-mortem human brain (Monoranu et al., 2009) and a buffer where metal complexes remain stable throughout separation (Ammann, 2002). Size exclusion chromatography separates proteins according to hydrodynamic volume rather than specific molecular weight, so predictions of protein mass are estimates only and cannot be used as confirmation of protein identity (Hagège et al., 2015). Confirmation of a specific protein would require a more traditional proteomics workflow. The molecular weight range of the column used here is 500-150,000 Da, and iron chromatograms are dominated by two peaks, one corresponding to a high molecular weight consistent with ferritin (peak 1). The second dominant peak (3) eluting with a Ve/Vo of 1.37 was not consistent with the transferrin standard, which falls into the mass range of peak 2, but was instead consistent with haemoglobin (Fig. 3b). Haemoglobin mRNA and protein expression have been confirmed in neurons, astrocytes and oligodendrocytes in rodents and humans using in situ hybridisation, microarray, quantitative reverse transcriptasepolymerase chain reaction and immunohistochemistry (Biagioli et al., 2009; Richter et al., 2009; Russo et al., 2013; Schelshorn et al., 2009). It has been suggested that brain cell haemoglobin may function to maintain oxygen homeostasis and protect against oxidative stress (Amri et al., 2017; Schelshorn et al., 2009; Shephard et al., 2014). Further, haemoglobin levels are altered in neurodegenerative diseases including Alzheimer’s disease, Parkinson’s disease and dementia with Lewy bodies (Ferrer et al., 2011; Shephard et al., 2014; Vanni et al., 2018). Despite this recent evidence that haemoglobin is expressed in brain cells, we cannot discount that at least some of the signal contributing to the haemoglobin-containing peak is due to blood contamination. Potential blood contamination is an inherent difficulty of investigating iron biology in post-mortem human tissue. Approaches that incorporate direct metal detection and traditional histological methods, such as those being developed by Hare et al., could provide ways around this in the future (Hare et al., 2014b). Using external calibration by injecting known amounts of iron as ferritin on-column, we found the amount of iron associated with each

Fig. 4. Integrated area under the curve analysis to determine the amount of iron associated with each peak from SEC-ICP-MS analysis. Integrated peak areas for peak 1 (a), peak 2 (b), peak 3 (c), peak 4 (d) and peak 5 (e). For each peak, a relative enrichment score was calculated to indicate the proportion of total soluble iron associated with each peak, compared to the overall mean. This is shown as a heat map (f). Mean ± SD is indicated for all data. Dotted lines indicate the overall mean and the shaded region indicates SD of the overall mean (one-way ANOVA with FDR correction; * p < 0.05, ** p < 0.01, **** p < 0.0001). 6

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

Table 5 Total iron content of peaks 1–5 per brain region (mean ± SD; n = 6). Region

Put Pons ACCx ECx OCx Thal Cbm SC Overall

Iron (ng) Peak 1

Peak 2

262 ± 115 41.4 ± 18.9 37.4 ± 10.4 71.2 ± 15.4 62.0 ± 11.8 65.1 ± 24.9 64.1 ± 7.43 20.4 ± 4.02 78.0 ± 82.5

6.59 2.84 1.67 3.35 3.03 2.26 2.01 1.12 2.86

± ± ± ± ± ± ± ± ±

2.24 0.32 0.46 1.06 0.44 0.40 0.18 0.15 1.80

Peak 3

Peak 4

36.6 ± 14.8 87.9 ± 71.3 19.7 ± 7.7 29.6 ± 13.3 58.9 ± 34.31 24.6 ± 11.9 31.8 ± 11.8 302.2 ± 15.6 39.9 ± 35.0

2.96 2.30 1.40 2.35 2.48 1.65 1.45 0.89 1.93

± ± ± ± ± ± ± ± ±

Peak 5 1.17 0.75 0.46 0.87 0.27 0.20 0.22 0.18 0.87

2.08 1.79 1.04 2.03 2.10 0.33 0.32 1.63 1.42

± ± ± ± ± ± ± ± ±

0.84 0.78 0.42 0.64 0.69 0.03 0.05 3.34 1.40

high energy output necessary for myelin synthesis that occurs rapidly during development (Todorich et al., 2009). Consistent with their high iron content, oligodendrocytes also contain high concentrations of transferrin (Connor et al., 1990), and continue to be metabolically active into human adulthood (Todorich et al., 2009). To directly compare iron content between cortical white and grey matter, we analysed grey matter tissue from parietal cortex as well as the immediate underlying white matter (Fig. 5a; Fig. S5). The measured iron content of white matter was consistent with previous reported measurements from other cortical white matter regions including temporal, frontal and occipital lobes (Langkammer et al., 2010). Total

generally considered that low molecular weight ligands, such as ascorbate and citric acid, effectively redox-silence free ferrous ions, mobilisation of which can catalyse formation of reactive oxygen species when in excess (Nilsson et al., 2002). Various approaches to quantifying the labile iron pool in cell culture, biofluids and tissue have been described, and is estimated to be in the μ-molar range (Breuer and Cabantchik, 2001; Gutteridge et al., 1981; Nilsson et al., 2002; Paffetti et al., 2006; Singh et al., 1990). This is equivalent to a peak apex of around 100 pg s−1, and in the non-diseased tissue analysed here, no apparent iron was associated with the column elution volume, indicating the absence of a detectable labile iron pool < 500 Da in mass. Consistent with this, recent evidence suggest very little iron-citrate exists in vivo (Dziuba et al., 2018). It should be noted, however, that while the cohort age and post-mortem interval were relatively consistent, redistribution from the labile iron pool to ferrorproteins with vacant high-affinity binding sites during the immediate post-mortem period cannot be excluded as a possible reason for the absence of a detectable labile iron pool. Similarly, an effect of the agonal period on iron redistribution cannot be excluded, however we do not have this clinical information for the tissue donors used in this study. Further, labile iron is prone to oxidation and precipitation under the oxidative conditions used so future studies under anerobic conditions are required to measure this pool. The use of fresh frozen tissue, as opposed to paraformaldehyde-fixed samples negates the likelihood of iron mobilisation and loss during preservation, but does not prevent it completely (Hare et al., 2014a). Investigating the labile iron pool in brain tissue would require a specific analysis, similar to that recently used in blood (Dziuba et al., 2018). It should be noted that iron in an intact, living brain is in a constant state of flux between cell types and ferroproteins (Moos et al., 2007). While total iron levels are unlikely to regionally redistribute in archived fresh frozen post-mortem brain (Hare et al., 2012), the nature of ex vivo analysis provides a static picture of brain iron metabolism at the time of sample collection. Eliminating potential sources of ex vivo perturbations, such as non-physiological pH or chemical fixatives provide greater confidence that an accurate representation of the native associations of iron and soluble ferroproteins is presented. However, autooxidation and exchange between proteins occurring from the point of sample collection to the time of analysis can never be totally discounted, particularly with no ‘true’ physiochemical standard against which they can be compared (Pfeiffer and Looker, 2017). 3.4. Iron and ferroprotein partitioning in white and grey matter MRI-based techniques can measure iron in cortical white and grey matter, though contrast as R2* relaxation from both iron and myelin produce some ambiguity in in vivo measures (Fukunaga et al., 2010). Imaging of post-mortem tissue has shown iron is higher in cortical white matter than grey matter (Hare et al., 2016a; Stüber et al., 2014). White matter is comprised of dense myelinated axons, and myelin-producing oligodendrocytes contain high concentrations of iron to support the

Fig. 5. Comparison between iron content in grey and white matter of parietal cortex. A schematic section of the parietal cortex is shown to illustrate where grey and white matter was sampled from (red box with grey matter coloured grey and white matter, white) (a). ICP-MS of total iron from grey matter and white matter of the parietal cortex (b) and the percentage distribution of iron between insoluble (c) and soluble (d) fractions (Student’s unpaired t-test; *** p < 0.001). 7

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

Iron chromatograms of soluble proteins were divided into the same five mass regions (Fig. 6; Fig. 2), with iron primarily associated with high molecular weight peak 1 (consistent with ferritin) in white matter (Fig. 7a). This increased amount corresponded with a greater proportion of total soluble iron being associated with this peak in white matter (Fig. 7b and j). Interestingly, there was no difference between grey and white matter for peak 2, which elutes at the same volume as transferrin, despite it being reported that white matter contains high concentrations of the protein (Fig. 7c,d) (Benkovic and Connor, 1993; Connor et al., 1990). To our knowledge, this is the first reported direct comparison of iron in parietal cortex grey matter and its underlying white matter. While the scale of difference between the two areas is consistent with studies on frontal cortex, it would be beneficial to perform a systematic analysis of grey/white matter differences throughout the brain, not only to understand the iron biology, but also to validate in vivo imaging using MRI-based techniques (Möller et al., 2019).

Fig. 6. Representative quantitative size exclusion chromatograms (n = 6; see Fig. S4, ESI) of soluble iron-binding proteins in grey matter and white matter of the parietal lobe.

4. Conclusions tissue iron content was significantly higher in white matter than grey matter (56.85 ± 6.54 and 38.55 ± 6.08 μg g−1; Fig. 5b). This amounted to a 47.5 % increase in iron in the white matter, consistent with differences observed in frontal cortex. Hallgren et al. determined iron was 45 % higher in white that grey matter using a chemical colorimetric assay, and Hare et al. reported 44 % higher iron in white matter using laser ablation-ICP-MS (Hallgren and Sourander, 1958; Hare et al., 2016a). Compartmentalisation of iron between the soluble and insoluble fractions did not differ between white and grey matter, although both had a slightly greater percentage of insoluble iron than any of the other brain regions analysed (Fig. 5c,d; Table 3).

The study presented here provides an overview of iron in different regions of the healthy aged human brain. While focussing solely on iron, the study sets the scene for the continued development of speciation methods to profile metalloproteins throughout the human brain in order to fully appreciate the role of metals in neurobiology. This includes the development of approaches that use multiple dimensions of separation prior to elemental analysis, to improve resolution (Bishop et al., 2018; Lothian et al., 2013). SEC-ICP-MS is a well-established technique that has been used in a variety of biological contexts including human serum, malignancies, mouse neural tissue and neural cell cultures (Boulyga et al., 2004; Gercken and Barnes, 1991; Hare

Fig. 7. Integrated area under the curve analysis to determine the amount of iron associated with each peak from SEC-ICP-MS analysis of grey and white matter of the parietal lobe. Integrated peak areas and percentage of total soluble iron for peak 1 (a and b), peak 2 (c and d), peak 3 (e and f), peak 4 (g and h) and peak 5 (I and j). (Student’s unpaired t-test; * p < 0.05, ** p < 0.01). 8

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

et al., 2013b; Kameo, 2014; Roberts et al., 2014). The data presented here represents the first quantitative profiling of iron-binding proteins in the human brain using SEC-ICP-MS, and shows that the distribution of iron in the brain is generally consistent with expression of ferritin. Further, iron is differentially partitioned to specific protein masses depending on the brain region. This study is the first of its kind in nondiseased human brain tissue, forming an important resource for the study of brain iron in the context of both health and disease. The observation of a significant amount of iron associated with a molecular weight consistent with haemoglobin is particularly interesting in light of recent studies demonstrating a biological role for expression of this protein within brain cells and is deserving of further investigation. Future integration of multiple separation methods and complementary analytical techniques, such as imaging and proteomics, will likely present further evidence of the complexity and diversity of ferroproteins in the human brain.

Oxidative stress and caspase activation. Front. Endocrinol. 8, 67. Andrasi, E., Farkas, E., Gawlik, D., Rosick, U., Bratter, P., 2000. Brain iron and zinc contents of german patients with alzheimer disease. J. Alzheimers Dis. 2, 17–26. Aquino, D., Bizzi, A., Grisoli, M., Garavaglia, B., Bruzzone, M.G., Nardocci, N., Savoiardo, M., Chiapparini, L., 2009. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. Radiology 252, 165–172. Bartzokis, G., Beckson, M., Hance, D.B., Marx, P., Foster, J.A., Marder, S.R., 1997. MR evaluation of age-related increase of brain iron in young adult and older normal males. Magn. Reson. Imaging 15, 29–35. Bauernfeind, A.L., Babbitt, C.C., 2017. The predictive nature of transcript expression levels on protein expression in adult human brain. BMC Genomics 18, 322. Bélanger, M., Allaman, I., Magistretti, P.J., 2011. Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation. Cell Metab. 14, 724–738. Benjamini, Y., Krieger, A.M., Yekutieli, D., 2006. Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491–507. Benkovic, S.A., Connor, J.R., 1993. Ferritin, transferrin, and iron in selected regions of the adult and aged rat brain. J. Comp. Neurol. 338, 97–113. Biagioli, M., Pinto, M., Cesselli, D., Zaninello, M., Lazarevic, D., Roncaglia, P., Simone, R., Vlachouli, C., Plessy, C., Bertin, N., 2009. Unexpected expression of α-and β-globin in mesencephalic dopaminergic neurons and glial cells. Proc. Natl. Acad. Sci. 106, 15454–15459. Bilgic, B., Pfefferbaum, A., Rohlfing, T., Sullivan, E.V., Adalsteinsson, E., 2012. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage 59, 2625–2635. Bishop, D.P., Hare, D.J., Clases, D., Doble, P.A., 2018. Applications of liquid chromatography-inductively coupled plasma-mass spectrometry in the biosciences: a tutorial review and recent developments. Trends Analyt. Chem. 104, 11–21. Boulyga, S.F., Loreti, V., Bettmer, J., Heumann, K.G., 2004. Application of SEC-ICP-MS for comparative analyses of metal-containing species in cancerous and healthy human thyroid samples. Anal. Bioanal. Chem. 380, 198–203. Breuer, W., Cabantchik, Z.I., 2001. A fluorescence-based one-step assay for serum nontransferrin-bound iron. Anal. Biochem. 299, 194–202. Buijs, M., Doan, N., van Rooden, S., Versluis, M.J., van Lew, B., Milles, J., van der Grond, J., van Buchem, M.A., 2017. In vivo assessment of iron content of the cerebral cortex in healthy aging using 7-Tesla T2*-weighted phase imaging. Neurobiol. Aging 53, 20–26. Chen, J.-H., Shahnavas, S., Singh, N., Ong, W.-Y., Walczyk, T., 2013. Stable iron isotope tracing reveals significant brain iron uptake in adult rats. Metallomics 5, 167–173. Chen, J.-H., Singh, N., Tay, H., Walczyk, T., 2014. Imbalance of iron influx and efflux causes brain iron accumulation over time in the healthy adult rat. Metallomics 6, 1417–1426. Connor, J.R., Menzies, S.L., St Martin, S.M., Mufson, E.J., 1990. Cellular distribution of transferrin, ferritin, and iron in normal and aged human brains. J. Neurosci. Res. 27, 595–611. Crichton, R.R., Dexter, D.T., Ward, R.J., 2011. Brain iron metabolism and its perturbation in neurological diseases. J. Neural Transm. 118, 301–314. Cvetkovic, A., Menon, A., Thorgersen, M.P., Scott, J.W., Poole 2nd, F.L., Jenney Jr, F.E., Lancaster, A.W., Praissman, J.L., Shanmukh, S., Vaccaro, B.J., Trauger, S.A., Kalisiak, E., Apon, J.V., Siuzdak, G., Yannone, S.M., Tainer, J.A., Adams, M.W.W., 2010. Microbial metalloproteomes are largely uncharacterized. Nature 466, 779–782. Dixon, S.J., Stockwell, B.R., 2014. The role of iron and reactive oxygen species in cell death. Nat. Chem. Biol. 10, 9–17. Dziuba, N., Hardy, J., Lindahl, P.A., 2018. Low-molecular-mass iron in healthy blood plasma is not predominately ferric citrate. Metallomics 10, 802–817. Ferrer, I., Gómez, A., Carmona, M., Huesa, G., Porta, S., Riera-Codina, M., Biagioli, M., Gustincich, S., Aso, E., 2011. Neuronal hemoglobin is reduced in Alzheimer’s disease, argyrophilic grain disease, Parkinson’s disease, and dementia with Lewy bodies. J. Alzheimers Dis. 23, 537–550. Foster, A.W., Osman, D., Robinson, N.J., 2014. Metal preferences and metallation. J. Biol. Chem. 289, 28095–28103. Fukunaga, M., Li, T.-Q., van Gelderen, P., de Zwart, J.A., Shmueli, K., Yao, B., Lee, J., Maric, D., Aronova, M.A., Zhang, G., Leapman, R.D., Schenck, J.F., Merkle, H., Duyn, J.H., 2010. Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast. P. Natl. Acad. Sci. U. S. A. 107, 3834–3839. Genoud, S., Roberts, B.R., Gunn, A.P., Halliday, G.M., Lewis, S.J.G., Ball, H.J., Hare, D.J., Double, K.L., 2017. Subcellular compartmentalisation of copper, iron, manganese, and zinc in the Parkinson’s disease brain. Metallomics 9, 1447–1455. Gercken, B., Barnes, R.M., 1991. Determination of lead and other trace element species in blood by size exclusion chromatography and inductively coupled plasma/mass spectrometry. Anal. Chem. 63, 283–287. Gong, N.J., Wong, C.S., Hui, E.S., Chan, C.C., Leung, L.M., 2015. Hemisphere, gender and age-related effects on iron deposition in deep gray matter revealed by quantitative susceptibility mapping. NMR Biomed. 28, 1267–1274. Gutteridge, J.M., Rowley, D.A., Halliwell, B., 1981. Superoxide-dependent formation of hydroxyl radicals in the presence of iron salts. Detection of ‘free’iron in biological systems by using bleomycin-dependent degradation of DNA. Biochem. J. 199, 263–265. Haacke, E.M., Cheng, N.Y., House, M.J., Liu, Q., Neelavalli, J., Ogg, R.J., Khan, A., Ayaz, M., Kirsch, W., Obenaus, A., 2005. Imaging iron stores in the brain using magnetic resonance imaging. Magn. Reson. Imaging 23, 1–25. Hagège, A., Huynh, T.N.S., Hébrant, M., 2015. Separative techniques for metalloproteomics require balance between separation and perturbation. TRAC-Trend. Anal. Chem. 64, 64–74. Hallgren, B., Sourander, P., 1958. The effect of age on the non‐haemin iron in the human brain. J. Neurochem. 3, 41–51. Hare, D., Ayton, S., Bush, A., Lei, P., 2013a. A delicate balance: iron metabolism and

Funding Funding for this project was provided by the National Health & Medical Research Council and Australian Research Council. EJM is supported by an NHMRC-ARC Dementia Research Development Fellowship (GNT1105791); DJH is the recipient of an NHMRC Career Development Fellowship – Industry (GNT1122981) in partnership with Agilent Technologies Australia. AIB is an NHMRC Senior Principal Research Fellow (GNT 1103703), and DJH and AIB are supported by an ARC Discovery Project (DP180101248). BRR receives an NHMRC Boosting Dementia Research Leadership Fellowship (GNT1138673) and NHMRC project grant (GNT1164692). This work was also supported by an ARC Linkage Grant (LP14010095) to DJH and BRR, also supported by Agilent Technologies. The Florey Institute of Neuroscience and Mental Health acknowledge the strong support from the Victorian Government and in particular the funding from the Operational Infrastructure Support Grant. Declaration of Competing Interest DJH and BRR receive material and research support from Agilent Technologies, including co-funded Australian Government grants listed in the Acknowledgements. BRR receives research support from eMSion inc. The other authors declare no competing interests. Acknowledgements The authors would like to thank Dr Katherine Ganio, Dr Christopher McDevitt and the Bio21 Mass Spectrometry Proteomics Facility; Mr Fred Fryer from Agilent Technologies for providing technical assistance; and the Victorian Brain Bank, tissue donors and their families. Appendix A. The Peer Review Overview and Supplementary data The Peer Review Overview and Supplementary data associated with this article can be found in the online version, at doi:https://doi.org/ 10.1016/j.pneurobio.2019.101744. References Allen Institute for Brain Science, 2013. Technical White Paper: Microarray Data Normalisation Allen Human Brain Atlas v.1.Technical White Paper: Microarray Data Normalisation Allen Human Brain Atlas v.1. Acosta-Cabronero, J., Betts, M.J., Cardenas-Blanco, A., Yang, S., Nestor, P.J., 2016. In vivo MRI mapping of brain Iron deposition across the adult lifespan. J. Neurosci. 36, 364–374. Ammann, A.A., 2002. Determination of strong binding chelators and their metal complexes by anion-exchange chromatography and inductively coupled plasma mass spectrometry. J. Chromatogr. A 947, 205–216. Amri, F., Ghouili, I., Tonon, M.-C., Amri, M., Masmoudi-Kouki, O., 2017. Hemoglobinimproved Protection in cultured cerebral cortical astroglial cells: inhibition of

9

Progress in Neurobiology xxx (xxxx) xxxx

E.J. McAllum, et al.

Life Sci. 72, 709–727. Möller, H.E., Bossoni, L., Connor, J.R., Crichton, R.R., Does, M.D., Ward, R.J., Zecca, L., Zucca, F.A., Ronen, I.J., 2019. Iron, myelin, and the brain: neuroimaging meets neurobiology. Trends Neurosci. Monoranu, C.M., Apfelbacher, M., Grünblatt, E., Puppe, B., Alafuzoff, I., Ferrer, I., Al‐Saraj, S., Keyvani, K., Schmitt, A., Falkai, P., Schittenhelm, J., Halliday, G., Kril, J., Harper, C., McLean, C., Riederer, P., Roggendorf, W., 2009. pH measurement as quality control on human post mortem brain tissue: a study of the BrainNet Europe consortium. Neuropath. Appl. Neuro. 35, 329–337. Moos, T., Rosengren Nielsen, T., Skjorringe, T., Morgan, E.H., 2007. Iron trafficking inside the brain. J. Neurochem. 103, 1730–1740. Nilsson, U.A., Bassen, M., Sävman, K., Kjellmer, I., 2002. A simple and rapid method for the determination of" free" iron in biological fluids. Free Radic. Res. Commun. 36, 677–684. Paffetti, P., Perrone, S., Longini, M., Ferrari, A., Tanganelli, D., Marzocchi, B., Buonocore, G., 2006. Non-protein-bound iron detection in small samples of biological fluids and tissues. Biol. Trace Elem. Res. 112, 221–232. Paul, B., Hare, D.J., Bishop, D.P., Paton, C., Nguyen, V., Cole, N., Niedwiecki, M.M., Andreozzi, E., Vais, A., Billings, J.L., Bray, L., Bush, A.I., McColl, G., Roberts, B.R., Adlard, P.A., Finkelstein, D.I., Hellstrom, J., Hergt, J.M., Woodhead, J.D., Doble, P.A., 2015. Visualising mouse neuroanatomy and function by metal distribution using laser ablation-inductively coupled plasma-mass spectrometry imaging. Chem. Sci. 6, 5383–5393. Pfeiffer, C.M., Looker, A.C., 2017. Laboratory methodologies for indicators of iron status: strengths, limitations, and analytical challenges. Am. J. Clin. Nutr. 106 (Suppl), 1606S–1614S. Pirpamer, L., Hofer, E., Gesierich, B., De Guio, F., Freudenberger, P., Seiler, S., Duering, M., Jouvent, E., Duchesnay, E., Dichgans, M., Ropele, S., Schmidt, R., 2016. Determinants of iron accumulation in the normal aging brain. Neurobiol. Aging 43, 149–155. Rajan, M.T., Jagannatha Rao, K.S., Mamatha, B.M., Rao, R.V., Shanmugavelu, P., Menon, R.B., Pavithran, M.V., 1997. Quantification of trace elements in normal human brain by inductively coupled plasma atomic emission spectrometry. J. Neurol. Sci. 146, 153–166. Ramos, P., Santos, A., Pinto, N.R., Mendes, R., Magalhães, T., Almeida, A., 2014. Iron levels in the human brain: a post-mortem study of anatomical region differences and age-related changes. J. Trace Elem. Med. Biol. 28, 13–17. Richter, F., Meurers, B.H., Zhu, C., Medvedeva, V.P., Chesselet, M.F., 2009. Neurons express hemoglobin α‐and β‐chains in rat and human brains. J. Comp. Neurol. 515, 538–547. Roberts, B.R., Doecke, J.D., Rembach, A., Yévenes, F.L., Fowler, C.J., McLean, C.A., Lind, M., Volitakis, I., Masters, C.L., Bush, A.I., Hare, D.J., research group, A., 2016. Rubidium and potassium levels are altered in Alzheimer’s disease brain and blood but not in cerebrospinal fluid. Acta Neuropathol. Commun. 4, 119. Roberts, B.R., Lim, N.K.H., McAllum, E.J., Donnelly, P.S., Hare, D.J., Doble, P.A., Turner, B.J., Price, K.A., Chun Lim, S., Paterson, B.M., Hickey, J.L., Rhoads, T.W., Williams, J.R., Kanninen, K.M., Hung, L.W., Liddell, J.R., Grubman, A., Monty, J.F., Llanos, R.M., Kramer, D.R., Mercer, J.F.B., Bush, A.I., Masters, C.L., Duce, J.A., Li, Q.X., Beckman, J.S., Barnham, K.J., White, A.R., Crouch, P.J., 2014. Oral treatment with CuII(atsm) increases mutant SOD1 in vivo but protects motor neurons and improves the phenotype of a transgenic mouse model of amyotrophic lateral sclerosis. J. Neurosci. 34, 8021–8031. Russo, R., Zucchelli, S., Codrich, M., Marcuzzi, F., Verde, C., Gustincich, S., 2013. Hemoglobin is present as a canonical α2β2 tetramer in dopaminergic neurons. Biochim. Biophys. Acta 1834, 1939–1943. Schelshorn, D.W., Schneider, A., Kuschinsky, W., Weber, D., Krüger, C., Dittgen, T., Bürgers, H.F., Sabouri, F., Gassler, N., Bach, A., 2009. Expression of hemoglobin in rodent neurons. J. Cereb. Blood Flow Metab. 29, 585–595. Shephard, F., Greville-Heygate, O., Marsh, O., Anderson, S., Chakrabarti, L.J.M., 2014. A mitochondrial location for haemoglobins—dynamic distribution in ageing and Parkinson’s disease. Mitochondrion 14, 64–72. Singh, S., Hider, R.C., Porter, J.B., 1990. A direct method for quantification of nontransferrin-bound iron. Anal. Biochem. 186, 320–323. Stockwell, B.R., Angeli, J., Bayir, H., Bush, A.I., Conrad, M., Dixon, S.J., Fulda, S., Gascón, S., Hatzios, S.K., Kagan, V.E., Noel, K., Jiang, X., Linkermann, A., Murphy, M.E., Overholtzer, M., Oyagi, A., Pagnussat, G.C., Park, J., Ran, Q., Rosenfeld, C.S., Salnikow, K., Tang, D., Torti, F.M., Torti, S.V., Toyokuni, S., Woerpel, K.A., Zhang, D.D., 2017. Ferroptosis: a regulated cell death Nexus linking metabolism, redox biology, and disease. Cell 171, 273–285. Stüber, C., Morawski, M., Schäfer, A., Labadie, C., Wähnert, M., Leuze, C., Streicher, M., Barapatre, N., Reimann, K., Geyer, S., Spemann, D., Turner, R., 2014. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. Neuroimage 93, 95–106. Todorich, B., Pasquini, J.M., Garcia, C.I., Paez, P.M., Connor, J.R., 2009. Oligodendrocytes and myelination: the role of iron. Glia 57, 467–478. Vanni, S., Zattoni, M., Moda, F., Giaccone, G., Tagliavini, F., Haïk, S., Deslys, J.-P., Zanusso, G., Ironside, J.W., Carmona, M., 2018. Hemoglobin mRNA changes in the frontal cortex of patients with neurodegenerative diseases. Front. Neurosci. 12, 8. Ward, R.J., Zucca, F.A., Duyn, J.H., Crichton, R.R., Zecca, L., 2014. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol. 13, 1045–1060.

diseases of the brain. Front. Aging Neurosci. 5, 34. Hare, D.J., Arora, M., Jenkins, N.L., Finkelstein, D.I., Doble, P.A., Bush, A.I., 2015a. Is early-life iron exposure critical in neurodegeneration? Nat. Rev. Neurol. 11, 536–544. Hare, D.J., Cardoso, B., Szymlek-Gay, E.A., Biggs, B.-A., 2018. Neurological effects of iron supplementation in infancy: finding the balance between health and harm in ironreplete infants. Lancet Child Adolesc. Health 2, 144–156. Hare, D.J., Doecke, J.D., Faux, N.G., Rembach, A., Volitakis, I., Fowler, C.J., Grimm, R., Doble, P.A., Cherny, R.A., Masters, C.L., 2015b. Decreased plasma iron in Alzheimer’s disease is due to transferrin desaturation. ACS Chem. Neurosci. 6, 398–402. Hare, D.J., George, J.L., Bray, L., Volitakis, I., Vais, A., Ryan, T.M., Cherny, R.A., Bush, A.I., Masters, C.L., Adlard, P.A., Doble, P.A., Finkelstein, D.I., 2014a. The effect of paraformaldehyde fixation and sucrose cryoprotection on metal concentration in murine neurological tissue. J. Anal. At. Spectrom. 29, 565–570. Hare, D.J., Gerlach, M., Riederer, P., 2012. Considerations for measuring iron in postmortem tissue of Parkinson’s disease patients. J. Neural Transm. 119, 1515–1521. Hare, D.J., Grubman, A., Ryan, T.M., Lothian, A., Liddell, J.R., Grimm, R., Matsuda, T., Doble, P.A., Cherny, R.A., Bush, A.I., 2013b. Profiling the iron, copper and zinc content in primary neuron and astrocyte cultures by rapid online quantitative size exclusion chromatography-inductively coupled plasma-mass spectrometry. Metallomics 5, 1656–1662. Hare, D.J., Lei, P., Ayton, S., Roberts, B.R., Grimm, R., George, J.L., Bishop, D.P., Beavis, A.D., Donovan, S.J., McColl, G., Volitakis, I., Masters, C.L., Adlard, P.A., Cherny, R.A., Bush, A.I., Finkelstein, D.I., Doble, P.A., 2014b. An iron–dopamine index predicts risk of parkinsonian neurodegeneration in the substantia nigra pars compacta. Chem. Sci. 5, 2160–2169. Hare, D.J., Raven, E.P., Roberts, B.R., Bogeski, M., Portbury, S.D., McLean, C.A., Masters, C.L., Connor, J.R., Bush, A.I., Crouch, P.J., Doble, P.A., 2016a. Laser ablation-inductively coupled plasma-mass spectrometry imaging of white and gray matter iron distribution in Alzheimer’s disease frontal cortex. Neuroimage 137, 124–131. Hare, D.J., Roberts, B.R., McColl, G.J., 2016b. Profiling changes to natively-bound metals during Caenorhabditis elegans development. RSC Adv. 6, 113689–113693. Hawrylycz, M.J., Lein, E.S., Guillozet-Bongaarts, A.L., Shen, E.H., Ng, L., Miller, J.A., van de Lagemaat, L.N., Smith, K.A., Ebbert, A., Riley, Z.L., Abajian, C., Beckmann, C.F., Bernard, A., Bertagnolli, D., Boe, A.F., Cartagena, P.M., Chakravarty, M.M., Chapin, M., Chong, J., Dalley, R.A., Daly, B., Dang, C., Datta, S., Dee, N., Dolbeare, T.A., Faber, V., Feng, D., Fowler, D.R., Goldy, J., Gregor, B.W., Haradon, Z., Haynor, D.R., Hohmann, J.G., Horvath, S., Howard, R.E., Jeromin, A., Jochim, J.M., Kinnunen, M., Lau, C., Lazarz, E.T., Lee, C., Lemon, T.A., Li, L., Li, Y., Morris, J.A., Overly, C.C., Parker, P.D., Parry, S.E., Reding, M., Royall, J.J., Schulkin, J., Sequeira, P., Slaughterbeck, C.R., Smith, S.C., Sodt, A.J., Sunkin, S.M., Swanson, B.E., Vawter, M.P., Williams, D., Wohnoutka, P., Zielke, R.H., Geschwind, D.H., Hof, P.R., Smith, S.M., Koch, C., Grant, S.G.N., Jones, A.R., 2012. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399. Ince, P.G., Shaw, P.J., Candy, J.M., Mantle, D., Tandon, L., Ehmann, W.D., Markesbery, W.R., 1994. Iron, selenium and glutathione peroxidase activity are elevated in sporadic motor neuron disease. Neurosci. Lett. 182, 87–90. Kakhlon, O., Cabantchik, Z.I., 2002. The labile iron pool: characterization, measurement, and participation in cellular processes. Free. Radical. Bio. Med. 33, 1037–1046. Kameo, S., 2014. Simple analysis method for Metallothionein-1, -2 and -3 in the brain by one-step size-exclusion column HPLC on-line coupling with inductively coupled plasma mass spectrometry. J. Anal. Bioanal. Techniques 5, 6. Kühn, L.C., 2015. Iron regulatory proteins and their role in controlling iron metabolism. Metallomics 7, 232–243. Langkammer, C., Krebs, N., Goessler, W., Scheurer, E., Ebner, F., Yen, K., Fazekas, F., Ropele, S., 2010. Quantitative MR imaging of brain iron: a postmortem validation study. Radiology 257, 455–462. Langkammer, C., Schweser, F., Krebs, N., Deistung, A., Goessler, W., Scheurer, E., Sommer, K., Reishofer, G., Yen, K., Fazekas, F., Ropele, S., Reichenbach, J.R., 2012. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 62, 1593–1599. Levi, S., Rovida, E., 2009. The role of iron in mitochondrial function. BBA. Gen. Subjects 1790, 629–636. Li, W., Wu, B., Batrachenko, A., Bancroft-Wu, V., Morey, R.A., Shashi, V., Langkammer, C., De Bellis, M.D., Ropele, S., Song, A.W., Liu, C., 2014. Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan. Hum. Brain Mapp. 35, 2698–2713. Lill, R., Hoffmann, B., Molik, S., Pierik, A.J., Rietzschel, N., Stehling, O., Uzarska, M.A., Webert, H., Wilbrecht, C., Mühlenhoff, U., 2012. The role of mitochondria in cellular iron–sulfur protein biogenesis and iron metabolism. BBA. Mol. Cell Res. 1823, 1491–1508. Lothian, A., Hare, D.J., Grimm, R., Ryan, T.M., Masters, C.L., Roberts, B.R., 2013. Metalloproteomics: principles, challenges and applications to neurodegeneration. Front. Aging Neurosci. 5, 35. Lothian, A., Roberts, B.R., 2016. Standards for quantitative metalloproteomic analysis using size exclusion ICP-MS. J. Vis. Exp. e53737. Maher, B.A., Ahmed, I.A.M., Karloukovski, V., MacLaren, D.A., Foulds, P.G., Allsop, D., Mann, D.M.A., Torres-Jardón, R., Calderon-Garciduenas, L., 2016. Magnetite pollution nanoparticles in the human brain. P. Natl. Acad. Sci. U. S. A. 113, 10797–10801. McCarthy, R.C., Kosman, D.J., 2015. Iron transport across the blood–brain barrier: development, neurovascular regulation and cerebral amyloid angiopathy. Cell. Mol.

10