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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Bursting noise in gene expression dynamics: linking microscopic and mesoscopic models
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“Results in Escherichia coli and yeast cells show that extrinsic noise dominates the total noise (Colman-Lerner et al, 2005), especially at high protein abundances (Taniguchi et al, 2010), whereas fluctuations at low-protein copy numbers are owing to intrinsic noise.”
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Recent Developments in Single-Cell RNA-Seq of Microorganisms
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Urchueguía-Fornes, A., Galbusera, L., Chauvin, D., Bellement-Theroue, G., Julou, T., & van Nimwegen, E. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001491 · Cited by 44
“The noise floor ac likely reflects the minimal noise that every promoter is subject to due to general fluctuations in the physiological state of the cell including overall transcription, translation, mRNA decay, and growth [1,6].”
BackgroundInfluentialJia, C., & Grima, R. (2022) · iScience · doi:10.1016/j.isci.2022.105746 · Cited by 46
Transcription factor binding kinetics constrain noise suppression via negative feedback
Grönlund, A., Lötstedt, P., & Elf, J. (2013) · Nature Communications · doi:10.1038/ncomms2867 · Cited by 39
Pathway dynamics can delineate the sources of transcriptional noise in gene expression
Ham, L., Jackson, M., & Stumpf, M. P. (2021) · eLife · doi:10.7554/elife.69324 · Cited by 37
Normanno, D., Dahan, M., & Darzacq, X. (2012) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2012.02.001 · Cited by 42
Bokes, P., King, J. R., Wood, A. T. A., & Loose, M. (2013) · Bulletin of Mathematical Biology · doi:10.1007/s11538-013-9811-z · Cited by 40
“Genes are typically present at low copy numbers, while proteins can be highly abundant (Lu et al. 2007); mRNA transcripts are scarce in prokaryotic cells (Taniguchi et al. 2010), but they can be copious in eukaryotes (Zenklusen et al. 2008).”
BackgroundSingle-cell protein dynamics reproduce universal fluctuations in cell populations
Brenner, N., Braun, E., Yoney, A., Susman, L., Rotella, J., & Salman, H. (2015) · The European Physical Journal E · doi:10.1140/epje/i2015-15102-8 · Cited by 42
Electron beam fabrication of a microfluidic device for studying submicron-scale bacteria
Moolman, M. C., Huang, Z., Krishnan, S. T., Kerssemakers, J. W. J., & Dekker, N. H. (2013) · Journal of Nanobiotechnology · doi:10.1186/1477-3155-11-12 · Cited by 44
“[18] in their study of protein and mRNA expression at the single-molecule level inside individual living cells.”
BackgroundProtein Counting in Single Cancer Cells
Schubert, S. M., Walter, S. R., Manesse, M., & Walt, D. R. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b00146 · Cited by 46
Mapping the landscape of metabolic goals of a cell
Zhao, Q., Iffland-Stettner, A., Reznik, E., Paschalidis, I. C., & Segrè, D. (2016) · Genome biology · doi:10.1186/s13059-016-0968-2 · Cited by 42
“We should emphasize that, as described recently [14], integration of gene expression data to help predict fluxes is still problematic, partly due to the non-trivial relationship between mRNA and protein levels [15, 16].”
BackgroundTools for Genomic and Transcriptomic Analysis of Microbes at Single-Cell Level
Chen, Z., Chen, L., & Zhang, W. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01831 · Cited by 44
“For example, methods using fluorescent reporter proteins coupling with high-throughput data acquisition approaches such as flow cytometry have been widely applied for detecting gene expression heterogeneities within the microbial population (Taniguchi et al., 2010; Roberfroid et al., 2016).”
MethodExploiting Interdata Relationships in Next-generation Proteomics Analysis
Vitrinel, B., Koh, H. W. L., Kar, F. M., Maity, S., Rendleman, J., Choi, H., & Vogel, C. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.mr118.001246 · Cited by 41
“Single cell analysis will also expand our views on the relationship between protein and mRNA concentrations (83), as has been discussed for bacteria many years ago (89).”
BackgroundPhage integration alters the respiratory strategy of its host
Carey, J. N., Mettert, E. L., Fishman-Engel, D. R., Roggiani, M., Kiley, P. J., & Goulian, M. (2019) · eLife · doi:10.7554/elife.49081 · Cited by 40
Frequency modulation of a bacterial quorum sensing response
Bettenworth, V., van Vliet, S., Turkowyd, B., Bamberger, A., Wendt, H., McIntosh, M., Steinchen, W., Endesfelder, U., & Becker, A. (2022) · Nature Communications · doi:10.1038/s41467-022-30307-6 · Cited by 41
NF-κB memory coordinates transcriptional responses to dynamic inflammatory stimuli
Wang, A. G., Son, M., Kenna, E., Thom, N., & Tay, S. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111159 · Cited by 44
Physiological and molecular characterisation of cadmium stress in Schmidtea mediterranea
Plusquin, M., Stevens, A., Belleghem, F. V., DeGheselle, O., Roten, A. V., Vroonen, J., Blust, R., Cuypers, A., Artois, T., & Smeets, K. (2012) · The International Journal of Developmental Biology · doi:10.1387/ijdb.113485mp · Cited by 42
“This discrepancy might be due to the fact that enzyme activities are also modified post-transcriptionally (Hansen et al., 2007), the half-life of mRNA and proteins varies (Taniguchi et al., 2010) or multiple isoforms of the studied molecules exist.”
BackgroundBacterial responses to antibiotics and their combinations
Mitosch, K., & Bollenbach, T. (2014) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12190 · Cited by 39
Exploiting Natural Fluctuations to Identify Kinetic Mechanisms in Sparsely Characterized Systems
Hilfinger, A., Norman, T. M., & Paulsson, J. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.002 · Cited by 40
“…correlation coefficients and CVs, allowing for any dynamically fluctuating translation rates in the stochastic mRNA-protein model proposed in Taniguchi et al. (2010). x 1 / l 1 x 1 + 1 x 2 / fðuÞx 1 x 2 + 1 x 1 / x 1 =t 1 x 1 À 1 x 2 / x 2 =t 2 x 2 À 1 (Equation 5) where X 1 denotes mRNA, X…”
InfluentialGifford, S. M., Becker, J. W., Sosa, O. A., et al. (2016) · mBio · doi:10.1128/mbio.01279-16 · Cited by 43
Papadopoulos, D. K., Skouloudaki, K., Engström, Y., Terenius, L., Rigler, R., Zechner, C., Vukojević, V., & Tomancak, P. (2019) · Development · doi:10.1242/dev.168179 · Cited by 41
The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian
Weidemann, D. E., Holehouse, J., Singh, A., Grima, R., & Hauf, S. (2023) · Science Advances · doi:10.1126/sciadv.adh5138 · Cited by 38
Quantitative proteomics reveals the central changes of wheat in response to powdery mildew
Fu, Y., Zhang, H., Mandal, S. N., Wang, C., Chen, C., & Ji, W. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.09.006 · Cited by 42
Pendar, H., Platini, T., & Kulkarni, R. (2013) · Physical Review E · doi:10.1103/physreve.87.042720 · Cited by 40
“Science 327(5969): 1142–1145 [11] Taniguchi Y, Choi P, Li G, Chen H, Babu M, Hearn J, Emili A, Xie X (2010) Quantifying E.”
BackgroundWurpel, D. J., Moriel, D. G., Totsika, M., Easton, D. M., & Schembri, M. A. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2014.12.005 · Cited by 44
Salehi‐Reyhani, A., Gesellchen, F., Mampallil, D., Wilson, R., Reboud, J., Ces, O., Willison, K. R., Cooper, J. M., & Klug, D. R. (2014) · Analytical Chemistry · doi:10.1021/ac5033758 · Cited by 43
Origins and consequences of transcriptional discontinuity
Suter, D. M., Molina, N., Naef, F., & Schibler, U. (2011) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2011.09.004 · Cited by 43
“It is therefore not surprising that the distribution of many proteins in E. coli is relatively heterogeneous across an isogenic cell population [11], and in both bacteria and yeast, a significant fraction of heterogeneity at the protein level has been linked to variance in the number of mRNA…”
BackgroundResultThe Influence of Promoter Architectures and Regulatory Motifs on Gene Expression in Escherichia coli
Rydenfelt, M., García, H. G., Cox, R. S., & Phillips, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114347 · Cited by 41
Quantifying Position-Dependent Codon Usage Bias
Hockenberry, A. J., Sirer, M. I., Amaral, L. A. N., & Jewett, M. C. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu126 · Cited by 39
“Additionally, data of transcript abundances were downloaded from Taniguchi et al. (2010) and Shiroguchi et al. (2012) and again filtered for genes that we were able to map back to the genome.”
BackgroundMethodInfluentialUsing variability in gene expression as a tool for studying gene regulation
Padovan-Merhar, O. M., & Raj, A. (2013) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1243 · Cited by 42
Chromosome-centric approach to overcoming bottlenecks in the Human Proteome Project
Archakov, А., Zgoda, V. G., Kopylov, A. T., Naryzhny, S., Chernobrovkin, A. L., Ponomarenko, E. A., & Lisitsa, A. (2012) · Expert Review of Proteomics · doi:10.1586/epr.12.54 · Cited by 43
SSB binds to the RecG and PriA helicases in vivo in the absence of DNA
Yu, C., Tan, H. Y., Choi, M., Stanenas, A. J., Byrd, A. K., Raney, K. D., Cohan, C. S., & Bianco, P. R. (2016) · Genes to Cells · doi:10.1111/gtc.12334 · Cited by 42
“…of exponentially growing cells where SSB is present at 1–2000 tetramers per cell, RecG is present at seven molecules per cell and PriA is present at 30–50 enzymes per cell (Shlomai & Kornberg 1980; Meyer & Laine 1990; Lloyd 1991; Tanaka et al. 2003; Ishihama et al. 2008; Taniguchi et al. 2010).”
BackgroundLemoine, A., Delvigne, F., Bockisch, A., Neubauer, P. B., & Junne, S. G. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.03.020 · Cited by 43
“Hence, highly expressed genes generate a lownoise compared to the low level 15 expressed genes, that yield increased noise (Silander et al., 2012; Taniguchi et al., 2010).”
BackgroundEffects of growth rate and promoter activity on single-cell protein expression
Nordholt, N., van Heerden, J. H., Kort, R., & Bruggeman, F. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-05871-3 · Cited by 45
The Chemical Fluctuation Theorem governing gene expression
Park, S. J., Song, S., Yang, G., Kim, P. M., Yoon, S., Kim, J., & Sung, J. (2018) · Nature Communications · doi:10.1038/s41467-017-02737-0 · Cited by 38
Molecular Time Sharing through Dynamic Pulsing in Single Cells
Park, J. H., Dies, M., Lin, Y., Hormoz, S., Smith-Unna, S. E., Quinodoz, S. A., Hernández-Jiménez, M. J., García‐Ojalvo, J., Locke, J. C. W., & Elowitz, M. B. (2018) · Cell Systems · doi:10.1016/j.cels.2018.01.011 · Cited by 45
Bertaux, F., Marguerat, S., & Shahrezaei, V. (2018) · Royal Society Open Science · doi:10.1098/rsos.172234 · Cited by 40
“Realistic [73] parameters for E. coli gene expression have been used ( k m = 0.28min − 1 , γ m = 0.14 min − 1 , k p = 0.94min − 1 , μ = 2 doublings h − 1 ).”
InfluentialSun, J., Cui, F., Zhang, R., Gao, Z., Ji, J., Ren, Y., Pi, F., Zhang, Y., & Sun, X. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b02854 · Cited by 42
Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
Cao, Z., & Grima, R. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0967 · Cited by 40
Lepore, A., Taylor, H. N., Landgraf, D., Okumuş, B., Jaramillo-Riveri, S., McLaren, L., Bakshi, S., Paulsson, J., & Karoui, M. E. (2019) · Scientific Reports · doi:10.1038/s41598-019-44278-0 · Cited by 41
Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise
Schmiedel, J. M., Carey, L. B., & Lehner, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11116-w · Cited by 40
Single Molecule Data Analysis: An Introduction
Tavakoli, M., Taylor, J. N., Li, C., Komatsuzaki, T., & Pressé, S. (2017) · Advances in chemical physics · doi:10.1002/9781119324560.ch4 · Cited by 47
Cell-to-Cell Diversity in a Synchronized Chlamydomonas Culture As Revealed by Single-Cell Analyses
Garz, A., Sandmann, M., Rading, M., et al. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.07.026 · Cited by 41
Bershtein, S., Choi, J., Bhattacharyya, S., Budnik, B., & Shakhnovich, E. I. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.03.051 · Cited by 41
“Second, since DHFR is present at a low copy number (only 40 copies/cell) (Taniguchi et al., 2010), its mutants are less likely to cause aggregation-associated toxicity.”
BackgroundMicrofluidic Platforms for Single-Cell Protein Analysis
Liu, Y., & Singh, A. K. (2013) · SLAS TECHNOLOGY · doi:10.1177/2211068213494389 · Cited by 41
“To link single-cell genomics with proteomics, Taniguchi et al.(64) reported a microfluidic device–based approach to quantifying the Escherichia coli proteome with single-molecule sensitivity.”
MethodInfluentialChen, W.-H., van Noort, V., Lluch‐Senar, M., Hennrich, M. L., Wodke, J. A. H., Yus, E., Alibés, A., Roma, G., Mende, D. R., Pesavento, C., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw004 · Cited by 42
Hershko-Shalev, T., Odenheimer-Bergman, A., Elgrably‐Weiss, M., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005975 · Cited by 41
Transcriptional precision and accuracy in development: from measurements to models and mechanisms
Bentovim, L., Harden, T. T., & DePace, A. H. (2017) · Development · doi:10.1242/dev.146563 · Cited by 41
Linear Noise Approximation for a Class of Piecewise Deterministic Markov Processes
Modi, S., Soltani, M., & Singh, A. (2018) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2018.8431767 · Cited by 41
Analysis of lipoprotein transport depletion in Vibrio cholerae using CRISPRi
Caro, F., Place, N. M., & Mekalanos, J. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906158116 · Cited by 41
“In bacteria, mRNA levels are generally correlated with protein levels (26).”
BackgroundResponses of Methanosarcina barkeri to acetate stress
He, P., Duan, H., Han, W., Liu, Y., Shao, L., & Lü, F. (2019) · Biotechnology for Biofuels · doi:10.1186/s13068-019-1630-5 · Cited by 42
Metabolic stress promotes stop-codon readthrough and phenotypic heterogeneity
Zhang, H., Lyu, Z., Fan, Y., Evans, C. R., Barber, K. W., Banerjee, K., Igoshin, O. A., Rinehart, J., & Ling, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013543117 · Cited by 42
Macro-to-Micro Structural Proteomics: Native Source Proteins for High-Throughput Crystallization
Totir, M. A., Echols, N., Nanao, M., Gee, C. L., Moskaleva, A., Gradia, S., Iavarone, A. T., Berger, J. M., May, A. P., Zubieta, C., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032498 · Cited by 43
“coli have shown that essential proteins are present with at least ten copies per cell and are present in higher concentrations than many non-essential proteins under steady state growth conditions [24].”
BackgroundNoell, S. E., Hellweger, F. L., Temperton, B., & Giovannoni, S. J. (2023) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00124-22 · Cited by 40
Gene expression noise is affected differentially by feedback in burst frequency and burst size
Bokes, P., & Singh, A. (2016) · Journal of Mathematical Biology · doi:10.1007/s00285-016-1059-4 · Cited by 35
“Stochastic expression of genes drives significant randomfluctuations (noise) in protein copy numbers over time in single cells (Elowitz et al. 2002; Bar-Even et al. 2006; Raser and O’Shea 2005; Taniguchi et al. 2010; Blake et al. 2003; Kærn et al. 2005; Eldar and Elowitz 2010; Munsky et al. 2012).”
BackgroundGoñi‐Moreno, Á., Benedetti, I., Kim, J., & de Lorenzo, V. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.6b00397 · Cited by 39
Kawade, K., & Tsukaya, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185050 · Cited by 39
Intracellular Signaling by the comRS System in Streptococcus mutans Genetic Competence
Underhill, S. A. M., Shields, R. C., Kaspar, J. R., et al. (2018) · mSphere · doi:10.1128/msphere.00444-18 · Cited by 40
Hybridization-based capture of pathogen mRNA enables paired host-pathogen transcriptional analysis
Betin, V., Peñaranda, C., Bandyopadhyay, N., Yang, R., Abitua, A., Bhattacharyya, R. P., Fan, A. C., Avraham, R., Livny, J., Noam Shoresh, F., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55633-6 · Cited by 40
Advances of Single-Cell Protein Analysis
Liu, L., Chen, D., Wang, J., & Chen, J. (2020) · Cells · doi:10.3390/cells9051271 · Cited by 41
“Although single-cell genomic and transcriptomic analysis with high throughputs have developed rapidly to address the issue of cellular heterogeneity in recent years, studies have located poor correlations between RNA and protein levels in single cells [14].”
BackgroundAkpinar, F., Timm, A. C., & Yin, J. (2015) · Journal of Virology · doi:10.1128/jvi.02190-15 · Cited by 40
“This dependence was modeled using a gamma distribution, which has precedence as a flexible distribution for describing various biological measures (61, 62).”
MethodImaging Translation in Single Cells Using Fluorescent MicroscopyPreprint
Chao, J. A., Yoon, Y. J., & Singer, R. H. (2012) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a012310 · Cited by 42
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“The model-based core proteome was defined to be the list of 356 genes (Dataset S1) that are required for growth across all 333 ME model simulations for E. coli K-12 MG1655.”
MethodInfluentialBuskila, A.-A. A., Kannaiah, S., & Amster‐Choder, O. (2014) · RNA Biology · doi:10.4161/rna.36135 · Cited by 42
Temporal Expression-based Analysis of Metabolism
Collins, S. B., Reznik, E., & Segrè, D. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002781 · Cited by 38
“Recently, a number of experimental studies [15,28] have addressed the question of correlation between mRNA and protein abundance.”
BackgroundTakashima, S., Saito, H., Takahashi, N., Imai, K., Kudo, S., Atari, M., Saito, Y., Motoyama, S., & Minamiya, Y. (2013) · Tumor Biology · doi:10.1007/s13277-013-1556-7 · Cited by 39
Protein Microarrays and Biomarkers of Infectious Disease
Natesan, M., & Ulrich, R. G. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11125165 · Cited by 39
Davison, J. R., Dorival, J., Rabeharindranto, H., et al. (2014) · Chemical Science · doi:10.1039/c3sc53511h · Cited by 39
A Rapid Response Thin-Film Plasmonic-Thermoelectric Light Detector
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Synthetic Biology: Engineering Living Systems from Biophysical Principles
Bartley, B., Kim, K., Medley, K., & Sauro, H. M. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.02.013 · Cited by 41
Exploiting nonionic surfactants to enhance fatty alcohol production in Rhodosporidium toruloides
Liu, D., Geiselman, G. M., Coradetti, S. T., Cheng, Y., Kirby, J., Prahl, J., Jacobson, O., Sundstrom, E. R., Tanjore, D., Skerker, J. M., et al. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27285 · Cited by 39
“Such variations originate from naturally inherent factors such as uneven cell division, stochastic gene expression and protein activities (Lidstrom & Konopka, 2010; Müller, Harms, & Bley, 2010; Taniguchi et al., 2010).”
Quantifying the Central Dogma in the p53 Pathway in Live Single Cells
Hafner, A., Reyes, J. S., Stewart-Ornstein, J., Tsabar, M., Jambhekar, A., & Lahav, G. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.001 · Cited by 37
Mechanism of ribosome rescue by alternative ribosome-rescue factor B
Chan, K.-H., Petrychenko, V., Mueller, C. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17853-7 · Cited by 39
Imaging the future: the emerging era of single‐cell spatial proteomics
Paul, I., White, C. A., Turcinovic, I., & Emili, A. (2020) · FEBS Journal · doi:10.1111/febs.15685 · Cited by 41
Self-citationHouser, J. R., Barnhart, C. S., Boutz, D. R., Carroll, S. M., Dasgupta, A., Michener, J. K., Needham, B. D., Papoulas, O., Sridhara, V., Sydykova, D. K., et al. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004400 · Cited by 38
“[12] 1018 NA YFP fusion 137 95% confidence FISH”
BackgroundWang, Y., Zhang, Z., Wang, H., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130178 · Cited by 38
“It plays an important role in both fundamental biology research [1 – 3] and clinical applications [4] regarding cell morphology analysis and cell behavior characterization.”
BackgroundThe Low Noise Limit in Gene Expression
Dar, R. D., Razooky, B. S., Weinberger, L. S., Cox, C. D., & Simpson, M. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140969 · Cited by 35
“However, recent experiments have demonstrated that transcription of highly expressed genes occurs in stochastic bursts in bacteria [4, 9, 15, 43] and eukaryotic cells [6, 8], and a general mechanism for mediating this transcriptional bursting in E coli was recently reported [44].”
BackgroundMethodInfluential
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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