I've been attempting to process a small dataset of ribosomes sitting on a thin continuous amorphous carbon film in CisTEM. CisTEM's particle picker still does a great job picking particles despite the increased background noise that comes with the carbon film, but 2D classification results in a set of classes that look uniformly like fuzzy spheres. Importing the same particle coordinates into Relion results in 2D classes that look recognizably like ribosomes, using essentially the default settings in Relion. However, I'm still interesting in using CisTEM to process this and similar future datasets.
I've tried varying amplitude contrast (from 0.07 to 0.15), the starting and final resolution (from 40-60 A and 8 to 25 A, respectively), the low pass filter (from 300 to 700 A), and various particle mask radii (from tight to generous), and smoothing factor (from 0.1 to 1.0), and -- at least individually -- none of these seem to change the results much.
I'll attach a sample of a 2D class average from CisTEM (all classes come out looking like this) vs Relion (using the same particle set) to this post.
What settings would you recommend for 2D classification of particles on thin carbon films?