CryoVERSE: A Foundation Model across Cryo-EM Observation Types and Scales

Cryogenic electron microscopy (cryo-EM) reconstructs three-dimensional macromolecular structures from noisy two-dimensional observations spanning diverse data types and spatial scales. Existing representation-learning approaches, however, typically specialize in individual observation types, limiting knowledge transfer across single-particle analysis (SPA) and cryogenic electron tomography (cryo-ET) workflows. We introduce CryoVERSE (Cryo-EM Versatile Encoder for Representations across Scales), a unified foundation model jointly pretrained on four pre-reconstruction 2D cryo-EM observation types: SPA micrographs, SPA particles, cryo-ET tilt images, and tilt-derived cryo-ET particles. CryoVERSE uses a scale-flexible shared encoder with pose-aware contrastive learning and masked patch-level prediction to capture transferable global and spatial representations across these observation types and scales. Across diverse downstream evaluations, CryoVERSE improves molecular structure classification, projection-pose classification, and particle quality ranking, transfers effectively to dense particle localization in micrographs, and preserves structural identity and acquisition-angle information in cryo-ET. These results demonstrate that joint pretraining across cryo-EM observation types and scales can yield transferable global and spatial representations within a shared backbone, providing a unified representation basis for diverse pre-reconstruction cryo-EM analysis tasks.