DUIL: Deep Unsupervised Inverse Learning for in situ Macromolecular Morphology Identification

Emerging microscopic technologies such as cryo-electron tomography (cryo-ET) provide direct 3D visualization of macromolecules within the cell, enabling analysis of their in situ morphology. This morphology can be regarded as an SE(3)-invariant, denoised volumetric representation of subvolumes extracted from tomograms, termed as subtomograms. Morphology identification from a set of subtomograms is formulated as an inverse problem of estimating a set of template morphologies and per-subtomogram SE(3) transformations with respect to any one of the templates. The existing expectation-maximization-based solution to this end often struggles with high structural heterogeneity and requires manual selection of a large number of hyperparameters. Addressing this issue, we present a novel deep unsupervised learning framework called DUIL. Given a set of subtomograms, DUIL first models their SE(3)-invariant morphological code using a siamese-like neural network with a multi-choice learning module. The learned morphological codes are clustered and used to generate a set of template morphologies through a generator network. The generated templates are used as references to estimate SE(3) transformations for each subtomograms through latent optimization. The subtomograms with identical template morphologies are then aligned and averaged to iteratively refine the templates. Experiments on simulated and real cryo-ET datasets demonstrate clear improvements over prior methods, including the discovery of previously unidentified macromolecular morphologies.