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Contribution of Mass Spectrometry-Based Proteomics to Discoveries in Developmental Biology

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Advancements of Mass Spectrometry in Biomedical Research

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1140))

Abstract

Understanding multicellular organism development from a molecular perspective is no small feat, yet this level of comprehension affords clinician-scientists the ability to identify root causes and mechanisms of congenital diseases. Inarguably, the maturation of molecular biology tools has significantly contributed to the identification of genetic loci that underlie normal and aberrant developmental programs. In combination with cell biology approaches, these tools have begun to elucidate the spatiotemporal expression and function of developmentally-regulated proteins. The emergence of quantitative mass spectrometry (MS) for biological applications has accelerated the pace at which these proteins can be functionally characterized, driving the construction of an increasingly detailed systems biology picture of developmental processes. Here, we review the quantitative MS-based proteomic technologies that have contributed significantly to understanding the role of proteome regulation in developmental processes. We provide a brief overview of these methodologies, focusing on their ability to provide precise and accurate proteome measurements. We then highlight the use of discovery-based and targeted mass spectrometry approaches in model systems to study cellular differentiation states, tissue phenotypes, and spatiotemporal subcellular organization. We also discuss the current application and future perspectives of MS proteomics to study PTM coordination and the role of protein complexes during development.

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References

  1. Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018). Using single-cell genomics to understand developmental processes and cell fate decisions. Molecular Systems Biology, 14(4), e8046.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Xie, D., Chen, C. C., Ptaszek, L. M., Xiao, S., Cao, X., Fang, F., et al. (2010). Rewirable gene regulatory networks in the preimplantation embryonic development of three mammalian species. Genome Research, 20(6), 804–815.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Deng, Q., Ramskold, D., Reinius, B., & Sandberg, R. (2014). Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science, 343(6167), 193–196.

    Article  CAS  PubMed  Google Scholar 

  4. Session, A. M., Uno, Y., Kwon, T., Chapman, J. A., Toyoda, A., Takahashi, S., et al. (2016). Genome evolution in the allotetraploid frog Xenopus laevis. Nature, 538(7625), 336–343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Diez-Roux, G., Banfi, S., Sultan, M., Geffers, L., Anand, S., Rozado, D., et al. (2011). A high-resolution anatomical atlas of the transcriptome in the mouse embryo. PLoS Biology, 9(1), e1000582.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wilk, R., Hu, J., Blotsky, D., & Krause, H. M. (2016). Diverse and pervasive subcellular distributions for both coding and long noncoding RNAs. Genes & Development, 30(5), 594–609.

    Article  CAS  Google Scholar 

  7. Sollner, J. F., Leparc, G., Hildebrandt, T., Klein, H., Thomas, L., Stupka, E., et al. (2017). An RNA-Seq atlas of gene expression in mouse and rat normal tissues. Scientific Data, 4, 170185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gucinski, A. C., & Boyne II, M. T. (2012). Evaluation of intact mass spectrometry for the quantitative analysis of protein therapeutics. Analytical Chemistry, 84(18), 8045–8051.

    Article  CAS  PubMed  Google Scholar 

  9. Old, W. M., Meyer-Arendt, K., Aveline-Wolf, L., Pierce, K. G., Mendoza, A., Sevinsky, J. R., et al. (2005). Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Molecular & Cellular Proteomics, 4(10), 1487–1502.

    Article  CAS  Google Scholar 

  10. Venable, J. D., Dong, M. Q., Wohlschlegel, J., Dillin, A., & Yates, J. R. (2004). Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nature Methods, 1(1), 39–45.

    Article  CAS  PubMed  Google Scholar 

  11. Panchaud, A., Scherl, A., Shaffer, S. A., von Haller, P. D., Kulasekara, H. D., Miller, S. I., et al. (2009). Precursor acquisition independent from ion count: How to dive deeper into the proteomics ocean. Analytical Chemistry, 81(15), 6481–6488.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gillet, L. C., Navarro, P., Tate, S., Rost, H., Selevsek, N., Reiter, L., et al. (2012). Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis. Molecular & Cellular Proteomics, 11(6), O111 016717.

    Article  CAS  Google Scholar 

  13. Silva, J. C., Denny, R., Dorschel, C. A., Gorenstein, M., Kass, I. J., Li, G. Z., et al. (2005). Quantitative proteomic analysis by accurate mass retention time pairs. Analytical Chemistry, 77(7), 2187–2200.

    Article  CAS  PubMed  Google Scholar 

  14. Picotti, P., Bodenmiller, B., Mueller, L. N., Domon, B., & Aebersold, R. (2009). Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell, 138(4), 795–806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S., & Coon, J. J. (2012). Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Molecular & Cellular Proteomics, 11(11), 1475–1488.

    Article  CAS  Google Scholar 

  16. Hsu, J. L., Huang, S. Y., Chow, N. H., & Chen, S. H. (2003). Stable-isotope dimethyl labeling for quantitative proteomics. Analytical Chemistry, 75(24), 6843–6852.

    Article  CAS  PubMed  Google Scholar 

  17. Thompson, A., Schafer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., et al. (2003). Tandem mass tags: A novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Analytical Chemistry, 75(8), 1895–1904.

    Article  CAS  PubMed  Google Scholar 

  18. Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., et al. (2004). Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Molecular & cellular proteomics : MCP, 3(12), 1154–1169.

    Article  CAS  Google Scholar 

  19. Oda, Y., Huang, K., Cross, F. R., Cowburn, D., & Chait, B. T. (1999). Accurate quantitation of protein expression and site-specific phosphorylation. Proceedings of the National Academy of Sciences of the United States of America, 96(12), 6591–6596.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., et al. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics, 1(5), 376–386.

    Article  CAS  Google Scholar 

  21. Kruger, M., Moser, M., Ussar, S., Thievessen, I., Luber, C. A., Forner, F., et al. (2008). SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell, 134(2), 353–364.

    Article  PubMed  CAS  Google Scholar 

  22. McClatchy, D. B. & Yates III, J. R. (2008). Stable isotope labeling of mammals (SILAM). CSH protocols, 2008, pdb prot4940.

    Google Scholar 

  23. Krijgsveld, J., Ketting, R. F., Mahmoudi, T., Johansen, J., Artal-Sanz, M., Verrijzer, C. P., et al. (2003). Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics. Nature Biotechnology, 21(8), 927–931.

    Article  CAS  PubMed  Google Scholar 

  24. Sury, M. D., Chen, J. X., & Selbach, M. (2010). The SILAC fly allows for accurate protein quantification in vivo. Molecular & Cellular Proteomics, 9(10), 2173–2183.

    Article  CAS  Google Scholar 

  25. Hilger, M., & Mann, M. (2012). Triple SILAC to determine stimulus specific interactions in the Wnt pathway. Journal of Proteome Research, 11(2), 982–994.

    Article  CAS  PubMed  Google Scholar 

  26. Geiger, T., Wisniewski, J. R., Cox, J., Zanivan, S., Kruger, M., Ishihama, Y., et al. (2011). Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nature Protocols, 6(2), 147–157.

    Article  CAS  PubMed  Google Scholar 

  27. Wenger, C. D., Lee, M. V., Hebert, A. S., McAlister, G. C., Phanstiel, D. H., Westphall, M. S., et al. (2011). Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nature Methods, 8(11), 933–935.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. McAlister, G. C., Nusinow, D. P., Jedrychowski, M. P., Wuhr, M., Huttlin, E. L., Erickson, B. K., et al. (2014). MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Analytical Chemistry, 86(14), 7150–7158.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sonnett, M., Yeung, E., & Wuhr, M. (2018). Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster. Analytical Chemistry, 90(8), 5032–5039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lowe, R., Shirley, N., Bleackley, M., Dolan, S., & Shafee, T. (2017). Transcriptomics technologies. PLoS Computational Biology, 13(5), e1005457.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Schwanhausser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., et al. (2011). Global quantification of mammalian gene expression control. Nature, 473(7347), 337–342.

    Article  PubMed  CAS  Google Scholar 

  32. Gao, Y., Liu, X., Tang, B., Li, C., Kou, Z., Li, L., et al. (2017). Protein expression landscape of mouse embryos during pre-implantation development. Cell Reports, 21(13), 3957–3969.

    Article  CAS  PubMed  Google Scholar 

  33. Casas-Vila, N., Bluhm, A., Sayols, S., Dinges, N., Dejung, M., Altenhein, T., et al. (2017). The developmental proteome of Drosophila melanogaster. Genome Research, 27(7), 1273–1285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kislinger, T., Cox, B., Kannan, A., Chung, C., Hu, P., Ignatchenko, A., et al. (2006). Global survey of organ and organelle protein expression in mouse: Combined proteomic and transcriptomic profiling. Cell, 125(1), 173–186.

    Article  CAS  PubMed  Google Scholar 

  35. Abramsson, A., Westman-Brinkmalm, A., Pannee, J., Gustavsson, M., von Otter, M., Blennow, K., et al. (2010). Proteomics profiling of single organs from individual adult zebrafish. Zebrafish, 7(2), 161–168.

    Article  CAS  PubMed  Google Scholar 

  36. Kim, M. S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., et al. (2014). A draft map of the human proteome. Nature, 509(7502), 575–581.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., et al. (2014). Mass-spectrometry-based draft of the human proteome. Nature, 509(7502), 582–587.

    Article  CAS  PubMed  Google Scholar 

  38. Geiger, T., Velic, A., Macek, B., Lundberg, E., Kampf, C., Nagaraj, N., et al. (2013). Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Molecular & Cellular Proteomics, 12(6), 1709–1722.

    Article  CAS  Google Scholar 

  39. Wang, R., Liu, X., Kuster-Schock, E., & Fagotto, F. (2012). Proteomic analysis of differences in ectoderm and mesoderm membranes by DiGE. Journal of Proteome Research, 11(9), 4575–4593.

    Article  CAS  PubMed  Google Scholar 

  40. Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., et al. (2015). Cell type- and brain region-resolved mouse brain proteome. Nature Neuroscience, 18(12), 1819–1831.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ding, C., Li, Y., Guo, F., Jiang, Y., Ying, W., Li, D., et al. (2016). A cell-type-resolved liver proteome. Molecular & Cellular Proteomics, 15(10), 3190–3202.

    Article  CAS  Google Scholar 

  42. Ebisuya, M., & Briscoe, J. (2018). What does time mean in development? Development, 145(12), dev164368.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Toma, K., Wang, T. C., & Hanashima, C. (2016). Encoding and decoding time in neural development. Development, Growth & Differentiation, 58(1), 59–72.

    Article  Google Scholar 

  44. Grun, D., Kirchner, M., Thierfelder, N., Stoeckius, M., Selbach, M., & Rajewsky, N. (2014). Conservation of mRNA and protein expression during development of C. elegans. Cell Reports, 6(3), 565–577.

    Article  PubMed  CAS  Google Scholar 

  45. Alli Shaik, A., Wee, S., Li, R. H., Li, Z., Carney, T. J., Mathavan, S., et al. (2014). Functional mapping of the zebrafish early embryo proteome and transcriptome. Journal of Proteome Research, 13(12), 5536–5550.

    Article  CAS  PubMed  Google Scholar 

  46. Sun, L., Bertke, M. M., Champion, M. M., Zhu, G., Huber, P. W., & Dovichi, N. J. (2014). Quantitative proteomics of Xenopus laevis embryos: Expression kinetics of nearly 4000 proteins during early development. Scientific Reports, 4, 4365.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Peshkin, L., Wuhr, M., Pearl, E., Haas, W., Freeman Jr., R. M., Gerhart, J. C., et al. (2015). On the relationship of protein and mRNA dynamics in vertebrate embryonic development. Developmental Cell, 35(3), 383–394.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Presler, M., Van Itallie, E., Klein, A. M., Kunz, R., Coughlin, M. L., Peshkin, L., et al. (2017). Proteomics of phosphorylation and protein dynamics during fertilization and meiotic exit in the Xenopus egg. Proceedings of the National Academy of Sciences of the United States of America, 114(50), E10838–E10847.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. van Hoof, D., Krijgsveld, J., & Mummery, C. (2012). Proteomic analysis of stem cell differentiation and early development. Cold Spring Harbor Perspectives in Biology, 4(3), a008177.

    PubMed  PubMed Central  Google Scholar 

  50. Melo-Braga, M. N., Meyer, M., Zeng, X., & Larsen, M. R. (2015). Characterization of human neural differentiation from pluripotent stem cells using proteomics/PTMomics--current state-of-the-art and challenges. Proteomics, 15(4), 656–674.

    Article  CAS  PubMed  Google Scholar 

  51. Yocum, A. K., Gratsch, T. E., Leff, N., Strahler, J. R., Hunter, C. L., Walker, A. K., et al. (2008). Coupled global and targeted proteomics of human embryonic stem cells during induced differentiation. Molecular & Cellular Proteomics, 7(4), 750–767.

    Article  CAS  Google Scholar 

  52. Kim, S. Y., Kim, M. J., Jung, H., Kim, W. K., Kwon, S. O., Son, M. J., et al. (2012). Comparative proteomic analysis of human somatic cells, induced pluripotent stem cells, and embryonic stem cells. Stem Cells and Development, 21(8), 1272–1286.

    Article  CAS  PubMed  Google Scholar 

  53. Chaerkady, R., Kerr, C. L., Kandasamy, K., Marimuthu, A., Gearhart, J. D., & Pandey, A. (2010). Comparative proteomics of human embryonic stem cells and embryonal carcinoma cells. Proteomics, 10(7), 1359–1373.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Boue, S., Paramonov, I., Barrero, M. J., & Izpisua Belmonte, J. C. (2010). Analysis of human and mouse reprogramming of somatic cells to induced pluripotent stem cells. What is in the plate? PLoS One, 5(9), e12664.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Hao, J., Li, W., Dan, J., Ye, X., Wang, F., Zeng, X., et al. (2013). Reprogramming- and pluripotency-associated membrane proteins in mouse stem cells revealed by label-free quantitative proteomics. Journal of Proteomics, 86, 70–84.

    Article  CAS  PubMed  Google Scholar 

  56. Sauls, K., Greco, T. M., Wang, L., Zou, M., Villasmil, M., Qian, L., et al. (2018). Initiating events in direct cardiomyocyte reprogramming. Cell Reports, 22(7), 1913–1922.

    Article  CAS  PubMed  Google Scholar 

  57. Osinalde, N., Aloria, K., Omaetxebarria, M. J., & Kratchmarova, I. (2017). Targeted mass spectrometry: An emerging powerful approach to unblock the bottleneck in phosphoproteomics. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 1055–1056, 29–38.

    Article  PubMed  CAS  Google Scholar 

  58. Aebersold, R., Burlingame, A. L., & Bradshaw, R. A. (2013). Western blots versus selected reaction monitoring assays: Time to turn the tables? Molecular & Cellular Proteomics, 12(9), 2381–2382.

    Article  CAS  Google Scholar 

  59. Deutsch, D. R., Frohlich, T., Otte, K. A., Beck, A., Habermann, F. A., Wolf, E., et al. (2014). Stage-specific proteome signatures in early bovine embryo development. Journal of Proteome Research, 13(10), 4363–4376.

    Article  CAS  PubMed  Google Scholar 

  60. Simicevic, J., Moniatte, M., Hamelin, R., Ahrne, E., & Deplancke, B. (2015). A mammalian transcription factor-specific peptide repository for targeted proteomics. Proteomics, 15(4), 752–756.

    Article  CAS  PubMed  Google Scholar 

  61. Morales Betanzos, C., Federspiel, J. D., Palubinsky, A. M., McLaughlin, B., & Liebler, D. C. (2016). Dynamic phosphorylation of apoptosis signal regulating kinase 1 (ASK1) in response to oxidative and electrophilic stress. Chemical Research in Toxicology, 29(12), 2175–2183.

    Article  CAS  PubMed  Google Scholar 

  62. Lin, C. C., Kitagawa, M., Tang, X., Hou, M. H., Wu, J., Qu, D. C., et al. (2018). CoA synthase regulates mitotic fidelity via CBP-mediated acetylation. Nature Communications, 9(1), 1039.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Mathias, R. A., Greco, T. M., Oberstein, A., Budayeva, H. G., Chakrabarti, R., Rowland, E. A., et al. (2014). Sirtuin 4 is a lipoamidase regulating pyruvate dehydrogenase complex activity. Cell, 159(7), 1615–1625.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hashimoto, Y., Kinoshita, N., Greco, T. M., Jean Beltran, P. M., Federspiel, J. D., Naoto, U., et al. (2019). Mechanical force induces phosphorylation-mediated signaling that underlies tissue response and robustness in Xenopus embryos. Cell Systems, 8(3), 226–241.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wu, M. Y., & Hill, C. S. (2009). Tgf-beta superfamily signaling in embryonic development and homeostasis. Developmental Cell, 16(3), 329–343.

    Article  CAS  PubMed  Google Scholar 

  66. Beenken, A., & Mohammadi, M. (2009). The FGF family: Biology, pathophysiology and therapy. Nature Reviews. Drug Discovery, 8(3), 235–253.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Steinhart, Z., & Angers, S. (2018). Wnt signaling in development and tissue homeostasis. Development, 145(11), dev14658.

    Article  CAS  Google Scholar 

  68. Larsen, M. R., Thingholm, T. E., Jensen, O. N., Roepstorff, P., & Jorgensen, T. J. (2005). Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Molecular & Cellular Proteomics, 4(7), 873–886.

    Article  CAS  Google Scholar 

  69. Nuhse, T. S., Stensballe, A., Jensen, O. N., & Peck, S. C. (2003). Large-scale analysis of in vivo phosphorylated membrane proteins by immobilized metal ion affinity chromatography and mass spectrometry. Molecular & Cellular Proteomics, 2(11), 1234–1243.

    Article  CAS  Google Scholar 

  70. Rush, J., Moritz, A., Lee, K. A., Guo, A., Goss, V. L., Spek, E. J., et al. (2005). Immunoaffinity profiling of tyrosine phosphorylation in cancer cells. Nature Biotechnology, 23(1), 94–101.

    Article  CAS  PubMed  Google Scholar 

  71. Guan, K. L., Yu, W., Lin, Y., Xiong, Y., & Zhao, S. (2010). Generation of acetyllysine antibodies and affinity enrichment of acetylated peptides. Nature Protocols, 5(9), 1583–1595.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Xu, G., Paige, J. S., & Jaffrey, S. R. (2010). Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling. Nature Biotechnology, 28(8), 868–873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Krauchunas, A. R., Horner, V. L., & Wolfner, M. F. (2012). Protein phosphorylation changes reveal new candidates in the regulation of egg activation and early embryogenesis in D. melanogaster. Developmental Biology, 370(1), 125–134.

    Article  CAS  PubMed  Google Scholar 

  74. Peuchen, E. H., Cox, O. F., Sun, L., Hebert, A. S., Coon, J. J., Champion, M. M., et al. (2017). Phosphorylation dynamics dominate the regulated proteome during early Xenopus development. Scientific Reports, 7(1), 15647.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Henry, R. A., Singh, T., Kuo, Y. M., Biester, A., O’Keefe, A., Lee, S., et al. (2016). Quantitative measurement of histone tail acetylation reveals stage-specific regulation and response to environmental changes during Drosophila development. Biochemistry, 55(11), 1663–1672.

    Article  CAS  PubMed  Google Scholar 

  76. Liu, Z., Zhang, Q. B., Bu, C., Wang, D., Yu, K., Gan, Z., et al. (2018). Quantitative dynamics of proteome, Acetylome, and Succinylome during stem-cell differentiation into hepatocyte-like cells. Journal of Proteome Research, 17(7), 2491–2498.

    Article  CAS  PubMed  Google Scholar 

  77. Na, C. H., Jones, D. R., Yang, Y., Wang, X., Xu, Y., & Peng, J. (2012). Synaptic protein ubiquitination in rat brain revealed by antibody-based ubiquitome analysis. Journal of Proteome Research, 11(9), 4722–4732.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Rose, C. M., Isasa, M., Ordureau, A., Prado, M. A., Beausoleil, S. A., Jedrychowski, M. P., et al. (2016). Highly multiplexed quantitative mass spectrometry analysis of Ubiquitylomes. Cell Systems, 3(4), 395–403.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Cristea, I. M., Williams, R., Chait, B. T., & Rout, M. P. (2005). Fluorescent proteins as proteomic probes. Molecular & Cellular Proteomics, 4(12), 1933–1941.

    Article  CAS  Google Scholar 

  80. Yao, Z., Darowski, K., St-Denis, N., Wong, V., Offensperger, F., Villedieu, A., et al. (2017). A global analysis of the receptor tyrosine kinase-protein phosphatase interactome. Molecular Cell, 65(2), 347–360.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Joshi, P., Greco, T. M., Guise, A. J., Luo, Y., Yu, F., Nesvizhskii, A. I., et al. (2013). The functional interactome landscape of the human histone deacetylase family. Molecular Systems Biology, 9, 672.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Breitkreutz, A., Choi, H., Sharom, J. R., Boucher, L., Neduva, V., Larsen, B., et al. (2010). A global protein kinase and phosphatase interaction network in yeast. Science, 328(5981), 1043–1046.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Diner, B. A., Li, T., Greco, T. M., Crow, M. S., Fuesler, J. A., Wang, J., et al. (2015). The functional interactome of PYHIN immune regulators reveals IFIX is a sensor of viral DNA. Molecular Systems Biology, 11(1), 787.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., et al. (2015). A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), 712–723.

    Article  CAS  PubMed  Google Scholar 

  85. Huttlin, E. L., Ting, L., Bruckner, R. J., Gebreab, F., Gygi, M. P., Szpyt, J., et al. (2015). The BioPlex network: A systematic exploration of the human interactome. Cell, 162(2), 425–440.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Malovannaya, A., Lanz, R. B., Jung, S. Y., Bulynko, Y., Le, N. T., Chan, D. W., et al. (2011). Analysis of the human endogenous coregulator complexome. Cell, 145(5), 787–799.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Guruharsha, K. G., Rual, J. F., Zhai, B., Mintseris, J., Vaidya, P., Vaidya, N., et al. (2011). A protein complex network of Drosophila melanogaster. Cell, 147(3), 690–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Tandon, P., Miteva, Y. V., Kuchenbrod, L. M., Cristea, I. M., & Conlon, F. L. (2013). Tcf21 regulates the specification and maturation of proepicardial cells. Development, 140(11), 2409–2421.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Kaltenbrun, E., Greco, T. M., Slagle, C. E., Kennedy, L. M., Li, T., Cristea, I. M., et al. (2013). A Gro/TLE-NuRD corepressor complex facilitates Tbx20-dependent transcriptional repression. Journal of Proteome Research, 12(12), 5395–5409.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Wang, J., Cantor, A. B., & Orkin, S. H. (2009). Tandem affinity purification of protein complexes in mouse embryonic stem cells using in vivo biotinylation. Current Protocols in Stem Cell Biology, 11(1), 1B–5.

    Google Scholar 

  91. Hashimoto, Y., Shirane, M., Matsuzaki, F., Saita, S., Ohnishi, T., & Nakayama, K. I. (2014). Protrudin regulates endoplasmic reticulum morphology and function associated with the pathogenesis of hereditary spastic paraplegia. The Journal of Biological Chemistry, 289(19), 12946–12961.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Kennedy, L., Kaltenbrun, E., Greco, T. M., Temple, B., Herring, L. E., Cristea, I. M., et al. (2017). Formation of a TBX20-CASZ1 protein complex is protective against dilated cardiomyopathy and critical for cardiac homeostasis. PLoS Genetics, 13(9), e1007011.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Waldron, L., Steimle, J. D., Greco, T. M., Gomez, N. C., Dorr, K. M., Kweon, J., et al. (2016). The cardiac TBX5 Interactome reveals a chromatin remodeling network essential for cardiac Septation. Developmental Cell, 36(3), 262–275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Christov, C. P., Dingwell, K. S., Skehel, M., Wilkes, H. S., Sale, J. E., Smith, J. C., et al. (2018). A NuRD complex from Xenopus laevis eggs is essential for DNA replication during early embryogenesis. Cell Reports, 22(9), 2265–2278.

    Article  CAS  PubMed  Google Scholar 

  95. Conlon, F. L., Miteva, Y., Kaltenbrun, E., Waldron, L., Greco, T. M., & Cristea, I. M. (2012). Immunoisolation of protein complexes from Xenopus. Methods in Molecular Biology, 917, 369–390.

    Article  CAS  PubMed  Google Scholar 

  96. Greco, T. M., Miteva, Y., Conlon, F. L., & Cristea, I. M. (2012). Complementary proteomic analysis of protein complexes. Methods in Molecular Biology, 917, 391–407.

    Article  CAS  PubMed  Google Scholar 

  97. Omori, Y., Zhao, C., Saras, A., Mukhopadhyay, S., Kim, W., Furukawa, T., et al. (2008). Elipsa is an early determinant of ciliogenesis that links the IFT particle to membrane-associated small GTPase Rab8. Nature Cell Biology, 10(4), 437–444.

    Article  CAS  PubMed  Google Scholar 

  98. Papaioannou, V. E. (2014). The T-box gene family: Emerging roles in development, stem cells and cancer. Development, 141(20), 3819–3833.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Dharmat, R., Eblimit, A., Robichaux, M. A., Zhang, Z., Nguyen, T. T., Jung, S. Y., et al. (2018). SPATA7 maintains a novel photoreceptor-specific zone in the distal connecting cilium. The Journal of Cell Biology, 217(8), 2851–2865.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Bienvenu, F., Jirawatnotai, S., Elias, J. E., Meyer, C. A., Mizeracka, K., Marson, A., et al. (2010). Transcriptional role of cyclin D1 in development revealed by a genetic-proteomic screen. Nature, 463(7279), 374–378.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Dey, A., Seshasayee, D., Noubade, R., French, D. M., Liu, J., Chaurushiya, M. S., et al. (2012). Loss of the tumor suppressor BAP1 causes myeloid transformation. Science, 337(6101), 1541–1546.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Havugimana, P. C., Hart, G. T., Nepusz, T., Yang, H., Turinsky, A. L., Li, Z., et al. (2012). A census of human soluble protein complexes. Cell, 150(5), 1068–1081.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Kristensen, A. R., Gsponer, J., & Foster, L. J. (2012). A high-throughput approach for measuring temporal changes in the interactome. Nature Methods, 9(9), 907–909.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Wan, C., Borgeson, B., Phanse, S., Tu, F., Drew, K., Clark, G., et al. (2015). Panorama of ancient metazoan macromolecular complexes. Nature, 525(7569), 339–344.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Gingras, A. C., Abe, K. T., & Raught, B. (2018). Getting to know the neighborhood: Using proximity-dependent biotinylation to characterize protein complexes and map organelles. Current Opinion in Chemical Biology, 48, 44–54.

    Article  PubMed  CAS  Google Scholar 

  106. Roux, K. J., Kim, D. I., Raida, M., & Burke, B. (2012). A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. The Journal of Cell Biology, 196(6), 801–810.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Kim, D. I., Birendra, K. C., Zhu, W., Motamedchaboki, K., Doye, V., & Roux, K. J. (2014). Probing nuclear pore complex architecture with proximity-dependent biotinylation. Proceedings of the National Academy of Sciences of the United States of America, 111(24), E2453–E2461.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Hesketh, G. G., Youn, J. Y., Samavarchi-Tehrani, P., Raught, B., & Gingras, A. C. (2017). Parallel exploration of interaction space by BioID and affinity purification coupled to mass spectrometry. Methods in Molecular Biology, 1550, 115–136.

    Article  CAS  PubMed  Google Scholar 

  109. Uezu, A., Kanak, D. J., Bradshaw, T. W., Soderblom, E. J., Catavero, C. M., Burette, A. C., et al. (2016). Identification of an elaborate complex mediating postsynaptic inhibition. Science, 353(6304), 1123–1129.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Gupta, G. D., Coyaud, E., Goncalves, J., Mojarad, B. A., Liu, Y., Wu, Q., et al. (2015). A dynamic protein interaction landscape of the human centrosome-cilium Interface. Cell, 163(6), 1484–1499.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Han, J., & Kaufman, R. J. (2017). Physiological/pathological ramifications of transcription factors in the unfolded protein response. Genes & Development, 31(14), 1417–1438.

    Article  CAS  Google Scholar 

  112. Rees, J. S., Li, X. W., Perrett, S., Lilley, K. S., & Jackson, A. P. (2015). Protein neighbors and proximity proteomics. Molecular & Cellular Proteomics, 14(11), 2848–2856.

    Article  CAS  Google Scholar 

  113. Rhee, H. W., Zou, P., Udeshi, N. D., Martell, J. D., Mootha, V. K., Carr, S. A., et al. (2013). Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science, 339(6125), 1328–1331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Lam, S. S., Martell, J. D., Kamer, K. J., Deerinck, T. J., Ellisman, M. H., Mootha, V. K., et al. (2015). Directed evolution of APEX2 for electron microscopy and proximity labeling. Nature Methods, 12(1), 51–54.

    Article  CAS  PubMed  Google Scholar 

  115. Hung, V., Lam, S. S., Udeshi, N. D., Svinkina, T., Guzman, G., Mootha, V. K., et al. (2017). Proteomic mapping of cytosol-facing outer mitochondrial and ER membranes in living human cells by proximity biotinylation. eLife, 6, e24463.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Branon, T. C., Bosch, J. A., Sanchez, A. D., Udeshi, N. D., Svinkina, T., Carr, S. A., et al. (2018). Efficient proximity labeling in living cells and organisms with TurboID. Nature Biotechnology, 36(9), 880–887.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Chen, C. L., Hu, Y., Udeshi, N. D., Lau, T. Y., Wirtz-Peitz, F., He, L., et al. (2015). Proteomic mapping in live Drosophila tissues using an engineered ascorbate peroxidase. Proceedings of the National Academy of Sciences of the United States of America, 112(39), 12093–12098.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Reinke, A. W., Mak, R., Troemel, E. R., & Bennett, E. J. (2017). In vivo mapping of tissue- and subcellular-specific proteomes in Caenorhabditis elegans. Science Advances, 3(5), e1602426.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Rees, J. S., Li, X. W., Perrett, S., Lilley, K. S., & Jackson, A. P. (2015). Selective proteomic proximity labeling assay using Tyramide (SPPLAT): A quantitative method for the proteomic analysis of localized membrane-bound protein clusters. Current Protocols in Protein Science, 80, 19.27.11–19.27.18.

    Article  Google Scholar 

  120. Graham, J. M. (2002). OptiPrep density gradient solutions for mammalian organelles. TheScientificWorldJOURNAL, 2, 1440–1443.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. McClatchy, D. B., Liao, L., Lee, J. H., Park, S. K., & Yates 3rd, J. R. (2012). Dynamics of subcellular proteomes during brain development. Journal of Proteome Research, 11(4), 2467–2479.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Christoforou, A., Mulvey, C. M., Breckels, L. M., Geladaki, A., Hurrell, T., Hayward, P. C., et al. (2016). A draft map of the mouse pluripotent stem cell spatial proteome. Nature Communications, 7, 8992.

    Article  PubMed  CAS  Google Scholar 

  123. Itzhak, D. N., Davies, C., Tyanova, S., Mishra, A., Williamson, J., Antrobus, R., et al. (2017). A mass spectrometry-based approach for mapping protein subcellular localization reveals the spatial proteome of mouse primary neurons. Cell Reports, 20(11), 2706–2718.

    Article  CAS  PubMed  Google Scholar 

  124. Deal, R. B., & Henikoff, S. (2011). The INTACT method for cell type-specific gene expression and chromatin profiling in Arabidopsis thaliana. Nature Protocols, 6(1), 56–68.

    Article  CAS  PubMed  Google Scholar 

  125. Amin, N. M., Greco, T. M., Kuchenbrod, L. M., Rigney, M. M., Chung, M. I., Wallingford, J. B., et al. (2014). Proteomic profiling of cardiac tissue by isolation of nuclei tagged in specific cell types (INTACT). Development, 141(4), 962–973.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Sigg, M. A., Menchen, T., Lee, C., Johnson, J., Jungnickel, M. K., Choksi, S. P., et al. (2017). Evolutionary proteomics uncovers ancient associations of cilia with signaling pathways. Developmental Cell, 43(6), 744–762 e711.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Smits, A. H., Lindeboom, R. G., Perino, M., van Heeringen, S. J., Veenstra, G. J., & Vermeulen, M. (2014). Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs. Nucleic Acids Research, 42(15), 9880–9891.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Lombard-Banek, C., Moody, S. A., & Nemes, P. (2016). Single-cell mass spectrometry for discovery proteomics: Quantifying translational cell heterogeneity in the 16-cell frog (Xenopus) embryo. Angewandte Chemie, 55(7), 2454–2458.

    Article  CAS  PubMed  Google Scholar 

  129. Lombard-Banek, C., Reddy, S., Moody, S. A., & Nemes, P. (2016). Label-free quantification of proteins in single embryonic cells with neural fate in the cleavage-stage frog (Xenopus laevis) embryo using capillary electrophoresis electrospray ionization high-resolution mass spectrometry (CE-ESI-HRMS). Molecular & Cellular Proteomics, 15(8), 2756–2768.

    Article  CAS  Google Scholar 

  130. Sun, L., Dubiak, K. M., Peuchen, E. H., Zhang, Z., Zhu, G., Huber, P. W., et al. (2016). Single cell proteomics using frog (Xenopus laevis) Blastomeres isolated from early stage embryos, which form a geometric progression in protein content. Analytical Chemistry, 88(13), 6653–6657.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Virant-Klun, I., Leicht, S., Hughes, C., & Krijgsveld, J. (2016). Identification of maturation-specific proteins by single-cell proteomics of human oocytes. Molecular & Cellular Proteomics, 15(8), 2616–2627.

    Article  CAS  Google Scholar 

  132. Zhu, Y., Piehowski, P. D., Zhao, R., Chen, J., Shen, Y., Moore, R. J., et al. (2018). Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nature Communications, 9(1), 882.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Budnik, B., Levy, E., Harmange, G., & Slavov, N. (2018). SCoPE-MS: Mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biology, 19(1), 161.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Roberts, B., Haupt, A., Tucker, A., Grancharova, T., Arakaki, J., Fuqua, M. A., et al. (2017). Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization. Molecular Biology of the Cell, 28(21), 2854–2874.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Dalvai, M., Loehr, J., Jacquet, K., Huard, C. C., Roques, C., Herst, P., et al. (2015). A scalable genome-editing-based approach for mapping multiprotein complexes in human cells. Cell Reports, 13(3), 621–633.

    Article  CAS  PubMed  Google Scholar 

  136. Holding, A. N. (2015). XL-MS: Protein cross-linking coupled with mass spectrometry. Methods, 89, 54–63.

    Article  CAS  PubMed  Google Scholar 

  137. Yu, C., & Huang, L. (2018). Cross-linking mass spectrometry: An emerging Technology for Interactomics and Structural Biology. Analytical Chemistry, 90(1), 144–165.

    Article  CAS  PubMed  Google Scholar 

  138. Kyriacou, E., & Heun, P. (2018). High-resolution mapping of centromeric protein association using APEX-chromatin fibers. Epigenetics & Chromatin, 11(1), 68.

    Article  CAS  Google Scholar 

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Acknowledgments

This research was supported by the NIH grants R01HL127640, R01HL135007, and R01HL126509 to I.M.C, and by the NINS Strategic International Research Exchange Promotion Program to Y.H. and I.M.C.

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  1. Yutaka Hashimoto and Todd M. Greco have contributed equally to this work.

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  1. Department of Molecular Biology, Lewis Thomas Laboratory, Princeton University, Princeton, NJ, USA

    Yutaka Hashimoto, Todd M. Greco & Ileana M. Cristea

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  1. Yutaka Hashimoto
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  2. Todd M. Greco
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Correspondence to Ileana M. Cristea .

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  1. Biochemistry and Proteomics Group, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY, USA

    Alisa G. Woods

  2. Biochemistry and Proteomics Group, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY, USA

    Costel C. Darie

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Hashimoto, Y., Greco, T.M., Cristea, I.M. (2019). Contribution of Mass Spectrometry-Based Proteomics to Discoveries in Developmental Biology. In: Woods, A., Darie, C. (eds) Advancements of Mass Spectrometry in Biomedical Research. Advances in Experimental Medicine and Biology, vol 1140. Springer, Cham. https://doi.org/10.1007/978-3-030-15950-4_8

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