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
13 No full text of the sources · 10 Own result · 7 Own interpretation · 3 Own method · 1 Supported so far · 1 Partly supported · 1 Couldn’t confirm · 1 Not checked yet · 1 No basis given · accuracy not yet measured
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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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The 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)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plainn = 5,447- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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]
Citations
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Cited by
Sorted by citations
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Noise analysis of genome-scale protein synthesis using a discrete computational model of translation
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Identification of direction in gene networks from expression and methylation
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“The definition in terms of mRNA expression is a pragmatic choice, as this can be easily measured in a high-throughput fashion and can serve as a surrogate for protein concentration under some circumstances [11].”
BackgroundMotif analysis for small-number effects in chemical reaction dynamics
Saito, N., Sughiyama, Y., & Kaneko, K. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4961675 · Cited by 8
“[1] Yuichi Taniguchi, Paul J Choi, Gene-Wei Li, Huiyi Chen,”
BackgroundPost-transcriptional regulation tends to attenuate the mRNA noise and to increase the mRNA gain
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Biophysical constraints determine the selection of phenotypic fluctuations during directed evolution
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“These studies show that the probability density of protein copy number across a population is consistently non-Gaussian and highly skewed, and reportedly well fit by gamma [15], extreme value (Fisher–Tippett–Gumbel [14] or Frechet [13]) or log-normal [12, 17] distributions, all of which are similar in shape.”
BackgroundGilliot, P.-A., & Gorochowski, T. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.07.515414 · Cited by 6
The ability to sense the environment is heterogeneously distributed in cell populationsPreprint
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“The gamma distribution was chosen here and in several other places because it has been shown to accurately approximate real distributions of protein/mRNA abundances ( Taniguchi et al., 2010 ).”
BackgroundHeterogeneous efflux pump expression underpins phenotypic resistance to antimicrobial peptidesPreprint
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“…coli and S. cerevisiae have indeed revealed that a tremendous amount of variability exists in the molecular make-up of genetically identical cells and that different molecular constituents of a cell can exhibit widely different patterns of variability (Newman et al., 2006; Taniguchi et al., 2010).”
BackgroundShewanella baltica Ecotypes Have Wide Transcriptional Variation under the Same Growth Conditions
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Noise, Information and Fitness in Changing Environments
Pedraza, J. M., Garcia, D. A., & Pérez-Ortiz, M. F. (2018) · Frontiers in Physics · doi:10.3389/fphy.2018.00083 · Cited by 5
“Experimentally, we have measurements at the single molecule level of mRNAs and proteins [47, 48], single cell transcription data [49, 50], and automated ways to observe populations [6] and single cells [51] in changing environments over long periods.”
MethodTechnologies for whole‐cell modeling: Genome‐wide reconstruction of a cell in silico
Kaizu, K., & Takahashi, K. (2023) · Development Growth & Differentiation · doi:10.1111/dgd.12897 · Cited by 4
“Next-generation sequencing and fluorescence imaging technologies have also been developed to quantify amounts of gene product in a single cell in vivo (Gorochowski et al., 2019; Lahtvee et al., 2017; Taniguchi et al., 2010).”
BackgroundReporter Gene-Based qRT-PCR Assay for Rho-Dependent Termination In Vivo
Monford Paul Abishek, N., Jeon, H. J., Wang, X., et al. (2023) · Cells · doi:10.3390/cells12222596 · Cited by 5
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Influence of multiplicative stochastic variation on translational elongation rates
Datta, S., & Seed, B. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191152 · Cited by 5
SHARMA, R. R., & Cherayil, B. J. (2013) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2013/10/p10029 · Cited by 5
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From noise to synthetic nucleoli: can synthetic biology achieve new insights?
Ciechonska, M., Grob, A., & Isalan, M. (2016) · Integrative Biology · doi:10.1039/c5ib00271k · Cited by 6
“contain sequences associated with higher extrinsic noise levels (15), while promoters associated with functionally important genes were characterized by decreased noise (16).”
BackgroundNAKAGAWA, M., & Togashi, Y. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00089 · Cited by 5
Multiscale Mathematical Modeling and Simulation of Cellular Dynamical Process
Nakaoka, S. (2014) · Methods in molecular biology · doi:10.1007/7651_2014_78 · Cited by 5
Sheng, J., Hod, E. A., Vlad, G., & Chavez, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-04842-7 · Cited by 6
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Reduction of translation rate stabilizes circadian rhythm and reduces the magnitude of phase shift
Nakajima, M., Koinuma, S., & Shigeyoshi, Y. (2015) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2015.06.158 · Cited by 5
Microfluidic Systems for Marine Biotechnology
Hamon, M., Dai, J. H., Jambovane, S. R., et al. (2015) · Springer eBooks · doi:10.1007/978-3-642-53971-8_20 · Cited by 5
Efficient and flexible implementation of Langevin simulation for gene burst production
Yan, C. S., Chepyala, S. R., Yen, C.-M., & Hsu, C. (2017) · Scientific Reports · doi:10.1038/s41598-017-16835-y · Cited by 5
“The other parameter we scanned is the protein burst size p , or equivalently protein production rate ( k p = 10 − 1000), which also covers the values observed in the experiment 28 .”
BackgroundInfluentialMulti-objective optimization for gene expression noise reduction in a synthetic gene circuit
Boada, Y., Vignoni, A., & Picó, J. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.376 · Cited by 5
Molecular Communication as a Biological System
Suda, T., & Nakano, T. (2018) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/seconw.2018.8396352 · Cited by 5
“Although some studies exist (analytical frameworks [7], single molecular level simulation techniques [11] and single molecule imaging techniques [12]), they are limited, and further research is necessary.”
BackgroundMaximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network
Firman, T., Amgalan, A., & Ghosh, K. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b07465 · Cited by 5
Chromosome and plasmid-borne PLacO3O1 promoters differ in sensitivity to critically low temperatures
Oliveira, S. M. D., Goncalves, N. S. M., Kandavalli, V. K., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-39618-z · Cited by 5
Predicting bacterial growth conditions from mRNA and protein abundances
Caglar, M. U., Hockenberry, A. J., & Wilke, C. O. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206634 · Cited by 5
Çelik, C., Bokes, P., & Singh, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60327-4_2 · Cited by 6
Fluctuating-rate model with multiple gene states
Li, J., Ge, H., & Zhang, Y. (2020) · Journal of Mathematical Biology · doi:10.1007/s00285-020-01538-2 · Cited by 4
Connecting Timescales in Biology: Can Early Dynamical Measurements Predict Long-Term Outcomes?
Tsabar, M., Lovitch, S. B., Jambhekar, A., & Lahav, G. (2021) · Trends in cancer · doi:10.1016/j.trecan.2020.12.008 · Cited by 5
“The complex relationships are due in part to the resolution of measurements as well as to differences between the half-lives of mRNAs and their cognate proteins [35,36].”
BackgroundDynamics of transcription of closely spaced promoters in Escherichia coli, one event at a time
Martins, L. D. O., Mäkelä, J., Häkkinen, A., et al. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.02.015 · Cited by 5
Protein Aggregation Acts as Strong Constraint During Evolution
Villar‐Piqué, A., & Ventura, S. (2012) · Springer eBooks · doi:10.1007/978-3-642-30425-5_6 · Cited by 5
Gene expression noise can promote the fixation of beneficial mutations in fluctuating environmentsPreprint
Schmutzer, M., & Wagner, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.13.947275 · Cited by 5
Çelik, C., Bokes, P., & Singh, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.25.061101 · Cited by 5
Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways
Manes, N. P., Calzola, J. M., Kaplan, P. R., Fraser, I. D. C., Germain, R. N., Meier‐Schellersheim, M., & Nita‐Lazar, A. (2022) · Scientific Data · doi:10.1038/s41597-022-01612-y · Cited by 4
Himeoka, Y., & Furusawa, C. (2024) · eLife · doi:10.1101/2023.10.18.562862 · Cited by 5
Cell-cycle dependence of transcription dominates noise in gene expression
Zopf, C. J., Quinn, K., Zeidman, J., & Maheshri, N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003161 · Cited by 4
“This difficulty of using static data to pinpoint origins of variability has been anticipated [5,23] although here we have shown that even static mRNA FISH data can reveal additional dynamic information by disaggregating mRNA distributions by cell-cycle phase.”
BackgroundA Vascularized Lung Tumoroid‐on‐Chip Model for the Accurate Assessment of Anti‐Invasion Agents
Wang, X., Zhu, G., Lu, J., Hu, X., Zheng, W., Yang, K., Zhang, W., Huang, Y., Hu, S., Zhao, B., et al. (2026) · Advanced Functional Materials · doi:10.1002/adfm.202526981 · Cited by 7
“Even though F-actin protein was not significantly upregulated in the transcriptome data, we speculate this observation may be due to the temporal lag between transcriptional and translational events, which has been reported in previous literature [66].”
Gouws, A. C., Kruger, H. G., Gheysens, O. S., Zeevaart, J. R., Govender, T., Naicker, T., & Ebenhan, T. (2022) · Angewandte Chemie · doi:10.1002/ange.202204955 · Cited by 4
Lewin, L. E., Daniels, K. G., & Hurst, L. D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011581 · Cited by 4
Molecular noise can minimize the collective sensitivity of a clonal heterogeneous cell population
Forment, M., & Rodrigo, G. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.12.023 · Cited by 4
Kinetically accessible yield (KAY) for redirection of metabolism to produce exo-metabolites
Rivera, J. G. L., Theisen, M. K., Chen, P.-W., & Liao, J. C. (2017) · Metabolic Engineering · doi:10.1016/j.ymben.2017.03.011 · Cited by 4
Effects of Extrinsic Noise are Promoter Kinetics Dependent
Bahrudeen, M. N. M., Startceva, S., & Ribeiro, A. S. (2017) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1145/3093293.3093295 · Cited by 4
“This model was derived from data from multiple studies, including genome-wide studies of RNA numbers variability [11, 2] and of the transcription dynamics of individual genes [13, 14].”
MethodQuantifying heterogeneity of stochastic gene expression
Iida, K., Obata, N., & Kimura, Y. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.01.003 · Cited by 3
Chen, W., Zhang, J., Li, F., Wang, C., Zhang, Y., Xia, A., Ni, L., & Jin, F. (2022) · mSystems · doi:10.1128/msystems.00963-22 · Cited by 4
“According to previous studies, variation in protein expression follows a gamma distribution when stochastic bursting dominates other sources of intracellular variation (14, 45), which is usually the case for constitutive promoters with a low transcription rate.”
BackgroundGuet, C. C., Bruneaux, L., Oikonomou, P., Aldana, M., & Cluzel, P. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1049255 · Cited by 4
“However, most gene regulatory networks produce a continuously varying distribution of outputs (i.e. analogue networks), and thus detecting the transmission of information from cell to cell especially at cell division can be very difficult (Taniguchi et al 2010, Austin et al 2006).”
BackgroundZhu, H., Xiong, Y., Jiang, Z., Liu, Q., & Wang, J. (2024) · Advanced Science · doi:10.1002/advs.202304548 · Cited by 4
Efstratiou, E., Feidantsis, K. G., Makri, V., Staikou, A. E., & Giantsis, I. Α. (2024) · Animals · doi:10.3390/ani14060857 · Cited by 6
“The latter may assist farmers to further improve the efficiency of snail farming.”
BackgroundStability and gene strand bias of lambda prophages and chromosome organization in Escherichia coli
Li, X., Gallardo, Ó., August, E., et al. (2024) · mBio · doi:10.1128/mbio.02078-23 · Cited by 4
Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation
Chauhan, V., Baptista, I. S. C., Arsh, A. M., Jagadeesan, R., Dash, S., & Ribeiro, A. S. (2024) · Biochemistry · doi:10.1021/acs.biochem.4c00012 · Cited by 5
Dóra, D., Revisnyei, P., Mihucz, A., Kiraly, P., Szklenárik, G., Dulka, E., Gálffy, G., & Lohinai, Z. (2024) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2024.1381170 · Cited by 4
“Furthermore, it is important to acknowledge that transcriptome data alone may not fully capture the metabolic changes occurring in the host and often shows poor correlation with both proteomic and metabolomic pro fi les (Taniguchi et al., 2010; Ma et al., 2019; Li L. et al., 2021).”
Saridis, M. R., Salmina, A. B., Korsakova, S. A., & Yurchenko, S. O. (2025) · Biotechnology Journal · doi:10.1002/biot.70065 · Cited by 6
A narrow range of transcript-error rates across the Tree of Life
Li, W., Baehr, S., Marasco, M. R., Reyes, L., Brister, D., Pikaard, C. S., Goût, J.-F., Vermulst, M., & Lynch, M. (2025) · Science Advances · doi:10.1126/sciadv.adv9898 · Cited by 5
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Mäkelä, J., Huttunen, H. J., Kandhavelu, M., Yli-Harja, O. P., & Ribeiro, A. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr471 · Cited by 4
Modeling and systematic analysis of biomarker validation using selected reaction monitoring
Atashpaz-Gargari, E., Braga-Neto, U., & Dougherty, E. R. (2014) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1186/s13637-014-0017-y · Cited by 4
From measuring noise toward integrated single-cell biology
Paszek, P. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00408 · Cited by 5
“However, existing analyses already suggested that this correlation might be in fact smaller than expected (Taniguchi et al., 2010).”
BackgroundNucleotides and nucleic acids; oligo- and polynucleotides
Loakes, D. (2012) · Royal Society of Chemistry eBooks · doi:10.1039/9781849734875-00169 · Cited by 4
Akben, S. B., & Alkan, A. (2015) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gummfd.63786 · Cited by 4
Fluorescent Reporter Genes and the Analysis of Bacterial Regulatory Networks
de Jong, H., & Geiselmann, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27656-4_2 · Cited by 4
Bayesian ABC-MCMC Classification of Liquid Chromatography–Mass Spectrometry Data
Banerjee, U., & Braga-Neto, U. (2015) · Cancer Informatics · doi:10.4137/cin.s30798 · Cited by 4
“N ( , ), , , , , , , , , 0 1 2 1 2 2 2 αμ βμ + = = pep (9)”
A stochastic model for post-transcriptional regulation of rare events in gene expression
Kumar, N., & Kulkarni, R. (2019) · Physical Biology · doi:10.1088/1478-3975/aafbef · Cited by 4
Svenningsen, M. S., Semsey, S., & Mitarai, N. (2019) · mSphere · doi:10.1128/msphere.00575-18 · Cited by 4
The Pros and Cons of Circular RNAs as miRNA Sponges
Duk, M. A., & Samsonova, M. G. (2021) · BIOPHYSICS · doi:10.1134/s0006350921010036 · Cited by 4
Acceleration of evolutionary processes by learning and extended Fisher's fundamental theorem
Nakashima, S., & Kobayashi, T. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013069 · Cited by 4
Chance and Necessity inBacillus subtilisDevelopment
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Controlling cell-to-cell variability with synthetic gene circuits
Azizoğlu, A., & Stelling, J. (2019) · Biochemical Society Transactions · doi:10.1042/bst20190295 · Cited by 4
Stochastic Analysis Demonstrates the Dual Role of Hfq in Chaperoning E. coli Sugar Shock Response
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Ballarini, P., & Duflot, M. (2015) · Theoretical Computer Science · doi:10.1016/j.tcs.2015.05.018 · Cited by 4
“transcription factors and promoter regions, is very small, ranging from one to a few at a given moment [56].”
BackgroundKinetics of gene expression in bacteria — From models to measurements, and back again
Ribeiro, A. S. (2012) · Canadian Journal of Chemistry · doi:10.1139/cjc-2012-0409 · Cited by 4
Ding, D., Shu, C., & Sun, X. S. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25789 · Cited by 4
“Although protein levels depend on mRNA levels, quantitative systemwide analyses of protein and mRNA expression have shown a nontrivial relationship between cellular protein and mRNA levels.(19) In addition, posttranslational modifications (such as methylation, acetylation, phosphorylation, etc.”
BackgroundConstraining the complexity of promoter dynamics using fluctuations in gene expression
Kumar, N., & Kulkarni, R. (2019) · Physical Biology · doi:10.1088/1478-3975/ab4e57 · Cited by 4
Liu, Y., Yang, J., Zhao, Z., Pu, Y., & Bai, F. (2014) · Science China Chemistry · doi:10.1007/s11426-014-5245-1 · Cited by 5
Single‐molecule Fluorescence Imaging Techniques
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Noise propagation shapes condition-dependent gene expression noise in Escherichia coliPreprint
Urchueguía-Fornes, A., Galbusera, L., Bellement-Theroue, G., Julou, T., & van Nimwegen, E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/795369 · Cited by 4
“Fluctuations in chromosome copy number, polymerase concentration, ribosome and charged tRNA concentrations, mRNA decay rates, and fluctuations in growth-rate itself, may all contribute to determining the noise floor.”
BackgroundMethodResultInfluentialKinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing dataPreprint
Jia, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00356 · Cited by 4
“5pg per cell) [41] and most genes only transcribe a small copy number of mRNA [42].”
BackgroundVisualizing active viral infection reveals diverse cell fates in synchronized algal bloom demisePreprint
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Topological Constraints on Noise Propagation in Gene Regulatory NetworksPreprint
Mahajan, T., Singh, A., & Dar, R. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.11.463999 · Cited by 4
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PISA: versatile microscope for 3D single molecule light sheet imagingPreprint
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Self-citationCondition-dependent amorphous protein agglomerates control cytoplasmic rheology
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Profiling the heterogeneity of microbial populations and communities at the single‐cell level
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“22 Therefore, we sought to use an approach where we convert the arbitrary RNA-seq units into absolute mRNA copy numbers by measuring the absolute mRNA copy number of a highly expressed and a poorly expressed mRNA.”
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“Experimental observations have indicated that the half-life of the mRNA is of the order of 5– 10min in bacteria, 32 and around 10h in humans.”
A library of reporters of the global regulators of gene expression in Escherichia coli
Dash, S., Jagadeesan, R., Baptista, I. S. C., Chauhan, V., Kandavalli, V. K., Oliveira, S. M. D., & Ribeiro, A. S. (2024) · mSystems · doi:10.1128/msystems.00065-24 · Cited by 3
“At least four cellular processes influence the ratio between RNA and protein levels in a cell: translation, RNA degradation, protein degradation, and cell division [which causes not only dilution but also, in some cases, asymmetries when partitioning proteins between sister cells (41–43)].”
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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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