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Getting Better Images Easier for Single Particle Cryo‐EM
Author(s) -
Cheng Anchi
Publication year - 2019
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2019.33.1_supplement.89.2
Subject(s) - cryo electron microscopy , computer science , projection (relational algebra) , single particle analysis , particle (ecology) , grid , orientation (vector space) , data mining , algorithm , chemistry , mathematics , biology , biochemistry , geometry , aerosol , organic chemistry , ecology
Single‐particle Cryo‐EM is a structural biology technique that has gained increasing popularity in recent years. An increasing number protein complex structures can now be reconstructed to resolutions sufficient to build atomic models similar to those obtained with x‐ray crystallography. The single particle cryo‐EM method relies on averaging millions of projection images of the same protein complex particles preserved in a thin‐layer of vitreous ice. These projection images also need to be distributed across a large number of orientational views so that they can be combined to reconstruct the three‐dimensional volume correctly. At National Resource for Automated Molecular Microscopy (NRAMM), methods are being developed to characterize cryo‐specimen preparation and improve its reproducibility. Efforts are also being made to increase the throughput and quality of automated data acquisition. Tools are made to efficiently support EM managers at large facilities such as the Simons Electron Microscopy Center (SEMC) and the recently established National Center for CryoEM Access and Training (NCCAT). For example, we developed a blotless grid preparation instrument, Spotiton, which by decreasing the time between sample application and cryo‐plunging, serves to amerliorate effects such as preferred orientation. By compensating for beam tilt and astigmatism, we also increased the data collection rate by at least 2‐fold without compromising data quality. In this presentation, I will describe some of these problems and illustrate our solutions for getting better data easier. Support or Funding Information NIH National Institute of General Medical Sciences (GM103310 and U24GM129539) Simons Foundation (349247) This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .