https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Eisenstein_SPACETomo2024Eisenstein SPACETomo - Revision history2026-05-24T20:25:04ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2024Eisenstein_SPACETomo&diff=4725&oldid=prevWikiSysop: Created page with "== Citation == Eisenstein, Fabian / Fukuda, Yoshiyuki / Danev, Radostin. Smart parallel automated cryo-electron tomography. 2024. Nature Methods, p. 1-4 == Abstract == In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time t..."2024-08-21T11:31:35Z<p>Created page with "== Citation == Eisenstein, Fabian / Fukuda, Yoshiyuki / Danev, Radostin. Smart parallel automated cryo-electron tomography. 2024. Nature Methods, p. 1-4 == Abstract == In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time t..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
<br />
Eisenstein, Fabian / Fukuda, Yoshiyuki / Danev, Radostin. Smart parallel automated cryo-electron tomography. 2024. Nature Methods, p. 1-4<br />
<br />
== Abstract ==<br />
<br />
In situ cryo-electron tomography enables investigation of macromolecules<br />
in their native cellular environment. Samples have become more readily<br />
available owing to recent software and hardware advancements. Data<br />
collection, however, still requires an experienced operator and appreciable<br />
microscope time to carefully select targets for high-throughput tilt series<br />
acquisition. Here, we developed smart parallel automated cryo-electron<br />
tomography (SPACEtomo), a workflow using machine learning approaches<br />
to fully automate the entire cryo-electron tomography process, including<br />
lamella detection, biological feature segmentation, target selection and<br />
parallel tilt series acquisition, all without the need for human intervention.<br />
This degree of automation will be essential for obtaining statistically<br />
relevant datasets and high-resolution structures of macromolecules in their<br />
native context.<br />
<br />
== Keywords ==<br />
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== Links ==<br />
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https://www.nature.com/articles/s41592-024-02373-9<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop