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Journal Article

Deep and fast label-free Dynamic Organellar Mapping

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/persons/resource/persons229376

Schessner,  Julia P.
Borner, Georg / Systems Biology of Membrane Trafficking, Max Planck Institute of Biochemistry, Max Planck Society;
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons293245

Albrecht,  Vincent
Borner, Georg / Systems Biology of Membrane Trafficking, Max Planck Institute of Biochemistry, Max Planck Society;
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons246490

Davies,  Alexandra K.
Borner, Georg / Systems Biology of Membrane Trafficking, Max Planck Institute of Biochemistry, Max Planck Society;
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons147529

Sinitcyn,  Pavel
Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons136451

Borner,  Georg H. H.
Borner, Georg / Systems Biology of Membrane Trafficking, Max Planck Institute of Biochemistry, Max Planck Society;
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Citation

Schessner, J. P., Albrecht, V., Davies, A. K., Sinitcyn, P., & Borner, G. H. H. (2023). Deep and fast label-free Dynamic Organellar Mapping. Nature Communications, 14(1): 5252. doi:10.1038/s41467-023-41000-7.


Cite as: https://hdl.handle.net/21.11116/0000-000D-D94A-C
Abstract
The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we enhance the performance of DOMs through data-independent acquisition (DIA) mass spectrometry. DIA-DOMs achieve twice the depth of our previous workflow in the same mass spectrometry runtime, and substantially improve profiling precision and reproducibility. We leverage this gain to establish flexible map formats scaling from high-throughput analyses to extra-deep coverage. Furthermore, we introduce DOM-ABC, a powerful and user-friendly open-source software tool for analyzing profiling data. We apply DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which identifies a subset of Golgi proteins that cycle through endosomes. An imaging time-course reveals different cycling patterns and confirms the quantitative predictive power of our translocation analysis. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.