Aug 28, 2024
Single particle analysis of α-Synuclein fibrils
- Kevin Sicking1,2,
- Rubén ernández-Busnadiego1,2,3,4
- 1University Medical Center Göttingen, Institute for Neuropathology, Göttingen, 37077 Germany;
- 2Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA;
- 3Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen, 37077, Germany;
- 4Faculty of Physics, University of Göttingen, Göttingen, 37077, Germany
External link: https://doi.org/10.1186/s13024-025-00889-y
Protocol Citation: Kevin Sicking, Rubén ernández-Busnadiego 2024. Single particle analysis of α-Synuclein fibrils. protocols.io https://dx.doi.org/10.17504/protocols.io.81wgbxozylpk/v1
Manuscript citation:
Brücke C, Al-Azzani M, Ramalingam N, Ramón M, Sousa RL, Buratti F, Zech M, Sicking K, Amaral L, Gelpi E, Chandran A, Agarwal A, Chaves SR, Fernández CO, Dettmer U, Lautenschläger J, Zweckstetter M, Busnadiego RF, Zimprich A, Outeiro TF (2025) A novel alpha-synuclein G14R missense variant is associated with atypical neuropathological features. Molecular Neurodegeneration 20(). doi: 10.1186/s13024-025-00889-y
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: December 29, 2023
Last Modified: August 28, 2024
Protocol Integer ID: 92909
Keywords: ASAPCRN, synuclein fibril, amyloid fibril, synuclein, syn fibril, structural information of these fibril, single particle analysis, specialized single particle analysis, fibril, dementia with lewy body, parkinson, including parkinson, syn
Funders Acknowledgements:
MJFF/ASAP
Grant ID: ASAP-000282
DFG
Grant ID: EXC 2067/1-390729940
Disclaimer
It is assumed the reader is familiar with the operation of the equipment mentioned herein. To avoid hazards, it is highly recommended to seek advice and training from experienced staff if this is not the case.
Abstract
Aggregates of α-Synuclein (α-Syn) have been implicated in the pathogenesis of a spectrum of disorders, including Parkinson’s disease, multiple system atrophy, and dementia with Lewy bodies. These aggregates comprise misfolded α-Syn in the form of amyloid fibrils. Structural information of these fibrils is valuable, as it provides insights into potentially harmful interactions facilitated by amyloid fibrils in their native environment. This comprehensive protocol describes the entire procedure for generating structural information of in vitro α-Syn fibrils through a specialized single particle analysis (SPA) approach (see Figure 1).
Attachments
937-2418.pdf
601KB
Guidelines
Plunging of α-Syn fibrils
The aim is to carefully place α-Syn filaments on cryo-EM grids and quickly freeze them to create a vitrified state in the fibril solution. This prevents the formation of damaging ice crystals that could harm the soluble proteins. It is crucial to keep the plunge-frozen sample close to -192 °C (liquid nitrogen temperature) to prevent any thawing. This ensures the α-Syn filaments remain structurally intact for accurate cryo-EM analysis.
Cryo-TEM data collection
The goal is to capture images, in the form of electron event representation (EER) frames, of α-Syn fibrils suspended in vitreous ice on cryo-EM grids. It is important to gather images on holes showing abundant α-Syn fibrils, which at the same time do not overlap significantly (though precise particle picking allows for effective data processing even with some overlapping filaments).
Preprocessing
During this step, EER frames undergo motion correction and conversion into MRC files. Subsequent processing steps will exclusively take place in RELION (Scheres, 2012), leveraging its specialized functions tailored for helical structures, even accommodating unique cases like amyloid helices. Thus, preprocessing also includes importing the MRC files into RELION. Additionally, preprocessing involves the correction of the Contrast Transfer Function (CTF) of images, enhancing the signal-to-noise ratio of images.
Particle picking in crYOLO
During this stage, the crYOLO network, a deep-learning neural network specializing in particle picking (Wagner et al., 2019), is trained and subsequently employed to predict filament positions on all micrographs. These predictions are then imported into RELION for particle extraction. This integrated approach ensures efficient and accurate identification of filament positions.
Note
Depending on the size of the dataset, a computer with at least one to two GPUs should be used. Then training the network and predicting filaments should be done in approximately 12 hours.
2D classifications to get a good particle set
From here on, data processing generally follows Lövestam & Scheres, 2022. The goal is to remove trash particles such as carbon edge picks, particles originating from overlapping filaments, and generally low-quality particles in a binned 2D classification. Since particles are extracted with a larger box size for this binned classification, the resulting class averages can be used to estimate the crossover distance and hence the helical properties of the amyloid helices (Scheres, 2020). Then, following the selection of reasonable classes, an unbinned 2D classification is carried out to identify particles contributing to beta-sheet separated class averages. Those high-quality particles will be used for 3D classifications later down the line.
Note
Again, depending on the size of the dataset, usage of a high-performance computing cluster is advised. Generally, at least four, for large datasets up to 16 GPUs should be used, especially for the unbinned classifications.
3D classification
The goal here is to generate a first 3D map that can be further refined during auto-refinement and various postprocessing steps. This is arguably the most difficult step in the entire workflow. Given the absence of distinct features in amyloid helices, the 3D alignment algorithm has difficulties converging on correct structures. For a more detailed exploration of this issue, refer to Scheres (2020). Consequently, initiating the 3D classification with correct helical parameters for twist and rise is crucial. The rise is fixed at 4.75 Å, but the twist may vary from polymorph to polymorph.
Note
The usage of a high-performance computing cluster is advised. Especially, if helical parameters are unknown and 3D classifications are used in a brute-force manner to narrow them down, then each classification run should have at least four, if possible 16 GPUs. Otherwise, a single iteration can easily take up to 12 hours.
3D Refinement and postprocessing
In this phase, the initial 3D map is refined using RELION's autorefinement process. However, the efficacy of auto refinement hinges on the high quality of the initial map, so only proceed if 3D classification gives reasonable results. Postprocessing plays a pivotal role in enhancing the map by correcting the B-factor. An essential aspect involves the creation and application of a mask to exclude the solvent area. It is worth noting that this is an iterative process where multiple refinement and postprocessing runs can be sequentially linked to further elevate the quality of the density map. Particularly in later iterations, refining helical parameters using the helical search option becomes valuable, and exploring different symmetry options is prudent in cases involving multiple protofilaments.
Note
3D refinements (and especially postprocessing tasks) usually do not need many computational resources, especially, if run with a small but high-quality particle dataset. Four GPUs should be plenty.
Enhancing the quality of the map / Postprocessing
Finally, in this postprocessing step, the quality of the particle set is bolstered through a series of measures. This includes refining the Contrast Transfer Function (CTF) parameters of particles, improving the motion correction of each particle, and selectively filtering out low-quality particles through 3D classifications, utilizing the refined maps as the initial model. These refinements collectively contribute to the overall fidelity and precision of the particle dataset, ensuring a more accurate representation in the final density map.
Materials
Plunging of α-Syn fibrils
- Tris-HCl buffer
| A | B | |
| Tris-HCl | 30 mM | |
| Buffered to pH 7.5 | ||
- Waterbath sonicator: Bioruptor UCD-200 Sonication System (Diagenode)
- Cryo-EM grids: R2/1, Cu 200 mesh grid (Quantifoil microtools)
- Plasma cleaner: PDC-3XG (Harrick)
- Vitrobot: Vitrobot Mark IV System (Thermo Fisher Scientific)
- Filter paper: Whatman 597 (Whatman)
Protocol references
References
Lövestam S, Scheres SHW. High-throughput cryo-EM structure determination of amyloids. Faraday Discuss. 2022;240(0):243-260. Published 2022 Nov 8. doi:10.1039/d2fd00034b
MotionCor2 User Manual, hpc.nih.gov, https://hpc.nih.gov/apps/RELION/MotionCor2-
UserManual-05-03-2018.pdf
Polinski NK, Volpicelli-Daley LA, Sortwell CE, et al. Best Practices for Generating and Using Alpha-Synuclein Pre-Formed Fibrils to Model Parkinson's Disease in Rodents. J Parkinsons
Dis. 2018;8(2):303-322. doi:10.3233/JPD-171248
Protocol for Generation of Pre-Formed Fibrils From Alpha-Synuclein Monomer, www.protocols.io, https://www.protocols.io/view/protocol-for-generation-of-pre-formedfibrils-from-rm7vz3ezxgx1/v1/protocols, doi: 10.17504/protocols.io.bws6pehe
Scheres SH. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J Struct Biol. 2012;180(3):519-530. doi:10.1016/j.jsb.2012.09.006
Scheres SHW. Amyloid structure determination in RELION-3.1. Acta Crystallogr D Struct Biol. 2020;76(Pt 2):94-101. doi:10.1107/S2059798319016577
Tutorial 3: Pick filaments using a model trained for your data, cryolo.readthedocs.io,
Wagner T, Merino F, Stabrin M, et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun Biol. 2019;2:218. Published 2019 Jun 19.
doi:10.1038/s42003-019-0437-z
Zheng SQ, Palovcak E, Armache JP, Verba KA, Cheng Y, Agard DA. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat Methods.
2017;14(4):331-332. doi:10.1038/nmeth.4193