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1. Cryo-EXLO for Cryo-TEM of FIB Specimens

   https://doi.org/10.1017/S1431927622005153  

   Giannuzzi, Lucille A; Colletta, Michael; Yu, Yue; Kourkoutis, Lena F; Iams, Andrew D; Beggs, Kyle; Kassab, Alain J (August 2022, Microscopy and Microanalysis)
2. Cryo STEM of Low Melting Point Metals Enabled by Cryo FIB and EXLO

   https://doi.org/10.1093/mam/ozae044.985  

   Colletta, Michael; Ford, Jamie; Michael, Joseph R; Giannuzzi, Lucille A; Muller, David A (July 2024, Microscopy and Microanalysis)
3. Cryo-FIB Milling and Lift-Out for Preparation of Specimens for Cryo-TEM

   https://doi.org/10.1017/S1431927617012223  

   Zachman, Michael J.; Noble, Jade M.; Kourkoutis, Lena F. (July 2017, Microscopy and Microanalysis)
4. Cryo-EXLO Manipulation of FIB Specimens for Cryo-TEM

   https://doi.org/10.1093/micmic/ozac029  

   Giannuzzi, Lucille A; Colletta, Michael; Yu, Yue; Kourkoutis, Lena F; Iams, Andrew D; Beggs, Kyle; Kassab, Alain J (January 2023, Microscopy and Microanalysis)

   Abstract This work describes cryogenic ex situ lift out (cryo-EXLO) of cryogenic focused ion beam (cryo-FIB) thinned specimens for analysis by cryogenic transmission electron microscopy (cryo-TEM). The steps and apparatus necessary for cryo-EXLO are described. Methods designed to limit ice contamination include use of an anti-frost lid, a vacuum transfer assembly, and a cryostat. Cryo-EXLO is performed in a cryostat with the cryo-shuttle holder positioned in the cryogenic vapor phase above the surface of liquid N2 (LN2) using an EXLO manipulation station installed inside a glove box maintained at < 10% relative humidity and inert (e.g., N2 gas) conditions. Thermal modeling shows that a cryo-EXLO specimen will remain vitreous within its FIB trench indefinitely while LN2 is continuously supplied. Once the LN2 is cut off, modeling shows that the EXLO specimen will remain vitreous for over 4 min, allowing sufficient time for the cryo-transfer steps which take only seconds to perform. Cryo-EXLO was applied successfully to cryo-FIB-milled specimen preparation of a polymer sample and plunge-frozen yeast cells. Cryo-TEM of both the polymer and the yeast shows minimal ice contamination with the yeast specimen maintaining its vitreous phase, illustrating the potential of cryo-EXLO for cryo-FIB-TEM of beam-sensitive, liquid, or biological materials. 

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5. Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization

   https://doi.org/10.1093/bioinformatics/btab794  

   Bandyopadhyay, Hmrishav; Deng, Zihao; Ding, Leiting; Liu, Sinuo; Uddin, Mostofa Rafid; Zeng, Xiangrui; Behpour, Sima; Xu, Min; Xu, ed., Jinbo (November 2021, Bioinformatics)

   Abstract MotivationCryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms. ResultsIn this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with ‘warp’ modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data. Availabilityand implementationhttps://github.com/xulabs/aitom. Supplementary informationSupplementary data are available at Bioinformatics online. 

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