Deep and fast label-free Dynamic Organellar Mapping
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-41000-7 |
| _version_ | 1827709921910587392 |
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| author | Julia P. Schessner Vincent Albrecht Alexandra K. Davies Pavel Sinitcyn Georg H. H. Borner |
| author_facet | Julia P. Schessner Vincent Albrecht Alexandra K. Davies Pavel Sinitcyn Georg H. H. Borner |
| author_sort | Julia P. Schessner |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:30:01Z |
| format | Article |
| id | doaj.art-c3f9e864d4644d02bc35bf043fda5009 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:30:01Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-c3f9e864d4644d02bc35bf043fda50092023-11-20T10:04:40ZengNature PortfolioNature Communications2041-17232023-08-0114111910.1038/s41467-023-41000-7Deep and fast label-free Dynamic Organellar MappingJulia P. Schessner0Vincent Albrecht1Alexandra K. Davies2Pavel Sinitcyn3Georg H. H. Borner4Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of BiochemistryDepartment of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of BiochemistryDepartment of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of BiochemistryComputational Systems Biochemistry Research Group, Max-Planck Institute of BiochemistryDepartment of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of BiochemistryAbstract 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.https://doi.org/10.1038/s41467-023-41000-7 |
| spellingShingle | Julia P. Schessner Vincent Albrecht Alexandra K. Davies Pavel Sinitcyn Georg H. H. Borner Deep and fast label-free Dynamic Organellar Mapping Nature Communications |
| title | Deep and fast label-free Dynamic Organellar Mapping |
| title_full | Deep and fast label-free Dynamic Organellar Mapping |
| title_fullStr | Deep and fast label-free Dynamic Organellar Mapping |
| title_full_unstemmed | Deep and fast label-free Dynamic Organellar Mapping |
| title_short | Deep and fast label-free Dynamic Organellar Mapping |
| title_sort | deep and fast label free dynamic organellar mapping |
| url | https://doi.org/10.1038/s41467-023-41000-7 |
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