Cryogenic electron tomography (cryoET) offers unparalleled views into the molecular architecture of cells. As no stains or fixation are used, electrons scatter off the native atoms, and all molecules contribute to the final tomogram. As a result, it can be challenging to identify proteins of interest, especially inside a crowded cellular environment. Recent developments in molecular tags for cryoET provide several options for identifying proteins in reconstructed tomograms, but these are often not appropriate for finding an area of interest when collecting data. To increase the utility and throughput of cryoET, future approaches should combine correlative light and electron microscopy (CLEM) with tagging, so that a single modification can be used at small and large spatial scales. Automation of the detection of tags in tomograms and correlation between imaging modalities using machine learning methods will help increase the throughput of these methods, making them more suitable for rare events or structure determination by sub-tomogram averaging.
Machala et al. (Wed,) studied this question.