Nanoscale Spatial Organization of ARC High‐ and Low‐Order Assemblies at Excitatory Synapses
Source abstract
Abstract
ABSTRACT The Activity‐Regulated Cytoskeleton‐Associated protein (ARC) plays a pivotal role in mediating synaptic plasticity in neuronal cells. In vitro studies suggest that ARC can form both high‐ and low‐order oligomers. Despite their potentially important functions, direct nanoscale observation of ARC assemblies in cells has been lacking due to its tightly regulated spatiotemporal expression, the small size of the structures, the absence of suitable labelling strategies, and background signals from freely diffusing cytosolic proteins. Here, we combine super‐resolution microscopy and time‐resolved fluorescence anisotropy measurements with precisely designed ARC‐tagging strategies to reveal the nanoscale spatial organization of ARC in neuronal environments, with a particular focus on excitatory synapses. We identify low‐order assemblies of ARC at synapses, that colocalize with surface AMPA receptors (AMPARs), semi‐circular organizations of ARC at the endocytic zone, consistent with a role in mediating AMPAR endocytosis, and particle‐like assemblies sized 60–80 nm predominantly localized in dendritic spines. Finally, using a combination of experiments and simulations, we show that ARC can directly induce membrane bending and lipid bilayer tubulation in the absence of other protein partners.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cellular transport and secretion · Lipid Membrane Structure and Behavior · Neuroscience and Neuropharmacology Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Applied Physics and Science for Life Laboratory KTH Royal Institute of Technology Stockholm Sweden
Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia
Max Planck Institute of Biochemistry Planegg Germany
Faculty of Physics and Center for NanoScience Ludwig Maximilian University München Germany
Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
Funding
Human Frontier Science Program · European Research Council
References (73)
Show source-provided references
1. 10.3389/fnmol.2018.0014510.3389/fnmol.2018.00145 ↗
2. 10.1016/j.pneurobio.2005.06.00310.1016/j.pneurobio.2005.06.003 ↗
3. 10.1038/nature0476910.1038/nature04769 ↗
4. 10.1523/JNEUROSCI.3864-08.200810.1523/JNEUROSCI.3864-08.2008 ↗
5. 10.1002/cne.2307310.1002/cne.23073 ↗
6. 10.1016/S0896-6273(00)80591-710.1016/S0896-6273(00)80591-7 ↗
7. 10.1016/j.neuron.2006.08.03310.1016/j.neuron.2006.08.033 ↗
8. 10.1523/ENEURO.0144-15.201610.1523/ENEURO.0144-15.2016 ↗
9. 10.1038/s41598-018-32821-410.1038/s41598-018-32821-4 ↗
10. 10.3389/fncel.2018.0036210.3389/fncel.2018.00362 ↗
11. 10.1016/j.semcdb.2017.09.00510.1016/j.semcdb.2017.09.005 ↗
12. 10.1038/nn.342910.1038/nn.3429 ↗
13. 10.3389/fnsyn.2017.0000810.3389/fnsyn.2017.00008 ↗
14. 10.1523/JNEUROSCI.2883-07.200710.1523/JNEUROSCI.2883-07.2007 ↗
15. 10.1523/JNEUROSCI.0008-20.202110.1523/JNEUROSCI.0008-20.2021 ↗
16. 10.5281/zenodo.376557010.5281/zenodo.3765570 ↗
17. 10.1016/j.bbagen.2015.03.00210.1016/j.bbagen.2015.03.002 ↗
18. 10.1042/BJ2014144610.1042/BJ20141446 ↗
19. 10.1016/j.cell.2017.12.02410.1016/j.cell.2017.12.024 ↗
20. 10.1016/j.molcel.2019.05.00410.1016/j.molcel.2019.05.004 ↗
21. 10.1016/j.jmb.2009.01.05810.1016/j.jmb.2009.01.058 ↗
22. 10.1371/journal.pone.005642410.1371/journal.pone.0056424 ↗
23. 10.1016/j.str.2019.04.00110.1016/j.str.2019.04.001 ↗
24. 10.1111/jnc.1455610.1111/jnc.14556 ↗
25. 10.1016/j.cell.2017.12.02210.1016/j.cell.2017.12.022 ↗
26. 10.1038/s41587-022-01489-710.1038/s41587-022-01489-7 ↗
27. 10.1021/acschemneuro.1c0074410.1021/acschemneuro.1c00744 ↗
28. 10.3389/fnmol.2023.114236110.3389/fnmol.2023.1142361 ↗
29. 10.1016/j.cell.2012.02.06210.1016/j.cell.2012.02.062 ↗
30. 10.1016/j.neuron.2006.08.03410.1016/j.neuron.2006.08.034 ↗
31. 10.1039/D0NR00227E10.1039/D0NR00227E ↗
32. 10.1021/acs.jpcb.3c0165810.1021/acs.jpcb.3c01658 ↗
33. 10.1073/pnas.040192310110.1073/pnas.0401923101 ↗
34. 10.1038/s41467-019-12301-710.1038/s41467-019-12301-7 ↗
35. 10.1038/s41592-024-02242-510.1038/s41592-024-02242-5 ↗
36. 10.1016/0896-6273(95)90299-610.1016/0896-6273(95)90299-6 ↗
37. 10.1007/s00418-014-1192-310.1007/s00418-014-1192-3 ↗
38. 10.1016/j.celrep.2017.09.04510.1016/j.celrep.2017.09.045 ↗
39. 10.7554/eLife.7438710.7554/eLife.74387 ↗
40. 10.1038/s41422-022-00697-910.1038/s41422-022-00697-9 ↗
41. 10.1016/j.neuron.2015.03.03010.1016/j.neuron.2015.03.030 ↗
42. 10.1523/JNEUROSCI.2381-12.201310.1523/JNEUROSCI.2381-12.2013 ↗
43. 10.1016/j.neuron.2013.03.00910.1016/j.neuron.2013.03.009 ↗
44. 10.1083/jcb.20130207110.1083/jcb.201302071 ↗
45. 10.1088/1361-6463/ab4c1310.1088/1361-6463/ab4c13 ↗
46. 10.3389/fmolb.2021.63062510.3389/fmolb.2021.630625 ↗
47. 10.1016/j.semcdb.2017.09.02910.1016/j.semcdb.2017.09.029 ↗
48. 10.1021/acs.biochem.7b0095910.1021/acs.biochem.7b00959 ↗
49. 10.1073/pnas.191467711710.1073/pnas.1914677117 ↗
50. R.Evans et al. “Protein Complex Prediction with AlphaFold‐Multimer ”BioRxiv(2021) Preprint at https://doi.org/10.1101/2021.10.04.463034.10.1101/2021.10.04.463034 ↗
51. 10.1073/pnas.1016184107/‐/DCSupplemental10.1073/pnas.1016184107/‐/DCSupplemental ↗
52. 10.1038/nmeth.380410.1038/nmeth.3804 ↗
53. 10.1038/s41586-023-05925-910.1038/s41586-023-05925-9 ↗
54. 10.1111/apha.1388610.1111/apha.13886 ↗
55. 10.1073/pnas.190033811610.1073/pnas.1900338116 ↗
56. 10.1126/science.115352910.1126/science.1153529 ↗
57. 10.14440/jbm.2014.3610.14440/jbm.2014.36 ↗
58. 10.1038/nnano.2016.9510.1038/nnano.2016.95 ↗
59. 10.1038/s41467-019-09616-w10.1038/s41467-019-09616-w ↗
60. 10.1016/j.softx.2015.06.00110.1016/j.softx.2015.06.001 ↗
61. 10.1016/j.str.2018.10.02410.1016/j.str.2018.10.024 ↗
62. 10.1002/jcc.2094510.1002/jcc.20945 ↗
63. 10.1021/acs.jpcb.7b1017510.1021/acs.jpcb.7b10175 ↗
64. 10.1016/j.bpj.2017.10.01710.1016/j.bpj.2017.10.017 ↗
65. 10.1021/acs.jctc.5b0020910.1021/acs.jctc.5b00209 ↗
66. 10.1021/ct300646g10.1021/ct300646g ↗
67. 10.1016/0263-7855(96)00018-510.1016/0263-7855(96)00018-5 ↗
68. 10.1002/jcc.2178710.1002/jcc.21787 ↗
69. 10.1016/j.bpr.2022.10005510.1016/j.bpr.2022.100055 ↗
70. 10.1038/nprot.2017.02410.1038/nprot.2017.024 ↗
71. M.Ester H.‐P.Kriegel J.Sander andX.Xu “A Density‐Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise ” inProceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD)(1996).
72. 10.1371/journal.pone.028961910.1371/journal.pone.0289619 ↗
73. 10.1038/s41467-018-05799-w10.1038/s41467-018-05799-w ↗