Global quantification of mammalian gene expression control

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Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.

At a glance


  1. Parallel quantification of mRNA and protein turnover and levels.
    Figure 1: Parallel quantification of mRNA and protein turnover and levels.

    a, Mouse fibroblasts were pulse labelled with heavy amino acids (SILAC, left) and the nucleoside 4-thiouridine (4sU, right). Protein and mRNA turnover was quantified by mass spectrometry and next-generation sequencing, respectively. b, Mass spectra of peptides from a high- and low-turnover protein reveal increasing heavy to light (H/L) ratios over time. c, Protein half-lives were calculated from log H/L ratios at all three time points using linear regression. d, Variability of linear regression slopes assessed by leave-one-out cross-validation was small.

  2. mRNA and protein levels and half-lives.
    Figure 2: mRNA and protein levels and half-lives.

    a, b, Histograms of mRNA (blue) and protein (red) half-lives (a) and levels (b). Proteins were on average 5 times more stable and 2,800 times more abundant than mRNAs and spanned a higher dynamic range. c, d, Although mRNA and protein levels correlated significantly, correlation of half-lives was virtually absent.

  3. Quantitative model of gene expression in growing cells.
    Figure 3: Quantitative model of gene expression in growing cells.

    a, mRNAs are synthesized with the rate vsr and degraded with a rate constant kdr. Proteins are translated and degraded with rate constants ksp and kdp, respectively. b, Calculated mRNA transcription rates show a uniform distribution. c, Calculated translation rate constants are not uniform. d, Translation rate constants of abundant proteins saturate between approximately 750 and 1,300 proteins per mRNA per hour. Red line shows the locally weighted fit (Lowess). Dashed lines indicate 95% confidence intervals of the Lowess maximum value calculated by bootstrapping.

  4. Impact of different rates and rate constants on protein abundance.
    Figure 4: Impact of different rates and rate constants on protein abundance.

    a, Protein levels are best explained by translation rates, followed by transcription rates. mRNA and protein stability is less important (left bar). b, In the replicate experiment mRNA levels explained 37% of protein levels in NIH3T3 cells (middle bar in a). c, The model explains 85% of variance in protein levels from measured mRNA levels (middle bar in a). The mouse fibroblast model has some predictive power for human orthologous genes in MCF7 cells (right bar in a). Error bars show 95% confidence intervals estimated by bootstrapping.

  5. Functional characteristics of genes with different mRNA and protein
    Figure 5: Functional characteristics of genes with different mRNA and protein half-lives.

    Genes were grouped according to their combination of mRNA and protein half-lives and analysed for enriched gene ontology terms. A heat map of enrichment P-values reveals functional similarities of genes with similar combinations of half-lives.

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Primary accessions

Sequence Read Archive


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Author information


  1. Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13092 Berlin, Germany

    • Björn Schwanhäusser,
    • Dorothea Busse,
    • Na Li,
    • Gunnar Dittmar,
    • Jana Wolf,
    • Wei Chen &
    • Matthias Selbach
  2. MicroDiscovery GmbH, Marienburger Str. 1, D-10405 Berlin, Germany

    • Johannes Schuchhardt


M.S. conceived, designed and supervised the experiments. B.S. performed wet-lab experiments, mass spectrometry and proteomic data analysis. D.B. and J.W. developed and employed the mathematical model. N.L. performed RNA-seq experiments. W.C. designed and supervised RNA-seq experiments. B.S., D.B., J.S., W.C. and M.S. analysed genome-wide data. G.D. helped in cycloheximide chase experiments and data analysis. B.S., D.B., J.S., J.W., W.C. and M.S. interpreted the data. M.S. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Sequences have been deposited in the Sequence Read Archive under accession code SRA030871.

Author details

Supplementary information

PDF files

  1. Supplementary Figures (2M)

    This file contains Supplementary Figures 1-12 with legends. This file was replaced on 13 February 2013 - see Selbach 11848 corrigendum for details.

  2. Supplementary Methods (739K)

    This file contains Supplementary Methods and Data, Supplementary Figures 1-4 with legends and additional references.

Excel files

  1. Supplementary Table 1 (31K)

    This table displays an overview of data reproducibility. This file was replaced on 13 February 2013 - see Selbach 11848 corrigendum for details.

  2. Supplementary Table 2 (72K)

    This table displays categories enriched in bins of genes with the specified combinations of protein and mRNA half-lives.

  3. Supplementary Table 3 (3.2M)

    This table displays protein and mRNA copy numbers, half-lives, transcription rates and translation rate constants in mouse fibroblasts (NIH 3T3). This file was replaced on 13 February 2013 - see Selbach 11848 corrigendum for details.


  1. Report this comment #24114

    Gilles Merlin said:

    Very nice work indeed. I would like to add that there may be a difference between the absolute abundance of a protein and the absolute abundance of a functional protein. For example, when I was working on a membrane receptor, depending on the cell lines, I could find small differences in the number of receptors per cell (as determined by ligand binding), while there could exist large differences of abundance as seen on Western blots, suggesting that, in some cases, proteins may be abundant, but not all molecules be functional, depending on their localization, post-translational modification, or else ...

  2. Report this comment #33464

    Marcelo Segura said:

    Definitely this was a must-do task and I hope new works like this start coming up, either repeats and in other species. The myth of a general principle of high coexpression for coexpressed proteins (or complexes) has been there for too long (or maybe is real, but clearly that has not been proven as most people assume).Even here the authors seemed compelled to show that the correlation messenger/protein is high. Notice how correlations were taken from logged values, which usually has the effect of improving the value in comparison to the correlation of the original variable. In any case the dissection of contributions from synthesis and degradation to this correlation is very illuminating.

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