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
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
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]
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Quantification of Intracellular Proteins in Single Cells Based on Engineered Picoliter Droplets
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Maximum Caliber Can Characterize Genetic Switches with Multiple Hidden Species
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Dual-Color Fluorescent Timer Enables Detection of Growth-Arrested Pathogenic Bacterium
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Rep and UvrD Antagonize One Another at Stalled Replication Forks and This Is Exacerbated by SSB
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Nanomolar Responsiveness of an Anaerobic Degradation Specialist to Alkylphenol Pollutants
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Chronology of transcriptome and proteome expression during early Arabidopsis flower development
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“This agrees with protein measurements in cells in optimal conditions ( 32 ).”
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“Thus, we obtained the difference in the noise level, Δη(2)≡η(2)p f λ T ð Þ −η(2)p gλ T ð Þ 1⁄2 , as a function of λ or other parameters across a range of in vivo biochemical parameters [25, 26, 62].”
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Self-citationOut-of-equilibrium gene expression fluctuations in the presence of extrinsic noise
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“For example, if fluctuations in ribosome concentration are the 375 main source of extrinsic noise, as it has been hypothesized in fast-growing bacteria [41], 376 the translation rate would be the main fluctuating parameter in a corresponding model 377 of stochastic gene expression.”
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Wehrens, M., Krah, L. H. J., Towbin, B. D., Hermsen, R., & Tans, S. J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113284 · Cited by 8
“9,10 17 These findings raise the question of whether cells employ regulatory mechanisms to adjust the proteome in response to stochastic internal metabolic fluctuations.”
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The optics inside an automated single molecule array analyzer
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Effects of Rate-Limiting Steps in Transcription Initiation on Genetic Filter Motifs
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“For that, we need to use models that account for the nature of gene expression, since genes with low expression levels are abundant in bacteria [2,3].”
MinION™: New, Long Read, Portable Nucleic Acid Sequencing Device
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The small 6C RNA of Corynebacterium glutamicum is involved in the SOS response
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Dynamics of sequestration-based gene regulatory cascades
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Molecular Biology in Tardigrades
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Plasmids for Independently Tunable, Low-Noise Expression of Two Genes
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Sequence-dependent model of genes with dual σ factor preference
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“This is well described by a function of the form: [33] ( N is the noise floor, a lower bound on the cell-to-cell variability of protein levels in clonal populations due to extrinsic factors [43]), in agreement with [33,44,45].”
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In search of noise-induced bimodality
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“It has been shown to appear when translation events occur in a bursting manner when a short-lived mRNA is transcribed [3].”
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Cole, J. A., & Luthey-Schulten, Z. A. (2014) · Israel Journal of Chemistry · doi:10.1002/ijch.201300147 · Cited by 10
Effects of gene length on the dynamics of gene expression
Ribeiro, A. S., Häkkinen, A., & Lloyd‐Price, J. (2012) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2012.10.002 · Cited by 9
Monitoring induced gene expression of single cells in a multilayer microchip
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Deciphering Complexity in Molecular Biophysics with Single-Molecule Resolution
Deniz, A. A. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.12.011 · Cited by 9
“in bursts that may be important in regulation [42, 43].”
BackgroundRoles of factorial noise in inducing bimodal gene expression
Liu, Y., Yuan, Z., Huang, L., & Zhou, T. (2015) · Physical Review E · doi:10.1103/physreve.91.062706 · Cited by 9
Noise–plasticity correlations of gene expression in the multicellular organism Arabidopsis thaliana
Hirao, K., Nagano, A. J., & Awazu, A. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.09.017 · Cited by 9
“…identical population under constant environmental conditions (Elowitz et al., 2002; Furusawa et al., 2005; Golding et al., 2005; Kaern et al., 2005; Newman et al., 2006; Chang et al., 2008; Konishi et al., 2008; Taniguchi et al., 2010; So et al., 2011; Silander et al., 2012; Woods, 2014).”
BackgroundSystems Analysis of Microbial Adaptations to Simultaneous Stresses
Carlson, R. P., Oshota, O., & Taffs, R. L. (2012) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-94-007-5055-5_7 · Cited by 10
Explicit Scale Simulation for analysis of RNA-sequencing with ALDEx2Preprint
Gloor, G. B., Nixon, M. P., & Silverman, J. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.21.563431 · Cited by 8
Exact Power Spectrum in a Minimal Hybrid Model of Stochastic Gene Expression Oscillations
Jia, C., Qian, H., & Zhang, M. Q. (2024) · SIAM Journal on Applied Mathematics · doi:10.1137/23m1560914 · Cited by 14
Hardo, G., Li, R., & Bakshi, S. (2024) · npj Imaging · doi:10.1038/s44303-024-00024-4 · Cited by 8
“…STED, or PALM 18–21 ) could help, or employing an imaging system with a shallower depth of field compared to the cell depth (such as a confocal microscope) could reduce the effects of projection and mitigate the resulting shift from diffraction effects (detailed in Supplementary Information 4).”
BackgroundMethodInfluentialConcentration buffering and noise reduction in non-equilibrium phase-separating systems
Zechner, C., & Jülicher, F. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101168 · Cited by 7
“27 As an example, in Figure 2B, we consider r A to have a correlation time of 1 = l 2 x 3 h (e.g., due to transcriptional fluctu-ations 15 or mRNA decay 28 ) together with a solute lifetime of 1 = k 2 x 14 h 28,29 and a partitioning time of 1 = k D x 2 : 6 min.”
Rapid Transcription Factor Fluctuations Drive Nonequilibrium Gene Regulatory Dynamics in Bacteria
Galbusera, L., Bellement-Theroue, G., Julou, T., & van Nimwegen, E. (2025) · PRX Life · doi:10.1103/nvk3-jsvm · Cited by 8
Designing Microbe–Semiconductor Interfaces for Semibiological Photosynthesis
Song, W. J. R., Quek, G., Short, M. I. M., Li, W., Reisner, E., & Liu, B. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00900 · Cited by 11
Minimal Effect of Gene Clustering on Expression inEscherichia coli
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“Digital images were captured on a Gel Doc XR imaging system (Bio-Rad) by transillumination of the film and band intensity was quantified on nonsaturated exposures with Quantity One Analysis software (Bio-Rad).”
BackgroundMethodInfluentialUsing Models to (Re-)Design Synthetic Circuits
McCallum, G., & Potvin-Trottier, L. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1032-9_3 · Cited by 7
Independent control of mean and noise by convolution of gene expression distributions
Gerhardt, K. P., Rao, S. D., Olson, E. J., Igoshin, O. A., & Tabor, J. J. (2021) · Nature Communications · doi:10.1038/s41467-021-27070-5 · Cited by 7
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Colloquium : Gene expression in growing cells: A biophysical primer
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Kinetic Modeling of Metabolic Networks
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Transcription dynamics of inducible genes modulated by negative regulations
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“…predictable and continuous fashion, the extensive single cell studies over last decades have shown that transcription is indeed a stochastic and discontinuous process (Thattai & van Oudenaarden, 2001; Paulsson, 2004; Golding et al., 2005; Yu et al., 2006; Raj et al., 2010; Taniguchi et al., 2010).”
BackgroundInfluentialElucidating the role of select cytoplasmic proteins in altering diffusion of integrin receptors
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“However, correlations between protein expression and integrin diffusion may be correlated [46].”
BackgroundCompetition enhances stochasticity in biochemical reactions
Firman, T., & Ghosh, K. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4816527 · Cited by 8
Nagaraja, K., & Braga-Neto, U. (2018) · Cancer Informatics · doi:10.1177/1176935118786927 · Cited by 8
“The initial values used here are consistent with values obtained experimentally in the work by Taniguchi et al 14 as well as the hyperparameter values used in the work by Atashpaz-Gargari et al. 5 Furthermore, these initial values are modified based on the data, as part of the prior calibration process described in Al…”
MethodSalmonella Single-Cell Metabolism and Stress Responses in Complex Host Tissues
Bumann, D. (2019) · Microbiology Spectrum · doi:10.1128/microbiolspec.bai-0009-2019 · Cited by 9
Mathematical models of gene expression
Robert, P. (2019) · Probability Surveys · doi:10.1214/19-ps332 · Cited by 8
“See Taniguchi [37].”
Metabolic mapping with plasmonic nanoparticle assemblies
Le, N. H., Ye, G., Peng, C., & Chen, J. I. L. (2020) · The Analyst · doi:10.1039/c9an02262g · Cited by 8
Information, programme, signal: dead metaphors that negate the agency of organisms
Soto, A. M., & Sonnenschein, C. (2020) · Interdisciplinary Science Reviews · doi:10.1080/03080188.2020.1794389 · Cited by 9
“As an example, protein and mRNA copy numbers vary from cell to cell in isogenic bacterial populations; also, in a given single cell, protein and mRNA copy numbers for any particular gene are uncorrelated (Taniguchi et al. 2010).”
BackgroundInoue, M., & Kaneko, K. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033183 · Cited by 9
“Moreover, each process controlling the cellular concentrations of chemicals is generally stochastic, as the number of molecules involved is sometimes not very large [14{16].”
BackgroundAvoidance of protein unfolding constrains protein stability in long-term evolution
Razban, R. M., Dasmeh, P., Serohijos, A. W. R., et al. (2021) · Biophysical Journal · doi:10.1016/j.bpj.2021.03.042 · Cited by 8
Comparison of bias and resolvability in single-cell and single-transcript methods
Rammohan, J., Lund, S. P., Alperovich, N., Paralanov, V., Strychalski, E. A., & Ross, D. (2021) · Communications Biology · doi:10.1038/s42003-021-02138-6 · Cited by 6
Ghimire, C., & Guo, P. (2021) · Biophysics Reports · doi:10.52601/bpr.2021.210003 · Cited by 8
“Alternatively, the number of fluorophores can be estimated by dividing the intensity of the fluorescent spot prior to photobleaching with that of a single fluorophore derived from the histogram of step size (Coffman et al. 2011; Leake et al. 2006; Taniguchi et al. 2010), although errors can occur due to the fluctuatio…”
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“…et al., 2004; Uyemura et al., 2005; Ulbrich and Isacoff, 2007), cytokinesis (Wu and Pollard, 2005), mitosis (Joglekar et al., 2006), transcription (Elf et al., 2007; Taniguchi et al., 2010; Tutucci et al., 2018), bacterial motility (Leake et al., 2006), and development (Gross et al., 2019).”
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“Some data of smFISH in isogenic population of both prokaryotic and mammalian cells fit into this scheme [14,21,68].”
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“On the other hand, as a major factor driving extrinsic noise (Swain et al. 2002; das Neves et al. 2010; Taniguchi et al. 2010; Johnston et al. 2012), noise saturation of ribosome-related genes is also compatible with the previous finding that natural selection favors the reduction of global…”
ResultGenome-wide screening reveals metabolic regulation of stop-codon readthrough by cyclic AMP
Lyu, Z., Villanueva, P., O’Malley, L., Murphy, P., Augenstreich, J., Briken, V., Singh, A., & Ling, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad725 · Cited by 9
“Hetero-geneity of gene expression is typically quantified using coefficient of variation (CV) ( 43 , 62 , 63 ).”
MethodNagarajan, T., Gayathri, M. K., Mack, J. P., Nyokong, T., Govindarajan, S., & Babu, B. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01162 · Cited by 10
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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