Insights into the regulation of protein abundance from proteomic and transcriptomic analyses
Open accessAbstract
Recent advances in next-generation DNA sequencing and proteomics provide an unprecedented ability to survey mRNA and protein abundances. Such proteome-wide surveys are illuminating the extent to which different aspects of gene expression help to regulate cellular protein abundances. Current data demonstrate a substantial role for regulatory processes occurring after mRNA is made — that is, post-transcriptional, translational and protein degradation regulation — in controlling steady-state protein abundances. Intriguing observations are also emerging in relation to cells following perturbation, single-cell studies and the apparent evolutionary conservation of protein and mRNA abundances. Here, we summarize current understanding of the major factors regulating protein expression.
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Other estimates for the contribution of mRNA levels in determining protein expression in mammals are lower than ours, suggesting that mRNA levels contribute 10%-40%.
“Other estimates for the contribution of mRNA levels in determining protein expression in mammals are lower than ours, suggesting that mRNA levels contribute 10%–40% (Maier, Guell & Serrano, 2009; Vogel & Marcotte, 2012).”
From System wide analyses have underestimated protein abundances and the importance of transcription in mammals (2014) · cites this paper as Vogel & Marcotte, 2012
In “Concentrations, production and turnover rates of mRNA and proteins at steady state” (page unavailable), the review says mRNA abundance explains approximately 40% of variation in protein concentration and includes mammalian cells among the organisms discussed. It does not provide the 10% estimate attributed to the cited sources collectively. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe regulation of senescence extends beyond transcription, and prior work has reported cases in which transcriptomic changes do not align with proteomic readouts.
“the regulation of senescence extends beyond transcription, and prior work has reported cases in which transcriptomic changes do not align with proteomic readouts [11,46,47,48].”
From Fluorescence-Guided Spatial Mapping of p21-Expressing Senescent Cells in Aged Liver at Single-Cell Resolution (2026) · cites this paper as [47]
The section “mRNA and protein levels in response to perturbation” reports cases in yeast and bacteria where changes in mRNA and protein abundance do not align. The Conclusions also describe regulation beyond transcription. Neither passage addresses senescence specifically, so the senescence-specific framing is not supported by this source. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredpQTL associations may capture regulatory mechanisms specific to proteins, explaining low correlations between mRNAs and their protein products.
“pQTL associations may capture regulatory mechanisms specific to proteins, explaining low correlations between mRNAs and their protein products (7).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [7]
The section on steady-state mRNA and protein levels reports that mRNA abundance explains only part of the variation in protein abundance and discusses regulation downstream of transcription. However, the passages do not address pQTL associations or say that those associations capture the regulatory mechanisms; that specific attribution is unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMessenger RNA and protein quantities often correlate poorly, emphasizing post-transcriptional control of protein synthesis and degradation.
“Several studies have reported that quantities of messenger RNA and proteins often correlate poorly, emphasizing the importance of post-transcriptional processes that control protein synthesis and degradation11.”
From Cell type- and brain region-resolved mouse brain proteome (2015) · cites this paper as [11]
mRNA abundance is not always a faithful proxy of protein expression across various organisms.
“Although transcriptomic profiling has provided insights into Tex cell biology, mRNA abundance is not always a faithful proxy of protein expression across various organisms18–22.”
From Proteotoxic stress response drives T cell exhaustion and immune evasion (2025) · cites this paper as [18]
The correlation between mRNA levels and their encoded protein levels is weak.
“Additionally, the correlation between levels of mRNA and their encoded proteins is weak8,9, while proteins are typically the molecule that most-directly act on disease.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [8]
mRNA and protein levels were modestly correlated, but over two thirds of these correlations were not statistically significant in the TCGA tumor set.
“mRNA and protein levels were modestly correlated, as earlier cell and animal model studies suggest36, but over two thirds of these correlations were not statistically significant in the TCGA tumor set.”
From Proteogenomic characterization of human colon and rectal cancer (2014) · cites this paper as [36]
Protein half-lives can vary many orders of magnitude and proteins have different translational rates.
“This is not surprising, since it known that protein half‐lives can vary many orders of magnitude and that proteins also have different translational rates (Schwanhäusser et al, 2011; Vogel & Marcotte, 2012).”
From Gene‐specific correlation of RNA and protein levels in human cells and tissues (2016) · cites this paper as Vogel & Marcotte, 2012
Comparisons of steady-state mRNA and protein abundances have revealed a large mismatch between mRNA and protein levels.
“comparing steady state mRNA and protein abundances has indicated a considerable discrepancy between mRNA and protein levels (5, 7–13).”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [5]
How much each step of RNA and protein turnover contributes to shifts in protein expression is still unclear and disputed.
“the quantitative contribution of each process to changes in protein expression levels (Fig. 1A) remains largely unknown and much debated (5, 6).”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [5]
Few studies have examined how mRNA, translation, and degradation changes shape differential protein expression during dynamic responses, and none has fully accounted for measurement error or disentangled interdependencies in the data.
“only a few studies (5, 12, 17–20) have attempted to shed light on how changes in each of these processes determine differential protein expression – either relative (ratios) or absolute (differences) – during dynamic responses and only one recent report (20) has attempted to quantitate each process. However, none of these has comprehensively accounted for experimental measurement errors, nor have”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [5]
The consensus across studies holds that, in a wide array of organisms, transcriptional regulation explains 30-50% of the variation in steady-state protein levels, leaving half or more to be explained by post-transcriptional regulatory processes.
“The consensus across these studies holds that, in a wide array of organisms, transcriptional regulation explains 30–50% of the variation in steady-state protein levels, leaving half or more to be explained by post-transcriptional regulatory processes [3, 7, 9–16].”
Between-study correlations are modest, with median r = 0.62 for mRNA measurements and 0.57 for protein measurements.
“Although correlations of replicates within studies are quite high [9], with median r = 0.97 for mRNA and 0.93 for protein levels, between-study correlations are far more modest, r = 0.62 for mRNA measurements and 0.57 for protein measurements (Fig 1C).”
Most quantification methods are biased toward detection of more abundant mRNAs and proteins.
“However, most quantification methods are biased toward detection of more abundant mRNAs and proteins [9].”
Noise in measurements can cause many of the observations attributed to post-transcriptional regulation.
“However, as frequently noted [1, 7, 9, 10, 19–21], noise in measurements can cause many of the observations attributed to post-transcriptional regulation.”
Within-study replicate correlations are high, with median r = 0.97 for mRNA and 0.93 for protein levels.
“Although correlations of replicates within studies are quite high [9], with median r = 0.97 for mRNA and 0.93 for protein levels, between-study correlations are far more modest, r = 0.62 for mRNA measurements and 0.57 for protein measurements (Fig 1C).”
Data that are not missing at random due to biased detection have reduced variance or restricted range.
“Data which are not missing at random (NMAR) in this way have reduced variance or restricted range.”
The largest pair of datasets covers 4,367 genes and shows an mRNA-protein correlation of r = 0.618 (r^2 = 0.38).
“The largest pair of datasets covers 4,367 genes and shows an mRNA–protein correlation of r = 0.618 (r 2 = 0.38, 38% of protein-level variance explained by mRNA levels), close to consensus values [9].”
Dynamic-range amplification must involve post-transcriptional variation.
“As previously noted [9], this dynamic-range amplification must involve post-transcriptional variation.”
Integration of RNA-seq with proteomics is controversial because the two measurements show generally low correlation (~0.40).
“Integration of RNA-seq with proteomics is controversial because the two measurements show generally low correlation (~0.40 [158, 159]).”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [159]
Protein abundances are more direct determinants of cellular functions than transcript levels.
“Yet protein abundances are more direct determinants of cellular functions (4)”
From Impact of regulatory variation from RNA to protein (2015) · cites this paper as [4]
Transcript levels can explain only one- to two-thirds of protein levels.
“Indeed, transcript levels can only explain one- to two-thirds of protein levels, underlining the importance of post-transcriptional control (Liu et al., 2016, Vogel and Marcotte, 2012).”
From A Multi-layered Quantitative In Vivo Expression Atlas of the Podocyte Unravels Kidney Disease Candidate Genes (2018) · cites this paper as Vogel and Marcotte, 2012
Absolute abundances of mRNA and protein correlate only moderately.
“However, several studies from different fields found that absolute abundances of mRNA and protein abundances correlate only moderately.”
From A Multi-layered Quantitative In Vivo Expression Atlas of the Podocyte Unravels Kidney Disease Candidate Genes (2018) · cites this paper as Vogel and Marcotte, 2012
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- Authors
- Vogel, C., & Marcotte, E. M.
- Affiliations
- New York University (US)
- The University of Texas at Austin (US)
- Corresponding author
- Vogel, Christine
- Marcotte, Edward M.
- Venue
- Nature Reviews Genetics
- Published
- 2012-03-13
- Volume
- 13
- Issue
- 4
- Pages
- 227-232
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3654667
- OpenAlex
- W1973268693
- Semantic Scholar
- 119839d2861f7293f7a982a421a577799a9dcac6
- Cited by
- 4,195
- References
- 54
- Citations by year
- 2026: 409 · 2025: 312 · 2024: 278 · 2023: 258 · 2022: 338 · 2021: 343 · 2020: 382 · 2019: 303 · 2018: 302 · 2017: 301 · 2016: 293 · 2015: 271 · 2014: 254 · 2013: 119 · 2012: 26
- FWCI
- 20.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Welch Foundation
- National Institute of General Medical Sciences (DP1GM106408, R01GM076536)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- protein abundance, mRNA levels, post-transcriptional regulation, translational regulation, protein degradation, gene expression regulation, mRNA-protein correlation, single-cell transcriptomics
- MeSH terms
- Proteins, RNA, Messenger, Proteome, Gene Expression Profiling, Proteomics
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