The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 5 of the 34 sources its claims cite were checked for support.
Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
Open accessAbstract
Devil in the Detail: Genetically identical cells in the same environment can show variation in gene expression that may cause phenotypic variation at the single-cell level. But how noisy are most genes? Taniguchi et al. (p. 533 ; see the Perspective by Tyagi ) now report single-cell global profiling of both messenger RNA (mRNA) and proteins in Escherichia coli using a yellow fluorescent protein fusion library. As well as a common extrinsic noise in high-abundance proteins, large fluctuations were observed in low-abundance proteins. Remarkably, in single-cell experiments, mRNA and protein levels for the same gene were uncorrelated.
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Claims the paper makes
5 of 34 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Such stochasticity is manifested in fluctuations of mRNA and protein copy numbers within a cell lineage over time, and in variations of mRNA and protein copy numbers among a population of genetically identical cells at a particular time (1, 2, 3, 4).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
- Not checked — no full text of the sourcecites [1] source
- Partly supportedcites [2] sourceExperimental — accuracy not yet measured
The Abstract says stochastic gene expression can produce variation among identical cells, and its model predicts cell-to-cell variability in mRNA levels for both eukaryotic and prokaryotic promoters. However, the paper's single-cell protein-reporter measurements were made in yeast, not bacteria; it does not establish that both protein and mRNA copy numbers differ among individual bacterial cells. (LLM, conf=0.83)
- Not checked — no full text of the sourcecites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Single molecule sensitivity was confirmed by abrupt photobleaching of membrane-bound YFPs expressed at low level (Fig. S14) (8, 18, 23).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
What the sources say
- Not checked — no full text of the sourcecites [8] source
- Not checked — no full text of the sourcecites [18] source
- Could not verifycites [23] sourceExperimental — accuracy not yet measured
The Results and discussion passage on deletion-mutant verification describes PCR checks, while Materials and methods > Growth tests describes absorbance measurements; neither addresses single-molecule imaging. Searches for photobleaching, membrane-bound YFP, and fluorescence imaging found no passage confirming the claimed sensitivity test. Page numbers were not available in our copy. (LLM, conf=0.78)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“We found that E. coli proteins generally had larger noise and Fano factors than yeast proteins, even for those present at similar copy numbers (Fig. S6) (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Comparison between E. coli and yeast
What the sources say
- Not checked — no full text of the sourcecites [18] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, we found that in a single cell mRNA and protein levels for the same gene are completely uncorrelated.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The distributions for 1,009 out of the 1,018 strains can be well fit by the gamma distribution, Eq. 2 (Fig. S20) (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Analysis of protein distributions
What the sources say
- Not checked — no full text of the sourcecites [18] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Below ten molecules per cell, ηp2 is inversely proportional to protein abundance, indicative of intrinsic noise.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“mRNA and protein copy numbers in a single cell are not correlated (r = 0.01 ± 0.03, SEM, N = 5,447).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The protein noise (ηp2 ≡ σp2/μp2) exhibits two distinct scaling properties (Fig. 2B).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The average protein abundances of essential genes are higher than those for all genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Average protein abundances span five orders of magnitude, ranging from 10-1 to 104 molecules per cell (Fig 2A).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 13
- Probably not a substantial claim. 3
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“In recent years, single-molecule fluorescence microscopy has been used to count mRNA (12-16) or protein (8, 17) molecules in individual cells, especially in bacteria. However, these methods have only been applied to limited number of specific genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
- Not checked — no full text of the sourcecites [12] source
- Supportedcites [13] source
The Results section (page not available) describes fluorescence microscopy used to count individual mRNA molecules from a specific reporter gene in mammalian cells. This paper supports the mRNA-counting citation as an example; the sentence’s broader reference to bacteria does not describe this study. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [14] source
- Supportedcites [15] source
The passage labeled Abstract describes using fluorescence in situ hybridization and microscopy to count individual comK mRNA molecules in single Bacillus subtilis cells. It also describes counting an engineered comK transcript, making this paper an example of the gene-specific, single-cell mRNA measurements for which it is cited. (LLM, conf=0.91)
- Supportedcites [16] source
The Abstract reports counting individual mRNAs in single yeast cells for different genes. The Discussion describes extending the method to analyze multiple genes simultaneously as future work. This paper supports the mRNA-counting example for which it is cited; the sentence assigns protein counting to other citations. (LLM, conf=0.91)
- Not checked — no full text of the sourcecites [8] source
- Not checked — no full text of the sourcecites [17] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, at higher expression levels (>10 molecules per cell), the noise reaches a plateau of ∼0.1 and does not decrease further, suggesting that each protein has at least 30% variation in its expression level.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“We found that the noise floor does not result from cell size effects, nor did it arise from measurement noise (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
What the sources say
- Not checked — no full text of the sourcecites [18] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We discovered large fluctuations in low abundance proteins as well as a common extrinsic noise in high abundance proteins.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We further demonstrate that the extrinsic noise is global to all high expression genes by analyzing the correlations between expression levels of 13 pairs of randomly selected genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The prevalence of proteins with very low copy number suggests that single-molecule experiments are necessary for bacteriology.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This striking result highlights the disconnect between proteome and transcriptome analyses of a single cell, as well as the need for single cell proteome analysis.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“A fluorescent protein reporter library of Saccharomyces cerevisiae (9) has proven to be extremely useful in protein profiling (10, 11).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, our result offers a cautionary note in interpreting single-cell transcriptome analysis and argues for the necessity for single cell proteome analysis.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“We attribute the additional noise to extrinsic noise (3), that is the slow variation of the values of a and b, which we confirm with real time observation of protein levels for four randomly selected high copy library strains.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
What the sources say
- Not checked — no full text of the sourcecites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that some proteins and most mRNAs of functional genes are present at low copy numbers in a bacterial cell, the single molecule sensitivity afforded by our measurements is necessary for the understanding of stochastic gene expression and regulation.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In E. coli, mRNA is typically degraded within minutes (Table S6) (18), whereas most proteins, including fluorescent proteins, have a lifetime longer than the cell cycle (34).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
What the sources say
- Not checked — no full text of the sourcecites [18] source
- Not matchedcites [34]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 1,400 strains attempted, 1,018 strains were confirmed by sequencing and showed no significant growth defects.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Because the same probe is used for all strains, the optimized hybridization efficiency is unbiased for every measured gene (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule RNA counting
What the sources say
- Not checked — no full text of the sourcecites [18] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“While single cell mRNA profiling has been carried out with cDNA microarray (6) and mRNA-seq (7), these studies did not have single molecule sensitivity and are not suitable for bacteria, which express mRNA at low copy numbers (8).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
Gene expression dynamics follow a kinetic scheme with transcription, translation, mRNA degradation, and protein degradation rates.
“We consider the kinetic scheme”
The authors state this as an assumption.
The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
“Assuming that there exist static or slowly varying heterogeneities of a and b, with distributions f (a) and g(b), respectively, the protein distribution is”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Stochastic simulations are necessary when reactions involving few particles such as those in genetic information processing and the charging of the tRNAs can lead to large variations in the state of the cell (Taniguchi et al., 2010).”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Taniguchi et al., 2010
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Although transcriptomic profiling has provided insights into Tex cell biology, mRNA abundance is not always a faithful proxy of protein expression across various organisms18–22.”
From Proteotoxic stress response drives T cell exhaustion and immune evasion (2025) · cites this paper as [22]
In the citing paper
“This finding contrasts with data from bacteria where productive protein expression is achieved with such low mRNA concentrations (Taniguchi et al., 2010).”
From Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells (2012) · cites this paper as Taniguchi et al., 2010
In the citing paper
“These methods hold promise for elucidating the epigenetic controls of cellular identity186,187.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [187]
Citations
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Stochastic steady state gain in a gene expression process with mRNA degradation control
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BackgroundMethodCounting proteins bound to a single DNA molecule
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“Quantification of protein number has been attempted using the fluorescence of the molecule one wishes to quantify as the standard [3].”
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“While such comparisons of mRNA and protein production have been addressed in a few genetically tractable lab adapted microbes (Abreu et al. 2009; Taniguchi et al. 2010), these comparisons are harder to address in non-model and/or environmental systems especially at a quantitative level.”
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“292 Based on previous theoretical [22,23] and experimental [24] studies we assume 293 that fluctuating phosphatase level follows a gamma distribution which is described by 294 two parameters - burst size (b, average number of molecules produced per burst) and 295 burst frequency (a, number of bursts per cell cycle).”
BackgroundHierarchical Post-transcriptional Regulation of Colicin E2 Expression in Escherichia coli
Lechner, M., Schwarz, M., Opitz, M., & Frey, E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005243 · Cited by 17
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“We took d , the probability that the mRNA is degraded during the production of a protein, to be 1 / 11 , giving a mean lifetime of 10 proteins for an mRNA (35).”
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“and fluorescence microscopy, one usually obtains data of protein concentrations, instead of protein copy numbers [11]. Let V >0 be a scaling parameter which usually denotes the average cell volume [30] or maximal protein copy number [31, 32], and let X V(t) = N(t)=V denote the concentration of the protein at time t…”
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Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing
Manes, N. P., Angermann, B., Koppenol-Raab, M., An, E., Sjoelund, V. H., Sun, J., Ishii, M., Germain, R. N., Meier‐Schellersheim, M., & Nita‐Lazar, A. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.048918 · Cited by 17
“Correlation between transcript and protein absolute abundance values has been reported previously for Escherichia coli (65, 66), Saccharomyces cerevisiae (65, 67–70), Schizosaccharomyces pombe (39), the HeLa (human epithelial) cell line (71, 72), the NIH/3T3 (mouse fibroblast) cell line (73), and…”
BackgroundProteomic analysis of Escherichia coli associated with urinary tract infections
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Measuring gene expression in single bacterial cells: recent advances in methods and micro-devices
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“Single-cell based imaging methods have been improved recently and provided useful information about not only the gene expression but also the spatial information about mRNA inside a single bacterium (Taniguchi et al., 2010).”
BackgroundStochastic single-gene autoregulation
Aquino, T., Abranches, E., & Nunes, A. (2012) · Physical Review E · doi:10.1103/physreve.85.061913 · Cited by 17
Growth-rate-dependent dynamics of a bacterial genetic oscillator
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“It is now widely accepted that stochasticity or noise in gene expression can give rise to phenotypic variations among clonal cells in homogeneous environments [10,11].”
BackgroundFundamental Constraints on the Abundances of Chemotaxis Proteins
Bitbol, A., & Wingreen, N. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.01.024 · Cited by 17
“roteomic studies (see also ‘http://ecoliwiki.net/colipedia’). All values are in copy numbers per cell. Two-component system Histidine kinase Response regulator (Histidine kinase / response regulator) (59) (101) (102) Other (59) (101) (102) Other CheA/ CheY: Chemotaxis 6,700 (16) 12 8,200 (16) EnvZ / OmpR: Osmolarity s…”
BackgroundMethodInfluentialFrom analog to digital models of gene regulation
Munsky, B. E., & Neuert, G. (2015) · Physical Biology · doi:10.1088/1478-3975/12/4/045004 · Cited by 17
Thanos, P. K., Zhuo, J., Robison, L. S., Kim, R., Ananth, M. R., Choai, I., Grunseich, A., Grissom, N. M., George, R., Delis, F., et al. (2016) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2016.09.008 · Cited by 20
Quantification of Low Copy Number Proteins in Single Cells
Shi, M., Geng, X., Wang, C., & Guan, Y. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02989 · Cited by 17
Nuclear Physics: Quantitative Single-Cell Approaches to Nuclear Organization and Gene ExpressionPreprint
Lionnet, T., Wu, B., Grünwald, D., Singer, R. H., & Larson, D. R. (2010) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2010.75.057 · Cited by 17
“For example, gene expression is strongly influenced by both kinetics and abundance of factors: First, complex kinetics result from the large number of steps leading from an upstream protein effector (the concentration and spatial distribution of a transcription factor) to its downstream product (the number of a partic…”
Li, M., Liu, L., Xi, N., & Wang, Y. (2014) · Chinese Science Bulletin · doi:10.1007/s11434-014-0290-x · Cited by 16
“Single-cell and single-molecule techniques have become invaluable experimental methods in the life sciences [66, 67].”
BackgroundPolar Localization of the Serine Chemoreceptor of Escherichia coli Is Nucleoid Exclusion-Dependent
Neeli‐Venkata, R., Startceva, S., Annila, T., & Ribeiro, A. S. (2016) · Biophysical Journal · doi:10.1016/j.bpj.2016.10.024 · Cited by 16
Deaconescu, A. M. (2021) · Transcription · doi:10.1080/21541264.2021.1982628 · Cited by 18
“One may conclude then that TCR is a housekeeping DNA repair mechanism that becomes particularly apparent under non-SOS conditions, when the concentrations of UvrA are quite low [1,59].”
BackgroundSassi, A. S., Garcia-Alcala, M., Aldana, M., & Tu, Y. (2022) · Physical Review X · doi:10.1103/physrevx.12.011051 · Cited by 20
RNase E and HupB dynamics foster mycobacterial cell homeostasis and fitness
Griego, A., Douché, T., Gianetto, Q. G., Matondo, M., & Manina, G. (2022) · iScience · doi:10.1016/j.isci.2022.104233 · Cited by 13
“Furthermore, the overall lack of correlation between mRNA and proteins points to the relevance of post-transcriptional regulation and RNA stability in gene expression variation (Swain, 2004; Hansen and Weinberger, 2019; Taniguchi et al., 2010; Arbel-Goren et al., 2016).”
BackgroundControlling gene expression timing through gene regulatory architecture
Ali, M. Z., & Brewster, R. C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009745 · Cited by 15
“coli varies from few 470 units to thousands [61] as such we vary translation rate (b) in a range of 471 0.”
BackgroundChoi, H., & Covert, M. W. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad435 · Cited by 17
“First, the expression of synthetase enzymes is known to be a noisy and stochastic process ( 17 ) and can lead to variable enzyme counts that rise and fall, sometimes dramatically, over the course of a cell cycle.”
Specific codons control cellular resources and fitness
Love, A. M., & Nair, N. U. (2024) · Science Advances · doi:10.1126/sciadv.adk3485 · Cited by 15
Living in a noisy world—origins of gene expression noise and its impact on cellular decision‐making
Pal, S., & Dhar, R. (2024) · FEBS Letters · doi:10.1002/1873-3468.14898 · Cited by 18
Microfluidic Bioreactors for Cellular Microarrays
Willaert, R., & Goossens, K. V. Y. (2015) · Fermentation · doi:10.3390/fermentation1010038 · Cited by 16
Coupling Between Noise and Plasticity in E. coli
Singh, G. P. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.008540 · Cited by 14
Modeling stochastic gene expression in growing cells
Gomez, D., Marathe, R., Bierbaum, V., & Klumpp, S. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.01.017 · Cited by 16
Guantes, R., Benedetti, I., Silva‐Rocha, R., & de Lorenzo, V. (2015) · The ISME Journal · doi:10.1038/ismej.2015.193 · Cited by 16
“Noise in gene expression, or stochastic fluctuations of the gene products (mRNAs and proteins) around their average values, is an unavoidable source of randomness and cell heterogeneity in isogenic cell populations, both in prokaryotes (Taniguchi et al., 2010) and eukaryotes (Newman et al., 2006).”
BackgroundCombining Molecular Observations and Microbial Ecosystem Modeling: A Practical Guide
Hellweger, F. L. (2019) · Annual Review of Marine Science · doi:10.1146/annurev-marine-010419-010829 · Cited by 16
Experimental evolution reveals a general role for the methyltransferase Hmt1 in noise buffering
You, S., Jhou, Y., Kao, C.-F., & Leu, J. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000433 · Cited by 15
Lineage EM algorithm for inferring latent states from cellular lineage trees
Nakashima, S., Sughiyama, Y., & Kobayashi, T. J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa040 · Cited by 18
Heterogeneity coordinates bacterial multi-gene expression in single cells
Han, Y., & Zhang, F. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007643 · Cited by 16
“across all conditions) copies per cell, ranking in the top 1% of all endogenous genes [25] and in agreement with RNA-seq measurements from the studied E.”
BackgroundUvrC Coordinates an O 2 -Sensitive [4Fe4S] Cofactor
Silva, R. M. B., Grodick, M. A., & Barton, J. K. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c01671 · Cited by 17
Bokes, P., Borri, A., Palumbo, P. J., & Singh, A. (2020) · Journal of Mathematical Biology · doi:10.1007/s00285-020-01512-y · Cited by 14
Measuring expression heterogeneity of single-cell cytoskeletal protein complexes
Vlassakis, J., Hansen, L. L., Higuchi-Sanabria, R., Zhou, Y., Tsui, C. K., Dillin, A., Huang, H., & Herr, A. E. (2021) · Nature Communications · doi:10.1038/s41467-021-25212-3 · Cited by 16
“The CQV is a metric of variance accounting for skewed distributions 63 , such as gamma-distributed protein expression 64 : F-actin level.”
BackgroundContinuous rate modeling of bacterial stochastic size dynamics
Nieto, C., Vargas-García, C. A., & Pedraza, J. M. (2021) · Physical review. E · doi:10.1103/physreve.104.044415 · Cited by 17
Cellular heterogeneity: yeast-side story
Pinheiro, S., Pandey, S., & Pelet, S. B. (2021) · Fungal Biology Reviews · doi:10.1016/j.fbr.2021.11.005 · Cited by 16
A spatially resolved stochastic model reveals the role of supercoiling in transcription regulation
Geng, Y., Bohrer, C., Yehya, N. N., Hendrix, H., Shachaf, L., Liu, J., Xiao, J., & Roberts, E. (2022) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1009788 · Cited by 18
“Bacterial transcription exhibits a large cell-to-cell variability [42,50,51,52], allowing a population of isogenic cells to exhibit heterogenous phenotypes and responses to various stimuli [53].”
BackgroundMethodStochastic Switching Induced Adaptation in a Starved Escherichia coli Population
Shimizu, Y., Tsuru, S., Ito, Y., Ying, B., & Yomo, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023953 · Cited by 15
“, whether the gene is essential or nonessential [9,10,11].”
BackgroundSmith, K. P., Fields, J. G., Voogt, R., et al. (2015) · PROTEOMICS · doi:10.1002/pmic.201400505 · Cited by 16
“Heterogeneity in individual protein abundance exists in bacterial cells [24].”
BackgroundPatterns of Bacterial and Archaeal Gene Expression through the Lower Amazon River
Satinsky, B. M., Smith, C. B., Sharma, S., et al. (2017) · Frontiers in Marine Science · doi:10.3389/fmars.2017.00253 · Cited by 16
“…microbial communities (Church et al., 2010; Moran et al., 2013; Satinsky et al., 2017) as well as laboratorygrown bacterial cultures (Neidhardt and Umbarger, 1987; Taniguchi et al., 2010; Gifford et al., 2016) and reflects a small and dynamic mRNA pool in Bacteria and Archaea (Moran et al., 2013).”
BackgroundWrede, F., & Hellander, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz420 · Cited by 16
Source and regulation of flux variability in Escherichia coli
Román, M. S., Cancela, H., & Acerenza, L. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-67 · Cited by 18
“coli [31] are used to impose constraints on fluxes in a metabolic reconstruction [20].”
MethodChemical Kinetics for the Microbial Safety of Foods Treated with High Pressure Processing or Hurdles
Doona, C. J., Feeherry, F. E., Ross, E. W., et al. (2016) · Food Engineering Reviews · doi:10.1007/s12393-015-9138-7 · Cited by 16
“The essence of this postulate was confirmed, but the distribution function was experimentally determined to be the related, but more complicated, Gamma distribution function, and the ‘‘germination enzyme’’ was recognized to be a receptor [52].”
BackgroundRoot-Microbe Interactions: The Importance of Protein Secretion
De‐la‐Peña, C., & Vivanco, J. M. (2010) · Current Proteomics · doi:10.2174/157016410793611819 · Cited by 16
“Because of the lack of correlation between the transcriptome and the proteome of an organism [112], functional analysis of secreted proteins would represent the precise biological function of how bacteria react to the presence of different plants.”
BackgroundMeasuring expression heterogeneity of single-cell cytoskeletal protein complexes
Vlassakis, J., Hansen, L. L., Higuchi-Sanabria, R., Zhou, Y., Tsui, C. K., Dillin, A., Huang, H., & Herr, A. E. (2020) · doi:10.1101/2020.09.12.294801 · Cited by 16
The accuracy of telling time via oscillatory signals
Monti, M., & ten Wolde, P. R. (2016) · Physical Biology · doi:10.1088/1478-3975/13/3/035005 · Cited by 14
“[53] Y Taniguchi, P J Choi, G W Li, H Chen, M Babu, J Hearn, A Emili, and X S Xie.”
BackgroundStochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations
Sun, L., Ashcroft, P., Ackermann, M., & Bonhoeffer, S. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz199 · Cited by 17
“…the frequency of cells with active efflux DNA agrees with the prediction of the telegraph process (∼ 2.5%), while the average number of efflux mRNA (∼ 0.04) and efflux proteins (∼ 4) per cell are within experimentally reported ranges (Moran et al., 2013; Cai et al., 2006; Taniguchi et al., 2010).”
BackgroundInfluentialLiu, N., Huang, Y., Zhang, H., Wang, T., Chen, T., Zhang, A., Chen, B., Yin, Y., Song, M., Qu, G., et al. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c05989 · Cited by 16
Hyperosmotic Shock Transiently Accelerates Constriction Rate in Escherichia coli
Sun, J., Shi, H., & Huang, K. C. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.718600 · Cited by 16
“Microfluidic devices are powerful tools for monitoring growth and division dynamics at single-cell resolution (Taniguchi et al., 2010; Wang et al., 2010; Campos et al., 2014; Taheri-Araghi et al., 2015; Camsund et al., 2020), particularly during acute environmental changes such as osmotic shocks (Pilizota and Shaevitz…”
BackgroundDuan, D., Hanson, M., Holland, D. O., & Johnson, M. E. (2022) · Scientific Reports · doi:10.1038/s41598-022-09259-w · Cited by 15
Nucleoside diphosphate kinases 1 and 2 regulate a protective liver response to a high-fat diet
Iuso, D., Garcia-Saez, I., Couté, Y., Yamaryo-Botté, Y., Boeri Erba, E., Adrait, A., Zeaiter, N., Tokarska-Schlattner, M., Jilkova, Z. M., Boussouar, F., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh0140 · Cited by 14
Bacterial cell-size changes resulting from altering the relative expression of Min proteins
Vashistha, H., Jammal-Touma, J., Singh, K., Rabin, Y., & Salman, H. (2023) · Nature Communications · doi:10.1038/s41467-023-41487-0 · Cited by 15
Cao, W., Huang, C., Zhou, X., Zhou, S., & Deng, Y. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108404 · Cited by 17
MYMIV-AC2, a Geminiviral RNAi Suppressor Protein, Has Potential to Increase the Transgene Expression
Rahman, J., Karjee, S., & Mukherjee, S. K. (2012) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-012-9702-z · Cited by 15
Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks
Hallen, M. A., Li, B., Tanouchi, Y., Tan, C., West, M., & You, L. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002209 · Cited by 16
“Recent singlemolecule fluorescent measurements have allowed experimental determination of molecule number distributions in Escherichia coli, thus measuring both intrinsic and extrinsic noise [41].”
BackgroundInvestment in rapid growth shapes the evolutionary rates of essential proteins
Vieira‐Silva, S., Touchon, M., Abby, S. S., & Rocha, E. P. C. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110972108 · Cited by 16
“The distribution of the concentration of proteins in bacterial cells follows approximately a log-normal distribution, where most proteins are present at low concentrations and a small percentage of highly expressed proteins (HEP) account for the majority of the proteome (4, 5).”
BackgroundCharacterizing rate limiting steps in transcription from RNA production times in live cells
Häkkinen, A., & Ribeiro, A. S. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv744 · Cited by 14
A stochastic analysis of autoregulation of gene expression
Dessalles, R., Fromion, V., & Robert, P. (2017) · Journal of Mathematical Biology · doi:10.1007/s00285-017-1116-7 · Cited by 14
“The lifetime of an mRNA is μ−12 4min, see Taniguchi et al. (2010).”
BackgroundA novel method for quantitative measurements of gene expression in single living cells
Mandic, A., Strebinger, D., Regali, C., Phillips, N. E., & Suter, D. M. (2017) · Methods · doi:10.1016/j.ymeth.2017.04.008 · Cited by 15
Wei, J., Hu, X. T., Zou, X., & Tian, T. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0312-z · Cited by 14
“The availability of the large datasets provides unprecidental opportunities to study large-scale regulatory networks inside the cell by using various types of omics datasets [1, 2].”
BackgroundCsrA and its regulators control the time-point of ColicinE2 release in Escherichia coli
Götz, A., Lechner, M., Mader, A., von Bronk, B., Frey, E., & Opitz, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-24699-z · Cited by 15
Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data
Eugenio Cinquemani, B. (2018) · Processes · doi:10.3390/pr6090136 · Cited by 14
“Model experimental technologies in cellular biology allow one to measure reaction dynamics with single-cell resolution [7].”
BackgroundmRNA detection in budding yeast with single fluorophores
Wadsworth, G. M., Parikh, R. Y., Choy, J. S., & Kim, H. D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx568 · Cited by 14
“In many studies, single mRNA molecules were successfully probed with a smaller number of fluorophores: as few as four fluorophores for budding yeast (20) and a single fluorophore for Escherichia coli (21).”
BackgroundNanoplasmonic–Nanofluidic Single-Molecule Biosensors for Ultrasmall Sample Volumes
Špačková, B., Šípová, H., Käll, M., et al. (2020) · ACS Sensors · doi:10.1021/acssensors.0c01774 · Cited by 15
Walworth, N. G., Saito, M. A., Lee, M., McIlvin, M. R., Moran, D. M., Kellogg, R. M., Fu, F., Hutchins, D. A., & Webb, E. A. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00517 · Cited by 16
Bacterial survivors: evaluating the mechanisms of antibiotic persistence
Shi, X., & Zarkan, A. (2022) · Microbiology · doi:10.1099/mic.0.001266 · Cited by 16
Nonlinear biochemical signal processing via noise propagation
Kim, K. H., Qian, H., & Sauro, H. M. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4822103 · Cited by 14
Focus on Membrane Differentiation and Membrane Domains in the Prokaryotic Cell
Boekema, E. J., Scheffers, D., van Bezouwen, L. S., Bolhuis, H., & Folea, I. M. (2013) · Microbial Physiology · doi:10.1159/000351361 · Cited by 15
“…and the development of better fluorescent reagents, both methods have been recently applied to detect RNA in bacterial cells, despite the inherent limitations, that is, the small cell size, the low copy number and fast turnover of mRNAs per cell [Taniguchi et al., 2010; Nevo-Dinur et al., 2012].”
MethodAnalysis of gene expression levels in individual bacterial cells without image segmentation
Kwak, I. H., Son, M., & Hagen, S. J. (2012) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2012.03.117 · Cited by 15
“Each histogram can be fit to a gamma distribution to yield the parameters a and b which characterize the rates of transcription and translation respectively [4,27].”
BackgroundPhenotypic deconstruction of gene circuitry
Lomnitz, J. G., & Savageau, M. A. (2013) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4809776 · Cited by 17
“The co-operativity for induction is 2, 18,19 the capacity for regulation is about 100, 18,19 the steady-state ratio of regulator to mRNA is 30, 31,35 the rate constant for mRNA decay is 20.8 h (cid:2) 1 , 4,17 and the rate constant for loss of the stable repressor has a value of ð ln2 Þ = T h…”
BackgroundInfluentialLabeling native bacterial RNA in live cells
Toran, P. T., Smolina, I., Driscoll, H., et al. (2014) · Cell Research · doi:10.1038/cr.2014.47 · Cited by 15
Biochemical aspects of bacterial strategies for handling the incomplete translation processes
Shimizu, Y. (2014) · Frontiers in Microbiology · doi:10.3389/fmicb.2014.00170 · Cited by 15
Improved Statistical Analysis of Low Abundance Phenomena in Bimodal Bacterial Populations
Reinhard, F., & van der Meer, J. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078288 · Cited by 14
“Such detection is typically performed by either epifluorescence microscopy and digital image analysis [3,5,9,31–33] or by flow cytometry, if expression of the fluorescent reporter protein is sufficiently high [4,17,34–38].”
BackgroundMethodGérard, C., & Novák, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083372 · Cited by 14
“coli [47], in yeast [48], as well as in mammals [49] show an expression of messenger RNA of about tens of copies per cell, while the expression of proteins is in the range of hundreds or thousands of copies per cell.”
BackgroundTrauth, S., & Bischofs, I. B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098360 · Cited by 14
“Especially microscopy-based assays are very promising to accurately quantify the weak signals by deconvolution [10,33,34].”
BackgroundTuson, H. H., Aliaj, A., Brandes, E. R., et al. (2016) · ChemPhysChem · doi:10.1002/cphc.201600035 · Cited by 15
Distribution of data in cellular electrophysiology: Is it always normal?
Kula, R., Bébarová, M., Matejovič, P., et al. (2020) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2020.05.008 · Cited by 15
Deterministic and Stochastic Descriptions of Gene Expression Dynamics
Marathe, R., Bierbaum, V., Gomez, D., & Klumpp, S. (2012) · Max Planck Institute for Plasma Physics · Cited by 15
Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics
Stein-O’Brien, G., Ainslie, M. C., & Fertig, E. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.03.008 · Cited by 14
Extending the dynamic range of transcription factor action by translational regulation
Sokolowski, T. R., Walczak, A. M., Bialek, W., & Tkačik, G. (2016) · Physical review. E · doi:10.1103/physreve.93.022404 · Cited by 14
Re-defining how mRNA degradation is coordinated with transcription and translation in bacteriaPreprint
Kim, S.-H., Wang, Y., Hassan, A., & Kim, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.18.588412 · Cited by 11
Evidence for a sexual dimorphism in gene expression noise in metazoan species
Díaz-Castillo, C. (2015) · PeerJ · doi:10.7717/peerj.750 · Cited by 14
Ying, B., Seno, S., Matsuda, H., & Yomo, T. (2017) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2017.03.148 · Cited by 12
RNAP Promoter Search and Transcription Kinetics in Live E. coli Cells
Bettridge, K. E., Harris, F. E., Yehya, N. N., et al. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c09142 · Cited by 14
Skalnik, C. J., Cheah, S., Yang, M. Y., Wolff, M., Spangler, R. K., Talman, L., Morrison, J. H., Peirce, S. M., Agmon, E., & Covert, M. W. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011232 · Cited by 16
Recent methodological advances towards single-cell proteomics
Kim, S., Kamarulzaman, L., & Taniguchi, Y. (2023) · Proceedings of the Japan Academy Series B · doi:10.2183/pjab.99.021 · Cited by 15
Self-citation
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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