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]
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BackgroundMethodSpatially-resolved metabolic cooperativity within dense bacterial colonies
Cole, J. A., Kohler, L., Hedhli, J., & Luthey-Schulten, Z. A. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0155-1 · Cited by 106
“Estimating the colony acs expression based on the fluorescence ratio computed above and published gapA expression data [37] yields an average value of approximately 704 Acs proteins per cell, most of which should be concentrated at the top and sides of the colony (Figures S6 & S7).”
MethodGene Architectures that Minimize Cost of Gene Expression
Frumkin, I., Schirman, D., Rotman, A., Li, F., Zahavi, L., Mordret, E., Asraf, O., Wu, S., Levy, S. F., & Pilpel, Y. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.11.007 · Cited by 101
“One potential reason is that too low of mRNA levels might lead to increased expression noise (Taniguchi et al., 2010) or increased response time to an environmental signal (Gasch and Werner-Washburne, 2002).”
Lv, Y., Qian, S., Du, G., Chen, J., Zhou, J., & Xu, P. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.03.009 · Cited by 101
Heins, A., & Weuster‐Botz, D. (2018) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-018-1922-3 · Cited by 105
“However, single cell transcriptomics would be of great benefit as levels of gene products like proteins and messenger RNAs vary significantly between single cells in a population [25, 61, 151].”
BackgroundInfluentialBimodal gene expression in noncooperative regulatory systems
Ochab-Marcinek, A., & Tabaka, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008965107 · Cited by 100
Droplet-Based Microfluidics: Enabling Impact on Drug Discovery
Dressler, O. J., Maceiczyk, R. M., Chang, S., & deMello, A. J. (2013) · SLAS DISCOVERY · doi:10.1177/1087057113510401 · Cited by 98
Eling, N., Richard, A. C., Richardson, S., Marioni, J. C., & Vallejos, C. A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.011 · Cited by 99
“The latter arises because biological noise is negatively correlated with protein abundance (BarEven et al., 2006; Newman et al., 2006; Taniguchi et al., 2010) or mean RNA expression (Brennecke et al.”
BackgroundReverse vaccinology in the 21st century: improvements over the original design
Donati, C., & Rappuoli, R. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12046 · Cited by 101
Obtaining a Panel of Cascade Promoter-5′-UTR Complexes in Escherichia coli
Zhou, S., Ding, R., Chen, J., Du, G., Li, H., & Zhou, J. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00006 · Cited by 100
Fast bacterial growth reduces antibiotic accumulation and efficacy
Łapińska, U., Voliotis, M., Lee, K. K., Campey, A., Stone, M. R. L., Tuck, B., Phetsang, W., Zhang, B., Tsaneva-Atanasova, K. T., Blaskovich, M. A. T., et al. (2022) · eLife · doi:10.7554/elife.74062 · Cited by 99
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Measuring enzyme activity in single cells
Kovarik, M. L., & Allbritton, N. L. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.01.003 · Cited by 99
“Intrinsic noise arises from stochastic fluctuations in biochemical events, such as binding of an enzyme to its substrate, whereas extrinsic noise is correlated to a physical parameter, such as microenvironment, cell cycle stage, or even intrinsic noise in an upstream event [10-11].”
BackgroundLi, W., Chen, T., Chen, Z., Fei, P., Yu, Z., Pang, Y., & Huang, Y. (2012) · Lab on a Chip · doi:10.1039/c2lc40125h · Cited by 99
Quantifying post-transcriptional regulation in the development of Drosophila melanogaster
Becker, K., Bluhm, A., Casas-Vila, N., Dinges, N., Dejung, M., Sayols, S., Kreutz, C., Roignant, J., Butter, F., & Legewie, S. (2018) · Nature Communications · doi:10.1038/s41467-018-07455-9 · Cited by 98
Muntel, J., Fromion, V., Goelzer, A., Maaß, S., Mäder, U., Büttner, K., Hecker, M., & Becher, D. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.032631 · Cited by 101
“This is in accordance to studies in Escherichia coli (29).”
ResultNoise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study
Roberts, E., Magis, A. T., Ortiz, J. O., Baumeister, W. P., & Luthey-Schulten, Z. A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002010 · Cited by 99
“coli, maintaining population heterogeneity even at high levels of gene expression [85].”
Delayed Protein Synthesis Reduces the Correlation between mRNA and Protein Fluctuations
Gedeon, T., & Bokes, P. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.06.025 · Cited by 95
“The former scenario represents a nonessential protein, or alternatively a transcription factor, whereas the latter can be thought of as a ubiquitous product of a housekeeping gene (5).”
BackgroundMethodInfluentialBiochemical complexity drives log‐normal variation in genetic expression
Beal, J. (2017) · Engineering Biology · doi:10.1049/enb.2017.0004 · Cited by 96
Bacterial quorum sensing and phenotypic heterogeneity: how the collective shapes the individual
Striednig, B., & Hilbi, H. (2021) · Trends in Microbiology · doi:10.1016/j.tim.2021.09.001 · Cited by 100
Soluble oligomerization provides a beneficial fitness effect on destabilizing mutationsPreprint
Bershtein, S., Mu, W., & Shakhnovich, E. I. (2011) · Nature Precedings · doi:10.1073/pnas.1118157109 · Cited by 98
Noise in gene expression is coupled to growth ratePreprint
Keren, L., van Dijk, D., Weingarten-Gabbay, S., Davidi, D., Jona, G., Weinberger, A., Milo, R., & Segal, E. (2015) · Genome Research · doi:10.1101/gr.191635.115 · Cited by 89
“Variability in expression is anti-correlated to population average gene expression, which in turn is tightly coupled to growth rate (Tyson et al. 1979; Ingraham et al. 1983; Bar-Even et al. 2006; Newman et al. 2006; Brauer et al. 2008; Klumpp et al. 2009; Taniguchi et al. 2010; Keren et al. 2013).”
BackgroundInfluentialLabhsetwar, P., Cole, J. A., Roberts, E., Price, N. D., & Luthey-Schulten, Z. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222569110 · Cited by 94
“have been shown to exhibit considerable copy number correlation (6, 23, 24).”
BackgroundMethodInfluentialSoluble expression of proteins correlates with a lack of positively-charged surface
Chan, P., Curtis, R., & Warwicker, J. (2013) · Scientific Reports · doi:10.1038/srep03333 · Cited by 92
Microfluidics for biological measurements with single-molecule resolution
Streets, A., & Huang, Y. (2013) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2013.08.013 · Cited by 99
“The ability of microfluidics to isolate, manipulate, and optically analyze single cells is especially advantageous for the quantification of protein expression in single cells with single-molecule resolution and has enabled the characterization of the stochastic nature of gene expression [65], direct counting of scarc…”
BackgroundComparing protein folding in vitro and in vivo: foldability meets the fitness challenge
Hingorani, K. S., & Gierasch, L. M. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.11.007 · Cited by 98
Making lineage decisions with biological noise: Lessons from the early mouse embryo
Simon, C. S., Hadjantonakis, A., & Schröter, C. J. (2018) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.319 · Cited by 92
“, 2004), recent studies have shown that this is not necessarily true at the single-cell level (Albayrak et al., 2016; Taniguchi et al., 2010).”
BackgroundCiryam, P., Morimoto, R. I., Vendruscolo, M., Dobson, C. M., & O’Brien, E. P. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1213624110 · Cited by 93
Transcript level and sequence determinants of protein abundance and noise in Escherichia coli
Guimaraes, J. C., Rocha, M., & Arkin, A. P. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku126 · Cited by 98
“Many single-cell studies have reported a strong dependence between noise and mean expression level (28,31,33,35).”
BackgroundMethodInfluentialReconstruction of bacterial transcription-coupled repair at single-molecule resolution
Fan, J., Leroux‐Coyau, M., Savery, N. J., & Strick, T. R. (2016) · Nature · doi:10.1038/nature19080 · Cited by 93
“In this manner GGR components may actively participate in repair even in uninduced, non-SOS conditions, where abundance of UvrA is extremely low – in the 20 nM range, i.e. only about ten dimers per cell [19, 20] – and therefore also subject to large fluctuations.”
BackgroundThe Escherichia coli Peripheral Inner Membrane Proteome
Papanastasiou, M., Orfanoudaki, G., Koukaki, M., Kountourakis, N., Sardis, M. F., Aivaliotis, M., Karamanou, S., & Economou, A. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.024711 · Cited by 98
Ma, X., Zhao, Z., Xiao, L., Xu, W., Kou, Y., Zhang, Y., Wu, G., Wang, Y., & Du, Z. (2021) · Nature Methods · doi:10.1038/s41592-021-01216-1 · Cited by 85
Feedback Control of Gene Expression Variability in the Caenorhabditis elegans Wnt Pathway
Ji, N., Middelkoop, T. C., Mentink, R. A., Betist, M. C., Tonegawa, S., Mooijman, D., Korswagen, H. C., & van Oudenaarden, A. (2013) · Cell · doi:10.1016/j.cell.2013.09.060 · Cited by 90
“This value is greater than the average measurement of 1.6 in Escherichia coli (Taniguchi et al., 2010) yet is over 10-fold lower than those reported for mammalian mRNAs (Raj et al., 2006).”
BackgroundInfluentialBiological Phosphorus Recovery: Review of Current Progress and Future Needs
Yang, Y., Shi, X., Ballent, W., et al. (2017) · Water Environment Research · doi:10.2175/106143017x15054988926424 · Cited by 90
“The polyphosphate accumulating organisms (PAOs) responsible for EBPR can enrich phosphorus up to 4 to 8% or even higher (Orhon, 1997; Tchobanoglous et al., 2003).”
BackgroundDonati, S., Kuntz, M. A., Pahl, V., Farke, N., Beuter, D., Glatter, T., Gomes‐Filho, J. V., Randau, L., Wang, C., & Link, H. (2020) · Cell Systems · doi:10.1016/j.cels.2020.10.011 · Cited by 83
“This mechanism may ensure a high constancy of metabolic flux despite expression noise (Newman et al., 2006; Taniguchi et al., 2010) or mutations that occur during the evolution of metabolic networks (McCloskey et al., 2018a).”
BackgroundReview of methods to probe single cell metabolism and bioenergetics
Vasdekis, A. E., & Stephanopoulos, G. N. (2014) · Metabolic Engineering · doi:10.1016/j.ymben.2014.09.007 · Cited by 89
Liu, J. K., O’Brien, E. J., Lerman, J. A., Zengler, K., Palsson, B. Ø., & Feist, A. M. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0110-6 · Cited by 94
“Even though the correlation between the transcriptome and proteome is poor on a protein-to-transcript level [56,57], RNA-seq is a robust currently-available omic data-source which covers genome-scale expression in all compartments.”
BackgroundClassic reaction kinetics can explain complex patterns of antibiotic action
Wiesch, P. A. Z., Abel, S., Gkotzis, S., Ocampo, P., Engelstädter, J., Hinkley, T., Magnus, C., Waldor, M. K., Udekwu, K. I., & Cohen, T. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa8760 · Cited by 85
Regulatory mechanisms and networks couple the different phases of gene expression
Dahan, O., Gingold, H., & Pilpel, Y. (2011) · Trends in Genetics · doi:10.1016/j.tig.2011.05.008 · Cited by 88
Kannaiah, S., Livny, J., & Amster‐Choder, O. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.08.013 · Cited by 83
Keegstra, J. M., Kamino, K., Anquez, F., Lazova, M. D., Emonet, T., & Shimizu, T. S. (2017) · eLife · doi:10.7554/elife.27455 · Cited by 82
Simple and Precise Counting of Viable Bacteria by Resazurin-Amplified Picoarray Detection
Hsieh, K., Zec, H. C., Chen, L., Kaushik, A. M., Mach, K. E., Liao, J. C., & Wang, T. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b02096 · Cited by 86
Sampaio, N. M. V., Blassick, C. M., Andreani, V., Lugagne, J., & Dunlop, M. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115032119 · Cited by 84
Multivariate moment closure techniques for stochastic kinetic models
Lakatos, E., Ale, A. B. F., Kirk, P., & Stumpf, M. P. H. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4929837 · Cited by 80
Long-Lived Intracellular Single-Molecule Fluorescence Using Electroporated Molecules
Crawford, R., Torella, J. P., Aigrain, L., Plochowietz, A., Gryte, K., Uphoff, S., & Kapanidis, A. N. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.09.057 · Cited by 86
Ullman, G., Walldén, M., Marklund, E. G., Mahmutovic, A., Razinkov, I., & Elf, J. (2012) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0025 · Cited by 83
“Single-protein counting experiments in vivo reveal that isogenic cells under seemingly identical experimental conditions display considerable diversity in expression [6].”
BackgroundNoise Propagation in Synthetic Gene Circuits for Metabolic Control
Oyarzún, D. A., Lugagne, J., & Stan, G.-B. V. (2014) · ACS Synthetic Biology · doi:10.1021/sb400126a · Cited by 84
Gaudet, S., Spencer, S. L., Chen, W. W., & Sorger, P. K. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002482 · Cited by 93
“To date, we have only measured ten pairwise correlations (creating a joint distribution for five protein species) but in real cells, extrinsic noise will correlate all proteins to some degree unless they are actively regulated otherwise [35].”
BackgroundInfluentialKoehorst, J. J., van Dam, J. C. J., van Heck, R. G. A., Saccenti, E., dos Santos, V. A. P. M., Diez, M. S., & Schaap, P. J. (2016) · Scientific Reports · doi:10.1038/srep38699 · Cited by 84
“Associations between gene essentiality and low variation in protein abundance have been observed in E. coli 32 .”
Warne, D. J., Baker, R. E., & Simpson, M. J. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0943 · Cited by 97
“gulation of gene expression, are stochastic [1,7,77,95,107]; that is, randomness or noise is an essential component of living things. Internal and external factors are responsible for this randomness [36,78,119,128], particularly within systems where low copy numbers of certain chemical species greatly aect the system…”
BackgroundQuantification and discovery of sequence determinants of protein‐per‐mRNA amount in 29 human tissues
Eraslan, B., Wang, D., Gušić, M., Prokisch, H., Hallström, B. M., Uhlén, M., Asplund, A., Pontén, F., Wieland, T., Hopf, T. A., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188513 · Cited by 82
“…had been reported before for the unicellular eukaryotes baker’s yeast (Lackner et al, 2007) and fission yeast (Csárdi et al, 2015) yet appears to be absent in the prokaryote E. coli, for which mRNA and protein levels across genes obey a nearly linear relationship (Taniguchi et al, 2010).”
BackgroundDifferential lysine acetylation profiles of Erwinia amylovora strains revealed by proteomics
Wu, X., Vellaichamy, A., Wang, D., Zamdborg, L., Kelleher, N. L., Huber, S. C., & Zhao, Y. F. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.12.001 · Cited by 85
“coli [37,38] and examined their gene ontology (Fig.”
BackgroundIn search of principles for a Theory of Organisms
Longo, G. O., Montévil, M., Sonnenschein, C., & Soto, A. M. (2015) · Journal of Biosciences · doi:10.1007/s12038-015-9574-9 · Cited by 84
“How does intrinsic variation manifest itself? One obvious example is the unequal distribution of macromolecules and organelles during cell division (Huh and Paulsson 2011); another is stochastic gene expression (Kupiec 1983; Taniguchi et al. 2010; Tyagi 2010; Marinov et al. 2014).”
BackgroundMitosch, K., Rieckh, G., & Bollenbach, T. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.001 · Cited by 83
Beebout, C. J., Eberly, A. R., Werby, S. H., Reasoner, S. A., Brannon, J. R., De, S., Fitzgerald, M. J., Huggins, M., Clayton, D. B., Cegelski, L., et al. (2019) · mBio · doi:10.1128/mbio.02400-18 · Cited by 83
ArfA Recruits RF2 into Stalled Ribosomes
Shimizu, Y. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.08.007 · Cited by 85
Xiong, Y. B., Coradetti, S. T., Li, X., Gritsenko, M. A., Clauss, T. R., Petyuk, V., Camp, D., Smith, R. D., Cate, J. H. D., Yang, F., et al. (2014) · Fungal Genetics and Biology · doi:10.1016/j.fgb.2014.05.005 · Cited by 85
Extended Nanofluidic Immunochemical Reaction with Femtoliter Sample Volumes
Shirai, K., Mawatari, K., & Kitamori, T. (2013) · Small · doi:10.1002/smll.201302709 · Cited by 85
Progress in quantitative single-molecule localization microscopy
Deschout, H. G., Shivanandan, A., Annibale, P., Scarselli, M., & Rađenović, A. (2014) · Histochemistry and Cell Biology · doi:10.1007/s00418-014-1217-y · Cited by 83
“while omics-scale abundance data with single-molecule sensitivity can be obtained from conventional fluorescence microscopy (Taniguchi et al. 2010), the spatial resolution is limited due to the diffraction of light.”
BackgroundThe emergence of proteome-wide technologies: systematic analysis of proteins comes of age
Breker, M., & Schuldiner, M. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3821 · Cited by 85
Universal Protein Fluctuations in Populations of Microorganisms
Salman, H., Brenner, N., Tung, C.-K., Elyahu, N., Stolovicki, E., Moore, L., Libchaber, A. J., & Braun, E. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.238105 · Cited by 83
Extrinsic Noise Driven Phenotype Switching in a Self-Regulating Gene
Assaf, M., Roberts, E., Luthey-Schulten, Z. A., & Goldenfeld, N. D. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.058102 · Cited by 85
Bacteria differently regulate mRNA abundance to specifically respond to various stresses
Bartholomäus, A., Fedyunin, I., Feist, P., Sin, C., Zhang, G., Valleriani, A., & Ignatova, Z. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0069 · Cited by 81
“This poor correlation is often attributed to different time scales of transcript and protein turnover [27].”
BackgroundResultConditional Moment Closure Schemes for Studying Stochastic Dynamics of Genetic Circuits
Soltani, M., Vargas-García, C. A., & Singh, A. (2015) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2453158 · Cited by 75
Stochastic Phenotype Transition of a Single Cell in an Intermediate Region of Gene State Switching
Ge, H., Qian, H., & Xie, X. S. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.078101 · Cited by 79
Self-citationTranscriptional Control in Marine Copiotrophic and Oligotrophic Bacteria with Streamlined Genomes
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Visualizing active viral infection reveals diverse cell fates in synchronized algal bloom demise
Vincent, F., Sheyn, U., Porat, Z., Schatz, D., & Vardi, A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021586118 · Cited by 79
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Is information a proper observable for biological organization?
Longo, G. O., Miquel, P.-A., Sonnenschein, C., & Soto, A. M. (2012) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2012.06.004 · Cited by 82
“For example, protein and mRNA copy numbers vary from cell to cell in isogenic bacterial populations, and in a given single cell, protein and mRNA copy numbers for any given gene are uncorrelated (Taniguchi et al. 2010).”
Nikolić, N., Barner, T., & Ackermann, M. (2013) · BMC Microbiology · doi:10.1186/1471-2180-13-258 · Cited by 81
“In our recent work [31], we showed that signals from such plasmid-based fluorescent reporters were significantly correlated with directly measured levels of mRNA as well as with measurements of translational reporters [32], although the latter association was weaker.”
Schubert, C. (2011) · Nature · doi:10.1038/480133a · Cited by 81
A Complete Pathway Model for Lipid A Biosynthesis in Escherichia coli
Emiola, A., George, J., & Andrews, S. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121216 · Cited by 87
“For example, Taniguchi et al. [79] observed that E . coli grown on minimal medium had a 150 minute generation time.”
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“We first investigated whether the gamma distribution provides a generally valid model for the distribution of protein levels, as others have suggested [22,27-31].”
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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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