Simultaneous and quantitative monitoring transcription factors in human embryonic stem cell differentiation using mass spectrometry–based targeted proteomics

Abstract

Human embryonic stem cells (hESCs) can be self-propagated indefinitely in culture while holding the capacity to generate almost all cell types. Although this powerful differentiation ability of hESCs has become a potential source of cell replacement therapies, application of stem cells in clinical practice relies heavily on the exquisite control of their developmental fate. In general, an essential first step in differentiation is to exit the pluripotent state, which is precariously balanced and depends on a variety of factors, mainly centering on the core transcriptional mechanism. To date, much evidence has indicated that transcription factors such as Sox2, Oct4, and Nanog control the self-renewal and pluripotency of hESCs. Their expression displays a restricted spatial-temporal pattern and their small changes in level can significantly affect directed differentiation and the cell type derived. So far, few assays have been developed to monitor this process. Herein, we provided a mass spectrometry (MS)–based approach for simultaneous and quantitative monitoring of these transcription factors, in an attempt to provide insight into their contributions in hESC differentiation.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Vallier L, Touboul T, Brown S, Cho C, Bilican B, Alexander M, et al. Signaling pathways controlling pluripotency and early cell fate decisions of human induced pluripotent stem cells. Stem Cells. 2009;27(11):2655–66. https://doi.org/10.1002/stem.199.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, Zucker JP, et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell. 2005;122(6):947–56. https://doi.org/10.1016/j.cell.2005.08.020.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell. 2006;125(2):301–13. https://doi.org/10.1016/j.cell.2006.02.043.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Chen X, Xu H, Yuan P, Fang F, Huss M, Vega VB, et al. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell. 2008;133(6):1106–17. https://doi.org/10.1016/j.cell.2008.04.043.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Adachi K, Suemori H, Yasuda SY, Nakatsuji N, Kawase E. Role of SOX2 in maintaining pluripotency of human embryonic stem cells. Genes Cells. 2010;15(5):455–70. https://doi.org/10.1111/j.1365-2443.2010.01400.x.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Kopp JL, Ormsbee BD, Desler M, Rizzino A. Small increases in the level of Sox2 trigger the differentiation of mouse embryonic stem cells. Stem Cells. 2008;26(4):903–11. https://doi.org/10.1634/stemcells.2007-0951.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Rizzino A. Concise review: the Sox2-Oct4 connection: critical players in a much larger interdependent network integrated at multiple levels. Stem Cells. 2013;31(6):1033–9. https://doi.org/10.1002/stem.1352.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Niwa H, Miyazaki J, Smith AG. Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells. Nat Genet. 2000;24(4):372–6. https://doi.org/10.1038/74199.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Wang Z, Oron E, Nelson B, Razis S, Ivanova N. Distinct lineage specification roles for NANOG, OCT4, and SOX2 in human embryonic stem cells. Cell Stem Cell. 2012;10(4):440–54. https://doi.org/10.1016/j.stem.2012.02.016.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Addona TA, Abbatiello SE, Schilling B, Skates SJ, Mani DR, Bunk DM, et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol. 2009;27(7):633–41. https://doi.org/10.1038/nbt.1546.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Li N, Nemirovskiy OV, Zhang Y, Yuan H, Mo J, Ji C, et al. Absolute quantification of multidrug resistance-associated protein 2 (MRP2/ABCC2) using liquid chromatography tandem mass spectrometry. Anal Biochem. 2008;380(2):211–22. https://doi.org/10.1016/j.ab.2008.05.032.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Gillette MA, Carr SA. Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat Methods. 2013;10(1):28–34. https://doi.org/10.1038/nmeth.2309.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Takeda K, Mizushima T, Yokoyama Y, Hirose H, Wu X, Qian Y, et al. Sox2 is associated with cancer stem-like properties in colorectal cancer. Sci Rep. 2018;8(1):17639. https://doi.org/10.1038/s41598-018-36251-0.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Atakan S, Bayiz H, Sak S, Poyraz A, Vural B, Yildirim AS, et al. Autologous anti-SOX2 antibody responses reflect intensity but not frequency of antigen expression in small cell lung cancer. BMC Clin Pathol. 2014;14:24. https://doi.org/10.1186/1472-6890-14-24.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Yang F, Zhang J, Yang H. OCT4, SOX2, and NANOG positive expression correlates with poor differentiation, advanced disease stages, and worse overall survival in HER2(+) breast cancer patients. Onco Targets Ther. 2018;11:7873–81. https://doi.org/10.2147/OTT.S173522.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Sherman-Samis M, Onallah H, Holth A, Reich R, Davidson B. SOX2 and SOX9 are markers of clinically aggressive disease in metastatic high-grade serous carcinoma. Gynecol Oncol. 2019;153(3):651–60. https://doi.org/10.1016/j.ygyno.2019.03.099.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Stenman UH. Immunoassay standardization: is it possible, who is responsible, who is capable? Clin Chem. 2001;47(5):815–20.

    CAS  Article  Google Scholar 

  18. 18.

    Haab BB, Geierstanger BH, Michailidis G, Vitzthum F, Forrester S, Okon R, et al. Immunoassay and antibody microarray analysis of the HUPO Plasma Proteome Project reference specimens: systematic variation between sample types and calibration of mass spectrometry data. Proteomics. 2005;5(13):3278–91. https://doi.org/10.1002/pmic.200401276.

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics. 2006;5(4):573–88. https://doi.org/10.1074/mcp.M500331-MCP200.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Picotti P, Aebersold R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods. 2012;9(6):555–66. https://doi.org/10.1038/nmeth.2015.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Ong SE, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol. 2005;1(5):252–62. https://doi.org/10.1038/nchembio736.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Wasinger VC, Zeng M, Yau Y. Current status and advances in quantitative proteomic mass spectrometry. Int J Proteomics. 2013;2013:180605. https://doi.org/10.1155/2013/180605.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Vidova V, Spacil Z. A review on mass spectrometry-based quantitative proteomics: targeted and data independent acquisition. Anal Chim Acta. 2017;964:7–23. https://doi.org/10.1016/j.aca.2017.01.059.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Yuan F, Fang KH, Cao SY, Qu ZY, Li Q, Krencik R, et al. Efficient generation of region-specific forebrain neurons from human pluripotent stem cells under highly defined condition. Sci Rep. 2015;5:18550. https://doi.org/10.1038/srep18550.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Surmacz B, Fox H, Gutteridge A, Fish P, Lubitz S, Whiting P. Directing differentiation of human embryonic stem cells toward anterior neural ectoderm using small molecules. Stem Cells. 2012;30(9):1875–84. https://doi.org/10.1002/stem.1166.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Siller R, Greenhough S, Naumovska E, Sullivan GJ. Small-molecule-driven hepatocyte differentiation of human pluripotent stem cells. Stem Cell Reports. 2015;4(5):939–52. https://doi.org/10.1016/j.stemcr.2015.04.001.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Wisniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359–62. https://doi.org/10.1038/nmeth.1322.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Barnidge DR, Dratz EA, Martin T, Bonilla LE, Moran LB, Lindall A. Absolute quantification of the G protein-coupled receptor rhodopsin by LC/MS/MS using proteolysis product peptides and synthetic peptide standards. Anal Chem. 2003;75(3):445–51.

    CAS  Article  Google Scholar 

  29. 29.

    http://www.fda.gov/downloads/Drugs/Guidances/ucm070107.pdf, Guidance for Industry: Bioanalytical Method Validation, US Department of Health and Human Services, Food and Drug Administration, accessed November 24, 2020.

  30. 30.

    Xu F, Yang T, Fang D, Xu Q, Chen Y. An investigation of heat shock protein 27 and P-glycoprotein mediated multi-drug resistance in breast cancer using liquid chromatography-tandem mass spectrometry-based targeted proteomics. J Proteome. 2014;108:188–97. https://doi.org/10.1016/j.jprot.2014.05.016.

    CAS  Article  Google Scholar 

  31. 31.

    Yang T, Xu F, Xu J, Fang D, Yu Y, Chen Y. Comparison of liquid chromatography-tandem mass spectrometry-based targeted proteomics and conventional analytical methods for the determination of P-glycoprotein in human breast cancer cells. J Chromatogr B Analyt Technol Biomed Life Sci. 2013;936:18–24. https://doi.org/10.1016/j.jchromb.2013.07.023.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Vialas V, Sun Z, Loureiro y Penha CV, Carrascal M, Abian J, Monteoliva L, et al. A Candida albicans PeptideAtlas. J Proteome. 2014;97:62–8. https://doi.org/10.1016/j.jprot.2013.06.020.

    CAS  Article  Google Scholar 

  33. 33.

    Fusaro VA, Mani DR, Mesirov JP, Carr SA. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol. 2009;27(2):190–8. https://doi.org/10.1038/nbt.1524.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Liu L, Zhong T, Xu Q, Chen Y. Efficient molecular imprinting strategy for quantitative targeted proteomics of human transferrin receptor in depleted human serum. Anal Chem. 2015;87(21):10910–9. https://doi.org/10.1021/acs.analchem.5b02633.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Jiang WT, Liu L, Chen Y. Simultaneous detection of human C-terminal p53 isoforms by single template molecularly imprinted polymers (MIPs) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based targeted proteomics. Anal Chem. 2018;90(5):3058–66. https://doi.org/10.1021/acs.analchem.7b02890.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Lynch KL. CLSI C62-a: a new standard for clinical mass spectrometry. Clin Chem. 2016;62(1):24–9. https://doi.org/10.1373/clinchem.2015.238626.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Zhang X, Huang CT, Chen J, Pankratz MT, Xi J, Li J, et al. Pax6 is a human neuroectoderm cell fate determinant. Cell Stem Cell. 2010;7(1):90–100. https://doi.org/10.1016/j.stem.2010.04.017.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Engert S, Burtscher I, Liao WP, Dulev S, Schotta G, Lickert H. Wnt/beta-catenin signalling regulates Sox17 expression and is essential for organizer and endoderm formation in the mouse. Development. 2013;140(15):3128–38. https://doi.org/10.1242/dev.088765.

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Barthelery M, Salli U, Vrana KE. Nuclear proteomics and directed differentiation of embryonic stem cells. Stem Cells Dev. 2007;16(6):905–19. https://doi.org/10.1089/scd.2007.0071.

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, et al. The human transcription factors. Cell. 2018;172(4):650–65. https://doi.org/10.1016/j.cell.2018.01.029.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Jaenisch R, Young R. Stem cells, the molecular circuitry of pluripotency and nuclear reprogramming. Cell. 2008;132(4):567–82. https://doi.org/10.1016/j.cell.2008.01.015.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513(7518):382–7. https://doi.org/10.1038/nature13438.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Zhang B, Whiteaker JR, Hoofnagle AN, Baird GS, Rodland KD, Paulovich AG. Clinical potential of mass spectrometry-based proteogenomics. Nat Rev Clin Oncol. 2019;16(4):256–68. https://doi.org/10.1038/s41571-018-0135-7.

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE, et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell. 2016;166(3):755–65. https://doi.org/10.1016/j.cell.2016.05.069.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors are grateful for the financial support from Natural Science Foundation of China (21722504, 21675089), SEU-NJMU cooperation project (2242017K3DN12), SEU-NJMU-CPU cooperation project (2242019K3DNZ2), Primary Research & Development Plan of Jiangsu Province (BE2018725), and Open Foundation of State Key Laboratory of Reproductive Medicine [SKLRM-GA201804] awarded to Dr. Chen, and Natural Science Foundation of China (21605086), Natural Science Fund Project of Colleges in Jiangsu Province (16KJB150028) and Fundamental Research Funds for the Central Universities(2042019kf0129) awarded to Dr. Xu.

Author information

Affiliations

Authors

Contributions

Mengying Xu and Jianxiang Cao did the MS experiments and performed the statistical analysis. Lei Xu prepared the cells for the study. Yechen Hu and Feifei Xu drafted the manuscript. Yan Liu and Yun Chen designed the study and provide technological supports. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yan Liu or Yun Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This study was approved by the Institutional Review Board of Nanjing Medical University, Nanjing, China.

Additional information

Publisher’s note

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

Supplementary information

ESM 1

(PDF 932 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xu, M., Xu, L., Cao, J. et al. Simultaneous and quantitative monitoring transcription factors in human embryonic stem cell differentiation using mass spectrometry–based targeted proteomics. Anal Bioanal Chem 413, 2081–2089 (2021). https://doi.org/10.1007/s00216-021-03160-7

Download citation

Keywords

  • Human embryonic stem cells
  • Cell differentiation
  • Transcription factors
  • Mass spectrometry–based targeted proteomics
  • Protein quantification and monitoring