In the past, researchers studying cellular complexity used a reductionist approach by separating and analyzing individual molecular compoenents. While this “divide-and-conquer” approach has been successful, it is well known that biological functions typically involve the coordinated actions of multiple molecules. Therefore, there is a need for methods to study structures in their native cellular environment (in situ). Cryo electron tomography (cryo-ET) is a powerful imaging technique that can reveal the submolecular structure of cells with minimal disturbance to the cell. This post aims to give readers a brief background introduction of cryo-ET, as well as single particle analysis (SPA) cryo-EM that studies purified protein samples, reviews the latest cryo-ET workflows, and discusses the potential of machine learning and deep learning in cryo-ET.

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