CoChat
Sign Up
Cited by 111

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution

Martinez-Val, Ana, Bekker-Jensen, Dorte B., Steigerwald, Sophia, Koenig, Claire, Østergaard, Ole, Mehta, Adi, Tran, Trung, Sikorski, Krzysztof, Torres-Vega, Estefanía, Kwasniewicz, Ewa, Brynjólfsdóttir, Sólveig Hlín, Frankel, Lisa B., Kjøbsted, Rasmus, Krogh, Nicolai, Lundby, Alicia, Bekker-Jensen, Simon, Lund-Johansen, Fridtjof, Olsen, Jesper V.·2021·Nature Communications
DOI 10.1038/s41467-021-27398-yPMC PMC8651693

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of protein networks in cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmark the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in vitro in HeLa cells and in vivo in mouse tissues. Finally, we investigate the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io .

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-021-27398-y

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Proteomic analyses have detected phosphorylated SNX3 S72 and SNX12 S73 only in cytosolic fractions, whereas their nonphosphorylated counterparts associate with membrane-bound organelles.

    “proteomic analyses have detected phosphorylated SNX3 S72 and SNX12 S73 only in cytosolic fractions, whereas their nonphosphorylated counterparts associate with membrane-bound organelles (41).”

    From Phosphorylation of SNX17 impedes activation of Retriever-mediated sorting (2025) · cites this paper as [41]

    The Results section on subcellular phosphoproteomes distinguishes cytosolic fractions 1–2 from membrane and organelle fractions 3–4, and reports a phosphorylation-dependent difference for RPS6. Searches for SNX3, SNX12, S72 and S73 found no passage in our copy reporting the claimed distribution of those proteins or their nonphosphorylated counterparts. Page numbers were not available in the indexed passages. (LLM, conf=0.73)

    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

Flagged first, then by citations

Research works printed but not linked: 62

  1. Liu, Y., Beyer, A. & Aebersold, R. On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell 165, 535–550 (2016). · DOI · Paper page
  2. Kim, W. et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol. Cell 44, 325–340 (2011). · DOI
  3. Sharan, R., Ulitsky, I. & Shamir, R. Network-based prediction of protein function. Mol. Syst. Biol. 3, 88 (2007). · DOI
  4. Gallic, Lle et al. Transcriptional Repressor ERF Is a Ras/Mitogen-Activated Protein Kinase Target That Regulates Cellular Proliferation. Mol. Cell. Biol. 19, 4121–4133 (1999). · DOI
  5. Francavilla, C. et al. Multilayered proteomics reveals molecular switches dictating ligand-dependent EGFR trafficking. Nat. Struct. Mol. Biol. 23, 608–618 (2016). · DOI
  6. Singh, N. & Bhalla, N. Moonlighting Proteins. Ann. Rev. Genet. 54, 265–285 (2020). · DOI
  7. Sirover, M. A. Subcellular dynamics of multifunctional protein regulation: Mechanisms of GAPDH intracellular translocation. J. Cell. Biochem. 113, 2193–2200 (2012). · DOI
  8. Alvarez-Paggi, D. et al. Multifunctional Cytochrome c: Learning New Tricks from an Old Dog. Chem. Rev. 117, 13382–13460 (2017). · DOI
  9. Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590, 649–654 (2021). · DOI
  10. Stenström, L. et al. Mapping the nucleolar proteome reveals a spatiotemporal organization related to intrinsic protein disorder. Mol. Syst. Biol. 16, e9469 (2020). · DOI
  11. Roux, K. J., Kim, D. I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–810 (2012). · DOI · Paper page
  12. Lobingier, B. T. et al. An Approach to Spatiotemporally Resolve Protein Interaction Networks in Living. Cells Cell 169, 350–360.e12 (2017). · DOI
  13. Williams, C. C., Jan, C. H. & Weissman, J. S. Targeting and plasticity of mitochondrial proteins revealed by proximity-specific ribosome profiling. Sci. (80-.). 346, 748–751 (2014). · DOI · Paper page
  14. Geladaki, A. et al. Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun. 10, 331 (2019). · DOI
  15. Orre, L. M. et al. SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization. Mol. Cell 73, 166–182.e7 (2019). · DOI
  16. Itzhak, D. N. et al. A Mass Spectrometry-Based Approach for Mapping Protein Subcellular Localization Reveals the Spatial Proteome of Mouse Primary Neurons. Cell Rep. 20, 2706–2718 (2017). · DOI · Paper page
  17. Itzhak, D. N., Tyanova, S., Cox, J. & Borner, G. H. H. Global, quantitative and dynamic mapping of protein subcellular localization. Elife 5, e16950 (2016). · DOI · Paper page
  18. Jean Beltran, P. M., Mathias, R. A. & Cristea, I. M. A Portrait of the Human Organelle Proteome In Space and Time during Cytomegalovirus Infection. Cell Syst. 3, 361–373.e6 (2016). · DOI
  19. Krahmer, N. et al. Organellar Proteomics and Phospho-Proteomics Reveal Subcellular Reorganization in Diet-Induced Hepatic Steatosis. Dev. Cell 47, 205–221.e7 (2018). · DOI · Paper page
  20. Chuderland, D., Konson, A. & Seger, R. Identification and Characterization of a General Nuclear Translocation Signal in Signaling Proteins. Mol. Cell 31, 850–861 (2008). · DOI
  21. Lund-Johansen, F. et al. MetaMass, a tool for meta-analysis of subcellular proteomics data. Nat. Methods 13, 837–840 (2016). · DOI
  22. de la Rosa Carrillo, D., Sikorski, K., Khnykin, D., Wu, W. & Lund-Johansen, F. High-resolution antibody array analysis of proteins from primary human keratinocytes and leukocytes. PLoS One 13, e0209271 (2018). · DOI
  23. Batth, T. S. et al. Protein aggregation capture on microparticles enables multipurpose proteomics sample preparation. Mol. Cell. Proteom. 18, 1027–1035 (2019). · DOI
  24. Bekker-Jensen, D. B. et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol. Cell. Proteom. 19, 716–729 (2020). · DOI
  25. Gatto, L., Breckels, L. M., Wieczorek, S., Burger, T. & Lilley, K. S. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics 30, 1322–1324 (2014). · DOI
  26. Bateman, A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019). · DOI · Paper page
  27. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: Mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015). · DOI · Paper page
  28. Mendes, M. et al. Mapping the Spatial Proteome of Metastatic Cells in Colorectal Cancer. Proteomics 17, 1700094 (2017). · DOI
  29. Christoforou, A. et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nat. Commun. 7, 8992 (2016). · DOI
  30. Sousa, L. P. et al. Suppression of EGFR endocytosis by dynamin depletion reveals that EGFR signaling occurs primarily at the plasma membrane. Proc. Natl Acad. Sci. U. S. A. 109, 4419–4424 (2012). · DOI
  31. Vieira, A. V., Lamaze, C. & Schmid, S. L. Control of EGF receptor signaling by clathrin-mediated endocytosis. Sci. (80-.). 274, 2086–2089 (1996). · DOI · Paper page
  32. Buday, L. & Downward, J. Epidermal growth factor regulates p21ras through the formation of a complex of receptor, Grb2 adapter protein, and Sos nucleotide exchange factor. Cell 73, 611–620 (1993). · DOI
  33. Zheng, Y. et al. Temporal regulation of EGF signalling networks by the scaffold protein Shc1. Nature 499, 166–171 (2013). · DOI
  34. Visser Smit, G. D. et al. CBL controls EGFR fate by regulating early endosome fusion. Sci. Signal. 2, ra86–ra86 (2009). · DOI
  35. Lin, S. Y. et al. Nuclear localization of EGF receptor and its potential new role as a transcription factor. Nat. Cell Biol. 3, 802–808 (2001). · DOI
  36. Begley, M. J. et al. EGF-receptor specificity for phosphotyrosine-primed substrates provides signal integration with Src. Nat. Struct. Mol. Biol. 2015 2212 22, 983–990 (2015). · DOI
  37. Lundby, A. et al. Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites. Cell 179, 543–560.e26 (2019). · DOI
  38. Fernández-Vizarra, E., Enríquez, J. A., Pérez-Martos, A., Montoya, J. & Fernández-Silva, P. Tissue-specific differences in mitochondrial activity and biogenesis. Mitochondrion 11, 207–213 (2011). · DOI
  39. Schulz, S. et al. Protocol for the parallel isolation of intact mitochondria from rat liver, kidney, heart, and brain. Methods Mol. Biol. 1295, 75–86 (2015). · DOI
  40. Mootha, V. K. et al. Integrated Analysis of Protein Composition, Tissue Diversity, and Gene Regulation in Mouse Mitochondria. Cell 115, 629–640 (2003). · DOI · Paper page
  41. Hock, E.-M. et al. Hypertonic Stress Causes Cytoplasmic Translocation of Neuronal, but Not Astrocytic, FUS due to Impaired Transportin Function. CellReports 24, 987–1000.e7 (2018).
  42. Gong, X., Ming, X., Deng, P. & Jiang, Y. Mechanisms regulating the nuclear translocation of p38 MAP kinase. J. Cell. Biochem. 110, 1420–1429 (2010). · DOI
  43. Wang, X. et al. Complete inhibition of anisomycin and UV radiation but not cytokine induced JNK and p38 activation by an aryl-substituted dihydropyrrolopyrazole quinoline and mixed lineage kinase 7 small interfering RNA. J. Biol. Chem. 280, 19298–19305 (2005). · DOI
  44. Vind, A. C. et al. ZAKα Recognizes Stalled Ribosomes through Partially Redundant Sensor Domains. Mol. Cell 78, 700–713 (2020). · DOI
  45. Sauter, K. A. D., Magun, E. A., Iordanov, M. S. & Magun, B. E. ZAK is required for doxorubicin, a novel ribotoxic stressor, to induce SAPK activation and apoptosis in HaCaT cells. Cancer Biol. Ther. 10, 258–266 (2010). · DOI
  46. Henras, A. K., Plisson-Chastang, C., O’Donohue, M.-F., Chakraborty, A. & Gleizes, P.-E. An overview of pre-ribosomal RNA processing in eukaryotes. Wiley Interdiscip. Rev. RNA 6, 225–242 (2015). · DOI
  47. Gamalinda, M. et al. A hierarchical model for assembly of eukaryotic 60S ribosomal subunit domains. Genes Dev. 28, 198–210 (2014). · DOI
  48. Leidig, C. et al. 60S ribosome biogenesis requires rotation of the 5S ribonucleoprotein particle. Nat. Commun. 5, 1–8 (2014). · DOI
  49. Tafforeau, L. et al. The complexity of human ribosome biogenesis revealed by systematic nucleolar screening of pre-rRNA processing factors. Mol. Cell 51, 539–551 (2013). · DOI
  50. Bohnsack, K. E. & Bohnsack, M. T. Uncovering the assembly pathway of human ribosomes and its emerging links to disease. EMBO J. 38, e100278 (2019). 2019. · DOI
  51. Y., L., R., J. M., C., J., B., C. D. & S., S. B. The nucleolus exhibits an osmotically regulated gatekeeping activity that controls the spatial dynamics and functions of nucleolin. J. Biol. Chem. 283, 11823–11831 (2008). · DOI
  52. Kramer, H. F. & Goodyear, L. J. Exercise, MAPK, and NF-κB signaling in skeletal muscle. J. Appl. Physiol. 103, 388–395 (2007). · DOI · Paper page
  53. Hoffman, L., Jensen, C. C., Yoshigi, M. & Beckerle, M. Mechanical signals activate p38 MAPK pathway-dependent reinforcement of actin via mechanosensitive HspB1. Mol. Biol. Cell 28, 2661–2675 (2017). · DOI
  54. Chen, C. S. Mechanotransduction - A field pulling together? J. Cell Sci. 121, 3285–3292 (2008). · DOI
  55. Bekker-Jensen, D. B. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 11, 1–12 (2020). · DOI · Paper page
  56. van Oostrum, M. et al. Surfaceome dynamics reveal proteostasis-independent reorganization of neuronal surface proteins during development and synaptic plasticity. Nat. Commun. 11, 4990 (2020).
  57. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017). · DOI · Paper page
  58. R., B., F., S. J., C., M. D. & L., G. Nucleolin is required for RNA polymerase I transcription in vivo. Mol. Cell. Biol. 27, 937–948 (2007). · DOI
  59. Leutert, M., Rodríguez-Mias, R. A., Fukuda, N. K. & Villén, J. R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies. Mol. Syst. Biol. 15, e9021 (2019). · DOI
  60. Wieczorek, S. et al. DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics. Bioinformatics 33, 135–136 (2017). · DOI
  61. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015). · DOI · Paper page
  62. Lazar, C., Gatto, L., Ferro, M., Bruley, C. & Burger, T. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. J. Proteome Res. 15, 1116–1125 (2016). · DOI

Cited by

Sorted by citations

Metadata

Authors
Martinez-Val, A., Bekker-Jensen, D. B., Steigerwald, S., Koenig, C., Østergaard, O., Mehta, A., Tran, T., Sikorski, K., Torres-Vega, E., Kwasniewicz, E., Brynjólfsdóttir, S. H., Frankel, L. B., Kjøbsted, R., Krogh, N., Lundby, A., Bekker-Jensen, S., Lund-Johansen, F., & Olsen, J. V.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-12-07
Volume
12
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-021-27398-y
PMC
PMC8651693
Cited by
111
References
2
Language
en
Licence

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