Data independent acquisition enables deep and fast label-free dynamic organellar mapping
The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. We leveraged this gain to establish flexible map formats scaling from rapid analyses to ultra-deep coverage. Our fastest format takes only [~]2.5h/map and enables high-throughput experimental designs. Furthermore, we introduce DOM-QC, an open-source software tool for in-depth standardized analysis of DOMs and other profiling data. We then applied DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which revealed a subset of Golgi proteins that cycle through endosomes. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.