Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Mass spectrometry-based draft of the mouse proteome

Nature Methods volume 19, pages 803–811 (2022)Cite this article

Subjects

Abstract

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Proteomic map of mouse tissues.
Fig. 2: Consolidation of the mouse proteome.
Fig. 3: Proteomic expression landscapes in the mouse.
Fig. 4: Proteome comparative analysis across tissues and species.
Fig. 5: Linking large proteomic data collection with phenotypic drug and radiation response data.

Similar content being viewed by others

Data availability

The data supporting the findings of this study are available within the paper, the supplementary information and the public repositories. The M. musculus UniProt FASTA database (UP000000589) was downloaded from the UniProt website (https://www.uniprot.org/). The sORF database was downloaded from www.sorfs.org. The M. musculus Ensembl, MGI or NCBI databases, along with their annotation were downloaded from www.ensembl.org, www.informatics.jax.org or www.ncbi.nlm.nih.gov, respectively. The the Unimod database was downloaded from www.unimod.org. The GENCODE M19 transcriptome was downloaded from www.gencodegenes.org. Transcriptome sequencing and quantification data are available at ArrayExpress (www.ebi.ac.uk/arrayexpress) under the identifier E-MTAB-10276. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository71 with the dataset identifier PXD030983.

References

  1. The mouse genome. Nature 420, 510 (2002).

  2. Consortium, M. G. S. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).

  3. Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.0400782101 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Geiger, T. et al. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol. Cell. Proteomics 12, 1709–1722 (2013).

    Article  CAS  Google Scholar 

  5. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).

    Article  CAS  Google Scholar 

  6. Gessulat, S. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods 16, 509–518 (2019).

    Article  CAS  Google Scholar 

  7. Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature https://doi.org/10.1038/nature13319 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mergner, J. et al. Mass-spectrometry-based draft of the Arabidopsis proteome. Nature https://doi.org/10.1038/s41586-020-2094-2 (2020).

    Article  PubMed  Google Scholar 

  9. Bult, C. J. et al. Mouse Genome Database (MGD) 2019. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1056 (2019).

    Article  PubMed  Google Scholar 

  10. The UniProt Consortium. UniProt: a worldwide hub of protein knowledge The UniProt Consortium. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1049 (2019).

    Article  Google Scholar 

  11. Chick, J. M. et al. A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat. Biotechnol. 33, 743–749 (2015).

    Article  CAS  Google Scholar 

  12. Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).

    Article  CAS  Google Scholar 

  13. Uhlén, M. et al. Tissue-based map of the human proteome. Science https://doi.org/10.1126/science.1260419 (2015).

    Article  PubMed  Google Scholar 

  14. Edfors, F. et al. Gene‐specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. https://doi.org/10.15252/msb.20167144 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Franks, A., Airoldi, E. & Slavov, N. Post-transcriptional regulation across human tissues. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1005535 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Eraslan, B. et al. Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues. Mol. Syst. Biol. 15, e8513 (2019).

    Article  Google Scholar 

  17. Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

    Article  Google Scholar 

  18. Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

    Article  CAS  Google Scholar 

  19. Robles, M. S., Cox, J. & Mann, M. In-vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genet. 10, (2014).

  20. Hackam, D. G. & Redelmeier, D. A. Translation of research evidence from animals to humans. JAMA https://doi.org/10.1001/jama.296.14.1731 (2006).

    Article  PubMed  Google Scholar 

  21. Frejno, M. et al. Pharmacoproteomic characterisation of human colon and rectal cancer. Mol. Syst. Biol. https://doi.org/10.15252/msb.20177701 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Frejno, M. et al. Proteome activity landscapes of tumor cell lines determine drug responses. Nat. Commun. https://doi.org/10.1038/s41467-020-17336-9 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Nusinow, D. P. et al. Quantitative proteomics of the Cancer Cell Line Encyclopedia. Cell https://doi.org/10.1016/j.cell.2019.12.023 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yang, P. et al. The cytosolic nucleic acid sensor LRRFIP1 mediates the production of type i interferon via a Β-catenin-dependent pathway. Nat. Immunol. https://doi.org/10.1038/ni.1876 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Weichselbaum, R. R. et al. An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.0809242105 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Warmerdam, D. O. et al. PHF6 promotes non‐homologous end joining and G2 checkpoint recovery. EMBO Rep. https://doi.org/10.15252/embr.201948460 (2020).

    Article  PubMed  Google Scholar 

  27. Larsen, D. H. et al. The NBS1-Treacle complex controls ribosomal RNA transcription in response to DNA damage. Nat. Cell Biol. https://doi.org/10.1038/ncb3007 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ciccia, A. et al. Treacher Collins syndrome TCOF1 protein cooperates with NBS1 in the DNA damage response. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1422488112 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Weber, T. J. et al. Stable acinar progenitor cell model identifies treacle-dependent radioresistance. Radiat. Res. https://doi.org/10.1667/RR15342.1 (2019).

    Article  PubMed  Google Scholar 

  30. Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1131 (2019).

    Article  PubMed  Google Scholar 

  31. Johnson, N. et al. Compromised CDK1 activity sensitizes BRCA-proficient cancers to PARP inhibition. Nat. Med. https://doi.org/10.1038/nm.2377 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kim, S. S. et al. Impaired skin and mammary gland development and increased γ-irradiation-induced tumorigenesis in mice carrying a mutation of S1152-ATM phosphorylation site in Brca1. Cancer Res. https://doi.org/10.1158/0008-5472.CAN-09-2418 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Cortez, D., Wang, Y., Qin, J. & Elledge, S. J. Requirement of ATM-dependent phosphorylation of Brca1 in the DNA damage response to double-strand breaks. Science. https://doi.org/10.1126/science.286.5442.1162 (1999).

    Article  PubMed  Google Scholar 

  34. Strub, T. et al. SIRT6 haploinsufficiency induces BRAFV600E melanoma cell resistance to MAPK inhibitors via IGF signalling. Nat. Commun. https://doi.org/10.1038/s41467-018-05966-z (2018).

  35. Kim, E. J. & Juhnn, Y. S. Cyclic AMP signaling reduces sirtuin 6 expression in non-small cell lung cancer cells by promoting ubiquitin-proteasomal degradation via inhibition of the Raf-MEK-ERK (raf/mitogen-activated extracellular signal-regulated kinase/ extracellular signal-regulation. J. Biol. Chem. https://doi.org/10.1074/jbc.M114.633198 (2015).

  36. Cea, M. et al. Evidence for a role of the histone deacetylase SIRT6 in DNA damage response of multiple myeloma cells. Blood https://doi.org/10.1182/blood-2015-06-649970 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yuan, J. et al. SHROOM2 inhibits tumor metastasis through RhoA–ROCK pathway-dependent and -independent mechanisms in nasopharyngeal carcinoma. Cell Death Dis. https://doi.org/10.1038/s41419-019-1325-7 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lee, C., Scherr, H. M. & Wallingford, J. B. Shroom family proteins regulate γ-tubulin distribution and microtubule architecture during epithelial cell shape change. Development https://doi.org/10.1242/dev.02828 (2007).

    Article  PubMed  Google Scholar 

  39. Baker, S. J. et al. A contaminant impurity, not rigosertib, is a tubulin binding agent. Mol. Cell https://doi.org/10.1016/j.molcel.2020.05.024 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Jost, M. et al. Pharmaceutical-grade rigosertib is a microtubule-destabilizing agent. Mol. Cell https://doi.org/10.1016/j.molcel.2020.06.008 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mueller, S. et al. Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes. Nature https://doi.org/10.1038/nature25459 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ruprecht, B., Zecha, J., Zolg, D. P. & Kuster, B. in Proteomics: Methods and Protocols Vol. 1550 (eds Comai, L. et al.) 83–98 (Springer, 2017).

  43. Yu, P. et al. Trimodal mixed mode chromatography that enables efficient offline two-dimensional peptide fractionation for proteome analysis. Anal. Chem. 89, 8884–8891 (2017).

    Article  CAS  Google Scholar 

  44. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  Google Scholar 

  45. Savitski, M. M., Wilhelm, M., Hahne, H., Kuster, B. & Bantscheff, M. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol. Cell. Proteomics https://doi.org/10.1074/mcp.M114.046995 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  Google Scholar 

  47. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  CAS  Google Scholar 

  48. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing) (2014); https://www.R-project.org/

  49. Schmidt, T. et al. Universal Spectrum Explorer: a standalone (web-)application for cross-resource spectrum comparison. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.1c00096 (2021).

    Article  PubMed  Google Scholar 

  50. Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. Revigo summarizes and visualizes long lists of gene ontology terms. PLoS ONE https://doi.org/10.1371/journal.pone.0021800 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Apweiler, R. et al. Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1068 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Menschaert, G. & Fenyö, D. Proteogenomics from a bioinformatics angle: a growing field. Mass Spectrom. Rev. 36, 584–599 (2017).

    Article  CAS  Google Scholar 

  53. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  Google Scholar 

  54. Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).

    Article  CAS  Google Scholar 

  55. Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).

    Article  CAS  Google Scholar 

  56. Webb-Robertson, B. J. M. et al. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J. Proteome Res. https://doi.org/10.1021/pr501138h (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf (2002).

  58. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. https://doi.org/10.18637/jss.v033.i01 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  Google Scholar 

  60. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015).

    Article  Google Scholar 

  61. Hart, T., Komori, H., LaMere, S., Podshivalova, K. & Salomon, D. R. Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics 14, 778 (2013).

    Article  CAS  Google Scholar 

  62. Csárdi, G., Franks, A., Choi, D. S., Airoldi, E. M. & Drummond, D. A. Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast. PLoS Genet. https://doi.org/10.1371/journal.pgen.1005206 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics https://doi.org/10.1093/bioinformatics/bts635 (2013).

    Article  PubMed  Google Scholar 

  64. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. https://doi.org/10.1038/ng.2310 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Löytynoja, A. Phylogeny-aware alignment with PRANK. Methods Mol. Biol. https://doi.org/10.1007/978-1-62703-646-7_10 (2014).

    Article  PubMed  Google Scholar 

  67. Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. https://doi.org/10.1093/oxfordjournals.molbev.a026334 (2000).

    Article  PubMed  Google Scholar 

  68. Yang, Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msm088 (2007).

    Article  PubMed  Google Scholar 

  69. Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. https://doi.org/10.18637/jss.v017.i01 (2006).

    Article  Google Scholar 

  70. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

  71. Vizcaino, J. A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–91069 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to all members of the Kuster laboratory for fruitful discussions and creating reagents and tools used in this study. We thank the sample processing laboratory and the genomics and proteomics core facility at the German Cancer Center (DKFZ) in Heidelberg for RNA-seq, M. Raspe for help with the drug screen, F. Neff, V. Gailus-Durner and H. Fuchs for their contribution to early discussions of the project, B. Eraslan and J. Gagneur for providing access to the source code of the de novo mRNA motif analysis scripts, A. Hubauer, M. Krötz-Fahning, M. Zukowska, J. Götzfried and J. Manolow for expert laboratory assistance. This work was supported by the Deutsche Forschungsgemeinschaft (grant nos. SFB1321:P06 to D.S., P13 to G.S., P18 to B.K. and S01 to D.S., R.R. and G.S.), the German Federal Ministry of Education and Research (Infrafrontier grant no. 01KX1012 to M.H.d.A., ProteomeTools grant no. 031L0008A to B.K., DIAS grant no. 031L0168 to B.K.), the German Center for Diabetes Research (to M.H.d.A.) and the European Research Council (ERC AdG grant no. 833710 to B.K., ERC CoG grant no. 648521 to D.S.). Y.B. is grateful for postdoctoral fellowships from the Alexander von Humboldt Foundation, the Carl Friedrich von Siemens Foundation and the National Natural Science Foundation of China (grant no. 81600046). B.K., F.J. and R.R.H. acknowledge support from the Technical University of Munich Institute for Advanced Study, funded by the German Excellent Initiative and the European Seventh Framework Programme under grant agreement no. 291763. The IBM infrastructure hosting ProteomicsDB and Prosit is operated and maintained by the SAP University Competence Center of the Technical University Munich.

Author information

Author notes

  1. These authors contributed equally: Piero Giansanti, Patroklos Samaras, Yangyang Bian.

Authors and Affiliations

  1. Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany

    Piero Giansanti, Patroklos Samaras, Yangyang Bian, Martin Frejno, Julia Rechenberger, Johannes Krumm, Chien-Yun Lee, Ludwig Lautenbacher, Yun-Chien Chang, Florian P. Bayer, Tobias Schmidt, Matthew The, Mathias Wilhelm & Bernhard Kuster

  2. College of Life Science, Northwest University, Xi’an, China

    Yangyang Bian

  3. Bavarian Biomolecular Mass Spectrometry Center, Technical University of Munich, Freising, Germany

    Chen Meng & Bernhard Kuster

  4. Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany

    Andrea Coluccio, Hannah Jakubowsky, Nicole Wimberger, Chiara Falcomatà, Stefanie Bärthel, Roland Rad & Dieter Saur

  5. Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany

    Andrea Coluccio, Hannah Jakubowsky, Nicole Wimberger, Chiara Falcomatà, Stefanie Bärthel & Dieter Saur

  6. Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Andrea Coluccio, Hannah Jakubowsky, Sebastian Mueller, Nicole Wimberger, Chiara Falcomatà, Stefanie Bärthel, Roland Rad & Dieter Saur

  7. Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany

    Andrea Coluccio, Hannah Jakubowsky, Sebastian Mueller, Nicole Wimberger, Chiara Falcomatà, Stefanie Bärthel, Roland Rad & Dieter Saur

  8. Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Sophie Dobiasch & Stephanie E. Combs

  9. Institute of Radiation Medicine, Department of Radiation Sciences, Helmholtz Zentrum München, Neuherberg, Germany

    Sophie Dobiasch & Stephanie E. Combs

  10. German Cancer Consortium (DKTK), Munich, Germany

    Sophie Dobiasch, Stephanie E. Combs & Bernhard Kuster

  11. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Sophie Dobiasch, Stephanie E. Combs & Bernhard Kuster

  12. Population epigenetics and epigenomics, Technical University of Munich, Freising, Germany

    Rashmi R. Hazarika & Frank Johannes

  13. Institute of Advanced Study (IAS), Technical University of Munich, Freising, Germany

    Rashmi R. Hazarika, Frank Johannes & Bernhard Kuster

  14. Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Julia Calzada-Wack & Martin Hrabe de Angelis

  15. Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technical University of Munich, Munich, Germany

    Sebastian Mueller & Roland Rad

  16. Medical Clinic and Policlinic II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Zonera Hassan & Günter Schneider

  17. Chair of Experimental Genetics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

    Martin Hrabe de Angelis

  18. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Martin Hrabe de Angelis

  19. Computational Mass Spectrometry, Technical University of Munich, Freising, Germany

    Mathias Wilhelm

  20. University Medical Center Göttingen, Department of General, Visceral and Pediatric Surgery, Göttingen, Germany

    Günter Schneider

Authors
  1. Piero Giansanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patroklos Samaras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yangyang Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrea Coluccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Martin Frejno
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hannah Jakubowsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sophie Dobiasch
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rashmi R. Hazarika
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Julia Rechenberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Julia Calzada-Wack
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Johannes Krumm
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sebastian Mueller
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Chien-Yun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Nicole Wimberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ludwig Lautenbacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Zonera Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yun-Chien Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chiara Falcomatà
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Florian P. Bayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Stefanie Bärthel
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Tobias Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Roland Rad
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Stephanie E. Combs
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Matthew The
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Frank Johannes
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Dieter Saur
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Martin Hrabe de Angelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Mathias Wilhelm
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Günter Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Bernhard Kuster
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.G. performed (phosho)proteomic and transcriptomic experiments on tissue samples. Y.B., J.R., J.K., C.-Y.L., Y.-C.C. and F.P.B. performed (phosho)proteomic experiments on mPDAC cell lines. P.G., P.S., Y.B. and B.K. interpreted and visualized data. P.G., P.S., Y.B. and C.M. generated web resource databases. P.G., P.S., Y.B. M.F., C.M. and R.R.H. performed data analysis. L.L., T.S. and M.T. contributed analysis tools. A.C., H.J., S.M., N.W., Z.H., C.F. and S.B. isolated mPDAC cell cultures and performed the drug screen. J.C.-W. and M.H.d.A. selected and provided mouse tissue samples. S.D. performed the radioresistance assay. R.R., S.E.C., F.J., D.S., M.W., G.S. and B.K. supervised the study. P.G. and B.K. conceptualized the project and wrote the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Bernhard Kuster.

Ethics declarations

Competing interests

M.W. and B.K. are founders and shareholders of OmicScouts GmbH and MSAID GmbH. They have no operational role in either company. M.F. is founder, shareholder and CEO of MSAID GmbH. T.S. is founder and shareholder of MSAID GmbH. The contents of this study are unrelated to any commercial activities. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Leonard Foster and Lukas Käll for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11.

Reporting Summary

Peer Review File

Supplementary Table 1

Identified and quantified protein groups across the 41 organs and 66 mPDAC cell lines, from the ProteomicsDB pipeline.

Supplementary Table 2

Identified and quantified phosphorylation sites across the 41 organs and 66 mPDAC cell lines, from the MaxQuant analysis.

Supplementary Table 3

Peptide identifications for the SEPs and amino acid substitution analyses.

Supplementary Table 4

Gene expression values across the 29 organs subjected to RNA-seq analysis.

Supplementary Table 5

Protein/mRNA expression comparison.

Supplementary Table 6

Quantified mouse:human orthologous protein pairs across 21 matching tissues.

Supplementary Table 7

Radiation and drug sensitivity/resistance analyses.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Giansanti, P., Samaras, P., Bian, Y. et al. Mass spectrometry-based draft of the mouse proteome. Nat Methods 19, 803–811 (2022). https://doi.org/10.1038/s41592-022-01526-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01526-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research