Elevated intracellular copper induces CTR1 monomerization and prevents copper uptake

Domain: physics
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Copper is an essential element involved in various biochemical processes, such as mitochondrial energy production and antioxidant defense, but improper regulation can lead to cellular toxicity and disease. Copper Transporter 1 (CTR1) plays a key role in copper uptake and maintaining cellular copper homeostasis. Although CTR1 endocytosis was previously thought to reduce copper uptake when levels are high, it was unclear how rapid regulation is achieved. Using single-molecule localization microscopy and single-molecule neighbor density assays, we discover that elevated intracellular copper induces monomerization of the wild-type trimeric CTR1 prior to endocytosis, a response blocked in the endocytosis-deficient CTR1 (M150L) mutant. This monomerization correlates with a rapid halt in copper uptake. We propose that changes in CTR1 oligomerization state may regulate endocytosis and copper homeostasis.

Introduction
Copper (Cu), a metal vital for numerous biochemical processes including electron transfer and the mitigation of oxidative stress, poses a toxicity risk when its cellular levels are not precisely regulated1,2. Copper transporter 1 (CTR1), encoded by the SLC31A1 gene, stands at the forefront of this regulation as the primary transporter mediating copper uptake from the extracellular environment3–5. Unlike conventional ion transporters, CTR1 does not utilize ATP or secondary ion gradients; instead, it relies on local Cu concentration gradients and highly conserved methionine residues (Met-150 and Met-154) within its selectivity filter to facilitate selective copper binding and transport6–11.

CTR1 exists as a homotrimer. Recent studies have provided structural insights into its copper transport mechanism. The selectivity filter methionine triads exhibit conformational plasticity, with Cu(I) binding modulating their orientation to regulate transport efficiency12. Additionally, the intrinsically disordered N-terminal domain influences selectivity filter conformation, further fine-tuning copper uptake13,14. While these findings have advanced our understanding of CTR1’s molecular architecture and transport function, they do not fully explain how cells dynamically regulate CTR1 to prevent overload. The regulation of CTR1 abundance at the plasma membrane and thus cellular copper levels is intricately linked to the endocytic trafficking of CTR1. Under elevated Cu conditions, CTR1 undergoes endocytosis, effectively reducing its surface expression and limiting further Cu uptake15–18. This process, largely mediated by clathrin-mediated endocytosis (CME), is essential for either recycling CTR1 to the plasma membrane as copper levels normalize or targeting it for degradation under persistent copper stress16,18. Whether CME prevents CTR1-mediated Cu transport from endocytic vesicles into the cytoplasm is uncertain. Mutations within the copper-binding motif M150xxxM154 disrupt Cu uptake and prevent copper-induced CTR1 endocytosis19, suggesting that structural changes in CTR1 triggered by Cu binding play a direct role in determining its trafficking fate. By investigating the relationship between copper binding, structural changes in CTR1 and endocytosis, we discovered an unexpected mechanism of regulation of metal ion transport. Specifically, our single-molecule localization microscopy (SMLM) and single-molecule neighbor density (smND) assays revealed that CTR1 endocytosis is associated with de-oligomerization, consistent with a rapid regulatory response that limits copper entry.

Results
Computational modeling suggests a possible link between trimer stability and endocytosis
To understand how the endocytosis-deficient mutations influence copper uptake, we first employed computational modeling to compare the wild-type CTR1 with the endocytosis-deficient M150 mutant. In the absence of copper, the wild-type CTR1 assembles into a stable trimer, with FoldX analysis estimating its dissociation energy to be 34.56 kcal/mol (Methods)20. Using the Eris algorithm21, we simulated changes in CTR1’s thermodynamic stability. In the absence of copper, the leucine substitution at M150 (M150L) reduced the free energy change (ΔΔG) by −5.11 kcal/mol compared to the wild type. Our structural models also predicted that the bulkier leucine residue in the M150L mutant occludes the central transport pore, providing a potential physical basis for its transport deficiency (Fig. 1a, middle). With copper present, where M150L bound one copper at M154 while the wild type bound two coppers at M150 and M154, the ΔΔG of M150L further decreased to −9.62 kcal/mol. We also examined the alanine mutant M150A, characterized by a shorter side chain and defective CTR1 endocytosis, and found a ΔΔG of −4.75 kcal/mol without copper. Although the M154 site in the M150L mutant still coordinates copper, it is unlikely to be the dominant stabilizing factor, as the Cu(I)–M154 bond lengths, averaged from our MD simulations, are slightly longer (average 2.7 Å) than those in the wild-type (2.4 Å), suggesting weaker coordination (Fig. 1a, right). The results suggest that the M150L mutation significantly stabilizes the trimeric form of CTR1, reducing the free energy change compared to the wild type, especially in the presence of copper.

We further explored CTR1’s structural dynamics through molecular dynamics simulations in membrane environments. By measuring the root mean square deviation (RMSD) over 800 ns, we found that the overall structure of the CTR1 trimer, for both the M150L mutant and the wild type, remained consistent (Fig. 1b, c). These results indicate that the M150 mutation does not significantly alter the overall structure of the CTR1 trimer, regardless of the presence or absence of copper. After confirming the root-mean-square fluctuation (RMSF) stabilized after 900 ns (Fig. 1d), we further checked the RMSF data of both the wild type and the M150L mutant and found they are comparable in the presence of copper (1.64 ± 0.04 Å and 1.65 ± 0.05 Å for wild-type and M150L CTR1, respectively, Fig. 1e). In the absence of copper, the RMSF data increased, with the M150L (1.84 Å ± 0.07) demonstrating higher overall flexibility than that of the wild type (1.75 ± 0.05 Å), especially within the intracellular loop (residues 98–136) and cytosolic tail (residues 176–190, Fig. 1f). Of note, this average RMSF masks some regional difference. For example, subunit A of the wild type appears more flexible than in the M150L mutant. It suggests a potential asymmetry in how the mutation affects local flexibility.

Collectively, our computational data suggest that the M150L mutation may have a dual effect: it appears to physically occlude the copper transport channel and is predicted to thermodynamically stabilize the trimeric assembly. This led us to hypothesize that the impaired endocytosis of the mutant could be a direct result of this increased trimer stability. However, it remained critical to experimentally distinguish whether this ‘locked’ trimer state is the primary cause, or if the phenotype is an indirect consequence of the blocked transport preventing a necessary intracellular copper signal.

The CTR1 fusion with photoconvertible mEos4b retains transport activity, oligomerization, and Cu-induced endocytosis
The C-terminal HCH motif plays a crucial role in the oligomerization and proper folding of CTR14. To visualize and investigate the regulation of CTR1 in situ without functional perturbation22, we tagged human CTR1 with the photoconvertible fluorescent protein mEos4b (mEos), which was inserted into the C-terminus before the HCH motif (Fig. 2a, Supplementary Table 1). The generated CTR1 fusion protein, CTR1mEos, showed the expected full-length product (Supplementary Fig. 1a) and retained both copper uptake activity and Cu-responsive endocytosis, as validated through direct Cu measurement using ICP-MS and surface biotinylation. Specifically, ICP-MS quantification showed comparable intracellular copper levels between CTR1mEos and wild-type CTR1 under identical conditions, confirming that the fusion protein maintain effective copper transport activity (Supplementary Fig. 1b). Cells that overexpressed CTR1mEos showed reduced surface CTR1mEos abundance in the presence of high copper (Supplementary Fig. 1c; Supplementary Fig. 2), indicating effective Cu-induced endocytosis. Additionally, blue-native PAGE analyses demonstrated that CTR1mEos exhibits oligomeric patterns identical to wild-type CTR1 at the plasma membrane, further validating the structural integrity and native behavior of the fusion construct. Due to low endogenous CTR1 expression, heterotrimer formation between endogenous CTR1 and CTR1mEos is negligible (Supplementary Fig. 1d), affirming that observed oligomeric transitions primarily reflect the functional homotrimers of the exogenous protein rather than artifacts of overexpression or instability.

To better understand the initiation of CTR1 endocytosis, the small GTPase Rab5, a key regulator of early endocytosis, was used as a marker of endocytic sites at the cell surface. Rab5 coordinates the early steps of CME by interacting with effector proteins to control membrane identity, vesicle formation, and endosome fusion23,24. Activation of Rab5 at the membrane initiates the formation of endocytic vesicles, followed by the vesicle pinched from the plasma membrane. The Rab5 activity on the nascent endocytic vesicles is further reinforced by positive feedback loops involving Rabex-5 and Rabaptin-5. These interactions help stabilize the Rab5 domain on the vesicle membrane, promoting endosome fusion and maturation, which the mature endosomes further move into the cell (Fig. 2b)25–27.

To examine early endocytic events specifically, we used the total internal reflection of fluorescence (TIRF) imaging to selectively probe proteins near the plasma membrane within a ~50 nm depth (Supplementary Fig. 3). Our analysis of Rab5 intensity relative to puncta size showed that smaller puncta (under 0.3 μm², or around 600 nm in diameter) had a positive correlation with Rab5 intensity, indicating vesicle formation. However, as puncta size grew larger than this threshold, Rab5 intensity decreased as vesicles moved out of the TIRF imaging plane, reflecting endosome maturation (Fig. 2c). This bi-phase correlation between Rab5 intensity and puncta size agrees with the Rab5 activation mechanism during the early endosome formation26–28. We therefore focused on Rab5-positive (Rab5+) areas smaller than 0.3 μm² to study the roles of CTR1 oligomeric state in the early stages of endocytosis under different conditions.

The smND assay differentiates proteins in different oligomeric states
Using SMLM under TIRF illumination, we analyzed CTR1mEos within the Rab5+ areas (Fig. 2d, e) on the plasma membrane of fixed COS7 cells. This approach captures the early stages of CTR1-involved endocytosis. SMLM was able to resolve individual CTR1 subunits with ~10 nm resolution, similar to the experimentally determined CTR1 size (~10 nm length and 5 nm width)10, and enabled the detection of oligomeric states through our recently developed smND assay (Methods and Supplementary Methods).

The smND assay calculates neighbor density (ND) for individual CTR1 subunits, defined as the number of subunits detected within a 40 nm diameter circle. The aggregated ND values were visualized as a histogram, representing the experimental probability of neighbor density (PNDexp) distribution and depicting the frequency of encountering various densities of CTR1 subunits (Fig. 2e). We previously developed a theoretical model (PNDtheo) to explain how the protein oligomeric state is associated with the PND distribution29–31. Using a residue square-based algorithm to match PNDexp with PNDtheo, the smND assay identifies the subpopulation of membrane protein complexes with a typical error of 10% across various test conditions29. This method determines protein oligomeric states without directly resolving entire complexes. Instead, it uses precise localization data to map neighboring protein subunit distributions. By calculating the density of neighboring molecules, smND distinguishes different oligomeric states, such as monomers, dimers, and trimers, based on unique ND distribution patterns. Even when proteins are smaller than the SMLM resolution limit, smND accurately identifies and quantifies their oligomeric states, making it a powerful tool for studying protein behavior in cells.

We noticed that the weighted average number of subunits (N), calculated by summing the predicted oligomeric states and their respective percentages (Fig. 2e), can effectively differentiate proteins in different oligomeric states. To validate this, we used the interleukin-2 receptor alpha chain (Tac-antigen), a membrane protein known to form homodimers and exist in a monomer–dimer equilibrium in cells32, as a model system for evaluating the sensitivity of our smND assay. We generated two constructs by fusing either one or two copies of mEos4b to the Tac-antigen, resulting in TacmEos and Tac2mEos, respectively. TacmEos thus represented a mixture of mEos4b monomers and dimers, while Tac2mEos represented a mixture of dimers and tetramers. Importantly, both constructs retain the same Tac monomer-to-dimer ratio, as confirmed by immunoblotting (Supplementary Fig. 4a). The smND analysis (Supplementary Fig. 4b) showed larger N values in Tac2mEos compared to TacmEos, which reflects the increased mEos4b subunits and confirms the accuracy of our approach (Supplementary Fig. 4c). To assess whether copper treatment affects the oligomeric state of a protein unrelated to copper homeostasis, we further analyzed TacmEos under both basal and Cu-treated conditions using a sample size comparable to that for CTR1. Comparing TacmEos under these two conditions revealed no significant difference in N, indicating that Cu treatment does not alter the oligomeric state of the mEos tag itself (Supplementary Fig. 4d, e).

Compared to dimers, the analysis of trimeric proteins introduces an additional layer of complexity. In such cases, a mixture of monomers and trimers needs to be differentiated from pure dimer. To achieve accurate identification of trimeric protein subpopulations, we incorporated a dissociation model into the smND assay. This model enables the estimation of the upper and lower boundaries of different oligomeric states by factoring into the dissociation dynamics (Supplementary Methods and Table 3). As depicted in Fig. 2f, the CTR1 dissociation begins with the breakdown of a trimer (T) into a monomer (M) and a dimer (D), marked by an equilibrium constant K1. Subsequently, the dimer dissociates into two monomers, regulated by another equilibrium constant K2. The K1 and K2 dictate the relative abundances of different oligomeric forms. Two theoretical models define the dissociation extremes: Model 1, where the ratio K1/K2 approaches infinite (i.e., K2 approaching zero), posits a single dissociation scenario where trimer dissociation yields a monomer and a dimer (T → M+D). Conversely, Model 2, where the ratio K2/K1 approaches infinite (i.e., K1 approaching infinity), suggests that dimeric CTR1 is virtually nonexistent, leading to a direct dissociation of the trimer into three monomers (T → 3M). These models offer a framework to estimate the extreme dissociation conditions of trimeric proteins, enhancing our capacity to dissect the intricate oligomeric states of trimeric proteins within the complex environment of the cell.

Cu elevation induces surface CTR1mEos de-trimerization
Using CTR1 locations within the Rab5+ areas, we first conducted the two nearest pairwise distance (TNPD) analysis, leveraging spatial information to infer the oligomeric states of CTR1mEos under basal and high copper (100 μM CuCl2 for 15 min) conditions. We found an increase in TNPD for CTR1mEos under copper stress, which pointed to increased separation between CTR1mEos subunits (Fig. 3a). However, while TNPD analysis hints at potential changes, it falls short of distinguishing different CTR1 oligomeric forms. To resolve the CTR1 oligomeric states, we applied smND assay. Fitting the neighbor density (ND) distribution (Fig. 3b) showed that the fraction of trimer population decreased from 62.9 ± 8.8% to 41.7 ± 2.3% (Fig. 3c). The corresponding N (Fig. 3d) also decreased from 2.30 ± 0.18 to 1.87 ± 0.04, suggesting that copper induced CTR1 dissociation. To verify de-oligomerization of CTR1, we employed two de-trimerization kinetic models (Fig. 3e). The trimeric population of CTR1mEos decreased from 55.7 ± 14.2% to 24.2 ± 3.1% in Model 1, and from 66.5 ± 9.5% to 46.4 ± 2.0% in Model 2. Both models suggest that Cu induces a decrease in surface CTR1 oligomerization toward the monomeric form. Since copper elevation is known to trigger CTR1 endocytosis and reduce its surface abundance15–18, this mechanism, together with our findings of copper triggering the dissociation of CTR1 oligomers, suggests that copper-induced oligomeric changes and endocytosis may be coupled. Furthermore, while small conformational changes upon copper binding could theoretically alter inter-subunit distances, the extent of neighbor separation (>40 nm) required by smND strongly suggests genuine dissociation rather than minor structural adjustments. Thus, the observed copper-induced changes are consistent with authentic CTR1 trimer-to-monomer transitions rather than artifacts arising from conformational expansions alone.

CTR1(M150L)mEos variant lacks Cu-induced de-trimerization and endocytosis
Previous studies have shown that the mutation of methionine within the copper-binding motif M150xxxM154 disrupts copper-induced endocytosis. This motif forms an intramembrane pore essential for copper coordination in the trimeric CTR1 structure19. The M150L mutation, which stabilizes the trimer, led us to investigate its impact on CTR1’s oligomer dynamics and its connection to endocytosis.

We created a CTR1mEos variant with the M150L mutation (CTR1(M150L)mEos, Fig. 2a) and characterized its copper transport and endocytosis capacity. Biochemical results of CTR1(M150L)mEos showed a deficiency in Cu uptake and endocytosis. ICP-MS data of cells transfected with CTR1(M150L)mEos showed no significant changes in intracellular Cu levels compared to mEos4b controls in basal and Cu-stressed conditions (Supplementary Fig. 1b), confirming that the M150L mutation disrupted CTR1-mediated copper uptake. Surface biotinylation studies showed no change in the surface abundance of CTR1(M150L)mEos after 30 min of copper stimulation for (Supplementary Fig. 1c), consistent with previous findings that the M150 mutation impairs Cu-stimulated endocytosis.

Analyses of TNPD and PND distributions revealed no significant changes in CTR1(M150L)mEos’s oligomeric states in response to copper stress (Fig. 3f, g). The smND showed that the extent of Cu-induced de-trimerization for the CTR1(M150L)mEos (3%) and the change of N (ΔN = −0.1) was sharply reduced relative to the wild-type CTR1mEos (21% and ΔN as −0.44, Fig. 3c–e, h–j).

These findings indicate that the M150L mutation prevents both Cu-induced de-trimerization and CTR1 endocytosis, supporting the connections between these processes. However, before attributing the observed effects solely to changes in CTR1’s oligomeric states, we examined the broader landscape of cellular endocytosis under copper stress. This is necessary to discern whether copper’s influence exclusively CTR1 or affects more generally the cell’s endocytic machinery.

Cu stress selectively reduces CTR1 density without affecting global endocytosis
To explore the possibility that copper might influence protein endocytosis globally, we labeled cell surface proteins with wheat germ agglutinin (WGA), a general membrane protein marker, and examined the WGA uptake after copper treatments. The same copper treatment (100 μM CuCl2 for 15 min) that triggers endocytosis of CTR1 did not significantly increase in global endocytosis, as evidenced by unchanged sizes and numbers of WGA-positive puncta (Fig. 4a, b).

We then focused on Rab5 GTPase, a marker for early endosomes27,33 and examined the steady-state distribution of Rab5 on the plasma membrane in COS7 cells expressing either the wild-type CTR1 or the M150L mutant after copper treatment. No significant changes were observed in the Rab5+ puncta size or number, suggesting that neither copper nor CTR1 expression altered Rab5-associated endocytic processes (Fig. 4c, d).

Using a cell-based analysis, we found that copper treatment was associated with downward, non-significant trends in surface CTR1 density both in Rab5+ regions and in Rab5- regions (areas lacking Rab5 signal) for the wild-type. The endocytosis-deficient CTR1(M150L) mutant showed similarly directed but attenuated trends across both regions (Fig. 4e, f). Despite these interesting trends, the lack of statistical significance prevents pathway-specific conclusions from density alone. Notably, decreases outside Rab5+ regions could reflect either global surface depletion secondary to Rab5-associated internalization (reducing the overall pool), an additional Rab5-independent process, or both; the present data do not distinguish among these mechanisms. When considered alongside our single-molecule evidence that copper rapidly induces CTR1 monomerization prior to endocytosis, these observations align with a working model in which elevated intracellular copper precedes CTR1 monomerization and is accompanied by a shift in surface distribution; however, targeted experiments will be required to demonstrate any Rab5-independent contribution.

Intracellular Cu triggers compartment-specific CTR1 dissociation for cell survival
Our smND results (Fig. 3c–e) demonstrate that CTR1 exists in a dynamic equilibrium of different oligomeric states and shifts toward the monomeric state in response to elevated copper levels, which trigger endocytosis. While molecular dynamics simulations suggested that both the M150 mutation and copper binding at specific sites (M150 and M154) stabilize CTR1 trimers, experimental observations revealed a contrasting phenomenon: copper induces de-trimerization of wild-type CTR1 at early endocytic areas, a response blocked in the M150L mutant. This raises a key question: is endocytosis triggered directly by copper binding at Met150, or does the M150L mutant fail to internalize simply because it blocks copper uptake and prevents the intracellular signal required for endocytosis?

To distinguish between structural stabilization and cytosolic signaling as the cause of impaired endocytosis in the M150L mutant, we conducted additional experiments using a membrane-permeable copper ionophores, Cu-elesclomol (CuES)34,35. These reagents bypass CTR1-mediated uptake and directly elevate intracellular copper levels. The biotinylation assay shows that, in contrast to CuCl2 fails to trigger internalization of CTR1(M150L), cells treated with CuES robustly demonstrated decrease of surface CTR1 populations. This indicates a restoration of the mutant’s ability to internalize (Fig. 5a). Computational energy calculations reveal substantial intrinsic stability of the CTR1 trimer (dissociation energy 34.56 kcal/mol plus ΔΔG 9.62 kcal/mol), indicating that physiological dissociation under copper stress likely involves additional intracellular effectors or mechanical forces supplied by the recruited endocytic machinery. Thus, the key role of Met150 lies in enabling copper permeation, not in acting as a sensor or trigger itself.

To further delineate compartment-specific differences in CTR1 oligomerization upon copper stress, we compared CTR1 trimer populations in Rab5+ and Rab5– regions. WT CTR1 demonstrated a substantial and statistically significant reduction in trimer content within Rab5+ compartments (from ~63% to ~42%, Fig. 3c–e). Conversely, Rab5– areas exhibited only minor oligomeric shifts (from ~35% to ~33%, within smND uncertainty, Fig. 5b–e). These data imply that CTR1 dissociation primarily occurs at or near Rab5+ compartments, with Rab5– areas likely reflecting secondary equilibria or diffusional redistribution. For the endocytosis-deficient CTR1(M150L) mutant, changes in Rab5+ were minimal, and Rab5– exhibited an apparent reduction that did not reach significance (Fig. 5g–i); accordingly, we do not infer compartment-specific dissociation for the mutant. Overall, the wild-type pattern supports Rab5-proximal dissociation, while the M150L data remains inconclusive for Rab5– despite a visually larger decrease.

Finally, to directly test the functional importance of endocytosis as an essential protective pathway, we inhibited internalization using the dynamin inhibitor Dynasore during copper stress. Our results show a strong synergistic cytotoxicity of copper stress and endocytic blockage. As shown in Fig. 5j, after 24 h, treatment with either 100 µM CuCl2 or 80 µM Dynasore alone had a moderate impact on cell viability. In contrast, blocking endocytosis with Dynasore prior to copper exposure led to a profound loss of cell viability, with cells appearing rounded and detached. These results demonstrate that a functional endocytic pathway is also critical for protecting cells from acute copper toxicity.

Methods
Human cell lines
HEK293 and COS7 cells were purchased from ATCC for all the cellular experiments performed in the study. The cell lines were examined frequently under the microscope for proper morphology, but they were not authenticated. They were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco 11965126) supplemented with 10% fetal bovine serum (SAFC# 12306C), 1× GlutaMax (Gibco 35050061), and 1× sodium pyruvate (Gibco 11360070), at 37°C in a 5% CO2 ambient humidified incubator.

General reagents
Most of the general reagents were obtained from Sigma Aldrich and Thermo Fisher Scientific unless otherwise mentioned.

Plasmid constructions and sample preparation
DNA plasmid construction. The internal tagged CTR1mEos construct was generated by directly engineering the source hCTR1 plasmid (RC201980) which originally harbored human CTR1 fused with a Myc-DDK-tag at the C-terminus. The coding region of CTR1 (amino acid 185–190) and the following Myc-DDK-tag in the source plasmid was first removed by EcoRV digestion and replaced with the polymerase chain reaction (PCR)-amplified mEos4b-Flag fragment (Oligo #1 and #2, Supplementary Table 1) by using the NEBuilder HiFi DNA Assembly Cloning Kit (New England Biolabs E5520S). The resulting plasmid was linearized again by PmeI digestion, and the coding region of hCTR1 amino acids 185–190 with a stop codon was then restored at the C-terminus of the mEos4b-Flag through single-strand DNA assembling (Oligo #3, Supplementary Table 1). This modification resulted in a final construct with mEos4b-Flag internal insertion and kept 188 HCH domain at the fusion protein’s C-terminus.

The Cu-transfer deficient mutant construct, CTR1(M150L)mEos, was further generated by site-directed mutagenesis (Oligo #4 and #5, Supplementary Table 1) using Q5 site-directed mutagenesis kit (New England Biolab E0554S) following the manufacturer’s instructions.

The plasmid expressing CTLA4mEos was constructed by replacing the mEos2 fluorescent protein in the original plasmid pirespuro2-CTLA4-mEos2, a kind gift from Dr. Mike Heilemann (Addgene plasmid # 98285; RRID:Addgene_98285)47, with PCR-amplified mEos4b-Flag fragment (oligo #6 and #7, Supplementary Table 1) between the AgeI and NotI restriction sites for experimental consistency with our other constructs.

Plasmids expressing membrane-anchored single or tandem mEos4b were constructed to validate the smND assay. First, the Tac-mEos4b (TacmEos) construct was generated by inserting a PCR-amplified mEos4b-Flag fragment (oligo #8 and #9, Supplementary Table 1) into a pcDNA3.1/Zeo(-) plasmid which harbored the interleukin-2 receptor α (Tac) extracellular and transmembrane domains (amino acids 1–262, a gift from Dr. Chin-Yin Tai48,49) between EcoRI and BamHI. Then another mEos4b fragment was PCR amplified (oligo #10 and #11, Supplementary Table 1) and cloned into the TacmE plasmid between NotI and EcoRI to generate the Tac-mEos4b-mEos4b (Tac2mEos) construct. Plasmid expression CTLA4mEos were constructed by replacing mEos2 in the original plasmid with mEos4b between AgeI and NotI for experimental consistency with our other constructs. All plasmids were confirmed by Sanger sequencing prior to use.

Cell preparations and transfection. For imaging experiments, 1.5 × 105 COS7 cells were transfected with 1 μg plasmid DNA, either CTR1mEos or CTR1(M150L)mEos using Lipofectamine 2000 (Invitrogen 11668019) for 1 day. The next day, the cells in a 35-mm dish were detached using 300 μl TrypLE Express (Gibco 12604013) and then replated onto a custom-made, poly-L-lysine (Sigma P2636) coated glass-bottom imaging chamber made by no. 1.5H cover glass at a density of 10,000 cells/cm2 and grown in complete DMEM medium without phenol red (Gibco 31053028) for 2 days. Before imaging, cells were treated with or without 100 μM CuCl2 (Sigma C3279) in a complete growth medium for 15 min to induce CTR1 internalization. Cells were then fixed in 4% paraformaldehyde (Electron Microscopy Sciences 15710) in PBS at room temperature for 15 min.

Immunofluorescence staining and immunoblotting. For immunofluorescence staining, COS7 cells transfected with indicated plasmids were grown on a poly-L-lysin coated glass-bottom imaging chamber with 10,000 cells/cm2. Following specific copper treatments, cells were washed 3 times with ice-cold phosphate buffer saline (PBS) and fixed in 4% paraformaldehyde/PBS at room temperature for 20 min. Cells were permeabilized with 0.1% Triton X-100 prepared in the blocking buffer (2% bovine serum albumin (Sigma A7906), 4% normal goat serum (Gibco 16210064) in PBS) at room temperature for 20 min, and then further incubated in the blocking buffer for 30 minutes, followed by incubated with anti-Rab5 antibody (C8B1, Cell Signaling Technology 3547; 1:200 dilution) overnight at 4°C. After 3 times of wash with PBS, cells were probed with Alexa Fluor™ 633-conjugated secondary antibodies (Thermo Fisher Scientific A-21071; 1:500 dilution) for an additional 2 h at room temperature. After three final washes with PBS, the cells were ready for imaging.

For immunoblotting, cells were harvested in the lysis buffer (25 mM Tris pH 7.4, 150 mM NaCl, 0.1% SDS, 1% Triton-X-100 and 1x Halt Protease inhibitors cocktail (Thermo Scientific)) and incubated on ice for 60 min for collecting total proteins. Alternatively, the Mem-PER™ Plus Membrane Protein Extraction Kit (Thermo Scientific) was employed for collecting subcellular fractionated proteins. Cell lysates were further centrifugated at 16,000 × g for 15 min to remove insoluble substances. The protein concentrations were determined by BCA protein assay. Equal amounts of protein were prepared in Laemmli SDS sample buffer containing 2.5% β-mercaptoethanol, heated at 70°C for 5 min, and resolved in SDS-PAGE followed by transferred onto a PVDF membrane for immunoblotting. Anti-Flag mouse IgG (Sigma-Aldrich F1804, 1:1000 dilution), anti-Flag rabbit IgG (Rockland 600-401-383, 1:2500 dilution) or anti-SLC31A1/CTR1 rabbit IgG (Abcam ab129067, 1:1000 dilution) were used as primary antibodies and horseradish peroxidase (HRP)-conjugated anti-mouse or -rabbit IgG (Jackson ImmunoResearch Lab, 1:10000 dilution) was used as the secondary antibody. The blots were developed in Immobilon TM Western Chemiluminescent HRP Substrate (Millipore) and visualized by using ChemiDoc Imaging System TM (BioRad). The same blots were stripped and re-probed with anti-β-Actin (Sigma-Aldrich A5441, 1:5000 dilution), anti-Caveolin-1 rabbit IgG (Cell Signaling Technology 3267, 1:1000 dilution) or anti-Na+/K+-ATPase alpha1 IgG (Santa Cruze sc-21712, 1:1000 dilution) as a loading control. The band densitometry were quantified by using ImageLab Software TM (BioRad) or ImageJ (NIH). The specificity of the anti-Flag and anti-CTR1 antibodies were validated along with our experiments. (Supplementary Fig. 1) The validation information of all the antibodies used in this study is also provided in Supplementary Table 2.

Functionality assays
Surface biotinylation. To measure the abundance of the CTR1 protein on the cell surface under various stress conditions, HEK293 or COS7 cells were transfected with tagged constructs (i.e., CTR1mEos or CTR1(M150L)mEos) using Lipofectamine 2000 (Invitrogen 11668019) following the manufacturer’s instructions. After 48 h of transfection, cells were treated with either CuCl2 or bathocuproine disulfonic acid (BCS) to a final concentration of 50 μM or 100 μM, respectively, in the culture medium for 30 min. For ionophore treatment, CuES was freshly prepared by mixing elesclomol with equimolar amounts of CuCl2 in the tube for 5 min. The resulting complex was then applied to culture medium to a final concentration of 2 µM.

After treatment, surface proteins were labeled by 0.5 µg/mL EZ-Link™ sulfo-NHS-SS-biotin (Thermo Scientific 21331) on ice for 30 min. Cells were rinsed with an excess amount of 200 mM glycine to quench the excess reactive amine group on ice for an additional 15 min. The cells were then harvested in the lysis buffer (0.1% SDS, 1% Triton-X-100, and 1X Halt Protease inhibitor cocktail in PBS), followed by incubation at 4°C with rotation for 60 min. For each 35-mm dish, 250–300 µL of lysis buffer was used, while for each 60-mm dish, 500 µL of lysis buffer was applied. The insoluble material was removed by centrifugation at 16,000 × g for 15 min. Protein concentrations were determined by BCA protein assay.

Equal amounts of total proteins from each sample were incubated with 50 µl Pierce™ NeutrAvidin™ agarose beads (Thermo Scientific 29200) at 4°C overnight with rotation to bind the biotinylated proteins. The beads were washed with 1 mL lysis buffer three times to remove non-specific binding. The bound proteins were dissociated in 30 µl of 2X Laemmli SDS-sample buffer and resolved by immunoblotting, using an anti-Flag polyclonal antibody (Rockland 600-401-383) to detect the tagged CTR1 proteins. The same blots were re-probed with Na/K ATPase α1 (Santa Cruz sc-21712) or Caveolin-1 (Cell Signaling Technology 3267) antibodies as a loading control.

Plasma membrane fractionation and blue-native PAGE. To characterize the oligomeric property of CTR1mEos fusion proteins, COS7 cells were transfected with either non-tagged CTR1 (CTR1*) or mEos4b-tagged variants and treated with 100 µl CuCl2 as previously described. Following treatment, plasma membrane fraction were isolated using Yamamoto’s protocol with minor modifications50. The membrane fractions were solubilized with a native-compatible buffer containing 50 mM NaCl, 50 mM Imidazole/HCl, pH 7.0, 2 mM 6-aminohexanoic acid, 1 mM EDTA, 1x Protease Inhibitors, and 4.4% digitonin. The solubilized plasma fractions were mixed with Coomassie Brilliant Blue G-250 and resolved by blue-native PAGE following Wittig’s protocol51. To immunodetect CTR1, the gel was incubated in 1% SDS with 1% β-mercaptoethanol for 10 min. Proteins were then transferred to a PVDF membrane and probed with CTR1 antibody following standard immunoblotting procedures.

WGA surface labeling. To identify newly internalized vesicles that originated from the plasma membrane, we labeled cells with Alexa Fluor™ 555 or Alexa Fluor™ 647-conjugated WGA (Invitrogen W32466 or W32466) following the manufacturer’s instructions before inducing endocytosis. In preparation, cells expressing CTR1mEos or CTR1(M150L)mEos were grown in serum-free and phenol red-free DMEM medium (Gibco 31053028) with 50 μg/mL cycloheximide (CHX, Sigma C1988) for 1 h at 37°C. This treatment halted new protein synthesis and was followed by chilling on ice to suspend ongoing endocytosis. Cells were rinsed twice with ice-cold HBSS (Gibco 14175095) and incubated with WGA at a concentration of 2.5 μg/mL in HBSS on ice for 10 min. After removing excess staining solution with three rinses in ice-cold HBSS, the cells were incubated in complete growth medium containing serum and either just CHX (for basal conditions) or CHX plus Cu (for Cu-stressed conditions) to trigger endocytosis at 37°C for 15 min. Cells were then rinsed twice with ice-cold PBS and fixed with 3% paraformaldehyde (Electron Microscopy Sciences 15710) and 0.1% glutaraldehyde (Electron Microscopy Sciences 16365) in PBS. Approximately 1–1.5 mL of fixation buffer was applied to the 35-mm glass bottom dish, ensuring it completely covered the surface. After fixation, the cells were washed again with PBS before imaging using a wild-field epi-fluorescence microscope (Olympus CKX53) under 40× Objective (Olympus LUCPLFLN40XRC) for quantifying global endocytosis. The success of WGA-labeling and detecting internalization was demonstrated by the representative single section images taken by using confocal microscopy (Olympus FV3000).

Inductively coupled plasma mass spectrometry (ICP-MS). HEK293 cells expressing CTR1mEos or CTR1(M150L)mEos were used to quantify the intracellular Cu levels. HEK293 cells were exposed to 50 μM CuCl2 for either 30 or 60 min. After treatment, cells were scraped into PBS and collected by centrifugation at 1000 × g for 5 min. The cell pellet was digested in 50 μL of ultrapure 70% HNO3, heated at 70°C for 30 min and then at 95°C for an additional 2 hrs. Samples were left to cool down naturally to room temperature overnight to achieve complete mineralization.

Mineralized samples were further diluted in ultrapure water to ensure the final HNO3 concentrations were below 2%. These samples were then analyzed using a triple quadrupole inductively coupled plasma–mass spectrometry (Agilent 8800 ICP-QQQ MS). The analysis focused on isotopes 63Cu and 65Cu in helium MS/MS mode, and 32S and 34S in oxygen MS/MS mode, to ensure precise detection. Copper concentrations were determined against standard curves and the values obtained were normalized to the total sulfur content within the same samples, as assessed by ICP-MS, to ensure accuracy in quantification.

Cell viability assay. To assess cell viability under copper stress with endocytosis inhibition, COS7 cells were seeded in multi-well plates and cultured in DMEM medium containing 10% FBS overnight to ~70% confluency. The Cells were then serum-starved for 2 h before being treated with either 80 µM Dynasore (Calbiochem 324410) or an equivalent volume of the vehicle, DMSO (final 0.2%, VWR 97063-136) for an additional 30 minutes. Subsequently, copper stress was induced by adding CuCl2 to a final concentration of 100 µM in the medium containing 1% FBS. Cell viability was assessed after 24 h by bright-field microscopy. Adherent cells were considered viable, whereas non-adherent, rounded cells were classified as non-viable.

To avoid transport-deficiency confounding, we did not pursue a WT versus CTR1(M150L) cell-death comparison, because CTR1(M150L) fails to accumulate intracellular copper under challenge and any survival difference would be dominated by reduced Cu entry rather than oligomerization or endocytosis perse.

Structural conformation modeling and thermodynamics simulation analysis
Modeling and optimization of human CTR1 trimer structure. The copper transporter is a highly conserved protein across different species7,10,11, with the protein sequences of human CTR1 and Salmo salar (Atlantic salmon) Ctr1 being 77% identical. This high degree of conservation makes the X-ray structure of Salmo salar Ctr1 (sCtr1) an excellent template for modeling the human protein. To construct the trimeric structure of human CTR1 and its relevant mutants, we used the monomeric structure of sCtr1 (PDB ID: 6M97 for the copper-free form and 6M98 for the copper-bound form, with Cu+ present at the locations corresponding to human CTR1 M150 and M154) as templates for the SWISS-Model7,52. Using PyMol53–56, we aligned three copies of the predicted monomeric human CTR1 structure to the trimeric complex of sCtr1. This alignment resulted in a modeled trimeric structure of human CTR1, leveraging the structural information from the highly conserved sCtr1 to provide a reliable representation of the human protein.

Note that the published sCtr1 has an N-terminal (residues 1–40) truncation and a thermostabilized apocytochrome BRIL replacement at the intracellular loop (residues 94–120) of the original sCtr1 polypeptide7. To accurately represent the human CTR1 structure, we removed the extra polypeptide of BRIL and added back the missing residues in the crystal structure using Modeller57.

To ensure the structural viability of the human CTR1 model, an extensive structural integrity analysis was conducted using the Gaia server56, which revealed multiple steric clashes among atoms. To address these issues and ensure the model’s viability for further studies, Chiron53 was employed to efficiently address and resolve these clashes, optimizing the modeled CTR1 for further studies.

Evaluating CTR1 stability through computational mutagenesis. To evaluate the thermodynamic stability of wild-type CTR1, we used FoldX to estimate the dissociation energy of its trimeric structure20. FoldX calculated the binding energies between subunit pairs, namely A-B, B-C, and A-C, as −11.19 kcal/mol, −11.29 kcal/mol, and −12.08 kcal/mol, respectively. The total dissociation energy, obtained by summing these pairwise interactions, was ~34.56 kcal/mol, representing the energetic cost required to fully dissociate the trimer into individual subunits. Computational mutagenesis was employed using the software Eris21, a sophisticated tool designed for virtual mutagenesis and thermodynamic analysis. We used Eris to substitute the amino acid methionine at position 150 with leucine (M150L) and alanine (M150A) to assess their impact on the protein’s stability. The method hinges on the calculation of the change in free energy (ΔΔG) relative to the wild-type CTR1, providing a quantitative measure of the mutations’ effects.

Eris executes this analysis by initially repacking the side chains of the mutated sites and then employing discrete molecular dynamics (DMD) simulations to optimize the protein’s entire structure58–61. Eris calculates the free energy of the entire structure before and after the simulation. The discrepancy in free energy values serves as an indicator of how each mutation influences the overall stability of CTR1. Eris employs the Medusa force field, a physics-based energy function that accounts for key molecular interactions. These include both attractive and repulsive van der Waals forces, solvation energies, and hydrogen bonding—within the backbone, between side chains, and between side chains and the backbone. The force field also incorporates side-chain rotamer preferences based on backbone φ and ψ dihedral angles. The Met–Cu(I) interaction is modeled within the Medusa framework. Force fields are parameterized by training their energy terms, such as van der Waals and solvation, on high-resolution protein structures, ensuring that the native amino acid sequences possess the lowest free energy62.

Molecular dynamics simulations. To simulate CTR1 in a cellular context for analyzing CTR1 protein dynamics in membrane environments, we embedded CTR1 into a membrane and added counter ions using Charmm-GUI63. The membrane boundaries are between residues A66 and S97, Q125 and M154, and G158 and V181 on each chain of the trimer. The constructed membrane comprised of 80% POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and 20% cholesterol, reflecting a biologically relevant composition.

Molecular dynamics simulations were performed using the PMEMD.CUDA program in Amber 1864. We utilized the Amber force field, which includes well-validated parameters for Cu(I) coordination65. The protein was parameterized using the ff14SB force field, with the TIP3P water model and Li–Merz parameters for monovalent transition metal cations. The simulations utilized a 2-fs time step, maintained the conditions of constant pressure (1 bar), and achieved the temperature (300 K) by using the isothermal-isobaric (NPT) ensemble. To ensure charge neutrality in the system, sodium ions were introduced, along with additional Na+ and Cl- ions, to achieve a physiological ion concentration of ~0.15 M.

The initial simulation phase spanned 50,000 steps, equivalent to 100 ns, providing preliminary data on root-mean-square fluctuation (RMSF) and unadjusted root-mean-square deviation (RMSD) metrics. Subsequently, the first 50 ns of RMSD data were discarded to allow for system equilibration. The simulation was then extended by an additional 800 ns, enabling a more precise determination of RMSD and RMSF values. These measurements were computed using the Cpptraj tool within the Amber 18 framework, facilitating a comprehensive analysis of protein dynamics and stability within the membrane environment.

Single molecule localization microscopy (SMLM) and image analysis
Microscope setup. All single-molecule fluorescence experiments were conducted on an inverted fluorescence microscope (Olympus IX83) with the excitation and emission light paths detailed in Supplementary Fig. 3a, and Supplementary Table 1. In the excitation path, lasers with wavelengths of 405 nm, 488 nm, 552 nm, and 640 nm (Coherent, Inc.) were coaxially aligned using a series of long-pass dichroic mirrors (Chroma Tech. Co., ZT458-UF1, ZT488-UF2, and ZT532rdc-UF2). The laser power density for each wavelength was finely tuned using a combination of a half wave plate and a polarizer pair, optimizing conditions for the activation and detection of single molecules. These lasers were directed into the microscope via the back port and focused through a high numerical aperture objective (Olympus, UAPON 100X, N.A. = 1.49) to excite the fluorescent dyes or proteins under study. The typical laser spot size at the sample focal plane is ~60 μm in diameter. The power density of 405, 488, 552, and 640 nm lasers is 0.0708 W/cm2, 513 W/cm2, 184 W/cm2, and 251 W/cm2, respectively. In the emission path, the fluorescent signals from the excited fluorescent dyes or proteins were then collected by the objective and relayed to a highly sensitive CMOS camera for detection (Teledyne Photometric Inc.).

Quantifying TIRF illumination depth. To accurately quantify the evanescent field strength of our TIRF microscopy, we employed the raisin-cake scanning method (Supplementary Fig. 3b), which involves plotting field strength against z-depth to reveal the penetration depth of the evanescent field66.

We used 100 nm gold nanospheres as location markers, utilizing their scattering intensities to indicate evanescent field strength. The calibration slide was prepared by mixing 200 µL of a sonicated gold nanosphere solution with 1% agarose gel (0.005 g agarose in 500 µL ultrapure water). This mixture was then heated for 30 s in a microwave with a Petri dish water bath and allowed to cool to room temperature, ensuring even distribution and stability of the markers for imaging.

Imaging began with bright-field microscopy to identify the rough lateral locations of gold nanospheres on the glass coverslip surface. The z-location of each gold marker was then determined by re-imaging the sample under fluorescent mode with a 488 nm laser in epi-illumination, scanning from the bottom to the top of a 3 µm range in 10 nm steps. The scattering intensity of each gold nanosphere at each z-location was recorded. Summing the z-image stack from the epi movie revealed a clear point spread function (PSF) for each gold nanosphere. We calculated the center in the x and y dimensions and analyzed the mean intensity within a 3 × 3 pixel area along the z-stack to construct an intensity versus z trajectory. This trajectory’s peak represents the tightest focus of each gold nanosphere. To obtain the precise z location, each trajectory was fitted with a Gaussian function (Supplementary Fig. 3c). This single nanosphere-based analysis was repeated for ~84 gold nanospheres within the 400 nm-wide field of view.

To correct the slight tilt in our system (Supplementary Fig. 3d), we fit the z locations with the equation “z = ax + by + c” and subtracting z0 from all gold nanospheres. For assessing the evanescent field strength, the calibration slide was re-imaged under TIRF mode near the z0 plane. We conducted a 500 nm z-scan under TIRF configuration to identify the brightest gold nanospheres, which helped eliminate the influence of inhomogeneous objective focus volume and the tilted coverslip. After identifying the gold nanospheres with the brightest intensity at a given z from the z stack, we obtained the mean intensity from a 3 × 3 pixel area.

To estimate the noise level at the selected z plane, we created a histogram of the entire image’s intensity and selected the bottom 14% of pixel intensity to calculate the standard deviation of the intensity distribution. We then computed the signal-to-noise ratio (SNR) and used it as an indicator of field strength. Combining the z information from epi-illumination and SNR from TIRF illumination, we generated a field strength versus z-depth plot (Supplementary Fig. 3e). The exponential decay convoluted with a Gaussian function. The final penetration depth was determined to be 50 nm, providing a precise measure of the evanescent field strength.

Image and localize surface CTR1 and Rab5 positive areas. To specifically probe surface CTR1 and correlate their oligomeric state distribution to membrane trafficking hot spots (Rab5+ areas), we simultaneously determined the CTR1 and Rab5+ areas localizations using two-color fluorescence microscopy with total internal reflection (TIR) excitation. The TIR angle was controlled by a translational stage (Thorlabs Inc. LST300) on the back of the microscope. This setup enabled selective excitation of surface CTR1 and Rab5 by creating evanescent fields ~50 nm deep, ensuring targeted illumination of these proteins.

Surface CTR1 locations were identified using photoactivated localization microscopy (PALM), achieving a typical lateral resolution of ~20 nm. The CTR1mE was activated by a 405 nm laser (power ranging from 0.0708 W/cm2 to 7.08 W/cm2, gradually increased as needed) and simultaneously excited with a 552 nm laser at 184 W/cm2. The emitted red emission from mE was collected by the objective, filtered by a bandpass filter (ET610/60m, Chroma Tech. Co.) and detected by a CMOS camera with a 50 ms integration time.

Rab5 proteins, labeled with Alexa Fluor™ 633, were excited with a 640 nm laser at 251 W/cm2, filtered by a far-red bandpass filter (OSF-680/42, Chroma Tech. Co.), and detected accordingly. The total number of frames recorded for each cell varied between 53,000 to 118,000, depending on protein expression levels, to ensure complete imaging of all the proteins.

Image analysis. A custom-developed MATLAB program was employed to process fluorescence micrographs. It analyzed the fluorescence micrographs and quantified the locations of surface CTR1 and Rab5+ areas, including the diameters of Rab5+ zones. The images went through static background subtraction, PSF fitting, duplicate removal, and drift correction for accurate determination of CTR1 locations.

To remove the static background caused by gold markers or cell scattering in each image, we calculated a background image by averaging 50 images taken before and after the target image. Subtracting this from the target image yielded a background-subtracted image, which was then used to identify single-molecule events. Local bright pixels were detected using a local statistical maximum search algorithm. The local bright pixels were identified through the local maximum search algorithm. Once identified, each subregion (9 × 9 pixels) containing the fluorescent PSF was fitted with a two-dimensional (2D) Gaussian function Eq. (1), I(x,y), to determine the center location (x0, y0):

I(x,y) = b + e^((x-x0)^2/2σx^2 + (y-y0)^2/2σy^2) (1)

Note that the I(x,y) represents the spatial intensity distribution of the PSF; x0 and y0 specify the center position of the peak; σx and σy indicate the Gaussian function’s standard deviation, which controls the width of the bell shape.

The theoretical resolution, based on the Airy disc’s full width at half maximum (FWHM) for the PSF, is calculated using FWHM = 0.51λ/NA, where λ is the wavelength and NA is the numerical aperture of the objective lens. The FWHM for mEos4b-labeled CTR1 and Rab5 were ~200 nm and 230 nm, respectively. Localization error for each PSF was quantified using the Eq. (2)67:

Error_i = σ_i / sqrt(N) * sqrt(1 + 12 * a^2 / (N * σ_i^2) + 8 * (π * σ_i^2 * b^2) / (a^2 * N^2)) (2)

where σi is the standard deviation of the 2D Gaussian fit result, N is the total number of photons of the PSF, a is the pixel size, and b is the standard deviation of the background obtained by subtracting the fitted 2D Gaussian function from the background-subtracted image.

For accurate representation of CTR1 locations by single-molecule PSFs, only PSFs with an FWHM between 117 to 600 nm, localization error between 0 to 20 nm, and SNR larger than 3.6 were considered in the analysis (Supplementary Fig. 3f).

Single-molecule neighbor density (smND) assay
The smND assay (Supplementary Software 1) is a technique for determining the oligomeric states of proteins within cells. Using protein locations obtained from SMLM, smND calculates the density of neighboring protein molecules within a defined region, yielding the neighbor density (ND). This data is then used to generate a Probability of Neighbor Density (PND), which reflects the oligomeric states of the proteins. Critical factors, such as fluorophore activation efficiency and protein concentration, are incorporated into a theoretical model to ensure the experimental results align with actual cellular conditions. The weighted average subunit number (N) is used to quantify the average oligomeric state of a protein population (Supplementary Methods).

To specifically examine CTR1 oligomerization in distinct cellular compartments, analyses were performed separately for regions within Rab5+ compartments and for regions outside these compartments (Rab5– areas), using precise fitting, area selection, and background compensation methods (Supplementary Methods).

To benchmark smND performance, it is crucial to use appropriate oligomeric controls, for which there are two primary strategies. The first strategy uses well-characterized proteins that are known to self-assemble into defined oligomeric states in vivo, such as CD28 or CTLA4. A complementary strategy is to engineer constructs with a precisely controlled number of fluorophores per molecule. This approach provides an unambiguous ground truth for fluorophore stoichiometry, thereby directly testing the quantification capabilities of the smND model, independent of biological assembly equilibria. In this study, we leveraged both approaches for comprehensive validation. We employed CTLA4 as an expected homodimeric biological standard. However, we measured a steady-state population of ~75% dimers, reflecting its dynamic equilibrium in situ and confirming the assay’s sensitivity to mixed biological states (Supplementary Fig. 4f–h). We then used TacmEos (single mEos4b) and Tac2mEos (tandem mEos4b) as references with effectively ‘locked’ single- versus dual-chromophore stoichiometries anchored to the membrane via Tac-antigen. These constructs allowed benchmarking of smND performance under well-defined oligomeric and environmental conditions (https://doi.org/10.6084/m9.figshare.29483831). The smND assay provides a robust framework for detecting changes in oligomeric states under different conditions. While the assay may not provide absolute subpopulation values, it reliably defines the upper and lower limits of each population, making smND a valuable tool for studying protein oligomeric state changes within cells.

Analysis of global endocytosis
To quantify global endocytosis, COS7 cells were labeled with Alexa Fluor™ 555-conjugated WGA (Invitrogen W32464), following treatment with 100 μM CuCl2 for 15 min to induce endocytosis, the cells were fixed and imaged using a wide-field epi-fluorescence microscope (CKX53, Olympus). The size and number of internalized WGA-labeled objects were analyzed as indicators of global endocytosis.

To assess the numbers and sizes of internalized vesicles, the WGA-labeled objects were excited with a mercury lamp (Olympus U-HGLGPS) and visualized in the red channel through filter sets (BP 530-550/DM570/LP575) (Olympus U-FGW), and the acquired images were analyzed using NIH ImageJ/Fiji software. The red images were processed by background subtraction using the rolling-ball algorithm with a radius of 50 pixels. The images were then auto-thresholded using the ‘Moments’ algorithm to generate binary masks. These segmented WGA images were analyzed using the ‘Particle Analysis’ tool, with a filter size set to an area of 0.12–4 μm² and circularity of 0.04–1.00 to identify and quantify internalized WGA puncta. Bright-field images of the cells were used to determine the cell area.

To compare CTR1 density of Rab5+ to that of the outside Rab5 areas (Rab5-), COS7 cells were transfected with indicated CTR1mEos, stained with Rab5, imaged and analyzed as prior described. Rab5+ objects with diameters smaller than 600 nm were selected as Rab5+ areas. Meanwhile, for each selected Rab5+ area, another non-overlapping Rab5- area of the same size was randomly selected within the cells. This approach ensured equal numbers and total areas of Rab5+ and Rab5- regions for comparison. CTR1 density for each Rab5 area was calculated by dividing the number of CTR1 molecules by the area of the Rab5 region.

Quantification and statistical analysis
The study analyzed a total of 27 cells, 2776 Rab5+ and 2215 Rab5- areas, and 1,402,934 CTR1 protein locations cumulatively from all four conditions. The molecular densities and oligomeric states of CTR1 were initially assessed within each Rab5+ or Rab5- ROI across all cells with the similar CTR1 expression level were pooled and compared between the basal and copper-stressed conditions. Statistical evaluation was performed using the two-sample Student’s t test in MATLAB, Statistics and Machine Learning Toolbox, “ttest2” function. The calculations and statistical analyses for global endocytosis and other biochemistry data were performed using Excel and GraphPad Prism 9. The statistics of all data were summarized in figures and Source Data. To ensure robust statistical interpretation, the Shapiro–Wilk normality test was initially performed to assess data distribution. All the data were performed under single cell base and analyzed using the Student’s t test. Additionally, data points are explicitly shown using dots overlayed on bar graphs, enhancing data transparency and readability.

Sample size. No a priori power calculation was performed. Sample sizes were chosen based on prior work using this system and pilot variability estimates, and were sufficient to detect the reported effects with consistent direction and magnitude across ≥3 independent experiments. The unit of analysis is specified in each legend (typically cells for single-cell readouts; independent experiments for biochemistry assays). Exact n values appear on panels/legends and in Source Data.

Allocation and blinding. Cells were assigned to conditions by plate/well at the time of treatment. Randomization beyond plate/well allocation and blinding were not applicable because analyses used automated pipelines with pre-specified parameters applied uniformly across groups.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors investigate the molecular mechanism of how the transporter CTR1 regulates copper uptake into a cell. In a first part, the thermodynamic stability of wild-type CTR1 and an endocytosis-deficient mutant, CTR1(M150L), was calculated using molecular dynamics simulations. They then constructed a fusion of CTR1 with the photoactivatable fluorescent protein mEos4b (CTR1_mE), which was found to be fully functional. With this fusion, PALM experiments and quantitative analyses of the oligomeric state of CTR1_mE were performed at endocytic sites identified by Rab5. The authors found that high copper levels induce the de-oligomerization of CTR1 from a functional homotrimer to monomers. Together with the surface depletion of CTR1 under copper stress, the authors derive a comprehensive molecular model of copper uptake regulation by CTR1.\n\nA key to quantification methods are molecular controls. For that purpose, the authors chose the Tac antigen, which is present in the cells in a monomer-dimer equilibrium. Tac should be insensitive to high copper concentrations, but the observed shift in monomers and dimers is large, e.g. from 0.48 to 0.26 – these are very similar values to CTR1 (Figure 3). I understand that in case of Tac, that was found to be not significant (statistical analysis), whereas it was found significant for CTR1. More information and clarification is needed on this important point:\n- The authors used a t-Test. Does the data meet the criteria (normality)? Otherwise, Mann-Whitney would be an alternative.\n- Please show all data points (Figure 3, Ext Fig 4) instead of only bar plots, e.g. in a violin plot or in dots next to bars.\n- How do the authors interpret the discrepancy in significance? That would of course also depend on the type of test that should match the type of distribution and sample size in each scenario.\n- Given the large error (e.g., 0.48 and 0.26 being not distinguishable for Tac; in addition, the authors rate their method of quantification to have an accuracy of around 10%): how accurate is the estimate of the dimer population in Figure 3c and 3h? This is a general question, and unrelated to the multi-step model.\n- Why did the authors choose a reference protein with a mixed oligomeric state, rather than a pure dimer or trimer?\nThe quantification of protein oligomers from SMLM data has become increasingly important in recent years. In addition to the method chosen by the authors, an alternative approach that analyzes the kinetics of fluorescence emission to report protein oligomers (qPALM) exists and has been shown to be useful (e.g. PMIDs 38226794 and 38457493). I am not asking the authors to analyze their data using this method as well (although this should be directly possible and it would be interesting to compare the two methods); nevertheless, I would like to ask the authors to briefly mention and discuss both approaches in the discussion section, especially with regard to the points raised above. This will be helpful for the reader.\nOne limitation of the study is that the mechanism of de-trimerization remains unclear. The discussion here remains somewhat vague and points to an unknown “sensor protein”.\nMinor comments\nExtended Data Fig. 2, I find it difficult to see the internalization of CTR1 at high copper levels from the one cell shown. Could the authors rather show a larger panel of many cells (tens or more)?\nExtended Data Fig. 3, it is pretty much accepted in the field that an evanescent light field penetrates to around 100 nm into a cell; the authors made an extra effort to show that here, which I appreciate.\nThe abbreviation ‘mE’, for mEos4b, is a bit confusing to read and maybe unusual.", "reviewer_2": "The study tests an interesting and novel idea that the de-oligomerization of the copper transporter CTR1 is an initial step in CTR1 endocytosis, which effectively stops copper (Cu) uptake. While the concept is very intriguing, the evidence of de-oligomerization is not very strong (or not convincingly explained), and the mechanism detailing how a stable trimer dissociates into monomer has not yet been investigated. The work appears to be at the early stage.\nSpecific comments:\n1. The authors seem to assume that copper binding at M150 of CTR1 triggers endocytosis. There is little basis for such assumption. If this was the case, then each time Cu binds to CTR1 the protein would endocytose, i.e. CTR1 will act as transferrin receptor rather than a transporter. Furthermore, the C-terminal mutant that retains M150 but has the C-terminal HCH motif mutated does not endocytose, which suggests that the response to elevated copper comes from the cytosol. The alternative explanation for M150 mutant not being able to endocytose is that the mutation inhibits Cu uptake and eliminate cytosolic Cu sensing by CTR1. If the authors believe that their model is correct, this alternative scenario needs to be eliminated\n2. “The increased stability of the trimer in the mutant raises the possibility that the ability of CTR1 to alter its oligomeric state plays a critical role in its regulation of copper uptake” – Can the authors calculate the energetic cost of de-oligomerization?\n3. How does the insertion of 3 GFP-FLAG into a small and tightly packed trimeric protein, like CTR1 affect protein stability? AlphaFold3 analysis of the wild type and CTR1-GFP-Flag trimers suggests a significant destabilization of the trimer by the fusion. The authors should provide evidence that the stability of the wild-type and fusion CTR1 are similar. This can be done either computationally or experimentally (for example, by analysis of thermostability/urea sensitivity on nondenaturing gels or by correlation spectroscopy)\n4. Presence of monomers, dimers, and trimers at the plasma membrane at steady state needs to be verified independently because it may be a result of overexpression and/or instability of the fusion protein (see above). In addition to surface biotinylation experiments that convincingly show Cu induced endocytosis, the authors should perform blue native gel analysis for endogenous CTR1 to ensure that the fusion construct behaves identically, when it comes to monomers, dimers, and trimers.\n5. Can the authors exclude that the increased activity of CTR1mE is caused by CTR1mE homotrimer rather than heterotrimers with the endogenous CTR1? To better illustrate functionality of CTR1mE – the comparison should be made with the wild-type CTR1 expressed at the same level as CTR1mE.\n6. It is not clear how method with 10 nm resolution can resolve individual CTR1 subunits, which have a cross-section in the membrane of 5 nm. This needs to be explained better.\n7. Related, if the method does resolve CTR1 monomer from trimers then the individual CTR1 signals within Rab5 puncta should be markedly different from CTR1-signals outside Rab5 puncta, where functional CTR1 is uniformly trimer. Is this what the authors observe? Figure 2e (the CTR1 map) is difficult to interpret\n8. The smND assay is a new and very intriguing methodology and it has to be explained better. For example, can the authors exclude that the “de-oligomerization” is simply an increase in the distance between fluorophores due to copper-induced conformational changes in CTR1. It was demonstrated that CTR1 undergoes conformational changes upon Cu binding (PMID: 36409899). The authors should formally rule out this possibility.", "reviewer_3": "The Cu uptake transporter CTR1 undergoes endocytosis in response to excess Cu to maintain cellular Cu homeostasis. By employing single-molecule localization microscopy (SMLM) and single-molecule neighbor density (smND) assays, the authors demonstrate that excess Cu induces rapid monomerization of the trimeric CTR1 prior to endocytosis. This process inhibits Cu uptake and is impaired in the endocytosis-deficient CTR1 (M150L) mutant. Thus, authors conclude that de-oligomerization of CTR1, linked with endocytosis as a rapid mechanism to prevent excess Cu entry. While the study is interesting and important, a major limitation is the absence of data showing functional consequences of de-oligomerization of CTR1 in response to excess Cu, both in vitro and in vivo. For instance, experiments demonstrating Cu-induced cell death (cuproptosis) using CTR1 wild type and mutants are needed. Moreover, it is essential to perform live cell imaging of WT or mutant CTR1 with Rab5 in COS7 cells. Additional experiments are therefore required to increase the quality of this manuscript.", "reviewer_4": "The article titled \"Human Transporter De-oligomerization Regulates Copper Uptake into Cells\" presents a combined computational and experimental study. The authors demonstrate that an excess of copper induces the monomerization of CTR1, which typically transports copper as a trimer. This monomerization process is suggested to prevent copper overload.\nThe article is intriguing as it provides new insights into the still unclear aspects of copper internalization. However, several issues need to be addressed before the paper can be considered for publication.\nIn the introduction, where the mechanism of copper uptake is discussed, the authors should reference recent papers that have investigated the detailed mechanism of copper transport. For example, see Janos et al. https://doi.org/10.1017/qrd.2022.2 and Aupic et al. https://doi.org/10.1073/pnas.2214602119.\nThe authors claim in the first paragraph that the M150L mutant significantly stabilizes the trimeric form by decreasing the ΔΔG. However, examining the structure of CTR1, it appears that the selectivity filter of the CTR1 trimer may be affected by introducing a residue larger than methionine. Previous MD simulation studies have shown that methionine is already quite bulky. Have the authors performed actual MD simulations to verify the structural impact of this mutation on CTR1 stability?\nAnother aspect that needs clarification is that M150L is the bottom methionine triad in the structure, which means that the upper methionine triad M154 can still bind copper and stabilize the trimer in this manner. The authors should also consider the other M150L variant to ensure copper is not bound to the upper triad of Met in selectivity filter and therefore it is not stabilizing the trimer structure in this manner.\nAs far as I understand, MD simulations are performed in the absence and presence of copper. However, no reference to their parameterization is provided in the methods section. It is well known that this parameterization is not trivial and may require QM-based simulations.\nAdditionally, RMSF values are reported on page 3 with several decimal digits. Have the authors checked how the computed RMSF depends on the simulation length? The observed difference is probably smaller than the error and not significant. This is particularly relevant when the authors try to spot differences based on the RMSF.\nFurthermore, the experimental structure of CTR1 lacks residues from the N-terminus and part of the C-terminus. According to the methods, these parts have been added to the CTR1 model. If this is the case, since they are both disordered, how is their conformation modeled?\nThe results on the structure and copper-binding abilities of CTR1 are affected by the presence and conformation of the N-term and C-term residues see Aupic et al. https://doi.org/10.1073/pnas.2214602119 and Walke et al., Biophysical Journal, Volume 121, 1194-1204. Thus understanding their influence is a relevant point.\nThese parts contain several copper-binding residues, thus the authors should also create and test the oligomerization properties of these truncated variants (truncanted N-term and/or truncated C-term) in response to copper stress.\nSome contradictory statements are provided in the text. In the discussion, the authors mention the YxxM motif of the intracellular loops (please state exactly the residues you are considering). The authors mention that copper binding at these methionine sites stabilizes the trimer, preventing its dissociation. How does this occur? Can they better argument this point?\nIn this respect, can the authors increase the intracellular concentration of copper, without relying on CTR1 transport, and check whether it is really the intracellular increase of copper that is affecting the oligomerization status?"}, "author_response": "Point-by-point response to reviewers\n[Reply to all reviewers] Thank you for your critical and constructive comments. Following your\nsuggestion, we have revised the manuscript accordingly. Below, we respond (in red) to each\nquestion/comment (in black italic), with our additions to the text indicated (in blue).\nReviewer #1\nThe authors investigate the molecular mechanism of how the transporter CTR1 regulates copper\nuptake into a cell. In a first part, the thermodynamic stability of wild-type CTR1 and an endocytosis-\ndeficient mutant, CTR1(M150L), was calculated using molecular dynamics simulations. They then\nconstructed a fusion of CTR1 with the photoactivatable fluorescent protein mEos4b (CTR1_mE), which\nwas found to be fully functional. With this fusion, PALM experiments and quantitative analyses of the\noligomeric state of CTR1_mE were performed at endocytic sites identified by Rab5. The authors found\nthat high copper levels induce the de-oligomerization of CTR1 from a functional homotrimer to\nmonomers. Together with the surface depletion of CTR1 under copper stress, the authors derive a\ncomprehensive molecular model of copper uptake regulation by CTR1.\n1. A key to quantification methods are molecular controls. For that purpose, the authors chose the Tac\nantigen, which is present in the cells in a monomer-dimer equilibrium. Tac should be insensitive to\nhigh copper concentrations, but the observed shift in monomers and dimers is large, e.g. from 0.48\nto 0.26 – these are very similar values to CTR1 (Figure 3). I understand that in case of Tac, that was\nfound to be not significant (statistical analysis), whereas it was found significant for CTR1. More\ninformation and clarification is needed on this important point:\n1.1 The authors used a t-Test. Does the data meet the criteria (normality)? Otherwise, Mann-\nWhitney would be an alternative.\n[Reply 1.1] We sincerely appreciate the reviewer for pointing out the importance of ensuring that\nthe statistical test used is appropriate for our dataset. The reviewer correctly noted that a t-test\nassumes normality, which may not always be valid. To ensure the robustness of our statistical\napproach, we reexamined our data by performing a normality test (Shapiro-Wilk) on the datasets.\nOur analysis revealed that only the Cu-treated condition passed the normality test, indicating\nthat the assumptions required for a parametric t-test are not fully met. Given this, we reanalyzed\nthe data using the Mann-Whitney U test, a non-parametric alternative that does not assume\nnormal distribution. This reanalysis yielded the same conclusion as our original approach: CTR1\nundergoes a statistically significant shift in oligomeric state under high copper conditions,\nwhereas the population distribution of TacmEos remains unchanged, confirming that our findings\nare robust and not artifacts of the statistical method employed.\nWe have included details of the Shapiro-Wilk normality test and the Mann-Whitney U test in the\nMethods and clearly indicated the statistical methods used in the legend of Figure 3 and\nExtended Data Figure 4. These changes ensure transparency and reinforce the validity of our\nconclusions.\n[Changes in the Main text] To ensure robust statistical interpretation, the Shapiro-Wilk normality\ntest was initially performed to assess data distribution. Due to deviation from normality in some\ndatasets, statistical comparisons between experimental conditions were conducted using the\nnon-parametric Mann-Whitney U test rather than a parametric t-test. Additionally, data points are\nexplicitly shown using dots overlayed on bar graphs, enhancing data transparency and\nreadability.\n1.2 Please show all data points (Figure 3, Ext Fig 4) instead of only bar plots, e.g. in a violin plot or\nin dots next to bars.\n[Reply 1.2] To provide greater clarity on our data processing pipeline, we outline the sequence\nof data transformation leading to the reported results. First, SMLM and fluorescence microscopy\nwas used to detect individual CTR1 molecules and Rab5+ area, respectively. In each Rab5+ area,\nlocal protein concentration was determined by quantifying CTR1 molecules relative to the area.\nRab5+ area with similar protein concentrations were grouped for subsequent analysis. ND values\nat individual concentrations were then computed and compiled into ND distributions. The mean\nvalues and sem of data crossing different concentrations for each experimental condition were\ncomputed to generate Fig. 3b, 3g and Extended Data Fig. 4d (right). Note that ND distributions\nreflect a mixture of monomers, dimers, and trimers and are influenced by local concentration.\nThus, direct interpretation of raw ND data does not yield precise oligomeric composition. With\ndata grouped into defined concentration ranges, a theoretical model was applied to fit the ND\ndistributions. This process enabled the extraction of the relative fractions of different oligomers\nunder each condition. Quantitative results were derived from the fitted data. Both the weighted\naverage oligomeric state (N̅ ; e.g., Fig. 3d and 3i, Extended Data Fig. 4c and 4e right) and the\noligomer population (Fig. 3e and 3j, Extended Data Fig. 4b and 4e left) were generated from\nthese fitted results, where the mean and sem were calculated from the fitted results.\nTo further clarify the link between the ND distributions and the extracted oligomeric populations,\nwe have included raw data points in Fig. 3 and Extended data Fig. 4.\n[Changes in the Main text]\nFig. 3 | Copper-induced changes in CTR1 oligomeric states assessed by TNPD and smND Assays. a, b,\nProbability density functions of two nearest pairwise distance (TNPD, a) and neighbor density (ND, b) for CTR1mEos\nunder basal (open circles) and Cu-stressed (filled circles) conditions. Copper treatment increases TNPD and\nreduces ND, indicating larger separation between CTR1 units and de-trimerization of CTR1, respectively. c, d,\nExtracted oligomer populations (c) and weighted average number of subunits (Nഥ, d) for CTR1mEos show that copper\nsignificantly modulated oligomer populations and Nഥ in CTR1mEos. e, CTR1 trimer percentages extracted from smND\nwith two dissociation scenarios: Model 1 (T → M + D) and Model 2 (T → 3M). Model 1 and Model 2 provide the\nlower and upper limits of trimer populations, respectively. In both models, copper induced significant trimer\npopulations decrease. (f–j) Similar to (a–e) but for CTR1(M150L) mEos. No significant change in TNPD distributions\n(f) nor ND distributions (g). Copper does not affect the extracted oligomer populations (h), Nഥ (i), nor trimer\npopulation (j) in rab5+ areas, confirming that the M150L mutation locks CTR1 in a trimeric state. Data are\nsummarized from three independent experiments. Graphs (b, d, e, g, i, and j) are shown as mean ± SEM. Mann-\nWhitney test. *p < 0.05. See also Supplementary Table 3.\nExtended Data Fig. 4 | Validation of smND assay and Nഥ as a metric for evaluating oligomeric state\ncomposition using membrane-anchored TacmEos and Tac2mEos. a, Non-reduced immunoblotting analysis of\nTacmEos and Tac2mEos overexpressed in COS7, showing their oligomeric populations as 59% monomers and\n41% dimers for TacmEos, and 54% monomers and 46% dimers for Tac2mEos with no significant cleavage. Equal\namounts of the total protein were resolved in both non-reduced and reduced SDS-PAGE, and then the full-\nlength proteins were detected by using anti-Flag antibody. The corresponding percentages of the monomer\n(m) and dimer (d) populations within each fusion protein were quantified by the band intensities from the non-\nreduced gel. b, Comparison of oligomer populations of Tac-fusion proteins extracted from immunoblotting and\nsmND analysis. The immunoblotting and the smND data of TacmEos and Tac2mEos were collected under basal\nconditions, showing no significant (ns) differences in Tac oligomeric state distributions. c, Comparison of Nഥ of\nTacmEos and Tac2mEos extracted from the oligomer populations in immunoblotting (triangle) and smND analysis\n(circle). Values are closely matched across methods. d, Probability density functions of TNPD (left) and ND\n(right) results of TacmEos under basal (open circle) and Cu-stressed (closed circle) conditions. No significant\nchanges are observed, supporting the copper insensitivity of the Tac construct. e, Extracted dimer populations\nand the corresponding Nഥ values of TacmEos remained statistically unchanged between basal and Cu-treated\nconditions, confirming that the oligomeric state of the mEos tag is not affected by Cu treatment. f,\nImmunoblotting analysis of CTLA4mEos resolved in non-reducing (without βME) and reducing (with βME) SDS-\nPAGE showed over 90% of the CTLA4mEos as dimer. g, Probability density functions of TNPD (left) and ND\n(right) results of CTLA4mEos under basal condition. h. Extracted dimer populations and the corresponding\nNഥ values of CTLA4mEos showed 75% dimer population in situ. Panels b, c, e and h are shown as mean ± SEM.\n1.3 How do the authors interpret the discrepancy in significance? That would of course also depend\non the type of test that should match the type of distribution and sample size in each scenario.\n[Reply 1.3] The reviewer raised a question regarding the discrepancy in statistical significance\nbetween CTR1 and TacmEos despite similar population shifts. This discrepancy most likely stems\nfrom differences in sample size and data saturation, which affect statistical power. In our initial\nexperiments, our TacmEos dataset had a smaller sample size compared to CTR1, meaning that\nthe data were less saturated and exhibited greater variability between individual measurements.\nThis resulted in broader confidence intervals, reducing the ability of statistical tests to detect\nsignificant differences between basal and Cu-treated conditions. To address this, we conducted\nadditional smND experiments on TacmEos under both basal and Cu-treated conditions, increasing\nthe sample size comparable to CTR1 to enhance statistical precision.\nWith this expanded dataset, we now observe that the TacmEos population shift (10%) in response\nto Cu treatment is much smaller than previously estimated (22%). The updated analysis\n(Extended Data Fig. 4e) shows that TacmEos exhibits smaller changes, and statistical testing\nconsistently confirms no significant difference between basal and Cu-treated conditions. In\ncontrast, CTR1 shows a significant population shift under the same conditions. This reinforces\nthat copper-induced de-oligomerization is a specific feature of CTR1 and does not occur in\nTacmEos. Therefore, the difference in statistical significance is not due to an artifact of statistical\ntesting but rather reflects the fact that TacmEos does not respond to copper in the same way as\nCTR1.\nWe have incorporated these additional experiments and the corresponding updated analyses\ninto Extended Data Figure 4e in Reply 1.2. The main text and figure legends have also been\nrevised to clearly reflect the increased sample size and improved statistical precision,\nemphasizing the specificity of copper-induced oligomerization shifts in CTR1 compared to\nTacmEos.\n[Changes in the Main text] To assess whether copper treatment affects the oligomeric state of\na protein unrelated to copper homeostasis, we further analyzed TacmEos under both basal and\nCu-treated conditions using a sample size comparable to that for CTR1. Comparing TacmEos\nunder these two conditions revealed no significant difference in Nഥ, indicating that Cu treatment\ndoes not alter the oligomeric state of the mEos tag itself (Extended Data Fig. 4d,e).\n1.4 Given the large error (e.g., 0.48 and 0.26 being not distinguishable for Tac; in addition, the\nauthors rate their method of quantification to have an accuracy of around 10%): how accurate is\nthe estimate of the dimer population in Figure 3c and 3h? This is a general question, and\nunrelated to the multi-step model.\n[Reply 1.4] We acknowledge the reviewer's concern regarding the accuracy of dimer population\nestimates, particularly in relation to our reported 10% error. Our previous theoretical model\n(Analytical Chemistry, 2023 PMC10842889) estimated that the smND approach has an intrinsic\naccuracy of approximately 10% when extracting protein oligomeric states of proteins with\nmultiple oligomeric states under a saturated dataset. The TacmEos data under basal and copper\ncondition (see Reply 1.3) align well with this theoretical prediction, providing experimental\nvalidation that fluctuations within this range reflect the inherent variability of single-molecule\ndetection and analysis rather than systematic errors.\nGiven this accuracy threshold, population shifts close to 10% must be interpreted with caution,\nas they may fall within measurement uncertainty. Both CTR1mEos and CTR1(M150)mEos exhibit\ndimer populations below 10%, meaning that any detected dimer populations are near the\nresolution limit of our method and should be interpreted cautiously. However, the overall\npopulation shift for CTR1 remains robust (>20%) and well above the 10% uncertainty threshold,\nreinforcing that copper-induced de-oligomerization is a genuine effect for CTR1. The observed\ndimer population variability does not affect this conclusion.\nThus, while the smND approach has inherent limitations, the estimates of dimer populations in\nFig. 3c and 3h should be interpreted with caution due to their low abundance, but remain within\nthe expected resolution of this method, and the key conclusions remain valid: CTR1 undergoes\nsignificant oligomeric changes in response to Cu.\nWe have included clarifications in the Discussion section. These modifications explicitly note the\ncaution required when interpreting low-abundance dimer populations, thereby providing\ntransparent context regarding our method's inherent accuracy and reinforcing the robustness of\nour core conclusions regarding CTR1 oligomeric changes.\n[Changes in the Main text] The smND assay inherently provides an oligomeric state\nquantification accuracy within approximately 10%. Thus, observed dimer populations below or\nnear this threshold must be interpreted cautiously, reflecting the assay's resolution limits rather\nthan definitive biological variability.\n1.5 Why did the authors choose a reference protein with a mixed oligomeric state, rather than a pure\ndimer or trimer?\n[Reply 1.5] In principle, relying on a strictly dimeric protein would seem ideal for validating smND,\nbut in practice even “known” dimers tend to occupy multiple states in cells. Proteins are\nsynthesized as monomers and then assemble into their higher-order forms, typically creating an\nequilibrium of monomeric and oligomeric species rather than a single uniform structure. For\nexample, despite non-reduced blots classifying CTLA4 as a stable dimer, our smND data still\ndetect a substantial (about 25%) monomeric fraction in cells, indicating that prior biochemical\ndesignations may not fully reflect in vivo equilibria (Extended Data Fig. 4f-h in Reply 1.3). To\ncircumvent this ambiguity, we employed two constructs engineered to bypass potential re-\nequilibration steps: TacmEos, containing a single mEos, and Tac2mEos, which fuses two mEos4b\nunits in tandem (see details in Reply 1.3). Both are anchored at the plasma membrane via the\nTac antigen, ensuring consistent membrane localization. In Tac2mEos, the tandem arrangement\nphysically locks two fluorophores within a single polypeptide chain, acting as a “built-in dimer”\nreference, whereas TacmEos remains monomeric. This design sidesteps the variable assembly\npathways seen in ordinary proteins and provides a more definitive test platform for smND under\nwell-defined single- vs. dual-fluorophore conditions.\nTo clearly articulate our rationale for selecting these reference constructs and address the\nreviewer's concern, we have included an explicit description of our validation strategy using\nTacmEos and Tac2mEos in the Methods sections. Additionally, Extended Data Fig. 4f-h has been\nupdated to explicitly illustrate the presence of mixed oligomeric states in typical reference\nproteins, thereby clarifying the necessity and utility of our engineered reference constructs\n[Changes in the Main text] Although one might prefer a purely dimeric control, many\nbiochemically designated “dimers” still exhibit partial monomeric states in cells (Extended Data\nFig. 4f–h). To sidestep this ambiguity, we engineered TacmEos (single mEos4b) and Tac2mEos\n(tandem mEos4b) as references with effectively ‘locked’ single- versus dual-chromophore\nstoichiometries anchored to the membrane via Tac-antigen. These constructs allowed\nbenchmarking of smND performance under well-defined oligomeric conditions, free from\nconfounding self-assembly pathways.\n2. The quantification of protein oligomers from SMLM data has become increasingly important in\nrecent years. In addition to the method chosen by the authors, an alternative approach that analyzes\nthe kinetics of fluorescence emission to report protein oligomers (qPALM) exists and has been\nshown to be useful (e.g. PMIDs 38226794 and 38457493). I am not asking the authors to analyze\ntheir data using this method as well (although this should be directly possible and it would be\ninteresting to compare the two methods); nevertheless, I would like to ask the authors to briefly\nmention and discuss both approaches in the discussion section, especially with regard to the points\nraised above. This will be helpful for the reader.\n[Reply 2] We thank the reviewer for pointing out the relevant qPALM approach. Indeed, both\nqPALM and smND use single-molecule localization microscopy to quantify protein\noligomerization inside cells, yet they differ in how they collect and interpret data. qPALM counts\nall fluorescence bursts from photoactivated fluorescent proteins within each cluster—using\nknown monomeric (e.g., β1AR-mEos3.2 or CD86-mEos3.2) and dimeric (e.g., CTLA-4–mEos3.2\nor CD28-mEos3.2) references to calibrate two common sources of error: (i) blinking, whereby a\nsingle fluorophore produces multiple emission events, and (ii) incomplete labeling, whereby\nsome fraction of the tagged subunits never becomes fluorescent. This approach yields direct\nstoichiometry provided cluster densities are low enough to spatially separate each emission\ncluster (e.g., tens to a few hundreds of molecules per μm²) rather than having them overlap.\nsmND, by contrast, calculates the number of neighbors within a fixed radius around each\nlocalization, building a neighbor density histogram that is fitted with theoretical or simulated\ncurves for monomers, dimers, or higher-order oligomers (e.g., trimers, tetramers, or beyond).\nBoth methods provide super-resolution detail in intact cells and yield quantitative measures of\noligomerization.\nHowever, qPALM works best when well-characterized monomer/dimer references exist and\nwhen the expression (hence cluster density) remains sufficiently low to track all emission bursts\ndistinctly. smND is particularly advantageous for mapping diverse equilibrium mixtures or larger\ncomplexes (i.e., oligomeric states above dimer) but depends on selecting an appropriate\n“neighbor radius” and on having sparse enough labeling to avoid excessive random clustering.\nWe have now included a brief comparison of the qPALM and smND methodologies in the\nDiscussion section of our manuscript. This addition highlights their respective strengths,\nlimitations, and suitable applications, thereby providing readers with essential context for\ninterpreting our results and understanding the broader utility of each approach.\n[Changes in the Main text] In addition to our smND assay, alternative single-molecule\nmethodologies, such as qPALM, offer complementary approaches by counting fluorescence\nbursts per cluster, accounting for blinking and incomplete labeling via calibration with well-\ncharacterized monomeric or dimeric references. While qPALM can yield direct stoichiometry,\nit often requires low expression levels to separate clusters spatially, and accurate calibration\ncontrols for blinking behavior. By contrast, smND infers oligomeric states from local neighbor\ndensities, enabling analysis of multi-state protein complexes (e.g., monomers, dimers, trimers)\neven at moderate expression levels. Both approaches leverage single-molecule resolution in\nintact cells and can be complementary in dissecting complex equilibrium mixtures.\n3. One limitation of the study is that the mechanism of de-trimerization remains unclear. The discussion\nhere remains somewhat vague and points to an unknown “sensor protein”.\n[Reply 3] We agree that the detailed molecular mechanism of CTR1 de-oligomerization is not\nfully elucidated. We currently hypothesize an as-yet-unidentified intracellular sensor or adaptor\nthat binds CTR1’s cytosolic loop upon local Cu accumulation. However, fully identifying that\nsensor would require far more extensive biochemical and proteomic exploration. As our core\nfinding is that CTR1 subunits dissociate under Cu stress, and that endocytosis is coupled to this\nde‐oligomerization, regardless of the precise sensor involved. Providing detailed mechanism of\nde-trimerization is thus beyond our scope.\nWe have explicitly addressed this limitation in the Discussion section of the revised manuscript\nby clearly acknowledging the unidentified nature of the proposed intracellular sensor or adaptor.\nAdditionally, we suggest potential future experimental strategies, such as CRISPR-based\nscreens or pull-down mass spectrometry, that could help identify these important components in\nsubsequent studies.\n[Changes in the Main text] Although our study reveals significant copper-induced de-\noligomerization of CTR1 linked to its endocytosis, the precise molecular players facilitating this\nstructural transition remain unidentified. We hypothesize the involvement of intracellular sensors\nor adaptor proteins interacting with CTR1’s cytosolic domain in response to elevated copper\nlevels. Identifying these components is beyond the current study's scope and will require future\nproteomic or genetic screening approaches, such as CRISPR-based identification or affinity pull-\ndown mass spectrometry.\n4. (minor comments): Extended Data Fig. 2, I find it difficult to see the internalization of CTR1 at high\ncopper levels from the one cell shown. Could the authors rather show a larger panel of many cells\n(tens or more)?\n[Reply 4] We appreciate the reviewer's suggestion and have taken additional images to better\nillustrate the internalization behavior of CTR1mEos upon copper stimulation. We have expanded\nExtended Data Fig. 2 to include more representative cells under basal and Cu-stressed\nconditions. Due to the small size of endocytic vesicles, we further annotate colocalized puncta\nwith arrows and provide enlarged images to improve discernibility. This revision demonstrates\nthe reproducibility of CTR1 internalization upon copper treatment.\n[Changes in the Main text]\nExtended Data Fig. 2 | WGA-surface labeling tracks Cu-induced endocytosis of CTR1. The confocal\nimages of COS7 cells overexpressing CTR1mEos before (top Examples 1-5) and after (bottom Examples 1-\n5) treatment with 100 μM CuCl for 30 minutes. Cells were labeled with Alexa647-WGA to visualize\n2\nmembrane trafficking. The Example 1 images depict the distributions of CTR1mEos and Alexa647-WGA (left\ntwo panels). The right two panels show a merged image of CTR1mEos (green) and WGA (magenta), and the\nselected zoom in area (dash line box). Examples 2-5 show another four cells showing similar behaviors.\nAfter Cu stimulation, plasma membrane-derived internalized CTR1mEos and WGA were imaged\nsimultaneously. Arrows indicate colocalized puncta (white spots), indicating the endocytosis of CTR1\nthrough endocytic vesicles. The enlarged images of these vesicles are provided to facilitate clear\nidentification. The increase of WGA intracellular puncta and colocalization with CTR1mEos indicates effective\ncopper-induced endocytosis. Scale bar: 50 µm; zoom in area scale bar: 10 µm.\n5. (minor comments): Extended Data Fig. 3, it is pretty much accepted in the field that an evanescent\nlight field penetrates to around 100 nm into a cell; the authors made an extra effort to show that\nhere, which I appreciate.\n[Reply 5] We thank the reviewer for the compliment regarding Extended Data Fig. 3.\n6. (minor comments): The abbreviation ‘mE’, for mEos4b, is a bit confusing to read and maybe unusual.\n[Reply 6] We agree that the abbreviation “mE” may be confusing and have revised it as “mEos”\nfor clarity.\nReviewer #2\nThe study tests an interesting and novel idea that the de-oligomerization of the copper transporter\nCTR1 is an initial step in CTR1 endocytosis, which effectively stops copper (Cu) uptake. While the\nconcept is very intriguing, the evidence of de-oligomerization is not very strong (or not convincingly\nexplained), and the mechanism detailing how a stable trimer dissociates into monomer has not yet\nbeen investigated. The work appears to be at the early stage. Specific comments:\n[Reply] We appreciate the reviewer’s interest in the mechanistic aspects.\n7. The authors seem to assume that copper binding at M150 of CTR1 triggers endocytosis. There is\nlittle basis for such assumption. If this was the case, then each time Cu binds to CTR1 the protein\nwould endocytose, i.e. CTR1 will act as transferrin receptor rather than a transporter. Furthermore,\nthe C-terminal mutant that retains M150 but has the C-terminal HCH motif mutated does not\nendocytose, which suggests that the response to elevated copper comes from the cytosol. The\nalternative explanation for M150 mutant not being able to endocytose is that the mutation inhibits\nCu uptake and eliminate cytosolic Cu sensing by CTR1. If the authors believe that their model is\ncorrect, this alternative scenario needs to be eliminated.\n[Reply 7] We appreciate the reviewer’s thoughtful comment and acknowledge that our original\nphrasing might have led to misunderstanding. To clarify, we do not propose that copper binding\nat Met150 of CTR1 directly triggers endocytosis upon each binding event. Rather, our model,\nsupported by previous studies (Guo et al., JBC 2004; Petris et al., JBC 2003) and our own recent\nfindings, emphasizes that functional copper transport into the cytosol is critical for subsequent\nCTR1 endocytosis. In other words, copper must effectively pass through CTR1 into the cell to\nactivate intracellular signaling pathways that recruit endocytic machinery to CTR1.\nTo further test this hypothesis and address the alternative scenario proposed by the reviewer—\nthat the lack of endocytosis in the CTR1(M150L) mutant might simply result from impaired\ncopper uptake—we conducted additional biotinylation experiments using a copper ionophore\n(Cu-elesclomol) to artificially elevate intracellular copper. Notably, when cells expressing\nCTR1(M150L)mEos were treated with 2 μM of the membrane-permeable ionophore Cu-elesclomol\n(CuES), mutant CTR1 exhibited clear copper-induced internalization. This result compellingly\nindicates that elevated intracellular copper, rather than extracellular copper binding to the\nMet150 site alone, is the key factor driving CTR1 endocytosis. These results explicitly eliminate\nthe alternative scenario posed by the reviewer. The mutation at M150 does indeed impair copper\nuptake, but the essential trigger for CTR1 endocytosis originates intracellularly, after copper has\ntraversed the plasma membrane via CTR1, rather than from extracellular copper binding alone.\nWe have explicitly updated the Discussion section of our manuscript to clarify this critical point.\nSpecifically, we have now clearly described that functional intracellular copper transport, rather\nthan extracellular copper binding alone, is essential for initiating CTR1 endocytosis. Moreover,\nwe have included details of the new ionophore-based experiments, emphasizing that\nintracellular copper accumulation is a key regulator of CTR1 oligomerization and endocytosis.\n[Changes in the Main text] Our smND results (Fig. 3c–e) demonstrate that CTR1 exists in a\ndynamic equilibrium of different oligomeric states and shifts toward the monomeric state in\nresponse to elevated copper levels, which trigger endocytosis. While molecular dynamics\nsimulations suggested that both the M150 mutation and copper binding at specific sites (M150\nand M154) stabilize CTR1 trimers, experimental observations revealed a contrasting\nphenomenon: copper induces de-trimerization of wild-type CTR1 at early endocytic areas, a\nresponse blocked in the M150L mutant. This raises a key question: is endocytosis triggered\ndirectly by copper binding at Met150, or does the M150L mutant fail to internalize simply\nbecause it blocks copper uptake and prevents the intracellular signal required for endocytosis?\nTo distinguish between structural stabilization and cytosolic signaling as the cause of impaired\nendocytosis in the M150L mutant, we conducted additional experiments using a membrane-\npermeable copper ionophores, Cu-elesclomol (CuES).34,35 These reagents bypass CTR1-\nmediated uptake and directly elevate intracellular copper levels. The biotinylation assay shows\nthat, in contrast to CuCl₂ fails to trigger internalization of CTR1(M150L), cells treated with CuES\nrobustly demonstrated decrease of surface CTR1 populations. This indicates a restoration of the\nmutant’s ability to internalize (Fig. 5a). Computational energy calculations reveal substantial\nintrinsic stability of the CTR1 trimer (dissociation energy 34.56 kcal/mol plus ΔΔG 9.62 kcal/mol),\nindicating that physiological dissociation under copper stress likely involves additional\nintracellular effectors or mechanical forces supplied by the recruited endocytic machinery. Thus,\nthe key role of Met150 lies in enabling copper permeation, not in acting as a sensor or trigger\nitself.\nFig. 5 | Intracellular copper elevation triggers CTR1 dissociation primarily in Rab5+, not Rab5–,\ncompartments. a, Biotinylation assay comparing the surface abundance of the CTR1(M150L)mEos under different\ncopper treatments. Treatment with 100 µM CuCl₂ alone fails to trigger its internalization, whereas exposure to 2\nµM of the membrane-permeable CuES ionophore restores endocytosis. This confirms that elevated intracellular\ncopper, rather than extracellular copper binding, drives CTR1 internalization. Representative immunoblots show\ntotal and biotinylated surface CTR1(M150L)mEos normalized to the loading control Caveolin-1. (b–i), smND\nanalysis of CTR1 oligomerization in Rab5– compartments. Under basal conditions, wild-type CTR1mEos (b–e)\nshows minimal changes in trimer populations upon copper stress, shifting from approximately 53% to 47%,\nwithin the uncertainty of smND. Similarly, the endocytosis-deficient CTR1(M150L)mEos mutant (f–i) demonstrates\nnegligible oligomeric shifts (~62% to 57%) under copper treatment in Rab5– areas, confirming that significant\ncopper-induced dissociation of CTR1 occurs specifically in Rab5-positive compartments. Data represent mean ±\nSEM from three independent experiments.\n8. “The increased stability of the trimer in the mutant raises the possibility that the ability of CTR1 to\nalter its oligomeric state plays a critical role in its regulation of copper uptake” – Can the authors\ncalculate the energetic cost of de-oligomerization?\n[Reply 8] We appreciate the reviewer's insightful suggestion regarding the energetic analysis of\nCTR1 de-oligomerization. We performed computational energy calculations using FoldX, a\nwidely used protein modeling tool that combines empirical force fields with statistical potentials\nto predict binding energies and mutational effects on stability. For the CTR1 trimer, FoldX\nestimated the binding energies between subunits (chains A-B, B-C, and A-C) as −11.19 kcal/mol,\n−11.29 kcal/mol, and −12.08 kcal/mol, respectively. Summing these pairwise interactions\nsuggests a total energetic cost of ~34.56 kcal/mol for complete trimer dissociation.\nImportantly, our experimental data clearly demonstrate that despite the energetic stabilization of\nthe trimer in the presence of copper, even mutant CTR1 readily undergoes de-oligomerization\nwhen intracellular copper levels are sufficiently elevated. This apparent contradiction between\ncomputational stabilization and observed dissociation can be reconciled by our proposed model:\nintracellular copper accumulation activates endocytic machinery, providing an additional\nmechanical or energetic input required for trimer dissociation. Thus, the calculated energetic\nvalues represent intrinsic thermodynamic stabilities of CTR1 trimers, whereas the physiological\nde-oligomerization involves additional intracellular factors beyond simple thermodynamic\nequilibrium.\nWe have explicitly incorporated these energetic calculations and clarified their interpretation in\nthe Results and Methods sections (see Reply 7) section of our manuscript. Specifically, we now\nclearly indicate that intrinsic thermodynamic stability calculated by FoldX contrasts with the\nexperimentally observed physiological dissociation, emphasizing the necessity of additional\nintracellular factors for CTR1 de-oligomerization under copper stress.\n[Changes in the Main text] To evaluate the thermodynamic stability of wild-type CTR1, we used\nFoldX to estimate the dissociation energy of its trimeric structure.20 FoldX calculated the binding\nenergies between subunit pairs, namely A-B, B-C, and A-C, as −11.19 kcal/mol, −11.29 kcal/mol,\nand −12.08 kcal/mol, respectively. The total dissociation energy, obtained by summing these\npairwise interactions, was approximately 34.56 kcal/mol, representing the energetic cost\nrequired to fully dissociate the trimer into individual subunits.\n9. How does the insertion of 3 GFP-FLAG into a small and tightly packed trimeric protein, like CTR1\naffect protein stability? AlphaFold3 analysis of the wild type and CTR1-GFP-Flag trimers suggests\na significant destabilization of the trimer by the fusion. The authors should provide evidence that the\nstability of the wild-type and fusion CTR1 are similar. This can be done either computationally or\nexperimentally (for example, by analysis of thermostability/urea sensitivity on nondenaturing gels\nor by correlation spectroscopy).\n[Reply 9] The reviewer raises an important question regarding whether the insertion of mEos4b-\nFLAG destabilizes the trimeric structure of CTR1. To address this, we directly compared the\nfunctional activity of wild-type CTR1 and CTR1mEos by measuring intracellular copper levels\nunder basal and high-copper conditions. If the fusion construct were significantly destabilized or\nimpaired in trimer formation, we would expect a decrease in copper uptake efficiency, as only\nthe trimeric form of CTR1 is competent for copper transport. However, cells expressing wild-type\nCTR1 and those expressing CTR1mEos exhibit similar intracellular copper levels under both basal\nand 50 μM CuCl conditions, indicating that the fusion does not significantly disrupt the trimeric\n2\nstate or function of CTR1.\nIn addition to these functional assays, our previous membrane fractionation and immunoblotting\nexperiments (Extended Data Fig. 1a) show that CTR1mEos exhibits similar membrane localization\npatterns to wild-type CTR1, reinforcing that the fusion does not alter the stability or trafficking\nbehavior of the protein.\nTo further validate that CTR1mEos maintains trimeric stability, we also performed blue native\nPAGE as reviewer suggested in the later question to assess its oligomeric state under native\nconditions (Extended Data Fig. 1d). Both wild-type CTR1 and CTR1mEos exhibit the same\nbanding pattern, with four distinct bands observed, corresponding to different oligomeric states.\nWhile the exact relationship between band number and oligomeric state remains uncertain, the\npresence of the same four-band pattern in both constructs suggests that CTR1mEos maintains a\nsimilar oligomeric distribution to wild-type CTR1. The relative distribution of these bands remains\ncomparable between wild-type CTR1 and CTR1mEos, indicating that the fusion does not\nsignificantly alter the oligomeric equilibrium.\nTogether, these results collectively indicate that the mEos4b-FLAG fusion does not destabilize\nCTR1’s trimeric structure or impair its copper transport function. While AlphaFold3 predictions\nsuggest possible structural perturbations, our experimental functional data demonstrate that\nCTR1mEos remains fully capable of mediating copper uptake and retains its native oligomeric\nstate, supporting the validity of using this construct to study CTR1 oligomerization dynamics.\nWe have incorporated the experimental validation data into Extended Data Fig. 1, explicitly\ndescribing these stability and oligomeric analyses in both the figure legend and the Methods\nsection. Additionally, the manuscript now clearly emphasizes the agreement between our\nfunctional assays, membrane localization, and native PAGE experiments, collectively reinforcing\nthe conclusion that the mEos4b-FLAG fusion does not significantly affect CTR1 stability or its\nfunctional oligomeric state.\n[Changes in the Main text] The generated CTR1 fusion protein, CTR1mEos, showed the\nexpected full-length product (Extended Data Fig. 1a) and retained both copper uptake activity\nand Cu-responsive endocytosis, as validated through direct Cu measurement using ICP-MS and\nsurface biotinylation. Specifically, ICP-MS quantification showed comparable intracellular copper\nlevels between CTR1mEos and wild-type CTR1 under identical conditions, confirming that the\nfusion protein maintains effective copper transport activity (Extended Data Fig. 1b). Cells that\noverexpressed CTR1mEos showed reduced surface CTR1mEos abundance in the presence of high\ncopper (Extended Data Fig. 1c; Extended Data Fig. 2), indicating effective Cu-induced\nendocytosis. Additionally, blue-native PAGE analyses demonstrated that CTR1mEos exhibits\noligomeric patterns identical to wild-type CTR1 at the plasma membrane, further validating the\nstructural integrity and native behavior of the fusion construct. Due to extremely low endogenous\nCTR1 expression, heterotrimer formation between endogenous CTR1 and CTR1mEos is\nnegligible (Extended Data Fig. 1d), affirming that observed oligomeric transitions primarily reflect\nthe functional homotrimers of the exogenous protein rather than artifacts of overexpression or\ninstability.\nExtended Data Fig. 1 | Characterization of CTR1 fusion proteins. a, Immunoblotting of COS7 cells expressing\nmEos4b-Flag (mEos4b), CTR1mEos, or CTR1(M150L)mEos proteins. Cells were fractionated into total (T),\ncytosolic (C), and membrane (M) fractions. Equal amounts of protein from each fraction were probed with an anti-Flag\nantibody. A duplicate membrane was probed with Na/K ATPase and α-Actin to serve as markers for the plasma\nmembrane and cytosol, respectively. The asterisk (*) denotes a non-specific band. b, Intracellular copper levels in\nHEK293 cells expressing mEos4b-Flag, non-tagged CTR1 (CTR1*), CTR1mEos or CTR1(M150L)mEos, measured by\nICP-MS (top). The copper levels were normalized to exogenous CTR1 expression detected by immunoblotting\n(bottom). Both CTR1* and CTR1mEos exhibit increased copper uptake under copper-treated conditions, confirming\nthat the mEos4b-Flag tag does not impair CTR1's transport function. c, Immunoblot of surface biotinylated\nCTR1mEos and CTR1(M150L)mEos under basal, copper-stressed (Cu), and copper-deficient (BCS) conditions.\nReduced surface CTR1mEos levels upon copper treatment indicate effective endocytosis, which is absent in\nCTR1(M150L)mEos. d, Blue-native PAGE (BN-PAGE) of plasma membrane fractions isolated from COS7 cells\ntransfected with various CTR1 constructs as indicated or mock control. Due to the low abundance of CTR1 protein\nwhich cannot be directly visualized on the PAGE, the PAGE was further transfer to PVDF blot and subject to\nimmunoblotting using ant-CTR1 antibody to detect all the CTR1. Multiple high molecular weight bands (brackets)\nare observed for all constructs, indicating the presence of high-order oligomers. Notably, endogenous CTR1 is not\ndetectable under our experimental conditions, as shown by the absence of signal in the mock lane. An equal\nproportion of membrane extracts were resolved in parallel under standard SDS-PAGE and immunoblotting probed\nwith Na/K ATPase served as a loading control.\n10. Presence of monomers, dimers, and trimers at the plasma membrane at steady state needs to be\nverified independently because it may be a result of overexpression and/or instability of the fusion\nprotein (see above). In addition to surface biotinylation experiments that convincingly show Cu\ninduced endocytosis, the authors should perform blue native gel analysis for endogenous CTR1 to\nensure that the fusion construct behaves identically, when it comes to monomers, dimers, and\ntrimers.\n[Reply 10] The reviewer raises an important concern regarding whether the observed monomer,\ndimer, and trimer populations might be artifacts of overexpression and/or instability of the fusion\nprotein rather than reflecting the true biological behavior of CTR1.\nWe first exclude the possibility of instability of the fusion protein. Extended Data Fig. 1a showing\nboth wild-type and mutant CTR1 fusion proteins are enriched in membrane fraction, supporting\nthat they are indeed membrane-anchored. In addition, no obvious cleavage fragments\ncorresponding to soluble mEos4b, which was performed in parallel serving as reference on the\nblot, were detected in the cytosol fractions. It supports the stability of these CTR1 fusion proteins\nin our expression system.\nTo further confirm that the observed oligomeric states are not artifacts of overexpression, we\nperformed blue native PAGE using plasma membrane fractions from COS7 cells to assess the\noligomeric distribution of CTR1mEos under native conditions (Extended Data Fig. 1d). The results\nshow that wild-type CTR1 and CTR1mEos exhibit the same banding pattern, with multiple distinct\nbands corresponding to different oligomeric states. Importantly, the presence of these bands in\nplasma membrane-enriched fractions confirms that these oligomeric species exist at the plasma\nmembrane, rather than being a consequence of overexpression artifacts or cytosolic aggregation.\nThe overall distribution of oligomeric species remains comparable between wild-type CTR1 and\nCTR1mEos, further supporting that the fusion construct does not alter the oligomeric behavior of\nCTR1 at the plasma membrane.\nTo comprehensively address overexpression issue, we consider the possible ways in which\noverexpression could hypothetically affect oligomeric distribution and demonstrate that none of\nthese scenarios account for our observed results.\nScenario 1: Overexpression leads to a higher population of monomers\nIf overexpression were responsible for the observed oligomeric shift, we would expect a\nsubstantial fraction of monomers even under basal conditions. However, our smND analysis\nshows that CTR1 remains predominantly trimeric at steady state, and monomeric and dimeric\nspecies only increase upon copper exposure, directly contradicting the hypothesis that\noverexpression passively drives monomer formation.\nScenario 2: Overexpression leads to a higher population of trimers\nAnother possible overexpression-driven artifact could be the stabilization of trimer formation, as\nhigher protein abundance increases the likelihood of trimeric interactions. In this case, we would\nexpect that upon copper exposure, an even greater fraction of CTR1 molecules would remain in\nthe trimeric state due to mass action favoring oligomerization. However, our smND analysis\nreveals the opposite effect, where the N̅ decreases in the copper rather than increases in\nresponse to copper. If overexpression were passively driving trimerization, the population of\nmonomers should remain low regardless of copper levels, but our data demonstrate a controlled\nshift toward monomers upon copper treatment.\nScenario 3: Overexpression causes a random distribution of oligomeric States\nAnother possibility is that overexpression disrupts CTR1’s regulated oligomeric equilibrium,\nleading to a broad and unstructured distribution of monomers, dimers, and trimers. If this were\nthe case, we would expect the ND distribution to be highly variable, lacking a defined oligomeric\npreference. However, our data show that CTR1 follows a structured, condition-dependent\ntransition: under basal conditions, trimers predominate, whereas copper exposure results in a\ncontrolled increase in monomers. The specificity of this transition is inconsistent with a random\noverexpression effect and instead indicates a tightly regulated mechanism.\nAdditional evidence supporting a regulated oligomeric transition\nTo further exclude overexpression artifacts, we tested whether CTR1mEos functions identically to\nwild-type CTR1 by measuring intracellular copper levels under basal and high-copper conditions.\nSince only trimeric CTR1 can efficiently mediate copper transport, if overexpression disrupted\nits native oligomeric state, we would expect a measurable difference in copper uptake. However,\nour results show that cells expressing CTR1mEos and wild-type CTR1 accumulate intracellular\ncopper at similar levels, confirming that the fusion construct retains normal trimeric function. This\nfurther supports that CTR1mEos faithfully recapitulates the native behavior of wild-type CTR1 and\nthat its oligomeric transition is not an artifact of overexpression.\nBy considering all possible overexpression artifacts (whether favoring monomers, trimers, or a\nrandom distribution), we demonstrate that none can explain the observed results. Instead, our\ndata consistently show that CTR1 undergoes a regulated, biologically driven transition from\ntrimer to monomer upon copper exposure, regardless of expression level. Importantly, our smND\nanalysis of CTR1 expression levels directly shows that higher CTR1 concentrations favor trimer\nformation rather than monomer formation, further confirming that the monomeric shift we\nobserve in the presence of copper is not an artifact of overexpression but a controlled regulatory\nevent.\nWe acknowledge the reviewer’s request for a blue-native gel analysis of endogenous CTR1 to\nconfirm physiological oligomeric distributions. Due to the low endogenous CTR1 abundance in\nour COS7/HEK293 lines and difficulties in maintaining complex integrity under native conditions,\nwe have not yet obtained robust BN-PAGE data for endogenous CTR1. Nonetheless, our\nfunctional assays (unchanged copper uptake, stable membrane localization, and regulated\ntrimer-to-monomer shift under Cu stress) collectively indicate that the fusion construct mirrors\nthe native behavior. We plan to explore BN-PAGE or other native analyses (e.g., crosslinking,\ncorrelation spectroscopy) in future work to further corroborate these findings.\nWe have explicitly incorporated the blue native PAGE results into Extended Data Fig. 1 and\nupdated the corresponding figure legends and Methods section to highlight these additional\nexperiments (see Reply 9). Furthermore, we included a discussion in the manuscript addressing\npotential artifacts from overexpression, outlining the scenarios discussed above, and explicitly\nclarifying why our experimental evidence strongly supports the physiological relevance of the\nobserved oligomeric distributions.\n11. Can the authors exclude that the increased activity of CTR1mE is caused by CTR1mE homotrimer\nrather than heterotrimers with the endogenous CTR1? To better illustrate functionality of CTR1mE\n– the comparison should be made with the wild-type CTR1 expressed at the same level as CTR1mE.\n[Reply 11] While it is theoretically possible for heterotrimers to form, our experimental evidence\nsuggests that this scenario is highly unlikely. Our blue native PAGE analysis using plasma\nmembrane fractions from COS7 cells (Extended Data Fig. 1d, Reply 9) reveals that endogenous\nCTR1 is not detectable under our experimental setup (mock lane). This indicates that the\nendogenous CTR1 expression level is extremely low in our system, making the formation of\nheterotrimers with CTR1mEos highly improbable. Given the overexpression of CTR1mEos, the vast\nmajority of CTR1 complexes at the plasma membrane are expected to be composed of the\nexogenous protein rather than mixed with undetectable levels of endogenous CTR1.\nAdditionally, we acknowledge the reviewer’s request for a comparison with wild-type CTR1\nexpressed at the same level as CTR1mEos. While absolute expression matching can be\nchallenging, our functional assays (e.g., intracellular copper accumulation) show that CTR1mEos\nexhibits comparable activity to wild-type CTR1 under identical experimental conditions. This\nfurther supports that the increased activity of CTR1mEos arises from its functional homotrimers\nrather than potential interactions with endogenous CTR1.\nBased on these findings, we conclude that while heterotrimer formation is highly unlikely due to\nthe absence of detectable endogenous CTR1 in our system. The functional activity of CTR1mEos\nis thus primarily attributed to its homotrimeric state, reinforcing the validity of using this construct\nto study CTR1 oligomerization and function.\nWe have clearly described the blue native PAGE analysis and clarified the low expression levels\nof endogenous CTR1 in the updated Extended Data Fig. 1d. Additionally, we have explicitly\naddressed the unlikely formation of heterotrimers in the Discussion section, emphasizing that\nthe functional activity observed for CTR1mEos reflects its homotrimeric state. These revisions\nstrengthen the manuscript by transparently addressing the potential concern regarding\nheterotrimer formation.\n12. It is not clear how method with 10 nm resolution can resolve individual CTR1 subunits, which have\na cross-section in the membrane of 5 nm. This needs to be explained better.\n[Reply 12] We fully agree that smND does not directly resolve the 5 nm structure of a single\ncomplex. Instead, it captures the 2D coordinates of each fluorescent subunit and uses the spatial\ndistribution (i.e., how close or far subunits appear) to distinguish oligomeric states. Smaller\nexpansions (~1–2 nm) wouldn’t register as “separate spots” if they remain within the same ~40\nnm region of interest. So truly “split” subunits are typically more than 20–40 nm apart. We have\nclarified this point in the Methods (“Single‐molecule neighbor density assay”) to avoid confusion.\nWe have provided more detailed working principles of smND in Reply 2, and emphasized how\none can resolve oligomeric states with a resolution lower than the protein complex itself in\nDiscussion.\n[Changes in the Main text] Although the method does not directly resolve proteins smaller than\nits ~10 nm resolution limit, it effectively discriminates oligomeric states through spatial\ndistribution patterns, and reliably distinguishing monomers from higher-order oligomers like\ntrimers.\n13. Related, if the method does resolve CTR1 monomer from trimers then the individual CTR1 signals\nwithin Rab5 puncta should be markedly different from CTR1-signals outside Rab5 puncta, where\nfunctional CTR1 is uniformly trimer. Is this what the authors observe? Figure 2e (the CTR1 map)\nis difficult to interpret\n[Reply 13] In response to the reviewer’s suggestion, we examined whether our method\ndistinguishes CTR1’s oligomeric state inside (Rab5+) and outside (Rab5–) Rab5-labeled\ncompartments, we performed a detailed comparison of Rab5+ and Rab5– regions. To identify\nRab5– areas, we first excluded Rab5+ compartments and then selected random regions\ndisplaying comparable CTR1 locations and signal intensities for both the M150L mutant and WT\nCTR1 under the same treatment conditions. Here, intensity refers to the height of the CTR1\nsignal relative to the coverslip, ensuring that the chosen Rab5– regions closely matched the\nRab5+ areas in protein distribution.\nSeveral molecular mechanisms could explain how CTR1 detrimerizes under copper stress. One\npossibility is that Rab5+ endosomes serve as the primary site for trimer reduction, leading to a\nmore pronounced loss of trimers in these compartments. Another possibility is that Rab5–\nregions play an equally or more effective role in detrimerization, producing comparable or greater\nchanges outside Rab5+ areas. A third scenario is that CTR1 detrimerizes uniformly throughout\nthe membrane, making no specific compartment the main site. Our experimental data help us\nevaluate which pathway dominates.\nOur data revealed a clear compartment-specific difference: WT CTR1 exhibited a marked\ndecrease in trimer content within Rab5+ compartments under copper stress (from ~65% to\n~44%). In comparison, the trimeric population in Rab5– regions showed only minor changes\nunder copper stress (from ~53% to ~47%). These results suggest the model where Rab5+\nendosomes serve as primary sites for copper-induced CTR1 detrimerization. We interpret the\nconsistently lower trimer content and limited copper-induced changes observed in Rab5– areas\nas potentially reflecting lateral diffusion of CTR1 subunits from their primary dissociation sites at\nRab5+ compartments, combined with the inherently heterogeneous membrane environment of\nRab5– regions. Rab5– areas, lacking specialized factors that stabilize trimeric CTR1, may\nrepresent an equilibrated mixture of oligomeric states, influenced by transient interactions and\ndiffusional processes. Furthermore, the M150L mutant showed minimal responsiveness in both\nRab5+ (from ~71% to ~66%) and Rab5– areas (from ~62% to ~57%) under copper stress,\nindicating that functional copper transport and subsequent intracellular signaling are necessary\nto trigger CTR1 dissociation effectively. Collectively, these findings strengthen the conclusion\nthat Rab5+ compartments predominantly catalyze copper-induced CTR1 dissociation, with\nRab5– areas reflecting secondary or diffusive processes rather than primary regulatory events.\nPast studies have shown that CTR1 primarily undergoes endocytosis via CME, involving regions\neventually marked by Rab5 as early endosomes. Thus, the observed trimer dissociation\npredominantly in Rab5+ areas could align well with CME-derived endocytic vesicle formation,\nsuggesting that dissociation may occur shortly before or during the initial internalization steps\nmediated by clathrin-coated vesicles. Although our current data alone do not conclusively\ndetermine the precise spatial-temporal relationship between CTR1 dissociation and CME, the\nsignificant oligomeric shift observed in Rab5+ compartments is consistent with the notion that\nthese areas could represent hotspots for dissociation events linked to established CME\nmechanisms. The modest changes seen in Rab5– compartments might reflect subsequent\ndiffusion of dissociated CTR1 subunits away from primary dissociation sites or local\nheterogeneities in membrane environments rather than active copper-induced dissociation.\nFurther detailed studies will be needed to fully validate and clarify this integrated mechanism.\nWe have incorporated these comparative analyses of CTR1 oligomeric states in Rab5+ and\nRab5– compartments into the revised Results section and added Fig. 5 (Reply 7) to highlight\nthese findings, clearly emphasizing Rab5+ compartments as primary sites for copper-induced\nCTR1 detrimerization. Analysis Details were provided in the Supplementary Method section. On\nthe other hand, we also update Fig. 2e to improve the readability of the CTR1 map.\n[Changes in the Main text] To further delineate compartment-specific differences in CTR1\noligomerization upon copper stress, we compared CTR1 trimer populations in Rab5+ and Rab5–\nregions. WT CTR1 demonstrated a substantial and statistically significant reduction in trimer\ncontent within Rab5+ compartments (from ~65% to ~44%, Fig. 3c–e). Conversely, Rab5– areas\nexhibited only minor oligomeric shifts (from ~53% to ~47%, within smND uncertainty, Fig. 5b–e).\nThese data imply that CTR1 dissociation primarily occurs at or near Rab5+ compartments, with\nRab5– areas likely reflecting secondary equilibria or diffusional redistribution. In contrast, the\nendocytosis-deficient M150L mutant showed minimal changes (~5%) in trimer populations in\nboth Rab5+ (71% to 66%, Fig. 3h) and Rab5– (62% to 57%, Fig. 5g) compartments, indicating\nimpaired responsiveness to copper-induced intracellular signals required for CTR1 dissociation.\n[Changes in Supplementary Methods] CTR1 population analysis of Rab5– area. To analyze\nthe oligomeric states of CTR1 localized outside Rab5 compartments (Rab5– areas), a systematic\napproach involving several sequential processing steps was followed: defining Rab5– regions,\nexcluding unsuitable regions, compensating single-molecule background noise, accurately\ncalculating local concentrations, and performing theoretical PND analysis. Rab5– regions were\ndefined by selecting areas outside these identified Rab5+ compartments. To ensure reliable\nanalysis, regions too close to gold fiducial markers, cell edges, or imaging frame boundaries\nwere excluded. Single-molecule background noise was compensated by sampling molecule\ndensity in regions outside the cellular boundaries and subsequently subtracting a corresponding\nnumber of randomly selected mEos4b coordinates from each Rab5– region.\nAccurate calculation of local CTR1 concentrations was critical for theoretical PND modeling. We\ncalculated the concentration within each Rab5– region using polygons precisely constructed\naround grouped mEos4b molecule locations. Subsequently, regions were sorted based on their\nCTR1 concentration. To ensure statistical reliability and saturation, concentration groups with\nfewer than five distinct Rab5– regions were excluded from subsequent analysis.\nFinally, measured PND distributions were computed from mEos4b coordinates, and theoretical\nPND distributions were calculated using parameters such as the total number of molecules, area\ndensity, and fluorophore activation efficiency. These theoretical distributions were used to extract\noligomeric subpopulations from experimentally measured PND distributions.\n[Change of Fig. 2e]\nFig. 2 | Quantification of CTR1 oligomeric states in endocytic vesicles using TIRF imaging and single-\nmolecule localization microscopy. a, Schematic representation of wild-type (CTR1mEos) and mutant\n(CTR1(M150L) mEos) constructs. b, Illustration of the relationship between Rab5 intensity and endocytic vesicle\npositions under TIRF illumination. Rab5 marks early/sorting endosomes, with higher intensity observed in smaller,\nnewly formed vesicles near the plasma membrane and decreasing signal as vesicles mature and move deeper into\nthe cell. c, Rab5 intensity plotted against vesicle size in COS7 cells expressing wild-type CTR1mEos (WT) and\nCTR1(M150L)mEos (M150L) under basal and Cu-stressed conditions. All conditions show similar profiles: Rab5\nintensity increases with puncta size up to 0.4 μm² and then decreases, indicating comparable endocytic progression\nacross samples. Data represent mean ± SEM; two-way ANOVA (see Supplementary Table 3). d, Representative\nimages of COS7 cells showing the transmission (top), CTR1mEos TIRF (middle), and Rab5-mCherry TIRF (bottom)\nsignals. e, Workflow for the smND assay. Neighboring CTR1 molecules within a 40 nm radius were counted to\ngenerate a ND histogram. Experimental ND distributions (PND ) were fitted with theoretical distributions (PND )\nexp theo\nto obtain CTR1 oligomer populations and Nഥ. f, Two dissociation models used to estimate the lower (Model 1) and\nupper (Model 2) bounds of CTR1 trimer populations from smND data.\n14. The smND assay is a new and very intriguing methodology and it has to be explained better. For\nexample, can the authors exclude that the “de-oligomerization” is simply an increase in the distance\nbetween fluorophores due to copper-induced conformational changes in CTR1. It was demonstrated\nthat CTR1 undergoes conformational changes upon Cu binding (PMID: 36409899). The authors\nshould formally rule out this possibility.\n[Reply 14] We understand the concern. Indeed, if CTR1’s transmembrane helices partially splay\napart, it might appear as a “distance increase.” However, our smND analysis relies on each\nfluorescent subunit’s location being separated by >40 nm to be counted as “different neighbors.”\nA small splay of 1–2 nm would not cause subunits to appear ~40 nm apart. Moreover, even if\nthe trimer partially expands, we would not expect a large fraction of subunits to lose their\nimmediate neighbors altogether. The best explanation for losing neighbors in a 40 nm radius is\ngenuine subunit separation (true dissociation). We will add a short discussion acknowledging\nthat small conformational changes are possible but would not alone explain a substantial fraction\nof “monomer-like” distributions in the Result section.\n[Changes in the Main text] Furthermore, while small conformational changes upon copper\nbinding could theoretically alter inter-subunit distances, the extent of neighbor separation (>40\nnm) required by smND strongly suggests genuine dissociation rather than minor structural\nadjustments. Thus, the observed copper-induced changes are consistent with authentic CTR1\ntrimer-to-monomer transitions rather than artifacts arising from conformational expansions alone.\nReviewer #3\n15. The Cu uptake transporter CTR1 undergoes endocytosis in response to excess Cu to maintain\ncellular Cu homeostasis. By employing single-molecule localization microscopy (SMLM) and single-\nmolecule neighbor density (smND) assays, the authors demonstrate that excess Cu induces rapid\nmonomerization of the trimeric CTR1 prior to endocytosis. This process inhibits Cu uptake and is\nimpaired in the endocytosis-deficient CTR1 (M150L) mutant. Thus, authors conclude that de-\noligomerization of CTR1, linked with endocytosis as a rapid mechanism to prevent excess Cu entry.\nWhile the study is interesting and important, a major limitation is the absence of data showing\nfunctional consequences of de-oligomerization of CTR1 in response to excess Cu, both in vitro and\nin vivo. For instance, experiments demonstrating Cu-induced cell death (cuproptosis) using CTR1\nwild type and mutants are needed. Moreover, it is essential to perform live cell imaging of WT or\nmutant CTR1 with Rab5 in COS7 cells. Additional experiments are therefore required to increase\nthe quality of this manuscript.\n[Reply 15] We agree that live-cell imaging of CTR1 dynamics and more in-depth physiological\nreadouts (e.g., cuproptosis) would be valuable. However, these would significantly extend the\nscope of our current study. Our major focus here is to establish that CTR1 undergoes a structural\nchange (de-oligomerization) that precedes endocytosis in elevated copper. We do plan future\nwork to explore real-time CTR1 de-oligomerization, as well as the specific cytotoxic endpoints\n(cell viability or cell-death pathways). We have mentioned these follow-up directions in the\nDiscussion. (Future directions: more related to membrane-trafficking related questions, such as\nthe choice of trafficking routes or the fate of internalized CTR1).\nWe have updated the Discussion section of our manuscript to clearly outline these limitations\nand specify our planned future research directions (see Reply 3). Specifically, we now highlight\nour intention to pursue real-time imaging experiments and physiological assays such as\ncuproptosis in subsequent studies, thus transparently addressing the reviewer's suggestions\nwhile maintaining clarity regarding the scope and focus of our current findings.\nReviewer #4\nThe article titled \"Human Transporter De-oligomerization Regulates Copper Uptake into Cells\" presents\na combined computational and experimental study. The authors demonstrate that an excess of copper\ninduces the monomerization of CTR1, which typically transports copper as a trimer. This\nmonomerization process is suggested to prevent copper overload.The article is intriguing as it provides\nnew insights into the still unclear aspects of copper internalization. However, several issues need to be\naddressed before the paper can be considered for publication.\n16. In the introduction, where the mechanism of copper uptake is discussed, the authors should\nreference recent papers that have investigated the detailed mechanism of copper transport. For\nexample, see Janos et al. https://doi.org/10.1017/qrd.2022.2 and Aupic et al.\nhttps://doi.org/10.1073/pnas.2214602119.\n[Reply 16] We appreciate the reviewer’s suggestion to incorporate recent studies that have\ninvestigated the molecular mechanism of copper transport. The work by Janos et al. (QRB\nDiscovery, 2022) and Aupič et al. (PNAS, 2022) significantly contributes to our understanding of\nCTR1 function at the molecular level. Janos et al. utilized molecular dynamics (MD) and quantum\nmechanics/molecular mechanics (QM/MM) simulations to reveal the conformational plasticity of\nmethionine triads in CTR1's selectivity filter, demonstrating that these residues regulate Cu(I)\ntransport efficiency. Aupič et al. combined MD simulations with circular dichroism to show that\nintrinsically disordered regions (IDRs) at the N-terminal domain of CTR1 modulate copper\nuptake by affecting the selectivity filter’s conformational state.\nWhile these studies have provided crucial insights into CTR1-mediated copper permeation,\nthey\ndo not directly address the relationship between CTR1's oligomeric state and endocytosis in\nregulating copper uptake. Our study uniquely integrates SMLM with smND analysis to uncover\nhow excess copper induces CTR1 de-trimerization, a structural transition that precedes\nendocytosis. Unlike previous work, which focuses on the structural determinants of copper entry\nthrough CTR1, our study reveals a regulatory mechanism linking transporter oligomerization to\nendocytic trafficking. This distinction is critical, as it establishes a new paradigm wherein CTR1\nstructural transitions actively contribute to controlling cellular copper levels beyond mere\npermeation through the transporter.\nTo address the reviewer’s concern, we have revised the Introduction to integrate these\nreferences appropriately. We explicitly highlight how prior studies have contributed to our\nunderstanding of CTR1 selectivity and structural flexibility while emphasizing that our work\nprovides novel insights into how copper-induced de-oligomerization serves as a regulatory\nmechanism coupling transporter function with cellular trafficking processes.\n[Changes in the Main text] CTR1 exists as a homotrimer. Recent studies have provided\nstructural insights into its copper transport mechanism. The selectivity filter methionine triads\nexhibit conformational plasticity, with Cu(I) binding modulating their orientation to regulate\ntransport efficiency.12 Additionally, the intrinsically disordered N-terminal domain influences\nselectivity filter conformation, further fine-tuning copper uptake.13,14 While these findings have\nadvanced our understanding of CTR1’s molecular architecture and transport function, they do\nnot fully explain how cells dynamically regulate CTR1 to prevent overload.\n17. The authors claim in the first paragraph that the M150L mutant significantly stabilizes the trimeric\nform by decreasing the ΔΔG. However, examining the structure of CTR1, it appears that the\nselectivity filter of the CTR1 trimer may be affected by introducing a residue larger than methionine.\nPrevious MD simulation studies have shown that methionine is already quite bulky. Have the authors\nperformed actual MD simulations to verify the structural impact of this mutation on CTR1 stability?\n[Reply 17] Yes, as stated in the manuscript, “We further explored CTR1's structural dynamics\nthrough molecular dynamics simulations in membrane environments”. These simulations\nconfirm that the M150L substitution does not disrupt the overall stability of the trimeric structure\nbut instead reinforces inter-subunit interactions.\nWe have now detailed these molecular dynamics simulations and their outcomes in the Methods\nand Results sections of the manuscript. Specifically, the revised manuscript explicitly states that\nthe MD simulations confirm the M150L substitution enhances inter-subunit interactions without\ndisturbing the global trimeric structure (see Reply 8).\n18. Another aspect that need clarification is that M150L is the bottom methionine triad in the structure,\nwhich means that the upper methionine triad M154 can still bind copper and stabilize the trimer in\nthis manner. The authors should also consider the other M150L variant to ensure copper is not\nbound to the upper triad of Met in selectivity filter and therefore it is not stabilizing the trimer structure\nin this manner.\n[Reply 18] In the wild-type structure, copper interacts with all three M154 residues with bond\nlengths of approximately 2.4 Å, consistent with typical Cu(I)-S(Met) coordination distances (~2.2-\n2.4 Å). In the M150L mutant, copper exhibits slightly longer bond lengths with M154 residues\n(2.9 Å, 2.6 Å, and 2.7 Å), suggesting a weaker interaction than in the wild type. This analysis\nindicates that the trimer structure is unlikely to be stabilized via copper-mediated interactions at\nthe upper triad in the M150L variant.\nWe have incorporated this structural analysis into the Results section of our revised manuscript,\nclearly specifying the altered copper-binding geometry in the M150L variant. The update\nexplicitly reflects these bond-length measurements, clarifying that the reduced capacity of the\nupper triad stabilizes the trimer structure via copper interactions in the M150L mutant.\n[Changes in the Main text] Although the M154 site in the M150L mutant still coordinates copper,\nit is unlikely to be the dominant stabilizing factor, as the Cu(I)–M154 bond lengths in the mutant\n(average 2.7 Å) are slightly longer than those in the wild-type (2.4 Å), suggesting weaker\ncoordination (Fig. 1a).\nFig. 1 | Computational modeling reveals increased stability of the CTR1(M150L) trimer. a, Top: structural\nmodels of CTR1 under side (left) and top views (right). Bottom: zoom in area showing Cu(I) coordination in CTR1\n(left) and the M150L mutant (right) coordinating two Cu(I) ions at Met150 and Met154 sites. The Cu(I)–Met154 bond\nis longer in the mutant (~2.7 Å) than in the wild type (~2.4 Å), indicating weaker interaction. b, c, Root-mean-square\ndeviation (RMSD) of wild-type CTR1 (blue) and CTR1(M150L) (red) in the presence (b) and absence (c) of copper\nover 800–1000 ns. Both constructs maintain overall structural stability under all conditions. d, Time evolution of root-\nmean-square fluctuation (RMSF) averaged across all residues shows stabilization after 900 ns. e, RMSF of wild-\ntype CTR1 and CTR1(M150L) in the presence of copper shows similar patterns. f, RMSF of wild-type CTR1 and\nCTR1(M150L) in the absence of copper highlights increased flexibility in the M150L mutant, particularly in the\nintracellular loop (dark gray area) and cytosolic tail regions (light gray area).\n19. As far as I understand, MD simulations are performed in the absence and presence of copper.\nHowever, no reference to their parameterization is provided in the methods section. It is well known\nthat this parameterization is not trivial and may require QM-based simulations.\n[Reply 19] Thank you for your comment. We utilized the Amber force field, which includes well-\nvalidated parameters for Cu(I) coordination. While QM-based simulations provide an alternative\napproach, prior studies (Schwartz R, et al. Protein Science. 2022;31(12):e4464) have\ndemonstrated that Amber’s force field parameters for Cu(I) yield reliable results for\nmetalloproteins. The study by Schwartz et al. (2022) that simulated Cu bond distances (~2.3-\n2.4) matched experimental X-ray crystallography data.\nWe have now explicitly referenced our parameterization approach in the Methods section for\nclarity.\n[Changes in the Main text] We utilized the Amber force field, which includes well-validated\nparameters for Cu(I) coordination.55\n20. Additionally, RMSF values are reported on page 3 with several decimal digits. Have the authors\nchecked how the computed RMSF depends on the simulation length? The observed difference is\nprobably smaller than the error and not significant. This is particularly relevant when the authors try\nto spot differences based on the RMSF.\n[Reply 20] We have investigated how the RMSF values stabilize over increasing simulation time\nas reviewer suggested, confirming that our reported differences are consistent throughout the\ntrajectory and not artifacts of sampling duration. We have also reduced the number of decimal\ndigits for clarity, and the key observation remains that the M150L mutant shows greater flexibility\nin the absence of copper (1.84 Å) compared to wild-type (1.75 Å), while their copper-bound forms\nremain similar (1.64 Å vs 1.65 Å).\nWe have incorporated the new RMSF stability analysis into the revised manuscript as an\nadditional figure (Fig. 1d in Reply 18), indicating that the reported RMSF differences are robust\nand consistent over simulation time.\n[Changes in the Main text] After confirming the root-mean-square fluctuation (RMSF) stabilized\nafter 900 ns (Fig. 1d), we further check the RMSF data of both the wild type and the M150L\nmutant are comparable in the presence of copper (1.64 Å and 1.65 Å for wild-type and M150L\nCTR1, respectively, Fig. 1e).\n21. Furthermore, the experimental structure of CTR1 lacks residues from the N-terminus and part of the\nC-terminus. According to the methods, these parts have been added to the CTR1 model. If this is\nthe case, since they are both disordered, how is their conformation modeled?\n[Reply 21] Thank you for your comment. The missing N- and C-terminal residues were modeled\nusing MODELLER. While these regions lack well-defined secondary structure, MODELLER\ngenerated plausible conformations with physical energy potentials. This initial model was\nsubsequently refined through MD simulations.\nWe have updated the Methods section to describe the use of MODELLER for generating initial\nconformations of the missing N- and C-terminal residues, as well as detailing their subsequent\nrefinement via MD simulations.\n[Changes in the Main text] To accurately represent the human CTR1 structure, we removed\nthe extra polypeptide of BRIL and added back the missing residues in the crystal structure\nusing Modeller.53\n22. The results on the structure and copper-binding abilities of CTR1 are affected by the presence and\nconformation of the N-term and C-term residues see Aupic et al.\nhttps://doi.org/10.1073/pnas.2214602119 and Walke et al., Biophysical Journal, Volume 121, 1194-\n1204. Thus understanding their influence is a relevant point. These parts contain several copper-\nbinding residues, thus the authors should also create and test the oligomerization properties of\nthese truncated variants (truncanted N-term and/or truncated C-term) in response to copper stress.\n[Reply 22] We appreciate the reviewer’s suggestion to examine the influence of the N-terminal\n(N-term) and C-terminal (C-term) regions on CTR1’s structure and function, particularly in light\nof previous studies (Aupič et al., PNAS, 2022; Walke et al., Biophys. J., 2022). These studies\nprovide valuable insights into how the N-term and C-term contribute to copper binding and\nconformational changes in CTR1, highlighting their potential regulatory roles in Cu transport.\nHowever, neither of these studies specifically investigates CTR1's oligomeric state or how its de-\noligomerization is linked to endocytosis, which is the central focus of our work.\nOur study directly addresses how CTR1 de-oligomerization serves as a regulatory mechanism\nfor copper uptake and endocytosis, providing a new perspective on how CTR1 abundance at\nthe plasma membrane is dynamically controlled. Using SMLM and smND analysis, we\ndemonstrate that high copper levels induce CTR1 de-trimerization, a key step preceding\nendocytosis. This mechanism is distinct from the copper-binding dynamics of the N-term and C-\nterm, as it directly regulates transporter internalization rather than modulating Cu permeation\nthrough the selectivity filter.\nWhile it is possible that the N-term and C-term influence CTR1’s overall structure, the core\nregulatory mechanism we describe, copper-induced de-oligomerization leading to endocytosis,\nis driven by structural transitions in the transmembrane domain of CTR1, where oligomerization\nis primarily controlled. Thus, the suggestion to generate and test truncated variants (lacking the\nN-term and/or C-term) in response to copper stress, while interesting, is beyond the scope of\nthe present study. Future work could investigate whether N-term or C-term truncations modulate\nCTR1 de-oligomerization or affect its interaction with endocytic machinery, but our current data\nstrongly support a model in which CTR1 de-trimerization is the primary regulatory step linking\ncopper sensing to transporter removal from the membrane.\nWe have acknowledged this important consideration and updated the Introduction section (see\nReply 16) of our manuscript to address the potential roles of N-terminal and C-terminal regions,\nreferencing recent studies. Additionally, we highlight this topic as a relevant direction for future\nresearch to explore their influence on CTR1 oligomerization and endocytosis.\n23. Some contradictory statements are provided in the text. In the discussion, the authors mention the\nYxxM motif of the intracellular loops (please state exactly the residues you are considering). The\nauthors mention that copper binding at these methionine sites stabilizes the trimer, preventing its\ndissociation. How does this occur? Can they better argument this point?\n[Reply 23] We appreciate the reviewer’s careful reading of our discussion and the opportunity\nto clarify this point. The YNSM motif was referenced in the context of endocytic regulation, not\nas a Cu-binding site. The key Cu-binding residues in CTR1 are M150 and M154, located within\nthe selectivity filter of the transmembrane domain, where they directly coordinate Cu(I) and\nfacilitate transport. Our data show that Cu binding at these sites increases the stability of the\ntrimer, reinforcing its structural integrity. However, at high cellular Cu concentrations, CTR1\nundergoes a regulated transition from a trimeric to a monomeric state, which precedes its\nendocytosis. This suggests that Cu coordination initially stabilizes the transporter, but excessive\nCu exposure promotes structural changes that shift CTR1 toward an endocytic fate. In contrast,\nthe YNSM motif is relevant to endocytic regulation, likely serving as a recognition site for\nintracellular trafficking machinery. To prevent any misinterpretation, we have revised the\ndiscussion to explicitly state that Cu binding occurs at M150 and M154, ensuring that the role of\nthe YNSM motif is only discussed in the context of CTR1 internalization.\nWe have revised the Discussion section to clearly specify that copper binding occurs at the M150\nand M154 residues and explicitly state that the YNSM motif is relevant solely for endocytic\nregulation. These clarifications ensure the accurate interpretation of the roles of different regions\nwithin CTR1.\n[Changes in the Main text] Building on our results, a plausible model is that intracellular copper,\ntransported through CTR1, is sensed by a downstream factor that recruits endocytic components\nto the intracellular region of CTR1. The YNSM motif, located in the cytosolic loop of CTR1\n(residues 103–106, Fig. 1a), has been proposed as a potential docking site for endocytic\nmachinery.36 This recruitment may generate the mechanical force or conformational change\nnecessary to destabilize the trimer and promote the shift toward the monomeric state. In this\nmodel, copper binding at Met150 and Met154, located within the transmembrane domain, serves\nto coordinate Cu⁺ and facilitate transport. Binding at these sites stabilizes the trimer structure\nunder basal conditions, consistent with our MD simulations. However, when intracellular copper\nrises to a threshold, endocytic recruitment is activated, leading to regulated trimer dissociation\nand internalization. This framework explains how CTR1 can remain trimeric during normal\ntransport yet undergo de-trimerization once copper homeostasis is threatened. In the M150L\nmutant, copper coordination and transport are disrupted, so intracellular copper does not rise,\nand the endocytic machinery is not engaged—leaving the trimer structurally intact and\nendocytosis blocked.\n24. In this respect, can the authors increase the intracellular concentration of copper, without relying on\nCTR1 transport, and check whether it is really the intracellular increase of copper that is affecting\nthe oligomerization status?\n[Reply 24] We appreciate the reviewer’s suggestion and fully agree that it is the intracellular\ncopper that triggers the structural (de-oligomerization) changes in CTR1. As detailed in our\nReply 7, we conducted experiments using the copper ionophore Cu-elesclomol, which increases\nintracellular copper independently of CTR1-mediated transport. These results confirm that the\nelevation of intracellular copper alone is sufficient to reduce surface CTR1 level, suggesting as\nthe outcome of changes in CTR1 oligomerization and subsequent endocytosis. We refer the\nreviewer to Reply 7 for a detailed discussion of these findings.\nPoint-by-point response to reviewers\n[Reply to all reviewers] Thank you for your critical and constructive comments. Following your\nsuggestion, we have revised the manuscript accordingly. Below, we respond (in red) to each\nquestion/comment (in black italic), with our additions to the text indicated (in blue).\nReviewer #1 (Remarks to the Author):\nI thank the authors for responding to and addressing all my comments.\nThere are a few points – minor, yet I see them as mandatory – that need further attention:\n1. The authors integrated the qPALM method in the discussion (lines 390ff), which I think is very\nhelpful for the reader. Yet, references to further direct the interested reader are missing. Some\noriginal work should be integrated, e.g. PMID 27466316 and PMID 31937565. Along these lines:\n‘blinking’ is not an artefact in PALM, it is rather valuable kinetic information that reports on molecule\nnumbers (in qPALM, qPAINT etc.).\n[Reply 1-1] We thank the reviewer for these important suggestions. We have now included the\nrecommended citations (PMID: 27466316 and 31937565) to provide the reader with key original\nworks on the qPALM method. We also agree that \"blinking\" is a valuable photophysical property\nexploited by these quantitative methods and not simply an artifact. We have revised the text to reflect\nthis more accurate description.\n[Changes in Discussion: Limitations of the study] qPALM leverages the stochastic photophysical\nbehavior of fluorescent proteins, such as blinking and photoactivation, to determine stoichiometry,\naccounting for factors like incomplete labeling via calibration with well-characterized monomeric or\ndimeric references.45,46\n2. I politely disagree in pointing the reader towards not using dimeric proteins as a reference. There\nare various dimeric reference proteins available (CD28 being a a very robust one; CTLA4 reported\nas well); another option are single and dual-labelled monomeric proteins. I propose to rephrase this\nsection and recommend to the reader to implement appropriate controls.\n[Reply 1-2] We thank the reviewer for this valuable clarification, and we agree completely. Our\nintention was not to discourage the use of standard dimeric controls, but to highlight that in vivo\nequilibria can be complex. We have rephrased this section to remove any ambiguity. We now\nexplicitly acknowledge the value of well-established dimeric references like CD28 or CTLA4 and\nframe our engineered Tac-tandem constructs as a complementary strategy that provides an\nunambiguous ground truth for fluorophore number, thereby serving as a robust control for the smND\nquantification itself.\n[Changes in Methods: Single-molecule neighbor density (smND) assay] To benchmark smND\nperformance, it is crucial to use appropriate oligomeric controls, for which there are two primary\nstrategies. The first strategy uses well-characterized proteins that are known to self-assemble into\ndefined oligomeric states in vivo, such as CD28 or CTLA4. A complementary strategy is to engineer\nconstructs with a precisely controlled number of fluorophores per molecule. This approach provides\nan unambiguous ground truth for fluorophore stoichiometry, thereby directly testing the quantification\ncapabilities of the smND model, independent of biological assembly equilibria. In this study, we\nleveraged both approaches for comprehensive validation. We employed CTLA4 as an expected\nhomodimeric biological standard. However, we measured a steady-state population of ~75% dimers,\nreflecting its dynamic equilibrium in situ and confirming the assay's sensitivity to mixed biological\nstates (Extended Data Fig. 4f–h). We then used TacmEos (single mEos4b) and Tac2mEos (tandem\nmEos4b) as references with effectively ‘locked’ single- versus dual-chromophore stoichiometries\nanchored to the membrane via Tac-antigen. These constructs allowed benchmarking of smND\nperformance under well-defined oligomeric conditions.\n3. Please mention the origin and the exact name for the CTLA4 plasmids.\n[Reply 1-3] We apologize for this omission. We have now added the specific details for the CTLA4\nplasmid to the Methods section.\n[Changes in Methods: DNA plasmid construction] The plasmid expressing CTLA4mEos was\nconstructed by replacing the mEos2 fluorescent protein in the original plasmid pirespuro2-CTLA4-\nmEos2, a kind gift from Mike Heilemann (Addgene plasmid # 98285; RRID:Addgene_98285),47 with\nmEos4b between the AgeI and NotI restriction sites for experimental consistency with our other\nconstructs.\n4. How does incomplete labelling affect the results of the method the authors use? The detection\nefficiency of mEos is around 50% (this value varies in different reports). I do understand the\napproach the authors use is based on reference data generated from molecules of known\noligomers; in my view, one or two sentences in the discussion that point at this would suffice, and it\nwill help the interested reader.\n[Reply 1-4] This is a critical point, and we thank the reviewer for prompting us to clarify it. Our smND\ntheoretical model explicitly accounts for incomplete labeling and detection through the photo-\nactivation efficiency (PE) parameter, which is a cornerstone of the PND calculation. For example,\ntheo\na trimer where only two of the three mEos4b tags are detected (due to stochastic photoactivation)\nwill contribute to the experimental neighbor density distribution in a predictable way. The model\ncalculates the probability of such events using a binomial distribution based on the PE value and\nincorporates this into the theoretical fitting curves for each oligomeric state. This makes the method\nrobust to incomplete detection, as it statistically models the expected distribution of observed\nneighbors for a given true oligomeric state, rather than assuming 100% detection. We have added\na sentence to the Discussion to clarify this.\n[Changes in Discussion: Limitations of the study] A key strength of the smND method is that its\ntheoretical framework explicitly accounts for the incomplete detection of fluorophores (e.g., a photo-\nactivation efficiency for mEos proteins of ~40-50%).44 By modeling the probable number of detected\nsubunits for each oligomeric state, the method can robustly determine oligomeric populations even\nwhen not all subunits within a complex are localized.\n5. I appreciate that the authors added imaging data in Extended Figure 2. However, it is not possible\nto see single receptor clusters even in the zoom images (that have a scale of 10 µm). I wonder\nwhether there are isolated clusters at all, and what the molecular density of this receptor is – as\ncompared to the physiological density of CTR1.\n[Reply 1-5] We thank the reviewer for this observation, which allows us to clarify the purpose of\nExtended Figure 2. The reviewer is correct that single receptor clusters are not resolved in these\nimages. These confocal images were intended solely to demonstrate the general biological function\nof copper-induced endocytosis of our CTR1mEos construct, by showing the co-internalization of the\ncell-surface marker (WGA) and CTR1mEos into intracellular puncta upon copper treatment. This\nvalidates that the tagged protein traffics correctly. All quantitative analysis of oligomeric state was\nperformed using SMLM under TIRF illumination (presented in Figure 3), which provides the\nnecessary single-molecule resolution. We have revised the figure caption of Extended Figure 2 to\nmake its purpose clear and avoid any implication that these images are used for single-molecule\nanalysis.\n[Changes in Extended Figure 2] Note that these confocal images are intended to demonstrate the\ncopper-induced co-internalization of surface-labeled CTR1mEos and the membrane dye WGA,\nconfirming the endocytic competence of the fusion protein. They are not used for single-molecule or\noligomeric state analysis, which is performed using SMLM as shown in Figure 3.\nAll of the above points can be addressed with minor editing.\n[Reply] We thank the reviewer for their constructive feedback. We agree, and we appreciate that\nthese minor edits have helped improve the quality and clarity of our work.\nReviewer #2 (Remarks to the Author):\nThis reviewer appreciates the authours' thoughtful and constructive response to the critique. The\nrevised manuscript is significantly improved: it is clearer, it includes new and useful controls, and it is,\noverall, more convincing. I have no further concerns.\n[Reply] We thank the reviewer for their positive feedback and support of our manuscript.\nReviewer #3 (Remarks to the Author):\n1. In response to this reviewer's comments, authors responded that their major focus is to establish\nthat CTR1 undergoes a structural change (de-oligomerization) that precedes endocytosis in\nelevated copper, which prevents excess Cu entry. While study is important in terms of showing\nrelationship between structural change and endocytosis, it is essential to show the functional\nconsequence of preventing endocytosis, such as excess Cu entry and Cu-induced cell death at\nleast in vitro. Thus, authors should perform additional experiments to show the functional\nsignificance of de-oligomerization of CTR1, which is not difficult to do, to increase the quality of\nthis paper.\n[Reply 3-1] We thank the reviewer for their persistence on this critical point. Their suggestion\nprompted us to perform additional functional experiments to directly test the importance of the\ncomplete CTR1 regulatory pathway for cell survival. We sought to test the hypothesis that de-\ntrimerization and endocytosis are two cooperative and equally essential components of the cellular\ndefense against copper toxicity.\nTo test the hypothesis that endocytosis provides a necessary protective mechanism, we used the\ndynamin inhibitor Dynasore to specifically block the internalization step. Our new results show a\nstrong synergistic lethality between endocytosis inhibition and copper stress. While high copper or\n80 µM Dynasore alone caused only moderate effects, blocking endocytosis before adding high\ncopper resulted in massive cell death within 24 hours. This finding demonstrates that de-\ntrimerization alone is an insufficient defense over time. It strongly supports our model that\nendocytosis plays a crucial, non-redundant role. As noted in our discussion, we propose that this\nsecond step is essential to clear the dissociated, inactive subunits from the plasma membrane,\nthereby preventing their potential re-assembly into functional, copper-transporting trimers.\nWe have included these important findings in Figure 5j and have added a new paragraph to the\nResults and updated the Discussion. We are grateful for the reviewer's suggestion, as these new\nexperiments further strengthened the functional significance and overall impact of our manuscript.\n[Changes in the Results: Intracellular Cu triggers compartment-specific CTR1 dissociation\nfor cell survival] Finally, to directly test the functional importance of endocytosis as an essential\nprotective pathway, we inhibited internalization using the dynamin inhibitor Dynasore during copper\nstress. Our results show a strong synergistic cytotoxicity of copper stress and endocytic blockage.\nAs shown in Fig. 5j, after 24 hours, treatment with either 100 µM CuCl or 80 µM Dynasore alone\nhad a moderate impact on cell viability. In contrast, blocking endocytosis with Dynasore prior to\ncopper exposure led to a profound loss of cell viability, with cells appearing rounded and detached.\nThese results demonstrate that a functional endocytic pathway is also critical for protecting cells\nfrom acute copper toxicity.\nFig. 5 | Intracellular copper elevation triggers CTR1 dissociation primarily in Rab5+, not Rab5–,\ncompartments. a, Biotinylation assay comparing the surface abundance of the CTR1(M150L)mEos under different\ncopper treatments (top). Normalized CTR1 intensity plot (normalized to the loading control Caveolin-1, bottom)\nshows that treatment with 100 µM CuCl alone fails to trigger its internalization, whereas exposure to 2 µM of the\nmembrane-permeable CuES ionophore restores endocytosis. This confirms that elevated intracellular copper,\nrather than extracellular copper bind ₂ ing, drives CTR1 internalization. (b–i), smND analysis of CTR1\noligomerization in Rab5– compartments. Under basal conditions, wild-type CTR1mEos (b–e) shows minimal\nchanges in trimer populations upon copper stress, shifting from approximately 53% to 47%, within the uncertainty\nof smND. The endocytosis-deficient CTR1(M150L)mEos mutant (f–i) demonstrates negligible oligomeric shifts\n(~62% to 57%) under copper treatment in Rab5– areas, confirming that significant copper-induced dissociation of\nCTR1 occurs specifically in Rab5+ compartments. Data represent mean ± SEM from three independent\nexperiments. j, Representative bright-field images of COS7 cells after 24 hours of treatment with vehicle (DMSO),\n100 µM CuCl , 80 µM Dynasore, or Dynasore and CuCl combined. A profound loss of cell viability (i.e., cells\nappearing rounded and detached) is observed only in the combined treatment, demonstrating that blocking\nendocytosis s₂ensitizes cells to copper-induced death. ₂\n[Changes in Discussion: Benefits of CTR1 de-trimerization in copper regulation] However, if\nthese monomers were to persist on the plasma membrane, they could potentially re-assemble into\nfunctional trimers, counteracting the initial protective effect. Our functional data support this model,\nshowing that when endocytosis is blocked with Dynasore, cells become hypersensitive to copper\ntoxicity (Fig. 5j). This highlights the cooperative nature of the defense: de-trimerization provides the\nrapid stopgap, while endocytosis provides the essential follow-through by clearing the dissociated\nsubunits from the membrane to ensure a permanent shutdown of copper uptake and promote cell\nsurvival.\n[Changes in Methods: Cell viability assay] To assess cell viability under copper stress with\nendocytosis inhibition, COS7 cells were seeded in multi-well plates and cultured in DMEM medium\ncontaining 10% FBS overnight to approximately 70% confluency. The Cells were then serum-starved\nfor 2 hours before being treated with either 80 µM Dynasore (Calbiochem 324410) or an equivalent\nvolume of the vehicle, DMSO (final 0.2%, VWR 97063-136) for an additional 30 minutes.\nSubsequently, copper stress was induced by adding CuCl to a final concentration of 100 µM in the\nmedium containing 1% FBS. Cell viability was assessed after 24 hours by bright-field microscopy.\n₂\nAdherent cells were considered viable, whereas non-adherent, rounded cells were classified as non-\nviable.\nReviewer #4 (Remarks to the Author):\nI believe the authors’ discovery of CTR1 deoligomerization importantly contributes to understanding\nCTR1 function and regulation, and merits publication in Nature Communications.\n[Reply] We thank the reviewer for their positive assessment of our work's contribution and their\nsupport for its publication in Nature Communications!\n1. However, I am not fully convinced that their computational modelling supports the claim that the\nM150L mutation stabilizes the trimeric structure of Ctr1. Their experiments with Cu ionophore\nclearly show that deoligomerization is caused by an increase in intracellular Cu concentration and\nis not primarily driven by intrinsic thermodynamic stability of the CTR1 trimeric transmembrane\ndomain or how the latter is affected by copper binding at M150 or M154. This suggests that the\nM150L mutation, as the authors also state, primarily prevents deoligomerization by preventing\ncopper internalization and not by stabilizing the trimeric assembly. Therefore, I think that the\nestimation of the effect of the M150L mutation on CTR1 oligomerization affinity is not really\nrelevant for the paper’s main outcome and due its relative speculative nature perhaps detracts\nmore than it adds to the scientific quality of the paper. In any way, while the authors address many\npoints previously raised, some further improvements are needed before publication.\n[Reply 4-1] We are very grateful to the reviewer for this insightful and constructive critique. We\nagree that our experimental data, particularly the Cu-ionophore experiment, provide the most direct\nand compelling evidence that CTR1 de-oligomerization is an active process triggered by intracellular\ncopper elevation, rather than a passive process governed by the intrinsic thermodynamic stability\nof the transmembrane domains. The reviewer's point that computational modeling may detract from\nthis stronger, clearer experimental narrative is well-taken.\nFollowing the reviewer's suggestion to reconsider the role of this section, we have re-framed the\nsection to present it not as a definitive conclusion about stability, but as an exploration of a plausible,\nhypothesis-generating idea that required direct experimental testing. We believe this new structure\nstrengthens the paper's narrative. The modeling now serves to pose a key question: is the M150L\nphenotype driven by intrinsic structural stability or by its failure to transport copper? This framing\nhelps to \"pave the way\" for our subsequent experimental results, highlighting how the ionophore\nexperiment, in particular, was crucial for distinguishing between these two possibilities. We have\nrewritten the first section of the Results to reflect this new, hypothesis-driven approach and hope\nthe reviewer finds that this revision fully addresses their concern by better integrating the\ncomputational work into the paper's core logical flow.\n2. Authors write on line 95:\n“However, in the absence of copper, the RMSF data showed increased flexibility in M150L (1.84 Å\ncompared to the wild type 1.75 Å), especially within the intracellular loop (residues 98 to 136) and\ncytosolic tail (residues 176 to 190, Fig. 1f).”\nFirstly, while the authors now show that the average RMSF stabilizes after 900 ns, no error\nestimate is provided, therefore it is hard to estimate whether the relatively small 0.09 Å difference\nis significant. Moreover, panel 1f appears to show that in subunit A CTR1 is in fact more flexible\nthan CTR1 (M150L). Additionally, in the transmembrane part that should be most relevant for the\noligomerization, the differences seem particularly negligible and in any case do not suggest\ngreater flexibility of the wt variant. I suggest the authors at a minimum include error estimates,\neither by employing the block average method on the final 500 ns of trajectories or by treating\nsubunits as simulation replicas.\n[Reply 4-2] We thank the reviewer for these insightful comments on the RMSF analysis. As\nsuggested, we have now included standard error estimates for the RMSF values, calculated using\na block averaging method over 100 ns intervals from the simulation trajectories. These error bars\nhave been added to the revised version of Fig. 1f. We also appreciate the reviewer’s sharp\nobservation regarding the flexibility of subunit A. We agree this is an interesting point and have\nadded a sentence to the manuscript discussing this potential asymmetry and what it might suggest\nabout the mutation's localized effects.\n[Changes in the Results: Computational modeling suggests a possible link between trimer\nstability and endocytosis] In the absence of copper, the RMSF data increased, with the M150L\n(1.84 Å ± 0.07) demonstrating higher overall flexibility than that of the wild type (1.75 ± 0.05 Å),\nespecially within the intracellular loop (residues 98 to 136) and cytosolic tail (residues 176 to 190,\nFig. 1f). Of note, this average RMSF masks some regional difference. For example, subunit A of the\nwild type appears more flexible than in the M150L mutant. It suggests a potential asymmetry in how\nthe mutation affects local flexibility.\n3. The most convincing evidence about the effect of M150L mutation on the thermodynamic stability\nof the CTR1 trimer comes from computational mutagenesis. Can the authors add a brief\nexplanation regarding the scoring function employed by the Eris algorithm for calculating the free\nenergy and how it treats the Met-Cu(I) interaction. Does the DMD optimization significantly affect\nthe geometry of the metal binding site?\n[Reply 4-3] Thank you for the suggestion. We have now added a detailed description of the Eris\nalgorithm's scoring function to the Methods section. To summarize, Eris uses the Medusa force field,\na physics-based energy function that accounts for van der Waals forces, solvation, and hydrogen\nbonding, with parameters from the cited Yin et al. 2007 paper. Regarding the second point, the DMD\noptimization has a negligible impact on the metal binding site's geometry. Eris primarily uses DMD\nto relieve any atomic clashes after side-chain repacking. Upon inspection of the resulting structures,\nwe confirmed that the backbone of the binding site remained essentially unchanged, with deviations\nof less than 0.1 Å.\n[Changes in the Method: Evaluating CTR1 stability through computational mutagenesis] Eris\nemploys the Medusa force field, a physics-based energy function that accounts for key molecular\ninteractions. These include both attractive and repulsive van der Waals forces, solvation energies,\nand hydrogen bonding—within the backbone, between side chains, and between side chains and\nthe backbone. The force field also incorporates side-chain rotamer preferences based on backbone\nφ and ψ dihedral angles. The Met–Cu(I) interaction is modeled within the Medusa framework. The\nparameterization of the force field was described previously.62\nMinor points:\n4. In figure 1d, the grid lines and the y-axis tics do not match.\n[Reply 4-4] We have corrected the axis ticks in the figure.\n5. In the following sentence, starting on line 75, the word ‘change’ is unnecessary and leads to\nconfusion.\nIn the absence of copper, the leucine substitution at M150 (M150L) reduced the free energy change\n(ΔΔG) by −5.11 kcal/mol compared to the wild type.\n[Reply 4-5] We have removed the redundant word \"change\" from the sentence describing the free\nenergy calculation.\n6. Authors state on line 81:\n“Although the M154 site in the M150L mutant still coordinates copper, it is unlikely to be the dominant\nstabilizing factor, as the Cu(I)–M154 bond lengths in the mutant (average 2.7 Å) are slightly longer\nthan those in the wild-type (2.4 Å), suggesting weaker coordination (Fig. 1a).”\nPlease clarify if these distances stem from MD simulations.\n[Reply 4-6] We have clarified that the Cu(I)–Met154 bond lengths were averaged from the MD\nsimulation trajectories.\n[Changes in Results: Computational modeling suggests a possible link between trimer\nstability and endocytosis] Although the M154 site in the M150L mutant still coordinates copper, it\nis unlikely to be the dominant stabilizing factor, as the Cu(I)–M154 bond lengths, averaged from our\nMD simulations, are slightly longer (average 2.7 Å) than those in the wild-type (2.4 Å), suggesting\nweaker coordination (Fig. 1a, right).\n7. In the method section, line 641, the authors state:\n“We utilized the Amber force field, which includes well-validated parameters for Cu(I) coordination.60”\nI assume they employed the non-bonded copper model, however the reference they give (Schwartz\nR, et al. Protein Science. 2022;31(12):e4464) describes the development of parameters for a bonded\ncopper model that are not provided directly provided with Amber/AmberTools. If they have indeed\nused the non-bonded model the reference should be removed. Additionally, the authors should state\nexplicitly which protein force field (ff12SB/ff14SB,…), water model (TIP3P/OPC,…) and ion\nparameters (Li-Merz/Young-Cheatham,…) they employed.\n[Reply 4-7] We sincerely thank the reviewer for identifying this important error in our methods\nsection. The reviewer is correct; we used a non-bonded model for Cu(I), and the citation to Schwartz\net al. was inappropriate and has been removed to avoid confusion. We have now explicitly stated\nthe complete force field and parameter sets used in our simulations: the ff14SB protein force field,\nthe TIP3P water model, and the Li–Merz parameters for monovalent metal ions, including Cu(I). The\nMethods section has been updated accordingly.\n[Changes in the Method: Molecular Dynamics Simulations] The protein was parameterized\nusing the ff14SB force field, with the TIP3P water model and Li–Merz parameters for monovalent\ntransition metal cations.\nPoint-by-point response to reviewers\n[Reply to all reviewers] Thank you for your critical and constructive comments. Below, we respond (in\nred) to each question/comment (in black italic), with our additions to the text indicated (in blue).\nReviewer #1 (Remarks to the Author):\nI thank the authors for responding to and addressing all my comments. The revised manuscript has\nsignificantly improved, and there are no more open points from my side.\n[Reply 1] We sincerely thank the reviewer for the careful evaluation and supportive feedback. We are\ngrateful for the constructive suggestions provided in earlier rounds, which helped strengthen the\nmanuscript.\nReviewer #3 (Remarks to the Author):\n1. Although the reviewer requested that the authors should demonstrate the functional\nconsequences of CTR1 de-oligomerization in response to intracellular Cu elevation such as Cu-\ninduced cell death using both CTR1 wild-type and the endocytosis-deficient CTR1-M150L mutant,\nthis experiment was not included in the revised manuscript. Instead, the authors only assessed\nthe effect of the endocytosis inhibitor Dynasore on CuCl -induced cell death.\nTherefore, the revision does not adequately address the original concern.\n₂\n[Reply 3-1] We appreciate the reviewer’s emphasis on functional consequences. We agree that\nassessing cell viability under copper stress is an important orthogonal readout. However, a direct\nWT vs. CTR1(M150L) cell-death comparison would be intrinsically confounded: CTR1(M150L) is\ncopper-transport deficient and does not accumulate intracellular Cu to the same extent under\nchallenge. Any survival difference between WT and M150L would therefore primarily reflect impaired\nCu entry in the mutant rather than the specific contributions of CTR1 oligomeric state or endocytosis.\nFor this reason, we did not pursue the WT vs. M150L viability experiment. To make this rationale\nexplicit for readers, we have added a short paragraph in the Discussion explaining why we did not\npursue a WT–CTR1(M150L) cell-death assay.\nInstead, our revised manuscript provides three convergent lines of evidence that together connect\nCTR1 structural state, trafficking, and a cell-level outcome—without relying on a transport-deficient\nmutant comparison:\n1. Temporal ordering (structure before trafficking): Using TNPD/smND analyses within Rab5 early\nendocytic compartments under TIRF, we observe that elevating intracellular Cu is associated\n⁺\nwith a shift toward lower-order/monomeric CTR1 prior to endocytosis (Results; Fig. 3a–e). This\nestablishes sequence (monomerization precedes internalization) while avoiding a causal\noverclaim.\n2. Trigger specificity (intracellular Cu is sufficient): Using a membrane-permeable ionophore (Cu-\nelesclomol) to raise intracellular Cu, we observe robust CTR1 internalization by surface\nbiotinylation (Results; Fig. 5a). This indicates that intracellular Cu—not extracellular binding\nalone—is sufficient to induce the trafficking response.\n3. Orthogonal functional outcome (cell viability under stress): Dynamin inhibition (Dynasore) plus\nCu produces a pronounced loss of viability at 24 h, whereas either treatment alone has only\nmodest effects (Results; Fig. 5j). This is consistent with a protective role for endocytosis during\nCu overload and provides the requested functional readout at the cell level.\nTaken together, these data support a temporally ordered and mechanistically plausible model in\nwhich elevated intracellular Cu is associated with early CTR1 de-oligomerization in Rab5\n⁺\ncompartments and coincides with endocytosis that limits further Cu entry; blocking endocytosis\nsensitizes cells to Cu toxicity at the population level.\n[Changes in the METHOD DETAILS: Cell viability assay] To avoid transport-deficiency\nconfounding, we did not pursue a WT versus CTR1(M150L) cell-death comparison, because\nCTR1(M150L) fails to accumulate intracellular copper under challenge and any survival difference\nwould be dominated by reduced Cu entry rather than oligomerization or endocytosis per se.\nReviewer #5 (Remarks to the Author):\nI am content with the authors' response to my previous comments. I have no additional concerns\nregarding publication.\n[Reply] We sincerely thank the reviewer for the careful evaluation and supportive feedback. We are\ngrateful for the constructive suggestions provided in earlier rounds, which helped strengthen the\nmanuscript."}]

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