Absolute quantification of transcription factors during cellular differentiation using multiplexed targeted proteomics

Journal name:
Nature Methods
Year published:
Published online


The cellular abundance of transcription factors (TFs) is an important determinant of their regulatory activities. Deriving TF copy numbers is therefore crucial to understanding how these proteins control gene expression. We describe a sensitive selected reaction monitoring–based mass spectrometry assay that allowed us to determine the copy numbers of up to ten proteins simultaneously. We applied this approach to profile the absolute levels of key TFs, including PPARγ and RXRα, during terminal differentiation of mouse 3T3-L1 pre-adipocytes. Our analyses revealed that individual TF abundance differs dramatically (from ~250 to >300,000 copies per nucleus) and that their dynamic range during differentiation can vary up to fivefold. We also formulated a DNA binding model for PPARγ based on TF copy number, binding energetics and local chromatin state. This model explains the increase in PPARγ binding sites during the final differentiation stage that occurs despite a concurrent saturation in PPARγ copy number.

At a glance


  1. Workflow for the absolute quantification of TFs in 3T3-L1 cells.
    Figure 1: Workflow for the absolute quantification of TFs in 3T3-L1 cells.

    (a) Left, preparation of 3T3-L1 total nuclear protein extract (NE). Cells are lysed at the indicated differentiation time point (D0–D6; D, day; H, hour), after which nuclear proteins are extracted. The resulting protein mixture is separated by SDS-PAGE, and TF bands are excised from the gel. Right, preparation of in vitro–expressed SH-tagged TFs. The constructs are expressed as heavy-labeled versions (*), purified by glutathione S-transferase (GST) affinity and separated by SDS-PAGE. Bands containing the heavy-labeled constructs, here SH-RXRα-GST*, are excised from the gel. Center, each nuclear extract band to be quantified is mixed with a gel slice of the in vitro–expressed TF construct, spiked with known amounts of light SH-quant tag and digested in gel. SH-quant features a C-terminal trypsin-cleavable fluorescent tag (here termed JPT) that is used to quantify this quantotypic peptide. The resulting peptide mixtures are quantified by SRM using proteotypic peptides selected by performing shotgun mass spectrometry analyses on each in vitro–expressed TF. Quantification of each TF requires a separate experiment in this configuration. (b) Schematic of the quantification approach as outlined in a. (c, i) RXRα protein sequence with tryptic peptides sequences used for quantitation highlighted in green and magenta. (c, ii) SRM chromatogram of the five RXRα proteotypic fragments and the SH-quant tag. (c, iii,iv) Enlarged view of the SH-quant peptide abundance for its light and heavy isoforms (iii) and their transitions (Pinpoint software) (iv).

  2. Summary of RXR[alpha] and PPAR[gamma] levels quantified by SRM.
    Figure 2: Summary of RXRα and PPARγ levels quantified by SRM.

    (a) RXRα and PPARγ concentrations at each sampled differentiation time point (day 0, hour 2 and days 1, 2, 4 and 6), expressed as femtomoles per microgram of nuclear extract (NE), in three individual biological replicates. (b) Absolute RXRα and PPARγ levels displayed as copies per cell during terminal fat cell differentiation (mean ± s.e.m.; n = 3 biological replicates).

  3. Quantitative modeling of genome-wide PPAR[gamma] DNA binding.
    Figure 3: Quantitative modeling of genome-wide PPARγ DNA binding.

    (a) Cumulative number of genomic sites in H3K27ac regions at day 0 (D0; gray line), day 2 (D2; dashed line) and day 6 (D6; black line). The x axis runs from the strongest-affinity (that is, 1,000-fold stronger than nonspecific sites) to medium-affinity (that is, 60-fold stronger) sites. (b) Number of detected bound loci at D0 (gray), D2 (dashed) and D6 (black) during 3T3-L1 terminal differentiation as a function of the detection threshold on the expected occupancy. The model takes into account the measured PPARγ copies per cell (nucleus) and the distribution of accessible high-affinity sites. (c) Temporal pattern for the predicted number of detected PPARγ-bound sites (dashed), actual number of measured sites (black) and protein copy number (gray). The number of sites shows an exponential-like increase, whereas the protein copy-number graph reflects saturation.

  4. Properties of the SH-quant tag variants.
    Figure 4: Properties of the SH-quant tag variants.

    (a) Peptide tags specifically designed for multiplex absolute quantification. RT, retention time; M, molecular mass; H, hydrogen. (b) Liquid chromatography (LC)-SRM chromatogram showing the extent of separation of the peaks belonging to the SH-quant peptide variants; proper separation was achieved for most tags (numbered as in a) using a short LC gradient of 60 min. (c) LC-SRM chromatogram of a multiplexed nuclear extract sample obtained with a 120-min gradient. (d) Enlarged view of the area shaded in c, showing the extracted ion current of isobaric SH-quant tags 9 and 1 (m/z = 491.2) in a multiplex sample. Top, representation including other peptide species; bottom, filtered representation including the two tags only. (e) Calculation of the peak areas of the light and heavy peptide counterparts using Pinpoint. The difference in physicochemical properties of two isobaric SH-quant tags 9 and 1 resulted in separate elution times, 30.7 min (9; AAEVTSLYK) and 35 min (1; AADITSLYK), respectively. Identical elution times were observed between the light and heavy counterparts in each of the ten SH-quant tags.

  5. Simultaneous monitoring of the nuclear abundance of ten TFs during terminal adipogenesis via spiking of in vitro-expressed full-length TFs.
    Figure 5: Simultaneous monitoring of the nuclear abundance of ten TFs during terminal adipogenesis via spiking of in vitro–expressed full-length TFs.

    (a) Multiplex SRM transition profiles of selected best-responding tryptic peptides from all ten TFs (light and heavy) during a 120-min liquid chromatography–SRM run. (b) Enlarged view (minutes 29–36) of the shaded region in a, illustrating the complexity of the mixture and the separation quality achieved with the multiplex SRM analysis on nuclear extract sample. (c) Calculated endogenous levels of all ten TFs detected at day 0, day 2 and day 4 (mean ± s.e.m.; n = 4–6 technical replicates). NE, nuclear extract.


  1. Simicevic, J. & Deplancke, B. DNA-centered approaches to characterize regulatory protein-DNA interaction complexes. Mol. Biosyst. 6, 462468 (2010).
  2. Kim, H.D., Shay, T., O'Shea, E.K. & Regev, A. Transcriptional regulatory circuits: predicting numbers from alphabets. Science 325, 429432 (2009).
  3. Bussemaker, H.J., Foat, B.C. & Ward, L.D. Predictive modeling of genome-wide mRNA expression: from modules to molecules. Annu. Rev. Biophys. Biomol. Struct. 36, 329347 (2007).
  4. Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nat. Rev. Genet. 10, 443456 (2009).
  5. Stormo, G.D. & Zhao, Y. Determining the specificity of protein-DNA interactions. Nat. Rev. Genet. 11, 751760 (2010).
  6. Biggin, M.D. Animal transcription networks as highly connected, quantitative continua. Dev. Cell 21, 611626 (2011).
  7. Vaquerizas, J.M., Kummerfeld, S.K., Teichmann, S.A. & Luscombe, N.M. A census of human transcription factors: function, expression and evolution. Nat. Rev. Genet. 10, 252263 (2009).
  8. Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 69406945 (2003).
  9. Picotti, P., Bodenmiller, B., Mueller, L.N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795806 (2009).
  10. Picotti, P. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266270 (2013).
  11. Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577583 (2005).
  12. Brun, V. et al. Isotope-labeled protein standards: toward absolute quantitative proteomics. Mol. Cell Proteomics 6, 21392149 (2007).
  13. Hanke, S., Besir, H., Oesterhelt, D. & Mann, M. Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the attomole level. J. Proteome Res. 7, 11181130 (2008).
  14. Stergachis, A.B., MacLean, B., Lee, K., Stamatoyannopoulos, J.A. & MacCoss, M.J. Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods 8, 10411043 (2011).
  15. Zeiler, M., Straube, W.L., Lundberg, E., Uhlen, M. & Mann, M. A protein epitope signature tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines. Mol. Cell Proteomics 11, O111.009613 (2012).
  16. Pratt, J.M. et al. Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Nat. Protoc. 1, 10291043 (2006).
  17. Holzmann, J., Pichler, P., Madalinski, M., Kurzbauer, R. & Mechtler, K. Stoichiometry determination of the MP1-p14 complex using a novel and cost-efficient method to produce an equimolar mixture of standard peptides. Anal. Chem. 81, 1025410261 (2009).
  18. Singh, S., Springer, M., Steen, J., Kirschner, M.W. & Steen, H. FLEXIQuant: a novel tool for the absolute quantification of proteins, and the simultaneous identification and quantification of potentially modified peptides. J. Proteome Res. 8, 22012210 (2009).
  19. Wepf, A., Glatter, T., Schmidt, A., Aebersold, R. & Gstaiger, M. Quantitative interaction proteomics using mass spectrometry. Nat. Methods 6, 203205 (2009).
  20. Proc, J.L. et al. A quantitative study of the effects of chaotropic agents, surfactants, and solvents on the digestion efficiency of human plasma proteins by trypsin. J. Proteome Res. 9, 54225437 (2010).
  21. Kuhn, E. et al. Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma. Mol. Cell Proteomics 11, M111.013854 (2012).
  22. Ahmed, M., Neville, M.J., Edelmann, M.J., Kessler, B.M. & Karpe, F. Proteomic analysis of human adipose tissue after rosiglitazone treatment shows coordinated changes to promote glucose uptake. Obesity (Silver Spring) 18, 2734 (2010).
  23. Molina, H. et al. Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J. Proteome Res. 8, 4858 (2009).
  24. Mirzaei, H., McBee, J.K., Watts, J. & Aebersold, R. Comparative evaluation of current peptide production platforms used in absolute quantification in proteomics. Mol. Cell Proteomics 7, 813823 (2008).
  25. Dupuis, A., Hennekinne, J.A., Garin, J. & Brun, V. Protein Standard Absolute Quantification (PSAQ) for improved investigation of staphylococcal food poisoning outbreaks. Proteomics 8, 46334636 (2008).
  26. Nielsen, R. et al. Genome-wide profiling of PPARγ:RXR and RNA polymerase II occupancy reveals temporal activation of distinct metabolic pathways and changes in RXR dimer composition during adipogenesis. Genes Dev. 22, 29532967 (2008).
  27. Kaplan, T. et al. Quantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila development. PLoS Genet. 7, e1001290 (2011).
  28. Segal, E., Raveh-Sadka, T., Schroeder, M., Unnerstall, U. & Gaul, U. Predicting expression patterns from regulatory sequence in Drosophila segmentation. Nature 451, 535540 (2008).
  29. Mikkelsen, T.S. et al. Comparative epigenomic analysis of murine and human adipogenesis. Cell 143, 156169 (2010).
  30. Rey, G. et al. Genome-wide and phase-specific DNA-binding rhythms of BMAL1 control circadian output functions in mouse liver. PLoS Biol. 9, e1000595 (2011).
  31. Raghav, S.K. et al. Integrative genomics identifies the corepressor SMRT as a gatekeeper of adipogenesis through the transcription factors C/EBPβ and KAISO. Mol. Cell 46, 335350 (2012).
  32. Siersbæk, R. et al. Extensive chromatin remodelling and establishment of transcription factor 'hotspots' during early adipogenesis. EMBO J. 30, 14591472 (2011).
  33. Lamesch, P. et al. hORFeome v3.1: a resource of human open reading frames representing over 10,000 human genes. Genomics 89, 307315 (2007).
  34. Hens, K. et al. Automated protein-DNA interaction screening of Drosophila regulatory elements. Nat. Methods 8, 10651070 (2011).
  35. Whiteaker, J.R. et al. A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat. Biotechnol. 29, 625634 (2011).
  36. Farnham, P.J. Insights from genomic profiling of transcription factors. Nat. Rev. Genet. 10, 605616 (2009).
  37. John, S. et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat. Genet. 43, 264268 (2011).
  38. 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, 53835392 (2002).
  39. Nesvizhskii, A.I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 46464658 (2003).
  40. Desiere, F. et al. Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry. Genome Biol. 6, R9 (2005).
  41. Rosen, E.D. & MacDougald, O.A. Adipocyte differentiation from the inside out. Nat. Rev. Mol. Cell Biol. 7, 885896 (2006).
  42. Thakur, S.S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, M110.003699 (2011).
  43. Prakash, A. et al. Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development. J. Proteome Res. 8, 27332739 (2009).
  44. Picotti, P. et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 7, 4346 (2010).
  45. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966968 (2010).
  46. Brownridge, P. & Beynon, R.J. The importance of the digest: proteolysis and absolute quantification in proteomics. Methods 54, 351360 (2011).
  47. Jaquinod, M. et al. Mass spectrometry-based absolute protein quantification: PSAQ™ strategy makes use of “noncanonical” proteotypic peptides. Proteomics 12, 12171221 (2012).
  48. Guidance for industry: bioanalytical method validation. left fencehttp://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM070107.pdfright fence (Food and Drug Administration, US Department of Health and Human Services, 2001).
  49. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 10171018 (2011).
  50. Vlieghe, D. et al. A new generation of JASPAR, the open-access repository for transcription factor binding site profiles. Nucleic Acids Res. 34, D95D97 (2006).

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Author information

  1. These authors contributed equally to this work.

    • Jovan Simicevic,
    • Adrien W Schmid &
    • Paola A Gilardoni


  1. Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

    • Jovan Simicevic,
    • Paola A Gilardoni,
    • Sunil K Raghav,
    • Irina Krier,
    • Carine Gubelmann &
    • Bart Deplancke
  2. Proteomics Core Facility, School of Life Sciences, EPFL, Lausanne, Switzerland.

    • Adrien W Schmid &
    • Marc Moniatte
  3. Laboratory of Computational Systems Biology, Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland.

    • Benjamin Zoller &
    • Felix Naef
  4. Swiss Institute of Bioinformatics, Geneva, Switzerland.

    • Frédérique Lisacek


Conceived and planned the study: J.S., A.W.S., P.A.G., M.M. and B.D. Prepared the manuscript: J.S., A.W.S., P.A.G., B.Z., F.N., M.M. and B.D. Performed wet bench experiments: J.S., A.W.S., P.A.G., S.K.R. and C.G. Performed mass spectrometry data analysis: A.W.S. Performed biological data analysis: J.S., P.A.G. and B.D. Performed modeling: B.Z., I.K., F.N. and B.D. All the authors discussed the results and commented on the paper.

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The authors declare no competing financial interests.

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Supplementary information

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  1. Supplementary Text and Figures (10.9MB)

    Supplementary Figures 1–18, Supplementary Tables 1–4 and Supplementary Note

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  1. Supplementary Data 1 (74.7 KB)

    Proteotypic peptide selection for the ten TFs (multiplex)

  2. Supplementary Data 2 (60.9 KB)

    Precursor-to-product–ion transitions selected for SRM (tenTFs)

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