Localization of Organelle Proteins Using Data-Independent Acquisition (DIA-LOP)

作者：Kieran J A McCaskie, Charlotte Hutchings, Renata Feret, Y. Kim, Lisa M. Breckels, Michael J. Deery, Kathryn S. Lilley · 发表于：Molecular & Cellular Proteomics · 年份：2025 · DOI：10.1016/j.mcpro.2025.101047 · 被引用次数：6 · 研究领域：Advanced Proteomics Techniques and Applications、Mass Spectrometry Techniques and Applications、RNA and protein synthesis mechanisms

Subcellular localization within the proteome fundamentally influences cellular processes; however, the development of high-throughput techniques to allow proteome-wide mapping of the cell has proven difficult. Here we present DIA-LOP, an approach capable of high-throughput spatial proteome mapping with in-depth subcellular resolution. This unified framework integrates differential-ultracentrifugation (DC) with ion-mobility-based data-independent acquisition mass spectrometry, alongside data processing using DIA-NN and spatial analysis within the pRoloc bioinformatics pipeline. We obtain the largest DIA-based subcellular proteomics map, with 8242 protein identifications across 13 organellar compartments in U-2 OS cells. Within the same experimental pipeline, we compare DC fractionation with an alternate detergent-based protocol using either DIA or data-dependent acquisition (DDA) mass spectrometry approaches, highlighting the increased subcellular resolution of the DC approach and the increased proteome coverage when DIA is applied. We demonstrate the ability of DIA-LOP to inform clinical studies by identifying and mapping disease-related proteins within our osteosarcoma cell model. With impressive coverage and resolution, DIA-LOP provides a straightforward, high-throughput tool for biochemical discovery. This study thus informs potential users of subcellular proteomics strategies that employ biochemical fractionation of the optimal workflows to achieve high proteome coverage ...