# Isotopic Evidence for Disrupted Copper Metabolism in Amyotrophic Lateral Sclerosis

Lucie Sauzéat, Emilien Bernard, Armand Perret-Liaudet, Isabelle Quadrio, Alain Vighetto, Pierre Krolak-Salmon, Emmanuel Broussolle, Pascal Leblanc, Vincent Balter

<figure><img src="image_1.png" /></figure>

## To cite this version:

Lucie Sauzéat, Emilien Bernard, Armand Perret-Liaudet, Isabelle Quadrio, Alain Vighetto, et al. "Isotopic Evidence for Disrupted Copper Metabolism in Amyotrophic Lateral Sclerosis." iScience, 2018, 6, pp.264-271. 10.1016/j.isci.2018.07.023. hal-02323863

HAL Id: hal-02323863

https://hal.science/hal-02323863

Submitted on 17 Mar 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Article

# Isotopic Evidence for Disrupted Copper Metabolism in Amyotrophic Lateral Sclerosis

<figure><img src="image_3.png" /></figure>

Lucie Sauzéat,
Emilien Bernard,
Armand Perret-Liaudet, ...,
Emmanuel Broussolle, Pascal Leblanc, Vincent Balter

vincent.balter@ens-lyon.fr

## HIGHLIGHTS

Redox-active metals are implicated in ALS through oxidative stress

Concentrations of these metals in CSFs of patients with ALS are non-specific

Copper stable isotope composition in CSFs of patients with ALS are specific

Isotopic balance between CSFs and brain is probably the mechanism
## Article

# Isotopic Evidence for Disrupted Copper Metabolism in Amyotrophic Lateral Sclerosis

Lucie Sauzéat,<fnref n="1" /> Emilien Bernard,<fnref n="2" /> Armand Perret-Liaudet,<fnref n="3" />,<fnref n="4" /> Isabelle Quadrio,<fnref n="3" />,<fnref n="4" /> Alain Vighetto,<fnref n="5" />,<fnref n="6" />,<fnref n="9" /> Pierre Krolak-Salmon,<fnref n="6" /> Emmanuel Broussolle,<fnref n="7" /> Pascal Leblanc,<fnref n="8" />,<fnref n="10" /> and Vincent Balter<fnref n="1" />,<fnref n="10" />,<fnref n="11" />,*

### SUMMARY

Redox-active metals are thought to be implicated in neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). To address this point, we measured the concentrations of 12 elements and, for the first time, the stable isotope compositions of copper (redox-active) and zinc (redox-inactive) in human cerebrospinal fluids of 31 patients with ALS, 11 age-matched controls (CTRL), and 14 patients with Alzheimer disease. We first show that metal concentrations weakly discriminate patients with ALS from the two other groups. We then report that zinc isotopic compositions are similar in the three groups, but that patients with ALS have significantly 65 copper-enriched isotopic compositions relative to CTRL and patients with AD. This result unambiguously demonstrates that copper is implicated in ALS. We suggest that this copper isotopic signature may result from abnormal protein aggregation in the brain parenchyma, and propose that isotopic analysis is a potential tool that may help unraveling the molecular mechanisms at work in ALS.

### INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is one of the most harmful neurodegenerative diseases characterized by the progressive deterioration of the upper and lower motor neurons, leading to muscle weakness and atrophy, severe paralysis, and finally death typically within 3–5 years after symptom onset (Narasimhan, 2015). Currently, although genetic factors have been identified to play a role in familial ALS, which represents only 10% of the reported cases (Taylor et al., 2016), the leading mechanisms accounting for motor neuron degeneration in sporadic ALS (i.e., 90% of ALS cases) remains unknown. In addition, because of the complexity of the pathology, reliable markers (Savage, 2017) and consequently efficient treatments are still missing (Scott, 2017). Recently, biomarkers in cerebrospinal fluid (CSF) including concentrations of neurofilament light chain (Lu et al., 2015) and chemokines (Lind et al., 2016) have been identified. However, concentrations of neurofilament light chain in CSF have also been reported to be elevated in other neurological disorders such as Alzheimer disease (AD) (Zetterberg et al., 2016) and Parkinson disease (Bäckström et al., 2015). Robust and specific ALS markers are still scarce.

As for other neurodegenerative diseases, the progression of ALS is associated with the production of free radicals such as reactive oxygen species (Barnham and Bush, 2014), the production of which can be favored by the presence of free, redox-active metals like copper (Cu) (Barnham and Bush, 2014). Conversely, these metals can also be catalytic cofactors of several enzymes, like Cu/Zn superoxide dismutase (SOD1), involved in free radical detoxification by catalyzing highly toxic products (i.e., superoxide) to less dangerous species such as dioxygen and hydrogen peroxide (Valentine et al., 2005). Redox-active metals thus play a pivotal role in both the pro- and anti-oxidant homeostasis, and it is expected that dysregulation affecting these pathways will be characterized by significant elemental impairment. In mice models of familial ALS caused by mutations in SOD1, Cu has been observed to accumulate in the spinal cord (Tokuda et al., 2013). Metal dysregulations have also been observed for other redox-active metals in CSFs of patients with ALS including iron and manganese (Barnham and Bush, 2014; Roos et al., 2013). Zinc (Zn), which is not redox active, but binds to SOD1, has also been reported to be dysregulated in CSFs of patients with ALS (Hozumi et al., 2011). Comparing published results generally leads to equivocal conclusions about the usefulness of metal concentrations in CSFs to diagnose and study ALS (Figure S1).

Contrary to concentrations, stable isotope compositions may offer a more comprehensive view on biological reactions and neurological disease progression. This is due to two main reasons. First, stable isotope

<footnote n="1">¹Université de Lyon, ENS de Lyon, CNRS, LGL-TPE, 69007 Lyon, France</footnote>

<footnote n="2">²Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Centre de Ressources et de Compétence SLA de Lyon, Service de Neurologie C, Bron, France</footnote>

<footnote n="3">³Université de Lyon, CNRS UMR5292, INSERM U1028, BioRan, Lyon, France</footnote>

<footnote n="4">⁴Hospices Civils de Lyon, Neurobiology Laboratory, Biochemistry and Molecular Biology Department, Lyon, France</footnote>

<footnote n="5">⁵Service Neurocognition et Neuroophtalmologie, Hôpital Neurologique, 59 Boulevard Pinel, 69677 Bron Cedex, France</footnote>

<footnote n="6">⁶Centre Mémoire Ressources Recherche de Lyon, Hospices Civils de Lyon, Hôpital des Charpennes, Villeurbanne, France</footnote>

<footnote n="7">⁷Université de Lyon, Faculté de Médecine Lyon Sud Charles Mérieux, Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Lyon, France</footnote>

<footnote n="8">⁸Institut NeuroMyoGène, CNRS UMR5310, INSERM U1217, Faculté de Médecine Rockefeller, Université Claude Bernard Lyon I, 8 Avenue Rockefeller, 69373 Lyon Cedex 08, France</footnote>

<footnote n="9">⁹Université Lyon 1, Hospices Civils de Lyon, Centre de Recherche en Neurosciences de Lyon, équipe IMPACT, Lyon, France</footnote>

<footnote n="10">¹⁰These authors contributed equally</footnote>

<footnote n="11">¹¹Lead Contact</footnote>

<footnote>*Correspondence: vincent.balter@ens-lyon.fr</footnote>

<footnote>https://doi.org/10.1016/j.isci. 2018.07.023</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 1. Principal Component Analysis of the Results**</figcaption></figure>

The PCA allows the distinction of patients with ALS (green points) from CTRL subjects (yellow points) and subjects with AD (pink points). Concentrations and isotopic compositions are normalized to their SD. The two principal components (PC1 and PC2) are represented and explain ~50% of the total variance in chemical composition. For each component, black straight lines show the loading factors (i.e., the weight of each variable on the principal components). Circles and squares points stand for male and female subjects, respectively. No significant variation is observed between the male and the female subjects within the ALS group.

compositions are measured with a precision of about two orders of magnitude better than concentrations. Sec-
ond, the intensity of enrichment or depletion of a metal in a given compartment is hardly predictable, whereas
isotopic fractionation, i.e., the variation of the natural abundances of stable isotope ratios between coexisting
compartments, can usually be quantitatively predicted by ab initio calculations (Albarède et al., 2016). In blood,
Cu isotope compositions (δ<sup>65</sup>Cu) exhibit, for example, significant differences between men and women (Jaouen
et al., 2012) as well as during aging (Jaouen et al., 2013). In addition, the δ<sup>65</sup>Cu value varies in pathological con-
ditions such as cancer (Balter et al., 2015), Wilson disease, or liver cirrhosis (Costas-Rodriguez et al., 2016).
Regarding neurodegenerative disorders, although Cu isotope compositions seem to be insensitive to the
SOD1<sup>G93A</sup> mutation in the brain of mouse model (Enge et al., 2017), they are highly responsive in models of prion
protein knockout (PrP-KO) mice (Büchl et al., 2008; Miller et al., 2016). Zinc isotope compositions (δ<sup>66</sup>Zn) were also
scrutinized in mouse models of neurodegenerative diseases. In PrP-KO mice, the brain δ<sup>66</sup>Zn values were heavier
than in wild-type controls (Büchl et al., 2008), a pattern also observed in mutant mouse (APPswe/PSEN1dE9)
developing Alzheimer-like disease (Moynier et al., 2017).

In the present work, to further explore the potential of elemental concentrations and isotopic compositions for clinical studies of ALS, we measured the concentration of 12 major and trace elements and the Cu and Zn isotopic compositions in the CSFs of patients with ALS (n = 31), age-matched controls (CTRL, n = 11), and patients with AD (n = 14).

RESULTS

Major and trace element concentrations as well as Cu and Zn isotopic compositions measured in CSFs of
subjects with ALS, CTRL, and subjects with AD are all reported in Data S1.

ALS-Related Dysregulations of Major and Trace Element Concentrations

To evaluate the whole pattern of variations, we used a principal component analysis (PCA) (Figure 1). The
most noticeable feature is the chemical distinction between CSFs of patients with ALS and CTRL as
<figure><img src="image_5.png" /><figcaption>**Figure 2. Copper (δ⁶⁵Cu) and Zinc (δ⁶⁶Zn) Isotopic Variability in Cerebrospinal Fluids**</figcaption></figure>

(A and B) (A) Copper (δ⁶⁵Cu) and (B) zinc (δ⁶⁶Zn) isotopic compositions measured in CSFs of CTRL (yellow dots), patients with AD (red dots), and patients with ALS (green dots). Circle and square points are for male and female subjects, respectively. For each boxplot, the central mark is the median; the edges of the box are the first (i.e., 25th percentiles) and the third quartiles (i.e., 75th percentiles), respectively; and the points outside the boxes extend to the most extreme data points (i.e., not considered outliers). Significance level (i.e., p value) was determined using non-parametric “two-sided,” Wilcoxon-Mann-Whitney U tests. **p** < 0.005, *p* < 0.05.

illustrated by the y principal component axis (Figure 1). Subjects with ALS have, for example, significant lower Fe concentrations (Wilcoxon-Mann-Whitney, p value = 2.426 × 10⁻³) and Mn concentrations (Wilcoxon-Mann-Whitney, p value = 2.206 × 10⁻³) (Figure 1) but higher Rb concentrations (Wilcoxon-Mann-Whitney, p value = 1.003 × 10⁻²) (Figure 1 and Data S1). Conversely, Zn concentrations (Wilcoxon-Mann-Whitney, p value = 0.0715) and Cu concentrations (Wilcoxon-Mann-Whitney, p value = 0.1610) do not exhibit any systematics (Figure S1), an observation that also holds for other trace and major elements such as Sr, S, P, or Na (Data S1). Although these results are in line with a couple of studies, e.g., Ostachowicz et al. (2006), Kanias and Kapaki (1997), and Kapaki et al. (1997), opposite or equivocal results have also been reported (Figure S1 and references therein).

## ALS-Related Dysregulations of Cu-Zn Isotopic Compositions

Regarding the Cu and Zn isotopic compositions, Cu isotopic ratios in ALS (median δ⁶⁵CuALS = 0.01‰, 25th percentile = -0.10‰, 75th percentile = 0.18‰) are significantly heavier (Wilcoxon-Mann-Whitney, p value = 0.0146) than CTRL (median δ⁶⁵CuCTRL = -0.20‰, 25th percentile = -0.26‰, 75th percentile = -0.02‰) (Figure 2A). By contrast, no significant difference is observed between Zn isotopic compositions of ALS (median δ⁶⁶ZnALS = 0.15‰, 25th percentile = 0.07‰, 75th percentile = 0.20‰) and CTRL (median δ⁶⁶ZnCTRL = 0.19‰, 25th percentile = 0.15‰, 75th percentile = 0.23‰) (Wilcoxon-Mann-Whitney, p value = 0.1764) (Figure 2B).
Similar observations can be made by comparing the δ⁶⁵Cu value of patients with ALS and AD (median δ⁶⁵Cu<sub>AD</sub> = -0.06‰, 25th percentile = -0.23‰; 75th percentile = 0.01‰), patients with AD being chemically indistinguishable from CTRL subjects (Wilcoxon-Mann-Whitney, p value = 0.6638) (Figure 2A). However, subjects with AD tend to have a slightly lower δ⁶⁶Zn value (median δ⁶⁶Zn = 0.10‰, 25th percentile = -0.01‰; 75th percentile = 0.15‰) than CTRL subjects (Wilcoxon-Mann-Whitney, p value = 0.0426).

Effect of Biological and Clinical Parameters on Elemental Concentrations and Isotopic Compositions

Biological (gender and age at sample collection) and clinical (localization of first symptoms, Awaji criteria, ALSFRS-R score, delay between sampling date and first visible symptoms) parameters are given in Table S1. Focusing on the ALS and CTRL groups, neither clinical parameters nor biological parameters show significant association with elemental concentrations and Cu-Zn isotopic compositions. An illustration is the similar δ⁶⁵Cu values observed between male and female subjects in the ALS group (median δ⁶⁵Cu<sub>ALS-female</sub> = -0.02‰, 25th percentile = -0.16‰, 75th percentile = 0.07‰; median δ⁶⁵Cu<sub>ALS-male</sub> = +0.01‰, 25th percentile = -0.10‰, 75th percentile = 0.20‰) (Wilcoxon-Mann-Whitney, p value = 0.2721) as well as between patients with ALS characterized by different site at onset (i.e., lower limbs, ML; upper limbs, MS; bulbar, bulb) (median δ⁶⁵Cu<sub>ALS-MI</sub> = 0.08‰, 25th percentile = 0.01‰, 75th percentile = 0.14‰; median δ⁶⁵Cu<sub>ALS-MS</sub> = -0.06‰, 25th percentile = -0.15‰, 75th percentile = 0.07‰; and median δ⁶⁵Cu<sub>ALS-bulb</sub> = -0.03‰, 25th percentile = -0.13‰, 75th percentile = 0.18‰) (Table S1).

DISCUSSION

Disruption of the homeostasis of metals such as Cu and Zn is a key feature of neurodegenerative diseases, including ALS, leading to multiple abnormalities in the CSF, brain, and spinal cord, where they are inappropriately redistributed (Barnham and Bush, 2014). The changes in the distribution of metals seem to be controlled by their sequestration within misfolded protein aggregates as shown, for example, in amyloid-β (Aβ) plaques within the AD brain (Pithadia and Lim, 2012). In ALS, misfolded protein aggregates including TAR DNA-binding protein 43 (TDP-43), fused in sarcoma (FUS) (Pokrishevsky et al., 2012), and SOD1 (Valentine et al., 2005) have been reported to be present in the brain. Described as the main pathological hallmark of ALS, these proteins, like Aβ for AD, may concentrate metals. The local accumulation is likely to deplete the surrounding environment, ultimately scavenging the CSF burden, leading to decreased CSF concentrations. However, no change in elemental concentrations in patients with ALS, or AD, compared with CTRL, is observed in the present study, and more generally in the literature (Figure S1 and reference therein). The absence of any systematics may result from the natural wide range of metal concentrations in human fluids (e.g., Iyengar and Wolttiez, 1988). A more robust pattern of variations of metal concentrations is probably specific to each patient and would be more accessible through analysis of co-horts. The absence of any systematics may also result from exogenous contamination. Collection, storage, and preparation of CSFs can be sensitive to trace element contamination (Garçon et al., 2017). CSFs are made of 99% water and have very low metal contents; hence the risk of contamination is high. In this study, we ensure the absence of external contamination by quantifying the chemical content that may be released by sampling procedures and storage in tubes as well as dropper-type pipettes used to collect and conserve CSF. A complete description of the procedure is given in Transparent Methods. We also took care of low-acid blanks before and after each column chemistry, and we only used vinyl gloves as suggested by Garçon et al. (2017).

One advantage of using isotopic composition over concentration for a given element is that isotopic
fractionation is not dependent on the amount of the element. Isotopic compositions are thus theoreti-
cally more reliable biomarkers than concentrations. Here, we found that CSFs of patients with ALS
have significantly heavier Cu isotopic composition compared with age-matched CTRL (Wilcoxon-
Mann-Whitney, p value = 0.0146) and also tend to be different from those of patients with AD by having
slightly heavier δ⁶⁵Cu (Wilcoxon-Mann-Whitney, p value = 0.0510) (Figure 2A), whereas again no distinc-
tion is observed for Cu concentration (Figure S1). Using receiver operating characteristic (ROC) analysis,
we determined a δ⁶⁵Cu cutoff value of -0.05‰ with a sensitivity and a specificity of 73% and 65% respec-
tively, and an accuracy of 76% (Figure S2). This is a modest score compared, for instance, with the results
obtained by Pasinetti et al. (2006), with a sensitivity, specificity, and accuracy of 91%, 97%, and 95%,
respectively, using three protein concentrations in CSF. Noteworthy is the specificity of the CSF δ⁶⁵Cu
values of ALS as illustrated by the absence of significant δ⁶⁵Cu difference between subjects with AD
and CTRL subjects (Wilcoxon-Mann-Whitney, p value = 0.6638) (Figure 2A). Further additional studies
and evidence of the mechanisms at work are undoubtedly needed to improve the present results achieved by the ROC test.

Recently, Moynier et al. (2017) showed that brains of mice with AD have higher δ⁶⁶Zn value than wild-type mice and suggested that this isotopic enrichment may result from the formation of Aβ plaques in the brain parenchyma binding preferentially heavier Zn isotopes. Following this assumption, if the binding of a metal in the protein aggregates of the brain is associated with an isotopic fractionation, this must be balanced in the CSF, the brain and CSF being two complementary reservoirs in a closed system, the CNS. At first glance, our results on patients with AD support the hypothesis of an isotopic equilibrium between brain and CSF, because patients with AD exhibit CSF depleted in heavier Zn isotopes relative to CTRL subjects. However, this requires further analysis of CSF of patients with AD and CTRL subjects, which is beyond the scope of the present work. Focusing on ALS, a similar reasoning can be proposed with aggregation of SOD1 in the brain associated with a Cu isotopic fractionation. Direct evidence could be obtained by measuring the Cu isotope composition of aggregates, but here we can test if the assumption of an isotopic equilibrium between brain and CSF resists mass conservation laws. The concentration of Cu in brain varies between 3 and 5 µg/g (Scheiber et al., 2014). The brain volume ranges from 1,300 to 1,500 mL leading to a brain Cu burden (M<sub>B</sub>) of 3.9–7.5 mg. Regarding CSF, the concentration of Cu ranges from 0.02 to 0.2 µg/mL (Table S1; Iyengar and Wolttiez, 1988) and, with a volume of 150–250 mL, this gives a total Cu mass (M<sub>CSF</sub>) of 3–5 µg. As a first approximation, the CNS can be considered as a closed system at steady state and, δ⁶⁵Cu<sub>B</sub> and δ⁶⁵Cu<sub>CSF</sub> being the Cu isotopic composition in brain and CSF, respectively, one can write that:

$$M_B \cdot \delta^{65}Cu_B \leftrightarrow M_{CSF} \cdot \delta^{65}Cu_{CSF}$$

If the isotopic composition of Cu varies in one compartment by a factor Δ, this must be balanced in the other one such that:

$$M_B \cdot \Delta\delta^{65}Cu_B = M_{CSF} \cdot \Delta\delta^{65}Cu_{CSF}$$

It is thus possible to calculate the Cu isotopic offset in the CSF (Δδ⁶⁵Cu<sub>CSF</sub>) that would result from the incorporation of Cu in the brain with an isotopic fractionation Δδ⁶⁵Cu<sub>B</sub>. The assumption that the CNS is a closed system is made for the sake of simplicity for purposes of mass balance calculations. Physiologically, metals and other molecules are supplied in the CNS by blood, but classically stay there and accumulate with time. The inward flux of Cu in the CNS by time unit is unknown but should be very small compared with the mass of the CNS, considering that the Cu amount in blood (~5 µg) is three orders of magnitude less than that in CNS. Results of the calculations are illustrated by Figure 3. Intuitively, because there are between 78 and 2,500 more Cu in the brain than in the CSF, the Δδ⁶⁵Cu<sub>B</sub> value must be very small. Indeed, when the highest M<sub>B</sub>/M<sub>CSF</sub> ratio is considered (2,500), a contribution of only 0.01% of pathological Cu associated with a Δδ⁶⁵Cu<sub>B</sub> value of 0.5‰, can trigger a Δδ⁶⁵Cu<sub>CSF</sub> offset of almost 0.2‰, which is the observed Cu isotopic difference between ALS and CTRL. An identical Δδ⁶⁵Cu<sub>CSF</sub> offset is obtained with a contribution of pathological Cu of 0.5% when the lowest M<sub>B</sub>/M<sub>CSF</sub> ratio of 78 is considered. Altogether, these results show that minute proportions of aggregates formation associated with relevant Cu isotopic fractionation can likely explain the observed differences between the CSFs δ⁶⁵Cu values of CTRL and patients with ALS. Whatever the proportion of formed aggregates, the increase of the ALS δ⁶⁵Cu values implies for mass balance requirement that ⁶³Cu preferentially binds in aggregates. Measuring the Cu isotopic composition of protein aggregates and normal adjacent areas in autopsies of brains of patients with ALS, and/or in brains of ALS animal model like the SOD1<sup>G93A</sup> mice, can be the aim of future experiments to challenge this hypothesis.

Another way to explain the specific Cu isotopic composition of ALS would involve a differential Cu isotope fractionation between detergent-soluble SOD1 and aggregated, insoluble, SOD1. In mutant (G37R and G93A) SOD1 proteins from SOD1-ALS transgenic mice spinal cords, Lelie et al. (2011) found that aggregated, insoluble, SOD1 is metal depleted, whereas soluble SOD1 is highly Cu metallated. Exchange of Cu between soluble and aggregated SOD1 is unlikely because soluble SOD1 is thought to be highly stable. Lelie et al. (2011) also argued that aggregation occurs when SOD1 is in its immature, unmodified, apo form (Shaw et al., 2008). All these results suggest that the aggregation of SOD1 leads to the over-metallation of soluble SOD1. The abnormal Cu metallation of soluble SOD1 can be accompanied by different isotopic fractionation from normal conditions, which can explain the present results. Measurement of the Cu
<figure><img src="image_6.png" /><figcaption>**Figure 3. Cu Isotope Mass Balance between CSF and Brain**</figcaption></figure>

Calculation of the Cu isotopic offset in the CSF (Δδ<sup>65</sup>Cu<sub>CSF</sub>) that results from the incorporation of Cu in the brain (% of exchanged Cu) associated with an isotopic fractionation Δδ<sup>65</sup>Cu<sub>B</sub>. The calculations are made with two values of Δδ<sup>65</sup>Cu<sub>B</sub> (0.5‰ in red and 0.05‰ in blue) and two extreme values of the M<sub>B</sub>/M<sub>CSF</sub> ratio. The results thus define two areas in red and blue that overlap giving the dark blue area. The results discussed in the text corresponding to some proportions of exchanged Cu for a Δδ<sup>65</sup>Cu<sub>CSF</sub> value of 0.2‰, which represents the difference between CTRL and ALS δ<sup>65</sup>Cu mean values, are illustrated.

isotope composition of the soluble and insoluble fractions of SOD1-ALS transgenic mice spinal cords
would help to confirm this hypothesis.

To conclude, our results, i.e., the first to report Cu and Zn isotopic compositions in CSFs of patients with ALS and AD and CTRL subjects, demonstrate that Cu isotopic measurements in CSF may offer a more comprehensive view of the ALS metallome than elemental concentrations and may potentially reinforce any diagnosis. Similarly, the lighter δ<sup>66</sup>Zn value observed in the CSF of patients with AD compared with that of CTRL subjects may also offer promising information regarding Zn dyshomeostasis in AD. Increasing the number of Cu and Zn isotopic measurements in CSFs of ALS and AD, respectively, as well as in other neurodegenerative pathologies, such as Parkinson or Huntington diseases, would undoubtedly challenge the proposition that δ<sup>65</sup>Cu and δ<sup>66</sup>Zn values in CSF are probably future candidate biomarkers of neurodegenerative diseases.

METHODS

All methods can be found in the accompanying *Transparent Methods supplemental file*.

SUPPLEMENTAL INFORMATION

Supplemental Information includes Transparent Methods, three figures, one table, and two data files and
can be found with this article online at https://doi.org/10.1016/j.isci.2018.07.023.

ACKNOWLEDGMENTS

We thank three anonymous reviewers for their very helpful comments, Philippe Télouk for his assistance during isotopic measurements, and Mélanie Simon for her help regarding chemical procedures. Thanks are also due to the clinicians Dr. Anne Nove-Josserand and Dr. Dominique Minier, the chiefs of the Division of Neurology at Hospital North of Villefranche sur Saône and at William Morey Hospital in Châlon sur
Saône, respectively. V.B. and L.S. are grateful to Fondation Bullukian and Fondation Mérieux as well as the Ecole Polytechnique Fédérale of Lausanne (EPFL) for their financial support. P.L. received support from the Association pour la Recherche sur la SLA (ARSLA), the NeuroDis Foundation, the AFM (AFM-MyoNeuralp), the CNRS, and INSERM.

## AUTHOR CONTRIBUTIONS

Conceptualization, E. Bernard, E. Broussolle, P.L., and V.B.; Methodology, L.S. and V.B.; Investigation, L.S., E. Bernard, A.P.-L. I.Q. A.V., P.K.-S., and E. Broussolle; Resources, E. Bernard, A.P.-L., I.Q., A.V., P.K.-S., E. Broussolle, and V.B.; Writing - Original Draft, L.S., E. Bernard, E. Broussolle, P.L., and V.B.; Writing - Reviews and Editing, L.S., E. Bernard, E. Broussolle, P.L., and V.B.; Funding Acquisition, V.B.

## DECLARATION OF INTERESTS

The authors have no financial interests to declare.

Received: June 1, 2018

Revised: July 8, 2018

Accepted: July 26, 2018

Published: August 31, 2018

## SUPPORTING CITATIONS

Costa et al., 2012; del Campo et al., 2012; Ihara et al., 2013; Jaouen et al., 2016; Kapaki et al., 1989; Maréchal et al., 1999; McKhann et al., 2011.

## REFERENCES

Albarède, F., Télouk, P., Balter, V., Bondanese, V.P., Albalat, E., Oger, P., Bonaventura, P., Miossec, P., and Fujii, T. (2016). Medical applications of Cu, Zn, and S isotope effects. *Metallomics* 8, 1056-1070.

Bäckström, D.C., Eriksson Domellöf, M., Linder, J., Olsson, B., Öhrfelt, A., Trupp, M., et al. (2015). Cerebrospinal Fluid patterns and the risk of future dementia in early, incident Parkinson disease. *JAMA Neurol.* 72, 1175-1182.

Balter, V., Nogueira da Costa, A., Bondanese, V.P., Jaouen, K., Lamboux, A., Sangrajang, S., et al. (2015). Natural variations of copper and sulfur stable isotopes in blood of hepatocellular carcinoma patients. Proc. Natl. Acad. Sci. USA 112, 982-985.

Barnham, K.J., and Bush, A.I. (2014). Biological metals and metal-targeting compounds in major neurodegenerative diseases. Chem. Soc. Rev. 43, 6727-6749.

Büchl, A., Hawkesworth, C.J., Ragnarsdottir, K.V., and Brown, D.R. (2008). Re-partitioning of Cu and Zn isotopes by modified protein expression. *Geochem. Trans.* 9, 11.

Costa, J., Swash, M., and de Carvalho, M. (2012). Awaji criteria for the diagnosis of amyotrophic lateral sclerosis. Arch. Neurol. 69, 1410.

Costas-Rodriguez, M., Delanghe, J., and Vanhaecke, F. (2016). High-precision isotopic analysis of essential mineral elements in biomedicine: natural isotope ratio variations as potential diagnostic and/or prognostic markers. *Trends Anal. Chem.* 76, 182-193.

del Campo, M., Mollenhauer, B., Bertolotto, A., Engelborghsen, S., Hampel, H., Simonsen, A.H., et al. (2012). Recommendations to standardize

preanalytical confounding factors in Alzheimer's and Parkinson's disease cerebrospinal fluid biomarkers: an update. *Biomarkers Med.* 6, 419-430.

Enge, T.G., Ecroyd, H., Jolley, D.F., Yerbury, J.J., and Dosseto, A. (2017). Longitudinal assessment of metal concentrations and copper isotope ratios in the G93A SOD1 mouse model of amyotrophic lateral sclerosis. *Metallomics* 9, 161-174.

Garçon, M., Sauzéat, L., Carlson, R.W., Shirey, S.B., Simon, M., Balter, V., et al. (2017). Nitrile, latex, neoprene and vinyl gloves: a primary source of contamination for trace element and Zn isotopic analyses in geological and biological samples. *Geostand. Geoanal. Res.* 41, 367-380.

Hozumi, I., Hasegawa, T., Honda, A., Ozawa, K., Hayashi, Y., Hashimoto, K., et al. (2011). Patterns of levels of biological metals in CSF differ among neurodegenerative diseases. *J. Neurol. Sci.* 303, 95-99.

Ihara, Y., Nobukuni, K., Takata, H., and Hayabara, T. (2013). Oxidative stress and metal content in blood and cerebrospinal fluid of amyotrophic lateral sclerosis patients with and without a Cu, Zn-superoxide dismutase mutation. *Neurol. Res.* 27, 105-108.

Iyengar, V., and Wolttiez, J. (1988). Trace elements in Human clinical specimens: evaluation of literature data to identify reference values. *Clin. Chem.* 34, 474-481.

Jaouen, K., Balter, V., Herrscher, E., Lamboux, A., Télouk, P., and Albarède, F. (2012). Fe and Cu stable isotopes in archeological human bones and their relationship to sex. *Am. J. Phys. Anthropol.* 148, 334-340.

Jaouen, K., Gibert, M., Lamboux, A., Télouk, P., Fourel, F., Albarède, F., et al. (2013). Is aging recorded in blood Cu and Zn isotope compositions? *Metallomics* 5, 1016-1024.

Jaouen, K., Beasley, M., Schoeninger, M., Hublin, J.-J., and Richards, M.P. (2016). Zinc isotope ratios of bones and teeth as new dietary indicators: results from a modern food web (Koobi Fora, Kenya). *Sci. Rep.* 6, 1-8.

Kanias, G.D., and Kapaki, E. (1997). Trace elements, age, and sex in amyotrophic lateral sclerosis disease. *Biol. Trace Elem. Res.* 56, 187-201.

Kapaki, E., Segditsa, C., and Papageorgiou, C. (1989). Zinc, copper and magnesium concentration in serum and CSF of patients with neurological disorders. *Acat. Neurol. Scand.* 79, 373-378.

Kapaki, E., Zournas, C., Kanias, G., Zambelis, T., Kakami, A., and Papageorgiou, C. (1997). Essential trace element alterations in amyotrophic lateral sclerosis. *J. Neurol. Sci.* 147, 171-175.

Lelie, H.L., Liba, A., Bourassa, M.W., Chattopadhyay, M., Chan, P.K., Gralla, E.B., Miller, L.M., Borchelt, D.R., Valentine, J.S., and Whitelegge, J.P. (2011). Copper and zinc metallation status of copper-zinc superoxide dismutase from amyotrophic lateral sclerosis transgenic mice. *J. Biol. Chem.* 286, 2795-2806.

Lind, A.-L., Wu, D., Freyhult, E., Bodolea, C., Ekegren, T., Larsson, Å., et al. (2016). A multiplex protein panel applied to cerebrospinal fluid reveals three new biomarker candidates in ALS but none in neuropathic pain patients. *PLoS One* 11, e0149821.
Lu, C.H., Macdonald-Wallis, C., Gray, E., Pearce, N., Petzold, A., Norgren, N., et al. (2015). Neurofilament light chain: a prognostic biomarker in amyotrophic lateral sclerosis. Neurology 84, 2247-2257.

Maréchal, C.N., Télouk, P., and Albarède, F. (1999). Precise analysis of copper and zinc isotopic compositions by plasma-source mass spectrometry. Chem. Geol. 156, 251-273.

McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R., Jr., Kawas, C.H., et al. (2011). The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7, 263-269.

Miller, K.A., Keenan, C.M., Martin, G.R., Jirik, F.R., Sharkey, K.A., and Wieser, M.E. (2016). The expression levels of cellular prion protein affect copper isotopic shifts in the organs of mice. J. Anal. Spectrom. 31, 2015-2022.

Moynier, F., Foriel, J., Shaw, A.S., and Le Borgne, M. (2017). Distribution of Zn isotopes during Alzheimer's disease. Geochem. Persp. Lett. 3, 142-150.

Narasimhan, S.D. (2015). A brief history of ALS. Cell 161, 181-183.

Ostachowicz, B., Lankosz, M., Tomik, B., Adamek, D., Wobrauschek, P., Streli, C., et al. (2006).

Analysis of some chosen elements of cerebrospinal fluid and serum in amyotrophic lateral sclerosis patients by total reflection X-ray fluorescence. Spectrochim. Acta Part B At. Spectrosc. 61, 1210-1213.

Pasinetti, G.M., Ungar, L.H., Lange, D.J., Yemul, S., Deng, H., Yuan, X., Brown, R.H., Cudkowicz, M.E., Newhall, K., Peskind, E., et al. (2006). Identification of potential CSF biomarkers in ALS. Neurology 66, 1218-1222.

Pithadia, A., and Lim, M.H. (2012). Metal-associated amyloid-β in Alzheimer's disease. Curr. Opin. Chem. Biol. 16, 67-73.

Pokrishevsky, E., Grad, L.I., Yousefi, M., Wang, J., Mackenzie, I.R., and Cashman, N.R. (2012). Aberrant localization of FUS and TDP43 is associated with misfolding of SOD1 in amyotrophic lateral sclerosis. PLoS One 7, 1-9.

Roos, P.M., Vesterberg, O., Syversen, T., Flaten, T.P., and Nordberg, M. (2013). Metal concentrations in cerebrospinal fluid and blood plasma from patients with amyotrophic lateral sclerosis. Biol. Trace Elem. Res. 151, 159-170.

Savage, N. (2017). Calculating disease. Nature 550, S115.

Scheiber, I.F., Mercer, J.F.B., and Dringen, R. (2014). Metabolism and functions of copper in brain. Prog. Neurobiol. 116, 33-57.

Scott, A. (2017). On the treatment trail for ALS. Nature 550, S120.

Shaw, B.F., Lelie, H.L., Durazo, A., Nersissian, A.M., Xu, G., Chan, P.K., Gralla, E.B., Tiwari, A., Hayward, L.J., Borchelt, D.R., et al. (2008). Detergent-insoluble aggregates associated with amyotrophic lateral sclerosis in transgenic mice contain primarily full-length, unmodified superoxide dismutase-1. J. Biol. Chem. 283, 8340-8350.

Taylor, J.P., Brown, R.H., and Cleveland, D.W. (2016). Decoding ALS: from genes to mechanism. Nature 539, 197-206.

Tokuda, E., Okawa, E., Watanabe, S., Ono, S.-I., and Marklund, S.L. (2013). Dysregulation of intracellular copper homeostasis is common to transgenic mice expressing human mutant superoxide dismutase-1s regardless of their copper-binding abilities. Neurobiol. Dis. 54, 308-319.

Valentine, J.S., Doucette, P.A., and Zittin Potter, S. (2005). Copper-Zinc superoxide dismutase and amyotrophic lateral sclerosis. Annu. Rev. Biochem. 74, 563-593.

Zetterberg, H., Skillbäck, T., Mattsson, N., Trojanowski, J.Q., Portelius, E., Shaw, L.M., et al. (2016). Association of cerebrospinal fluid neurofilament light concentration with Alzheimer disease progression. JAMA Neurol. 73, 60.
# Supplemental Information

## Isotopic Evidence for Disrupted

## Copper Metabolism in Amyotrophic

## Lateral Sclerosis

Lucie Sauzéat, Emilien Bernard, Armand Perret-Liaudet, Isabelle Quadrio, Alain Vighetto, Pierre Krolak-Salmon, Emmanuel Broussolle, Pascal Leblanc, and Vincent Balter
<figure><img src="image_7.png" /><figcaption>**Supplementary Figure S1: Elemental concentrations variability in CSF of ALS and CTRL subjects, related to Figure 1.** Compilation of a) Cu and b) Zn concentrations of cerebrospinal fluids (CSFs) in both control (yellow) and amyotrophic lateral sclerosis subjects (green), from the literature and the present study. Error bars represent two standard deviations of the mean. Cu diagram is represented with a logarithm scale. Data from the present study are labeled in red, and literature data, in italic from 1 to 7, are for Kapaki et al. (1997), Hozumi et al. (2011), Ihara et al. (2013), Kanias et al. (1997), Kapaki et al. (1989), Roos et al. (2013), Ostachowicz et al. (2006), respectively.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Supplementary Figure S2: Receiver Operating Curve (ROC), related to Figure 2.</figcaption></figure>

2. ROC analysis of the δ<sup>65</sup>Cu values in CSF of ALS patients compared to that of CTRL subjects.
<figure><img src="image_9.png" /><figcaption>**Supplementary Figure S3: Estimation of blank levels, related to Figure 2.**</figcaption></figure>

Amount of trace element released by sampling and storage tubes as well as dropper-like pipette normalized to the amount of elements initially present in 2mL of cerebrospinal fluids (CSFs) (i.e. the average volume analyzed in this study). The grey field represents values below the limit of detection (DL) defined following IUPAC guideline (i.e. $DL_i=xb_i + k*sb_i$ where k=3, $xb_i$ and $sb_i$ are respectively the mean and the standard deviation of the number of counts measured in blanks). When the amount detected in the solution was lower the limit of detection, data were represented as equal to the detection limit.
<table><caption>Supplementary Table S1: General information and patients' clinical records, related to Figure 1</caption><thead><tr><th>Sample Name</th><th>Gender</th><th>Age (years) at sample collection</th><th>Localization of first symptoms *</th><th>Awaji criteria</th><th>ALSFRS-R **</th><th>Time (months) between sampling date and first symptoms</th></tr></thead><tbody><tr><td>ALS1</td><td>Male</td><td>45</td><td>MI</td><td>definite</td><td>44</td><td>7</td></tr><tr><td>ALS2</td><td>Male</td><td>62</td><td>MS</td><td>definite</td><td>39</td><td>6</td></tr><tr><td>ALS4</td><td>Male</td><td>81</td><td>MS</td><td>definite</td><td>40</td><td>6</td></tr><tr><td>ALS5</td><td>Male</td><td>46</td><td>bulb</td><td>definite</td><td>40</td><td>12</td></tr><tr><td>ALS6</td><td>Male</td><td>34</td><td>MS</td><td>definite</td><td>42</td><td>12</td></tr><tr><td>ALS7</td><td>Male</td><td>65</td><td>bulb</td><td>definite</td><td>44</td><td>12</td></tr><tr><td>ALS8</td><td>Male</td><td>86</td><td>MI</td><td>possible</td><td>37</td><td>6</td></tr><tr><td>ALS9</td><td>Male</td><td>68</td><td>MI</td><td>definite</td><td>45</td><td>12</td></tr><tr><td>ALS10</td><td>Male</td><td>73</td><td>MS</td><td>definite</td><td>37</td><td>6</td></tr><tr><td>ALS11</td><td>Male</td><td>64</td><td>MS</td><td>probable</td><td>28</td><td>6</td></tr><tr><td>ALS12</td><td>Male</td><td>60</td><td>MS</td><td>definite</td><td>33</td><td>6</td></tr><tr><td>ALS13</td><td>Male</td><td>60</td><td>bulb</td><td>definite</td><td>34</td><td>6</td></tr><tr><td>ALS14</td><td>Male</td><td>57</td><td>MI</td><td>definite</td><td>32</td><td>24</td></tr><tr><td>ALS 15</td><td>Male</td><td>49</td><td>bulb</td><td>definite</td><td>41</td><td>6</td></tr><tr><td>ALS 16</td><td>Male</td><td>50</td><td>MS</td><td>probable</td><td>39</td><td>12</td></tr><tr><td>ALS 17</td><td>Male</td><td>69</td><td>MI</td><td>probable</td><td>45</td><td>36</td></tr><tr><td>ALS 18</td><td>Male</td><td>60</td><td>bulb</td><td>definite</td><td>39</td><td>24</td></tr><tr><td>ALS 19</td><td>Male</td><td>49</td><td>bulb</td><td>definite</td><td>39</td><td>12</td></tr><tr><td>ALS 20</td><td>Male</td><td>75</td><td>MI</td><td>probable</td><td>35</td><td>12</td></tr><tr><td>ALS 21</td><td>Male</td><td>70</td><td>MS</td><td>definite</td><td>23</td><td>24</td></tr><tr><td>ALS 22</td><td>Male</td><td>76</td><td>bulb</td><td>definite</td><td>42</td><td>12</td></tr><tr><td>ALS 23</td><td>Male</td><td>54</td><td>bulb</td><td>definite</td><td>43</td><td>7</td></tr><tr><td>ALS 24</td><td>Male</td><td>44</td><td>MI</td><td>probable</td><td>46</td><td>24</td></tr><tr><td>ALS 25</td><td>Female</td><td>71</td><td>MS</td><td>probable</td><td>43</td><td>4</td></tr><tr><td>ALS 26</td><td>Female</td><td>64</td><td>bulb</td><td>definite</td><td>40</td><td>6</td></tr><tr><td>ALS 27</td><td>Female</td><td>63</td><td>bulb</td><td>definite</td><td>40</td><td>24</td></tr><tr><td>ALS 28</td><td>Female</td><td>67</td><td>MI</td><td>definite</td><td>40</td><td>5</td></tr><tr><td>ALS 29</td><td>Female</td><td>69</td><td>MI</td><td>probable</td><td>34</td><td>6</td></tr><tr><td>ALS 30</td><td>Female</td><td>64</td><td>MI</td><td>probable</td><td>40</td><td>9</td></tr><tr><td>ALS 31</td><td>Female</td><td>45</td><td>MS</td><td>definite</td><td>41</td><td>24</td></tr><tr><td>ALS 32</td><td>Female</td><td>43</td><td>MI</td><td>probable</td><td>42</td><td>6</td></tr><tr><td>CTRL2</td><td>Male</td><td>49</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL3</td><td>Male</td><td>62</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL4</td><td>Male</td><td>69</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL5</td><td>Male</td><td>48</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL6</td><td>Male</td><td>46</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL7</td><td>Male</td><td>67</td><td></td><td>n/a</td><td></td><td></td></tr><tr><td>CTRL8</td><td>Male</td><td>60</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL9</td><td>Male</td><td>44</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL10</td><td>Male</td><td>70</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL11</td><td>Male</td><td>58</td><td></td><td></td><td></td><td></td></tr><tr><td>CTRL 12</td><td>Male</td><td>79</td><td></td><td></td><td></td><td></td></tr><tr><td>AD4</td><td>Male</td><td>75</td><td></td><td></td><td></td><td></td></tr><tr><td>AD5</td><td>Male</td><td>78</td><td></td><td></td><td></td><td></td></tr><tr><td>AD6</td><td>Male</td><td>68</td><td></td><td></td><td></td><td></td></tr><tr><td>AD7</td><td>Male</td><td>68</td><td></td><td></td><td></td><td></td></tr><tr><td>AD8</td><td>Male</td><td>78</td><td></td><td></td><td></td><td></td></tr><tr><td>AD9</td><td>Male</td><td>63</td><td></td><td></td><td></td><td></td></tr><tr><td>AD10</td><td>Male</td><td>70</td><td></td><td>n/a</td><td></td><td></td></tr><tr><td>AD11</td><td>Male</td><td>59</td><td></td><td></td><td></td><td></td></tr><tr><td>AD12</td><td>Male</td><td>63</td><td></td><td></td><td></td><td></td></tr><tr><td>AD13</td><td>Male</td><td>71</td><td></td><td></td><td></td><td></td></tr><tr><td>AD14</td><td>Male</td><td>73</td><td></td><td></td><td></td><td></td></tr><tr><td>AD15</td><td>Male</td><td>73</td><td></td><td></td><td></td><td></td></tr><tr><td>AD16</td><td>Male</td><td>79</td><td></td><td></td><td></td><td></td></tr><tr><td>AD17</td><td>Male</td><td>78</td><td></td><td></td><td></td><td></td></tr></tbody></table>

**Footnote:**

"n/a" stands for unspecified value

CTRL stands for control subjects, ALS and AD are for Amyotrophic lateral sclerosis and Alzheimer patients respectively

*Site at onset: - MI=lower limbs

- MS=upper limbs

- bulb=bulbar

**ALSFRS-R stands for the revised version of the Amyotrophic Lateral Sclerosis Rating scale
**Transparent methods**

*Subjects and samples*

In this study, all subjects were hospitalized in the Department of Neurology for diagnosis purposes, which included cerebrospinal fluid (CSF) analyses among other tests. All patients signed informed consent about the potential use of their CSF for further research purposes and an ethical approval was also delivered by the local Ethics committee of the Hospices Civils de Lyon (date of delivery: 7th October, 2016). We investigated the CSF of 31 ALS patients diagnosed using the Awaji criteria (Costa et al., 2012). They were compared to a group of 25 patients suffering from neurocognitive complaint referred to expert memory clinic linked to Lyon Center for Memory Resources and Research. These 25 patients underwent lumbar puncture using a standard procedure (del Campo et al., 2012) in the context of clinical diagnosis of neurodegenerative diseases. Cerebrospinal fluid biomarker for AD pathology (i.e total and phosphorylated TAU proteins and Amyloid beta 1-42 peptide) were performed blind to the clinical diagnosis in the Neurochemistry Unit of Lyon University Hospital using commercially available enzyme-linked immunosorbent assays (INNOTEST hTau-Ag, INNOTEST phosphorylated-Tau181, and INNOTEST Aβ1-42; Fujirebio Europe) according to the manufacturer's instructions. The Neurochemistry Unit participated in the Alzheimer Association Quality Control Program for these biomarkers. Diagnoses were finally proposed in the framework of multidisciplinary consultation taking into account medical history, caregivers interviews, neurologic examination, neuropsychological evaluation, brain imaging and results of cerebrospinal fluid biomarker for AD pathology. AD diagnostic was excluded in 11 of these 25 patients who were finally classified as suffering mainly from psychiatric conditions and were then considered as controls for this study. The remaining 14 patients met the diagnostic criteria of Alzheimer 's disease according to McKhann et al. (2011). Both male and female from different ages ranging from 34 to 86 years old were studied. To avoid any sample bias, we also considered diverse ALS cases i.e. characterized by different onset brain location (bulbar, lower and upper limbs), distinct value of Awaji criteria and ALSFRS as well as variable time of symptom onset (6 to 36 months). All the details are summarized in Supplementary Table S1.
## Major and trace element concentrations

All chemical analyses were carried out in clean laminar flow hoods using double-distilled acids to avoid any exogenous contaminations. Samples were first weighted and then dissolved in a mixture of 15M HNO₃ and H₂O₂ (30%) in Savillex® beakers at 120°C for about 72h. When dissolved, major and trace element concentrations were measured in a small aliquot on an ICP-AES (iCAP 6000 Radial) and a quadrupole ICP-MS Thermo iCap-Q respectively at the Ecole Normale Supérieure (ENS) of Lyon following the method described in Garçon et al. (2017). Trace and major element concentrations are reported in ng/mL and µg/mL respectively in Supplementary Dataset 1. Briefly, the concentrations were calculated using calibration curves based on multi-elemental solutions. These solutions were also used to monitor and correct the instrumental drift over the analytical session. Oxide interference and analytical drift were also corrected using indium (In) and scandium (Sc) addition as internal standards for trace and major elements, respectively. The precision of the results was assessed by complete duplicate and re-run analyses (referred as “dup” and “bis” samples respectively in Supplementary Dataset 1) and accuracy and reproducibility were monitored by replication of certified reference materials (1577c, DORM2) and an in-house standards (OEP, FBS) measured as unknown samples (Supplementary Dataset 2). The results are generally reproducible and consistent within 10% (2sd) of previous published data (see Supplementary Dataset 2). Based on the analysis of reference standards and duplicates analyses, we therefore estimate that the measurement precision is, on average, better than 10% for both major and trace elements.

## Copper and zinc isotopic compositions

Samples were purified by ion-exchange chromatography using quartz columns filled with 1.8mL of Bio-Rad AGMP-1 (100-200 mesh) anion-exchange resin. Copper and zinc were successively eluted with 20mL of HCl 7M + 0.001% H₂O₂ and 10mL of HNO₃ 0.5M respectively following the procedure described by Maréchal et al. (1999). The total procedural blanks were on average 0.4 ng for Cu (n=7) and 3.0 ng for Zn (n=7), which is below the amount of element isolated from the sample and available for isotopic measurement (i.e. average CuCSFs and ZnCSFs of 20 ng). Blank contribution in the samples is therefore higher for Zn than for Cu. To
ensure the quality of the Zn isotopic data, we quantified the impact of this exogenous contamination using the mixing equation provided by Garçon et al. (2017). Tubes and pipettes used to collect and store the CSFs cannot release significant amount that may account for exogenous contamination (see below *Effect of exogenous contamination induced by tubes and pipettes on major and trace element concentrations measured in CSFs*). Conversely, gloves contain high Zn content that may be easily mobilized during sample preparation (Garçon et al., 2017). Gloves are probably the main source of contamination. With a δ<sup>66</sup>Zn<sub>gloves</sub> of 0.10 ± 0.32 ‰ (2sd), we show that for Zn = 20 ng (i.e. the content available for isotopic measurement in the samples), no significant shift can however be induced beyond the measurement uncertainties (i.e. ±0.07 ‰, 2sd) (Supplementary figure 3) ensuring the reliability of our isotopic data measurements.

The isotopic compositions of Cu and Zn are measured by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS, Nu500) in wet plasma conditions following the procedure described by Maréchal et al. (1999). On the day of analyses, Cu and Zn purified solutions are diluted in a Zn-doped solution (Zn JMC 3-0749L, Johnson Matthey Royston, UK) and a Cu-doped solution (Cu SRM 976, National Institute of Standards and Technology, Gaithersburg, MD, USA), respectively, to match the concentration of the standard mixture run between the samples (between 75 and 300 µg.L<sup>-1</sup> depending on the sample). The delta values (expressed in ‰) are reported relative to the isotopic solution reference material NIST SRM 976 for Cu and JMC 3-0749L for Zn and are referred as:

$$ \delta^{65}\text{Cu} = \left[ \frac{\left( \frac{^{65}\text{Cu}}{^{63}\text{Cu}} \right)_{\text{sample}}}{\left( \frac{^{65}\text{Cu}}{^{63}\text{Cu}} \right)_{\text{standard}}} - 1 \right] \times 1000 \quad \text{and} \quad \delta^{66}\text{Zn} = \left[ \frac{\left( \frac{^{66}\text{Zn}}{^{64}\text{Zn}} \right)_{\text{sample}}}{\left( \frac{^{66}\text{Zn}}{^{64}\text{Zn}} \right)_{\text{standard}}} - 1 \right] \times 1000 $$

Instrumental mass fractionation and temporal drift is corrected with an exponential law using the elemental-doping method and standard-sample bracketing respectively as recommended by Maréchal et al. (1999). The precision and the accuracy of Cu and Zn isotopic ratios were assessed by repeated measurements of re-run and duplicate samples and by reference materials (1577c, bovine liver) and in-house standard solutions (OEP, sheep plasma), respectively. The average δ<sup>66</sup>Zn of reference materials 1577c and OEP were -0.19 ± 0.04 (2sd, n=4) and +0.64 ± 0.06 (2sd, n=2), respectively, which is in good agreement with our in-house previous average values: δ<sup>66</sup>Zn<sub>1577c</sub> = -0.19 ± 0.05 (2sd, n=13) and δ<sup>66</sup>Zn<sub>OEP</sub> = +0.72 ± 0.08 (2sd, n=15) as well as with previous published results (δ<sup>66</sup>Zn<sub>1577c</sub> = -0.13 ± 0.02 (2sd,
n=4), 37) (Jaouen et al., 2016) (Supplementary Dataset 2). For δ⁶⁵Cu, we measured
+0.35 ± 0.07 (2sd, n=4) for 1577c and -1.16 ± 0.03 (2sd, n=3) for OEP which is also
in agreement with our in-house reference values: δ⁶⁵Cu₁₅₇₇c = +0.37 ± 0.12 (2sd,
n=10) and δ⁶⁵CuOEP = -1.14 ± 0.09 (2sd, n=20) (Supplementary Dataset 2). Based on
these results, we estimate the 2sd analytical uncertainty of our isotopic measurements
at ± 0.07. Note that the long-term precision based on the repeated measurements of
the pure Zn JMC 3-0749L and Cu SRM 976 solutions run every two samples are
very similar to these values (± 0.05 ‰ (2s, n = 140)).

Principal component analysis

In this study, we used a correlation-based principal component analysis
(PCA) to discriminate ALS patients from AD patients and age-matched CTRL as
well as to quantify the effect of the ALS disease on elemental concentrations and Cu-
Zn isotopic compositions. The method consists in identifying new variables called
principal components (PCs), which are linear combination of the original variables
and along which data variation is maximal. The variables include the chemical
concentrations of 12 major and trace elements measured in cerebrospinal fluids of
ALS, AD and control subjects, as well as δ⁶⁵Cu and δ⁶⁶Zn values. All data were
normalized, and samples with incomplete data were excluded. PCA was
implemented in MATLAB™.

Boxplot

In the present study, all the boxplot diagrams were implemented in MATLAB™ and significance level was determined using a non-parametric ‘two-sided’, Wilcoxon-Mann-Whitney U-test. For each boxplot, the central mark is the median, the edges of the box are the first (i.e. 25th percentiles) and third quartiles (i.e. 75th percentiles) respectively and the whiskers extend to the most extreme data points (i.e. not considered outliers).

ROC

Receiver Operating Curve (ROC) was used to evaluate the reliability of δ⁶⁵Cu
values as a potential ALS diagnostic test with the aim to confirm the presence of
ALS disease but also to rule out the presence of this pathology in healthy subjects. In
this study, the ROC test was implemented in MATLAB™ and four distinctive
parameters were obtained: 1) the cut-off value *i.e.* the threshold value discriminating ALS form CTRL subjects; 2) the true positive rate (*i.e.* sensitivity) corresponding to the probability that a result will be higher than the cut-off value when the ALS disease is present; 3) the false positive rate (*i.e.* 1-specificity) defined as the probability that a result will be lower than the cut-off value when the ALS disease is not present; 4) The area under the curve (AUC). Optimal performance test correspond to a sensitivity and specificity of 100% and an AUC of 1.

*Effect of exogenous contamination induced by tubes and pipettes on major and trace element concentrations measured in CSFs.*

Cerebrospinal fluids (CSFs) are made of 99% water and have low amount of major and trace elements. To ensure the absence of external contamination in CSFs during sample collection and/or storage, we quantify the content of the most contaminable trace elements (*i.e.* Mn, Fe, Cu, Zn, Rb and Sr as well as Mg) that may be released by tubes and pipettes being directly in contact with the samples. This includes sampling (CSF-A: polypropylene, Sarstedt, 10mL, 92x15.3mm and CSF-B: polypropylene, Sarstedt, 5mL, 57x15.3mm) and storage tubes (CSF-C: polypropylene, Sarstedt, 1.5mL) as well as dropper-type pipette.

The detailed procedure consist of putting 2mL of HNO₃ 0.5M + 2ppb indium (In) in the tubes, before storing them 3 weeks in a fridge. The volume of 2mL corresponds to the average volume of CSFs analyzed in this study. The duration of the test correspond to the approximate period for which CSF samples were stored in the tubes. To assess the amount of trace elements released by dropper-type pipette, we used a slightly different technique. For this test, we pour 2mL of HNO₃ 0.5M + 2ppb In in clean savillex® beaker and washed the pipette three times with the solution. An aliquot of each solution is then analyzed on the Thermo iCap-Q mass spectrometer following the method described in the *Major and trace element concentrations* section.

For all the elements measured in this study, the amount released by tubes and pipette that have been in contact with low concentrated HNO₃ acid is always lower (<0.01) than the quantity present in the CSF samples (Supplementary figure S3).

Only one exception is noted for the amount of Zn released by the dropper-type pipette, but this amount remains relatively low (<0.1) compared to the amount initially present in the samples. Although we used low concentrated acid solutions, it
is important to note that chemical elements are more easily released in acidic solution
than when they are in contact with non-acidic CSF samples. Therefore, our study
likely provides maximized results being ten times higher that the real amounts
released by tubes and pipettes when they are only filled with CSFs.

Altogether, these results show that storage and sampling tubes and dropper-
type pipettes cannot induce significant exogenous contamination and bias the Mg and
trace element concentrations including Fe, Cu, Zn, Mn, Rb and Sr measured in CSFs.
Similar conclusion can also be drawn for Ca, K, Na, P and S, the latters being
initially more concentrated in CSFs compared to trace elements.