SubCellBarCode: Integrated workflow for robust classification and visualization of spatial proteome

We have developed a mass spectrometry (MS) and bioinformatics-based pipeline to generate a proteome-wide resource of protein subcellular localization across multiple human cancer cell lines (www.subcellbarcode.org). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample prep, as well as a dry-lab protocol covering quantitative MS-data analysis, machine-learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline using MS-data generated by three different peptide prefractionation approaches, HiRIEF-LC-MS, High-pH reverse phase fractionation and direct analysis without pre-fractionation using long gradient LC-MS.