Colonna, M., and Brioschi, S. (2020). Neuroinflammation and neurodegeneration in human brain at single-cell resolution. Nat. Rev. Immunol. 20, 81-82. 10.1038/s41577-019-0262-0.
Wilson, D.M., 3rd, Cookson, M.R., Van Den Bosch, L., Zetterberg, H., Holtzman, D.M., and Dewachter, I. (2023). Hallmarks of neurodegenerative diseases. Cell 186, 693-714. 10.1016/j.cell.2022.12.032.
Cacace, R., Sleegers, K., and Van Broeckhoven, C. (2016). Molecular genetics of early-onset Alzheimer’s disease revisited. Alzheimers Dement. 12, 733-748. 10.1016/j.jalz.2016.01.012.
Lambert, J.C., Ibrahim-Verbaas, C.A., Harold, D., Naj, A.C., Sims, R., Bellenguez, C., DeStafano, A.L., Bis, J.C., Beecham, G.W., Grenier-Boley, B., et al. (2013). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452-1458. 10.1038/ng.2802.
Gatz, M., Reynolds, C.A., Fratiglioni, L., Johansson, B., Mortimer, J.A., Berg, S., Fiske, A., and Pedersen, N.L. (2006). Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168-174. 10.1001/archpsyc.63.2.168.
Karran, E., Mercken, M., and De Strooper, B. (2011). The amyloid cascade hypothesis for Alzheimer’s disease: an appraisal for the development of therapeutics. Nat. Rev. Drug Discov. 10, 698-712. 10.1038/nrd3505.
Moujalled, D., Strasser, A., and Liddell, J.R. (2021). Molecular mechanisms of cell death in neurological diseases. Cell Death Differ. 28, 2029-2044. 10.1038/s41418-021-00814-y.
Korczyn, A.D., and Grinberg, L.T. (2024). Is Alzheimer disease a disease? Nat. Rev. Neurol. 20, 245-251. 10.1038/s41582-024-00940-4.
Yang, W.S., SriRamaratnam, R., Welsch, M.E., Shimada, K., Skouta, R., Viswanathan, V.S., Cheah, J.H., Clemons, P.A., Shamji, A.F., Clish, C.B., et al. (2014). Regulation of ferroptotic cancer cell death by GPX4. Cell 156, 317-331. 10.1016/j.cell.2013.12.010.
Seiler, A., Schneider, M., Forster, H., Roth, S., Wirth, E.K., Culmsee, C., Plesnila, N., Kremmer, E., Radmark, O., Wurst, W., et al. (2008). Glutathione peroxidase 4 senses and translates oxidative stress into 12/15-lipoxygenase dependent- and AIF-mediated cell death. Cell Metab. 8, 237-248. 10.1016/j.cmet.2008.07.005.
Friedmann Angeli, J.P., Schneider, M., Proneth, B., Tyurina, Y.Y., Tyurin, V.A., Hammond, V.J., Herbach, N., Aichler, M., Walch, A., Eggenhofer, E., et al. (2014). Inactivation of the ferroptosis regulator Gpx4 triggers acute renal failure in mice. Nat. Cell Biol. 16, 1180-1191. 10.1038/ncb3064.
Dixon, S.J., Lemberg, K.M., Lamprecht, M.R., Skouta, R., Zaitsev, E.M., Gleason, C.E., Patel, D.N., Bauer, A.J., Cantley, A.M., Yang, W.S., et al. (2012). Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 149, 1060-1072. 10.1016/j.cell.2012.03.042.
Zheng, J., and Conrad, M. (2025). Ferroptosis: when metabolism meets cell death. Physiol. Rev. 105, 651-706. 10.1152/physrev.00031.2024.
Conrad, M., and Wahida, A. (2025). The many paths ascending to ferroptosis. Nat. Chem. Biol. 21, 18-19. 10.1038/s41589-024-01794-z.
Dixon, S.J., and Olzmann, J.A. (2024). The cell biology of ferroptosis. Nat. Rev. Mol. Cell Biol. 25, 424-442. 10.1038/s41580-024-00703-5.
Smith, A.C., Mears, A.J., Bunker, R., Ahmed, A., MacKenzie, M., Schwartzentruber, J.A., Beaulieu, C.L., Ferretti, E., et al. FORGE Canada Consortium, Majewski, J., (2014). Mutations in the enzyme glutathione peroxidase 4 cause Sedaghatian-type spondylometaphyseal dysplasia. J. Med. Genet. 51, 470-474. 10.1136/jmedgenet-2013-102218.
Ingold, I., Berndt, C., Schmitt, S., Doll, S., Poschmann, G., Buday, K., Roveri, A., Peng, X., Porto Freitas, F., Seibt, T., et al. (2018). Selenium utilization by GPX4 is required to prevent hydroperoxide-induced ferroptosis. Cell 172, 409-422.e21. 10.1016/j.cell.2017.11.048.
Liu, H., Forouhar, F., Seibt, T., Saneto, R., Wigby, K., Friedman, J., Xia, X., Shchepinov, M.S., Ramesh, S.K., Conrad, M., et al. (2022). Characterization of a patient-derived variant of GPX4 for precision therapy. Nat. Chem. Biol. 18, 91-100. 10.1038/s41589-021-00915-2.
Padmanabhan Nair, V., Liu, H., Ciceri, G., Jungverdorben, J., Frishman, G., Tchieu, J., Cederquist, G.Y., Rothenaigner, I., Schorpp, K., Klepper, L., et al. (2021). Activation of HERV-K(HML-2) disrupts cortical patterning and neuronal differentiation by increasing NTRK3. Cell Stem Cell 28, 1566-1581.e8. 10.1016/j.stem.2021.04.009.
Tschuck, J., Padmanabhan Nair, V., Galhoz, A., Zaratiegui, C., Tai, H.-M., Ciceri, G., Rothenaigner, I., Tchieu, J., Stockwell, B.R., Studer, L., et al. (2024). Suppression of ferroptosis by vitamin A or radical-trapping antioxidants is essential for neuronal development. Nat. Commun. 15, 7611. 10.1038/s41467-024-51996-1.
Yant, L.J., Ran, Q., Rao, L., Van Remmen, H., Shibatani, T., Belter, J.G., Motta, L., Richardson, A., and Prolla, T.A. (2003). The selenoprotein GPX4 is essential for mouse development and protects from radiation and oxidative damage insults. Free Radic. Biol. Med. 34, 496-502. 10.1016/S0891-5849(02)01360-6.
Kagan, V.E., Mao, G., Qu, F., Angeli, J.P.F., Doll, S., Croix, C.S., Dar, H.H., Liu, B., Tyurin, V.A., Ritov, V.B., et al. (2017). Oxidized arachidonic and adrenic PEs navigate cells to ferroptosis. Nat. Chem. Biol. 13, 81-90. 10.1038/nchembio.2238.
Mishima, E., Ito, J., Wu, Z., Nakamura, T., Wahida, A., Doll, S., Tonnus, W., Nepachalovich, P., Eggenhofer, E., Aldrovandi, M., et al. (2022). A non-canonical vitamin K cycle is a potent ferroptosis suppressor. Nature 608, 778-783. 10.1038/s41586-022-05022-3.
Nakamura, T., Hipp, C., Santos Dias Mourao, A., Borggrafe, J., Aldrovandi, M., Henkelmann, B., Wanninger, J., Mishima, E., Lytton, E., Emler, D., et al. (2023). Phase separation of FSP1 promotes ferroptosis. Nature 619, 371-377. 10.1038/s41586-023-06255-6.
Nakamura, T., Ito, J., Mourao, A.S.D., Wahida, A., Nakagawa, K., Mishima, E., and Conrad, M. (2024). A tangible method to assess native ferroptosis suppressor activity. Cell Rep. Methods 4, 100710. 10.1016/j.crmeth.2024.100710.
Ito, J., Nakamura, T., Toyama, T., Chen, D., Berndt, C., Poschmann, G., Mourao, A.S.D., Doll, S., Suzuki, M., Zhang, W., et al. (2024). PRDX6 dictates ferroptosis sensitivity by directing cellular selenium utilization. Mol. Cell 84, 4629-4644.e9. 10.1016/j.molcel.2024.10.028.
Ito, J., Nakagawa, K., Kato, S., Hirokawa, T., Kuwahara, S., Nagai, T., and Miyazawa, T. (2015). Direct separation of the diastereomers of phosphatidylcholine hydroperoxide bearing 13-hydroperoxy-9Z,11E-octadecadienoic acid using chiral stationary phase high-performance liquid chromatography. J. Chromatogr. A 1386, 53-61. 10.1016/j.chroma.2015.01.080.
Scheerer, P., Borchert, A., Krauss, N., Wessner, H., Gerth, C., Hohne, W., and Kuhn, H. (2007). Structural basis for catalytic activity and enzyme polymerization of phospholipid hydroperoxide glutathione peroxidase-4 (GPx4). Biochemistry 46, 9041-9049. 10.1021/bi700840d.
Labrecque, C.L., and Fuglestad, B. (2021). Electrostatic drivers of GPx4 interactions with membrane, lipids, and DNA. Biochemistry 60, 2761-2772. 10.1021/acs.biochem.1c00492.
Durr, U.H.N., Gildenberg, M., and Ramamoorthy, A. (2012). The magic of bicelles lights up membrane protein structure. Chem. Rev. 112, 6054-6074. 10.1021/cr300061w.
Warschawski, D.E., Arnold, A.A., Beaugrand, M., Gravel, A., Chartrand, E., and Marcotte, I. (2011). Choosing membrane mimetics for NMR structural studies of transmembrane proteins. Biochim. Biophys. Acta 1808, 1957-1974. 10.1016/j.bbamem.2011.03.016.
Cozza, G., Rossetto, M., Bosello-Travain, V., Maiorino, M., Roveri, A., Toppo, S., Zaccarin, M., Zennaro, L., and Ursini, F. (2017). Glutathione peroxidase 4-catalyzed reduction of lipid hydroperoxides in membranes: The polar head of membrane phospholipids binds the enzyme and addresses the fatty acid hydroperoxide group toward the redox center. Free Radic. Biol. Med. 112, 1-11. 10.1016/j.freeradbiomed.2017.07.010.
Wirth, E.K., Bharathi, B.S., Hatfield, D., Conrad, M., Brielmeier, M., and Schweizer, U. (2014). Cerebellar hypoplasia in mice lacking selenoprotein biosynthesis in neurons. Biol. Trace Elem. Res. 158, 203-210. 10.1007/s12011-014-9920-z.
Coppens, S., Lehmann, S., Hopley, C., and Hirtz, C. (2023). Neurofilament-light, a promising biomarker: Analytical, metrological and clinical challenges. Int. J. Mol. Sci. 24, 11624. 10.3390/ijms241411624.
Yuan, A., and Nixon, R.A. (2021). Neurofilament proteins as biomarkers to monitor neurological diseases and the efficacy of therapies. Front. Neurosci. 15, 689938. 10.3389/fnins.2021.689938.
Hofmann, A., Hasler, L.M., Lambert, M., Kaeser, S.A., Graber-Sultan, S., Obermuller, U., Kuder-Buletta, E., la Fougere, C., Laske, C., Voglein, J., et al. (2024). Comparative neurofilament light chain trajectories in CSF and plasma in autosomal dominant Alzheimer’s disease. Nat. Commun. 15, 9982. 10.1038/s41467-024-52937-8.
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T.K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017). A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276-1290.e17. 10.1016/j.cell.2017.05.018.
Leng, F., and Edison, P. (2021). Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat. Rev. Neurol. 17, 157-172. 10.1038/s41582-020-00435-y.
Tastan, B., and Heneka, M.T. (2024). The impact of neuroinflammation on neuronal integrity. Immunol. Rev. 327, 8-32. 10.1111/imr.13419.
White, L.D., and Barone, S., Jr. (2001). Qualitative and quantitative estimates of apoptosis from birth to senescence in the rat brain. Cell Death Differ. 8, 345-356. 10.1038/sj.cdd.4400816.
Perez-Riverol Y, Bandla C, Kundu DJ, Kamatchinathan S, Bai J, Hewapathirana S, John NS, Prakash A, Walzer M, Wang S, Vizcaíno JA The PRIDE database at 20 years: 2025 update. Nucleic Acids Res 2025;53:D543–D553. doi:10.1093/nar/gkae1011.
Doll, S., Freitas, F.P., Shah, R., Aldrovandi, M., da Silva, M.C., Ingold, I., Goya Grocin, A., Xavier da Silva, T.N., Panzilius, E., Scheel, C.H., et al. (2019). FSP1 is a glutathione-independent ferroptosis suppressor. Nature 575, 693-698. 10.1038/s41586-019-1707-0.
Hameyer, D., Loonstra, A., Eshkind, L., Schmitt, S., Antunes, C., Groen, A., Bindels, E., Jonkers, J., Krimpenfort, P., Meuwissen, R., et al. (2007). Toxicity of ligand-dependent Cre recombinases and generation of a conditional Cre deleter mouse allowing mosaic recombination in peripheral tissues. Physiol. Genomics 31, 32-41. 10.1152/physiolgenomics.00019.2007.
Casanova, E., Fehsenfeld, S., Mantamadiotis, T., Lemberger, T., Greiner, E., Stewart, A.F., and Schutz, G. (2001). A CamKIIα iCre BAC allows brain-specific gene inactivation: CamKllα iCre BAC Allows Brain-Specific Gene Inactivation. Genesis 31, 37-42. 10.1002/gene.1078.
Uus A, Zhang T, Jackson LH, Roberts TA, Rutherford MA, Hajnal JV, Deprez M Deformable Slice-to-Volume Registration for Motion Correction of Fetal Body and Placenta MRI. IEEE Trans. Med. Imaging. 2020;39:2750–2759. doi:10.1109/TMI.2020.2974844.
Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., et al. (2012). 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30, 1323-1341. 10.1016/j.mri.2012.05.001.
Pang, Z., Chong, J., Zhou, G., de Lima Morais, D.A., Chang, L., Barrette, M., Gauthier, C., Jacques, P.-E., Li, S., and Xia, J. (2021). MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388-W396. 10.1093/nar/gkab382.
Kabsch, W. (2010). XDS. Acta Crystallogr., D 66, 125-132. 10.1107/S0907444909047337.
Evans, P.R., and Murshudov, G.N. (2013). How good are my data and what is the resolution? Acta Crystallogr., D 69, 1204-1214. 10.1107/S0907444913000061.
Emsley, P., Lohkamp, B., Scott, W.G., and Cowtan, K. (2010). Features and development of coot. Acta Crystallogr., D 66, 486-501. 10.1107/S0907444910007493.
Murshudov, G.N., Vagin, A.A., and Dodson, E.J. (1997). Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr., D 53, 240-255. 10.1107/S0907444996012255.
Afonine, P.V., Grosse-Kunstleve, R.W., Echols, N., Headd, J.J., Moriarty, N.W., Mustyakimov, M., Terwilliger, T.C., Urzhumtsev, A., Zwart, P.H., and Adams, P.D. (2012). Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr., D 68, 352-367. 10.1107/S0907444912001308.
Bostock, M.J., Holland, D.J., and Nietlispach, D. (2012). Compressed sensing reconstruction of undersampled 3D NOESY spectra: application to large membrane proteins. J. Biomol. NMR 54, 15-32. 10.1007/s10858-012-9643-4.
Vranken, W.F., Boucher, W., Stevens, T.J., Fogh, R.H., Pajon, A., Llinas, M., Ulrich, E.L., Markley, J.L., Ionides, J., and Laue, E.D. (2005). The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins 59, 687-696. 10.1002/prot.20449.
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M.Y., Geiger, T., Mann, M., and Cox, J. (2016). The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731-740. 10.1038/nmeth.3901.
Cox, J., and Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367-1372. 10.1038/nbt.1511.
Cox, J., Hein, M.Y., Luber, C.A., Paron, I., Nagaraj, N., and Mann, M. (2014). Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513-2526. 10.1074/mcp.M113.031591.
Le, S., Josse, J., and Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 25, 1-18. 10.18637/jss.v025.i01.
Gu, Z., Eils, R., and Schlesner, M. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847-2849. 10.1093/bioinformatics/btw313.
Smedley, D., Haider, S., Durinck, S., Pandini, L., Provero, P., Allen, J., Arnaiz, O., Awedh, M.H., Baldock, R., Barbiera, G., et al. (2015). The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res. 43, W589-W598. 10.1093/nar/gkv350.
Yu, G., Wang, L.-G., Han, Y., and He, Q.-Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284-287. 10.1089/omi.2011.0118.
Liberzon, A., Birger, C., Thorvaldsdottir, H., Ghandi, M., Mesirov, J.P., and Tamayo, P. (2015). The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417-425. 10.1016/j.cels.2015.12.004.
Balduzzi, S., Rucker, G., and Schwarzer, G. (2019). How to perform a meta-analysis with R: a practical tutorial. Evid. Based Ment. Health 22, 153-160. 10.1136/ebmental-2019-300117.
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., and Ishiguro-Watanabe, M. (2023). KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587-D592. 10.1093/nar/gkac963.
Langfelder, P., and Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559. 10.1186/1471-2105-9-559.
Franzen, O., Gan, L.-M., and Bjorkegren, J.L.M. (2019). PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (Oxford) 2019, baz046. 10.1093/database/baz046.
Erdmann, G., Schutz, G., and Berger, S. (2007). Inducible gene inactivation in neurons of the adult mouse forebrain. BMC Neurosci. 8, 63. 10.1186/1471-2202-8-63.
Barski, J.J., Dethleffsen, K., and Meyer, M. (2000). Cre recombinase expression in cerebellar Purkinje cells. Genesis 28, 93-98. 10.1002/1526-968X(200011/12)28:3/4<93::AID-GENE10>3.0.CO;2-W.
Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., and Schnabel, J.A. (2012). Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med. Image Anal. 16, 1550-1564. 10.1016/j.media.2012.07.004.
Fuchs, H., Aguilar-Pimentel, J.A., Amarie, O.V., Becker, L., Calzada-Wack, J., Cho, Y.-L., Garrett, L., Holter, S.M., Irmler, M., Kistler, M., et al. (2018). Understanding gene functions and disease mechanisms: Phenotyping pipelines in the German Mouse Clinic. Behav. Brain Res. 352, 187-196. 10.1016/j.bbr.2017.09.048.
Holter, S.M., Garrett, L., Einicke, J., Sperling, B., Dirscherl, P., Zimprich, A., Fuchs, H., Gailus-Durner, V., Hrabe de Angelis, M., and Wurst, W. (2015). Assessing cognition in mice. Curr. Protoc. Mouse Biol. 5, 331-358. 10.1002/9780470942390.mo150068.
Garrett, L., Zhang, J., Zimprich, A., Niedermeier, K.M., Fuchs, H., Gailus-Durner, V., Hrabe de Angelis, M., Vogt Weisenhorn, D., Wurst, W., and Holter, S.M. (2015). Conditional reduction of adult born doublecortin-positive neurons reversibly impairs selective behaviors. Front. Behav. Neurosci. 9, 302. 10.3389/fnbeh.2015.00302.
Kabiri, Y., Eberhagen, C., Schmitt, S., Knolle, P.A., and Zischka, H. (2021). Isolation and electron microscopic analysis of liver cancer cell mitochondria. Methods Mol. Biol. 2277, 277-287. 10.1007/978-1-0716-1270-5_17.
Shi, Y., Kirwan, P., and Livesey, F.J. (2012). Directed differentiation of human pluripotent stem cells to cerebral cortex neurons and neural networks. Nat. Protoc. 7, 1836-1846. 10.1038/nprot.2012.116.
Matyash, V., Liebisch, G., Kurzchalia, T.V., Shevchenko, A., and Schwudke, D. (2008). Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid Res. 49, 1137-1146. 10.1194/jlr.D700041-JLR200.
Chong, J., Wishart, D.S., and Xia, J. (2019). Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinform. 68, e86. 10.1002/cpbi.86.
Bracher, A., Morozov, V., and Mourao, A. (2018). Munich Crystallography BAG (European Synchrotron Radiation Facility). 10.15151/ESRF-ES-2113997880.
Agirre, J., Atanasova, M., Bagdonas, H., Ballard, C.B., Basle, A., Beilsten-Edmands, J., Borges, R.J., Brown, D.G., Burgos-Marmol, J.J., Berrisford, J.M., et al. (2023). The CCP4 suite: integrative software for macromolecular crystallography. Acta Crystallogr. D Struct. Biol. 79, 449-461. 10.1107/S2059798323003595.
Sattler, M. (1999). Heteronuclear multidimensional NMR experiments for the structure determination of proteins in solution employing pulsed field gradients. Prog. Nucl. Magn. Reson. Spectrosc. 34, 93-158. 10.1016/S0079-6565(98)00025-9.
Farrow, N.A., Muhandiram, R., Singer, A.U., Pascal, S.M., Kay, C.M., Gish, G., Shoelson, S.E., Pawson, T., Forman-Kay, J.D., and Kay, L.E. (1994). Backbone dynamics of a free and phosphopeptide-complexed Src homology 2 domain studied by 15N NMR relaxation. Biochemistry 33, 5984-6003. 10.1021/bi00185a040.
Gaussmann, S., Gopalswamy, M., Eberhardt, M., Reuter, M., Zou, P., Schliebs, W., Erdmann, R., and Sattler, M. (2021). Membrane interactions of the peroxisomal proteins PEX5 and PEX14. Front. Cell Dev. Biol. 9, 651449. 10.3389/fcell.2021.651449.
Chaney, L.K., and Jacobson, B.S. (1983). Coating cells with colloidal silica for high yield isolation of plasma membrane sheets and identification of transmembrane proteins. J. Biol. Chem. 258, 10062-10072. 10.1016/S0021-9258(17)44606-0.
Paparelli, L., Corthout, N., Pavie, B., Wakefield, D.L., Sannerud, R., Jovanovic-Talisman, T., Annaert, W., and Munck, S. (2016). Inhomogeneity based characterization of distribution patterns on the plasma membrane. PLoS Comput. Biol. 12, e1005095. 10.1371/journal.pcbi.1005095.
Escamilla-Ayala, A.A., Sannerud, R., Mondin, M., Poersch, K., Vermeire, W., Paparelli, L., Berlage, C., Koenig, M., Chavez-Gutierrez, L., Ulbrich, M.H., et al. (2020). Super-resolution microscopy reveals majorly mono- and dimeric presenilin1/γ-secretase at the cell surface. eLife 9, e56679. 10.7554/eLife.56679.
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012). Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676-682. 10.1038/nmeth.2019.
Kulak, N.A., Geyer, P.E., and Mann, M. (2017). Loss-less nano-fractionator for high sensitivity, high coverage proteomics. Mol. Cell. Proteomics 16, 694-705. 10.1074/mcp.O116.065136.
R Core Team. (2022). R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing). www.r-project.org.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289-300. 10.1111/j.2517-6161.1995.tb02031.x.
Trumbach, D., Graf, C., Putz, B., Kuhne, C., Panhuysen, M., Weber, P., Holsboer, F., Wurst, W., Welzl, G., and Deussing, J.M. (2010). Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models. BMC Syst. Biol. 4, 159. 10.1186/1752-0509-4-159.
Frisch, M., Klocke, B., Haltmeier, M., and Frech, K. (2009). LitInspector: literature and signal transduction pathway mining in PubMed abstracts. Nucleic Acids Res. 37, W135-W140. 10.1093/nar/gkp303.
Berriz, G.F., King, O.D., Bryant, B., Sander, C., and Roth, F.P. (2003). Characterizing gene sets with FuncAssociate. Bioinformatics 19, 2502-2504. 10.1093/bioinformatics/btg363.
Haytural, H., Benfeitas, R., Schedin-Weiss, S., Bereczki, E., Rezeli, M., Unwin, R.D., Wang, X., Dammer, E.B., Johnson, E.C.B., Seyfried, N.T., et al. (2021). Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis. Sci. Data 8, 312. 10.1038/s41597-021-01090-8.
Varemo, L., Nielsen, J., and Nookaew, I. (2013). Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378-4391. 10.1093/nar/gkt111.