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Cell surface thermal proteome profiling tracks perturbations and drug targets on the plasma membrane


Numerous drugs and endogenous ligands bind to cell surface receptors leading to modulation of downstream signaling cascades and frequently to adaptation of the plasma membrane proteome. In-depth analysis of dynamic processes at the cell surface is challenging due to biochemical properties and low abundances of plasma membrane proteins. Here we introduce cell surface thermal proteome profiling for the comprehensive characterization of ligand-induced changes in protein abundances and thermal stabilities at the plasma membrane. We demonstrate drug binding to extracellular receptors and transporters, discover stimulation-dependent remodeling of T cell receptor complexes and describe a competition-based approach to measure target engagement of G-protein-coupled receptor antagonists. Remodeling of the plasma membrane proteome in response to treatment with the TGFB receptor inhibitor SB431542 leads to partial internalization of the monocarboxylate transporters MCT1/3 explaining the antimetastatic effects of the drug.

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Fig. 1: CS-TPP enables selective monitoring of thermal stability and abundance changes of plasma membrane proteins.
Fig. 2: Target engagement to receptor kinases.
Fig. 3: Monitoring T cell activation by CS-TPP.
Fig. 4: Targeting integrins and heat shock proteins.
Fig. 5: Target engagement to the G-protein-coupled receptor CXCR4.

Data availability

All source data are available in the main text or the supplementary materials. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE59 partner repository with the dataset identifier PXD016249. Annotations of proteins were based on the UniProt database (14 December 2016,

Code availability

An implementation of the above described CS-TPP analysis procedure can be found at or at


  1. Yin, H. & Flynn, A. D. Drugging membrane protein interactions. Annu. Rev. Biomed. Eng. 18, 51–76 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Guan, Y. et al. Kinetics of small molecule interactions with membrane proteins in single cells measured with mechanical amplification. Sci. Adv. 1, e1500633 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. Lomenick, B. et al. Target identification using drug affinity responsive target stability (DARTS). Proc. Natl Acad. Sci. USA 106, 21984–21989 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 32, 1036–1044 (2014).

    CAS  PubMed  Article  Google Scholar 

  5. Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).

    PubMed  Article  CAS  Google Scholar 

  6. Martinez Molina, D. et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87 (2013).

    PubMed  Article  CAS  Google Scholar 

  7. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).

    CAS  PubMed  Article  Google Scholar 

  8. Rutkowska, A. et al. A modular probe strategy for drug localization, target identification and target occupancy measurement on single cell level. ACS Chem. Biol. 11, 2541–2550 (2016).

    CAS  PubMed  Article  Google Scholar 

  9. Frei, A. P., Moest, H., Novy, K. & Wollscheid, B. Ligand-based receptor identification on living cells and tissues using TRICEPS. Nat. Protoc. 8, 1321–1336 (2013).

    PubMed  Article  CAS  Google Scholar 

  10. Reinhard, F. B. M. et al. Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nat. Methods 12, 1129–1131 (2015).

    CAS  PubMed  Article  Google Scholar 

  11. Kawatkar, A. et al. CETSA beyond soluble targets: a broad application to multipass transmembrane proteins. ACS Chem. Biol. 14, 1913–1920 (2019).

    CAS  PubMed  Article  Google Scholar 

  12. Kalxdorf, M., Gade, S., Eberl, H. C. & Bantscheff, M. Monitoring cell surface N-glycoproteome dynamics by quantitative proteomics reveals mechanistic insights into macrophage differentiation. Mol. Cell. Proteomics 16, 770–785 (2017).

  13. Zeng, Y., Ramya, T. N. C., Dirksen, A., Dawson, P. E. & Paulson, J. C. High-efficiency labeling of sialylated glycoproteins on living cells. Nat. Methods 6, 207–209 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Cvjetkovic, A. et al. Detailed analysis of protein topology of extracellular vesicles–evidence of unconventional membrane protein orientation. Sci. Rep. 6, 36338 (2016).

  15. Leuenberger, P. et al. Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability. Science 355, eaai7825 (2017).

  16. Haltia, T. & Freire, E. Forces and factors that contribute to the structural stability of membrane proteins. Biochim. Biophys. Acta 1228, 1–27 (1995).

    PubMed  Article  Google Scholar 

  17. Gagnon, K. B. & Delpire, E. Physiology of SLC12 transporters: lessons from inherited human genetic mutations and genetically engineered mouse knockouts. Am. J. Physiol. Cell Physiol. 304, C693–C714 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  18. Falivelli, G. et al. Attenuation of eph receptor kinase activation in cancer cells by coexpressed ephrin ligands. PLoS ONE 8, e81445 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. Kanatani, Y. et al. Role of CD14 expression in the differentiation-apoptosis switch in human monocytic leukemia cells treated with 1alpha,25-dihydroxyvitamin D3 or dexamethasone in the presence of transforming growth factor beta1. Cell Growth Differ. Mol. Biol. J. Am. Assoc. Cancer Res. 10, 705–712 (1999).

    CAS  Google Scholar 

  20. Nyhan, K. C. et al. Jagged/Notch signalling is required for a subset of TGFβ1 responses in human kidney epithelial cells. Biochim. Biophys. Acta 1803, 1386–1395 (2010).

    CAS  PubMed  Article  Google Scholar 

  21. Romero, M. F., Chen, A.-P., Parker, M. D. & Boron, W. F. The SLC4 family of bicarbonate (HCO3) transporters. Mol. Asp. Med. 34, 159–182 (2013).

    CAS  Article  Google Scholar 

  22. Fransvea, E., Angelotti, U., Antonaci, S. & Giannelli, G. Blocking transforming growth factor-beta up-regulates E-cadherin and reduces migration and invasion of hepatocellular carcinoma cells. Hepatol. 47, 1557–1566 (2008).

    CAS  Article  Google Scholar 

  23. Zhang, Q. et al. LY2157299 monohydrate, a TGF-βR1 inhibitor, suppresses tumor growth and ascites development in ovarian cancer. Cancers (2018).

  24. Halder, S. K., Beauchamp, R. D. & Datta, P. K. A specific inhibitor of TGF-beta receptor kinase, SB-431542, as a potent antitumor agent for human cancers. Neoplasia 7, 509–521 (2005).

  25. Miranda-Gonçalves, V. et al. Hypoxia-mediated upregulation of MCT1 expression supports the glycolytic phenotype of glioblastomas. Oncotarget 7, 46335–46353 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  26. Xu, R.-G. et al. MCT1 promotes tumor progression through regulating epithelial-mesenchymal transition in pancreatic cancer. Int. J. Clin. Exp. Pathol. 10, 3243–3250 (2017).

    CAS  Google Scholar 

  27. Gray, A. L., Coleman, D. T., Shi, R. & Cardelli, J. A. Monocarboxylate transporter 1 contributes to growth factor-induced tumor cell migration independent of transporter activity. Oncotarget 7, 32695–32706 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  28. Payen, V. L. et al. Monocarboxylate transporter MCT1 promotes tumor metastasis independently of its activity as a lactate transporter. Cancer Res. 77, 5591–5601 (2017).

    CAS  PubMed  Article  Google Scholar 

  29. Gaetke, L. M., Chow-Johnson, H. S. & Chow, C. K. Copper: toxicological relevance and mechanisms. Arch. Toxicol. 88, 1929–1938 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Virginio, C., Church, D., North, R. A. & Surprenant, A. Effects of divalent cations, protons and calmidazolium at the rat P2X7 receptor. Neuropharmacology 36, 1285–1294 (1997).

    CAS  PubMed  Article  Google Scholar 

  31. Gómez, M., González, A., Sáez, C. A. & Moenne, A. Copper-induced membrane depolarizations involve the induction of mosaic TRP channels, which activate VDCC leading to calcium increases in ulva compressa. Front. Plant Sci. 7, 754 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  32. Matsuzaki, S. et al. Annexin A4-conferred platinum resistance is mediated by the copper transporter ATP7A. Int. J. Cancer 134, 1796–1809 (2014).

    CAS  PubMed  Article  Google Scholar 

  33. Trickett, A. & Kwan, Y. L. T cell stimulation and expansion using anti-CD3/CD28 beads. J. Immunol. Methods 275, 251–255 (2003).

    CAS  PubMed  Article  Google Scholar 

  34. Pozzi, N. et al. Defective surface expression of attractin on T cells in patients with common variable immunodeficiency (CVID). Clin. Exp. Immunol. 123, 99–104 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Tan, C. S. H. et al. Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 359, 1170–1177 (2018).

    CAS  PubMed  Article  Google Scholar 

  36. Birnbaum, M. E. et al. Molecular architecture of the αβ T cell receptor-CD3 complex. Proc. Natl Acad. Sci. USA 111, 17576–17581 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. Morra, M., Zubiaur, M., Terhorst, C., Sancho, J. & Malavasi, F. CD38 is functionally dependent on the TCR/CD3 complex in human T cells. FASEB J. 12, 581–592 (1998).

    CAS  PubMed  Article  Google Scholar 

  38. Voisinne, G., Gonzalez de Peredo, A. & Roncagalli, R. CD5, an undercover regulator of TCR signaling. Front. Immunol. 9, 2900 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Stillwell, R. & Bierer, B. E. T cell signal transduction and the role of CD7 in costimulation. Immunol. Res. 24, 31–52 (2001).

    CAS  PubMed  Article  Google Scholar 

  40. Kumar, A. et al. CXCR4 physically associates with the T cell receptor to signal in T cells. Immunity 25, 213–224 (2006).

    CAS  PubMed  Article  Google Scholar 

  41. Muhammad, A. et al. Sequential cooperation of CD2 and CD48 in the buildup of the early TCR signalosome. J. Immunol. 182, 7672–7680 (2009).

    CAS  PubMed  Article  Google Scholar 

  42. Lioudyno, M. I. et al. Orai1 and STIM1 move to the immunological synapse and are up-regulated during T cell activation. Proc. Natl Acad. Sci. USA 105, 2011–2016 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. Dragovich, M. A. et al. SLAMF6 clustering is required to augment T cell activation. PLoS ONE 14, e0218109 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Yu, M. et al. Regulation of T cell receptor signaling by activation-induced zinc influx. J. Exp. Med. 208, 775–785 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Okiyoneda, T., Apaja, P. M. & Lukacs, G. L. Protein quality control at the plasma membrane. Curr. Opin. Cell Biol. 23, 483–491 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Triantafilou, K., Triantafilou, M. & Dedrick, R. L. A CD14-independent LPS receptor cluster. Nat. Immunol. 2, 338–345 (2001).

    CAS  PubMed  Article  Google Scholar 

  47. Hyun, S. Y. et al. Development of a novel Hsp90 inhibitor NCT-50 as a potential anticancer agent for the treatment of non-small cell lung cancer. Sci. Rep. 8, 1–16 (2018).

    Article  CAS  Google Scholar 

  48. Huang, J. & Wang, H. Hsp83/Hsp90 physically associates with insulin receptor to promote neural stem cell reactivation. Stem Cell Rep. 11, 883–896 (2018).

    CAS  Article  Google Scholar 

  49. Gaetani, M. et al. Proteome integral solubility alteration: a high-throughput proteomics assay for target deconvolution. J. Proteome Res. 18, 4027–4037 (2019).

    CAS  PubMed  Article  Google Scholar 

  50. Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc. 10, 1567–1593 (2015).

    CAS  PubMed  Article  Google Scholar 

  51. Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C. & Geromanos, S. J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteom. 5, 144–156 (2006).

    CAS  Article  Google Scholar 

  52. Becher, I. et al. Affinity profiling of the cellular kinome for the nucleotide cofactors ATP, ADP, and GTP. ACS Chem. Biol. 8, 599–607 (2013).

    CAS  PubMed  Article  Google Scholar 

  53. Savitski, M. M. et al. Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers. Anal. Chem. 83, 8959–8967 (2011).

    CAS  PubMed  Article  Google Scholar 

  54. Savitski, M. M. et al. Targeted data acquisition for improved reproducibility and robustness of proteomic mass spectrometry assays. J. Am. Soc. Mass. Spectrom. 21, 1668–1679 (2010).

    CAS  PubMed  Article  Google Scholar 

  55. Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013).

    CAS  PubMed  Article  Google Scholar 

  56. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Article  Google Scholar 

  57. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. Methodol. 57, 289–300 (1995).

    Google Scholar 

  58. Poole, W., Gibbs, D. L., Shmulevich, I., Bernard, B. & Knijnenburg, T. A. Combining dependent P-values with an empirical adaptation of Brown’s method. Bioinforma. 32, i430–i436 (2016).

    CAS  Article  Google Scholar 

  59. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  Article  Google Scholar 

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We thank J. Stuhlfauth, N. Garcia-Altrieth, K. Beß and B. Dlugosch for the supporting cell culture production; M. Bösche, T. Rudi, M. Klös-Hudak, K. Kammerer and M. Steidel for assistance with mass spectrometry and C. Boecker and T. Mathieson for the IT and computational support.

Author information

Authors and Affiliations



M.K. and H.C.E. designed the experiments. M.K. and I.G. performed the CS-TPP experiments. I.B. and S.K. performed lactate assay experiments. M.K. analyzed the CS-TPP data. N.K performed TPCA analysis. I.B. reviewed the figures. M.M.S. gave scientific advice. M.K., H.C.E. and M.B. wrote the manuscript. M.B. and H.C.E. supervised the work.

Corresponding authors

Correspondence to H. Christian Eberl or Marcus Bantscheff.

Ethics declarations

Competing interests

M.K., I.G., S.K., H.C.E. and M.B. are GSK employees. M.M.S. is a GSK shareholder.

Additional information

Peer review information Arunima, Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Table 1

Source data for the comparison of conventional TPP and TPP with cell surface enrichment on the example of cellular treatment with 1 µM ouabain.

Supplementary Table 2

Source data for all CS-TPP experiments.

Supplementary Table 3

Source data the gene ontology enrichment analyses comparing proteins with substantially different melting points (ΔTM > ±4 °C) between conventional TPP and TPP with additional cell surface enrichment.

Supplementary Table 4

Source data for the correlation of thermal stability with protein properties.

Supplementary Table 5

Summary of information about additionally affected proteins in CS-TPP experiments.

Supplementary Table 6

Source data for the TPCA analysis.

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Kalxdorf, M., Günthner, I., Becher, I. et al. Cell surface thermal proteome profiling tracks perturbations and drug targets on the plasma membrane. Nat Methods 18, 84–91 (2021).

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