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Data independent acquisition enables deep and fast label-free dynamic organellar mapping

View ORCID ProfileJulia P. Schessner, Vincent Albrecht, View ORCID ProfileAlexandra K. Davies, View ORCID ProfilePavel Sinitcyn, View ORCID ProfileGeorg H.H. Borner
doi: https://doi.org/10.1101/2021.11.09.467934
Julia P. Schessner
1Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
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Vincent Albrecht
1Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
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Alexandra K. Davies
1Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
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Pavel Sinitcyn
2Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
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Georg H.H. Borner
1Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
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  • For correspondence: borner@biochem.mpg.de
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Abstract

The membrane compartments of eukaryotic cells organize the proteome into dynamic reaction spaces that control protein activity. This ‘spatial proteome’ and its changes can be captured systematically by our previously established Dynamic Organellar Maps (DOMs) approach, which combines cell fractionation and shotgun-proteomics into a profiling analysis of subcellular localization. Our original method relied on data dependent acquisition (DDA), which is inherently stochastic, and thus offers limited depth of analysis across replicates. Here we adapt DOMs to data independent acquisition (DIA), in a label-free format, and establish an automated data quality control tool to benchmark performance. Matched for mass spectrometry (MS) runtime, DIA-DOMs provide double the depth relative to DDA-DOMs, with substantially improved precision and localization prediction performance. Matched for depth, DIA-DOMs provide organellar maps in a third of the runtime. To test the DIA-DOMs performance for comparative applications, we mapped subcellular localization changes in response to starvation/disruption of lysosomal pH in HeLa cells, revealing a subset of Golgi proteins that cycle through endosomes. DIA-DOMs offer a superior workflow for label-free spatial proteomics, with a broad application spectrum in cell and biomedical research.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 09, 2021.
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Data independent acquisition enables deep and fast label-free dynamic organellar mapping
Julia P. Schessner, Vincent Albrecht, Alexandra K. Davies, Pavel Sinitcyn, Georg H.H. Borner
bioRxiv 2021.11.09.467934; doi: https://doi.org/10.1101/2021.11.09.467934
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Data independent acquisition enables deep and fast label-free dynamic organellar mapping
Julia P. Schessner, Vincent Albrecht, Alexandra K. Davies, Pavel Sinitcyn, Georg H.H. Borner
bioRxiv 2021.11.09.467934; doi: https://doi.org/10.1101/2021.11.09.467934

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