Real-time and decision taking selection of single-particles during automated cryo-EM sessions based on neuro-fuzzy method

• Intelligent TEM control allows automated, unsupervised and efficient cryo-EM data collection.
• The system includes ANFIS-based detection of single-particles during the automated sessions.
• A classic FIS system makes appropriate decisions during the session, as would an expert microscopist.

Cryo-electron microscopy (cryo-EM) is a three-dimensional (3D) averaging technique that makes use of two-dimensional (2D) images of biological macromolecules preserved in a thin layer of vitreous ice. Recent advances in the field have facilitated the evolution of cryo-EM towards atomic resolution, and the technique provides 3D maps with detailed description of biological macromolecules. Data acquisition at the transmission electron microscope (TEM) is the first crucial step during the single-particle analysis workflow in cryo-EM. In order to exploit the potential of this structural technique for atomic or near-atomic resolution, the initial collection must allow recording of large datasets and, hence, requires operating the TEM in automated mode. The quality of the acquired dataset relies, however, on the expertise of researchers and unsupervised operations might result in low data quality. This work presents the first expert system integrated in a novel scheme to automate cryo-EM data acquisition in a TEM. This development takes advantage of fuzzy logic systems to integrate the working mode of an expert in a linguistic manner and to learn from acquired data through an adaptive network. A new method based on different image-processing algorithms and on adaptive neuro-fuzzy inference systems (ANFIS) identifies, in an unsupervised manner, the single-particles present in cryo-EM images during the automated acquisition on a TEM. This single-particle identification system is integrated in a new intelligent control scheme to automate cryo-EM data acquisition. A classic fuzzy inference system (FIS) was programmed to make appropriate decisions during the session. The designed system can be trained for a specific sample and allows for unsupervised but efficient data collection imitating the working mode of an experienced microscopist.