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1.
bioRxiv ; 2024 May 13.
Artigo em Inglês | MEDLINE | ID: mdl-38328036

RESUMO

CryoEM democratization is hampered by access to costly plunge-freezing supplies. We introduce methods, called CryoCycle, for reliably blotting, vitrifying, and reusing clipped cryoEM grids. We demonstrate that vitreous ice may be produced by plunging clipped grids with purified proteins into liquid ethane and that clipped grids may be reused several times for different protein samples. Furthermore, we demonstrate the vitrification of thin areas of cells prepared on gold-coated, pre-clipped grids.

2.
IUCrJ ; 10(Pt 1): 77-89, 2023 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-36598504

RESUMO

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.


Assuntos
Processamento de Imagem Assistida por Computador , Software , Microscopia Crioeletrônica/métodos , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Elétrons
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