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Cell type-selective secretome profiling in vivo

Abstract

Secreted polypeptides are a fundamental axis of intercellular and endocrine communication. However, a global understanding of the composition and dynamics of cellular secretomes in intact mammalian organisms has been lacking. Here, we introduce a proximity biotinylation strategy that enables labeling, detection and enrichment of secreted polypeptides in a cell type-selective manner in mice. We generate a proteomic atlas of hepatocyte, myocyte, pericyte and myeloid cell secretomes by direct purification of biotinylated secreted proteins from blood plasma. Our secretome dataset validates known cell type–protein pairs, reveals secreted polypeptides that distinguish between cell types and identifies new cellular sources for classical plasma proteins. Lastly, we uncover a dynamic and previously undescribed nutrient-dependent reprogramming of the hepatocyte secretome characterized by the increased unconventional secretion of the cytosolic enzyme betaine–homocysteine S-methyltransferase (BHMT). This secretome profiling strategy enables dynamic and cell type-specific dissection of the plasma proteome and the secreted polypeptides that mediate intercellular signaling.

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Fig. 1: Biotinylation of secreted polypeptides in cell culture.
Fig. 2: In vivo labeling of the hepatocyte secretome.
Fig. 3: Proteomics of hepatocyte-secreted plasma proteins.
Fig. 4: Dynamic and nutrient-dependent reprogramming of the hepatocyte secretome.
Fig. 5: Nutrient-induced unconventional secretion of hepatocyte BHMT.
Fig. 6: A secretome atlas across four diverse cell types in mice.

Data availability

The authors declare that data supporting the findings of this study are available within the paper and its Supplementary Information files. Figs. 3, 5 and 6 have associated raw data provided in Supplementary Tables 14. All proteomic data generated here are publicly available on ProteomeXchange under project accession no. PXD021602. Source data are provided with this paper.

References

  1. 1.

    Rorsman, P. & Braun, M. Regulation of insulin secretion in human pancreatic islets. Annu. Rev. Physiol. 75, 155–179 (2013).

    CAS  PubMed  Google Scholar 

  2. 2.

    Gaceb, A., Barbariga, M., Özen, I. & Paul, G. The pericyte secretome: potential impact on regeneration. Biochimie 155, 16–25 (2018).

    CAS  PubMed  Google Scholar 

  3. 3.

    Lim, J. M., Wollaston-Hayden, E. E., Teo, C. F., Hausman, D. & Wells, L. Quantitative secretome and glycome of primary human adipocytes during insulin resistance. Clin. Proteomics 11, 20 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Rabouille, C. Pathways of unconventional protein secretion. Trends Cell Biol. 27, 230–240 (2017).

    CAS  PubMed  Google Scholar 

  5. 5.

    Eichelbaum, K., Winter, M., Diaz, M. B., Herzig, S. & Krijgsveld, J. Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat. Biotechnol. 30, 984–990 (2012).

    CAS  PubMed  Google Scholar 

  6. 6.

    Yang, A. C. et al. Multiple click-selective tRNA synthetases expand mammalian cell-specific proteomics. J. Am. Chem. Soc. 140, 7046–7051 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Shin, J. et al. Comparative analysis of differentially secreted proteins in serum-free and serum-containing media by using BONCAT and pulsed SILAC. Sci. Rep. 9, 3096 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Eichelbaum, K. & Krijgsveld, J. Combining pulsed SILAC labeling and click-chemistry for quantitative secretome analysis. Methods Mol. Biol. 1174, 101–114 (2014).

    CAS  PubMed  Google Scholar 

  9. 9.

    Witzke, K. E. et al. Quantitative secretome analysis of activated Jurkat cells using click chemistry-based enrichment of secreted glycoproteins. J. Proteome Res. 16, 137–146 (2017).

  10. 10.

    Kim, D. I. et al. An improved smaller biotin ligase for BioID proximity labeling. Mol. Biol. Cell 27, 1188–1196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    May, D. G., Scott, K. L., Campos, A. R. & Roux, K. J. Comparative application of BioID and TurboID for protein-proximity biotinylation. Cells 9, 1070 (2020).

    CAS  PubMed Central  Google Scholar 

  14. 14.

    Octeau, J. C. et al. An optical neuron–astrocyte proximity assay at synaptic distance scales. Neuron 98, 49–66 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Long, J. Z. et al. The secreted enzyme PM20D1 regulates lipidated amino acid uncouplers of mitochondria. Cell 166, 424–435 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Long, J. Z. et al. Ablation of PM20D1 reveals N-acyl amino acid control of metabolism and nociception. Proc. Natl Acad. Sci. USA 115, E6937–E6945 (2018).

    CAS  PubMed  Google Scholar 

  17. 17.

    Kim, J. T. et al. Cooperative enzymatic control of N-acyl amino acids by PM20D1 and FAAH. eLife 9, e55211 (2020).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Jackson, A. et al. Heat shock induces the release of fibroblast growth factor 1 from NIH 3T3 cells. Proc. Natl Acad. Sci. USA 89, 10691–10695 (1992).

    CAS  PubMed  Google Scholar 

  19. 19.

    Yan, Z., Yan, H. & Ou, H. Human thyroxine binding globulin (TBG) promoter directs efficient and sustaining transgene expression in liver-specific pattern. Gene 506, 289–294 (2012).

    CAS  PubMed  Google Scholar 

  20. 20.

    Uezu, A. et al. Identification of an elaborate complex mediating postsynaptic inhibition. Science 353, 1123–1129 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Pályi-Krekk, Z. et al. EGFR and ErbB2 are functionally coupled to CD44 and regulate shedding, internalization and motogenic effect of CD44. Cancer Lett. 263, 231–242 (2008).

    PubMed  Google Scholar 

  22. 22.

    Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130 (2009).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Samuel, V. T. Fructose induced lipogenesis: from sugar to fat to insulin resistance. Trends Endocrinol. Metab. 22, 60–65 (2011).

    CAS  PubMed  Google Scholar 

  24. 24.

    Softic, S. et al. Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. J. Clin. Invest. 127, 4059–4074 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Softic, S. et al. Dietary sugars alter hepatic fatty acid oxidation via transcriptional and post-translational modifications of mitochondrial proteins. Cell Metab. 30, 735–753 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    PubMed Central  Google Scholar 

  27. 27.

    Teng, Y. W., Mehedint, M. G., Garrow, T. A. & Zeisel, S. H. Deletion of betaine–homocysteine S-methyltransferase in mice perturbs choline and 1-carbon metabolism, resulting in fatty liver and hepatocellular carcinomas. J. Biol. Chem. 286, 36258–36267 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Qin, S. et al. Identification of organ-enriched protein biomarkers of acute liver injury by targeted quantitative proteomics of blood in acetaminophen- and carbon-tetrachloride-treated mouse models and acetaminophen overdose patients. J. Proteome Res. 7, 3724–3740 (2016).

    Google Scholar 

  29. 29.

    Wang, B. et al. Construction and analysis of compact muscle-specific promoters for AAV vectors. Gene Ther. 15, 1489–1499 (2008).

    CAS  PubMed  Google Scholar 

  30. 30.

    Schnütgen, F. et al. A directional strategy for monitoring Cre-mediated recombination at the cellular level in the mouse. Nat. Biotechnol. 21, 562–565 (2003).

    PubMed  Google Scholar 

  31. 31.

    Canli, Ö. et al. Myeloid cell-derived reactive oxygen species induce epithelial mutagenesis. Cancer Cell 32, 869–883 (2017).

    CAS  PubMed  Google Scholar 

  32. 32.

    Cuervo, H. et al. PDGFRβ-P2A-CreERT2 mice: a genetic tool to target pericytes in angiogenesis. Angiogenesis 20, 655–662 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Postic, C. et al. Dual roles for glucokinase in glucose homeostasis as determined by liver and pancreatic β cell-specific gene knock-outs using Cre recombinase. J. Biol. Chem. 274, 305–315 (1999).

    CAS  PubMed  Google Scholar 

  34. 34.

    Zimmers, T. A. et al. Induction of cachexia in mice by systemically administered myostatin. Science 296, 1486–1488 (2002).

    CAS  PubMed  Google Scholar 

  35. 35.

    McPherron, A. C., Lawler, A. M. & Lee, S. J. Regulation of skeletal muscle mass in mice by a new TGF-β superfamily member. Nature 387, 83–90 (1997).

    CAS  PubMed  Google Scholar 

  36. 36.

    Hu, E., Liang, P. & Spiegelman, B. M. AdipoQ is a novel adipose-specific gene dysregulated in obesity. J Biol. Chem. 271, 10697–10703 (1996).

    CAS  PubMed  Google Scholar 

  37. 37.

    Scherer, P. E., Williams, S., Fogliano, M., Baldini, G. & Lodish, H. F. A novel serum protein similar to C1q, produced exclusively in adipocytes. J. Biol. Chem. 270, 26746–26749 (1995).

    CAS  PubMed  Google Scholar 

  38. 38.

    Delaigle, A. M., Senou, M., Guiot, Y., Many, M. C. & Brichard, S. M. Induction of adiponectin in skeletal muscle of type 2 diabetic mice: in vivo and in vitro studies. Diabetologia 49, 1311–1323 (2006).

    CAS  PubMed  Google Scholar 

  39. 39.

    Piñeiro, R. et al. Adiponectin is synthesized and secreted by human and murine cardiomyocytes. FEBS Lett. 579, 5163–5169 (2005).

    PubMed  Google Scholar 

  40. 40.

    Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–D658 (2006).

    CAS  PubMed  Google Scholar 

  41. 41.

    Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Mouchiroud, M. et al. The hepatokine Tsukushi is released in response to NAFLD and impacts cholesterol homeostasis. JCI Insight 4, e129492 (2019).

    PubMed Central  Google Scholar 

  44. 44.

    Barb, D., Bril, F., Kalavalapalli, S. & Cusi, K. Plasma fibroblast growth factor 21 is associated with severity of nonalcoholic steatohepatitis in patients with obesity and type 2 diabetes. J. Clin. Endocrinol. Metab. 104, 3327–3336 (2019).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Xiong, X. et al. Mapping the molecular signatures of diet-induced NASH and its regulation by the hepatokine Tsukushi. Mol. Metab. 20, 128–137 (2019).

    CAS  PubMed  Google Scholar 

  46. 46.

    Krahmer, N. et al. Organellar proteomics and phospho-proteomics reveal subcellular reorganization in diet-induced hepatic steatosis. Dev. Cell 47, 205–221 (2018).

    CAS  PubMed  Google Scholar 

  47. 47.

    Hailemariam, M. et al. S-Trap, an ultrafast sample-preparation approach for shotgun proteomics. J. Proteome Res. 17, 2917–2924 (2018).

    CAS  PubMed  Google Scholar 

  48. 48.

    Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    CAS  PubMed  Google Scholar 

  49. 49.

    Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).

    CAS  PubMed  Google Scholar 

  50. 50.

    Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

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

    CAS  PubMed  Google Scholar 

  52. 52.

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Google Scholar 

  53. 53.

    Michaud, S. A. et al. Molecular phenotyping of laboratory mouse strains using 500 multiple reaction monitoring mass spectrometry plasma assays. Commun. Biol. 1, 78 (2018).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the Long, Bertozzi, Svensson and Abu-Remaileh labs for helpful discussions. We gratefully acknowledge the staff at the Penn Vector Core for the production of AAVs. This work was supported by the US National Institutes of Health (DK105203 and DK124265 to J.Z.L. and K00CA21245403 to N.M.R.) and the Stanford Diabetes Research Center (P30DK116074).

Author information

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Authors

Contributions

W.W. generated all constructs used for this study and performed all mouse experiments. N.M.R. performed all proteomic analyses. A.C.Y. and S.M.T. performed histological and immunofluorescence analyses of livers. J.T.K., V.L.L. and M.G.-C. assisted in tissue collection and processing for mouse experiments. C.R.B. and J.Z.L. supervised the work. W.W., N.M.R. and J.Z.L. conceived experiments and wrote the manuscript.

Corresponding author

Correspondence to Jonathan Z. Long.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13

Reporting Summary

Supplementary Table 1

LC–MS/MS proteomic analysis of streptavidin-purified plasma proteins from mice transduced with AAV-Tbg-Mem, AAV-Tbg-Cyto or AAV-Tbg-ER viruses or control mice. Statistical comparisons were made using ANOVA and Student’s two-way t-test.

Supplementary Table 2

LC–MS/MS proteomic analysis of streptavidin-purified liver proteins from mice transduced with AAV-Tbg-Mem, AAV-Tbg-Cyto or AAV-Tbg-ER viruses.

Supplementary Table 3

LC–MS/MS proteomic analysis of streptavidin-purified plasma from control mice (chow) without viral transduction or mice transduced with AAV-Tbg-Cyto after 2 weeks of chow or HFHS diet. Statistical comparisons were made using ANOVA and Student’s two-way t-test.

Supplementary Table 4

LC–MS/MS proteomic analysis of streptavidin-purified plasma from mice. The following cell types correspond to the following mouse genotypes and viruses: hepatocytes (albumin-Cre, AAV-FLEx), myocytes (WT mice, AAV-tMCK), pericytes (Pdgfrb-creERT2, AAV-FLEx), myeloid cells (LysM-creERT2, AAV-FLEx). Statistical comparisons were made using ANOVA.

Source data

Source Data Fig. 1

Uncropped gel images for Fig. 1.

Source Data Fig. 2

Uncropped gel images for Fig. 2.

Source Data Fig. 4

Uncropped gel images for Fig. 4.

Source Data Fig. 5

Uncropped gel images for Fig. 5.

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Wei, W., Riley, N.M., Yang, A.C. et al. Cell type-selective secretome profiling in vivo. Nat Chem Biol 17, 326–334 (2021). https://doi.org/10.1038/s41589-020-00698-y

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