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Proximity labeling methods for proteomic analysis of membrane proteins

Zafra, Francisco, Piniella, Dolores·2022·Journal of Proteomics
DOI 10.1016/j.jprot.2022.104620
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Abstract

Membrane proteins constitute the filter that controls the cellular traffic of nutrients, ions and other essential molecules, as well as the transmission of signals across the membrane. These proteins interact with other proteins in the cytosol, cytoskeleton or the extracellular side of the membrane, giving rise to complex interactomes that are distributed throughout the various lipid microdomains of the membrane plane. In this manner, complex networks of protein-protein and protein-lipid interactions are formed which regulate the most diverse biological functions, and disturbance of these networks can lead to disease. Therefore, characterization of these interactomes is a priority for current biomedical sciences. Traditionally, such studies have largely depended on solubilization/dissociation of the essential components of multiprotein complexes with detergents of various strength. However, this technique may result in the loss of certain components of such complexes, especially those whose binding is weak or transient. Moreover, protein solubilization can lead to the formation of non-native spurious interactions. As an alternative, proximity labelling (PL) techniques have been developed in recent years that can identify interactors of the protein of interest in a native cellular environment, prior to solubilization. In this article, we review the recent advances in PL and explore the new possibilities they offer for the characterization of membrane interactomes. SIGNIFICANCE…

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Show all 8 references

Research works printed but not linked: 135

  1. Silva, 1867, Maintaining social contacts: the physiological relevance of organelle interactions, Biochim. Biophys. Acta Mol. Cell Res., 2020
  2. Wallin, 1998, Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms, Protein Sci., 7, 1029, 10.1002/pro.5560070420 · DOI
  3. Overington, 2006, How many drug targets are there?, Nat. Rev. Drug Discov., 5, 993, 10.1038/nrd2199 · DOI · Paper page
  4. Ratkeviciute, 2021, Methods for the solubilisation of membrane proteins: the micelle-aneous world of membrane protein solubilisation, Biochem. Soc. Trans., 49, 1763, 10.1042/BST20210181 · DOI
  5. Varnaitė, 2016, Meet the neighbors: mapping local protein interactomes by proximity-dependent labeling with BioID, Proteomics, 16, 2503, 10.1002/pmic.201600123 · DOI · Paper page
  6. Weissinger, 2021, RNA proximity labeling: a new detection tool for RNA-protein interactions, Molecules, 26, 10.3390/molecules26082270 · DOI
  7. Trinkle-Mulcahy, 2019, Recent advances in proximity-based labeling methods for interactome mapping, F1000Res, 8, 10.12688/f1000research.16903.1 · DOI
  8. Roux, 2012, A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells, J. Cell Biol., 196, 801, 10.1083/jcb.201112098 · DOI · Paper page
  9. Hung, 2016, Spatially resolved proteomic mapping in living cells with the engineered peroxidase APEX2, Nat. Protoc., 11, 456, 10.1038/nprot.2016.018 · DOI
  10. Gingras, 2019, Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles, Curr. Opin. Chem. Biol., 48, 44, 10.1016/j.cbpa.2018.10.017 · DOI
  11. Liu, 2018, A proximity-tagging system to identify membrane protein-protein interactions, Nat. Methods, 15, 715, 10.1038/s41592-018-0100-5 · DOI
  12. Xie, 2021, PUPIL enables mapping and stamping of transient electrical connectivity in developing nervous systems, Cell Rep., 37, 10.1016/j.celrep.2021.109853 · DOI
  13. Sun, 2021, A new method for studying RNA-binding proteins on specific RNAs, Bio Protoc., 11
  14. Lam, 2015, Directed evolution of APEX2 for electron microscopy and proximity labeling, Nat. Methods, 12, 51, 10.1038/nmeth.3179 · DOI
  15. Rhee, 2013, Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging, Science, 339, 1328, 10.1126/science.1230593 · DOI · Paper page
  16. Li, 2014, New insights into the DT40 B cell receptor cluster using a proteomic proximity labeling assay, J. Biol. Chem., 289, 14434, 10.1074/jbc.M113.529578 · DOI
  17. Cijsouw, 2018, Mapping the proteome of the synaptic cleft through proximity labeling reveals new cleft proteins, Proteomes, 6, 10.3390/proteomes6040048 · DOI
  18. Li, 2020, Cell-surface proteomic profiling in the fly brain uncovers wiring regulators, Cell, 180, 373, 10.1016/j.cell.2019.12.029 · DOI · Paper page
  19. Wu, 2017, Identification of lectin counter-receptors on cell membranes by proximity labeling, Glycobiology, 27, 800, 10.1093/glycob/cwx063 · DOI
  20. Ke, 2021, Spatiotemporal profiling of cytosolic signaling complexes in living cells by selective proximity proteomics, Nat. Commun., 12, 71, 10.1038/s41467-020-20367-x · DOI
  21. Choi-Rhee, 2004, Promiscuous protein biotinylation by Escherichia coli biotin protein ligase, Protein Sci., 13, 3043, 10.1110/ps.04911804 · DOI
  22. Kim, 2014, Probing nuclear pore complex architecture with proximity-dependent biotinylation, Proc. Natl. Acad. Sci. U. S. A., 111, E2453, 10.1073/pnas.1406459111 · DOI · Paper page
  23. Kim, 2016, An improved smaller biotin ligase for BioID proximity labeling, Mol. Biol. Cell, 27, 1188, 10.1091/mbc.E15-12-0844 · DOI
  24. Ramanathan, 2018, RNA-protein interaction detection in living cells, Nat. Methods, 15, 207, 10.1038/nmeth.4601 · DOI
  25. Kido, 2020, AirID, a novel proximity biotinylation enzyme, for analysis of protein-protein interactions, Elife, 9, 10.7554/eLife.54983 · DOI
  26. Liu, 2013, Imaging trans-cellular neurexin-neuroligin interactions by enzymatic probe ligation, PLoS One, 8
  27. Branon, 2018, Efficient proximity labeling in living cells and organisms with TurboID, Nat. Biotechnol., 36, 880, 10.1038/nbt.4201 · DOI · Paper page
  28. May, 2020, Comparative application of BioID and TurboID for protein-proximity biotinylation, Cells, 9, 10.3390/cells9051070 · DOI
  29. Zhao, 2021, ultraID: a compact and efficient enzyme for proximity-dependent biotinylation in living cells, Biochemistry
  30. Fairhead, 2015, Site-specific biotinylation of purified proteins using BirA, Methods Mol. Biol., 1266, 171, 10.1007/978-1-4939-2272-7_12 · DOI
  31. Jan, 2014, Principles of ER cotranslational translocation revealed by proximity-specific ribosome profiling, Science, 346, 1257521, 10.1126/science.1257521 · DOI
  32. Han, 2019, Directed evolution of split APEX2 peroxidase, ACS Chem. Biol., 14, 619, 10.1021/acschembio.8b00919 · DOI
  33. Martell, 2016, A split horseradish peroxidase for the detection of intercellular protein-protein interactions and sensitive visualization of synapses, Nat. Biotechnol., 34, 774, 10.1038/nbt.3563 · DOI
  34. De Munter, 2017, Split-BioID: a proximity biotinylation assay for dimerization-dependent protein interactions, FEBS Lett., 591, 415, 10.1002/1873-3468.12548 · DOI
  35. Schopp, 2017, Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes, Nat. Commun., 8, 15690, 10.1038/ncomms15690 · DOI
  36. Kwak, 2020, Contact-ID, a tool for profiling organelle contact sites, reveals regulatory proteins of mitochondrial-associated membrane formation, Proc. Natl. Acad. Sci. U. S. A., 117, 12109, 10.1073/pnas.1916584117 · DOI
  37. Cho, 2020, Split-TurboID enables contact-dependent proximity labeling in cells, Proc. Natl. Acad. Sci. U. S. A., 117, 12143, 10.1073/pnas.1919528117 · DOI
  38. Santos-Barriopedro, 2021, Off-the-shelf proximity biotinylation for interaction proteomics, Nat. Commun., 12, 5015, 10.1038/s41467-021-25338-4 · DOI
  39. Bar, 2018, Biotinylation by antibody recognition-a method for proximity labeling, Nat. Methods, 15, 127, 10.1038/nmeth.4533 · DOI
  40. Mair, 2019, Proximity labeling of protein complexes and cell-type-specific organellar proteomes in Arabidopsis enabled by TurboID, Elife, 8, 10.7554/eLife.47864 · DOI
  41. Zhang, 2019, TurboID-based proximity labeling reveals that UBR7 is a regulator of N NLR immune receptor-mediated immunity, Nat. Commun., 10, 3252, 10.1038/s41467-019-11202-z · DOI
  42. Xiong, 2021, In vivo proteomic mapping through GFP-directed proximity-dependent biotin labelling in zebrafish, Elife, 10, 10.7554/eLife.64631 · DOI
  43. Khan, 2018, In planta proximity dependent biotin identification (BioID), Sci. Rep., 8, 9212, 10.1038/s41598-018-27500-3 · DOI
  44. Rosenthal, 2021, A toolbox for efficient proximity-dependent biotinylation in zebrafish embryos, Mol. Cell. Proteomics, 20, 10.1016/j.mcpro.2021.100128 · DOI
  45. Groves, 2017, Fatty acid synthase inhibits the O-GlcNAcase during oxidative stress, J. Biol. Chem., 292, 6493, 10.1074/jbc.M116.760785 · DOI
  46. Cutler, 2020, Integrative phosphoproteome and interactome analysis of the role of Ubash3b in BCR-ABL signaling, Leukemia, 34, 301, 10.1038/s41375-019-0535-4 · DOI
  47. Vandemoortele, 2019, A well-controlled BioID design for endogenous bait proteins, J. Proteome Res., 18, 95
  48. Lambert, 2015, Proximity biotinylation and affinity purification are complementary approaches for the interactome mapping of chromatin-associated protein complexes, J. Proteome, 118, 81, 10.1016/j.jprot.2014.09.011 · DOI
  49. Roux, 2018, BioID: a screen for protein-protein interactions, Curr. Protoc. Protein Sci., 91, 19.23.1, 10.1002/cpps.51 · DOI
  50. Motani, 2020, BioID screening of biotinylation sites using the avidin-like protein Tamavidin 2-REV identifies global interactors of stimulator of interferon genes (STING), J. Biol. Chem., 295, 11174, 10.1074/jbc.RA120.014323 · DOI
  51. Frankenfield, 2020, Development and comparative evaluation of endolysosomal proximity labeling-based proteomic methods in human iPSC-derived neurons, Anal. Chem., 92, 15437, 10.1021/acs.analchem.0c03107 · DOI
  52. Liu, 2020, Combined proximity labeling and affinity purification-mass spectrometry workflow for mapping and visualizing protein interaction networks, Nat. Protoc., 15, 3182, 10.1038/s41596-020-0365-x · DOI
  53. Cheah, 2017, A simple elution strategy for biotinylated proteins bound to streptavidin conjugated beads using excess biotin and heat, Biochem. Biophys. Res. Commun., 493, 1522, 10.1016/j.bbrc.2017.09.168 · DOI
  54. Kim, 2018, BioSITe: a method for direct detection and quantitation of site-specific Biotinylation, J. Proteome Res., 17, 759, 10.1021/acs.jproteome.7b00775 · DOI
  55. Udeshi, 2017, Antibodies to biotin enable large-scale detection of biotinylation sites on proteins, Nat. Methods, 14, 1167, 10.1038/nmeth.4465 · DOI
  56. Rafiee, 2020, Protease-resistant streptavidin for interaction proteomics, Mol. Syst. Biol., 16, 10.15252/msb.20199370 · DOI
  57. Yates, 2019, Recent technical advances in proteomics, F1000Res., 8
  58. Dodds, 2019, Ion mobility spectrometry: fundamental concepts, instrumentation, applications, and the road ahead, J. Am. Soc. Mass Spectrom., 30, 2185, 10.1007/s13361-019-02288-2 · DOI · Paper page
  59. Armean, 2013, Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments, Mol. Cell. Proteomics, 12, 1, 10.1074/mcp.R112.019554 · DOI
  60. Sears, 2019, BioID as a tool for protein-proximity labeling in living cells, Methods Mol. Biol., 2012, 299, 10.1007/978-1-4939-9546-2_15 · DOI
  61. Habel, 2021, Biotin proximity labeling for protein-protein interaction discovery: the BioID method, Methods Mol. Biol., 2261, 357, 10.1007/978-1-0716-1186-9_22 · DOI
  62. Lee, 2008, Direct identification of biotinylated proteins from proximity labeling (spot-BioID), Methods Mol. Biol., 2019, 97
  63. Cho, 2020, Proximity labeling in mammalian cells with TurboID and split-TurboID, Nat. Protoc., 15, 3971, 10.1038/s41596-020-0399-0 · DOI · Paper page
  64. Hesketh, 2017, Parallel exploration of interaction space by BioID and affinity purification coupled to mass spectrometry, Methods Mol. Biol., 1550, 115, 10.1007/978-1-4939-6747-6_10 · DOI
  65. Ramirez, 2021, Context-specific and proximity-dependent labeling for the proteomic analysis of spatiotemporally defined protein complexes with Split-BioID, Methods Mol. Biol., 2247, 303, 10.1007/978-1-0716-1126-5_17 · DOI
  66. Tan, 2020, An optimized protocol for proximity biotinylation in confluent epithelial cell cultures using the peroxidase APEX2, STAR Protoc., 1, 10.1016/j.xpro.2020.100074 · DOI
  67. Bosch, 2021, Proximity-dependent labeling methods for proteomic profiling in living cells: an update, Wiley Interdiscip. Rev. Dev. Biol., 10, 10.1002/wdev.392 · DOI
  68. Kushner, 2021, Use of proximity labeling in cardiovascular research, JACC Basic Transl. Sci., 6, 598, 10.1016/j.jacbts.2021.01.005 · DOI
  69. Zhou, 2021, The evolving capabilities of enzyme-mediated proximity labeling, Curr. Opin. Chem. Biol., 60, 30, 10.1016/j.cbpa.2020.06.013 · DOI
  70. Samavarchi-Tehrani, 2020, Proximity dependent Biotinylation: key enzymes and adaptation to proteomics approaches, Mol. Cell. Proteomics, 19, 757, 10.1074/mcp.R120.001941 · DOI
  71. Paek, 2017, Multidimensional tracking of GPCR signaling via peroxidase-catalyzed proximity labeling, Cell, 169, 338, 10.1016/j.cell.2017.03.028 · DOI
  72. Lobingier, 2017, An approach to spatiotemporally resolve protein interaction networks in living cells, Cell, 169, 350, 10.1016/j.cell.2017.03.022 · DOI
  73. Bareja, 2018, The proximity-labeling technique BioID identifies sorting nexin 6 as a member of the insulin-like growth factor 1 (IGF1)-IGF1 receptor pathway, J. Biol. Chem., 293, 6449, 10.1074/jbc.RA118.002406 · DOI
  74. Abrams, 2020, Fibroblast growth factor homologous factors tune arrhythmogenic late NaV1.5 current in calmodulin binding-deficient channels, JCI Insight, 5, 10.1172/jci.insight.141736 · DOI
  75. Park, 2020, Kir2.1 Interactome mapping uncovers PKP4 as a modulator of the Kir2.1-regulated inward rectifier potassium currents, Mol. Cell. Proteomics, 19, 1436, 10.1074/mcp.RA120.002071 · DOI
  76. Piniella, 2018, Identification of novel regulatory partners of the glutamate transporter GLT-1, Glia, 66, 2737, 10.1002/glia.23524 · DOI
  77. Bartolomé-Martín, 2019, Identification of potassium channel proteins Kv7.2/7.3 as common partners of the dopamine and glutamate transporters DAT and GLT-1, Neuropharmacology, 161, 10.1016/j.neuropharm.2019.03.011 · DOI
  78. Piniella, 2021, Identification by proximity labeling of novel lipidic and proteinaceous potential partners of the dopamine transporter, Cell. Mol. Life Sci., 10.1007/s00018-021-03998-1 · DOI
  79. Swayampakula, 2017, The interactome of metabolic enzyme carbonic anhydrase IX reveals novel roles in tumor cell migration and invadopodia/MMP14-mediated invasion, Oncogene, 36, 6244, 10.1038/onc.2017.219 · DOI · Paper page
  80. Iizuka, 2021, Serine-threonine kinase TAO3-mediated trafficking of endosomes containing the invadopodia scaffold TKS5α promotes cancer invasion and tumor growth, Cancer Res., 81, 1472, 10.1158/0008-5472.CAN-20-2383 · DOI
  81. Simons, 1997, Functional rafts in cell membranes, Nature, 387, 569, 10.1038/42408 · DOI · Paper page
  82. Shah, 2015, RaftProt: mammalian lipid raft proteome database, Nucleic Acids Res., 43, D335, 10.1093/nar/gku1131 · DOI
  83. Zheng, 2009, Contributions of quantitative proteomics to understanding membrane microdomains, J. Lipid Res., 50, 1976, 10.1194/jlr.R900018-JLR200 · DOI
  84. Miyagawa-Yamaguchi, 2014, Expressed glycosylphosphatidylinositol-anchored horseradish peroxidase identifies co-clustering molecules in individual lipid raft domains, PLoS One, 9, 10.1371/journal.pone.0093054 · DOI
  85. Awoniyi, 2020, Novel players and large-scale protein dynamics of BCR activation revealed by APEX2 proximity labelling of lipid rafts, Immunology
  86. Ludwig, 2013, Molecular composition and ultrastructure of the caveolar coat complex, PLoS Biol., 11, 10.1371/journal.pbio.1001640 · DOI
  87. Mendoza-Topaz, 2018, BioID identifies proteins involved in the cell biology of caveolae, PLoS One, 13, 10.1371/journal.pone.0209856 · DOI
  88. Xu, 2021, Proteomic insights into synaptic signaling in the brain: the past, present and future, Mol. Brain, 14, 37, 10.1186/s13041-021-00750-5 · DOI
  89. van Gelder, 2021, Neuroproteomics of the synapse: subcellular quantification of protein networks and signaling dynamics, Mol. Cell. Proteomics, 20, 10.1016/j.mcpro.2021.100087 · DOI
  90. Uezu, 2016, Identification of an elaborate complex mediating postsynaptic inhibition, Science, 353, 1123, 10.1126/science.aag0821 · DOI · Paper page
  91. Spence, 2019, In vivo proximity proteomics of nascent synapses reveals a novel regulator of cytoskeleton-mediated synaptic maturation, Nat. Commun., 10, 386, 10.1038/s41467-019-08288-w · DOI
  92. Hamdan, 2020, Mapping axon initial segment structure and function by multiplexed proximity biotinylation, Nat. Commun., 11, 100, 10.1038/s41467-019-13658-5 · DOI
  93. Shao, 2018, New technologies for analysis of extracellular vesicles, Chem. Rev., 118, 1917, 10.1021/acs.chemrev.7b00534 · DOI
  94. Burton, 2021, Enriching extracellular vesicles for mass spectrometry, Mass Spectrom. Rev.
  95. Cheerathodi, 2021, Epstein-Barr virus LMP1 modulates the CD63 interactome, Viruses, 13, 10.3390/v13040675 · DOI
  96. Siva Sankar, 2021, Protein complexes and neighborhoods driving autophagy, Autophagy, 17, 2689, 10.1080/15548627.2020.1847461 · DOI
  97. Le Guerroué, 2017, Autophagosomal content profiling reveals an LC3C-dependent piecemeal mitophagy pathway, Mol. Cell, 68, 786, 10.1016/j.molcel.2017.10.029 · DOI
  98. Lamb, 2016, TBC1D14 regulates autophagy via the TRAPP complex and ATG9 traffic, EMBO J., 35, 281, 10.15252/embj.201592695 · DOI
  99. Heo, 2019, Integrated proteogenetic analysis reveals the landscape of a mitochondrial-autophagosome synapse during PARK2-dependent mitophagy, Sci. Adv., 5, eaay4624, 10.1126/sciadv.aay4624 · DOI
  100. Jia, 2018, Galectins control mTOR in response to endomembrane damage, Mol. Cell, 70, 120, 10.1016/j.molcel.2018.03.009 · DOI
  101. Jia, 2020, AMPK, a regulator of metabolism and autophagy, is activated by lysosomal damage via a novel galectin-directed ubiquitin signal transduction system, Mol. Cell, 77, 951, 10.1016/j.molcel.2019.12.028 · DOI
  102. Jia, 2020, Galectin-3 coordinates a cellular system for lysosomal repair and removal, Dev. Cell, 52, 69, 10.1016/j.devcel.2019.10.025 · DOI · Paper page
  103. Davies, 2018, AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A, Nat. Commun., 9, 3958, 10.1038/s41467-018-06172-7 · DOI
  104. Martin, 2019, STK38 kinase acts as XPO1 gatekeeper regulating the nuclear export of autophagy proteins and other cargoes, EMBO Rep., 20, 10.15252/embr.201948150 · DOI
  105. Leidal, 2020, The LC3-conjugation machinery specifies the loading of RNA-binding proteins into extracellular vesicles, Nat. Cell Biol., 22, 187, 10.1038/s41556-019-0450-y · DOI
  106. Tu, 2021, Global proximity Interactome of the human macroautophagy pathway, Autophagy, 1
  107. Huang, 2020, Current and emerging approaches for studying inter-organelle membrane contact sites., front cell, Dev. Biol., 8, 195
  108. Jing, 2015, Proteomic mapping of ER-PM junctions identifies STIMATE as a regulator of Ca2+ influx, Nat. Cell Biol., 17, 1339, 10.1038/ncb3234 · DOI
  109. Hua, 2017, VAPs and ACBD5 tether peroxisomes to the ER for peroxisome maintenance and lipid homeostasis, J. Cell Biol., 216, 367, 10.1083/jcb.201608128 · DOI · Paper page
  110. Cabukusta, 2020, Human VAPome analysis reveals MOSPD1 and MOSPD3 as membrane contact site proteins interacting with FFAT-related FFNT motifs, Cell Rep., 33, 10.1016/j.celrep.2020.108475 · DOI
  111. Feng, 2020, Identifying the cardiac dyad proteome in vivo by a BioID2 Knock-in strategy, Circulation, 141, 940, 10.1161/CIRCULATIONAHA.119.043434 · DOI
  112. Guo, 2014, E-cadherin interactome complexity and robustness resolved by quantitative proteomics, Sci. Signal., 7, rs7, 10.1126/scisignal.2005473 · DOI
  113. Van Itallie, 2014, Biotin ligase tagging identifies proteins proximal to E-cadherin, including lipoma preferred partner, a regulator of epithelial cell-cell and cell-substrate adhesion, J. Cell Sci., 127, 885
  114. Van Itallie, 2013, The N and C termini of ZO-1 are surrounded by distinct proteins and functional protein networks, J. Biol. Chem., 288, 13775, 10.1074/jbc.M113.466193 · DOI
  115. Fredriksson, 2015, Proteomic analysis of proteins surrounding occludin and claudin-4 reveals their proximity to signaling and trafficking networks, PLoS One, 10, 10.1371/journal.pone.0117074 · DOI
  116. Van Itallie, 2018, MARCKS-related protein regulates cytoskeletal organization at cell-cell and cell-substrate contacts in epithelial cells, J. Cell Sci., 131, 10.1242/jcs.210237 · DOI
  117. Emond, 2021, Proximity-dependent proteomics reveals extensive interactions of Protocadherin-19 with regulators of rho GTPases and the microtubule cytoskeleton, Neuroscience, 452, 26, 10.1016/j.neuroscience.2020.09.033 · DOI
  118. Shafraz, 2020, Mapping transmembrane binding partners for E-cadherin ectodomains, Proc. Natl. Acad. Sci. U. S. A., 117, 31157, 10.1073/pnas.2010209117 · DOI
  119. Takai, 2008, Nectins and nectin-like molecules: roles in contact inhibition of cell movement and proliferation, Nat. Rev. Mol. Cell Biol., 9, 603, 10.1038/nrm2457 · DOI
  120. Baskaran, 2021, Proximity proteomics identifies PAK4 as a component of Afadin-Nectin junctions, Nat. Commun., 12, 5315, 10.1038/s41467-021-25011-w · DOI
  121. Palmer, 2003, Multiple roles of ephrins in morphogenesis, neuronal networking, and brain function, Genes Dev., 17, 1429, 10.1101/gad.1093703 · DOI
  122. Lahaie, 2019, The endosomal sorting adaptor HD-PTP is required for ephrin-B:EphB signalling in cellular collapse and spinal motor axon guidance, Sci. Rep., 9, 11945, 10.1038/s41598-019-48421-9 · DOI
  123. Perez White, 2017, EphA2 proteomics in human keratinocytes reveals a novel association with afadin and epidermal tight junctions, J. Cell Sci., 130, 111
  124. Hynes, 2002, Integrins: bidirectional, allosteric signaling machines, Cell, 110, 673, 10.1016/S0092-8674(02)00971-6 · DOI
  125. Zaidel-Bar, 2007, Functional atlas of the integrin adhesome, Nat. Cell Biol., 9, 858, 10.1038/ncb0807-858 · DOI
  126. Dong, 2016, Proximity biotinylation provides insight into the molecular composition of focal adhesions at the nanometer scale, Sci. Signal., 9, rs4, 10.1126/scisignal.aaf3572 · DOI
  127. Te Molder, 2020, Comparative interactomics analysis reveals potential regulators of α6β4 distribution in keratinocytes, Biol. Open, 9
  128. Sahgal, 2019, GGA2 and RAB13 promote activity-dependent β1-integrin recycling, J. Cell Sci., 132, 10.1242/jcs.233387 · DOI
  129. Rahikainen, 2019, Talin-mediated force transmission and Talin rod domain unfolding independently regulate adhesion signaling, J. Cell Sci., 132, 10.1242/jcs.226514 · DOI
  130. Myllymäki, 2021, Proximity-dependent biotinylation (BioID) of integrin interaction partners, Methods Mol. Biol., 2217, 57, 10.1007/978-1-0716-0962-0_6 · DOI
  131. Chastney, 2020, Topological features of integrin adhesion complexes revealed by multiplexed proximity biotinylation, J. Cell Biol., 219, 10.1083/jcb.202003038 · DOI
  132. Go, 2021, A proximity-dependent biotinylation map of a human cell, Nature, 595, 120, 10.1038/s41586-021-03592-2 · DOI
  133. Dumrongprechachan, 2021, Cell-type and subcellular compartment-specific APEX2 proximity labeling reveals activity-dependent nuclear proteome dynamics in the striatum, Nat. Commun., 12, 4855, 10.1038/s41467-021-25144-y · DOI
  134. Hobson, 2022, Subcellular proteomics of dopamine neurons in the mouse brain, Elife, 11, 10.7554/eLife.70921 · DOI
  135. Rayaprolu, 2021, Cell type-specific biotin labeling in vivo resolves regional neuronal proteomic differences in mouse brain, Neuroscience

Cited by

Metadata

Authors
Zafra, F., & Piniella, D.
Venue
Journal of Proteomics
Publisher
Elsevier BV
Published
2022-07
Volume
264
Pages
104620
ISSN
1874-3919
DOI
10.1016/j.jprot.2022.104620
Cited by
22
References
8
Language
en
Licence
Funders
  • España Ministerio de Ciencia e Innovación (RTI2018-098712-B-100)
  • Ministerio de Ciencia e Innovación
  • Fundación Ramón Areces

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