{"entity": "researcher", "timestamp": "2026-09-23T21:38:21.132Z", "family": "Arslan", "given": "Taner", "initials": "T", "orcid": "0000-0002-2388-1811", "affiliations": ["Department of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/4fcf442970d44f5dac8736fbd96ba550.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/4fcf442970d44f5dac8736fbd96ba550"}}, "publications": [{"entity": "publication", "iuid": "aca3aa26e32c4944a569c3eae541d186", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/aca3aa26e32c4944a569c3eae541d186.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/aca3aa26e32c4944a569c3eae541d186"}}, "title": "SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry.", "authors": [{"family": "Arslan", "given": "Taner", "initials": "T", "orcid": "0000-0002-2388-1811", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4fcf442970d44f5dac8736fbd96ba550.json"}}, {"family": "Pan", "given": "Yanbo", "initials": "Y", "orcid": "0000-0001-9442-7782", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/51c4acd7c38841d7b259026d6311d88b.json"}}, {"family": "Mermelekas", "given": "Georgios", "initials": "G"}, {"family": "Vesterlund", "given": "Mattias", "initials": "M", "orcid": "0000-0001-9471-6592", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e443cdb01cc44a7fa786941b5c0b62d6.json"}}, {"family": "Orre", "given": "Lukas M", "initials": "LM"}, {"family": "Lehti\u00f6", "given": "Janne", "initials": "J", "orcid": "0000-0002-8100-9562", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/561efcf32e2648c8a10fee692fc4e908.json"}}], "type": "journal article", "published": "2022-08-00", "journal": {"title": "Nat Protoc", "issn": "1750-2799", "volume": "17", "issue": "8", "pages": "1832-1867", "issn-l": null}, "abstract": "The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for 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 preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .", "doi": "10.1038/s41596-022-00699-2", "pmid": "35732783", "labels": [], "xrefs": [{"db": "pii", "key": "10.1038/s41596-022-00699-2"}], "notes": [], "created": "2026-09-23T10:15:13.323Z", "modified": "2026-09-23T10:53:41.425Z"}, {"entity": "publication", "iuid": "9779940ec8694f7b85936f39d787c714", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9779940ec8694f7b85936f39d787c714.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9779940ec8694f7b85936f39d787c714"}}, "title": "Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms.", "authors": [{"family": "Lehti\u00f6", "given": "Janne", "initials": "J", "orcid": "0000-0002-8100-9562", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/561efcf32e2648c8a10fee692fc4e908.json"}}, {"family": "Arslan", "given": "Taner", "initials": "T", "orcid": "0000-0002-2388-1811", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4fcf442970d44f5dac8736fbd96ba550.json"}}, {"family": "Siavelis", "given": "Ioannis", "initials": "I"}, {"family": "Pan", "given": "Yanbo", "initials": "Y", "orcid": "0000-0001-9442-7782", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/51c4acd7c38841d7b259026d6311d88b.json"}}, {"family": "Socciarelli", "given": "Fabio", "initials": "F"}, {"family": "Berkovska", "given": "Olena", "initials": "O", "orcid": "0000-0002-8811-0591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/887be29288744e6c860e1e00a2501dce.json"}}, {"family": "Umer", "given": "Husen M", "initials": "HM"}, {"family": "Mermelekas", "given": "Georgios", "initials": "G"}, {"family": "Pirmoradian", "given": "Mohammad", "initials": "M"}, {"family": "J\u00f6nsson", "given": "Mats", "initials": "M"}, {"family": "Brunnstr\u00f6m", "given": "Hans", "initials": "H", "orcid": "0000-0001-7402-138X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1b51d0768d1a4c31bba8cf898f962a09.json"}}, {"family": "Brustugun", "given": "Odd Terje", "initials": "OT"}, {"family": "Purohit", "given": "Krishna Pinganksha", "initials": "KP"}, {"family": "Cunningham", "given": "Richard", "initials": "R"}, {"family": "Foroughi Asl", "given": "Hassan", "initials": "H"}, {"family": "Isaksson", "given": "Sofi", "initials": "S"}, {"family": "Arbajian", "given": "Elsa", "initials": "E"}, {"family": "Aine", "given": "Mattias", "initials": "M", "orcid": "0000-0002-0851-5952", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9782fed0f6f241b081264c3c0c2fef7f.json"}}, {"family": "Karlsson", "given": "Anna", "initials": "A", "orcid": "0000-0001-6974-5965", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8119afcc89de4612af587fc5f9894414.json"}}, {"family": "Kotevska", "given": "Marija", "initials": "M"}, {"family": "Gram Hansen", "given": "Carsten", "initials": "C"}, {"family": "Drageset Haakensen", "given": "Vilde", "initials": "V"}, {"family": "Helland", "given": "\u00c5slaug", "initials": "\u00c5"}, {"family": "Tamborero", "given": "David", "initials": "D"}, {"family": "Johansson", "given": "Henrik J", "initials": "HJ", "orcid": "0000-0003-4729-4205", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e8acb3ba52a54c7a82f2a096516a8e05.json"}}, {"family": "Branca", "given": "Rui M", "initials": "RM"}, {"family": "Planck", "given": "Maria", "initials": "M"}, {"family": "Staaf", "given": "Johan", "initials": "J", "orcid": "0000-0001-5254-5115", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3c7e552718d34d09a9a11aa54e90e37c.json"}}, {"family": "Orre", "given": "Lukas M", "initials": "LM"}], "type": "journal article", "published": "2021-11-00", "journal": {"title": "Nat Cancer", "issn": "2662-1347", "volume": "2", "issue": "11", "pages": "1224-1242", "issn-l": null}, "abstract": "Despite major advancements in lung cancer treatment, long-term survival is still rare, and a deeper understanding of molecular phenotypes would allow the identification of specific cancer dependencies and immune evasion mechanisms. Here we performed in-depth mass spectrometry (MS)-based proteogenomic analysis of 141 tumors representing all major histologies of non-small cell lung cancer (NSCLC). We identified six distinct proteome subtypes with striking differences in immune cell composition and subtype-specific expression of immune checkpoints. Unexpectedly, high neoantigen burden was linked to global hypomethylation and complex neoantigens mapped to genomic regions, such as endogenous retroviral elements and introns, in immune-cold subtypes. Further, we linked immune evasion with LAG3 via STK11 mutation-dependent HNF1A activation and FGL1 expression. Finally, we develop a data-independent acquisition MS-based NSCLC subtype classification method, validate it in an independent cohort of 208 NSCLC cases and demonstrate its clinical utility by analyzing an additional cohort of 84 late-stage NSCLC biopsy samples.", "doi": "10.1038/s43018-021-00259-9", "pmid": "34870237", "labels": [], "xrefs": [{"db": "mid", "key": "EMS133264"}, {"db": "pmc", "key": "PMC7612062"}, {"db": "pii", "key": "10.1038/s43018-021-00259-9"}], "notes": [], "created": "2026-09-23T07:36:20.274Z", "modified": "2026-09-23T07:36:20.545Z"}]}