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]
Citations
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“In this work, we use the following rela-109 tionship between protein noise and mRNA copy number [49, 50]: where CV is the coefficient of variation of protein num-112 ber, τ m is the mRNA lifetime, τ p is the protein lifetime, Here, we take p at the peak value of the fitness function before…”
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“In both mammalian and bacterial systems, measurements of protein levels do not necessarily correlate to transcripts levels at the individual cell level 18,19 .”
Kulathungage, H., Aretakis, J. R., Vaidya, K., Al-Husini, N., Muthunayake, N. S., Kim, S., & Schrader, J. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.22.648521
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Congruence between noise and plasticity in protein expression
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“4) might imply a substantial contribution of post-transcriptional process, including both protein synthesis and protein/ mRNA degradation 28 .”
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“In the context of a single cell, cellular volumes tend to be quite small, gene products (and genes themselves) often in low number, and the causes of noise can be conceptualized as coming from: (i) intrinsically random waiting times for molecular events; (ii) cell-to-cell variability [3–7].”
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Eriksson, H., Schlegel, S., Kjellin, J., & Koskiniemi, S. (2025) · mBio · doi:10.1128/mbio.02083-25
Wang, Z., Mei, Z., Wang, X., et al. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06284-0
“These post-transcriptional regulatory mechanisms are crucial in determining the final protein expression level, as mRNA abundance directly impacts the rate of protein synthesis during translation [8, 9].”
COTree: A Statistical Framework for Deciphering Cell-Resolved Multi-Omics TrajectoriesPreprint
Yuan, B., Wei, R., Brier, T. A., Fu, E., Thornburg, Z. R., Luthey-Schulten, Z. A., & Wang, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.30.685684
“…characterization of reaction kinetics, whole-cell models computationally track the temporal evolution of thousands of molecular species and intermediates across genomic, transcriptomic, proteomic, and metabolomic layers (Bremer and Dennis, 2008; Taniguchi et al., 2010; Park et al., 2016).”
Effects of cooling on E. coli ’s DNA organization, structure, and gene expression
Arsh, A. M., Azevedo, M. M., & Ribeiro, A. S. (2025) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00153-25
Montaner, M., Montes, M., Alajarin, F., et al. (2025) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkaf408
Closed-form information capacity of canonical signaling models
Komorowski, M. (2025) · Communications Physics · doi:10.1038/s42005-025-02425-2
“This corresponds to biological systems where the strength of noise grows linearly with the signal, which is often exactly or approximately observed [41, 43–45].”
Martin, N., Kleine-Schultjann, J., París, E., Boussau, Q., Dumont, A., Duplus‐Bottin, H., Prieux, M., Bazin, A., Modolo, L., Yvert, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.22.689898
Yehya, N. N., Bohrer, C. H., Yu, Q., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.23.690031
Sequence Dependent Properties of the Expression Dynamics of Genes and Gene Networks
Potapov, I. S., Mäkelä, J., Yli-Harja, O. P., & Ribeiro, A. S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33636-2_18
Additional file 2 of 3 ′-5 ′ crosstalk contributes to transcriptional bursting
Cavallaro, M., Walsh, M. D., Jones, M., Teahan, J., Tiberi, S., Finkenstädt, B., & Hebenstreit, D. (2021) · Figshare · doi:10.6084/m9.figshare.13719211
Rettenbacher, L. A., & von der Haar, T. (2024) · Figshare · doi:10.6084/m9.figshare.26556647
Additional file 1 of 3 ′-5 ′ crosstalk contributes to transcriptional bursting
Cavallaro, M., Walsh, M. D., Jones, M., Teahan, J., Tiberi, S., Finkenstädt, B., & Hebenstreit, D. (2021) · Figshare · doi:10.6084/m9.figshare.13719208
Savinov, A., Brandsen, B. M., Angell, B. E., Cuperus, J. T., & Fields, S. (2021) · Figshare · doi:10.6084/m9.figshare.16829509
Savinov, A., Brandsen, B. M., Angell, B. E., Cuperus, J. T., & Fields, S. (2021) · Figshare · doi:10.6084/m9.figshare.16829512
Klein, S. M., Stern, D., & Seeber, F. (2020) · Figshare · doi:10.6084/m9.figshare.13059649
Klein, S. M., Stern, D., & Seeber, F. (2020) · Figshare · doi:10.6084/m9.figshare.13059637
Klein, S. M., Stern, D., & Seeber, F. (2020) · Figshare · doi:10.6084/m9.figshare.13059646
Rettenbacher, L. A., & von der Haar, T. (2024) · Figshare · doi:10.6084/m9.figshare.26556644
Schmitz, A., & Zhang, F. (2021) · Figshare · doi:10.6084/m9.figshare.14151221
Agonkoui, C., Cao, H., Dabo‐Niang, S., et al. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11166216
“Single-cell data acquisition methods such as mass cytometry [2] and dynamic profiling of fluorescent fusion proteins [3] were developed years ago and are commonly found in modern biological labs.”
Toward single-cell control: noise-robust perfect adaptation in biomolecular systems
Lim, D., Moon, S., Song, Y. M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67736-y
Kscheschinski, B., Chauvin, D., Julou, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.02.696648
“In particular, for E. coli it has been estimated that the expression noise of genes whose average expression is larger than 10 proteins per cell is dominated by extrinsic noise 4 .”
InfluentialBayesian inference of gene regulatory networks at stochastic steady state
Gupta, A., Yoon, R., & Josić́, K. (2026) · Journal of The Royal Society Interface · doi:10.1098/rsif.2026.0040
“…applications ranging from the characterization of synthetic gene circuits [2,3] and the analysis of single-cell transcriptomic time-series data [4], to the identification of dysregulated regulatory interactions in disease contexts, such as cancer and developmental disorders, where key circuit…”
InfluentialInduced tolerance to UV stress drives survival heterogeneity in isogenic E. coli cell populations
Ichikawa, S., Tanoue, M., Takeuchi, J., Fujitani, T., Goto, M., Sanda, R., Matsuo, E., Shimada, Y., & Singh, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-36328-1
Regulatory mechanisms of the trade off between Th17 cells and Treg cells
Huang, L., Chen, Z., & Jiao, F. (2026) · Journal of Biological Physics · doi:10.1007/s10867-026-09701-4
Persky, Z., Mattioli, C. C., Garcia, K. Z., Azran, O., Avraham, R., & Dar, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.01.703127 · Cited by 1
“Chief among these is the extremely low abundance of bacterial mRNAs, typically fewer than one transcript per gene per cell ( 21 , 22 ), their lack of polyadenylation, and their encapsulation within rigid cell walls that impede efficient extraction (22).”
Growth-dependent sensory bet-hedging enhances collective navigationPreprint
Avgidis, F., Jurgelyte, R., Emonet, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.13.705748
“Previous studies have shown that stochastic gene expression can lead to significant variability in protein levels among isogenic cells, with one study suggesting that proteins in E. coli have a baseline variation of 30% in their expression level [43].”
Ahsan, T., Zang, C., Pei, X., Huang, Y., Liang, C., & Irfan, M. (2026) · Journal of Biological Engineering · doi:10.1186/s13036-026-00640-1 · Cited by 1
“The enrichment of the “Aminoacyl-tRNA biosynthesis” pathway indicates increased translation ability, which may be required to meet the increased demand of protein synthesis during fermentation [62].”
Lister, K. C., Wong, C., Uttam, S., Parisien, M., Stecum, P., Brown, N., Cai, W., Ho-Tieng, D., Hooshmandi, M., Gu, N., et al. (2026) · eLife · doi:10.7554/elife.100451.2
Hart, A., Shin, S., & Nguyen, L. K. (2026) · eLife · doi:10.7554/elife.87710.2
Protein overabundance is driven by growth robustness
Choi, H. J., Lo, T. W., Cutler, K. J., Huang, D., Will, W. R., & Wiggins, P. A. (2026) · Science Advances · doi:10.1126/sciadv.adz9623
Growth-rate-dependent protein production in bacteria
Borkowski, O. (2013) · theses.fr (ABES) · doi:10.70675/433e453azd16cz4b03z81bazb07c122d24bf
Cycline G, contrôle de la transcription et stabilité du développement
Dardalhon-Cuménal, D. (2015) · theses.fr (ABES) · doi:10.70675/a91e9af4z581dz489ez9767za2969ab64eaf
Emergence of a complex logic gate from the integration of slippage-induced frameshift mechanismsPreprint
Bhatti, R., & Saikrishnan, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.01.715767
Dessalles, R. (2017) · theses.fr (ABES) · doi:10.70675/c5e4ab0cz7b22z4358z805fzdb49c0b12e75 · Cited by 1
Prédiction de la structure de contrôle de bactéries par optimisation sous incertitude
Faqir, M. A. E. (2016) · doi:10.70675/e76d6675z95f3z4b5dz9680z188e2c799625
“Cette approximation est cohérente avec l’observation expérimentale présentée dans [66].”
Yamamoto, S. P. (2026) · Journal of Food Protection · doi:10.1016/j.jfp.2026.100772
Investigation of E. coli genome complexity by means of fluorescent reporters of gene expression
Brambilla, E. (2014) · theses.fr (ABES) · doi:10.70675/9ef281afzaf9dz4424zb084ze3fa8699d2d8
“Figure A4 Figure from (Taniguchi et al., 2010) showing the linear scaling of noise in lowly expressed genes, whose expression is mainly governed by intrinsic noise, and the plateau of noise for highly expressed genes, whose variation of expression is due to the fluctuation of global cell condition.”
InfluentialExpanding the phenotypic and chemical space in Escherichia coli
Dominguez, C. L. H. (2016) · doi:10.70675/0f9658a3ze0f1z4b9bzbc80zdf911d328d1c
Organization of Bacterial Cell Pole
Altinoglu, I. (2018) · doi:10.70675/f398cdebza844z484az9c5cz377e8388e0a3
Single molecule protein detection for proteomic profiling
Leclerc, S. (2018) · theses.fr (ABES) · doi:10.70675/62e90461zb705z42e1z8661z3db23d7ca70d
“Existing techniques require genetic manipulation, antibody or sophisticated devices and often yield relative levels of protein abundance (A. J. Hughes et al. 2014; Shi et al. 2012; Taniguchi et al. 2010; Albayrak et al. 2016; Darmanis et al. 2016).”
InfluentialOptimisation de la respiration du nitrate à travers l'organisation de ses acteurs
Bulot, S. (2019) · theses.fr (ABES) · doi:10.70675/1c379a96z0deez4407zbf06z5144fecbf1f1
Study of regulatory factors of translation in Escherichia coli
Nguyen, H. L. (2019) · theses.fr (ABES) · doi:10.70675/db3173c3z33a7z411bzb16ez15631ed1cfbe
“Taniguchi and colleagues confirmed in E. coli at the single cell level this low correlation (Taniguchi et al., 2010).”
ResultThe coupling of carbon and iron cycles in the Southern Ocean through microbial metabolism
Debeljak, P. (2019) · theses.fr (ABES) · doi:10.70675/e025a5aaz43e9z43c8z88edzd49b528ec0af
Expression stochastique des gènes chez Bacillus subtilis
Deloupy, A. (2018) · theses.fr (ABES) · doi:10.70675/93a8dd64z144bz4d4bza6f9zb5b664984135
Mistretta, M. (2021) · theses.fr (ABES) · doi:10.70675/a814c67cz9630z40f6z8867z43d3267f3290
Activity of antibiotics and non-antibiotics substances on Staphylococcus aureus biofilms
Liu, J. (2021) · theses.fr (ABES) · doi:10.70675/82fcd1d2z59faz4737z8e68z66b59b1b7ccf
Advanced microscopies for the study of motility behavior in predating Myxococcus xanthus
Rombouts, S. (2021) · theses.fr (ABES) · doi:10.70675/878d2581z340dz4dcdz9096zb809379f14a5 · Cited by 1
Statistical biophysics models of immune repertoire dynamics
Koraichi, M. B. (2022) · theses.fr (ABES) · doi:10.70675/625cdda6zca1dz443dzb6f4z3637549945b7
Detection of Protein–Protein Interactions in Escherichia coli With Single Molecule Sensitivity
Davi, M., & Ladant, D. (2026) · Advanced Science · doi:10.1002/advs.202510093
“Interestingly, it has been reported that a significant fraction of all proteins (about half of the 1000 tested) in an E. coli cell might be present at low levels, that is, below ten molecules per cell [ 53 ].”
Tat, J., Lay, F. D., Stevens, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.14.718270
“This decoupling likely reflects differences in mRNA and protein turnover dynamics 35-38 .”
Cellular remodeling during photosymbiosis establishment revealed by single-cell dual proteomicsPreprint
Yang, C.-L., Kamal, M. M., Ravael, L., Wei, C.-Y., Chen, Y., Magno, S., Chen, C., Lin, P.-Y., Hsu, C., Leu, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719821
“Individual differences are inherent in unicellular organisms, yet such heterogeneity can be obscured by bulk population sampling 21,22 .”
Tien, H., Meda, R. S., Shastry, S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719525
“ED1b): Li 2014 ribosome profiling[24], Mori 2021 DIA-MS[25], and Taniguchi 2010 single-cell YFP[26].”
Impact of variability in cell generation times on cell-to-cell variability of protein concentrationsPreprint
Ali, S. Y., Prasad, A., Singh, A., & Das, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.23.720286
“• Proteins are produced in bursts, under the assumption of very fast relative degradation of mRNA compared to proteins [21, 29, 78] – as a result, protein concentration augments as ( x → x + ∆ x ) with an exponentially distributed (i.e. 1 b exp( − ∆ x/b ) ) burst size ∆ x , having mean ⟨ ∆ x ⟩ = b .”
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0004
Developmental Plasticity, Ecology and Evolution
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0007
Origin and Integration of Biological Processes
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0003
Implications and Anticipations
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0008
Bradford, K. J. (2026) · doi:10.1093/9780197846032.001.0001
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.005.0001
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.002.0005
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.002.0006
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0001
Albeck, J. G. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.002.0004
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0005
Population-based Threshold Models
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0002
Bradford, K. J. (2026) · Oxford University Press eBooks · doi:10.1093/9780197846032.003.0006
Xiong, Y., Fang, X., & Wang, J. (2026) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c02056
Spectral analysis of chemical fluctuations of biomolecules in living cells
Kang, J., Song, S., Kim, J., & Sung, J. (2026) · The Journal of Chemical Physics · doi:10.1063/5.0331567
Fitness-Driven Scaling Laws between mRNA and Protein Levels
Yan, Y., & Lin, J. (2026) · Physical Review Letters · doi:10.1103/gkzp-r681
Alves, Â., Pombinho, R., & Cabanes, D. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1832970
“…eukaryotic single-cell RNA-sequencing (scRNA-seq) protocols have a transcript detection limit of 10 copies per cell (Bagnoli et al., 2018; Ziegenhain et al., 2017), prokaryotic scRNA-seq implies lower average of mRNA copies (0.4 copies/cell) (Bartholomäus et al., 2016; Taniguchi et al., 2010).”
InfluentialLyu, Z., Augenstreich, J., Briken, V., Singh, A., Mori, M., Hwa, T., & Ling, J. (2026) · Science Advances · doi:10.1126/sciadv.aed2171 · Cited by 2
Jain, R., Vardhan, H., Singhal, N., Vashisth, P., Chaudhary, P., Pandey, A., Kishore, K., & Gaur, A. (2026) · Hybrid Advances · doi:10.1016/j.hybadv.2026.100694
Chen, L.-Y., & Yan, P. (2026) · Water Research · doi:10.1016/j.watres.2026.126451
Kamarulzaman, L., Kim, S., Hidaka, T., Tsuchida, M., & Taniguchi, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-74840-0 · Cited by 2
“As proteome and transcriptome measurements are not always correlated 5 – 9 , we next compared the extent to which our single-cell proteome data obtained by single-cell PAGE-PISA are in agreement with reported single-cell RNA sequencing (scRNA-seq) 41 during cardiomyocyte differentiation.”
Self-citationImpact of competition between precursor and mature microRNAs on stochastic gene expression
Adhikary, R., & Das, D. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.07.010 · Cited by 1
Stochastic synthesis-degradation processes: First-passage properties and connections with resetting
Anonymous, & Boyer, D. (2026) · Physical review. E · doi:10.1103/c96q-zyp5
“Protein synthesis often occurs in bursts [7, 52, 53], with nonexponential waiting times between transcription events, as observed in both bacterial and mammalian gene regulation [54, 55].”
Hart, A., Shin, S., & Nguyen, L. K. (2026) · eLife · doi:10.7554/elife.87710.3
Speare, L., Zhao, L., Pavelsky, M. N., et al. (2026) · mBio · doi:10.1128/mbio.03970-24
“S7B), which is comparable to other bacterial species during exponential growth in a lab setting ( E. coli: 1,380–1,800 mRNAs/ cell) (25, 26).”
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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