TITLE: Single particle analysis of α-Synuclein fibrils
AUTHORS: Kevin Sicking, Rubén Fernández-Busnadiego

[DESCRIPTION]
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). 






[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.

 


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.


 

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.


 

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.


 

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.

[STEPS]
SECTION: Preparation of α-Syn Pre-Formed Fibrils from α-Syn monomers
1. To produce pre-formed fibrils for structural analysis, adhere to the standardized protocol outlined here: [Protocol for Generation of Pre-formed Fibrils](https://www.protocols.io/view/protocol-forgeneration-of-pre-formed-fibrils-from-rm7vz3ezxgx1/v1).
SECTION: Plunging of α-Syn fibrils
2. Dilute α-Syn fibril solution to 0.5 1629 using the Tris-HCl buffer.

SECTION: Plunging of α-Syn fibrils
3. To separate large clumps of α-Syn aggregates, sonicate the fibril solution for 10 min on medium settings, 0.5 s ON/OFF in a water bath sonicator.

SECTION: Plunging of α-Syn fibrils
4. Glow discharge grids for 40 s in a plasma cleaner. The carbon side should point upwards.

SECTION: Plunging of α-Syn fibrils
5. Prepare the Vitrobot for plunging.
SECTION: Plunging of α-Syn fibrils
5.1. Fill up the water tank, cool down the plunging stage, fill up the plunging cup with liquid 2:1 ethane/propane, insert blotting paper.


 

SECTION: Plunging of α-Syn fibrils
5.2. Set the plunging settings: Blot-force 7, Blot-time 5, 10°C, 100% Humidity.
SECTION: Plunging of α-Syn fibrils
6. Apply 3 µL fibril solution on the carbon side of the grids, then immediately plunge.

 

SECTION: Plunging of α-Syn fibrils
7. Store the grids in grid boxes in liquid nitrogen until clipping. From now on, the grids should always be immersed in liquid nitrogen!
SECTION: Cryo-TEM data collection
8. 
 

Clip the grids to prepare loading the Titan Krios TEM.

 

SECTION: Cryo-TEM data collection
9. Using a loading station, load the clipped grids into the autoloader magazine.
SECTION: Cryo-TEM data collection
9.1. Double-check the proper placement of the grids
SECTION: Cryo-TEM data collection
9.2. Insert the magazine into the nanocab.
SECTION: Cryo-TEM data collection
8.1. Confirm that the carbon side is oriented upward in the clipped grid.
SECTION: Cryo-TEM data collection
10. Insert the nanocab into the autoloader of the Krios and load the magazine.
SECTION: Cryo-TEM data collection
11. Examine the grids to identify squares displaying holes filled with α-Syn filaments.
SECTION: Cryo-TEM data collection
11.1. Squares with thicker ice usually contain more α-Syn filaments that were more evenly distributed over the entire hole.
SECTION: Cryo-TEM data collection
11.2. If the ice is too thin, then it may happen that α-Syn filaments bundle together at the edge of a hole, leaving the middle region free.
SECTION: Cryo-TEM data collection
11.3. Avoid grids with large, crowded aggregates (visible as shadowy spots even in higher magnifications).

 

SECTION: Cryo-TEM data collection
12. Set up the automatic data collection using EPU.
SECTION: Cryo-TEM data collection
12.1. Exclude holes with ice contaminations, dense aggregates, fissures, or those that are too close to the edge of the square.
SECTION: Cryo-TEM data collection
12.2. Pixel size of 0.92 to 0.72 Å/px (130,000x to 165,000x magnification).
SECTION: Cryo-TEM data collection
12.3. Electron dose of 40 e-/Å2
SECTION: Cryo-TEM data collection
12.4. -2.4 to -1.2 m defocus range.
SECTION: Cryo-TEM data collection
12.5. 10 eV energy slit.
SECTION: Cryo-TEM data collection
13. Calibrate the microscope.
SECTION: Cryo-TEM data collection
13.1. Autoeucentric by beam-tilt.
SECTION: Cryo-TEM data collection
13.6. Using Sherpa and using an unobstructed beam (go to a hole in the grid).

Zero-loss centering of the energy slit
Tune Isochromaticity
Tune Magnifications
Tune Distortions
SECTION: Cryo-TEM data collection
13.5. Calibrate image shifts.
SECTION: Cryo-TEM data collection
13.4. Autocoma (over carbon).
SECTION: Cryo-TEM data collection
13.3. Autostigmate (over carbon).
SECTION: Cryo-TEM data collection
13.2. Autofocus (over carbon).
SECTION: Cryo-TEM data collection
14. Collect 5,000 to 10,000 images.
SECTION: Preprocessing
15. Motion correction and conversion of EER files into MRC files.

 


SECTION: Preprocessing
15.1. Use MotionCor2 for motion correction and EER to MRC conversion. 
For more information, please consult the MotionCor2 which can be accessed at the following link: MotionCor2 User Manual.
EER fractionation: Each EER frame group should receive ~1 e-/Å2total dose.
Use standard settings for motion correction, however, use 5x5 patches for enhanced local motion correction.
SECTION: Preprocessing
16. Import the images into RELION.
SECTION: Preprocessing
16.1. In the RELION project folder create a folder called “input”. Then create a symbolic link to the motion corrected images within this folder.
SECTION: Preprocessing
16.2. 
 

SECTION: Preprocessing
16.3. Within RELION, create an Import job. Select “input/*.mrc” to designate raw input files (the wildcard “*” includes all .mrc links in the input folder). Ensure accurate entries for pixel size, voltage, spherical aberration, and amplitude contrast. Additionally, supply an MTF file for the detector used in data acquisition to enhance processing precision.
SECTION: Preprocessing
17. 
 

SECTION: Preprocessing
18. Correct the CTF.
SECTION: Preprocessing
18.1. Use CTFFIND4.1 on dose-weighted images with standard settings (spherical aberration of 2.7, amplitude contrast of 0.07). Additional parameters involve a box size of 512, resolution limits of 30 Å (lower) and 4 Å (higher), and a defocus search range spanning from 3000 to 50,000 Å in 500 Å increments (100 Å steps for astigmatism).
SECTION: Particle picking in crYOLO
19. Particle picking generally adheres to the crYOLO filament data picking tutorial, which can be
accessed at the following link:
https://cryolo.readthedocs.io/en/stable/tutorials/tutorial_overview.html#tutorial-3-pick-filaments-using-a-model-trained-for-your-data
SECTION: Particle picking in crYOLO
19.1. Manually pick filaments on 50-100 micrographs.
SECTION: Particle picking in crYOLO
19.2. Use those particle coordinates as training data for the crYOLO network.
SECTION: Particle picking in crYOLO
19.3. Use the trained network to predict particle / filament positions on all micrographs.
SECTION: Particle picking in crYOLO
19.4. Using the crYOLO boxmanager, check if the network picked reasonable filaments, if not try to increase the training data and repeat.
SECTION: Particle picking in crYOLO
19.5. 
 

SECTION: Particle picking in crYOLO
20. 
 

SECTION: Particle picking in crYOLO
21. Importing particle coordinates into RELION exploiting RELIONs manual picking interface.
SECTION: Particle picking in crYOLO
21.1. Start a manual picking job in RELION.

Use the CTF-corrected images.
Choose start-end coordinates of helices.
SECTION: Particle picking in crYOLO
21.2. Pick filaments on a single image, save the coordinates, and then end the job. This will create the directory environment necessary for the import of crYOLO coordinates.
SECTION: Particle picking in crYOLO
21.3. Go to the output folder of the Manual picking job and delete all .star files.
SECTION: Particle picking in crYOLO
21.4. Copy all the .star files from the STAR_START_END output folder of crYOLO into the output folder of the Manual picking job.
SECTION: Particle picking in crYOLO
21.5. Rename the .star files in the Manual picking output folder from *.star to *manualpick.star. RELION will now recognize the crYOLO coordinate files.
SECTION: Particle picking in crYOLO
21.6. Continue the Manual picking job. The manual picking interface should show the filaments that were picked by crYOLO. Confirm whether the network was picked reasonably. If not, then train the network on a bigger training set. See Figure 2A for an exemplary picked micrograph.
SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
22. Extract binned particles.


SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
22.1. Use imported crYOLO coordinates as particle set (from the fake Manual picking job) and CTF-corrected images.
SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
22.2. Extract with a large box size (756 px), then downsample 3 to 6 times to speed up calculations and signal quality.
SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
22.3. Make sure to switch on helical extraction. For amyloid helices, the rise is usually 4.75 Å, and following Lövestam & Scheres, extract every third unique asymmetric unit.
SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
22.4. Settings:
Extract
Particle box size: 768
Rescale particles: Yes
Rescaled size: 256 (bin3)
Helix
Extract helical segments: Yes
Tube diameter: 200 (whatever is reasonable)
Number of unique asymmetrical units: 3
Helical rise: 4.75
SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
23. 2D classification

SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
23.1. Use the extracted binned particles.
SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
23.2. Settings:
CTF
Do CTF-correction: Yes
 Ignore CTFs until first peak: Yes

 

2. Optimization:
No. classes: Usually so that I have 1.000 to 10.000 particles per class up to a maximum of 100   classes.
Reg. param. T: 2
Use EM algorithm: Yes
Mask diameter: 0.95 * pixel size * box size

3. Sampling

Perform image alignment: Yes
In-plane angular sampling: 6
Offset search range: 5
Offset search step: 1
Allow coarser sampling: No

4. Helix

Classify 2D helical segments: Yes
Tube diameter: 200
Do bimodal angular searches: Yes
Angular search range – psi: 6
Restrict helical offsets to rise: Yes
 Helical rise: 4.75

5. See Figure 2B for exemplary binned 2D classes.

SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
24. Selection of good particles

SECTION: 2D classifications to get a good particle set - Binned initial 2D classification
24.1. At this stage, only classes containing good particles will be chosen (see Figure 2B, panels 2-4). That is classes whose class average distinctly resembles a helical tube of an amyloid protein. Ensure that homogeneous groups are selected, for example, refraining from mixing fibrils containing multiple protofilaments (Figure 2B-2 and 2B-4) with single protofilament fibrils (Figure 2B-3).

 





SECTION: 2D classifications to get a good particle set - Unbinned 2D classification
25. Extract unbinned particles. Use a box size of 256 to 384 px.
SECTION: 2D classifications to get a good particle set - Unbinned 2D classification
26. Run another 2D classification.
SECTION: 2D classifications to get a good particle set - Unbinned 2D classification
26.1. Settings:
1. Optimization:
Decrease the class number and mask size according to the box size used.
Increase the number of iterations if the algorithm does not converge within 25 iterations.  
2. Sampling:
 In-plane angular sampling: 1
Offset search range: 10
Offset search step: 1
Allow coarser sampling: No
SECTION: 2D classifications to get a good particle set - Unbinned 2D classification
27. Selection of beta-sheet classes.
SECTION: 2D classifications to get a good particle set - Unbinned 2D classification
27.1. At this stage, select only classes that contain beta-sheet separated classes. Again, make sure not to mix different polymorphs together into the same selection as this will prevent obtaining a good 3D class later down the line. See Figure 2C for exemplary 2D classes showing beta-sheet separated helices.
SECTION: 3D classification - Find the helical parameters
28. If the crossover distance is visible from the micrographs or from binned 2D classes, use that to conveniently calculate the helical parameters. Otherwise, use a brute-force approach to find the correct helical parameters.
SECTION: 3D classification -Run the 3D classification
31. Use the beta-sheet separation showing classes as input particles. 
SECTION: 3D classification - Find the helical parameters
29. Brute force option #1: Use inimodel2d to generate a range of initial models.
               This is the computationally more parsimonious brute force method. Use class averages that show clear beta-sheet separated filaments with a range of different possible crossover distances to generate initial models using RELIONs inimodel2d program. If the crossover is correct and the averages cover most of the helix, then the resulting initial model may reveal a surprising amount of detail about the amyloid structure. Even in the absence of clear polypeptide backbone signals, arising artifacts give hints whether the crossover used (i.e., the underlying twist) is too large or too small which allows narrowing down the possible twist range. See Figure 2D for exemplary 3D classes showing the distinct artifacts arising from false twist settings.


SECTION: 3D classification - Find the helical parameters
30. Brute force option #2: Use 3D classifications directly with range of twist
              An alternative approach is to opt for 3D classifications directly, systematically testing various settings for the twist through brute force. Depending on the specified twist range and step size, this necessitates executing 10-20 3D classifications, which comes at a significant computational expense. Like the results from the inimodel2d program, results from 3D classification may hint at the correct twist depending on arising artifacts (see Figure 2D).


SECTION: 3D classification -Run the 3D classification
32. Settings:
SECTION: 3D classification -Run the 3D classification
32.1. Reference
Reg. map is on absolute greyscale: No
Initial low-pass filter: 25
Symmetry: C1
SECTION: 3D classification -Run the 3D classification
32.2. CTF
Do CTF-correction: Yes
Ignore CTFs until first peak: Yes
SECTION: 3D classification -Run the 3D classification
32.3. Optimization
No. of classes: 3-4
Reg. param. T: 15-30 (Note: This helps to align amyloid particles. However, keep in mind that the resolution estimates between the iterations are highly overestimated and that there is a risk of  overfitting the data!)
No. of iterations: 25-35 (depending on whether the algorithm can converge on a structure within the number of iterations provided).
Mask: 0.95*box size*pixel size
SECTION: 3D classification -Run the 3D classification
32.4. Sampling
Angular sampling interval: 1.8 degrees
Offset search range: 5
Offset search step: 1
SECTION: 3D classification -Run the 3D classification
32.5. Helix
Do helical reconstruction: Yes
Tube diameter (inner, outer): -1, 180
Apply helical symmetry: Yes
Number of unique asymmetrical units: 3
Initial twist: Use different twists if you want to brute force, otherwise use a twist valuable calculated beforehand.
Initial rise: 4.75 Å
Central Z length: 30%
SECTION: 3D classification - Select particles belonging to high-resolution 3D class
33. Select classes that at least shows hints of the polypeptide backbone when looking at Z slices of the resulting model. An example of a good 3D class can be found in Figure 2D-2.


SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
34. 
 

SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
35. Select particles and map from best class of previous 3D classification as input particles and initial model.
SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
36. Settings:
SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
36.1. Reference
Absolute greyscale: Yes
Initial low-pass: 20
Symmetry: C1
SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
36.2. CTF
Do CTF-correction: Yes
Ignore CTFs until first peak: Yes
SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
36.3. Optimization
Mask diameter: Pixel size * box size * 0.95
SECTION: 3D Refinement and postprocessing - Create a mask
37. To generate an appropriate mask for postprocessing, fine-tune the initial binarization threshold.
SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
36.4. Auto-sampling
Initial angular sampling: 1.8 degrees
Initial offset range: 10
Initial offset step: 1
Local searches from auto-sampling: 1.8 degrees
Use finger angular sampling faster: No
SECTION: 3D Refinement and postprocessing - Initial 3D autorefinement
36.5. Helix
Do helical reconstruction: Yes
Tube diameter (inner, outer): -1, 180
Apply helical symmetry: Yes
No. unique asymm. Units: 3
Initial twist & rise: same as 3D class., 4.75
Central Z length: 20-30%
Searches: No
SECTION: 3D Refinement and postprocessing - Create a mask
37.1. Download the best class from the previous successful 3D classification.
SECTION: 3D Refinement and postprocessing - Create a mask
37.2. Apply a low-pass filter to 15 Å (using `relion_image_handler –I 3D_classfication_map.mrc–-o lpf15A_3D_classification_map.mrc–-lowpass 15`)
SECTION: 3D Refinement and postprocessing - Create a mask
37.3. Open the filtered map in Chimera. Inside Chimera, adjust the threshold settings until no "dust" particles are visible.
SECTION: 3D Refinement and postprocessing - Create a mask
38. 
 

SECTION: 3D Refinement and postprocessing - Create a mask
39. Settings
SECTION: 3D Refinement and postprocessing - Create a mask
39.1. Mask
Lowpass filter: 15
Initial binarization threshold: Take from chimera
Extend binary map: 0-7
Soft-edge: 0-7
SECTION: 3D Refinement and postprocessing - Postprocessing
40. Apply postprocessing to enhance the quality of the map and achieve a more accurate resolution estimate, particularly if the regularization parameter T was adjusted during autorefinement.
SECTION: 3D Refinement and postprocessing - Postprocessing
41. Use one of the two half-maps obtained from the 3D autorefinement in conjunction with the generated mask for postprocessing. Make sure to input the MTF of the detector in the "Sharpen" tab. 
SECTION: 3D Refinement and postprocessing - Iterative refinement runs
42. To further enhance the quality of the map, refinements and postprocessing can be conducted iteratively, typically 2-3 times. This process may include the utilization of symmetry (commonly observed in amyloids, such as C2 symmetry) or optimizing helical parameters through local searches in the "Heli" tab. It also makes sense in later refinements filter applied to increase the resolution of the initial model by decreasing the lowpass filter applied (e.g., to 10 or 5 Å).
SECTION: Enhancing the quality of the map / Postprocessing
43. CTF refinement
SECTION: Enhancing the quality of the map / Postprocessing
43.1. To increase the quality of the map even further, refine the CTF for each particle using CTF refinements. Use the particles from the latest 3D refinement job and the postprocess.star file from the latest postprocessing job. 
SECTION: Enhancing the quality of the map / Postprocessing
43.2. 1st Refinement: Anisotropic Magnification, settings in the “fit” tab:
Estimate (anisotropic) magnification: Yes
Perform CTF parameter fitting: Yes
Everything else: No
SECTION: Enhancing the quality of the map / Postprocessing
43.3. 2nd Refinement: Defocus & Astigmatism, settings in the “fit” tab:
Perform CTF parameter fitting: Yes
Fit Defocus: Per-particle
Fit Astigmatism: Per-micrograph
Everything else: No
SECTION: Enhancing the quality of the map / Postprocessing
43.4. 3rd Refinement: Beamtilt, trefoil & higher order, settings in the “fit” tab:
Estimate beamtilt: Yes
Estimate trefoil: Yes
Estimate 4th order aberrations: Yes
SECTION: Enhancing the quality of the map / Postprocessing
44. 3D refinement / 3D classifications
SECTION: Enhancing the quality of the map / Postprocessing
44.1. With the particles corrected for CTF, another 3D refinement is conducted, incorporating the search for helical parameters and exploration of various helical symmetries. Using the refined maps as an initial model, it is advisable to perform another 3D classification to selectively filter particles. Care should be taken not to excessively low-pass filter the initial model map during this process. Additionally, the particle stack can be filtered to exclude particles with very high defocus (>20,000 Å). This could speed up refinements/classifications without affecting the quality of the reconstruction too much.
SECTION: Enhancing the quality of the map / Postprocessing
45. Bayesian polishing
SECTION: Enhancing the quality of the map / Postprocessing
45.1. If RELIONs internal motion correction was used, Bayesian polishing can be utilized to increase the particle quality even further.