User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

10.3791/62832 ◽  
2021 ◽  
Author(s):  
Anil Kumar ◽  
Surekha P. ◽  
Sahil Gulati ◽  
Somnath Dutta
Keyword(s):  
Electron Microscopy ◽  
Data Acquisition ◽  
High Throughput ◽  
Single Particle ◽  
Cryo Electron Microscopy ◽  
Data Acquisition Software ◽  
Automated Data Acquisition ◽  
User Friendly ◽  
Acquisition Software

scholarly journals High-Throughput Single Particle Cryo-Electron Microscopy Applied to the 26S Proteasome

2011 ◽  
Vol 17 (S2) ◽  
pp. 124-125
Author(s):  
S Nickell ◽  
F Beck ◽  
E Sakata ◽  
A Korinek ◽  
W Baumeister ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
High Throughput ◽  
Single Particle ◽  
26S Proteasome ◽  
Cryo Electron Microscopy

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

scholarly journals Optimized data acquisition workflow by sample thickness determination

2020 ◽  
Author(s):  
Jan Rheinberger ◽  
Gert Oostergetel ◽  
Guenter P Resch ◽  
Cristina Paulino
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Data Collection ◽  
Data Acquisition ◽  
Data Storage ◽  
Single Particle ◽  
Collection Efficiency ◽  
Sample Thickness ◽  
Cryo Electron Microscopy ◽  
Key Parameter

AbstractSample thickness is a known key parameter in cryo-electron microscopy (cryo-EM) and can affect the amount of high-resolution information retained in the image. Yet, common data acquisition approaches in single particle cryo-EM do not take it into account. Here, we demonstrate how the sample thickness can be determined before data acquisition, allowing to identify optimal regions and restrict data collection to images with preserved high-resolution details. This quality over quantity approach, almost entirely eliminates the time- and storage-consuming collection of suboptimal images, which are discarded after a recorded session or during early image processing due to lack of high-resolution information. It maximizes data collection efficiency and lowers the electron microscopy time required per dataset. This strategy is especially useful, if the speed of data collection is restricted by the microscope hardware and software, or if data transfer, data storage and computational power are a bottleneck.SynopsisDetermining sample thickness, a key parameter in single particle cryo-electron microscopy, before data acquisition, and targeting only optimal areas, maximizes the data output from a single particle cryo-electron microscopy session. Scripts and optimized workflows for EPU and SerialEM are presented utilizing this concept.

scholarly journals Data acquisition software for the CMS strip tracker

2008 ◽  
Vol 119 (2) ◽  
pp. 022008 ◽  
Author(s):  
R Bainbridge ◽  
G Baulieu ◽  
S Bel ◽  
J Cole ◽  
N Cripps ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Data Acquisition Software ◽  
Acquisition Software

The control and data acquisition software for the gamma-ray spectroscopy ATCA sub-systems of the JET-EP2 enhancements

2008 ◽  
Vol 83 (2-3) ◽  
pp. 346-349 ◽  
Author(s):  
A. Neto ◽  
J. Sousa ◽  
B. Carvalho ◽  
H. Fernandes ◽  
R.C. Pereira ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Gamma Ray ◽  
Data Acquisition Software ◽  
Gamma Ray Spectroscopy ◽  
Acquisition Software

scholarly journals Single-particle cryo-EM—How did it get here and where will it go

Science ◽  
2018 ◽  
Vol 361 (6405) ◽  
pp. 876-880 ◽  
Author(s):  
Yifan Cheng
Keyword(s):  
Electron Microscopy ◽  
Structural Biology ◽  
Single Particle ◽  
Electron Crystallography ◽  
The Past ◽  
Cryo Electron Microscopy ◽  
Technological Advances ◽  
The Future ◽  
Electron Cryotomography

Cryo–electron microscopy, or simply cryo-EM, refers mainly to three very different yet closely related techniques: electron crystallography, single-particle cryo-EM, and electron cryotomography. In the past few years, single-particle cryo-EM in particular has triggered a revolution in structural biology and has become a newly dominant discipline. This Review examines the fascinating story of its start and evolution over the past 40-plus years, delves into how and why the recent technological advances have been so groundbreaking, and briefly considers where the technique may be headed in the future.

scholarly journals In situ structure determination using single particle cryo-electron microscopy images

2020 ◽  
Author(s):  
Jing Cheng ◽  
Bufan Li ◽  
Long Si ◽  
Xinzheng Zhang
Keyword(s):  
Electron Microscopy ◽  
Structure Determination ◽  
Single Particle ◽  
Structure Refinement ◽  
Target Function ◽  
Target Protein ◽  
Particle Analysis ◽  
Cryo Electron Microscopy ◽  
Tilt Series

AbstractCryo-electron microscopy (cryo-EM) tomography is a powerful tool for in situ structure determination. However, this method requires the acquisition of tilt series, and its time consuming throughput of acquiring tilt series severely slows determination of in situ structures. By treating the electron densities of non-target protein as non-Gaussian distributed noise, we developed a new target function that greatly improves the efficiency of the recognition of the target protein in a single cryo-EM image without acquiring tilt series. Moreover, we developed a sorting function that effectively eliminates the false positive detection, which not only improves the resolution during the subsequent structure refinement procedure but also allows using homolog proteins as models to recognize the target protein. Together, we developed an in situ single particle analysis (isSPA) method. Our isSPA method was successfully applied to solve structures of glycoproteins on the surface of a non-icosahedral virus and Rubisco inside the carboxysome. The cryo-EM data from both samples were collected within 24 hours, thus allowing fast and simple structural determination in situ.

scholarly journals Data Acquisition Software for Sign Language Recognition

2019 ◽  
Vol 148 (3) ◽  
pp. 205-211
Author(s):  
Manuel Eleazar Martínez-Gutiérrez ◽  
José Rafael Rojano-Cáceres ◽  
Edgard Benítez-Guerrero ◽  
Héctor Eduardo Sánchez-Barrera
Keyword(s):  
Data Acquisition ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Data Acquisition Software ◽  
Acquisition Software
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