Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Plasticity of ether lipids promotes ferroptosis susceptibility and evasion

Abstract

Ferroptosis—an iron-dependent, non-apoptotic cell death process—is involved in various degenerative diseases and represents a targetable susceptibility in certain cancers1. The ferroptosis-susceptible cell state can either pre-exist in cells that arise from certain lineages or be acquired during cell-state transitions2,3,4,5. However, precisely how susceptibility to ferroptosis is dynamically regulated remains poorly understood. Here we use genome-wide CRISPR–Cas9 suppressor screens to identify the oxidative organelles peroxisomes as critical contributors to ferroptosis sensitivity in human renal and ovarian carcinoma cells. Using lipidomic profiling we show that peroxisomes contribute to ferroptosis by synthesizing polyunsaturated ether phospholipids (PUFA-ePLs), which act as substrates for lipid peroxidation that, in turn, results in the induction of ferroptosis. Carcinoma cells that are initially sensitive to ferroptosis can switch to a ferroptosis-resistant state in vivo in mice, which is associated with extensive downregulation of PUFA-ePLs. We further find that the pro-ferroptotic role of PUFA-ePLs can be extended beyond neoplastic cells to other cell types, including neurons and cardiomyocytes. Together, our work reveals roles for the peroxisome–ether-phospholipid axis in driving susceptibility to and evasion from ferroptosis, highlights PUFA-ePL as a distinct functional lipid class that is dynamically regulated during cell-state transitions, and suggests multiple regulatory nodes for therapeutic interventions in diseases that involve ferroptosis.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genome-wide CRISPR screens identify peroxisome components as contributors to ferroptosis susceptibility.
Fig. 2: The polyunsaturated ether lipid biosynthesis pathway mediates the pro-ferroptotic roles of peroxisomes.
Fig. 3: Cancer cells initially dependent on GPX4 downregulate PUFA-ePLs to evade ferroptosis.
Fig. 4: Neurons and cardiomyocytes acquire increased PUFA-ePLs and gain sensitivity to ferroptosis during differentiation.

Data availability

Lists of genes scored significantly in the OVCAR-8 CRISPR screening experiment are provided in Supplementary Data 1; raw sequencing data of the CRISPR screening have been deposited in the Gene Expression Omnibus via accession number GSE151062. Input gene list and output gene sets of GeLiNEA and GSEA analysis are included in Supplementary Data 2. Lipidomics and metabolomics data are available as Supplementary Data 37, 9, 10. Raw exome sequencing and RNA-seq data are available in the Gene Expression Omnibus under accession code GSE148297, and processed data—including top variants and differentially expressed genes in the tumour-derived ferroptosis-resistant cells—are listed in Supplementary Data 8Source data are provided with this paper.

Code availability

Code for the GeLiNEA is available on GitHub (https://github.com/broadinstitute/GeLiNEA).

References

  1. 1.

    Stockwell, B. R. et al. Ferroptosis: a regulated cell death nexus linking metabolism, redox biology, and disease. Cell 171, 273–285 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Matsushita, M. et al. T cell lipid peroxidation induces ferroptosis and prevents immunity to infection. J. Exp. Med. 212, 555–568 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Zou, Y. et al. A GPX4-dependent cancer cell state underlies the clear-cell morphology and confers sensitivity to ferroptosis. Nat. Commun. 10, 1617 (2019).

    ADS  Article  Google Scholar 

  4. 4.

    Viswanathan, V. S. et al. Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature 547, 453–457 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Hangauer, M. J. et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature 551, 247–250 (2017).

    ADS  CAS  Article  Google Scholar 

  6. 6.

    Yang, W. S. et al. Regulation of ferroptotic cancer cell death by GPX4. Cell 156, 317–331 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    Eaton, J. K. et al. Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles. Nat. Chem. Biol. 16, 497–506 (2020).

    CAS  Article  Google Scholar 

  8. 8.

    Doll, S. et al. ACSL4 dictates ferroptosis sensitivity by shaping cellular lipid composition. Nat. Chem. Biol. 13, 91–98 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    CAS  Article  Google Scholar 

  10. 10.

    Islinger, M., Voelkl, A., Fahimi, H. D. & Schrader, M. The peroxisome: an update on mysteries 2.0. Histochem. Cell Biol. 150, 443–471 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Lodhi, I. J. & Semenkovich, C. F. Peroxisomes: a nexus for lipid metabolism and cellular signaling. Cell Metab. 19, 380–392 (2014).

    CAS  Article  Google Scholar 

  12. 12.

    Dean, J. M. & Lodhi, I. J. Structural and functional roles of ether lipids. Protein Cell 9, 196–206 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Piano, V. et al. Discovery of inhibitors for the ether lipid-generating enzyme AGPS as anti-cancer agents. ACS Chem. Biol. 10, 2589–2597 (2015).

    CAS  Article  Google Scholar 

  14. 14.

    Zou, Y. et al. Cytochrome P450 oxidoreductase contributes to phospholipid peroxidation in ferroptosis. Nat. Chem. Biol. 16, 302–309 (2020).

    CAS  Article  Google Scholar 

  15. 15.

    Saito, K. et al. Lipidomic signatures and associated transcriptomic profiles of clear cell renal cell carcinoma. Sci. Rep. 6, 28932 (2016).

    ADS  CAS  Article  Google Scholar 

  16. 16.

    Dixon, S. J. et al. Human haploid cell genetics reveals roles for lipid metabolism genes in nonapoptotic cell death. ACS Chem. Biol. 10, 1604–1609 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Honsho, M. & Fujiki, Y. Plasmalogen homeostasis – regulation of plasmalogen biosynthesis and its physiological consequence in mammals. FEBS Lett. 591, 2720–2729 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Braverman, N. E. & Moser, A. B. Functions of plasmalogen lipids in health and disease. Biochim. Biophys. Acta 1822, 1442–1452 (2012).

    CAS  Article  Google Scholar 

  19. 19.

    Messias, M. C. F., Mecatti, G. C., Priolli, D. G. & de Oliveira Carvalho, P. Plasmalogen lipids: functional mechanism and their involvement in gastrointestinal cancer. Lipids Health Dis. 17, 41 (2018).

    Article  Google Scholar 

  20. 20.

    Yuki, K., Shindou, H., Hishikawa, D. & Shimizu, T. Characterization of mouse lysophosphatidic acid acyltransferase 3: an enzyme with dual functions in the testis. J. Lipid Res. 50, 860–869 (2009).

    CAS  Article  Google Scholar 

  21. 21.

    Rashba-Step, J. et al. Phospholipid peroxidation induces cytosolic phospholipase A2 activity: membrane effects versus enzyme phosphorylation. Arch. Biochem. Biophys. 343, 44–54 (1997).

    CAS  Article  Google Scholar 

  22. 22.

    Doll, S. et al. FSP1 is a glutathione-independent ferroptosis suppressor. Nature 575, 693–698 (2019).

    ADS  CAS  Article  Google Scholar 

  23. 23.

    Bersuker, K. et al. The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis. Nature 575, 688–692 (2019).

    ADS  CAS  Article  Google Scholar 

  24. 24.

    Gallego-García, A. et al. A bacterial light response reveals an orphan desaturase for human plasmalogen synthesis. Science 366, 128–132 (2019).

    ADS  Article  Google Scholar 

  25. 25.

    Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    CAS  Article  Google Scholar 

  26. 26.

    Alim, I. et al. Selenium drives a transcriptional adaptive program to block ferroptosis and treat stroke. Cell 177, 1262–1279 (2019).

    CAS  Article  Google Scholar 

  27. 27.

    Fang, X. et al. Ferroptosis as a target for protection against cardiomyopathy. Proc. Natl Acad. Sci. USA 116, 2672–2680 (2019).

    CAS  Article  Google Scholar 

  28. 28.

    Encinas, M. et al. Sequential treatment of SH-SY5Y cells with retinoic acid and brain-derived neurotrophic factor gives rise to fully differentiated, neurotrophic factor-dependent, human neuron-like cells. J. Neurochem. 75, 991–1003 (2000).

    CAS  Article  Google Scholar 

  29. 29.

    Engelmann, B. Plasmalogens: targets for oxidants and major lipophilic antioxidants. Biochem. Soc. Trans. 32, 147–150 (2004).

    CAS  Article  Google Scholar 

  30. 30.

    Ginsberg, L., Rafique, S., Xuereb, J. H., Rapoport, S. I. & Gershfeld, N. L. Disease and anatomic specificity of ethanolamine plasmalogen deficiency in Alzheimer’s disease brain. Brain Res. 698, 223–226 (1995).

    CAS  Article  Google Scholar 

  31. 31.

    Schilder, R. J. et al. Metallothionein gene expression and resistance to cisplatin in human ovarian cancer. Int. J. Cancer 45, 416–422 (1990).

    CAS  Article  Google Scholar 

  32. 32.

    Cholody, W. M. et al. Derivatives of fluorene, anthracene, xanthene, dibenzosuberone and acridine and uses thereof. US Patent WO2008140792A1 (2012).

  33. 33.

    Shimada, K. et al. Global survey of cell death mechanisms reveals metabolic regulation of ferroptosis. Nat. Chem. Biol. 12, 497–503 (2016).

    CAS  Article  Google Scholar 

  34. 34.

    Paynter, N. P. et al. Metabolic predictors of incident coronary heart disease in women. Circulation 137, 841–853 (2018).

    Article  Google Scholar 

  35. 35.

    Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    ADS  CAS  Article  Google Scholar 

  36. 36.

    Wang, T., Lander, E. S. & Sabatini, D. M. Single guide RNA library design and construction. Cold Spring Harb. Protoc. 2016, pdb.prot090803 (2016).

    Article  Google Scholar 

  37. 37.

    Wang, T., Lander, E. S. & Sabatini, D. M. Viral packaging and cell culture for CRISPR-based screens. Cold Spring Harb. Protoc. 2016, pdb.prot090811 (2016).

    Article  Google Scholar 

  38. 38.

    Drummen, G. P. C., van Liebergen, L. C. M., den Kamp, J. A. F. O. & Post, J. A. C11-BODIPY581/591, an oxidation-sensitive fluorescent lipid peroxidation probe: (micro)spectroscopic characterization and validation of methodology. Free Radic. Biol. Med. 33, 473–490 (2002).

    CAS  Article  Google Scholar 

  39. 39.

    McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    Article  Google Scholar 

  40. 40.

    Kedare, S. B. & Singh, R. P. Genesis and development of DPPH method of antioxidant assay. J. Food Sci. Technol. 48, 412–422 (2011).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank J. Pan, E. S. Leshchiner, H. Li, X. Rong and X. Wang for discussions; K. Sigmund for sharing lentiviruses and other reagents; and the Broad Institute Genetic Perturbation Platform for providing gene editing and shRNA reagents. This work is supported in part by the NCI’s Cancer Target Discovery and Development (CTD2) Network (grant number U01CA217848, awarded to S.L.S.). L.A.B. was supported by a grant from the Mathers Foundation. R.A.W. received support from the National Institutes of Health (NIH) (P01 CA080111 and R35 CA220487), Breast Cancer Research Foundation, Advanced Medical Research Foundation, Samuel Waxman Cancer Research Foundation and Ludwig Center for Molecular Oncology. Y.Z. was supported by the National Cancer Institute of the NIH under award number K99CA248610. W.S.H. was supported by a postdoctoral fellowship from the Jane Coffin Childs Memorial Fund. V.V.P. is supported by the New Horizon UROP Fund/MIT. N.B. is supported by a Department of Defense Peer Reviewed Cancer Research Program Horizon Award (W81XWH-19-1-0257).

Author information

Affiliations

Authors

Contributions

Y.Z., W.S.H. and E.L.R. conceived the project, performed the experiments and analysed data. E.T.G., V.V.P., S.P., B.F., J.F. and H.R.K. assisted with the experiments and interpreted data. A.A.D. and C.B.C. performed metabolomics profiling. W.W. and J.K.E. performed chemical syntheses. N.B. prepared the lipid nanoparticles with input from P.T.H., P.M. and L.A.B. assisted with the cardiomyocyte experiments and data interpretation. J.K.E. performed the DPPH assay. F.R. assisted with animal experiments. V.D. and P.A.C. developed GeLiNEA and assisted with computational analysis. Y.Z., W.S.H., S.L.S. and R.A.W. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Yilong Zou or Robert A. Weinberg or Stuart L. Schreiber.

Ethics declarations

Competing interests

S.L.S. serves on the Board of Directors of the Genomics Institute of the Novartis Research Foundation (‘GNF’); is a shareholder and serves on the Board of Directors of Jnana Therapeutics; is a shareholder of Forma Therapeutics; is a shareholder and advises Kojin Therapeutics, Kisbee Therapeutics, Decibel Therapeutics and Eikonizo Therapeutics; serves on the Scientific Advisory Boards of Eisai Co., Ltd., Ono Pharma Foundation, Exo Therapeutics and F-Prime Capital Partners; and is a Novartis Faculty Scholar. Kojin Therapeutics in particular explores the medical potential of cell plasticity related to ferroptosis. P.A.C. is an advisor to Pfizer, Inc. The other authors declare no conflict of interest relevant to this study.

Additional information

Peer review information Nature thanks Marcus Conrad, Scott Dixon and Ronald Wanders for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 CRISPR screens identify peroxisome components as contributors to ferroptosis sensitivity.

a, STRING Protein Network analysis of the top OVCAR-8 screening hits. b, STRING Protein Network analysis of the top 786-O screening hits. c, Schematic diagram showing simplified illustration of the principle and work flow for the GeLiNEA method. d, Table showing the top pathways enriched in the 786-O screen hits using GeLiNEA. Overlap is the number of genes in common between a gene set and the screening hit list, nConnections is the number of connections between a gene set and the screening hit list, P values are computed using Supplementary Equation (1) and q values are P values adjusted for multiple testing using the Benjamini–Hochberg correction method. e, Table showing the top pathways enriched in OVCAR-8 screen hits using GeLiNEA. f, Venn diagram showing the overlapped CRISPR screen hits in OVCAR-8 and 786-O cells. g, Volcano plots showing the top hits in OVCAR-8 and 786-O genome-wide CRISPR screens. For presentation purposes, only genes that are enriched in the RSL3- or ML210-treated condition by ≥ 1.5 fold (log2FC ≥ 0.585) are plotted. See Methods for data analysis methods.

Extended Data Fig. 2 Peroxisomes contribute to ferroptosis sensitivity in renal and ovarian carcinoma cells.

a, Fluorescent imaging analysis of peroxisome abundances, reported by PTS1–GFP signal, in OVCAR-8 (top) and 786-O (bottom) cells expressing sgNC, or PEX3, PEX10-targeting sgRNAs. Scale bar, 50 μm. Peroxisome quantifications are shown on the right as violin plots. OVCAR-8 sgNC, n = 295; PEX3 sg1, n = 318; PEX10 sg1, n = 301; 786-O sgNC, n = 604; PEX3 sg1, n = 617; PEX10 sg1, n = 543. Lines in violin plots indicate median and quartiles. b, Fluorescent imaging analysis of peroxisome abundances, reported by PTS1–GFP signal, in OVCAR-8 and 786-O cells expressing sgNC, or PEX12-targeting sgRNAs. Scale bar, 50 μm. Peroxisome quantifications are shown on the right as violin plots. OVCAR-8 sgNC, n = 1,032; PEX12 sg1, n = 259; PEX12 sg2, n = 444; 786-O sgNC, n = 1,139, PEX12 sg1, n = 623; PEX12 sg2, n = 1,326. Lines in violin plots indicate median and quartiles. c, Immunoblot analysis showing the PEX10 levels in OVCAR-8 cells expressing sgNC or PEX10-targeting sgRNAs. COX-IV was used as a loading control. Representative result of experiment performed in duplicate. See Supplementary Information for uncropped immunoblot images. d, Viability curves of 786-O cells expressing negative control (sgNC) or sgRNAs targeting PEX3, PEX10 or PEX12 treated with the indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative result of experiment performed in triplicate. e, Viability curves of PEX3 sg1-expressing OVCAR-8 cells rescued with sgRNA-resistant mouse Pex3 cDNA respectively and being treated with indicated concentrations of ML210 or RSL3. In this experiment, cellular viability was measured at 24 h of treatment, at which time point the PEX3-sg1-only cells were not yet dying. n = 4 biologically independent samples. Data from an experiment performed once. f, Viability curves of PEX10 sg1-expressing OVCAR-8 cells rescued with sgRNA-resistant mouse Pex10 cDNA respectively and being treated with indicated concentrations of ML210 or RSL3. In this experiment, cellular viability was measured at 24 h of treatment, at which time point the PEX10 sg1-only cells were not yet dying. n = 4 biologically independent samples. Data of experiment performed once. For viability curves, data are mean ± s.d.

Source data

Extended Data Fig. 3 Peroxisomes contribute to ferroptosis sensitivity via the ether lipid biosynthesis pathway.

a, Schematic of the known functional pathways involved in lipid metabolism and reduction of reactive oxygen species (ROS) in the peroxisome. VLCFA, very-long-chain fatty acids; BCFA, branched-chain fatty acids; DHAP, dihydroxyacetone phosphate; AGPS, alkylglycerone phosphate synthase. b, Volcano plots showing the lipidomic analysis of 786-O cells expressing sgNC or PEX3-targeting sgRNAs. n = 3 biologically independent samples. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. c, Viability curves of OVCAR-8 cells expressing negative control (sgNC), FAR1-targeting sgRNAs (left) or AGPS-targeting sgRNAs (right) and treated with indicated concentrations of RSL3 for 72 h. n = 3 biologically independent samples. Representative results of experiment performed in triplicate. d, Fluorescent imaging showing nuclear staining by Hoechst 33342 in OVCAR-8 cells with the indicated genetic perturbations and treated with vehicle (DMSO) or indicated concentrations of ML210 for 5 days. Representative images from experiment performed once, and each condition has three biological replicates. e, Relative growth rates measured by areas of live-cell coverage in OVCAR-8 cells expressing sgNC, AGPS sg2 or FAR1 sg2 and treated with indicated concentrations of ML210 for 5 days. n = 2 or 3 biologically independent samples. Data of experiment performed once. f, Immunoblot analysis of AGPS levels in 786-O cells expressing negative control (sgNC) or AGPS-targeting sgRNAs. g, Viability curves of 786-O cells expressing sgNC or AGPS-targeting sgRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative result of experiment performed in triplicate. h, Immunoblot analysis of FAR1 levels in HuH-7 cells expressing negative control (sgNC) or AGPS-targeting sgRNAs. β-Actin was used as a loading control. i, Viability curves of 786-O cells expressing sgNC or FAR1-targeting sgRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative result of experiment performed in triplicate. j, Immunoblot showing AGPS levels in OVCAR-8 cells expressing sgNC or AGPS sg2, and AGPS−/− single cell clone (SCC)#9. k, Viability curves of OVCAR-8 cells expressing sgNC or AGPS sg2, and AGPS−/− SCC9 treated with ML210 for 72 h. n = 3 biologically independent samples. Representative result of experiment performed in duplicate. l, Immunoblot showing FAR1 levels in OVCAR-8 cells expressing sgNC or FAR1 sg1, and FAR1−/− single cell clone (SCC)#9. m, Viability curves of OVCAR-8 cells expressing sgNC or FAR1 sg1, and FAR1−/− SCC9 treated with ML210 for 72 h. n = 3 biologically independent samples. Representative result of experiment performed in duplicate. Immunoblots are representative data of experiments performed twice. See Supplementary Information for uncropped immunoblot images. β-Actin or GAPDH was used as a loading control. For viability curves, data are mean ± s.d.

Source data

Extended Data Fig. 4 AGPS/FAR1 depletion blocks ether phospholipid synthesis and lipid peroxidation.

a, Volcano plots showing the lipidomic analysis of OVCAR-8 and 786-O cells expressing sgNC or FAR1-targeting sgRNAs. n = 3 biologically independent samples. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. b, Heat map showing the relative abundances of free fatty acids in wild-type and AGPS, FAR1 or PEX10-depleted OVCAR-8 cells. n = 3 biologically independent samples. Highlighted in red are polyunsaturated fatty acids that are enriched in response to AGPS or FAR1 knockout. c, Volcano plots showing free fatty acid lipidomic analysis in wild-type and AGPS, FAR1, PEX3 or PEX10-depleted 786-O cells. n = 3 biologically independent samples. Highlighted in red are free polyunsaturated fatty acids upregulated in the knockout cells. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. d, Representative gating strategy used in the flow cytometry analysis of BODIPY-C11 oxidation levels. e, Histogram showing the lipid peroxidation levels reported by the ratio between oxidized and reduced BODIPY-C11 levels in the indicated OVCAR-8 cells treated with DMSO or ML210 for 2 h. Plot of experiment performed once.

Extended Data Fig. 5 The ether lipid biosynthesis pathway, but not other peroxisomal pathways, contributes to ferroptosis susceptibility.

a, Immunoblot analysis of AGPS levels in cells expressing the indicated constructs. Plot of experiment performed once. b, Viability curves of AGPS sg2-expressing OVCAR-8 cells rescued with sgRNA-resistant mouse Agps cDNA and being treated with indicated concentrations of RSL3 for 24 h. n = 4 biologically independent samples. Plot of experiment performed once. c, Viability curves of FAR1 sg2-expressing OVCAR-8 cells rescued with sgRNA-resistant mouse Far1 cDNA and being treated with indicated concentrations of RSL3 for 24 h. n = 4 biologically independent samples. Plot of experiment performed once. d, Viability curves for OVCAR-8 cells expressing empty vector or cDNAs of mouse Agps, Far1, Pex3 or Pex10 and treated with indicated concentrations of ML210 or RSL3. In this experiment, cellular viability was read at 24 h of treatment (instead of normally at 72 h), at which time point the control cells were not yet dying. n = 4 biologically independent samples. Plot of experiment performed once. e, Viability curves of 786-O cells expressing non-targeting negative control shRNA (shNC) or FAR1-targeting shRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative data of experiment performed twice. f, Viability curves of 786-O cells expressing shNC or GNPAT-targeting shRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative data of experiment performed twice. g, Percentage of remaining DPPH levels in in vitro DPPH assay system containing indicated concentrations of ZINC-69435460 or ferrostatin-1 (Fer-1). n = 3 biologically independent samples. Data are mean ± s.d. ZINC-69435460 vs Fer-1 both at 1 mM, P = 6.05x10e-8. h, Relative viability of OVCAR-8 cells pre-treated with AGPS inhibitor ZINC-69435460 (ZINC) for 24 h, followed by ML210 treatment for another 72 h. n = 3 biologically independent samples. Data are mean ± s.e.m. Representative data of experiment performed in triplicate. For 0.25 μM ML210 conditions, 0 μM vs 150 μM ZINC, P = 0.000076; vs 250 μM ZINC, P = 0.000073; vs 350 μM ZINC, P = 0.00011; vs 500 μM ZINC, P = 0.00045. For 0.35 μM ML210 conditions, 0 μM vs 150 μM ZINC, P = 0.0017; vs 250 μM ZINC, P = 0.000011; vs 350 μM ZINC, P = 7.55x10e-9; vs 500 μM ZINC, P = 0.00015. i, Immunoblot analysis of catalase (CAT) protein levels in 786-O cells expressing sgNC or CAT-targeting sgRNAs. Plot of experiment performed once. j, Immunoblot analysis of superoxide dismutase 1 (SOD1) levels in 786-O cells expressing sgNC or SOD1-targeting sgRNAs. Plot of experiment performed once. k, Viability curves of 786-O cells expressing sgNC or SOD1- or CAT-targeting sgRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Plots of experiment performed once. β-Actin was used as a loading control in immunoblots. See Supplementary Information for uncropped immunoblot images. For viability curves, data are mean ± s.d. P values were calculated using two-tailed Student’s t-tests.

Source data

Extended Data Fig. 6 Peroxisomes and the ether lipid biosynthesis pathway contribute to ferroptosis in liver, endometrial and kidney cancers.

a, Fluorescent imaging analysis of peroxisome abundances, reported by PTS1–emGFP signal, in HuH-7 cells expressing the indicated sgRNAs. Scale bar, 25 μm. Representative images from experiment performed once, and each condition has three biological replicates. b, Violin plots showing the quantitation of peroxisomes in HuH-7 cells expressing indicated sgRNAs. HuH-7 sgNC, n = 250; PEX3 sg1, n = 231; PEX3 sg2, n = 256; PEX10 sg1, n = 600; PEX10 sg2, n = 455; PEX12 sg1, n = 295; PEX12 sg2, n = 278; AGPS sg1, n = 321; AGPS sg2, n = 304; FAR1 sg1, n = 411. Lines in violin plots indicate median and quartiles. c, Immunoblot analysis of AGPS protein levels in HuH-7 cells expressing negative control (sgNC) or AGPS-targeting sgRNAs. β-Actin was used as a loading control. Plot of experiment performed once. d, Viability curves for HuH-7 cells expressing the indicated sgRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative results from experiment performed twice. e, Viability curves for SNU-685 cells expressing the indicated sgRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative results from experiment performed once. f, Volcano plots showing the lipidomic analysis results15 comparing 49 pairs of ccRCC tumour and adjacent normal kidney tissues. n = 49 tumour samples, n = 49 normal samples. Two-tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. See Supplementary Information for uncropped immunoblot images. For viability curves, data are mean ± s.d.

Source data

Extended Data Fig. 7 AGPAT3 contributes to PUFA-ePL synthesis downstream of peroxisomes.

a, Immunoblot analysis of ACSL4 and LPCAT3 levels in 786-O cells expressing the indicated sgRNAs. Plot of experiment performed once. b, Immunoblot analysis of ACSL4 and LPCAT3 levels in OVCAR-8 cells expressing the indicated sgRNAs. Plot of experiment performed once. c, Immunoblotting of ACSL4, AGPS and FAR1 levels in the indicated OVCAR-8 cell lines. Representative result of experiment performed in duplicate. d, Viability curves of OVCAR-8 cells expressing sgNC or sgRNAs targeting the gene of interest (GOI) as indicated on top of each graph, and transduced with doxycycline (dox)-inducible ACSL4 sgRNA1TetOn construct. These cells were pre-treated with vehicle or dox, and then treated with indicated concentrations of ML210, or ML210+Fer-1 for 72 h. n = 3 biologically independent samples. Representative results from experiment performed twice. e, Volcano plot showing the changes in phospholipid levels in sgNC or AGPAT3-targeting sgRNA expressing 769-P cells. n = 3 biologically independent samples. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. f, Viability curves for 769-P cells expressing sgNC or AGPAT3-targeting sgRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative results from experiment performed in triplicate. g, Viability curves for 786-O and OVCAR-8 cells expressing sgNC or AGPAT3-targeting sgRNAs treated with indicated concentrations of RSL3 for 48 h. n = 3 (OVCAR-8) or n = 4 (786-O) biologically independent samples. Representative results from experiment performed in triplicate. h, Fluorescent imaging showing nuclear staining by Hoechst 33342 in OVCAR-8 cells with the indicated genetic perturbations and treated with vehicle (DMSO) or indicated concentrations of ML210 for 5 days. Representative images from experiment performed once, and each condition has three biological replicates. i, qRT–PCR analysis of relative AGPAT3 mRNA expression in 786-O and 769-P cells expressing non-targeting negative control shRNA (shNC) or AGPAT3-targeting shRNAs. B2M was used as a loading control. n = 3 biologically independent samples. Two-tailed Student’s t-test. j, Viability curves for 786-O, and 769-P cells expressing shNC or AGPAT3-targeting shRNAs treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative results from experiment performed in triplicate. k, Nucleotide traces in Sanger sequencing analysis showing the point mutation introduced in the mouse Agpat3E176A cDNA construct. l, Viability curves of AGPAT3 sg1-expressing OVCAR-8 cells rescued with sgRNA-resistant, wild-type mouse Agpat3 or Agpat3E176A mutant cDNA and being treated with indicated concentrations of RSL3 for 24 h. n = 4 biologically independent samples. Representative data of experiment performed twice. β-Actin or GAPDH was used as a loading control. See Supplementary Information for uncropped immunoblot images. For viability curves and bar graphs, data are mean ± s.d.

Source data

Extended Data Fig. 8 Polyunsaturated ether lipid nanoparticles increase cellular sensitivity to ferroptosis.

a, Schematic of the plasmalogen biosynthesis pathway. Genes marked in red highlight pro-ferroptotic genes identified from the CRISPR screens. b, Strategy used to deliver synthetic phospholipids to OVCAR-8 cells using nanoparticles, and the chemical structures of synthetic phospholipids used. c, Viability curves of OVCAR-8 cells expressing the indicated sgRNAs pre-treated with vehicle (sterilized water) or the specified PE nanoparticles, and then treated with indicated concentrations of ML210 or RSL3 for 72 h. n = 4 biologically independent samples. Veh, vehicle used to package the phospholipids. d, Viability curves of OVCAR-8 cells expressing the indicated sgRNAs pre-treated with vehicle or the specified PC nanoparticles, and then treated with indicated concentrations of ML210 or RSL3 for 72 h. n = 4 biologically independent samples. Veh, vehicle used to package the phospholipids. P value for comparing the relative viabilities of NP5: C18:0-C20:4PC treated cells (blue) with that of NP6: C18 (plasm)-C20:4PC (red): for ML210 = 0.0391 μM, P = 0.00741; for ML210 = 0.0781 μM, P = 0.00095; for ML210 = 0.156 μM, P = 0.00177; for ML210 = 0.3125 μM, P = 0.0101; for RSL3 = 0.0156 μM, P = 0.0000468; for RSL3 = 0.03125 μM, P = 0.00241; for RSL3 = 0.0625 μM, P = 0.00104. Two-tailed Student’s t-test. e, Viability curves of 786-O cells expressing the indicated sgRNAs pre-treated with vehicle or the specified PC nanoparticles, and then treated with ferroptosis inducers. n = 4 biologically independent samples. For viability curves, data are mean ± s.d.

Source data

Extended Data Fig. 9 Polyunsaturated plasmalogens promote lipid peroxidation in GPX4-inhibited cells.

a, Quantification of time-lapse imaging of lipid peroxidation levels reported by BODIPY-C11 oxidation in 786-O cells co-treated with ML210 and indicated PE nanoparticles or Lip-1. n = 3 biologically independent samples. b, Quantification of time-lapse imaging of lipid peroxidation levels reported by BODIPY-C11 oxidation in 786-O cells co-treated with ML210 and indicated PC nanoparticles or Lip-1. n = 3 biologically independent samples. c, Quantification of time-lapse imaging of lipid peroxidation levels reported by BODIPY-C11 oxidation in 786-O cells expressing the indicated sgRNAs treated with ML210 and indicated PE nanoparticles or Lip-1. n = 2 biologically independent samples. Nanoparticles were added at the same time as ML210. d, Quantification of time-lapse imaging of lipid peroxidation levels reported by BODIPY-C11 oxidation in 786-O cells treated with ML210 and indicated PE nanoparticles or Lip-1. n = 3 biologically independent samples. Nanoparticles were added 1 h after ML210 administration (indicated by the arrow, note a 10 min time is deduced for reagent and equipment handling). e, Quantification of time-lapse imaging of lipid peroxidation levels reported by BODIPY-C11 oxidation in 786-O cells treated with ML210 and indicated PC nanoparticles or Lip-1. n = 3 biologically independent samples. Nanoparticles were added 1 h after ML210 administration (indicated by the arrow, note a 10 min time is deduced for reagent and equipment handling). f, Volcano plots showing the lipidomic analysis of 786-O cells treated with ML210 or DMSO for 90 min. n = 3 biologically independent samples. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. For viability curves and lipid peroxidation time-lapse imaging quantification, data are mean ± s.d.

Source data

Extended Data Fig. 10 PUFA-ePL downregulation is associated with acquired ferroptosis resistance in vivo.

a, Immunoblotting analysis of GPX4, AGPS, FAR1 and ACSL4 levels of GPX4+/+ and GPX4−/− OVCAR-8 cells expressing sgNC or sgRNAs targeting each of AGPS, FAR1, and PEX3. β-tubulin was used as a loading control. Representative result of experiment performed in duplicate. b, Tumour growth curves from mice implanted with 786-O cells expressing sgNC or sgRNAs targeting each of AGPS, FAR1, PEX3 and AGPAT3. n = 5 mice per group, each mouse was injected with two tumours. Plot of experiment performed once. For day-42 tumour sizes, sgNC vs AGPS sg1, P = 0.496; sgNC vs FAR1 sg2, P = 0.899; sgNC vs PEX3 sg1, P = 0.066; sgNC vs AGPAT3 sg, P = 0.54. Two-tailed Student’s t-test. c, Tumour images showing the relative sizes of the xenograft tumours formed by 786-O cells expressing sgNC or sgRNAs targeting each of AGPS, FAR1, PEX3 and AGPAT3 and dissected at day 42. Results from experiment performed once. d, Relative viability of OVCAR-8 cells expressing sgNC, AGPS sg2 or FAR1 sg1, and AGPS−/− and FAR1−/− single-cell clones (SCC) over a 3-day time course. n = 3 biologically independent samples. Representative results of experiments performed in triplicates. For day-3 viability, sgNC vs AGPS sg2 bulk, P = 0.872; vs AGPS sg2 SCC9, P = 0.172; vs FAR1 sg1 bulk, P = 0.151; vs FAR1 sg1 SCC9, P = 0.01. Two tailed Student’s t-test. e, Relative sizes (left) and weights (right) of xenograft tumours dissected from immunocompromised mice injected with OVCAR-8 cells with the indicated genetic background. sgNC, n = 3 tumours, FAR1 sg1 bulk, n = 4 tumours, FAR1 sg1 SCC9, n = 5 tumours, AGPS sg2 bulk, n = 4 tumours, AGPS sg2 SCC9, n = 5 tumours. Two tailed Student’s t-test., ns, not significant (P > 0.05). Data of experiment performed once. f, Volcano plot showing the global lipidomic analysis comparing GPX4−/− FR2#a cells and GPX4+/+ wild-type 786-O cells isolated from xenograft tumours. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. n = 6 biologically independent samples. g, Volcano plot showing polar metabolomic analysis using HILIC-positive method and comparing GPX4−/− FR2#a (left) or FR2#d (right) cells and GPX4+/+ wild-type 786-O cells. n = 6 in each group. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. h, Volcano plot showing the free fatty acid lipidomic analysis comparing GPX4−/− FR2#a cells and GPX4+/+ wild-type 786-O cells. n = 6 biologically independent samples in each group. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. S1P, sphingosine-1-phosphate. i, Representative fluorescent images (left) of peroxisomes reported by PTS1–GFP expression and quantifications (right) in GPX4+/+ (WT), GPX4−/− FR2#a and FR2#d 786-O cells. Scale bar, 50 μm. WT-L, n = 1,320; WT-R, n = 863; FR2#a-L, n = 533; FR2#a-R, n = 512; FR2#d-L, n = 876; FR2#d-R, n = 1,019. Lines in violin plots indicate median and quartiles. Data of experiment performed once. j, Volcano plots showing the relative mRNA expression (RNA-seq) of 87 peroxisome and ether-lipid biosynthesis-related genes comparing GPX4+/+ and GPX4−/− FR2#d cells. n = 4 biologically independent samples. See Supplementary Information for statistical methods used. k, Heat map showing the relative mRNA expression of indicated genes GPX4+/+ (WT), GPX4−/− FR2#a and FR2#d 786-O cells analysed by RNA-seq. l, Immunoblotting analysis of AIFM2/FSP1 levels in GPX4+/+ (WT), GPX4−/− FR2#a and FR2#d 786-O cells. β-Actin was used as a loading control. Results from experiment performed once. See Supplementary Information for uncropped immunoblot images. For cell and tumour growth curves and bar graphs, data are mean ± s.d.

Source data

Extended Data Fig. 11 ER-resident enzyme plasmanylethanolamine desaturase/TMEM189 is dispensible for ferroptosis sensitivity in selected cancer cells.

a, Top genes that show co-dependency with TMEM189 (left) or AGPS (right) using the cancer dependency map (DepMap) database. PCE, Pearson correlation coefficient. b, Immunoblotting analysis of TMEM189 levels in 786-O cells expressing sgNC or TMEM189-targeting sgRNAs. Representative results of experiment performed twice. c, Viability curves for 786-O cells expressing sgNC or TMEM189-targeting sgRNAs and treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative results of experiment performed twice. d, Immunoblotting analysis of TMEM189 levels in 786-O cells expressing doxycycline (dox)-inducible TMEM189-shRNAs. β-Actin was used as a loading control. Results from experiment performed once. e, Viability curves for 786-O, OVCAR-8 and HuH-7 cells expressing dox-inducible TMEM189-targeting shRNAs, pretreated with vehicle (DMSO) or dox, and then treated with indicated concentrations of ML210 or RSL3. n = 4 biologically independent samples. Representative results of experiment performed twice. f, Immunoblotting analysis of TMEM189 protein levels in 786-O cells expressing empty vector (EV) or human TMEM189 cDNA construct. β-Actin was used as a loading control. Results from experiment performed once. g, Viability curves for 786-O cells expressing empty vector or TMEM189 cDNA and treated with indicated concentrations of ML210 or RSL3 for 48 h. n = 4 biologically independent samples. Representative results of experiment performed twice. β-Actin was used as a loading control in immunoblots. See Supplementary Information for uncropped immunoblot images. For viability curves, data are mean ± s.d.

Source data

Extended Data Fig. 12 Neurons and cardiomyocytes acquire increased ether-phospholipid levels and elevated sensitivity to ferroptosis.

a, Scheme showing the experimental strategy for neuronal differentiation of SH-SY5Y cells, and representative images showing the cell morphology at indicated stages. RA, retinoic acid; BDNF, brain-derived neurotrophic factor; FBS, fetal bovine serum. b, Immunoblot analysis showing the protein expression levels of relevant neuronal markers including MAP2 (microtubule associated protein 2), tyrosine hydroxylase, NeuN (neuronal nuclei antigen) and β-3-tubulin. Arrows indicate the band for the indicated full length protein, * indicates non-specific bands. β-Actin was used as a loading control. Representative results of experiment performed twice. c, Viability curves of SH-SY5Y parental cells and cells at day 6 of neuronal differentiation under the treatment of indicated concentrations of RSL3 for 48 h. n = 2 biologically independent samples. Representative results of experiment performed twice. d, Fluorescent images showing lipid peroxidation levels reported by BODIPY-C11 oxidation. Representative images of experiment performed in duplicate. e, Volcano plot showing free-fatty-acid lipidomics analysis in parental or differentiated SH-SY5Y cells. n = 3 biological replicates for the parental condition, n = 4 biological replicates for the day 6 and day 12 differentiation condition. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. f, Immunofluorescence images showing the expression of cardiac troponin T, a marker of differentiated human cardiomyocytes, and NKX2.5, a cardiac-lineage specific marker in the CP cells and CM. Scale bars,15 μm. Representative results of experiment performed twice. g, Viability curves of iPS cells and differentiated CM treated with indicated concentrations of ML210 or RSL3 for 24 h. n = 4 biologically independent samples. Results from experiment performed once. h, Bright-field images showing cardiomyocytes treated with ML210 undergoing cell death. Scale bars, 30 μm. Representative results of experiment performed twice. i, Bar plots showing relative viability of CMs treated with ML210 and indicated cell death inhibitors. z-VAD, z-VAD-FMK; Nec-1, necrostatin-1. n = 3 biologically independent samples. Representative results of experiment performed twice. 0 μM vs 0.25 μM ML210 treated conditions with additional DMSO treatment only, P = 0.00011. For 0.25 μM ML210 treated conditions, DMSO vs z-VAD, P = 0.089; DMSO vs Nec-1, P = 0.125; DMSO vs Fer-1, P = 0.00061; DMSO vs Lip-1, P = 0.00008. Two tailed Student’s t-test. j, Volcano plot showing free-fatty-acid lipidomics analysis in CP or differentiated CM. n = 2 biological replicates for CP, n = 4 biological replicates for CM. Two tailed Student’s t-test. Multiple-testing adjustment was performed using the Benjamini–Hochberg method. k, qRT–PCR analysis showing the relative abundances of PEX3 (left) or AGPS (right) mRNAs in cardiomyocytes treated with the indicated siRNAs. n = 2 biologically independent samples. See Supplementary Information for uncropped immunoblot images. For viability curves and bar graphs, data are mean ± s.d.

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Methods.

Reporting Summary

Supplementary Information

This file contains original immunoblot images.

Supplementary Data

Supplementary Dataset 1. Genome-wide ferroptosis suppressor CRISPR screen results in OVCAR-8 cells.

Supplementary Data

Supplementary Dataset 2. Input gene lists and top output gene sets in GeLiNEA and GSEA analysis.

Supplementary Data

Supplementary Dataset 3. Lipidomic analysis in wildtype and AGPS, FAR1, PEX10, or AGPAT3-depleted OVCAR-8 cells. n=3 biologically independent samples for each group.

Supplementary Data

Supplementary Dataset 4. Lipidomic analysis in wildtype and AGPS, FAR1, PEX3 or PEX10-depleted 786-O cells. n=3 biologically independent samples for each group.

Supplementary Data

Supplementary Dataset 5. Lipidomic analysis of wildtype and AGPAT3-sgRNA expressing 786-O and 769-P cells. n=3 biologically independent samples for each group.

Supplementary Data

Supplementary Dataset 6. Lipidomic analysis in ML210 or DMSO treated wildtype 786-O cells. The DMSO treated samples were also used in a dataset we previously reported3. n=3 biologically independent samples for each group.

Supplementary Data

Supplementary Dataset 7. Lipidomics, free fatty acids and polar metabolomics analysis of wildtype and ferroptosis-resistant 2 (FR2) 786-O cells. n=4 or 6 biologically independent samples for each group.

Supplementary Data

Supplementary Dataset 8. RNA-seq and Exome-seq analysis of wildtype and ferroptosis-resistant 2 (FR2) 786-O cells. n=4 biologically independent samples for RNA-Seq, n=2 biologically independent samples for Exome-seq.

Supplementary Data

Supplementary Dataset 9. Lipidomics and free fatty acid analysis of SH-SY5Y cells undergoing neuronal differentiation. N=4 biologically independent samples for the day 0 parental cells; similarly, n=3 for day 6 cells, and n=4 for day 12 cells.

Supplementary Data

Supplementary Dataset 10. Lipidomics and free fatty acid analysis of human cardiac progenitors and cardiomyocytes. N=2 biologically independent samples for cardiac progenitors; similarly, n=4 for cardiomyocytes.

Video 1

: Time-lapse imaging of RSL3-induced OVCAR-8 cell death. Cells were stained with propidium iodide (PI) to report nuclear permeability changes. Representative video of 2 technical replicates.

Video 2

: Time-lapse imaging showing the beating of iPSC-derived cardiomyocytes in culture. Cells were stained with Hoechst 33342 to stain the nucleus. Representative video of 2 independent biological replicates with 3-5 technical replicates.

Video 3

: Time-lapse imaging of differentiated cardiomyocytes treated with ML210. Treatment condition: 16 h of 150 nM ML210 treatment. Representative video of 4 technical replicates.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zou, Y., Henry, W.S., Ricq, E.L. et al. Plasticity of ether lipids promotes ferroptosis susceptibility and evasion. Nature 585, 603–608 (2020). https://doi.org/10.1038/s41586-020-2732-8

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing