The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 5 of the 34 sources its claims cite were checked for support.
Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
Open accessAbstract
Devil in the Detail: Genetically identical cells in the same environment can show variation in gene expression that may cause phenotypic variation at the single-cell level. But how noisy are most genes? Taniguchi et al. (p. 533 ; see the Perspective by Tyagi ) now report single-cell global profiling of both messenger RNA (mRNA) and proteins in Escherichia coli using a yellow fluorescent protein fusion library. As well as a common extrinsic noise in high-abundance proteins, large fluctuations were observed in low-abundance proteins. Remarkably, in single-cell experiments, mRNA and protein levels for the same gene were uncorrelated.
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Claims the paper makes
5 of 34 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Such stochasticity is manifested in fluctuations of mRNA and protein copy numbers within a cell lineage over time, and in variations of mRNA and protein copy numbers among a population of genetically identical cells at a particular time (1, 2, 3, 4).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
- Not checked — no full text of the sourcecites [1] source
- Partly supportedcites [2] sourceExperimental — accuracy not yet measured
The Abstract says stochastic gene expression can produce variation among identical cells, and its model predicts cell-to-cell variability in mRNA levels for both eukaryotic and prokaryotic promoters. However, the paper's single-cell protein-reporter measurements were made in yeast, not bacteria; it does not establish that both protein and mRNA copy numbers differ among individual bacterial cells. (LLM, conf=0.83)
- Not checked — no full text of the sourcecites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Single molecule sensitivity was confirmed by abrupt photobleaching of membrane-bound YFPs expressed at low level (Fig. S14) (8, 18, 23).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
What the sources say
- Not checked — no full text of the sourcecites [8] source
- Not checked — no full text of the sourcecites [18] source
- Could not verifycites [23] sourceExperimental — accuracy not yet measured
The Results and discussion passage on deletion-mutant verification describes PCR checks, while Materials and methods > Growth tests describes absorbance measurements; neither addresses single-molecule imaging. Searches for photobleaching, membrane-bound YFP, and fluorescence imaging found no passage confirming the claimed sensitivity test. Page numbers were not available in our copy. (LLM, conf=0.78)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“We found that E. coli proteins generally had larger noise and Fano factors than yeast proteins, even for those present at similar copy numbers (Fig. S6) (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Comparison between E. coli and yeast
What the sources say
- Not checked — no full text of the sourcecites [18] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, we found that in a single cell mRNA and protein levels for the same gene are completely uncorrelated.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The distributions for 1,009 out of the 1,018 strains can be well fit by the gamma distribution, Eq. 2 (Fig. S20) (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Analysis of protein distributions
What the sources say
- Not checked — no full text of the sourcecites [18] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Below ten molecules per cell, ηp2 is inversely proportional to protein abundance, indicative of intrinsic noise.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“mRNA and protein copy numbers in a single cell are not correlated (r = 0.01 ± 0.03, SEM, N = 5,447).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The protein noise (ηp2 ≡ σp2/μp2) exhibits two distinct scaling properties (Fig. 2B).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The average protein abundances of essential genes are higher than those for all genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Average protein abundances span five orders of magnitude, ranging from 10-1 to 104 molecules per cell (Fig 2A).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 13
- Probably not a substantial claim. 3
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“In recent years, single-molecule fluorescence microscopy has been used to count mRNA (12-16) or protein (8, 17) molecules in individual cells, especially in bacteria. However, these methods have only been applied to limited number of specific genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
- Not checked — no full text of the sourcecites [12] source
- Supportedcites [13] source
The Results section (page not available) describes fluorescence microscopy used to count individual mRNA molecules from a specific reporter gene in mammalian cells. This paper supports the mRNA-counting citation as an example; the sentence’s broader reference to bacteria does not describe this study. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [14] source
- Supportedcites [15] source
The passage labeled Abstract describes using fluorescence in situ hybridization and microscopy to count individual comK mRNA molecules in single Bacillus subtilis cells. It also describes counting an engineered comK transcript, making this paper an example of the gene-specific, single-cell mRNA measurements for which it is cited. (LLM, conf=0.91)
- Supportedcites [16] source
The Abstract reports counting individual mRNAs in single yeast cells for different genes. The Discussion describes extending the method to analyze multiple genes simultaneously as future work. This paper supports the mRNA-counting example for which it is cited; the sentence assigns protein counting to other citations. (LLM, conf=0.91)
- Not checked — no full text of the sourcecites [8] source
- Not checked — no full text of the sourcecites [17] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, at higher expression levels (>10 molecules per cell), the noise reaches a plateau of ∼0.1 and does not decrease further, suggesting that each protein has at least 30% variation in its expression level.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“We found that the noise floor does not result from cell size effects, nor did it arise from measurement noise (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
What the sources say
- Not checked — no full text of the sourcecites [18] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We discovered large fluctuations in low abundance proteins as well as a common extrinsic noise in high abundance proteins.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We further demonstrate that the extrinsic noise is global to all high expression genes by analyzing the correlations between expression levels of 13 pairs of randomly selected genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The prevalence of proteins with very low copy number suggests that single-molecule experiments are necessary for bacteriology.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This striking result highlights the disconnect between proteome and transcriptome analyses of a single cell, as well as the need for single cell proteome analysis.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“A fluorescent protein reporter library of Saccharomyces cerevisiae (9) has proven to be extremely useful in protein profiling (10, 11).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, our result offers a cautionary note in interpreting single-cell transcriptome analysis and argues for the necessity for single cell proteome analysis.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“We attribute the additional noise to extrinsic noise (3), that is the slow variation of the values of a and b, which we confirm with real time observation of protein levels for four randomly selected high copy library strains.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
What the sources say
- Not checked — no full text of the sourcecites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that some proteins and most mRNAs of functional genes are present at low copy numbers in a bacterial cell, the single molecule sensitivity afforded by our measurements is necessary for the understanding of stochastic gene expression and regulation.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In E. coli, mRNA is typically degraded within minutes (Table S6) (18), whereas most proteins, including fluorescent proteins, have a lifetime longer than the cell cycle (34).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
What the sources say
- Not checked — no full text of the sourcecites [18] source
- Not matchedcites [34]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 1,400 strains attempted, 1,018 strains were confirmed by sequencing and showed no significant growth defects.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Because the same probe is used for all strains, the optimized hybridization efficiency is unbiased for every measured gene (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule RNA counting
What the sources say
- Not checked — no full text of the sourcecites [18] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“While single cell mRNA profiling has been carried out with cDNA microarray (6) and mRNA-seq (7), these studies did not have single molecule sensitivity and are not suitable for bacteria, which express mRNA at low copy numbers (8).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
Gene expression dynamics follow a kinetic scheme with transcription, translation, mRNA degradation, and protein degradation rates.
“We consider the kinetic scheme”
The authors state this as an assumption.
The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
“Assuming that there exist static or slowly varying heterogeneities of a and b, with distributions f (a) and g(b), respectively, the protein distribution is”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Stochastic simulations are necessary when reactions involving few particles such as those in genetic information processing and the charging of the tRNAs can lead to large variations in the state of the cell (Taniguchi et al., 2010).”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Taniguchi et al., 2010
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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 [22]
In the citing paper
“This finding contrasts with data from bacteria where productive protein expression is achieved with such low mRNA concentrations (Taniguchi et al., 2010).”
From Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells (2012) · cites this paper as Taniguchi et al., 2010
In the citing paper
“These methods hold promise for elucidating the epigenetic controls of cellular identity186,187.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [187]
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Kovacs-Simon, A., Hemsley, C. M., Scott, A., Prior, J. L., & Titball, R. W. (2019) · BMC Microbiology · doi:10.1186/s12866-019-1469-8 · Cited by 30
“In an Escherichia coli single-cell study [57] the correlation coefficient between mRNA and protein levels of the same gene averaged zero for the genes tested.”
BackgroundAn objective function exploiting suboptimal solutions in metabolic networks
Wintermute, E. H., Lieberman, T. D., & Silver, P. A. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-98 · Cited by 30
Microfluidics: A new tool for microbial single cell analyses in human microbiome studies
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Single-cell microscopy of suspension cultures using a microfluidics-assisted cell screening platform
Okumuş, B., Baker, C. J., Arias-Castro, J. C., et al. (2017) · Nature Protocols · doi:10.1038/nprot.2017.127 · Cited by 30
What can we learn when fitting a simple telegraph model to a complex gene expression model?
Jiao, F., Li, J., Liu, T., Zhu, Y., Che, W., Bleris, L., & Jia, C. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012118 · Cited by 26
“…that the distributions of protein numbers in E. coli measured using single-molecule fluorescence microscopy often have a unimodal distribution [3] and the distributions of mRNA numbers measured using scRNA-seq often have a negative binomial or zero-inflated negative binomial distribution [66– 68].”
Sawyer, E. B., Phelan, J. E., Clark, T. G., & Cortes, T. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108695 · Cited by 25
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“[9, 11].”
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“Apparently, the observed strong transcriptional adaptation is not completely translated into an adaptation on the proteome level as observed for other adaptation processes (26).”
BackgroundThe fate of cell reprogramming
Karagiannis, P., & Yamanaka, S. (2014) · Nature Methods · doi:10.1038/nmeth.3109 · Cited by 32
Corrales‐Guerrero, L., Tal, A., Arbel‐Goren, R., Mariscal, V., Flores, E., Antonia Herrero, B., & Stavans, J. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005031 · Cited by 28
“Noise, extensively studied in unicellular organisms [6–8], has been shown to play a functional role in cellular decisions [9] and in the determination of developmental fates [10,11].”
BackgroundAdiabatic reduction of a model of stochastic gene expression with jump Markov process
Yvinec, R., Zhuge, C., Lei, J., & Mackey, M. C. (2013) · Journal of Mathematical Biology · doi:10.1007/s00285-013-0661-y · Cited by 31
“Rapid mRNA degradation has been observed in E. coli (and other bacteria), in which mRNA is typically degraded within minutes, whereas most proteins have a lifetime longer than the cell cycle (≥30 min for E. coli) (Taniguchi et al. 2010).”
BackgroundSong, C., Wang, F., Cheng, K., Wei, X., Bian, Y., Wang, K., Tan, Y., Wang, H., Ye, M., & Zou, H. (2013) · Journal of Proteome Research · doi:10.1021/pr400544j · Cited by 29
Mitochondria and the non‐genetic origins of cell‐to‐cell variability: More is different
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“coli [25], and found that for relatively abundant proteins, extrinsic factors contributed more than intrinsic ones.”
BackgroundDesign of a bistable switch to control cellular uptake
Oyarzún, D. A., & Chaves, M. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0618 · Cited by 27
Simulating biological processes: stochastic physics from whole cells to colonies
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“It has revealed the architecture of microtubule networks, and the dynamics of the molecular motors that traverse them in living cells [54, 55], the diffusive and subdiffusive motions of macromolecules like RNAP and ribosomes within and around the chromosome [56, 57], as well as the numbers and locations of individual…”
BackgroundControlling and exploiting cell-to-cell variation in metabolic engineering
Wang, T., & Dunlop, M. J. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.08.013 · Cited by 29
Ma, J., Tran, G., Wan, A. M. D., Young, E. W. K., Kumacheva, E., Iscove, N. N., & Zandstra, P. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-86087-4 · Cited by 30
Wilkinson, M., Wilkinson, O. J., Feyerherm, C., Fletcher, E. E., Wigley, D., & Dillingham, M. (2022) · bioRxiv · doi:10.7554/elife.83409 · Cited by 27
“9 likely sequesters all of the available RecBCD complex present at only ~10 copies per cell ( Lepore et al., 2019 ; Taniguchi et al., 2010 ; Taylor and Smith, 1980 ) in an inactive form, thereby protecting T7 DNA from degradation and prioritising the use of phage-recombinases ( Figure 5 ).”
Multimodality and Flexibility of Stochastic Gene Expression
Innocentini, G. C. P., Forger, M., Ramos, A. F., Radulescu, O., & Hornos, J. E. M. (2013) · Bulletin of Mathematical Biology · doi:10.1007/s11538-013-9909-3 · Cited by 26
Sequence Features of E. coli mRNAs Affect Their Degradation
Lenz, G., Doron‐Faigenboim, A., Ron, E. Z., Tuller, T., & Gophna, U. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028544 · Cited by 30
Characterization of the RNase R association with ribosomes
Małecki, M., Bárria, C., & Arraiano, C. M. (2014) · BMC Microbiology · doi:10.1186/1471-2180-14-34 · Cited by 29
“Based on approximate estimations of protein copy numbers in the cell, we can predict that in exponentially growing cells ribosomes are at least 100 fold more abundant than RNase R, which means that RNase R is only connected to a small fraction of the cellular ribosomes [24].”
Hassan, B. H., & Cronan, J. E. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.194191 · Cited by 30
Global Analysis of Viral Infection in an Archaeal Model System
Maaty, W. S. A., Steffens, J. D., Heinemann, J. V., Ortmann, A. C., Reeves, B. D., Biswas, S. K., Dratz, E. A., Grieco, P. A., Young, M., & Bothner, B. B. (2012) · Frontiers in Microbiology · doi:10.3389/fmicb.2012.00411 · Cited by 29
“This is due in part to differences in the turnover rates (half-life) of a given mRNA and the protein it codes for (Taniguchi et al., 2010).”
BackgroundNiwa, T., Fujiwara, K., & Taguchi, H. (2015) · FEBS Letters · doi:10.1002/1873-3468.12036 · Cited by 29
Living with noise: On the propagation of noise from molecules to phenotype and fitness
Bruggeman, F. J., & Teusink, B. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.02.010 · Cited by 25
Bursting onto the scene? Exploring stochastic mRNA production in bacteria
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Almario, J., Prigent-Combaret, C., Muller, D., & Moënne-Loccoz, Y. (2013) · Molecular Plant-Microbe Interactions · doi:10.1094/mpmi-11-12-0274-r · Cited by 26
Gene expression and protein abundance: Just how associated are these molecular traits?
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Universal protein distributions in a model of cell growth and division
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Adaptation by stochastic switching of a monostable genetic circuit in Escherichia coli
Tsuru, S., Yasuda, N., Murakami, Y., Ushioda, J., Kashiwagi, A., Suzuki, S., Mori, K., Ying, B., & Yomo, T. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.24 · Cited by 26
“Bistable structures are considered as evolved designs for adaptation (Acar et al, 2008), whereas monostability relies on a simpler design, which is much more common in regulatory networks (Newman et al, 2006; Taniguchi et al, 2010).”
Interplay of Gene Expression Noise and Ultrasensitive Dynamics Affects Bacterial Operon Organization
Ray, J. C. J., & Igoshin, O. A. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002672 · Cited by 27
“coli [43] to explore this trend for constitutively expressed physically interacting protein pairs (other network modules have insufficient data for such analysis).”
BackgroundMethodInfluentialModel reduction for stochastic chemical systems with abundant species
Smith, S., Cianci, C., & Grima, R. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4936394 · Cited by 28
Disentangling the behavioural variability of confined cell migration
Brückner, D. B., Fink, A., Rädler, J. O., & Broedersz, C. P. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0689 · Cited by 30
“in RNA-transcription [64], andwere attributed to cell-to-cell variations in the transcription rates [65].”
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“Imaging techniques are popular, but they require tagged bacterial cells or a probe to visualize the bacteria or the molecule of interest (8,9), making them less suitable to study environmental communities.”
BackgroundEffect of a post-translational modification mimic on protein translocation through a nanopore
Hoogerheide, D. P., Gurnev, P. A., Rostovtseva, T. K., & Bezrukov, S. M. (2020) · Nanoscale · doi:10.1039/d0nr01577f · Cited by 26
Mathis, A. D., Otto, R. M., & Reynolds, K. A. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1073 · Cited by 24
“(27), (iii) the gene was annotated in the bacterial Clusters of Orthologous Genes (COGs) Database (28), and (iv) the gene had a chromosomal YFP-fusion available (29).”
BackgroundNutrient concentrations in food display universal behaviour
Menichetti, G., & Barabási, A.-Ĺ. (2022) · Nature Food · doi:10.1038/s43016-022-00511-0 · Cited by 29
Downing, T. (2015) · Microorganisms · doi:10.3390/microorganisms3020236 · Cited by 26
“coli post-transcriptional processing disturbs expected correlations between mRNA and protein levels [246] – this includes AMR-driven regulatory changes by small RNAs [247].”
BackgroundSchellenberg, J. J., Verbeke, T. J., McQueen, P., Krokhin, O. V., Zhang, X., Alvare, G., Fristensky, B. W., Thallinger, G., Henrissat, B., Wilkins, J. A., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-567 · Cited by 28
“Further work will be required to determine the relation of these terms to cellular metabolism and each other as technologies to measure them continue to evolve [16,58,59].”
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“…these studies were based on large-scale measurements of this process generated by new technologies that have maturated recently (Arava et al., 2003; Newman et al., 2006; Kudla et al., 2009; Ingolia et al., 2009; Welch et al., 2009; Taniguchi et al., 2010; Uemura et al., 2010; Vogel et al., 2010).”
Zur, H., & Tuller, T. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s15-s1 · Cited by 27
“Concurrently, technologies to measure translation of mRNAs into proteins are now rapidly emerging, transforming our understanding of the proteome [1,9,15,17-31].”
BackgroundSalari, R., Wójtowicz, D., Zheng, J., Levens, D., Pilpel, Y., & Przytycka, T. M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002644 · Cited by 29
“Intrinsic and extrinsic fluctuations can be separated experimentally by utilizing dual reporter measurements [1,24], but experimental separation of transcriptional and translational components would additionally require single-cell measurements of both mRNA and protein copy numbers simultaneously [42].”
BackgroundA Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli
Adolfsen, K. J., & Brynildsen, M. P. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004562 · Cited by 27
“While experimental measurements on AHP [21], HPI [82], and HPII [82] are available, their concentrations vary with environment and growth phase, as shown in our reporter assays (S11 Fig).”
Preparation of Microbial Community cDNA for Metatranscriptomic Analysis in Marine Plankton
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Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population
Pavlou, A., Eugenio Cinquemani, B., Pinel, C., Giordano, N., Mathilde, V. M.-G., Mihalcescu, I., Geiselmann, J., & de Jong, H. (2025) · Nature Communications · doi:10.1038/s41467-024-55394-5 · Cited by 32
A Computational Framework for Analyzing Stochasticity in Gene Expression
Sherman, M. S., & Cohen, B. A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003596 · Cited by 25
Kopylov, A. T., Zgoda, V. G., Лисица, А. В., & Archakov, A. I. (2012) · PROTEOMICS · doi:10.1002/pmic.201100460 · Cited by 27
“To achieve a complete description of the proteome of tissues or biological fluids, technologies that could detect a single protein molecule in 103 cells would be of significant value [5].”
BackgroundProteomic profiling of a robust W olbachia infection in an A edes albopictus mosquito cell line
Baldridge, G. D., Baldridge, A. S., Witthuhn, B. A., Higgins, L., Markowski, T. W., & Fallon, A. M. (2014) · Molecular Microbiology · doi:10.1111/mmi.12768 · Cited by 26
“, 2011) and in Escherichia coli cells under varying growth conditions (Taniguchi et al., 2010), protein abundances and cognate mRNA levels are poorly correlated.”
BackgroundBounding the stationary distributions of the chemical master equation via mathematical programming
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“First, holistic investigations of well-characterized model organisms have 245 tracked every metabolite and protein to generate enzyme-directed functional rates in the bacterium 246 Escherichia coli (Taniguchi et al., 2010) and the yeast Saccharomyces cerevisiae (Ho et al.”
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Establishment of an immortal chicken embryo liver-derived cell line
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Genomics and proteomics in solving brain complexity
Kadakkuzha, B. M., & Puthanveettil, S. V. (2013) · Molecular BioSystems · doi:10.1039/c3mb25391k · Cited by 29
Decomposition and tunability of expression noise in the presence of coupled feedbacks
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Advancements and Potential Applications of Microfluidic Approaches—A Review
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“[158] have designed an automated image rotated microfluidic platform to compute the proteome of E.”
MethodComparison and Calibration of Different Reporters for Quantitative Analysis of Gene Expression
García, H. G., Lee, H. J., Boedicker, J. Q., & Phillips, R. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.06.026 · Cited by 27
“This observation is consistent with recently published results (7).”
BackgroundResultInfluentialDiverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies
Mu, X., & Zhang, F. (2023) · Journal of Industrial Microbiology & Biotechnology · doi:10.1093/jimb/kuad033 · Cited by 26
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Design of the lac gene circuit revisited
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Structural Basis of the Subcellular Topology Landscape of Escherichia coli
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“fluorescent protein lifetime depends on the growth rate of the bacteria (32, 33).”
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Distribution profiles and their dynamic transition in stochastic gene transcription
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The shape-shifting quasispecies of RNA: one sequence, many functional folds
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“Taniguchi et al explained the mechanism of the lack of mRNA-protein correlation using an elegant experiment [23].”
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Metadata
- Authors
- Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S.
- Affiliations
- Harvard University (US)
- University of Toronto (CA)
- Corresponding author
- Taniguchi, Yuichi
- Choi, Paul J.
- Li, Gene-Wei
- Chen, Huiyi
- Xie, X. Sunney
- Venue
- Science
- Published
- 2010-07-30
- Volume
- 329
- Issue
- 5991
- Pages
- 533-538
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC2922915
- OpenAlex
- W2053460598
- Cited by
- 1,982
- References
- 33
- Citations by year
- 2026: 60 · 2025: 77 · 2024: 85 · 2023: 91 · 2022: 102 · 2021: 110 · 2020: 122 · 2019: 127 · 2018: 153 · 2017: 117 · 2016: 158 · 2015: 157 · 2014: 156 · 2013: 171 · 2012: 161
- FWCI
- 60.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP1OD000277)
- Canadian Institutes of Health Research (77639_1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- Escherichia coli, single-cell proteomics, single-cell transcriptomics, gamma distribution, transcription rate, extrinsic noise, intrinsic noise, mRNA-protein correlation, gene expression variation
- MeSH terms
- Escherichia coli, Luminescent Proteins, Microscopy, Fluorescence, RNA, Bacterial, RNA, Messenger, Saccharomyces cerevisiae, Transcription, Genetic, Protein Biosynthesis, Gene Library, Gene Expression, In Situ Hybridization, Fluorescence, Proteome, Gene Expression Profiling, RNA Stability, Escherichia coli Proteins, Microfluidic Analytical Techniques
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