Hi everyone,
I’m a beginner in cryo-EM and currently working with my first dataset.
I’m studying curved protein filaments that form under acidic conditions (pH 2.3). The sample was collected from the pellet fraction after ultracentrifugation and resuspended in the same acidic buffer for purification and concentration. I used this sample to prepare grids with a Vitrobot using Quantifoil Cu R 1.2/1.3 grids. Data collection was performed on a JEOL CRYOARM 200 (200 kV) with a K3 camera in super-resolution mode, and 10,000 movie frames were collected.
Following the helical reconstruction tutorial in cryoSPARC, I performed Patch Motion Correction and Patch CTF Estimation, and then removed micrographs with CTF fits worse than 6 Å using Manually Curate Exposures (Figure 1).
Next, I performed manual picking and created 2D templates for Filament Tracer, which I used to detect filaments on 500 curated micrographs, followed by Inspect Picks for curation.
Filament Tracer settings:
・Filament diameter (Å): 70
・Separation distance between segments (diameters): 0.25
・Number of micrographs to process: 500
Inspect Picks thresholds:
・NCC score > 0.280
・Local power > 1287.000 and > 2374.000
I then extracted the particles using Extract from Micrographs (box size: 300 px) and ran 2D Classification. However, the resulting classes were very low resolution and often showed spiral-like striations in the center (Figure 3). Since the filaments are inherently flexible, I tried removing the highly curved classes and re-running 2D classification, but the results remained low resolution (Figure 4).
1st and 2nd 2D Classification settings:
Batchsize per class: 400
Here are my questions:
- Given the high flexibility of these filaments, I’m wondering if the standard helical reconstruction pipeline may not be suitable for this dataset. I’ve tried several 2D classification cleaning strategies suggested in previous forum discussions—for example, multiple ab initio classifications followed by 2D classification, or heterogeneous refinement followed by 2D classification, changing the extraction box size, etc. But in all cases, very few particles remain after classification, and the resulting ab initio models are not well-resolved. Do you have any advice or ideas for modifying the workflow to better handle such flexible filaments?
- The filaments I’m working with are not typical amyloids and seem to have irregular internal structure and high flexibility. Do you think a strategy that involves collecting significantly more data, and then isolating a subset of more structurally homogeneous particles from a large population, makes sense in order to eventually obtain clearer 2D classes or a 3D model?
Thank you very much for any suggestions or insights!
Best regards,
Yuki
