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

Journal name:
Nature Methods
Volume:
10,
Pages:
570–576
Year published:
DOI:
doi:10.1038/nmeth.2441
Received
Accepted
Published online

Abstract

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

Figures

  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.

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Author information

  1. These authors contributed equally to this work.

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

Affiliations

  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

Contributions

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.

Competing financial interests

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

Excel files

  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)

Additional data