Refinement/Classification parameters for noisy micrographs

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1

Dear all,
We are currently processing a dataset where a relatively small complex (300 kDa) is in very noisy environment (in cell), and thus the contrast is very low - particles are overlapping, very close to each other etc. We can locate the particles but we are stuck at 6-7 A with ~1 million particles.
Are there any specific parameters for 3D classification/heterogeneous refinement/non-uniform refinement one should try that can improve the reconstruction?

Best wishes
Dominik

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I’d see how much you can clean the dataset up in 2D, probably multiple times, then try using only the very best classes after multiple 2D rounds. Use a high number of classes, high target resolution and high initial particles per class, possibly with 2-4 final full iterations. Might take a few goes and playing with the parameters to find what works best. When you’ve got the cleanest particles possible, then try going back to 3D.