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This paper introduces a systematic meta-algorithm for the ab initio reconstruction of complex mixtures in cryo-electron microscopy (cryo-EM), addressing the challenges of reconstructing heterogeneous samples. By formalizing iterative classification and filtering techniques, the method achieves a remarkable 97% accuracy on a 45-class subset of the Tomotwin-100 dataset and 75% accuracy on the complete dataset. The approach not only successfully recovers ribosomal assembly states from unfiltered experimental data but also scales effectively with computational resources, paving the way for automated cryo-EM workflows.
Achieving 97% accuracy in reconstructing complex cryo-EM mixtures could revolutionize how we analyze heterogeneous biological samples.
We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strategies of iterative classification and filtering typically used by practitioners to sort impure, heterogeneous samples. To our knowledge, this is the first method that can successfully perform ab initio reconstruction on datasets containing dozens of distinct species. We obtain 97% accuracy on ab initio reconstruction of a 45-class subset of Tomotwin-100, 75% accuracy on the full Tomotwin-100 dataset, and demonstrate recovery of ribosomal assembly states from an unfiltered experimental cryo-EM dataset. Our approach's capability scales with compute and lays the foundation for automated cryo-EM workflows in modern experimental settings.