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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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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Lies and deception in bacterial gene regulation: the roles of nucleic acid decoys
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“However, with the increased availability of single-cell and single-molecule data, it has become possible to measure the distributions of molecular populations [4], [5], [6], [7], [8].”
BackgroundTension promoted circular probe for highly selective microRNA detection and imaging
Tang, Y., Wang, T., Chen, M., He, X. D., Qu, X., & Feng, X. (2016) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2016.05.010 · Cited by 14
Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells
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“The protein and mRNA distributions carry the signature of the dynamics of transcription and hence can be exploited to decipher the underlying mechanisms of transcriptional regulation (12,16,47,78).”
BackgroundDynamics of the HIV Gag Lattice Detected by Localization Correlation Analysis and Time-Lapse iPALM
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“o data for LacUV5 is r1/r−1 = 0.16[RNAP]/nM r2 = 0.095/sec, r3 = 2/sec [10] where [RNAP] is free RNA polymerase concentration. b,c) Fano factor as a function of mRNA number hmi with r/γ = 15.7, γ = ln(2)/(117sec) [19] and R = 10, 1, and 0.1. Solid lines are for the two-step model, while symbols are obtained by three-s…”
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FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW
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Alleviated Inhibition of Single Enzyme in Confined and Crowded Environment
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Deterministic and Stochastic Descriptions of Gene Expression Dynamics
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“While this simple estimate is certainly not an accurate description of such global noise, it clearly indicates that fluctuations in the growth rate can lead to noise in protein concentrations of the order of the observed extrinsic noise [44].”
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“Also, the low numbers of mRNA transcripts per cell we detected 318 are consistent with other studies that quantified mRNA transcripts per cell in other 319 prokaryotic microorganisms such as E. coli (Taniguchi et al. 2010), Burkholderia 320 thailandensis (Kang et al. 2011), and DvH (Qi et al. 2014).”
BackgroundResultInsights from protein-protein interaction studies on bacterial pathogenesis
Gagarinova, A. G., Phanse, S., Cygler, M., & Babu, M. (2017) · Expert Review of Proteomics · doi:10.1080/14789450.2017.1365603 · Cited by 14
“Since transcript levels alone do not accurately predict protein levels [10], proteomics is vital for the cataloging of protein abundances in diseased and healthy states, as well as for the mapping of protein macromolecular assemblies, protein–protein interactions (PPIs), and posttranslational modifications.”
BackgroundSelf-citationLuo, Y., Sun, W., Liu, C., Wang, G., & Fang, N. (2011) · Analytical Chemistry · doi:10.1021/ac201056z · Cited by 14
Fruzangohar, M., Ebrahimie, E., & Adelson, D. L. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/004911 · Cited by 14
“Bacteria are attractive organisms for GO analysis since they have less Post-transcriptional gene silencing compared to animals and plants (1) with gene expression levels moderately correlated with protein levels (2).”
BackgroundGenetic properties underlying transcriptional variability across different perturbations
Tsuru, S., & Furusawa, C. (2025) · Nature Communications · doi:10.1038/s41467-025-57642-8 · Cited by 13
Estimation of kinetic parameters of transcription from temporal single-RNA measurements
Zimmer, C., Häkkinen, A., & Ribeiro, A. S. (2015) · Mathematical Biosciences · doi:10.1016/j.mbs.2015.10.001 · Cited by 13
Salazar-Cavazos, E., & Santillán, M. (2013) · Bulletin of Mathematical Biology · doi:10.1007/s11538-013-9920-8 · Cited by 14
“This is in agreement with the experimental and theoretical results of Taniguchi et al. (2010), who measured the expression of several E. coli genes at the single molecule level, as well as with the common notion in engineering that systems with long characteristic times work as low-pass filters…”
Promoters adopt distinct dynamic manifestations depending on transcription factor context
Hansen, A. S., & Zechner, C. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209821 · Cited by 11
“As expected from previous studies (Bar-Even et al , 2006, Newman et al , 2006, Taniguchi et al , 2010), transcriptional noise uniformly decreases as transcriptional output increases for some genes such as DCS2 .”
Qin, W., Stärk, H., Müller, S., & Reemtsma, T. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.870931 · Cited by 13
What Has Genomics Taught an Evolutionary Biologist?
Zhang, J. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.01.005 · Cited by 16
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Harnessing microfluidic technology for bacterial single-cell analysis in mammals
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Moon, J.-H., Roh, D., Kwack, K. H., & Lee, J. (2023) · Japanese Dental Science Review · doi:10.1016/j.jdsr.2023.08.001 · Cited by 13
“Systematic investigations have revealed that the mRNA level (mean copy number) per gene in a single bacterial cell is only a few [30,31].”
BackgroundWang, X., Li, Y., & Jia, C. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0467 · Cited by 12
“In previous papers, the system size K is often understood to be the cell volume [5] or the typical protein number in the active gene state [78, 79].”
BackgroundBacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing
Walls, A. W., & Rosenthal, A. Z. (2024) · Transcription · doi:10.1080/21541264.2024.2334110 · Cited by 13
“This hypothesis is supported by the demonstrated non-correlation between levels of mRNA transcript and levels of expressed protein in an Escherichia coli model [56].”
ResultNoise robustness and metabolic load determine the principles of central dogma regulation
Lo, T. W., Choi, H. J., Huang, D., & Wiggins, P. A. (2024) · Science Advances · doi:10.1126/sciadv.ado3095 · Cited by 13
Studying transcriptional interactions in single cells at sufficient resolution
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“Most importantly, trapping regions are easily multiplexed on a chip, allowing a massive parallelization of a single experiment [15].”
BackgroundMethodGeneralized Schemes for High-Throughput Manipulation of the Desulfovibrio vulgaris Genome
Chhabra, S. R., Butland, G. P., Elias, D. A., Chandonia, J., Fok, O.-Y., Juba, T. R., Gorur, A., Allen, S., Leung, C. M., Keller, K. L., et al. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.05495-11 · Cited by 13
“In these localization studies, we consistently noted cell-to-cell variations in fluorescent signals in any given population, which may be attributed to corresponding differences in expression levels (38).”
Global coordination in adaptation to gene rewiring
Murakami, Y., Matsumoto, Y., Tsuru, S., Ying, B., & Yomo, T. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1366 · Cited by 12
Computation of Single-Cell Metabolite Distributions Using Mixture Models
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Modeling Protein Aggregation Kinetics: The Method of Second Stochasticization
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Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics
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Ribosome-Associated ncRNAs (rancRNAs) Adjust Translation and Shape Proteomes
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Stochastic Modeling of Cellular Networks
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Endocrinology: Advances through omics and related technologies
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Impact of the cell division cycle on gene circuits
Bierbaum, V., & Klumpp, S. (2015) · Physical Biology · doi:10.1088/1478-3975/12/6/066003 · Cited by 13
Systems Biology Characterization of Engineered Tissues
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Kuwahara, H., Arold, S. T., & Gao, X. (2015) · Integrative Biology · doi:10.1039/c5ib00107b · Cited by 13
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Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli
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Inferring gene regulation dynamics from static snapshots of gene expression variability
Joly-Smith, E., Wang, Z. J., & Hilfinger, A. (2021) · Physical review. E · doi:10.1103/physreve.104.044406 · Cited by 11
“For example, covariances between mRNA and protein levels have been used to test hypotheses about translation rates in bacteria [10].”
The effect of microRNA on protein variability and gene expression fidelity
Fan, R., & Hilfinger, A. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.01.027 · Cited by 12
Szoke, T., Goldberger, O., Albocher, N., Barsheshet, M., Dezorella, N., Nussbaum‐Shochat, A., Wiener, R., Schuldiner, M., & Amster‐Choder, O. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113393 · Cited by 14
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“Here, SX1747 is a K12 strain with CusS labeled by YFP on chromosome which comes from the E.coli Genetic Stock Center 25 .”
Robust and resource-optimal dynamic pattern formation of Min proteins in vivo
Ren, Z., Weyer, H., Sandler, M., Würthner, L., Fu, H., Tangtartharakul, C. B., Li, D., Sou, C., Villarreal, D. A., Kim, J. E., et al. (2025) · Nature Physics · doi:10.1038/s41567-025-02878-w · Cited by 12
A Protein Biosensor That Relies on Bending of Single DNA Molecules
Crawford, R., Kelly, D. J., & Kapanidis, A. N. (2012) · ChemPhysChem · doi:10.1002/cphc.201100881 · Cited by 12
Measuring Affinities of Fission Yeast Spindle Pole Body Proteins in Live Cells across the Cell Cycle
McCormick, C. D., Akamatsu, M., Ti, S.-C., et al. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.08.017 · Cited by 12
“Three tables, eight figures, Reference (46) and supporting methods and results are available at http://www.biophysj.org/biophysj/supplemental/ S0006-3495(13)00930-2.”
MethodGuantes, R., Cayrol, B., Busi, F., & Arluison, V. (2012) · Molecular BioSystems · doi:10.1039/c2mb05479e · Cited by 12
Rate-limiting steps in transcription dictate sensitivity to variability in cellular components
Mäkelä, J., Kandavalli, V. K., & Ribeiro, A. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-11257-2 · Cited by 12
“1B) with the variability in molecule numbers affecting different rate-limiting steps: (1) variability in molecule numbers affecting only τcc; (2) variability in molecule numbers affecting only τoc; (3) variability in molecule numbers affecting both τcc and τoc equally; (4) variability in numbers of two molecular speci…”
BackgroundMethodZarai, Y., & Tuller, T. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006055 · Cited by 12
Early fate of exogenous promoters in E. coli
Yousuf, M., Iuliani, I., Veetil, R. T., Seshasayee, A. S. N., Sclavi, B., & Lagomarsino, M. C. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1196 · Cited by 12
Czajka, J. J., Okumuş, B., Koffas, M. A. G., Blenner, M., & Tang, Y. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.08.003 · Cited by 12
“MutS, MutL and MutH) that are inherently low [40], are further repressed under stress conditions (such as nutrient limitation, extreme temperatures or pH) by the general stress regulon, namely RpoS in E.”
BackgroundChen, L., Zhao, B., Li, X., et al. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.129411 · Cited by 12
Bacterial transcription during growth arrest
Bergkessel, M. (2021) · Transcription · doi:10.1080/21541264.2021.1968761 · Cited by 12
“Many bacterial promoters have previously been shown to have “bursty” dynamics during exponential growth [135], and in stationary phase, such dynamics may be even more pronounced, especially if the difficulties of managing DNA supercoiling are exacerbated [136].”
BackgroundWen, Y., Zhao, J., He, H., Zhao, Q., & Liu, Z. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c03062 · Cited by 12
Gene regulation by a protein translation factor at the single-cell level
Dolcemascolo, R., Goiriz, L., Montagud‐Martínez, R., & Rodrigo, G. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010087 · Cited by 11
“To this end, we considered a Gamma distribution, which has been shown to describe quite well the stochasticity of genetic systems [28] and which emerges from ab initio calculations [29].”
BackgroundMethodPopulational Heterogeneity vs. Temporal Fluctuation in Escherichia coli Flagellar Motor Switching
Bai, F., Che, Y., Kami‐ike, N., Ma, Q., Minamino, T., Sowa, Y., & Namba, K. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.09.043 · Cited by 12
Estimation of GFP-tagged RNA numbers from temporal fluorescence intensity data
Häkkinen, A., & Ribeiro, A. S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu592 · Cited by 12
Phenotypic noise: effects of post‐transcriptional regulatory processes affecting mRNA
Arbel‐Goren, R., Tal, A., & Stavans, J. (2013) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1209 · Cited by 11
“basic mechanisms (Figure 4): (1) promoter competition in which binding of RNAP to one promoter precludes binding of another RNAP to the second promoter; (2) a ‘sitting duck’ mechanism whereby an elongating complex collides with an RNAP sitting on a second promoter, where it is in the process of transiting from an open…”
BackgroundA framework for scalable parameter estimation of gene circuit models using structural information
Kuwahara, H., Fan, M., Wang, S., & Gao, X. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt232 · Cited by 11
“Availability: http://sfb.kaust.edu.sa/Pages/Software.aspx Contact: xin.gao@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online.”
Lee, J., Kim, M., Park, J., et al. (2016) · Lab on a Chip · doi:10.1039/c6lc00116e · Cited by 12
“Such microfluidic devices enabled researchers to study microorganisms/cells at the single cell level [9, 10], and made it possible to investigate ensemble averaged cellular responses in a qualitative and/or quantitative manner [11, 12].”
BackgroundMarRA, SoxSR, and Rob encode a signal dependent regulatory network in Escherichia coli
Jain, K., & Saini, S. (2016) · Molecular BioSystems · doi:10.1039/c6mb00263c · Cited by 12
Correlated receptor transport processes buffer single-cell heterogeneity
Kallenberger, S. M., Unger, A. L., Legewie, S., Lymperopoulos, K., Klingmüller, U., Eils, R., & Herten, D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005779 · Cited by 10
“Moreover, overall correlations were observed for all cellular proteins, especially for proteins involved in the same biological pathways [66,67].”
BackgroundDevelopment of a Rapid Salivary Proteomic Platform for Oral Feeding Readiness in the Preterm Newborn
Khanna, P., Maron, J. L., & Walt, D. R. (2017) · Frontiers in Pediatrics · doi:10.3389/fped.2017.00268 · Cited by 12
A bacterial signaling system regulates noise to enable bet hedging
Carey, J. N., & Goulian, M. (2018) · Current Genetics · doi:10.1007/s00294-018-0856-2 · Cited by 13
“Published reports indicate that the average number of TorT and TorS proteins per cell is indeed very low, with only a few copies of each present during aerobic growth (Taniguchi et al. 2010; Li et al. 2014).”
Controlling E. coli Gene Expression Noise
Kim, K. H., Choi, K., Bartley, B., & Sauro, H. M. (2015) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2461135 · Cited by 12
Son, M., Kaspar, J. R., Ahn, S., Burne, R. A., & Hagen, S. J. (2018) · Molecular Microbiology · doi:10.1111/mmi.13992 · Cited by 12
“As is common for bacterial protein expression (Taniguchi et al., 2010), the histograms of PcomY expression (Supporting Fig.”
Timing effciency in small-RNA-regulated post-transcriptional processes
Biswas, K., & Ghosh, A. (2020) · Physical review. E · doi:10.1103/physreve.101.022418 · Cited by 13
Klein, S. M., Stern, D., & Seeber, F. (2020) · BMC Biotechnology · doi:10.1186/s12896-020-00646-7 · Cited by 12
Genome-wide genotype-phenotype associations in microbes
Feng, H., Yuan, Y., Zheng, Y., Xing, X., & Zhang, C. (2021) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2021.03.011 · Cited by 12
Multi-omic based production strain improvement (MOBpsi) for bio-manufacturing of toxic chemicals
Webb, J. P., Paiva, A. C., Rossoni, L., Alstrom-Moore, A., Springthorpe, V., Vaud, S., Yeh, V., Minde, D., Langer, S., Walker, H. J., et al. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2022.03.004 · Cited by 13
Wei, C., Li, M., Li, X., Lyu, J., & Zhu, X. M. (2022) · Advanced Biology · doi:10.1002/adbi.202200006 · Cited by 12
Localization of aggregating proteins in bacteria depends on the rate of addition
Scheu, K., Gill, R., Saberi, S., et al. (2014) · Frontiers in Microbiology · doi:10.3389/fmicb.2014.00418 · Cited by 12
Single microbe trap and release in sub-microfluidics
Vasdekis, A. E. (2013) · RSC Advances · doi:10.1039/c3ra40369f · Cited by 12
Trujillo, C., Cooper, M. M., & Klymkowsky, M. W. (2012) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.20585 · Cited by 12
Stochastic dynamics of complexation reaction in the limit of small numbers
Ghosh, K. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3590918 · Cited by 12
Site-Specific Covalent Labeling of DNA Substrates by an RNA Transglycosylase
Tota, E. M., & Devaraj, N. K. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c00861 · Cited by 11
Shaferman, M., Gencel, M., Alon, N., Alasad, K., Rotblat, B., Serohijos, A. W. R., Alfonta, L., & Bershtein, S. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad123 · Cited by 9
Feng, H., Li, F., Wang, T., Xing, X., Zeng, A., & Zhang, C. (2023) · Science Advances · doi:10.1126/sciadv.adg5296 · Cited by 12
“In addition, the outliers [in more extreme cases, one peak of the bimodal expression densities ( 31 )], which have a great impact on matching ( 21 , 30 ), can be viewed as being generated by a Gaussian component ( 33 – 35 ).”
InfluentialHigh-Throughput f-LAESI-IMS-MS for Mapping Biological Nitrogen Fixation One Cell at a Time
Dolatmoradi, M., Stopka, S. A., Corning, C., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c03651 · Cited by 11
A systematic model of the LC-MS proteomics pipeline
Sun, Y., Braga-Neto, U., & Dougherty, E. R. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s6-s2 · Cited by 11
“Protein concentration in the pooled control sample is modeled by a Gamma distribution in accordance with the observations in [13]: where t = 2 and θ = 1000 are the shape and scale parameters.”
MethodTetrameric UvrD Helicase Is Located at the E. Coli Replisome due to Frequent Replication Blocks
Wollman, A. J. M., Syeda, A. H., Howard, J. A. L., Payne-Dwyer, A. L., Leech, A., Warecka, D., Guy, C. P., McGlynn, P., Hawkins, M., & Leake, M. C. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168369 · Cited by 11
The transcription factor network ofE. colisteers global responses to shifts in RNAP concentration
Almeida, B. L. B., Bahrudeen, M. N. M., Chauhan, V., Dash, S., Kandavalli, V. K., Häkkinen, A., Lloyd‐Price, J., Palma, C. S. D., Baptista, I. S. C., Gupta, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac540 · Cited by 11
MetA is a “thermal fuse” that inhibits growth and protects Escherichia coli at elevated temperatures
Schink, S. J., Gough, Z., Biselli, E., Huiman, M. G., Chang, Y., Basan, M., & Gerland, U. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111290 · Cited by 13
Ecological divergence of syntopic marine bacterial species is shaped by gene content and expression
Nowinski, B., Feng, X., Preston, C. M., Birch, J. M., Luo, H., Whitman, W. B., & Moran, M. A. (2023) · The ISME Journal · doi:10.1038/s41396-023-01390-4 · Cited by 11
“4 transcripts per gene copy [61, 62]], as expected given growth rate differences.”
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“Namely, usually they exist in numbers ranging from 1 to a few [59, 60] and are located either at midcell, or randomly throughout the cytoplasm [61–63].”
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“In E. coli, the mean number of a protein is in general 100- to 1000-fold higher than the mean number of the corresponding mRNA [Taniguchi et al., 2010].”
BackgroundInfluentialMicroscopy of single FoF1‐ATP synthases— The unraveling of motors, gears, and controls
Börsch, M. (2013) · IUBMB Life · doi:10.1002/iub.1149 · Cited by 11
Facilitated diffusion buffers noise in gene expression
Schoech, A. P., & Zabet, N. R. (2014) · Physical Review E · doi:10.1103/physreve.90.032701 · Cited by 11
Ge, H., Wu, P., Qian, H., & Xie, X. S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006051 · Cited by 12
“Recent experiments have revealed that the dynamics of a single cell are essentially stochastic, as there is only a single copy of DNA inside a typical cell, which leads to stochastic mRNA and protein production [10, 11].”
Self-citationClass I histone deacetylases in retinal progenitors and differentiating ganglion cells
Saha, A., Tiwari, S., Dharmarajan, S., Otteson, D. C., & Belecky‐Adams, T. L. (2018) · Gene Expression Patterns · doi:10.1016/j.gep.2018.08.007 · Cited by 11
High-throughput cell-cycle imaging opens new doors for discovery
Kuwada, N. J., Traxler, B. A., & Wiggins, P. A. (2015) · Current Genetics · doi:10.1007/s00294-015-0493-y · Cited by 11
“Although this approach has the significant advantage that the cells are in log-phase growth, microfluidic devices are typically complex to operate and to scale-up to the proteome scale (Ullman et al. 2013; Taniguchi et al. 2010).”
BackgroundInfluentialSingle-cell analysis for proteome and related researches
Shao, X., Weng, L., Gao, M., & Zhang, X. (2019) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2019.115666 · Cited by 11
Bugs on Drugs: A Drosophila melanogaster Gut Model to Study In Vivo Antibiotic Tolerance of E. coli
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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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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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