PT  - JOURNAL ARTICLE
AU  - Julia P. Schessner
AU  - Vincent Albrecht
AU  - Alexandra K. Davies
AU  - Pavel Sinitcyn
AU  - Georg H.H. Borner
TI  - Data independent acquisition enables deep and fast label-free dynamic organellar mapping
AID  - 10.1101/2021.11.09.467934
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.11.09.467934
4099  - http://biorxiv.org/content/early/2021/11/09/2021.11.09.467934.short
4100  - http://biorxiv.org/content/early/2021/11/09/2021.11.09.467934.full
AB  - 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 StatementThe authors have declared no competing interest.