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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“[7] used images of about a thousand cells to obtain estimates of meanmRNAnumbers, mRNA Fano factors, mean protein numbers and protein number variances.”
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“6 (6), close to the Poisson expectation.”
A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data
Fruzangohar, M., Ebrahimie, E., & Adelson, D. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170486 · Cited by 25
Bruhn‐Olszewska, B., Szczepaniak, P., Matuszewska, E., Kuczyńska‐Wiśnik, D., Stojowska‐Swędrzyńska, K., Moruno-Algara, M., & Laskowska, E. (2018) · Microbiological Research · doi:10.1016/j.micres.2018.02.002 · Cited by 25
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“Similar to how these emerging techniques have been harnessed to reveal the basic biology of culturable microorganisms (Taniguchi et al., 2010), researchers studying the human microbiome have begun to apply these methods to obtain a highresolution view of host-associated microbial communities (Fig.”
BackgroundInfluentialAre gene loops the cause of transcriptional noise?
Hebenstreit, D. (2013) · Trends in Genetics · doi:10.1016/j.tig.2013.04.001 · Cited by 25
Type of noise defines global attractors in bistable molecular regulatory systems
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“…number observed in natural transcriptomes: 0:525 104 molecules/cell (Mortazavi et al., 2008; Galau et al., 1977). n E. coli: average protein copy number: 10 12104 molecules/cell (Taniguchi et al., 2010). o Mammals: the maximal protein copy number: 108 molecules/cell (Sims and Allbritton, 2007).”
InfluentialRapid Perturbation of Free‐Energy Landscapes: From In Vitro to In Vivo
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Emergence of Metabolite Provisioning as a By-Product of Evolved Biological Functions
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“Importantly, direct FP fusions to the C-termini of genes at their endogenous loci have been confirmed to maintain the endogenous expression level of these genes (Taniguchi et al., 2010).”
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Rapid label‐free quantitative analysis of the E. coli BL21(DE3) inner membrane proteome
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“Nevertheless, the low expression levels of integral IM proteins [5, 22] and their hydrophobic properties require demanding sample preparation methods that often entail”
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Protein-to-mRNA Ratios Are Conserved between Pseudomonas aeruginosa Strains
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“coli, the crossover regime between intrinsically and extrinsically dominated regimes lies at a concentration of 10 protein copies per cell (26).”
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“Proteomics data were taken from three sources to estimate cellular concentrations ( Schmidt et al., 2016; Taniguchi, 2010; Wi ́sniewski and Rakus, 2014 ).”
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High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression
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A Model for the Spatiotemporal Design of Gene Regulatory Circuits
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For the Greater (Bacterial) Good: Heterogeneous Expression of Energetically Costly Virulence Factors
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Three-Dimensional Single Particle Tracking and Its Applications in Confined Environments
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Nanofluidics for single-cell analysis
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Single‐Cell Metabolomics by Mass Spectrometry: Opportunities and Challenges
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Khoogar, R., Li, F., Chen, Y., Ignatius, M. S., Lawlor, E. R., Kitagawa, K., Huang, T. H. ‐., Phelps, D. A., & Houghton, P. J. (2022) · Cellular Oncology · doi:10.1007/s13402-021-00640-x · Cited by 23
Dash, S., Palma, C. S. D., Baptista, I. S. C., Almeida, B. L. B., Bahrudeen, M. N. M., Chauhan, V., Jagadeesan, R., & Ribeiro, A. S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac643 · Cited by 24
“Then, we plotted the mean expression levels in optimal conditions against the corresponding protein numbers reported in (34) (Supplementary Figure S12).”
BackgroundMethodInfluentialReal-time detection of response regulator phosphorylation dynamics in live bacteria
Butcher, R. J., & Tabor, J. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201204119 · Cited by 25
“In future studies, mNG tags could be fused to rr genes at their native chromosomal loci using established methods (75–77), analogous to a previously constructed library of chromosomal C-terminal yfp fusions for >1,000 individual genes (78).”
MethodA developmental basis for stochasticity in floral organ numbers
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“…models because the standard Gaussian distribution is the most basic distribution in statistics, the log-normal (Furusawa et al., 2005) and gamma (Taniguchi et al., 2010) distributions have been proposed as models for stochasticity in gene expression, and the beta distribution was previously…”
Ma, H., Gibson, E. A., Dittmer, P. J., Jimenez, R., & Palmer, A. E. (2012) · Journal of the American Chemical Society · doi:10.1021/ja2101592 · Cited by 23
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“Relevantly, their kinetics is both sequence-dependent and affected by regulatory molecules [14,15], which may explain how different genes differ so widely in kinetics of expression [16].”
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Modulation of gene transcription noise by competing transcription factors
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“The scenery becomes even worse as φ(t) could develop some highly nontrivial dynamics (Felmer et al. 2009; Tang 2010).”
BackgroundInfluentialA system mathematical model of a cell–cell communication network in amyotrophic lateral sclerosis
Shao, H., He, Y., Li, K. C., & Zhou, X. (2012) · Molecular BioSystems · doi:10.1039/c2mb25370d · Cited by 23
Wu, X., Oh, M., Kim, H. S., et al. (2012) · Frontiers in Plant Science · doi:10.3389/fpls.2012.00262 · Cited by 23
“Because the expression level of many proteins in the E. coli proteome has been determined (Ishihama et al., 2008; Taniguchi et al., 2010), we could readily determine whether FlagBRI1 was simply phosphorylating the most abundant bacterial proteins.”
BackgroundInfluentialArbel‐Goren, R., Tal, A., Parasar, B., Zorea, A., Costantino, N., Muñoz-García, J., Court, D. L., & Stavans, J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw273 · Cited by 22
“Second, each distribution corresponding to a given value of DTPA concentration is unimodal and similar to those characterizing mRNAs that are not sRNA targets (26), in spite of reports of a mixed double-negative feedback loop configuration involving RyhB and Fur (21).”
BackgroundUsing Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
Wiesch, P. A. Z., Clarelli, F., & Cohen, T. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005321 · Cited by 22
Illuminating Messengers: An Update and Outlook on RNA Visualization in Bacteria
van Gijtenbeek, L. A., & Kok, J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01161 · Cited by 24
“5 copies per cell in Escherichia coli) and therefore require a potentially bias-introducing pre-amplification step to prepare cDNA libraries (Taniguchi et al., 2010; Saliba et al., 2014; Liu and Trapnell, 2016).”
BackgroundInfluentialShaham, G., & Tuller, T. (2017) · DNA Research · doi:10.1093/dnares/dsx049 · Cited by 26
“Escherichia coli mean mRNA levels The mean mRNA levels were obtained from Taniguchi et al.(35) Data include mean mRNA levels and mRNA levels per cell cycle.”
MethodNoise propagation in an integrated model of bacterial gene expression and growth
Kleijn, I. T., Krah, L. H. J., & Hermsen, R. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006386 · Cited by 21
Single-Cell Metabolomics: Technology and Applications
Minakshi, P., Ghosh, M., Kumar, R., et al. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814919-5.00015-4 · Cited by 23
Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells
Sun, M., & Zhang, J. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008389 · Cited by 25
“By quantifying protein concentrations in individual isogenic cells cultured in a common environment, researchers have measured the expression noise for thousands of genes in the bacterium Escherichia coli [14] and unicellular eukaryote Saccharomyces cerevisiae [15].”
BackgroundConstruction of a RFP-lacZα bicistronic reporter system and its application in lead biosensing
Hui, C.-Y., Guo, Y. L., Liu, L., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228456 · Cited by 23
“Recombinant production of enzymatic and fluorescent reporters has greatly improved our ability to explore the potential molecular processes underlying gene expression and regulation in various living organisms [1, 2].”
BackgroundHeins, A., Reyelt, J., Schmidt, M., Kranz, H. D., & Weuster‐Botz, D. (2020) · Microbial Cell Factories · doi:10.1186/s12934-020-1283-x · Cited by 23
Digital Microfluidics for Sample Preparation in Low‐Input Proteomics
Steinbach, M. K., Leipert, J., Matzanke, T., & Tholey, A. (2024) · Small Methods · doi:10.1002/smtd.202400495 · Cited by 22
Strategy Revealing Phenotypic Differences among Synthetic Oscillator Designs
Lomnitz, J. G., & Savageau, M. A. (2014) · ACS Synthetic Biology · doi:10.1021/sb500236e · Cited by 22
Jȩdrak, J., & Ochab-Marcinek, A. (2016) · Physical review. E · doi:10.1103/physreve.94.032401 · Cited by 20
Bagnall, J., Rowe, W., Alachkar, N., Roberts, J., England, H., Clark, C. I., Platt, M., Jackson, D. A., Muldoon, M. R., & Paszek, P. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.007 · Cited by 22
Putting Proteomics Into Immunotherapy for Glioblastoma
Chen, L., Qin, D., Guo, X., Wang, Q.-X., & Li, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.593255 · Cited by 17
“The methods to study glioblastoma multiforme (GBM) heterogeneity are mainly genetic and transcriptomic profiling, which cannot reflect instant functional changes (50, 51).”
MethodDistinct molecular impact patterns of abamectin on Apis mellifera ligustica and Apis cerana cerana
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Self-citationZhou, Z., Tang, J., Cao, Q., Li, Z., & Ma, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264847 · Cited by 22
Information, Noise and Communication: Thresholds as Controlling Elements in Development
TREWAVAS, A. J. (2011) · Signaling and communication in plants · doi:10.1007/978-3-642-23524-5_2 · Cited by 21
“Careful measurements using a technique that could measure individual protein molecules indicated up to 15-fold variation (Taniguchi et al. 2010).”
BackgroundMethodStochastic Gene Expression in Cells: A Point Process Approach
Fromion, V., Leoncini, E., & Robert, P. H. (2013) · SIAM Journal on Applied Mathematics · doi:10.1137/120879592 · Cited by 22
“scent reporters, as the GFP (Green Fluorescent Protein), has allowed observations in live cells and the experimental quantication of the protein production at single cell level. See Taniguchi et al. [12] for the experimental characterization of a large number of messengers and proteins of E. coli. The good qualitative…”
ResultRiboregulation of the bacterial actin-homolog MreB by DsrA small noncoding RNA
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The current state of microbial proteomics: Where we are and where we want to go
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“coli [126], the authors found significant variation in abundance between individual cells.”
BackgroundMethods and applications of absolute protein quantification in microbial systems
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In-Depth Quantitative Proteomic Analysis of Trophozoites and Pseudocysts of Trichomonas vaginalis
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Single-Cell Proteomics: Technology and Applications
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One-shot analysis of translated mammalian lncRNAs with AHARIBO
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“…RNA engagement with the translational machinery by RIBO-seq or polysomal profiling ( Arava et al., 2003 ; Clamer et al., 2018 ; Eden et al., 2011 ; Taniguchi et al., 2010 ), and protein synthesis by mass spectrometry or metabolic labeling ( Aviner et al., 2013 ; Dieterich et al., 2006 ;…”
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Quantitative microfluidic biomolecular analysis for systems biology and medicine
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“A quantitative description of cellular behaviors is a major challenge in biology owing to the enormous number of different biomolecules all interacting in dynamically changing networks and across radically different spatiotemporal scales [5, 6].”
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“The double peaked (bimodal) distribution of PcomX activity under CSP stimulation was found empirically to be well characterized as a combination of two well-separated gamma distribution functions (Friedman et al., 2006; Taniguchi et al., 2010; Taniguchi et al., 2010) (as in Fig.”
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Gene expression noise can promote the fixation of beneficial mutations in fluctuating environments
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“These quantities agree well with single-cell observations [37, 87].”
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“, > 20 per cell) (38), total protein production would be consecutive while individual genes might exhibit bursty expression (78-80) When bursting has a large contribution to cell-to-cell variability, it is needed to be explicitly included in the model (Fig.”
Inference and uncertainty quantification of stochastic gene expression via synthetic models
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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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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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