AMP-activated protein kinase-driven lipid droplet dynamics govern melanoma sensitivity to polyunsaturated fatty acid and iron-induced ferroptosis

Domain: neuroscience
Published: 2025-12-17
Editorial outcome: major_revision

Abstract
Ferroptosis, a regulated form of cell death driven by lipid peroxidation, holds promise for targeting treatment-resistant cancer cells. Using a panel of melanoma cell lines, we uncover variability in the timing of ferroptosis onset upon exposure to iron and polyunsaturated fatty acids (PUFAs). This heterogeneity is linked to differences in PUFA sequestration into lipid droplets (LDs) and their subcellular distribution, particularly near lipid-metabolizing organelles such as mitochondria. In late-onset models, ferroptosis is delayed by peripheral LD retention and triggered by nutrient deprivation and AMP-activated protein kinase (AMPK) activation, which promotes LD trafficking toward mitochondria. Early responders bypass this mechanism. Our findings identify nutrient status and LD dynamics as key modulators of PUFA- and iron-induced ferroptosis, offering insights for therapeutic exploitation in cancer.

Introduction
The ultimate goal of therapeutic cancer intervention is to eliminate cancer cells with minimal effects on normal healthy tissue. Most current strategies are directed toward the selective induction of regulated cell death (RCD) in cancer cells. For a long time, apoptosis has been considered the primary mechanism of RCD and, therefore, selected as a critical target of many anticancer drugs. Cancer cells, however, often exploit mechanisms to evade apoptosis, leading to poor responses and contributing to therapy resistance1. The recent identification of other RCD mechanisms opens new avenues towards cancer treatment. One of these mechanisms is ferroptosis. Ferroptosis is an iron-dependent cell death propagated by polyunsaturated fatty acids (PUFA) peroxidation. A specific class of ferroptosis inducers, such as Erastin and RSL3, can induce this process. These compounds target the System Xc− cystine/glutamate antiporter SLC7A11 and glutathione peroxidase GPX4, which are involved in redox control and alleviating lipid peroxidation2–5. Since the discovery that ferroptosis contributes to tumor cell killing by several treatment modalities, including chemotherapy, radiotherapy, and immunotherapy, and that many treatment-resistant cancers remain responsive to ferroptosis inducers6–9, ferroptosis has come into the spotlight as a potential alternative strategy in cancer therapy and as a way to overcome drug resistance.

Emerging evidence reveals the relationship between melanoma differentiation status, sensitivity to ferroptosis, and lipid metabolism in the context of BRAF inhibition. Differentiated melanoma cells often possess enhanced antioxidant defenses, making them more resilient to ferroptosis despite exposure to BRAF inhibitors. In contrast, undifferentiated melanoma cells exhibit heightened sensitivity due to metabolic vulnerabilities, including altered iron metabolism and ineffective counteraction of oxidative stress10–13. Lipid metabolism further influences this sensitivity; undifferentiated cells may have impaired regulation of lipid peroxidation, leading to toxic lipid accumulation that triggers ferroptosis. Conversely, differentiated melanoma cells may leverage lipid saturation and storage to reduce oxidative stress effects14,15. Hence, modulation of this balance by increasing the cancer PUFA content may induce a critical vulnerability in undifferentiated cells. This has great therapeutic potential, as these cells have been shown to drive resistance to standard-of-care treatments, including targeted therapy and immune checkpoint blockade16.

In this study, to gain more insight into the mechanisms that contribute to cancer cells’ sensitivity to ferroptosis and to identify critical vulnerabilities that can be exploited in new combination cancer treatments, we examine the effect of iron and PUFA supplementation in a panel of drug-resistant and drug-sensitive melanoma cell lines. Interestingly, the sensitivity of melanoma cells to PUFA/iron-induced ferroptosis varies significantly depending on how they handle supplemented PUFAs. The sequestration of PUFAs within lipid droplets (LDs) and the subcellular distribution of these LDs relative to organelles, such as mitochondria, are key factors influencing the ferroptotic response to iron and PUFA supplementation. Notably, this response is affected by starvation-induced AMPK (AMP-activated protein kinase) activation, LD redistribution, and LD-mitochondria association. These findings highlight the roles of nutrient status and AMPK-mediated LD dynamics as modulators of PUFA- and iron-induced ferroptosis, offering insights into strategies to sensitize therapy-resistant cancer cells to cell death induction.

Results
Differential effect of PUFA and iron supplementation on ferroptosis induction in a panel of biologically diverse melanoma cell lines
To explore to what extent melanoma cells harboring distinct differentiation states and responsiveness to BRAF inhibitors (Table S1) are sensitive to natural inducers of ferroptosis, we exposed a panel of 8 well-characterized melanoma cell lines13,17 to a mixture of arachidonic acid (AA; 20-4 ω−6) and docosahexaenoic acid (DHA; 22-6 ω−3) to induce lipid polyunsaturation. We treated them with ferric ammonium citrate (Fe3+), a physiological form of non-transferrin-bound iron, which causes intracellular iron overload and induces ferroptosis in various biological systems18,19. Measurement of cell death at different timepoints using the SYTOX Green dead cell stain revealed remarkable differences in the time courses of cytotoxic response induction among the various cell lines, with M257, M229, M238 and M238R being early responders (>50% cell death after 48 h) and M249, M249R, 451Lu and 451LuR responding later or less severely (Fig. 1A). Notably, at the compound concentrations used, only the combination of PUFA and Fe3+ induced significant lethality (Fig. S1A), with no considerable difference in the uptake of PUFA and iron between groups (Fig. S1B). Notably, there was an inverse correlation between the differentiation status of the indicated melanoma cell lines and the time-dependent response to PUFA/Fe3+-induced cell death (Fig. S1C). Dedifferentiated cells with phenotypic plasticity, which confer them an adaptive drug tolerance/resistance, were susceptible to cytotoxicity induced by PUFA/Fe3+20,21. No association with resistance to BRAF-targeted treatment was observed.

Lipid peroxidation-mediated ferroptosis was supported by the accompanying increase in lipid peroxides, as measured by BODIPY 581/591 C11 staining, and the accumulation of malondialdehyde (MDA), a secondary product of lipid peroxidation (Fig. 1B, C). The induction of cell death was inhibited by either ferrostatin-1 or deferoxamine but not by the apoptosis inhibitor Z-VAD and the necroptosis inhibitor Nec-1s (Fig. 1D). This 2D cell line data was corroborated by monitoring the cytotoxicity of PUFA and iron supplementation in a 3D spheroid model generated from 451LuR cells. Treatment with PUFA/Fe3+ resulted in a gradual increase in SYTOX intensity over time, which was entirely prevented by ferrostatin (Fig. 1E, F).

Sequestration of PUFA into lipid droplets and their differential distribution determines the timing of ferroptosis induction
In exploring the molecular underpinnings of ferroptosis induced by PUFA/Fe3+, RNA sequencing across a comprehensive panel of models revealed that glycerolipid metabolism is among the top three enriched pathways. This finding was accompanied by a notable upregulation of triglyceride (TAG) synthesis genes in treated cells compared to controls (Figs. 2A, S2A). Given the established protective role of TAG-rich lipid droplets22–24, we investigated whether the differential sequestration of PUFA into LD-associated neutral lipids, primarily TAGs, contributes to the varying responses observed in melanoma models. Mass spectrometry-based lipidomics revealed that supplementation of cell cultures with dietary PUFA resulted in a limited change in the polyunsaturation degree of membrane phospholipids. In contrast, PUFA-containing neutral lipids, particularly DHA-TAGs and DHA-Cholesterol ester (CEs), increased dramatically across all melanoma cells (Figs. 2B, S2B). Similarly, the unbound fraction of PUFA significantly rose in both groups at an equal rate (Fig. 2C). The preferential partitioning of exogenous PUFA into LDs was further corroborated by Stimulated Raman Scattering (SRS) microscopy using a deuterated isotope of docosahexaenoic acid (DHA-d5), which demonstrated that exogenously administered PUFA was sequestered mainly in LDs rather than in plasma membranes, lysosomes, ER, or mitochondria (Fig. S2C, D). To further investigate the dynamics between saturated and unsaturated fatty acids within lipid droplets upon PUFA supplementation, we assessed changes in two additional Raman wavenumbers. The band around 1660 cm⁻¹, which indicates C=C stretching vibrations, serves as a marker for unsaturated fatty acids, while the 1440 cm⁻¹ band corresponds to the scissoring of the CH2–CH3 banding in saturated carbon chains25. As shown in Fig. S2E, F, signals for both saturated and unsaturated fatty acids were co-localized with lipid droplets. Notably, the ratio of unsaturated to saturated fatty acids increased following PUFA treatment compared to the control condition in both early and late-responder models (M257 and 451LuR). However, the difference in unsaturation levels between M257 and 451LuR was not statistically significant.

Fluorescent staining of LDs with BODIPY 493/503 (green) revealed a substantial increase in LD accumulation across all cell lines, irrespective of group classification (Fig. 2D). Consistent with RNA sequencing data indicating differential, time-dependent regulation of TAG synthesis genes, the analysis of 100 cells per cell line demonstrated distinct temporal dynamics in LD synthesis. Specifically, early responders (ER) showed a significant decrease in LD synthesis after 24 h, while late responders (LR) maintained a stable synthesis rate even at the 48-h mark (Fig. S2A, D). Further analysis of the BODIPY 493/503 fluorescence images revealed unique LD distribution patterns among the different models. Early responders presented numerous small, punctate LDs evenly distributed throughout the cell, with a notable concentration in the perinuclear region. In contrast, late responders showed a tendency for bigger LDs to cluster at the cell periphery (Fig. 2E, F). Interestingly, over time, these clustered LDs gradually dispersed into smaller droplets and migrated toward the perinuclear area (Fig. S2G, H). This observation suggests a dynamic regulation of LD distribution that may have important implications for cellular metabolism.

To investigate the role of lipid droplets in initiating ferroptosis, we modulated the accumulation of neutral lipids in these droplets by inhibiting LD biogenesis. Specifically, our study involved blocking diacylglycerol acyltransferases (DGAT1i T863 and DGAT2i PF-06424439) and utilizing RNAi-mediated knockdown of DGAT1 and DGAT2, which effectively eliminated LD formation (Figs. S2I, J, S6A). This intervention, particularly using the DGAT2 knockdown (KD) model, partially rescued cells from PUFA/Fe3+-induced lipid peroxidation and cell death, particularly in late responders (Figs. 2G–I, S2K, S2L). This observation aligns with our lipidomics data (Fig. S2M) and highlights the role of LDs in modulating cellular responses to PUFAs in melanoma models, particularly among late responders.

To clarify the causal relationship between lipid droplet (LD) distribution and sensitivity to ferroptosis, especially in late responders, we investigated LD movement by knocking down two genes: KIF5B, a motor protein that facilitates LD transport to the periphery in nutrient-rich conditions26,27, and spastin (SPAST gene), which facilitates inward dispersion of LDs during glucose starvation28. Image analysis revealed that KIF5B knockdown decreased LD clustering, while spastin knockdown increased it in 451LuR cells exposed to PUFA/Fe3+ (Figs. 2J, S6G). These changes in LD distribution were linked to significant alterations in lipid peroxidation and ferroptosis-related toxicity, as shown in Fig. 2K, L. Overall, these findings highlight that modulating LD dynamics and distribution significantly affects ferroptosis sensitivity in late-responder cells.

Energy stress-mediated AMPK activation promotes ferroptotic cell death by affecting lipid droplet dynamics
Given the well-established role of LDs as energy reservoirs, we investigated whether their differential and time-dependent translocation in late responders was linked to the cells’ energy demands. Quantification of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) revealed significantly higher activity in both oxidative phosphorylation (OXPHOS) and glycolysis pathways in late responders compared to early responders. Early responders, however, exhibited a lower overall bioenergetic capacity, which was slightly reduced by PUFA/Fe3+ exposure in both groups (Fig. 3A, B). This insight, combined with the gradual hydrolysis of PUFA-TAG over time (Fig. S3A), indicates that the varying adaptability of LDs in late responders is intricately connected to spatial energy metabolism and the onset of starvation processes. To further explore the impact of starvation and glucose depletion on LD translocation and ferroptosis induction, we treated M257 and 451LuR cells with PUFA/Fe3+ and replenished the culture medium with or without glucose after 20 h. Glucose deprivation led to decreased density of LD and increased cytotoxicity, whereas glucose supplementation notably alleviated cytotoxic effects in 451LuR late-responder cells but not in M257 early model (Fig. S3B, C).

In response to starvation, our RNA sequencing data revealed activation of AMPK signaling, particularly highlighted by increased expression of PRKAA1 (AMP-activated protein kinase) mRNA in late responders. Notably, this expression was further upregulated following PUFA treatment compared to early responders (Fig. S3C). Although we did not detect significant changes in the levels of phosphorylated AMPK (P-AMPK) or total AMPK protein among the groups (Fig. S3D, E), the observed upregulation of the mTOR gene and downregulation of both downstream targets of the AMPK pathway suggest that these cells have an enhanced ability to adapt metabolically under stress. This adaptability is further supported by the observed time-dependent upregulation of AMPK, which coincides with LD hydrolysis (Figs. 3D, S3F).

To demonstrate AMPK’s role in inducing ferroptosis, CRISPR-Cas9-mediated AMPK knockout significantly diminished PUFA/Fe3+-induced lipid peroxidation and cell death and increased AMPK KO proliferation (Figs. 3E, F, S3G). In contrast, activating AMPK with the activator A769662 increased lipid peroxidation and intensified PUFA/Fe3+-induced cytotoxicity, mimicking the effects of glucose deprivation. The AMPK inhibitor Compound C replicated the effects of the knockout (Fig. 3G, H). Notably, AMPK activation did not promote ferroptosis in 451LuR cells supplemented with monounsaturated fatty acid (MUFA), specifically oleate, rather than PUFA, highlighting the specific role of PUFA in this process (Fig. 3I, J). Similar outcomes were observed in other late-responder cell lines (Fig. S3H, I).

To further elucidate the mechanism by which AMPK mediates late-onset ferroptosis, we assessed the effect of AMPK modulation on the distribution of LDs. Confocal microscopy imaging revealed that depletion of AMPK, either pharmacologically or genetically, reinforced the clustering of bigger LDs in the peripheral extremities of 451LuR cells and prevented their dispersion in the cell body (Figs. 3K, S3J, K, S6B, C), while AMPK activation by EX-229 evoked the redistribution of LDs toward the cell center. Moreover, AMPK activation led to a significant increase in the size of lipid droplets and enhanced their subsequent hydrolysis over time (Figs. 3L, M, S3L, M). These effects were replicated in 3D 451LuR tumor spheroids treated with A769662 and Compound C (Fig. 3N, O). Collectively, these findings link lipid droplet translocation with energy demand and starvation, highlighting the role of AMPK activation in enhancing ferroptosis post-PUFA/Fe3+ supplementation.

Activated AMPK boosts LD-MT tethering through detyrosinated tubulin
Since starvation-mediated AMPK activation enhances both the dispersion and hydrolysis of LDs, we explored potential differential interactions between LDs and fatty acid-metabolizing organelles, such as mitochondria, peroxisomes, and lysosomes, across different groups. Confocal imaging of M257 and 451LuR cells with antibodies against mitochondria (MT), peroxisomes (PER), and lysosomes (LYS) revealed a time-dependent distribution pattern and organellar association of LD-associated PUFAs (Fig. 4A). In early responder cells, LDs localized closely with mitochondria and, to a lesser extent, with peroxisomes at early time points (12 h) after PUFA loading. Conversely, in late responders, LDs initially localized far from mitochondria and peroxisomes, only redistributing towards mitochondria-rich areas at later time points (48 h). Image-based quantification confirmed this time-dependent shift in LD-MT colocalization in late-responder cells (Fig. 4B). Live imaging using SRS-confocal microscopy of 451LuR cells treated with DHA-d5 and Fe3+ during starvation, co-stained with Mitotracker, CellLight Peroxisome-GFP, and Lysotracker, was consistent with fixed imaging results. DHA-d5 initially localized to lipid droplets at the cell periphery, subsequently migrating towards the perinuclear region and eventually overlapping with mitochondrial areas, particularly upon AMPK activation (Fig. S4A). Treatment with Etomoxir, a CPT1 inhibitor, reduced DHA-d5 accumulation in mitochondria in 451LuR cells at the starvation time point (48 h), with a slight increase in localization towards peroxisomes and lysosomes (Fig. S4B, C). Transmission electron microscopy (TEM) further revealed numerous close LD-MT appositions under stimulated conditions (Fig. 4C). TEM images revealed increased electron density in PUFA-loaded LDs, likely due to enhanced osmium staining associated with the unsaturated nature of DHA. Notably, upon AMPK activation, the lipid droplets appeared darker. This is likely attributable to the higher PUFA content, including DHA, which resulted in greater osmium binding and, consequently, increased electron density. Most dispersed LDs were in contact with mitochondria, and quantification confirmed that AMPK activation increased these LD-MT contact sites upon PUFA loading (Fig. 4D). This proximity promoted mitochondrial morphological changes, including decreased cristae and swollen mitochondria, particularly under ferroptosis-exacerbating conditions triggered by AMPK activation (Fig. 4E).

Building on earlier observations that induced AMPK to relocate LDs and mitochondria on detyrosinated microtubules, enhancing mitochondrial oxidation of LD-PUFA, we examined the impact of dietary PUFA and AMPK activator on tubulin termination. RNA sequencing analysis revealed significant differences in the expression of tubulin-related genes between the two groups, with VASH1—encoding a tyrosine carboxypeptidase (TCP) responsible for tubulin detyrosination—showing higher levels in late responders compared to early responders, particularly when treated with PUFA/Fe3+ (Fig. S4D, E).

PUFA/Fe3+ treatment showed a temporal downregulation of detyrosinated tubulin, followed by upregulation upon starvation or treatment with the AMPK activator A769662 (Fig. 4F). Interestingly, Parthenolide (PTN), an inhibitor of tubulin carboxypeptidase (TCP) (Fig. 4G), blocked LD dispersion and its interaction with mitochondria, partially reducing lipid peroxidation and rescuing cells from cytotoxicity in both groups (Figs. 4H–K, S4F). In summary, these findings underscore the crucial interplay between LDs and mitochondria in PUFA-induced ferroptosis.

Channeling of PUFAs between lipid droplets and mitochondria facilitates the induction of ferroptosis
Given the proximity of LDs and mitochondria, which facilitates the transfer of PUFAs into mitochondria, along with the observed accumulation of supplemented iron in mitochondria across both cell groups (Fig. S5A), we investigated whether mitochondria play a role in ferroptosis-induced cytotoxicity. SRS imaging-colocalization results and the upregulation of mRNA expression for genes involved in LD-mitochondria interaction (PLIN2, PLIN5), mitochondrial tethering (MFN1, MFN2), lipid import into mitochondria (CPT1), and beta-oxidation (ACADL) upon PUFA/Fe3+ loading (Figs. 5A, S5B) indicate a potential role of mitochondria. In contrast, genes involved in lysosomal or peroxisomal lipid metabolism (LAMP2, ACOX2, PEX5) were not affected by PUFA/Fe3+ treatment.

To further corroborate the involvement of mitochondria, we explored whether mitochondrial inhibition would impact sensitivity to ferroptosis in this group. Treatment with Etomoxir, which reduced the colocalization of PUFAs in mitochondria, also diminished cell death induction (Figs. 5B, S5C). Similarly, Antimycin A, an inhibitor of complex III of the oxidative phosphorylation pathway, and MitoTempo, a mitochondrial-targeted antioxidant, reduced cell death induction in 451LuR cells but not in M257 cells. Inhibitors of peroxisomal oxidation (3AT), lysosomal activity (CQ), or COX activity (LOR) had little effect individually but showed impact when combined with MitoTempo (Fig. S5D). In contrast to 451LuR cells, combined inhibition of all organelles, including lysosomes and mitochondria, provided only partial protection against PUFA/Fe3+-induced cytotoxicity in M257 cells (Fig. S5D).

Supporting the involvement of mitochondrial lipid utilization, CRISPR/Cas9-mediated knockout of CPT1 decreased the expression of enzymes involved in beta-oxidation (Figs. 5C, S6D, E), reduced the mitochondrial PUFA peroxide levels in 451LuR cells (Fig. 5D), and decreased their susceptibility to PUFA/iron-induced cell death (Fig. 5E). To further validate the role of mitochondria in PUFA-induced ferroptosis, we depleted mitochondrial function in 451LuR cells with the mitochondrial depolarizer carbonyl cyanide chlorophenylhydrazone (CCCP). We monitored mitochondrial function and cell death using SYTOX and tetramethylrhodamine ethylester (TMRE), which are sequestered by active mitochondria. Cell time-lapse imaging revealed that CCCP-induced mitochondrial dysfunction significantly reduced PUFA-mediated ferroptotic sensitivity (Fig. 5F–H). To further evaluate mitochondrial function, we measured mitochondrial lipid peroxidation, membrane potential, and oxidative phosphorylation (OCR). The AMPK activator significantly enhanced mitochondrial lipid peroxidation, as evidenced by the MitoPerOx probe and colocalization of oxidized BODIPY 581/591 C11 with MitoTracker (Figs. 5I, S5E, F). This was accompanied by a decreased oxygen consumption rate (OCR) and reduced mitochondrial membrane potential upon co-incubation of 451LuR cells with PUFA/Fe3+ (Fig. 5K, L). These data, alongside the increased levels of mitochondrial TCA metabolites such as citric acid (Fig. S5G), indicate that mitochondrial perturbations are linked to ferroptosis induction. The observed decrease in mitochondrial functionality corresponded with increased cell death, which could be mitigated by MitoTempo (Fig. 5J). Inhibition of lysosomal autophagy (and the endosomal pathway) and peroxisomal activity with chloroquine and 3-Amino-1,2,4-triazole (3-AT) did not significantly impact lipid peroxidation or cell death following AMPK activation (Fig. S5H, S5I). Similar results were observed when 451LuR cells in 3D culture were co-treated with modulators of AMPK, LD, and mitochondrial function (Fig. 4M, N). Together, these observations suggest that the mitochondrial interaction with LDs is pivotal in facilitating lipid peroxidation and ferroptosis, making mitochondria a key target for modulating ferroptosis-related cell death.

ER-MT-LD membrane contact site is a potential hub for lipid peroxidation
Given the relocation of LDs towards mitochondria triggered by AMPK activation, we explored the temporal mobilization of PUFAs from TAG to other lipid classes more profoundly using tracer lipidomics. Remarkably, our observations revealed that 13C-labeled DHA initially localized in TAGs but eventually accrued in phosphatidylcholine (PC) under conditions of nutrient deprivation (Fig. 6A). Notably, inhibiting DGAT resulted in diminished accumulation of DHA in both TAG and PC. Given the pivotal role of PC-PUFAs in mitochondrial homeostasis29–31, our subsequent analysis aimed to explore whether this lipid remodeling led to a heightened pro-ferroptotic lipid composition within the mitochondrial membranes. Lipidomics analysis of the mitochondrial membrane fractions 48 h post-PUFA/Fe3+ supplementation revealed an increased normalized incorporation of PUFAs into mitochondrial PC, particularly upon AMPK activation (Fig. 6B). This was accompanied by an increase in lipid peroxidation at the level of the mitochondrial membrane, as revealed by BODIPY 581/591 C11 staining of mitochondria in cells transfected with the mCherry-TOMM20 plasmid, which stains the mitochondrial membrane, as observed by super-resolution microscopy (Fig. 6C, D).

It is known that the transfer of PUFAs from TAGs to PC in mitochondrial membranes is mediated by a network of proteins at the interface of LDs, ER, and mitochondria32–36 (Fig. 6E). This involves inter-organellar tether proteins, including PLIN1, MFN1, and MFN2, which are upregulated upon PUFA/Fe3+ treatment (Fig. 5A), as well as enzymes involved in PC synthesis, i.e., choline phosphotransferase 1 (CHPT1) and choline/ethanolamine phosphotransferase 1 (CEPT1) (Fig. 6E). Transmission electron microscopy confirmed a close interaction between these organelles upon PUFA/Fe3+ treatment (Fig. 6F).

To explore the role of these interactions in PUFA-mediated ferroptosis, we suppressed the expression of CHPT1, CEPT1, MFN1, and MFN2. Notably, knockdown of CHPT1 and MFN2 led to the accumulation of PUFA-DAG, an increase in lipid droplets at the cell periphery away from mitochondria, restoration of membrane potential (as measured by TMRE fluorescence), reduced mitochondrial lipid peroxidation (MitoPerOx), and protection of PUFA/Fe3+-induced cell death (SYTOX) (Figs. 6G–L, S6F). Collectively, these findings indicate that the mitochondrial membrane, particularly at contact sites with the ER and lipid droplets, serves as a correlative hotspot for ferroptosis induction after PUFA/Fe3+ treatment.

Methods
This research was conducted in accordance with all applicable ethical guidelines and regulations under SBB approval 2192017/0767.

Cell culture
Cell lines M257, M229, M238, M238R, M249, M249R, 451Lu, and 451luR were kindly provided by Professor A. Ribas and R. Lo. All cell lines were propagated in DMEM High Glucose (Sigma-Aldrich), supplemented with 10% FBS (Gibco) and 4 mM glutamine (Thermo Fisher Scientific), and were periodically tested for mycoplasma contamination. Treatments were performed in DMEM High Glucose, supplemented with 2% FBS and the following chemicals: Docosahexaenoic acid (10μM, Sigma-Aldrich), arachidonic acid (10μM, Sigma-Aldrich), Docosahexaenoic Acid-d5 (10μM, Sanbio), Docosahexaenoic Acid Alkyne (10μM, Sanbio), Ferric ammonium citrate (10μM, Sigma-Aldrich), Ferrostatin (5μM, Sigma-Aldrich), Liproxtatin-1 (5μM, Sigma-Aldrich), Z-VAD (10μM, Sigma-Aldrich), Nec-1s (10μM, Sigma-Aldrich), T863 (10μM, Sigma-Aldrich), PF-06424439 (10μM, Sigma-Aldrich), Etomoxir (10μM, Cayman), Antimycin A (1μM, Sigma-Aldrich), Oligomycin (10μM, Sigma-Aldrich), FCCP (5μM, Sigma-Aldrich), A-769662 (70μM, Sigma-Aldrich), EX-229 (30μM, Selleck Chemicals), Compound C (2μM, Selleckchem), Rotenone (1μM, Tocris), Mitotempo (10μM, Sigma-Aldrich), Chloroquine (2μM, Sigma-Aldrich), Lornoxicam (20μM, Selleck Chemicals), 3-Amino-1,2,4-triazole; 3-AT (2mM, Sigma-Aldrich), Parthnolide (2μM, Sigma-Aldrich).

3D spheroid models
Spheroids were formed using 2×103 451LuR cells/well and were seeded in 96U bottom plate, Nunclon™ Sphera™ Microplates (Thermo Fisher Scientific) in DMEM High Glucose (Sigma-Aldrich) supplemented with 2% FBS and 4mM glutamine. The next day, spheroids were treated with the desired compounds. SYTOX green, at a final concentration of 1μM, was also added to the wells. Spheroid growth and green fluorescence were monitored using the IncuCyte ZOOM system (EssenBioScience) for at least 3 days. Images were taken at 2-h intervals. The intensity of SYTOX green was measured as the total green object integrated intensity (GCU×µM²/image) using Incucyte software.

Generation of knockout cell lines
For the knockout of a specific gene, sgRNAs were cloned into the pLentiCRISPRv2 plasmid (Addgene 52961) following the standard cloning protocol. A pool of four sgRNAs for CPT1 KO and AMPK KO was selected from the Brunello genome-wide knockout library.
HEK293T cells were seeded in T150 (TPP) flasks with 40 mL of DMEM containing 10% fetal bovine serum at 45% confluency to generate lentivirus particles and incubated overnight. After 1 day, cells were transfected using X-TremeGENE9 (Roche) with the pLentiCRISPRv2 KO plasmids, together with the packaging plasmids psPAX2 and envelope plasmid pMD2.G, and incubated overnight to form lentiviral particles coated with the VSV-G protein. ~16 h post-transfection, the medium was replaced by serum-free growth medium (DMEM + 1.1g/100 mL BSA and 20μg/mL gentamicin). The lentivirus supernatant was harvested ~48 h after transfection and stored at −80°C. Knockout cells were generated by transducing them with the lentiviral pool of 4 sgRNAs and selecting them with puromycin (1μg/ml) for 3 days. KO confirmation was conducted using Western blot and qPCR.

Generation of knockdown cell lines
451LuR cells were forward transfected with 10nM of Silencer-Select Negative Control siRNA (Thermo Fisher Scientific), two sequences targeting DGAT1 (s16568, s16569), DGAT2 (s85667, s85668), CHPT1 (s32552, s32553), CEPT1 (s20316, s20317), MFN1 (s31218, s31219), MFN2 (s19260, s19261), KIF5B (s731, s732), and SPASTIN (s13348, s13349) (Silencer-Select siRNA, Thermo Fisher Scientific) with lipofection according to the manufacturer’s recommendations (Lipofectamine RNAiMAX, Thermo Fisher Scientific). KD confirmation was conducted using qPCR.

RNA extraction and RT-qPCR
The cell lysate was extracted using the PureLink RNA Mini Kit (Invitrogen) according to the manufacturer’s protocol. 1μg RNA from each sample was reverse-transcribed to cDNA using a high-capacity cDNA Reverse Transcriptase Kit (Invitrogen, 18080044). Quantitative PCR reactions were performed using Fast SYBR Green Master Mix (Thermo Fisher Scientific, 4385612) with duplicate measurements on the LightCycler 96 Real-Time PCR instrument (Roche Life Science). 18S was used as an internal reference. Differences in mRNA levels compared with 18s were computed between vehicle and experimental groups using the ΔΔCt method. Primers used were:
hACADL: Fw 5‘-CTTGCATGGCGAAATATTGGG-3‘, Rv 5‘-ATAGATTGGCTGAACTCTGGC-3‘,
hACOX2: Fw 5‘-ACATGGCAAGAACAGCCTAC-3‘, Rv 5‘-CATAACAGCCAAGTGCTGAATTT-3‘,
hPRKAB1: Fw 5‘-ATTGATGATGAAATTACAGAAGCCA-3‘, Rv 5‘-GCATCTGAATCACTCCTTTGGC-3,
hATG5: Fw 5‘-TTTGCATCACCTCTGCTTTC-3‘, Rv 5‘-TAGGCCAAAGGTTTCAGCTT-3‘,
hATGL: Fw 5‘-GGCCAGACCCAGCTTCTTC-3‘, Rv 5‘-TAGGCCAAAGGTTTCAGCTT-3‘,
hCEPT1: Fw 5‘-ACCTTT TTGGCAATCTATGAT TCCA−3‘, Rv 5‘-TTGCCAACACCACCTGTGAA-3‘,
hCHPT1: Fw 5‘-GCATGTTGAGATTTGGAAAAGTGG−3‘, Rv 5‘-TCCAAGAACTGGAAGGATCTTCA-3‘,
hCD36: Fw 5‘-CAAAACGGCTGCAGGTCAAC-3‘, Rv 5‘-CCTCTGTG AGCCGCCTG-3‘,
hCPT1: Fw 5‘-GGAGACAGACACCATCCAGC-3‘, Rv 5‘-GCCAGACCTTGAAGTAGCGT-3‘,
hDGAT1: Fw 5‘-CACTGGGAGCTGAGGTGC-3‘, Rv 5‘-AGCATCACCACACACCAGTT-3‘,
hDGAT2: Fw 5‘-CATGGTCGAGGCCTCTTCTC-3‘, Rv 5‘-GGCTCTCCCACAACAGTG-3‘,
hLAMP2A: Fw 5‘-GGCTCCGTTTTCAGCATTGC-3‘, Rv 5‘-ACAGTCTGCTCTTTGTTGCAC-3‘,
hMFN1: Fw 5‘-AGTACATGGAGGATGTGCGC-3‘, Rv 5‘-CGAGCTTC CGACGGACTTAC-3‘,
hMFN2: Fw 5‘-GGGAAGGTGAAGCGCAATGT-3‘, Rv 5‘-AGTGGGGATGCATTCACCTC-3‘,
hPEX5: Fw 5‘-GGCGCTCCGCGAGAG−3‘, Rv 5‘-CGAGCTTCATGAGCGGGTT-3‘,
hPEX13: Fw 5‘-CAGCATCAACTGGGCAAGTG−3‘, Rv 5‘-GCACTTTGGGTTGTTGTTCTTTG−3‘,
hPLIN2: Fw 5‘-ATCAGCCATCAACTCAGATTGT-3‘, Rv 5‘-CCCCAGTCACAGTAGTCGTC-3‘,
hPLIN3: FW 5‘-CAGGTGACAGTGGAAGAACC-3‘, RV 5‘-CAGTCTTGATGTGCGGGTAG-3‘,
hPLIN5: FW 5‘-CTGGCCTACGAGCACTCTG-3‘, RV 5‘-TGCATGTGGTCTATCAGCTCC-3‘,
hPTGS2: Fw 5‘-GTTCCACCCGCAGTACAGAA-3‘, Rv 5‘-GCAGACATTCCTTTTCTCCTGT-3‘,
hRAB7A: Fw 5‘-CTCGCTTCTGTCCTCCGTTT-3‘, Rv 5‘-AGGTCATCCTTCAAACGCGG-3‘.

Western blotting
Western blotting was carried out on melanoma cell lines. Following ice-cold PBS washes, cells were collected in sample buffer (Thermo Fisher) supplemented with DTT (Sigma), sonicated, and boiled for 5 min. Equal amounts of protein were loaded onto precast gels (NuPAGE, Thermo Fisher), transferred to nitrocellulose membranes, and incubated with antibodies against phospho-AMPK (1/1000 dilution) (Cell Signaling, #2535), AMPK (1/1000 dilution) (Cell Signaling, #2532), and Anti-β-Tubulin Antibody (1/500 dilution) (Sigma-Aldrich, #AB3201). Representative uncropped blots are presented in the source data file.

TBARS assay (MDA)
3×106 cells were harvested after the desired incubation with PUFA/Fe3+. Malondialdehyde levels in sonicated cell lysates, tissue lysates, and serum were determined using a TBARS Assay Kit (Cayman, 700870) following the manufacturer’s instructions. The result was normalized to total cellular protein (BCA assay).

Iron assay
1×106 cells were harvested following 24-h treatment with PUFA/Fe3+. The relative amount of iron was measured using a colorimetric iron assay kit (Abcam, ab83366) according to the manufacturer’s protocol. A microplate reader was utilized to measure the absorbance at 593 nm for each well.

Fatty acid uptake assay
12×103 cells were plated in a 96-well black-sided plate. Following the desired incubation with PUFA/Fe3+, fatty acid uptake was measured using a fluorometric fatty acid uptake kit (Sigma-Aldrich, MAK156). Fluorescence intensity was quantified using excitation/emission wavelengths of 488/530 nm with a microplate reader.

Fatty acid oxidation assay
8×105 cells were seeded in 10 cm petri dishes. Upon stimulation with treatment, the cells were trypsinized and stained with three primary antibodies.; ACADVL, ACADM, and HADHA following the manufacturer’s protocol (Abcam, ab118183). The expression was measured by flow cytometry (BD FACSVerse, BD Biosciences) (excitation/emission = 488/594), and at least 1×104 cells were analyzed per sample using FlowJo.

Flow cytometric analysis
Briefly, 1.5×105 cells were seeded in a 6-well plate. The next day, cells were treated with stimuli and harvested in 300 μL HBSS containing 5 μM BODIPY 581/591 C11 or 500 nM MitoPerOx, and 500 nM Tetramethylrhodamine ethylester perchlorate (TMRE). They were then incubated for 30 min at 37°C. Cells were then resuspended in 300 μL of fresh HBSS and analyzed using flow cytometry (BD FACSVerse, BD Biosciences) with excitation/emission settings of 488/520 nm and 488/575-590 nm, respectively. At least 1×104 cells were analyzed per sample using FlowJo. Cells were initially gated (P1) based on forward scatter (FSC-A) and side scatter (SSC-A) to eliminate debris and doublets. A second gate (P2) using FSC height (FSC-H) versus FSC width (FSC-W) was applied to eliminate cell aggregates and isolate single cells. Within the P2 gate, target populations were identified based on fluorescence intensity in the relevant channels. To ensure accurate gating, Fluorescence Minus One (FMO) controls were used to assess fluorescence spillover, while unstained controls accounted for background autofluorescence.

Cell viability assessment using SYTOX™ Green
1×104 cells were seeded in a 96-well black cell carrier plate. The next day, cells were co-treated with stimuli and SYTOX green at a final concentration of 1 μM for at least 30 min at 37°C. The fluorescent signal was measured using a PerkinElmer microplate reader every 24 h with Excitation/Emission: 504/523 nm. At any desired measurement timepoints, 4 μl of 10× lysis solution (Promega) was added to each plate well to achieve maximal lysis and was incubated for 20 min at 37°C. The final measurement was performed using a microplate reader, and the cell death percentage was calculated as follows: % Cell Death = ((Fluorescence measured before lysis - Blank)/(Fluorescence measured after lysis - Blank)) × 100.

Proliferation assays
Proliferation curves were generated using an IncuCyte ZOOM system (EssenBioScience) on cells seeded in microplates (TPP or Nunc Edge) based on phase-contrast images taken at 2 h intervals for the duration of the experiments.

Immunoblotting analysis
Following ice-cold PBS washes, cells were collected in sample buffer (Thermo Fisher) supplemented with DTT (Sigma-Aldrich), sonicated, and boiled for 5 min. Equal amounts of protein were loaded onto precast gels (NuPAGE, Thermo Fisher), transferred to nitrocellulose membranes, and incubated with antibodies against CPT1A (ab128568, Abcam, 1:500), Phospho-AMPKα (2535S, Cell Signaling Technology, 1:1000), AMPKα (2532, Cell Signaling Technology, 1:1000), ATGL (sc-365278, Santa Cruz Biotechnology, 1:1000), GAPDH (5174, Cell Signaling, 1:5000), detyrosinated tubulin (AB-3201, Sigma-Aldrich, 1:1000), Protein Ladder (26619, Thermo Fisher).

Immunofluorescence imaging
1.5×105 Cells were seeded on 17-mm coverslips in 6-well plates. After treatment at various time points, washed cells in HBSS were incubated with optimized dyes, BODIPY 493/503 (2μM), for 30 min at 37°C. Then, coverslips were fixed by PFA 3.7% in PBS and were blocked with PBS containing 0.1% saponin and 5% goat serum for 1 h, following primary incubation with antibodies against TOMM20 (11802-1-AP, Proteintech, 1:200), PMP70 (ab3421, Abcam, 1:200), and LAMP2A (ab18528, Abcam, 1:200) overnight. After incubation with a secondary antibody, they were finally mounted in ProLong™ Glass Antifade Mountant with NucBlue™ Stain. Images were analyzed using high-resolution confocal Zeiss LSM900 Airyscan 2 with a 63× oil objective. 10 images were taken per coverslip, and at least three independent biological replicates were performed for each condition. We used the software NIS-Elements AR 5.21 (Nikon) for intensity and overlap analysis of the Airyscan images. For lipid droplet segmentation, the same threshold was applied per cell line. For mitochondria segmentation, the threshold values were manually adjusted per image.

Plasmid transfection
The plasmid, mCherry-TOMM20-N-10, was a gift from Michael Davidson (Addgene plasmid # 55146). For transfection into 451LuR cells, DNA plasmids were propagated in the Escherichia coli DH5α (TAKARA) strain and purified using the Plasmid Plus Midi Kit (QIAGEN). 1.5×105 cells were seeded in 35-mm dishes. The cells were transfected with mCherry-TOMM20-N-10 on the same day of treatment using Lipofectamine LTX Reagent with PLUS Reagent, as described in the manufacturer’s manual. Briefly, 1 μg of the plasmid DNA was mixed with 9 μl Lipofectamine LTX and 10 μl PLUS Reagent in 200 μl OptiMEM, vortexed, incubated at room temperature (RT) for 10 min, and added dropwise to the cells and incubated for 24–36 h before imaging of the cells.

Fluorescent staining of live cells
1.5×105 Cells were seeded in 35 mm imaging dishes. After the desired timepoints of treatment, they were once washed with HBSS and were incubated with trackers LysoTracker deep red (100nM, Invitrogen), MitoTracker CMXRos (100nM, Invitrogen), MitoTracker deep red (100nm, Invitrogen) and ER-Tracker™ Green (BODIPY™ FL Glibenclamide) (1μM, Invitrogen), or CellLight™ Peroxisome-GFP, BacMam 2.0 (30 particles per cell (PPC)), BODIPY 581/591 C11 (5μM, Invitrogen), Tetramethylrhodamine ethyl ester perchlorate (TMRE) (500nM, Sigma-Aldrich) all diluted in HBSS and were incubated for 30 min at 37°C with coverslips.

SRS imaging analysis
1.5×105 melanoma 451LuR cells from ATCC were seeded into 35 mm imaging dishes with a polymer coverslip bottom (No. 1.5H, ibidi) and treated with DHA-d5 (10 μM, Sanbio) and other desired inhibitors for a specified time. Cells were washed with HBSS (+CaCl2, +MgCl2, Gibco) and stained with LysoTracker deep red (100nM, Invitrogen), MitoTracker CMXRos (100nM, Invitrogen), and CellLight™ Peroxisome-GFP, BacMam 2.0 (30 particles per cell (PPC)) (Invitrogen) according to the manufacturer’s protocol. Cells were imaged in DMEM with no phenol red (4.5 g/l, Gibco) for fluorescent channels and SRS imaging.
SRS imaging was performed on an inverted Leica Stellaris 8 Coherent Raman Scattering laser-scanning microscope with the SRS option (Leica Microsystems, Mannheim, Germany). The PicoEmerald S Optical Parametric Oscillator (APE, Berlin, Germany) provided the fixed Stokes beam wavelength at 1031.306 nm and the tuneable pump beam wavenumber of 2129 cm−1 for DHA-d5 and 2852 cm−1 for lipid droplets. At the moment of imaging, the Stokes laser power was 0.1510 W, and the pump laser power was 0.2950 W; both were attenuated to 50% intensity. The pump and Stokes beams were spatially and temporally overlapped using two dichroic mirrors and a delay stage inside the laser system. They were then coupled into the laser-scanning microscope with optimized near-IR throughput. Unidirectional SRS images were acquired using a 40× objective, HC PL IR APO 40×, N.A. 1.10 water immersion lens (Leica Microsystems, Mannheim, Germany) with 3.1625 μs pixel dwell time over a 512×512 frame and a speed of 400 Hz. The resulting pixel size was 0.19 μm. The signal intensity was collected using an oil condenser (1.4 NA, Leica Microsystems, Mannheim, Germany) in the forward direction and demodulated using a Lock-In Amplifier (Zürich Instruments, Zürich, Switzerland). The F-SRS detector was used with a gain of 10. The imaging system was controlled by the software LasX version 4.6.1.27508.
Confocal fluorescence images were simultaneously acquired using the same instrument and objective. Excitation settings included a 488 nm laser power of 2.0 and a gain of 5.0, 561 nm with a laser power of 2.0 and a gain of 5.0, and 638 nm with a laser power of 2.0. Signals were collected with two HyDS detectors in analog mode through a 77.2 μm pinhole with 1 AU. A line average of 4 was applied for both SRS and fluorescent channels.

Transmission electron microscopy (TEM)
Cells were cultured on 35 mm glass-bottom dishes or coverslips made of Aclar till they reached 70–80% confluency. They were prefixed for 10 min at 37°C with prewarmed double-concentrated fixative (8% paraformaldehyde (PFA) + 1% glutaraldehyde (GA) in 0.2 M phosphate buffer (PB), pH 7.4), added directly into the culture dish in a 1:1 proportion to the amount of culture medium. Next, the cells were further fixed with 4% PFA + 2.5% GA in 0.1 M PB buffer (pH 7.4) for 2 h at room temperature (RT). After rinsing with 0,1 M PB (pH 7.4), 3×5 min, they were rinsed again with the same buffer 3×3 min, but this time on ice, and subsequently postfixed with 1% osmium tetroxide (OsO4) and 1.5% potassium ferrocyanide (K4Fe(CN)6) in 0.1 M PB (pH 7.4) for 60 min on ice. Then, they were washed five times for three minutes each with Milli-Q water on ice and incubated overnight in 1% aqueous uranyl acetate at 4°C. The next day the specimens were washed with Milli-Q water 5×7 min, incubated en bloc with lead aspartate (Walton’s lead aspartate: 0.02 M lead nitrate in 0.03 M L-aspartic acid, pH 5.5) for 30 min at +60°C, wash with Milli-Q water 5×3 min and dehydrated with graded series of ethanol: 1×10 min each step (30, 50, 70, 90% ethanol) at RT followed by, 2×10 min dehydration with pure ethanol on ice. Next, samples were infiltrated with the hard composition of AGAR resin, first with a 2:1 mixture of pure ethanol and resin for 1 h, next with a 1:1 mixture for 1 h, and finally with a 1:2 mixture of pure ethanol and resin overnight, all at RT. The following day, the samples were infiltrated with pure resin for 6 h, embedded in fresh, pure resin with beem capsule blocks on top, and polymerized for 48 h at 60°C.
Polymerized blocks were detached from the coverslips, and ultrathin sections (70 nm) were collected on formvar-coated copper grids. These sections were imaged at 80 kV using a Transmission Electron Microscope (JEOL JEM 1400; Tokyo, JN) equipped with an EMSIS Quemesa camera (11 Mpxl; Münster, DE).
Glutaraldehyde (GA) (25% solution, Electron Microscopy Services), Paraformaldehyde (PFA) (32% Aqueous solution, Electron Microscopy Services), Na2HPO4 (dibasic) and NaH2PO4 (monobasic) (Electron Microscopy Services), L-aspartic acid (Sigma-Aldrich), Lead nitrate (Electron Microscopy Services), Osmium tetroxide (OsO4) (4% Aqueous solution, Electron Microscopy Services), Potassium ferrocyanide (K4Fe(CN)6) (Sigma-Aldrich), Epon resin MNA (Agar scientific), Epon resin DDSA (Agar scientific), Epon resin Agar 100 (Agar scientific), Epon resin BDMA (Electron Microscopy Services), Uranyl acetate (Electron Microscopy Services).

Oxygen consumption rate measurements (OCR/ECAR)
2×105 cells were seeded on Seahorse Xp culture plates (103022-100, Agilent) in Seahorse XF Assay Medium (103335-100, Agilent), pH 7.4, containing 1 mM Sodium Pyruvate (S8636, Thermo Fisher Scientific), 4 mM Glutamine (G7513, Sigma-Aldrich), and 25 mM Glucose (8769, Sigma-Aldrich). Before analysis, cells were incubated for 1 h in a CO2 free incubator at 37°C. OCR was measured after serial injections of 3 μM Oligomycin (O4876, Sigma-Aldrich), 2 μM phenylhydrazone (FCCP, sc-203578, Santa Cruz Biotechnology), and 1 μM Antimycin A (A8674, Sigma-Aldrich). ECAR was measured after serial injections of 80 mM glucose, 3 μM oligomycin, and 500 mM 2-deoxy-D-glucose (D-8375, Sigma-Aldrich). Analysis was performed using Seahorse Wave Desktop Software (Agilent).

Lipidomics
For lipid extraction an amount of cell sort issue containing 10 μg of DNA was homogenized in 700 μL of water with a handheld sonicator and was mixed with 800 μl HCl (1 M): CH3OH 1:8 (v/v), 900 μl CHCl3, 200 μg/ml of the antioxidant 2,6-di-tert-butyl-4-methylphenol (BHT; Sigma-Aldrich) and 3 μl of SPLASH® LIPIDOMIX® Mass Spec Standard (#330707, Avanti Polar Lipids). After vortexing and centrifugation, the lower organic fraction was collected and evaporated using a Savant Speedvac spd111v (Thermo Fisher Scientific) at room temperature, and the remaining lipid pellet was stored at −20°C under argon.
Just before mass spectrometry analysis, lipid pellets were reconstituted in 100% ethanol. Lipid species were analyzed by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI/MS/MS) on a Nexera X2 UHPLC system (Shimadzu) coupled with a hybrid triple quadrupole/linear ion trap mass spectrometer (6500 + QTRAP system; ABSCIEX). Chromatographic separation was performed on a XBridge amide column (150 mm×4.6 mm, 3.5 μm; Waters) maintained at 35°C using mobile phase A (1 mM ammonium acetate in water-acetonitrile 5:95 (v/v)) and mobile phase B (1 mM ammonium acetate in water-acetonitrile 50:50 (v/v)) in the following gradient: (0–6 min: 0% B → 6% B; 6–10 min: 6% B → 25% B; 10–11 min: 25% B → 98% B; 11–13 min: 98% B → 100% B; 13–19 min: 100% B; 19–24 min: 0% B) at a flow rate of 0.7 mL/min which was increased to 1.5 mL/min from 13 min onwards. SM, CE, CER, DCER, HCER, and LCER were measured in positive ion mode with a precursor scan of 184.1, 369.4, 264.4, 266.4, 264.4, and 264.4, respectively. TAG, DAG, and MAG were measured in positive ion mode with a neutral loss scan for one of the fatty acyl moieties. PC, LPC, PE, LPE, PG, PI, and PS were measured in negative ion mode by fatty acyl fragment ions. Lipid quantification was performed by scheduled multiple reactions monitoring (MRM), the transitions being based on the neutral losses or the typical productions as described above. The instrument parameters were as follows: Curtain Gas = 35 psi; Collision Gas = 8 a.u. (medium); Ion Spray Voltage = 5500 V and −4500 V; Temperature = 550°C; Ion Source Gas 1 = 50 psi; Ion Source Gas 2 = 60 psi; Declustering Potential = 60 V and −80 V; Entrance Potential = 10 V and −10 V; Collision Cell Exit Potential = 15 V and −15 V.
The following fatty acyl moieties were taken into account for the lipidomic analysis: 14:0, 14:1, 16:0, 16:1, 16:2, 18:0, 18:1, 18:2, 18:3, 20:0, 20:1, 20:2, 20:3, 20:4, 20:5, 22:0, 22:1, 22:2, 22:4, 22:5 and 22:6 except for TG which considered: 16:0, 16:1, 18:0, 18:1, 18:2, 18:3, 20:3, 20:4, 20:5, 22:2, 22:3, 22:4, 22:5, 22:6.
Peak integration was performed with the MultiQuantTM software version 3.0.3. Lipid species signals were corrected for isotopic contributions (calculated with Python Molmass 2019.1.1) and were quantified based on internal standard signals and adhere to the guidelines of the Lipidomics Standards Initiative (LSI) (level 2 type quantification as defined by the LSI). Unpaired T-test p-values and FDR corrected p-values (using the Benjamini/Hochberg procedure) were calculated in Python StatsModels version 0.10.1. Pathway enrichment analysis of the lipidomics data was conducted using the Lipid Ontology (LION) enrichment analysis web application.

Metabolomics
10 μl of each sample was loaded into a Dionex UltiMate 3000 LC System (Thermo Fisher Scientific, Bremen, Germany) equipped with a C-18 column (Acquity UPLC-HSS T3 1.8 μm; 2.1×150 mm, Waters) coupled to a Q Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific) operating in negative ion mode. A step gradient was carried out using solvent A (10 mM TBA and 15 mM acetic acid) and solvent B (100% methanol). The gradient started with 5% of solvent B and 95% of solvent A and remained at 5% B until 2 min post-injection. A linear gradient to 37% B was carried out for 7 min and then increased to 41% until 14 min. Between 14 and 26 min, the gradient increased to 95% B and remained at 95% B for 4 min. At 30 min, the gradient returned to 5% B. The chromatography was stopped at 40 min. The flow rate was maintained at 0.25 mL/min, and the column was kept at 40°C throughout the analysis. The MS operated in full scan mode (m/z range: [70.0000-1050.0000]) using a spray voltage of 4.80 kV, capillary temperature of 300°C, sheath gas at 40.0, and auxiliary gas at 10.0. The AGC target was set at 3.0E+006 using a resolution of 140,000, with a maximum IT fill time of 512 ms. Data collection was performed using the Xcalibur software (Thermo Fisher Scientific). The data analyses were performed by integrating the peak areas (El-Maven – Polly - Elucidata).

Gene expression
For gene expression, cells were seeded in 6-well plates at a density of 1.5×105 cells per well. On the second day, the cells were treated with PUFA/Fe3+ for 18 h and 36 h. RNA was isolated using the PureLink™ RNA Mini Kit (12183018A, Invitrogen). Libraries were constructed, and bulk RNA-seq was performed by Novogene (UK).

Statistical analysis
Data were analyzed using GraphPad Prism 9.5.0. Statistical tests included unpaired t-tests, one-way ANOVA, and two-way ANOVA. For multiple comparisons, the Holm–Sidak correction was applied. Non-Gaussian data were analyzed using the Kruskal–Wallis test. Results are presented as mean ± standard deviation (SD), unless otherwise specified. Statistical significance was defined as p < 0.05, with significance levels indicated as follows: *p < 0.05, **p < 0.005, ***p < 0.0005, and ****p < 0.0001. No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study proposes that AMPK-mediated lipid droplet plasticity and dispersion dictate the differential sensitivity of melanoma cells to ferroptosis induced by cotreatment with PUFA and iron. However, the main issue with this manuscript is the lack of strong evidence showing that differences in AMPK activation or lipid droplet formation cause the differential sensitivity of various melanoma cell lines to ferroptosis.\nSpecifically, there are two main weaknesses in this model. First, it is unclear whether and how AMPK activation or lipid droplet formation differ among these cell lines. The difference in AMPK activation was not thoroughly analyzed under different conditions (normal or energy stress). Additionally, there is no significant difference in PUFA-containing neutral lipids or BODIPY 493/503 staining among these cell lines. The only noted difference is the somewhat arbitrary claim of variation in lipid droplet distribution patterns.\nSecondly and more importantly, to establish that differences in lipid droplet distribution play a causal role in mediating the differential sensitivity of these cell lines to ferroptosis, it is necessary to demonstrate that shifting the lipid droplet distribution from one pattern to another can alter the ferroptosis sensitivity phenotypes. Without this evidence, the observations remain correlational and cannot establish causality.\nOther comments:\nThe project aimed to understand the mechanisms underlying the differential ferroptosis sensitivity among various melanoma cell lines. There could be many factors explaining their differences in ferroptosis sensitivity. However, in Figure 2, the authors abruptly focus on lipid droplets without providing a clear rationale or conducting comprehensive analyses, such as gene expression profiling, across these cell lines. Additionally, the ferroptosis phenotypes in DGAT inhibition or knockdown cells are weak to moderate. Overall, the reviewer feels that lipid droplets may not be the primary mechanism underlying the differences among these cell lines.\nThe overall ferroptosis phenotypes in DGAT knockdown or AMPK KO cells are weak (for example, see Fig. 3F), which undermines the robustness of their conclusion. Additionally, they did not provide Western blot analysis to examine the knockdown or KO efficiency in the DGAT or AMPK cell lines.\nThe study involves many cellular components (lipid droplet, DGAT, AMPK, mitochondria, membrane contact between different organelles, CHPT1, MFN2 etc) but lacks coherence. It is unclear which operates upstream or downstream. The authors should provide a schematic to illustrate their model more clearly.", "reviewer_2": "The authors present that nutrient availability through redispersion of LDs plays key regulatory roles in PUFA-mediated ferroptosis induction. Starvation-dependent activation of AMPK renders the relocation of LDs toward mitochondria in the late responder melanoma cell lines. Overall, this is a high quality series of experiments that provides novel insights into the relationship between LDs subcellular (re)distribution and starvation-induced AMPK mitochondrial-associated PUFA-induced ferroptosis. My concerns are mostly minor.\n1. More BRAF wild-type melanoma cell lines may be needed to prove this conclusion “No association with resistance to BRAF-targeted treatment was observed.”.\n2. The authors perform SRS microscopy at 2852 cm-1 for LDs signal. Have the authors considered ratiometric approaches using Raman analysis to quantify the unsaturation degree (DOI: 10.1038/s41467-020-18376-x; 10.34133/2021/9860123; 10.1088/1361-6463/ac2175)? Using these approaches, the unsaturated lipids and distribution may be visualised using SRS microscopy.\n3. In Figure. S3B and 3C, the authors evaluate the effect of glucose deprivation on the dispersion of lipid droplets (LD) and cytotoxicity. Firstly, Figure. S3B lacks the confocal imaging of the unloaded PUFA/Fe3+ (+Glu) group. Besides, the effect of glucose deprivation should be also evaluated by readdition of glucose into the unloaded PUFA/Fe3+ (-Glu) group.\n4. The representative image of the fourth group in Fig. 3N is not obviously weaker than the second group, which seems not match with Fig. 3O.\n5. All western blots should have size markers and quantification.", "reviewer_3": "This is a very nice study that provides novel evidence linking metabolism, energy demands and ferroptotic responses in different melanoma cell phenotypes. Experiments are well controlled, thorough, using a wide variety of techniques to substantiate their conclusions.\nMajor comments:\nThere are more references to chemotherapy response and ferroptosis that to melanoma states/transcriptomic programs.\nGiven that the induction of ferroptosis and cytotoxicity is linked to melanoma cell phenotypes, a paragraph on this subject is required in the introduction.\nIn line with this, the authors should decide whether to include a couple of lines on the clinical relevance of BRAFi in melanoma (for context) or just delete any reference to drug response or combinatorial therapies. This study is all about ferroptosis with little translational/therapy data (which is perfectly ok). For instances, in Fig. S1A, while referring to BRAFi response in the text, no details on cell line response to BRAFi are provided.\nGiven the link between cell phenotype and response to ferroptotic stimuli. The mansucript is lacking a discussion of the results in the context of the differentiated phenotype. This is important in mlenaoma as differentiation is linked to key genes such as MITF, tyrosinase etc... that regulate melanin production and, more importantly, melanosome biology. The obvious link between melanosome biology and the results presented in this manuscript warrant a discussion on the matter.\nOther comments:\nLines 87-89 (Intro), the wording is confusing, given the importance of the message of this paragraph, it should be written with more clarity.\nFigure 2H: When quantifying LD density? Is it referring to perinuclear density of smaller LDs? If so, it should be better explained.\nFigure 3F, the effect of AMPK KO in cell death is minor, how do the authors explain it?\nWhat is the response of early responder cells to lack of glucose? Is AMPK activated as in late responder cells? What´s the basal activation of AMPK in early v late responders? This should be easily addressed with some blots.\nThe fact that accumulation of PUFA in LD is similar in Early and Late responders and that DGATi prevents access of PUFA to LD but this only affects proliferation in late responders (Fig. S2H) seems to indicate that the main event affecting proliferation is LD proximity rather than PUFA loading. How do the authors explain the result on proliferation?\nRegarding LD subcellular redistribution: how does it compare the tubulin detyrosination machinery between early and late responders? This should be addressed.\nMinor comments:\nCheck for typos: line 131: Cholsterol; Fig 4H: Pathnolide (examples)\nLine 248, it fatty acid oxidation rather than lipid oxidation would be more accurate\nLine 250: instead of proteins involved it´d be more correct to say expression of genes involved in…\nFig5SD: Chloroquine has no effect even in combination, it´s all down to mito and COXi. This should be indicated more precisely."}, "author_response": "REPLY TO THE REVIEWERS’ COMMENTS:\nWe thank the editor and the reviewers for their critical and constructive comments, which we address here point by point. We feel that these comments have substantially improved our manuscript and are grateful for this opportunity.\nReviewer #1 (Remarks to the Author):\nThis study proposes that AMPK-mediated lipid droplet plasticity and dispersion dictate the differential sensitivity of melanoma cells to ferroptosis induced by cotreatment with PUFA and iron. However, the main issue with this manuscript is the lack of strong evidence showing that differences in AMPK activation or lipid droplet formation cause the differential sensitivity of various melanoma cell lines to ferroptosis.\n1. Specifically, there are two main weaknesses in this model. First, it is unclear whether and how AMPK activation differ among these cell lines. The difference in AMPK activation was not thoroughly analyzed under different conditions (normal or energy stress).\nThank you for your insightful comments regarding the correlation between AMPK activation and ferroptosis sensitivity in our study. We appreciate the opportunity to clarify and strengthen our findings.\nTo address your concerns, we have quantitatively assessed AMPK levels in all cell lines (both at the RNA and protein levels) and have assessed phosphorylated AMPK (P-AMPK) levels under basal conditions and following PUFA treatment.\nInterestingly, our RNA sequencing analysis revealed differential expression of the PRKAA1 and PRKAA2 genes, which encode the alpha subunits of AMPK (Figure S3C). Notably: PRKAA2 exhibited a higher expression in early responders and an increase upon PUFA treatment in this group. No treatment effect was observed on PRKAA1 expression in the late responders, where the expression overall was low. In contrast, PRKAA1 was more expressed in late responders, with an increase in expression following PUFA treatment in both groups. Additionally, mTOR, a downstream target of the AMPK pathway, displayed a similar expression pattern to that of PRKAA1. Furthermore, we observed a decrease in HMGCR expression, consistent with its negative regulation by AMPK.\nWestern blot (WB) analysis revealed a slight increase in AMPK and p-AMPK levels following the PUFA treatment, particularly in the late responders (Figure S3D, S3E). This change was however, not statistically significant. Moreover, the antibodies do not distinguish AMPKα1 and AMPKα2 isoforms.\nTaken together, the differential expression of PRKAA1 at basal levels and its upregulation upon PUFA and nutrient stress in late responders, along with the more notable perinuclear localization of AMPK in late responders, support differences in AMPK in the different cell lines and under metabolic stress conditions.\nWe have included the relevant figures in the revised manuscript and modified the text accordingly (Highlighted in pink text). We hope these clarifications effectively address your concerns and strengthen the overall impact of our study.\nFigure S3C: mRNA expression levels of PRKAA1 and PRKAA2 across different melanoma cell lines under basal and PUFA treatment conditions. LRC (Late Responder Ctrl), LRT (Late Responder Treated with PUFA/Fe3+), ERC (Early Responder Ctrl), and ERT (Early Responder Treated with PUFA/Fe3+). Figures S3D, S3E: Western blot analysis and blot quantification showing the levels of P-AMPK, total AMPK, and GAPDH in early and late responders (two separate western blots) under basal and PUFA-treated conditions.\n2. Additionally, there is no significant difference in PUFA-containing neutral lipids or BODIPY 493/503 staining among these cell lines. The only noted difference is the somewhat arbitrary claim of variation in lipid droplet distribution patterns.\nThank you for your valuable feedback on our analysis of lipid droplets (LDs) and their relationship to differential ferroptosis sensitivity. Our work, illustrated in Figures S2G, S2H reveals insights into LD density and distribution.\nTo further investigate these variations, we quantified and classified LDs following treatment with PUFA and Fe3+ at 24 and 48 hours using images from BODIPY 493/503-stained cells. While lipidomic analyses demonstrated comparable PUFA incorporation into neutral lipids across all cell lines at 24 hours, as illustrated in Figure 2B, confocal imaging (n=100 cells per cell line) uncovered distinct temporal dynamics in lipid droplet (LD) synthesis and distribution (Figure 2D). Early responders (ER) exhibited a significant decrease in LD synthesis after 24 hours, whereas late responders (LR) maintained a consistent synthesis rate even at the 48-hour mark (Figure 2D). This finding was further supported by RNA sequencing data indicating time-dependent regulation of LD synthesis genes. Additionally, quantitative analysis revealed a distinctly more clustered LD phenotype in LR cells compared to the more dispersed pattern observed in ER cells (Figure 2F)(Highlighted in bright green text).\nAlong with the distinct temporal dynamics of LD synthesis, these findings emphasize critical differences in LD density that may not be immediately visible through qualitative analysis alone. Specifically, variations in LD distribution can influence the actual number of LDs in contact with metabolic organelles and available for metabolic processes. For instance, a peripheral clustering of LDs can lead to larger droplets, reducing the membrane surface area that comes into contact with metabolic organelles and thereby affecting metabolic efficiency. This indicates that the density of LDs accessible for metabolism is more relevant than the overall LD density. Moreover, the ability of LR cells to sustain higher rates of LD synthesis during the early phases of LD hydrolysis highlights significant metabolic differences between the two groups.\nFigure 2D, Quantitative measurement of BODIPY 493/503 intensity in a cohort of melanoma cells treated with PUFA/Fe3+ over a 48 h period. Figure 2F, Quantitative analysis of LD distribution from BODIPY 493/503 staining following PUFA/Fe3+ treatment in melanoma cohort over 100 cells in each condition. Figure S2A, KEGGid: hsa00561, mRNA expression of genes involved in glycerolipid metabolism was measured by RNA-Seq across various melanoma cell lines under basal and PUFA treatment conditions at 18 h and 36 h. The groups included LRC (Late Responder Ctrl), LRT (Late Responder Treated with PUFA/Fe3+), ERC (Early Responder Ctrl), and ERT (Early Responder Treated with PUFA/Fe3+), padj < 0.001.\n3. Secondly and more importantly, to establish that differences in lipid droplet distribution play a causal role in mediating the differential sensitivity of these cell lines to ferroptosis, it is necessary to demonstrate that shifting the lipid droplet distribution from one pattern to another can alter the ferroptosis sensitivity phenotypes. Without this evidence, the observations remain correlational and cannot establish causality.\nWe value your thoughtful comment highlighting the causal link between LD distribution and sensitivity to ferroptosis. To address this point, we conducted additional experiments to modulate LD distribution by knocking down Spastin (SPAST gene), a protein known to colocalize with LDs and mediate their dispersion via microtubule interactions during glucose starvation. In our study, we utilized RNAi to knock down Spastin in M257 and 451LUR cell lines. Post-knockdown, we performed imaging analyses to confirm changes in LD distribution. We assessed ferroptosis sensitivity using the Sytox cell death assay, confluency measurements with IncuCyte Zoom, and lipid peroxidation assays.\nOur image analysis indicated that Spastin knockdown resulted in impaired LD distribution and increased clustering of LDs at the cell periphery under PUFA/Fe3+ treatment upon starvation, compared to controls, specifically in 451LUR cells (Figure 2J). This altered distribution correlated with a significant reduction in lipid peroxide accumulation, mitigating ferroptosis-induced toxicity and promoting cell proliferation, as illustrated in Figures 2H, 2I, S2L, and S2M. These findings substantiate the hypothesis that LD distribution directly impacts ferroptosis sensitivity, reinforcing the causal link between LD distribution patterns and cellular responses to metabolic stress.\n40 µm\nFigure 2J, Confocal imaging of BODIPY 493/503 in M257 and 451LuR knocked down for negative control (NC) and Spastin (SPAST) gene treated with PUFA/Fe3+ at 72 h. Figure 2H, 2I, Effect of SPAST KD on cell death as measured by SYTOX Green and lipid peroxidation measured by C11BODIPY. Figure S2L, S2M, cell proliferation of M257 and 451LuR cells knocked down for SPAST and loaded with PUFA/Fe3+ ± co-treatment measured by IncuCyte Zoom.\n4. The project aimed to understand the mechanisms underlying the differential ferroptosis sensitivity among various melanoma cell lines. There could be many factors explaining their differences in ferroptosis sensitivity. in Figure 2, the authors abruptly focus on lipid droplets without providing a clear rationale or conducting comprehensive analyses, such as gene expression profiling across these cell lines.\nThank you for this comment and for the opportunity to clarify our rationale for focusing on lipid droplets in our study on ferroptosis sensitivity among melanoma cell lines. As noted, ferroptosis sensitivity is indeed regulated by various factors, including AMPK, GPX4, the system Xc−, the Nrf2 pathway, p53, ACSL4, CoQ10, and vitamin E, as described in the literature. Our specific interest lies in understanding how PUFAs affect ferroptosis sensitivity when combined with iron supplementation across different melanoma cell lines. Our emphasis on LDs is prompted by our observations that PUFAs are initially stored in LDs shortly after treatment, with minimal changes in other lipid species, such as membrane phospholipids, at this early stage. As the treatment progressed, we observed dynamic shifts in LD profiles, indicating a potential interaction between PUFA-laden LDs and the metabolic states of melanoma cells.\nIn response to Reviewer 1's suggestion on gene expression profiling, we have conducted RNA sequencing on ER and LR cell models at two time points following PUFA/Fe3+ treatment. KEGG pathway enrichment analysis of differentially expressed genes (DEGs) highlighted glycerolipid metabolism, including triacylglycerol (TAG) pathways, as among the top three ranked pathways following ferroptosis interventions, as shown in Figure 2A.\nFurther analysis of gene expression revealed significant upregulation of genes involved in glycerolipid metabolism in treated samples compared to controls in both groups (Figure S2A). Notably, several key genes associated with lipid droplet dynamics and glycerolipid synthesis, including GPAT3, AKR1B10, DGKG, and PLPP5, exhibited pronounced upregulation, particularly at earlier time points. This suggests enhanced processing of lipid droplets and increased turnover of PUFAs. Conversely, the downregulation of these genes during starvation indicates a potential shift toward LD hydrolysis and heightened susceptibility to ferroptosis. We have included these new data in the manuscript and have adapted the text accordingly (highlighted in orange).\nFigures 2A and S2A, KEGG enrichment analysis (2A) and KEGGid: hsa00561(S2A), mRNA expression of genes involved in glycerolipid metabolism was measured by RNA-Seq across various melanoma cell lines under basal and PUFA treatment conditions at 18 h and 36 h. The groups included LRC (Late Responder Ctrl), LRT (Late Responder Treated with PUFA/Fe3+), ERC (Early Responder Ctrl), and ERT (Early Responder Treated with PUFA/Fe3+), padj < 0.001.\n5. Additionally, the ferroptosis phenotypes in DGAT inhibition or knockdown cells are weak to moderate. Overall, the reviewer feels that lipid droplets may not be the primary mechanism underlying the differences among these cell lines. The overall ferroptosis phenotypes in DGAT knockdown or AMPK KO cells are weak (for example, see Fig. 3F), which undermines the robustness of their conclusion. Additionally, they did not provide Western blot analysis to examine the knockdown or KO efficiency in the DGAT or AMPK cell lines.\nTo address the observed partial rescue of ferroptosis upon DGAT inhibition or knockdown, our analysis highlighted two key factors that may explain the moderate phenotypes. First, the knockdown was performed separately for DGAT1 and DGAT2, as shown in Figure 2I. We observed differing levels of ferroptosis rescue between DGAT KD1 and KD2, indicating potential compensatory mechanisms from other DGAT isoforms. This suggests that targeting a single isoform may allow the remaining isoform to compensate, thereby reducing the observable phenotype. To further investigate this, we developed a double knockdown model targeting both isoforms. However, we did not observe a synergistic effect on ferroptosis prevention; instead, the DGAT2 KD model showed the most significant mitigation of ferroptosis (Figure 2I). This finding aligns with our results indicating significant changes in DGAT2 expression compared to the relatively stable expression of DGAT1. Therefore, we believe that the nearly 50% rescue of ferroptosis by DGAT2 KD, as depicted in Figure 2I, S2I, S2J, is significant enough to consider lipids as an important factor in ferroptosis induction. Nonetheless, other factors—such as the levels of free polyunsaturated fatty acids (PUFAs) within the cells, as detected by metabolomics (Figure 6A) may also influence overall cell health and contribute to cytotoxicity.\nFigure 2I, The percentage of cell death was measured by SYTOX green staining in the M257 and 451LuR DGAT KD cell model compared to NC KD control cells after 72 h treatment with PUFA/Fe3+. Figure S2K, S2L, Cell proliferation of DGAT KD M257 and 451LuR cells loaded with PUFA/Fe3+. Figure 6A, Heatmap showing free PUFA (DHA+ARA) and PC; phosphatidylcholine, TAG; triglyceride measured by metabolomics and tracer lipidomics in 451LuR cells at 24 h and 48 h\nIn the AMPK KO model, we established a clear connection between AMPK activation, lipid metabolism, and lipid peroxidation, as shown in Figures 3L and 3M. This finding was further accompanied by data from RNA sequencing across the panel of models, revealing AMPK signaling among the top five enriched pathways. Our data also demonstrated a significant reduction in lipid peroxidation, nearly returning to baseline levels in Figure 3F. While a partial rescue of the ferroptosis phenotype was noted, subsequent experiments with fresh AMPK KO 451LuR cells showed greater mitigation of ferroptosis (Figure 3E). This was further supported by a complete recovery in confluency measured by IncuCyte ZOOM following PUFA/Fe3+ treatment (Figure S3H).\nWe suspect that the passage number influenced the stability and adaptability of the AMPK KO model, impacting the interpretation of our initial results. Additionally, residual cytotoxicity observed in the AMPK KO model was mitigated by the apoptosis inhibitor, which underscores AMPK’s regulatory role in cell survival and energy homeostasis, indicating that these pathways may hinder the cells' ability to fully recover from stress.\nFigure 3E, Percentage of cell death was measured by SYTOX green staining in 451LuR AMPK KO cell model compared to AAVS Ctrl KO control cells after 48 h treatment with PUFA/Fe3+. Figure S3H, Cell proliferation of AMPK KO 451LuR cells loaded with PUFA/Fe3+± co-treatment with apoptosis inhibitor ZVAD, necroptosis inhibitor NEC-1S and ferroptosis inhibitor Ferrostatin measured by IncuCyteZoom.\nTo address the concerns about the efficiency of our KO models, we performed Western blot analyses for AMPK and CPT1, as detailed in Figures S6C and S6E. This is complemented by qPCR data shown in Figures S6B and S6D, providing robust validation of both protein and mRNA levels. For the knockdown models (DGAT, CHPT1, MFN2, and Spastin), we utilized qPCR for validation, given the temporary nature and high cost of generating large quantities of these models for Western blot analysis. qPCR is a recognized, sensitive, and accurate method for assessing mRNA levels, serving as a reliable indication of knockdown efficiency when paired with observed phenotypic outcomes.\nWe hope this clarification addresses your concerns and provides further insight into the experimental results.\n6. The study involves many cellular components (lipid droplet, DGAT, AMPK, mitochondria, membrane contact between different organelles, CHPT1, MFN2 etc) but lacks coherence. It is unclear which operates upstream or downstream. The authors should provide a schematic to illustrate their model more clearly.\nIn the supplementary material, we have included a schematic to illustrate the model.\nReviewer #2 (Remarks to the Author):\nThe authors present that nutrient availability through the redispersion of LDs plays key regulatory roles in PUFA-mediated ferroptosis induction. Starvation-dependent activation of AMPK renders the relocation of LDs toward mitochondria in the late responder melanoma cell lines. Overall, this is a high-quality series of experiments that provides novel insights into the relationship between LDs subcellular (re)distribution and starvation-induced AMPK mitochondrial-associated PUFA-induced ferroptosis. My concerns are mostly minor.\n1. More BRAF wild-type melanoma cell lines may be needed to prove this conclusion “No association with resistance to BRAF-targeted treatment was observed.”.\nOur study tested our hypothesis across 13 melanoma cell lines, including various BRAF mutations. Specifically, 9 cell lines are BRAF mutant, while only one is BRAF wild-type. Among the BRAF-mutated lines in response to targeted therapy, those designated with an “R” (M229R, M238R, M249R, and 451LuR) exhibited resistance to BRAFi therapy, whereas five other BRAF mutant cell lines demonstrated sensitivity to the treatment (M229, M238, M249, 451Lu, M233).\nImportantly, our analysis revealed no correlation between resistance to BRAF-targeted therapy and sensitivity to PUFA-induced ferroptosis within the BRAF-mutated cell lines. This lack of association indicates that the mechanisms driving resistance to BRAF therapy do not appear to directly influence susceptibility to ferroptosis induced by PUFAs.\nRegarding the inclusion of extra BRAF-wild type cell lines besides M257, we believe this is of limited relevance as the therapy is selectively used for BRAF-mutant patients. For that reason, we also excluded the wild-type cell line (M257) from our correlation analysis with resistance to BRAF-targeted therapy. This decision was made purposefully due to its limited relevance to BRAF-targeted therapy and the focus on the relationship between responses to BRAF-targeted therapy and sensitivity to PUFA-induced ferroptosis in the context of BRAF mutations.\nWhile we appreciate the reviewer's suggestion to include additional BRAF wild-type cell lines, we believe that our robust dataset offers valuable insights into the interplay between BRAF mutations, targeted therapy resistance, and sensitivity to ferroptosis.\nFigure S1A, Melanoma cell line annotation and clustering according to phenotypic subtype, response to BRAFi therapy, time course of response to PUFA/Fe3+, and LD dispersion. Figure B, Initial list of melanoma cell lines with different mutation status.\n2. The authors perform SRS microscopy at 2852 cm-1 for LDs signal. Have the authors considered ratiometric approaches using Raman analysis to quantify the unsaturation degree (DOI: 10.1038/s41467-020-18376-x; 10.34133/2021/9860123; 10.1088/1361-6463/ac2175)? Using these approaches, the unsaturated lipids and distribution may be visualized using SRS microscopy.\nThank you for your valuable point regarding the use of ratiometric approaches in Raman analysis to quantify the degree of unsaturation. We appreciate your suggestions and the valuable references you provided.\nIn our study, we employed SRS microscopy at 2852 cm⁻¹ to visualize lipid droplets, focusing on the CH stretching vibrations associated with both saturated and unsaturated fatty acids. While this method effectively allowed us to detect lipid distribution, we recognize that incorporating ratiometric analysis could further enhance our understanding of lipid unsaturation levels.\nTo investigate the dynamics between saturated and unsaturated fatty acids in lipid droplets within cells, we now utilized two additional Raman wavelengths. The band around 1660 cm⁻¹, associated with the C=C stretching vibrations, serves as an indicator of unsaturated fatty acids, while the 1440 cm⁻¹ band corresponds to the scissoring of CH2-CH3 banding in saturated carbon chains.\nAs shown in Figures S2E and S2F, we observed that the signals for both saturated and unsaturated fatty acids were colocalized with lipid droplets. Notably, the ratio of unsaturated to saturated fatty acids was greater following PUFA treatment compared to the control condition in both early and late responder models (M257 and 451LUR). The difference in unsaturation levels between M257 and 451LUR was not statistically significant. We have included these data in the revised manuscript and have adapted the text accordingly (Highlighted in blue text).\nWe appreciate your suggestion and recognize the potential benefits of ratiometric approaches, which we plan to explore in future studies to provide a more comprehensive analysis of lipid composition and its implications on cellular behavior. Thank you again for your constructive feedback.\nFigure S2E, S2F, SRS imaging and intensity quantification in M257 and 451LuR melanoma upon PUFA/Fe3+ treatment at 24 h and 48 h at Raman wavenumbers 1440 cm-1 (SFA) and 1660 cm-1 (PUFA).\n3. In Figure. S3B and 3C, the authors evaluate the effect of glucose deprivation on the dispersion of lipid droplets (LD) and cytotoxicity. Firstly, Figure. S3B lacks the confocal imaging of the unloaded PUFA/Fe3+ (+Glu) group. Besides, the effect of glucose deprivation should be also evaluated by readdition of glucose into the unloaded PUFA/Fe3+ (-Glu) group.\nThank you for your helpful feedback regarding Figures S3B and 3C. We appreciate your insights and have addressed your suggestions.\nWe have now included the confocal imaging data for the unloaded PUFA/Fe3+ (+Glu) group in figure S3K, S3L, as requested. This addition provides a clearer comparison of lipid droplet dispersion under the specified conditions.\nFurthermore, we have also conducted experiments to evaluate the effect of glucose deprivation with the re-addition of glucose to the unloaded PUFA/Fe3+ (-Glu) group on LD density and further TAG hydrolysis (Figure S3B, S3M). The results of this analysis are now included in the revised figures.\nFigure S3K, S3L Confocal imaging and quantification of LD-MT overlap in 451LuR cells loaded with PUFA/Fe3+ ± co-treatment with AMPK activator; EX-229 and AMPK inhibitor; Compound C or unloaded from PUFA with fresh medium ± Glucose. S3B, Quantification of LD density measured by the signal of BODIPY493/503 in 451LuR cells loaded and unloaded from PUFA ± Glucose. Figure S3M, TAG profiling of melanoma cells loaded with PUFA/Fe3+ upon AMPK modification or glucose deprivation.\n4. The representative image of the fourth group in Fig. 3N is not obviously weaker than the second group, which seems not match with Fig. 3O.\nWe acknowledge that the initial image may not have effectively represented the statistical analysis conducted on the three biological replicates. We have now selected a more suitable image that accurately reflects our results.\nFurthermore, in 3D cell models, interpreting the signal intensity of Sytox (or other fluorescent dyes) can be challenging, particularly when lower intensity is expected. Factors such as cell density and spatial organization can significantly impact dye uptake and retention, potentially resulting in higher fluorescence intensity even if fewer cells are actually permeable to the dye.\nWe appreciate your understanding of these complexities and have allowed ourselves to provide a clearer representation of our findings in the revised figure. Thank you for your valuable feedback.\n5. All western blots should have size markers and quantification.\nThank you for your feedback. We appreciate your attention to detail regarding the Western blot presentation.\nIn our revisions, we have made sure that all Western blots now prominently include size markers to indicate protein sizes. Furthermore, we have quantified the bands relative to the ladder for precise measurements. Please note that in Figure S3D, the ladder was washed away due to being set at a low level during the transfer. However, we have previously demonstrated band specificity for the same antibody in Figure 3D, where the ladder was used, and the raw images in the source file confirm the presence of a single band.\nWe hope these adjustments enhance the clarity and reliability of our data. Thank you for helping us improve our work.\nReviewer #3 (Remarks to the Author):\nThis is a very nice study that provides novel evidence linking metabolism, energy demands and ferroptotic responses in different melanoma cell phenotypes. Experiments are well controlled, thorough, using a wide variety of techniques to substantiate their conclusions.\nMajor comments: There are more references to chemotherapy response and ferroptosis that to melanoma states/transcriptomic programs.\n1. Given that the induction of ferroptosis and cytotoxicity is linked to melanoma cell phenotypes, a paragraph on this subject is required in the introduction. In line with this, the authors should decide whether to include a few lines on the clinical relevance of BRAF i. This study is all about ferroptosis with little translational/therapy data (which is perfectly ok). For instances, in Fig. S1A, while referring to BRAFi response in the text, no details on cell line response to BRAFi are provided.\nThank you for your valuable feedback regarding the need to address the relationship between ferroptosis induction, melanoma cell phenotypes, and the clinical relevance of BRAF inhibition. We have revised both the introduction and discussion sections to better engage with these aspects, highlighting the differences in response among melanoma subtypes and their implications for therapy resistance.\nIn particular, we have included a paragraph in the introduction that discusses how phenotype influences ferroptosis sensitivity in melanoma cells, highlighted in red, as well as the role of BRAF inhibitors (BRAFi) in this context. Additionally, we have provided more details on the responses of the cell lines to BRAFi, particularly in relation to the data shown in figure S1A.\nWe appreciate your guidance in enhancing the clarity and relevance of our manuscript, and we believe these revisions strengthen the overall discussion of our findings. Thank you again for your constructive comments.\n2. Given the link between cell phenotype and response to ferroptotic stimuli. The mansucript is lacking a discussion of the results in the context of the differentiated phenotype. This is important in mlenaoma as differentiation is linked to key genes such as MITF, tyrosinase etc... that regulate melanin production and, more importantly, melanosome biology. The obvious link between melanosome biology and the results presented in this manuscript warrant a discussion on the matter.\nThank you for your insightful comments on the connection between cell phenotype and response to ferroptotic stimuli, as well as your suggestion to explore the results in the context of melanoma differentiation.\nIn response, we have enhanced the manuscript with a better discussion on the relationship between differentiation status (as indicated by subtype), resistance to BRAF inhibition therapy, and the observed lipid droplet (LD) clustering patterns. This section explores the influence of melanosome biology and the regulatory roles of genes such as MITF, offering a deeper understanding of the intricate nature of ferroptosis sensitivity in melanoma. The updated text is highlighted in yellow at the end of the discussion. Highlighted in yellow.\n3. Lines 87-89 (Intro), the wording is confusing, given the importance of the message of this paragraph, it should be written with more clarity.\nThank you for your feedback on the wording in lines 87-89 of the introduction. We have paraphrased the text to enhance clarity and ensure the message is more understandable for readers. The revised version is highlighted in turquoise in the introduction section of the manuscript.\n4. Figure 2H: When quantifying LD density? Is it referring to perinuclear density of smaller LDs? If so, it should be better explained.\nThank you for pointing this out. In Figure 2H, the quantification of LD density specifically refers to the perinuclear density of smaller lipid droplets (LDs). We have revised the manuscript to clarify this point, with this explanation added at the end of line 145, ensuring that the information is clear for readers.\nFigure 3E, the effect of AMPK KO in cell death is minor. How do the authors explain it?\nThank you for your insightful observation regarding Figure 3E, a point also noted by Reviewer 1. While the effect of AMPK knockout on cell death seems minimal in this figure, a more comprehensive analysis reveals a significant impact on lipid metabolism and ferroptosis. Figures 3F, 3L and 3M clearly illustrate the strong connection between AMPK activation, lipid metabolism, and lipid peroxidation, with a marked reduction in lipid peroxidation levels in the AMPK knockout model, nearly returning to baseline.\nAlthough Figure 3E shows only partial recovery from the ferroptosis phenotype, subsequent experiments with freshly prepared AMPK knockout 451LuR cells demonstrated a more substantial mitigation of ferroptosis. Complete recovery in cell confluency following PUFA/Fe3+ treatment was observed using IncuCyte ZOOM. We attribute the initial results in Figure 3E to the passage number of the cells used, which may have affected the stability and adaptability of the AMPK knockout model. Additionally, the residual cytotoxicity observed in the AMPK knockout model was further reduced by an apoptosis inhibitor. This underscores AMPK's critical role in cell survival and energy homeostasis, suggesting that these pathways may interfere with the cells' capacity to fully recover from ferroptotic stress.\nFigure 3E, The percentage of cell death was measured by SYTOX green staining in 451LuR AMPK KO cell model compared to AAVS Ctrl KO control cells after 48 h treatment with PUFA/Fe3+± co-treatment with apoptosis inhibitor; ZVAD, necroptosis inhibitor; NEC-1S and ferroptosis inhibitor; Ferrostatin measured by IncuCyteZoom.\nFigure S3H, Cell proliferation of AMPK KO and AAVS1 KO 451LuR cells loaded with PUFA/Fe3+.\n5. What is the response of early responder cells to lack of glucose? Is AMPK activated as in late responder cells? What´s the basal activation of AMPK in early v late responders? This should be easily addressed with some blots.\nThank you for your insightful questions regarding the response of early responder cells to glucose deprivation and AMPK activation.\nAs illustrated in Figure 3C, M257 cells, identified as early responders, exhibited increased cytotoxicity following pre-treatment with PUFA and iron, followed by glucose depletion. Interestingly, unlike the late responder cell line 451LuR, the reintroduction of glucose did not affect TAG level and consequently did not alleviate cytotoxicity (Figure 3C, S3M). This, together with the data from Figure 3B, suggests that late responder cells possess a greater glycolytic capacity, which likely explains their different response to glucose deprivation compared to early responders. Our studies involving glucose and/or lipid depletion after PUFA and iron supplementation demonstrated that early responders rely more heavily on lipid metabolism compared to late responders, and lipid depletion had a more severe impact on cytotoxicity (Figure D, E). This dependency could explain why reintroducing glucose did not reverse ferroptosis in the early responder cells.\nFigure 3C, Cell death induction was revealed by SYTOX green staining in M257 (48 h) and 451LuR (72 h) following 20 h treatment with PUFA/Fe3+ and refreshing of medium with or without glucose (Glu). Figure S3M, TAG profiling in M257 (48 h) and 451LuR (48 h) following the same condition in 3C. Figure 3B, Aggregated OXPHOS parameters were plotted against aggregated glycolytic parameters for indicated melanoma cells at the basal level. Figure D, E, Cell death induction was revealed by SYTOX green staining in indicated melanoma cohort following the same condition in Figure 3C with/without Glucose or Lipid.\nRegarding AMPK activation, To address your concerns, we quantitatively assessed both AMPK and phosphorylated AMPK (P-AMPK) levels at the RNA and protein levels under basal conditions and following PUFA treatment.\nInterestingly, our RNA sequencing analysis revealed differential expression of the PRKAA1 and PRKAA2 genes, which encode the alpha subunits of AMPK (Figure S3C). Notably: PRKAA2 exhibited a higher expression in early responders and an increase upon PUFA treatment in this group. No treatment effect was observed on PRKAA1 expression in the late responders, where the expression overall was low. In contrast, PRKAA1 was more expressed in late responders, with an increase in expression following PUFA treatment in both groups. Additionally, the mTOR gene, a downstream target of the AMPK pathway, displayed a similar expression pattern to that of PRKAA1. Furthermore, we observed a decrease in HMGCR expression, consistent with its negative regulation by AMPK.\nWestern blot (WB) analysis revealed a slight increase in AMPK and p-AMPK levels following the PUFA treatment, particularly in the late responders (Figure S3D, S3E). This change was however, not statistically significant. Moreover, the antibodies do not distinguish AMPKα1 and AMPKα2 isoforms.\nImmunostaining analysis indicated that AMPK localization is predominantly confined to the perinuclear region in 451LuR late responder cells following PUFA treatment, compared to M257 early responder cells (Figure S3F). This nuclear localization is in line with its effects on metabolic reprogramming and the maintenance of cellular homeostasis.\nTaken together, the differential expression of PRKAA1 at basal levels and its upregulation upon PUFA and nutrient stress in late responders, along with the more notable perinuclear localization of AMPK in late responders, support in differences in AMPK in the different cell lines and under metabolic stress conditions.\nFigure S3C: mRNA expression levels of PRKAA1 and PRKAA2 across different melanoma cell lines under basal and PUFA treatment conditions. LRC (Late Responder Ctrl), LRT (Late Responder Treated with PUFA/Fe3+), ERC (Early Responder Ctrl), and ERT (Early Responder Treated with PUFA/Fe3+). Figures S3D, S3E: Western blot analysis and blot quantification showing the levels of P-AMPK, total AMPK, and GAPDH in early and late responders (two separate western blots) under basal and PUFA-treated conditions.\nThe fact that accumulation of PUFA in LD is similar in Early and Late responders and that DGATi prevents access of PUFA to LD but this only affects proliferation in late responders (Fig. S2H) seems to indicate that the main event affecting proliferation is LD proximity rather than PUFA loading. How do the authors explain the result on proliferation?\nThe reviewer correctly points out the apparent paradox: similar PUFA accumulation in LDs between early and late responders, despite DGATi's differential effect on proliferation. This observation supports the hypothesis that LD proximity and organization are critical factors alongside PUFA content in regulating both ferroptosis and proliferation.\nWhile the presence of PUFAs within LDs is essential for ferroptosis induction (supported by the observation that MUFAs, even when accumulating in LDs, do not induce ferroptosis; see Figures 3I and 3J, with further data on MUFA loading and LD relocalization to be presented), the cellular response to DGAT inhibition varies depending on pre-existing LD organization. We propose that inhibition of DGAT-dependent LD formation in cells with clustered LDs cells allows for efficient lipid reorganization and metabolic adaptation, mitigating ferroptosis and preserving proliferation. Conversely, cells with smaller, dispersed LDs (potentially exhibiting lower glycolytic capacity) have less capacity for rapid metabolic reprogramming upon DGAT inhibition. This limited adaptive response results in incomplete rescue from ferroptosis and a more pronounced impact on proliferation. Therefore, both PUFA content and the spatial organization and metabolic flexibility dictated by LD arrangement are crucial determinants of cellular response to DGAT inhibition and, ultimately, proliferation.\n6. Regarding LD subcellular redistribution, how does it compare the tubulin detyrosination machinery between early and late responders? This should be addressed.\nThank you for your insightful comment regarding the tubulin detyrosination machinery in early and late responders.\nInterestingly, our RNA sequencing analysis revealed significant differences in the expression of tubulin-related genes between the two groups, with VASH1—encoding a tyrosine carboxypeptidase (TCP) responsible for tubulin detyrosination—showing higher levels in late responders compared to early responders, particularly when they were treated with PUFA/Fe3+ (Figure S4D).\nHowever, quantification of detyrosinated tubulin at the protein level did not exhibit a significant difference between the two groups at the 24-hour mark, and the overall expression of detyrosinated tubulin was quite low in all cells. It's possible that this time point may not have been sufficient to capture expression differences at the protein level. Further quantification of detyrosinated tubulin at later time points, such as during starvation, could provide additional insights, paralleling our findings with phosphorylated AMPK (p-AMPK) and total AMPK levels. Additionally, early responders may employ other regulatory mechanisms or compensatory pathways to maintain tubulin tyrosination despite lower TCP expression (Figure S4E).\nThe inhibition of TCP using parthenolide, along with the subsequent rescue of ferroptosis in both groups, underscores the role of AMPK activation in balancing tubulin detyrosination and cellular stress responses and ultimately influencing ferroptosis sensitivity across various melanoma cell phenotypes (Figure S4F).\nWe appreciate your suggestion to investigate this relationship further, as it could yield valuable insights into the mechanistic foundations of ferroptosis sensitivity in tumor cells.\nFigure S4D: Heatmap demonstrating mRNA expression levels of Tubulin machinery across different melanoma cell lines under basal and PUFA treatment conditions. LRC (Late Responder Ctrl), LRT (Late Responder Treated with PUFA/Fe3+), ERC (Early Responder Ctrl), and ERT (Early Responder Treated with PUFA/Fe3+). Figures S3E and WB quantification show the levels of DT-Tubulin in early and late responders under basal and PUFA-treated conditions at 24 h. Figure S4F: Effect of TCP inhibition on cell death measured by SYTOX green staining in M257 and 451LuR in indicated conditions.\nMinor comments:\nCheck for typos: line 131: Cholsterol; Fig 4H: Pathnolide (examples)\nLine 248, it fatty acid oxidation rather than lipid oxidation would be more accurate\nLine 250: instead of proteins involved it´d be more correct to say expression of genes involved in…\nFig5SD: Chloroquine has no effect even in combination, it´s all down to mito and COXi. This should be indicated more precisely.\nWe have addressed all these minor issues in the text."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors addressed some questions but other concern remain.\nIn the previous review, the reviewer raised a critical point: “Secondly and more importantly, to establish that differences in lipid droplet distribution play a causal role in mediating the differential sensitivity of these cell lines to ferroptosis, it is necessary to demonstrate that shifting the lipid droplet distribution from one pattern to another can alter the ferroptosis sensitivity phenotypes. Without this evidence, the observations remain correlational and cannot establish causality.”\nIn other words, to demonstrate causality, the authors would need to manipulate a gene involved in lipid droplet distribution in such a way that it converts a late-responder (ferroptosis-resistant) cell line into a ferroptosis-sensitive state, or an early-responder (ferroptosis-sensitive) cell line into a resistant one.\nIn response, the authors knocked down the SPAST gene in M257 cells (an early-responder, ferroptosis-sensitive line) and 451LuR cells (a late-responder, ferroptosis-resistant line). However, as shown in Fig. 2K, SPAST knockdown did not alter ferroptosis sensitivity in M257 cells and instead further increased resistance in 451LuR cells, which are already ferroptosis-resistant. Therefore, this experiment does not address the reviewer's concern, as it does not demonstrate that altering lipid droplet distribution can causally shift ferroptosis sensitivity.", "reviewer_2": "The authors have addressed my concerns.", "reviewer_3": "The author have done a signficcant effort to address the points raised by all reviewers.\nI find the manuscript has improved substantially.\nOther than checking possible typos (ie. GPADH v GAPDH in FS3C) I tihnk the manuscript has reached the approppriate standards."}, "author_response": "Response to Reviewer #1 (Remarks to the Author):\nEditor/Reviewer comment: In the previous review, the reviewer raised a critical point: “Secondly and more importantly, to establish that differences in lipid droplet distribution play a causal role in mediating the differential sensitivity of these cell lines to ferroptosis, it is necessary to demonstrate that shifting the lipid droplet distribution from one pattern to another can alter the ferroptosis sensitivity phenotypes. Without this evidence, the observations remain correlational and cannot establish causality.” In other words, to demonstrate causality, the authors would need to manipulate a gene involved in lipid droplet distribution in such a way that it converts a late-responder (ferroptosis-resistant) cell line into a ferroptosis-sensitive state, or an early-responder (ferroptosis-sensitive) cell line into a resistant one. In response, the authors knocked down the SPAST gene in M257 cells (an early-responder, ferroptosis-sensitive line) and 451LuR cells (a late-responder, ferroptosis-resistant line). However, as shown in Fig. 2K, SPAST knockdown did not alter ferroptosis sensitivity in M257 cells; instead, it further increased resistance in 451LuR cells, which are already ferroptosis-resistant. Therefore, this experiment does not address the reviewer's concern, as it does not demonstrate that altering lipid droplet distribution can causally shift ferroptosis sensitivity.\nResponse: We fully recognize the importance of establishing causality in our findings. To this end, we had knocked down the SPAST gene, which encodes a protein that colocalizes with lipid droplets (LDs) and facilitates their dispersion via microtubule interactions during glucose starvation. In response to your remaining concern that “SPAST knockdown did not alter ferroptosis sensitivity in M257 cells and instead further increased resistance in 451LuR cells, which are already ferroptosis-resistant,” we would like to clarify the importance of timing in these effects.\nFigures 2K and 2L present data at the 72-hour (late) time point. At this stage, LDs have migrated inwards in 451LuR cells as illustrated in (Fig. 2J) and cells are sensitive to ferroptosis induction (late responders), showing approximately 80% cell death upon PUFA/Fe3+ treatment, preventable by ferrostatin (Fig. 2K). Upon SPAST knockdown, which blocks inward LD movement, LDs remain largely peripherally (Fig. 2J), resulting in a reduction of cell death to below 40% (Fig. 2K), accompanied by a similar reduction in lipid peroxidation (Fig. 2L). These findings in 451LuR support our hypothesis that LD redistribution contributes to ferroptosis sensitivity in late responders and contrasts with the SPAST knockdown effect in M257 cells (early responders), which already exhibit a dispersed pattern of LDs centrally, and inward LD movement is not required for ferroptosis sensitization in these cells.\nTo further corroborate this concept, and given the complexity of LD dynamics, which involves both inward and outward LD movement, we further investigated this causal link by knocking down KIF5B, a kinesin protein involved in outward LD transport (10.3389/fcell.2022.893375). In 451LuR cells, KIF5B knockdown led to increased central LD dispersal and reduced peripheral sequestration, leading to elevated lipid peroxidation and enhanced toxicity in 451LuR. We have incorporated these data in Figure 2J, 2K and 2L of the revised manuscript (also shown below).\nIn conclusion, our results demonstrate that modulating LD trafficking - via proteins such as spastin or KIF5B - significantly influences ferroptosis sensitivity in late-responder cells.\nWe hope this addresses the remaining concerns and are happy to provide further clarification if needed. Thank you again for your thoughtful review and consideration.\n(J) Confocal images of BODIPY 493/503 staining in 451LuR cells upon knockdown of KIF5B (48 h), Spastin (SPAST) (72 h), or negative control (NC), after treatment with PUFA/Fe3+. (K, L) Effect of KIF5B and SPAST knockdown on lipid peroxidation, assessed using C11BODIPY and cell death, as measured by SYTOX Green.\nResponse to Reviewer #2:\nThank you for your support and kind feedback. We appreciate your input throughout the process.\nResponse to Reviewer #3:\nThank you for acknowledging our efforts to address all reviewer points. We appreciate your constructive feedback on the improvements. The typo you noted has been corrected, and we have thoroughly reviewed the manuscript for accuracy."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors addressed my remaining concerns. The paper can be accepted for its publicaiton in the journal."}, "author_response": ""}]

Decision letter