What shapes template matching performance in cryogenic electron tomography in situ

Detecting specific biological macromolecules in cryogenic electron tomography (cryo-ET) data is frequently approached by applying cross-correlation-based 3D template matching. To reduce computational cost and noise, high binning is used to aggregate voxels before template matching. This remains a prevalent practice in both practical applications and method development. Here, we systematically evaluate the relation between template size, shape, and angular sampling to identify ribosomes in a ground truth annotated dataset. We show that at the commonly used binning, a detailed subtomogram average, a sphere, and the heart emoji [Formula] results in near-identical performance. Our findings indicate that with current template-matching practices, macromolecules can only be detected with high precision if their shape and size are sufficiently different from the background. Using theoretical considerations we rationalize our experimental results and discuss why at high binning, primarily low-frequency information remains and that template matching fails to be accurate because similarly shaped and sized macromolecules have similar low-frequency spectrums. We discuss these challenges and propose potential enhancements for future template-matching methodologies.