Genetically encoded multimeric tags for subcellular protein localization in cryo-EM
Abstract - Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we present a ligand-inducible labeling st...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
06 November 2023
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| In: |
Nature methods
Year: 2023, Jahrgang: 20, Heft: 12, Pages: 1900-1929 |
| ISSN: | 1548-7105 |
| DOI: | 10.1038/s41592-023-02053-0 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41592-023-02053-0 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41592-023-02053-0 
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| Verfasserangaben: | Herman K.H. Fung, Yuki Hayashi, Veijo T. Salo, Anastasiia Babenko, Ievgeniia Zagoriy, Andreas Brunner, Jan Ellenberg, Christoph W. Müller, Sara Cuylen-Haering & Julia Mahamid |
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| 520 | |a Abstract - Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we present a ligand-inducible labeling strategy for intracellular proteins based on fluorescent, 25-nm-sized, genetically encoded multimeric particles (GEMs). The particles exhibit recognizable structural signatures, enabling their automated detection in cryo-ET data by convolutional neural networks. The coupling of GEMs to green fluorescent protein-tagged macromolecules of interest is triggered by addition of a small-molecule ligand, allowing for time-controlled labeling to minimize disturbance to native protein function. We demonstrate the applicability of GEMs for subcellular-level localization of endogenous and overexpressed proteins across different organelles in human cells using cryo-correlative fluorescence and cryo-ET imaging. We describe means for quantifying labeling specificity and efficiency, and for systematic optimization for rare and abundant protein targets, with emphasis on assessing the potential effects of labeling on protein function. | ||
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