The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Global, quantitative and dynamic mapping of protein subcellular localization
Abstract
Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Spatial proteomics data from human and mouse cells reveal that SNX family members with a potential phosphorylation site in the conserved lipid-binding pocket are more abundant in the cytoplasm compared to those lacking this site.
“Spatial proteomics data from human and mouse cells further reinforce this trend, revealing that SNX family members with a potential phosphorylation site in the conserved lipid-binding pocket are more abundant in the cytoplasm compared to those lacking this site (43, 44).”
From Phosphorylation of SNX17 impedes activation of Retriever-mediated sorting (2025) · cites this paper as [43]
The Materials and methods (Bioinformatic analysis; page not provided) describe quantitative cytosolic and organellar distributions in human cells, so this paper is a source of spatial proteomics data. The Discussion (Accurate, quantitative and reproducible organellar maps; page not provided) compares its human-cell map with a separate mouse-cell study. Our copy does not report the claimed comparison between SNX family members with and without a potential phosphorylation site in the lipid-binding pocket. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097
Global analysis of protein localization in budding yeast
Huh, W.-K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S., & O'Shea, E. K. (2003) · Nature · doi:10.1038/nature02026 · Cited by 4,248 · cited 1×
“Genome-wide GFP-tagging in yeast circumvents the need for antibodies (Huh et al., 2003), but tags may inadvertently alter protein subcellular localisation, which is difficult to control for; in addition, serial imaging of cells for comparative purposes remains experimentally challenging (Breker et…”
BackgroundKulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719
Proteomic characterization of the human centrosome by protein correlation profiling
Andersen, J. S., Wilkinson, C. J., Mayor, T., Mortensen, P., Nigg, E. A., & Mann, M. (2003) · Nature · doi:10.1038/nature02166 · Cited by 1,319
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979
Self-citationThe quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816
A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards
Wiśniewski, J. R., Hein, M. Y., Cox, J., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.037309 · Cited by 690
Self-citation
Flagged first, then by citations
Research works printed but not linked: 34
- Attardi, 1969, Membrane-bound ribosomes in HeLa cells. I. Their proportion to total cell ribosomes and their association with messenger RNA, Journal of Molecular Biology, 44, 47, 10.1016/0022-2836(69)90404-5 · DOI
- Borner, 2014, Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein-protein interactions, Molecular Biology of the Cell, 25, 3178, 10.1091/mbc.E14-07-1198 · DOI
- Breker, 2013, A novel single-cell screening platform reveals proteome plasticity during yeast stress responses, Journal of Cell Biology, 200, 839, 10.1083/jcb.201301120 · DOI
- Calvo, 2016, MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins, Nucleic Acids Research, 44, D1251, 10.1093/nar/gkv1003 · DOI · Paper page
- Christoforou, 2016, A draft map of the mouse pluripotent stem cell spatial proteome, Nature Communications, 7, 8992, 10.1038/ncomms9992 · DOI
- Cox, 2014, Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ, Molecular & Cellular Proteomics, 13, 2513, 10.1074/mcp.M113.031591 · DOI · Paper page
- Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nature Biotechnology, 26, 1367, 10.1038/nbt.1511 · DOI · Paper page
- Deeb, 2015, Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles, Molecular & Cellular Proteomics, 14, 2947, 10.1074/mcp.M115.050245 · DOI
- Dunkley, 2004, Localization of organelle proteins by isotope tagging (LOPIT), Molecular & Cellular Proteomics, 3, 1128, 10.1074/mcp.T400009-MCP200 · DOI
- Fauconnier, 2009, Outliers detection with the minimum covariance determinant estimator in practice, Statistical Methodology, 6, 363, 10.1016/j.stamet.2008.12.005 · DOI
- Fisher, 1967, Electron microscope studies of the microvilli of HeLa cells, Journal of Cell Biology, 34, 569, 10.1083/jcb.34.2.569 · DOI
- Foster, 2006, A mammalian organelle map by protein correlation profiling, Cell, 125, 187, 10.1016/j.cell.2006.03.022 · DOI
- Gatto, 2014, A foundation for reliable spatial proteomics data analysis, Molecular & Cellular Proteomics, 13, 1937, 10.1074/mcp.M113.036350 · DOI
- Geiger, 2010, Super-SILAC mix for quantitative proteomics of human tumor tissue, Nature Methods, 7, 383, 10.1038/nmeth.1446 · DOI · Paper page
- Gilchrist, 2006, Quantitative proteomics analysis of the secretory pathway, Cell, 127, 1265, 10.1016/j.cell.2006.10.036 · DOI
- Grøvdal, 2004, Direct interaction of Cbl with pTyr 1045 of the EGF receptor (EGFR) is required to sort the EGFR to lysosomes for degradation, Experimental Cell Research, 300, 388, 10.1016/j.yexcr.2004.07.003 · DOI
- Humphrey, 2015, High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics, Nature Biotechnology, 33, 990, 10.1038/nbt.3327 · DOI
- Jones, 2014, Interdependent epidermal growth factor receptor signalling and trafficking, International Journal of Biochemistry & Cell Biology, 51, 23, 10.1016/j.biocel.2014.03.014 · DOI
- Larance, 2015, Multidimensional proteomics for cell biology, Nature Reviews Molecular Cell Biology, 16, 269, 10.1038/nrm3970 · DOI
- Lubas, 2011, Interaction profiling identifies the human nuclear exosome targeting complex, Molecular Cell, 43, 624, 10.1016/j.molcel.2011.06.028 · DOI
- Malapeira, 2011, ADAM17 (TACE) regulates TGFβ signaling through the cleavage of vasorin, Oncogene, 30, 1912, 10.1038/onc.2010.565 · DOI
- Marx, 2015, Mapping proteins with spatial proteomics, Nature Methods, 12, 815, 10.1038/nmeth.3555 · DOI
- Oda, 2005, A comprehensive pathway map of epidermal growth factor receptor signaling, Molecular Systems Biology, 1, 2005.0010, 10.1038/msb4100014 · DOI
- Ong, 2002, Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics, Molecular & Cellular Proteomics, 1, 376, 10.1074/mcp.M200025-MCP200 · DOI · Paper page
- Plotnikov, 2011, The MAPK cascades: signaling components, nuclear roles and mechanisms of nuclear translocation, Biochimica Et Biophysica Acta, 1813, 1619, 10.1016/j.bbamcr.2010.12.012 · DOI
- Quinn, 1984, Density of newly synthesized plasma membrane proteins in intracellular membranes II. Biochemical studies, Journal of Cell Biology, 98, 2142, 10.1083/jcb.98.6.2142 · DOI
- Raguz, 2007, Suppressor of T-cell receptor signalling 1 and 2 differentially regulate endocytosis and signalling of receptor tyrosine kinases, FEBS Letters, 581, 4767, 10.1016/j.febslet.2007.08.077 · DOI
- Scheltema, 2014, The Q Exactive HF, a Benchtop mass spectrometer with a pre-filter, high-performance quadrupole and an ultra-high-field Orbitrap analyzer, Molecular & Cellular Proteomics, 13, 3698, 10.1074/mcp.M114.043489 · DOI
- Smirle, 2013, Cell biology of the endoplasmic reticulum and the Golgi apparatus through proteomics, Cold Spring Harbor Perspectives in Biology, 5, a015073, 10.1101/cshperspect.a015073 · DOI
- Tiwari, 1987, Functional equivalents of interferon-mediated signals needed for induction of an mRNA can be generated by double-stranded RNA and growth factors, EMBO Journal, 6, 3373, 10.1002/j.1460-2075.1987.tb02659.x · DOI
- Uhlén, 2015, Proteomics. Tissue-based map of the human proteome, Science, 347, 1260419, 10.1126/science.1260419 · DOI · Paper page
- Varmuza, 2009, Introduction to Multivariate Statistical Analysis in Chemometrics, 10.1201/9781420059496 · DOI
- Zanivan, 2012, In vivo quantitative proteomics: the SILAC mouse, Methods in Molecular Biology, 757, 435, 10.1007/978-1-61779-166-6_25 · DOI
- Zeiler, 2012, A Protein Epitope Signature Tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines, Molecular & Cellular Proteomics, 11, O111.009613, 10.1074/mcp.O111.009613 · DOI · Paper page
Cited by
Sorted by citations
A subcellular map of the human proteome
Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Björk, L., Breckels, L. M., et al. (2017) · Science · doi:10.1126/science.aal3321 · Cited by 2,813
Mass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334
Mathieu, M., Névo, N., Jouve, M., Valenzuela, J. I., Maurin, M., Verweij, F. J., Palmulli, R., Lankar, D., Dingli, F., Loew, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24384-2 · Cited by 910
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 795
OpenCell: Endogenous tagging for the cartography of human cellular organization
Cho, N. H., Cheveralls, K. C., Brunner, A.-D., Kim, K., Michaelis, A. C., Raghavan, P., Kobayashi, H., Savy, L., Li, J. Y., Canaj, H., et al. (2022) · Science · doi:10.1126/science.abi6983 · Cited by 567
Self-citationMapping the Genetic Landscape of Human Cells
Horlbeck, M. A., Xu, A., Wang, M., Bennett, N. K., Park, C. Y., Bogdanoff, D., Adamson, B., Chow, E. D., Kampmann, M., Peterson, T. R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.010 · Cited by 319
Large-scale investigation of the reasons why potentially important genes are ignored
Stoeger, T., Gerlach, M., Morimoto, R. I., & Nunes Amaral, L. A. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2006643 · Cited by 261
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280
Quality Control of Orphaned Proteins
Juszkiewicz, S., & Hegde, R. S. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.07.001 · Cited by 165
Structure of the endosomal Commander complex linked to Ritscher-Schinzel syndrome
Healy, M. D., McNally, K. E., Butkovič, R., Chilton, M., Kato, K., Sacharz, J., McConville, C., Moody, E. R. R., Shaw, S., Planelles-Herrero, V. J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.04.003 · Cited by 91
Identification of a quality-control factor that monitors failures during proteasome assembly
Zavodszky, E., Peak-Chew, S.-Y., Juszkiewicz, S., Narvaez, A. J., & Hegde, R. S. (2021) · Science · doi:10.1126/science.abc6500 · Cited by 45
Phosphorylation of SNX17 impedes activation of Retriever-mediated sorting
Speidel, J. D., Yu, K., & Böttcher, R. T. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110222
Metadata
- Authors
- Itzhak, D. N., Tyanova, S., Cox, J., & Borner, G. H.
- Affiliations
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- ORCID
- Borner, Georg HH
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2016-06-09
- Volume
- 5
- ISSN
- 2050-084X
- PMC
- PMC4959882
- Cited by
- 649
- References
- 7
- Language
- en
- Licence
- Funders
- Deutsche Forschungsgemeinschaft (Gottfried Wilhelm Leibniz Preis)
- Louis-Jeantet Foundation
- Max-Planck-Gesellschaft
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
