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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
Cites
Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection
Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. A., Tomita, M., Wanner, B. L., & Mori, H. (2006) · Molecular Systems Biology · doi:10.1038/msb4100050 · Cited by 7,858
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
Global analysis of protein localization in budding yeast
Huh, W.-K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S., & O'Shea, E. K. (2003) · Nature · doi:10.1038/nature02026 · Cited by 4,248
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Stochastic mRNA Synthesis in Mammalian Cells
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040309 · Cited by 1,737
An efficient recombination system for chromosome engineering in Escherichia coli
Yu, D., Ellis, H. M., Lee, E.-C., Jenkins, N. A., Copeland, N. G., & Court, D. L. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.100127597 · Cited by 1,688
Intrinsic and extrinsic contributions to stochasticity in gene expression
Swain, P. S., Elowitz, M. B., & Siggia, E. D. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.162041399 · Cited by 1,661
Visualization of Single RNA Transcripts in Situ
Femino, A. M., Fay, F. S., Fogarty, K., & Singer, R. H. (1998) · Science · doi:10.1126/science.280.5363.585 · Cited by 1,504
Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., DeRisi, J. L., & Weissman, J. S. (2006) · Nature · doi:10.1038/nature04785 · Cited by 1,483
Real-Time Kinetics of Gene Activity in Individual Bacteria
Golding, I., Paulsson, J., Zawilski, S. M., & Cox, E. C. (2005) · Cell · doi:10.1016/j.cell.2005.09.031 · Cited by 1,468
Cited by
Sorted by citations
Items 901–1,000 of 2,008
Population transcriptomics with single‐cell resolution: A new field made possible by microfluidics
Plessy, C., Simon-Desbois, L., Fujii, T., & Carninci, P. (2012) · BioEssays · doi:10.1002/bies.201200093 · Cited by 24
Inference of Quantitative Models of Bacterial Promoters from Time-Series Reporter Gene Data
Stefan, D., Pinel, C., Pinhal, S., Eugenio Cinquemani, B., Geiselmann, J., & de Jong, H. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004028 · Cited by 21
“The effect of rapid responses in gene expression, within a single generation, may thus give rise to proteins persisting over several generations, endowing the cell with a memory of past events [9, 13].”
BackgroundGenome-wide differential gene expression in immortalized DF-1 chicken embryo fibroblast cell line
Kong, B. C., Lee, J. Y., Bottje, W. G., Lassiter, K., Lee, J., & Foster, D. N. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-571 · Cited by 24
“Although the connections to cellular phenotypes from mRNA expression data may not be as strong as expected due to the potential lack of correlation between mRNA expression and protein abundance (which have been reported previously in both prokaryotes and eukaryotes [23-27]), the highly and dramatically differentially…”
BackgroundGene expression scaled by distance to the genome replication site
Ying, B., Tsuru, S., Seno, S., et al. (2013) · Molecular BioSystems · doi:10.1039/c3mb70254e · Cited by 21
Pieper, R., Zhang, Q., Clark, D. J., Huang, S.-T., Suh, M., Braisted, J. C., Payne, S., Fleischmann, R. D., Peterson, S. N., & Tzipori, S. R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026554 · Cited by 20
“coli proteomic and transcriptomic data revealed surprisingly low correlations of protein and mRNA copy numbers [33].”
BackgroundBohrer, C. H., & Roberts, E. (2015) · BMC Biophysics · doi:10.1186/s13628-016-0027-0 · Cited by 21
“Several previous studies have utilized smFISH to quantify the mRNA distributions of various genes [7, 14, 18, 20].”
InfluentialSingle Cell Analysis of a Bacterial Sender-Receiver System
Ramalho, T., Meyer, A., Mückl, A., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0145829 · Cited by 21
“coli suggested that generally intrinsic noise is dominant at low expression levels, while extrinsic noise is dominant at high expression levels [45].”
BackgroundMcWhorter, F. Y., Smith, T. D., Luu, T. U., Rahim, M. K., Haun, J. B., & Liu, W. F. (2016) · Integrative Biology · doi:10.1039/c6ib00053c · Cited by 21
Liu, W., Chen, M., Bai, L., Zhuang, Z., Fan, C., Jiang, N., Zhao, J., Ma, S., & Xiang, X. (2017) · Scientific Reports · doi:10.1038/s41598-017-05724-z · Cited by 22
Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber
Firman, T., Balázsi, G., & Ghosh, K. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.08.057 · Cited by 21
Light-Activated Proteomic Labeling via Photocaged Bioorthogonal Non-Canonical Amino Acids
Adelmund, S. M., Ruskowitz, E. R., Farahani, P. E., Wolfe, J. V., & DeForest, C. A. (2018) · ACS Chemical Biology · doi:10.1021/acschembio.7b01023 · Cited by 21
Pushing the limits of detection for proteins secreted from single cells using quantum dots
Herrera, V., Hsu, S.-C. J., Rahim, M. K., Chen, C. C. L., Nguyen, L., Liu, W. F., & Haun, J. B. (2018) · The Analyst · doi:10.1039/c8an01083h · Cited by 22
Importance of Crowding in Signaling, Genetic, and Metabolic Networks
ten Wolde, P. R., & Mugler, A. (2014) · International review of cytology · doi:10.1016/b978-0-12-800046-5.00012-6 · Cited by 21
The mean and noise of protein numbers in stochastic gene expression
Kuang, J., Tang, M., & Yu, J. (2012) · Journal of Mathematical Biology · doi:10.1007/s00285-012-0551-8 · Cited by 21
Toggle switch: noise determines the winning gene
Jaruszewicz-Błońska, J., & Lipniacki, T. (2013) · Physical Biology · doi:10.1088/1478-3975/10/3/035007 · Cited by 19
Modification of the Glass Surface Property in PDMS-Glass Hybrid Microfluidic Devices
Kaneda, S., Ono, K., Fukuba, T., Nojima, T., Yamamoto, T., & Fujii, T. (2012) · Analytical Sciences · doi:10.2116/analsci.28.39 · Cited by 21
Industrial Biotechnology:Escherichia colias a Host
Theisen, M. K., & Liao, J. C. (2016) · Industrial Biotechnology · doi:10.1002/9783527807796.ch5 · Cited by 21
Yang, A., Song, J., Li, J., Li, Y., Bai, S., Zhou, C., Wang, M., Zhou, Y., Wen, K., Luo, M., et al. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c04749 · Cited by 20
Machine learning-powered multi-omics for food microbiology and smarter food safety
Bansal, S., Rodriguez, C. Z., Thompson‐Witrick, K. A., Wang, Y., Taft, D. H., & Zhang, B. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105145 · Cited by 21
Lloyd‐Price, J., Lehtivaara, M., Kandhavelu, M., et al. (2011) · Molecular BioSystems · doi:10.1039/c1mb05100h · Cited by 20
Zur, H., Aviner, R., & Tuller, T. (2016) · Scientific Reports · doi:10.1038/srep21635 · Cited by 20
Depletion of Shine-Dalgarno Sequences Within Bacterial Coding Regions Is Expression Dependent
Yang, C., Hockenberry, A. J., Jewett, M. C., & Amaral, L. A. N. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.032227 · Cited by 17
Liu, Z., Yang, Y., Sun, Z., et al. (2016) · Optical Materials · doi:10.1016/j.optmat.2016.09.046 · Cited by 20
Havugimana, P. C., Hu, P., & Emili, A. (2017) · Expert Review of Proteomics · doi:10.1080/14789450.2017.1374179 · Cited by 20
“Then, expanding the scope of the study, we successfully purified 1241 soluble protein baits, including low abundance cytosolic proteins present as low as one or fewer copies per cell [48].”
BackgroundSelf-citationMatange, N., Bodkhe, S., Patel, M., et al. (2018) · Biochemical Journal · doi:10.1042/bcj20180249 · Cited by 20
Martino, A. D., Gueudré, T., & Miotto, M. (2019) · Physical review. E · doi:10.1103/physreve.99.012417 · Cited by 20
Massively parallel gene expression variation measurement of a synonymous codon library
Schmitz, A., & Zhang, F. (2021) · BMC Genomics · doi:10.1186/s12864-021-07462-z · Cited by 16
Genomic Mutations Within the Host Microbiome: Adaptive Evolution or Purifying Selection
Zhang, J., & Knight, R. D. (2022) · Engineering · doi:10.1016/j.eng.2021.11.018 · Cited by 21
Bacterial Replication Initiation as Precision Control by Protein Counting
Fu, H., Xiao, F., & Jun, S. (2023) · PRX Life · doi:10.1103/prxlife.1.013011 · Cited by 22
Duanis‐Assaf, T., & Reches, M. (2024) · iScience · doi:10.1016/j.isci.2024.108803 · Cited by 20
“…developing methodologies for a combination of SCFS with AFM imaging and advanced molecular biology methods for single-cell transcriptomic analysis 57,58 can greatly enhance our understanding of the attachment mechanism, which can help in developing the next generation of antifouling coatings.”
MethodSamanta, P., Cooke, S. F., McNulty, R., Hormoz, S., & Rosenthal, A. (2024) · Nature Protocols · doi:10.1038/s41596-024-01002-1 · Cited by 19
Efficient fluorescence image normalization for time lapse movies
Schwarzfischer, M., Marr, C., Krumsiek, J., et al. (2011) · Site cant be reached · Cited by 20
Tyagi, S. (2010) · Science · doi:10.1126/science.1194036 · Cited by 20
Growth propagation of yeast in linear arrays of microfluidic chambers over many generations
Wang, L., Liu, J., Li, X., et al. (2011) · Biomicrofluidics · doi:10.1063/1.3668243 · Cited by 20
Temporal Profile of Gene Transcription Noise Modulated by Cross-Talking Signal Transduction Pathways
Sun, Q., Tang, M., & Yu, J. (2011) · Bulletin of Mathematical Biology · doi:10.1007/s11538-011-9683-z · Cited by 19
“The stochasticity of gene transcription has often been quantified by the noise or the noise strength (Elowitz et al. 2002; Kaern et al. 2005; Kaufmann and van Oudenaarden 2007; Paulsson 2004; Raj et al. 2006, 2010; Taniguchi et al. 2010).”
BackgroundMolecular basis for population variation: From SNPs to SAPs
Wu, J., & Zeng, R. (2012) · FEBS Letters · doi:10.1016/j.febslet.2012.07.036 · Cited by 20
“Xie’s group at Harvard University quantitatively analysed the correlation between protein and mRNA expression at single-molecule level and concluded that the protein and mRNA copy numbers for any given gene are uncorrelated [36].”
BackgroundKohn, G., Delvaux, D., Lakaye, B., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0043879 · Cited by 20
“coli with nearly 400 copies per cell [15].”
BackgroundDynamics of small genetic circuits subject to stochastic partitioning in cell division
Lloyd‐Price, J., Tran, H., & Ribeiro, A. S. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.04.018 · Cited by 18
“a The degradation rate was set to match a mean protein number of 20 molecules (Taniguchi et al., 2010), assuming no regulation and given the values of the other”
BackgroundAnalysis of Translation Elongation Dynamics in the Context of an Escherichia coli Cell
Vieira, J. R. P., Racle, J., & Vassily Hatzimanikatis, A. (2016) · Biophysical Journal · doi:10.1016/j.bpj.2016.04.004 · Cited by 19
Influence of gene copy number on self-regulated gene expression
Jȩdrak, J., & Ochab-Marcinek, A. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.08.018 · Cited by 17
Zhang, R., Yuan, H., Wang, S., et al. (2017) · Scientific Reports · doi:10.1038/srep42200 · Cited by 20
Jȩdrak, J., Kwiatkowski, M., & Ochab-Marcinek, A. (2019) · Physical review. E · doi:10.1103/physreve.99.042416 · Cited by 19
Kröber, E., & Schäfer, H. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01132 · Cited by 20
Post‐Transcriptional Noise Control
Hansen, M. M. K., & Weinberger, L. S. (2019) · BioEssays · doi:10.1002/bies.201900044 · Cited by 17
Models of protein production along the cell cycle: An investigation of possible sources of noise
Dessalles, R., Fromion, V., & Robert, P. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226016 · Cited by 25
“Finally, some assumptions considered in the two-stage model are questionable when applied in the context of Taniguchi et al., for example the fact that a death process acting on the proteins models the dilution effect due to cell volume growth (see [13]).”
InfluentialOrdered patterning of the sensory system is susceptible to stochastic features of gene expression
Giri, R., Papadopoulos, D. K., Posadas, D. M., Potluri, H. K., Tomančák, P., Mani, M., & Carthew, R. W. (2020) · eLife · doi:10.7554/elife.53638 · Cited by 21
Predicting Transcriptional Output of Synthetic Multi-input Promoters
Zong, D. M., Cinar, S., Shis, D. L., Josić́, K., Ott, W., & Bennett, M. R. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00165 · Cited by 18
Fulcher, J. M., Markillie, L. M., Mitchell, H. D., Williams, S., Engbrecht, K. M., Degnan, D. J., Bramer, L., Moore, R. J., Chrisler, W., Cantlon-Bruce, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.17.492137 · Cited by 20
NoisET: Noise Learning and Expansion Detection of T-Cell Receptors
Koraichi, M. B., Touzel, M. P., Mazzolini, A., Mora, T., & Walczak, A. M. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c05002 · Cited by 23
Transcriptional bursting dynamics in gene expression
Zhang, Q., Cao, W., Wang, J., Yin, Y., Sun, R., Tian, Z., Hu, Y., Tan, Y., & Zhang, B. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1451461 · Cited by 19
Stockmar, I., Feddersen, H., Cramer, K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-28472-0 · Cited by 19
Razban, R. M. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz119 · Cited by 21
From deterministic to fuzzy decision-making in artificial cells
Greiss, F., Daube, S. S., Noireaux, V., & Bar‐Ziv, R. (2020) · Nature Communications · doi:10.1038/s41467-020-19395-4 · Cited by 19
Szoke, T., Albocher, N., Govindarajan, S., Nussbaum‐Shochat, A., & Amster‐Choder, O. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016017118 · Cited by 18
Chen, M., Luo, S., Cao, M., Guo, C., Zhou, T., & Zhang, J. (2022) · Physical review. E · doi:10.1103/physreve.105.014405 · Cited by 19
Finding the Last Bits of Positional Information
McGough, L., Casademunt, H., Nikolić, M., Aridor, Z., Petkova, M. D., Gregor, T., & Bialek, W. (2024) · PRX Life · doi:10.1103/prxlife.2.013016 · Cited by 19
Single-molecule fluorescence imaging of photocatalytic nanomaterials
Zhang, S., Fan, D., Yan, Q., et al. (2024) · Journal of Materials Chemistry A · doi:10.1039/d4ta02347a · Cited by 19
Lim, Y. R., Kim, J., Park, S. J., Yang, G., Song, S., Chang, S.-K., Lee, N. K., & Sung, J. (2015) · Physical Review X · doi:10.1103/physrevx.5.031014 · Cited by 17
Nabu, S., Lawung, R., Isarankura-Na-Ayudhya, P., Isarankura‐Na‐Ayudhya, C., Roytrakul, S., & Prachayasittikul, V. (2014) · Journal of Medical Microbiology · doi:10.1099/jmm.0.067595-0 · Cited by 19
“Plausible explanations draw on (i) post-transcriptional mechanisms in controlling the rate of protein translation, (ii) the half-life of protein or mRNAs in vivo (Varshavsky, 1996), and (iii) the noises in protein and mRNA (Taniguchi et al., 2010).”
BackgroundResultBarhoom, S., Farrell, I., Shai, B., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt686 · Cited by 19
Quantifying Extrinsic Noise in Gene Expression Using the Maximum Entropy Framework
Dixit, P. D. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.05.010 · Cited by 15
“The limited treatment extrinsic noise has received (7,20,28) employs the linear fluctuation-dissipation like susceptibility analysis (20) or ad hoc assumptions about the nature of variation in extrinsic parameters (7,28).”
BackgroundControl of ribosome traffic by position-dependent choice of synonymous codons
Mitarai, N., & Pedersen, S. (2013) · Physical Biology · doi:10.1088/1478-3975/10/5/056011 · Cited by 20
In vitro and in vivo single-molecule fluorescence imaging of ribosome-catalyzed protein synthesis
Perez, C. E., & Gonzalez, R. L. (2011) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2011.11.002 · Cited by 19
“Together with a number of follow up studies [68,70], Xie and co-workers have demonstrated that random events, which can only be characterized using single-molecule approaches, play decisive roles in driving gene expression, regulating the composition of the proteome, and controlling the observed phenotype of living ce…”
BackgroundAbsolute quantification of protein copy number using a single-molecule-sensitive microarray
Burgin, E., Salehi‐Reyhani, A., Barclay, M., Brown, A. T., Kaplinsky, J., Novakova, M., Neil, M. A. A., Ces, O., Willison, K. R., & Klug, D. R. (2014) · The Analyst · doi:10.1039/c4an00091a · Cited by 19
Isothermal Amplification Strategies for Detection in Microfluidic Devices
Sarkar, S., Sabhachandani, P., & Konry, T. (2016) · Trends in biotechnology · doi:10.1016/j.tibtech.2016.09.006 · Cited by 19
Wu, F., Zhang, Q., & Wang, X. (2018) · Cell Systems · doi:10.1016/j.cels.2018.01.010 · Cited by 21
“So the variation of mRNA concentrations for each construct is insufficient to explain the fluorescence differences, which is in agreement with previous studies that protein and mRNA copy numbers in E. coli cells are uncorrelated (Lim et al., 2011; Taniguchi et al., 2010).”
Comparative studies of glycolytic pathways and channeling under in vitro and in vivo modes
Abernathy, M. H., Zhang, Y., Hollinshead, W. D., Wang, G., Baidoo, E. E. K., Liu, T., & Tang, Y. (2018) · AIChE Journal · doi:10.1002/aic.16367 · Cited by 18
Holland, D. O., & Johnson, M. E. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006022 · Cited by 18
“In addition, weakly expressed proteins are noisier(12) and thus less reliable for the cell.”
InfluentialPinho, N., Wiśniewski, J. R., Dias-Lopes, G., Vahia, L. S., Bombaça, A. C. S., Mesquita‐Rodrigues, C., Menna‐Barreto, R. F. S., Cupolillo, E., de Jesus, J. B., Padrón, G., et al. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008509 · Cited by 18
Conserved patterns in bacterial genomes: A conundrum physically tailored by evolutionary tinkering
Junier, I. (2014) · Comput. Biol. Chem. · doi:10.1016/j.compbiolchem.2014.08.017 · Cited by 21
Incorporation of flexible objectives and time-linked simulation with flux balance analysis
Birch, E. W., Udell, M. R., & Covert, M. W. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.12.009 · Cited by 18
“In the wild type case, such an objective will result in process reactant exchange to and from the metabolic network identical to the biomass reaction flux maximization case in Eq.”
BackgroundInfluentialGeneration of Femtoliter Reactor Arrays within a Microfluidic Channel for Biochemical Analysis
Ota, S., Kitagawa, H., & Takeuchi, S. (2012) · Analytical Chemistry · doi:10.1021/ac301204v · Cited by 19
Fei, S. S., Wilmarth, P. A., HITZEMANN, R. J., McWeeney, S. K., Belknap, J. K., & David, L. L. (2011) · Journal of Proteome Research · doi:10.1021/pr200133p · Cited by 18
The nonlinear dynamics and fluctuations of mRNA levels in cell cycle coupled transcription
Sun, Q., Jiao, F., Lin, G., Yu, J., & Tang, M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007017 · Cited by 17
Gene-Centric Model Approaches for Accurate Prediction of Pesticide Biodegradation in Soils
Rodríguez, L. C., Ingalls, B., Schwarz, E., Streck, T., Uksa, M., & Pagel, H. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c03315 · Cited by 18
Clustering single-cell RNA-seq data by rank constrained similarity learning
Mei, Q., Li, G., & Su, Z. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab276 · Cited by 22
Self-citationSavinov, A., Brandsen, B. M., Angell, B. E., Cuperus, J. T., & Fields, S. (2021) · Genome biology · doi:10.1186/s13059-021-02509-6 · Cited by 18
“…first, the role of evolved sequence context in modulating 3 ′ UTR motif function, and second, the lack of equivalence between effects on RNA level (via mRNA stability) and protein level, consistent with literature demonstrating a lack of correlation between protein and mRNA levels [60 – 63].”
Double‐Edged Role of Resource Competition in Gene Expression Noise and Control
Goetz, H., Stone, A., Zhang, R., Lai, Y., & Tian, X. (2022) · Advanced Genetics · doi:10.1002/ggn2.202100050 · Cited by 16
Automated Design of Synthetic Gene Circuits in the Presence of Molecular Noise
Sequeiros, C., Vázquez, C., Banga, J. R., & Otero‐Muras, I. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00033 · Cited by 20
Simas, R. G., Pessoa, A., & Long, P. F. (2023) · Journal of Industrial Microbiology & Biotechnology · doi:10.1093/jimb/kuad034 · Cited by 28
“In a given experiment for quantification of more than 1000 tagged proteins, about 50% of these were expressed at an average level of less than 10 copies per cell (Li and Xie, 2011 ), which agreed with data presented elsewhere (Taniguchi et al., 2010 ; Wi ´sniewski & Rakus, 2014 ).”
BackgroundOptimal Census by Quorum Sensing
Taillefumier, T. O., & Wingreen, N. S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004238 · Cited by 19
“functional form can be inferred for simple models of gene expression [25, 26].”
ZFP91—A Newly Described Gene Potentially Involved in Prostate Pathology
Paschke, L., Ruciński, M., Ziółkowska, A., et al. (2013) · Pathology & Oncology Research · doi:10.1007/s12253-013-9716-z · Cited by 18
Adamla, F., & Ignatova, Z. (2015) · Scientific Reports · doi:10.1038/srep10692 · Cited by 18
Microfluidics for bacterial imaging
Eland, L. E., Wipat, A., Lee, S., Park, S., & Wu, L. J. (2016) · Methods in microbiology · doi:10.1016/bs.mim.2016.10.002 · Cited by 19
Nucleoid and cytoplasmic localization of small RNAs inEscherichia coli
Sheng, H., Stauffer, W. T., Hussein, R., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx023 · Cited by 18
“This advantage is important for target proteins that exert their actions at low concentrations (82) and in systems that have high cooperativity, ultrasensitive switches or positive feedback (83,84).”
BackgroundCareful accounting of extrinsic noise in protein expression reveals correlations among its sources
Cole, J. A., & Luthey-Schulten, Z. A. (2017) · Physical review. E · doi:10.1103/physreve.95.062418 · Cited by 18
Stochastic protein multimerization, activity, and fitness
Hagner, K., Setayeshgar, S., & Lynch, M. (2018) · Physical review. E · doi:10.1103/physreve.98.062401 · Cited by 18
Guerra, P. R., Liu, G., Lemire, S., Nawrocki, A., Kudirkienė, E., Møller‐Jensen, J., Olsen, J. E., & Jelsbak, L. (2020) · Research in Microbiology · doi:10.1016/j.resmic.2019.12.001 · Cited by 18
Risser, F., Collin, S., Morais, R. D. S., Gruez, A., Chagot, B., & Weissman, K. J. (2020) · Journal of Structural Biology · doi:10.1016/j.jsb.2020.107581 · Cited by 18
Vasdekis, A. E., & Singh, A. (2020) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1512 · Cited by 18
Imaging-based screens of pool-synthesized cell libraries
Lawson, M. J., & Elf, J. (2021) · Nature Methods · doi:10.1038/s41592-020-01053-8 · Cited by 19
First passage time in post-transcriptional regulation by multiple small RNAs
Biswas, K., & Ghosh, A. (2021) · The European Physical Journal E · doi:10.1140/epje/s10189-021-00028-7 · Cited by 19
Non-Genetic Diversity in Chemosensing and Chemotactic Behavior
Moore, J. P., Kamino, K., & Emonet, T. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22136960 · Cited by 17
“By far, the most-studied source of phenotypic variability is stochastic gene expression (Figure 1b), where it is understood that transcription and translation events occur in bursts with variable frequency and size, leading to broad distributions of protein expression in isogenic cell populations [6,11,12,14].”
BackgroundCharacterizing Bacterial Gene Expression in Nitrogen Cycle Metabolism with RT-qPCR
Graham, J. E., Wantland, N. B., Campbell, M., & Klotz, M. G. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-386489-5.00014-2 · Cited by 18
Individual differences and evidence-based psychopharmacology
Belmaker, R. H., Bersudsky, Y., & Agam, G. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-110 · Cited by 18
“The same is true of protein expression in isogenic bacterial populations [5].”
BackgroundOrigin and Consequences of the Relationship between Protein Mean and Variance
Vallania, F., Sherman, M. S., Goodwin, Z. A., Mogno, I., Cohen, B. A., & Mitra, R. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102202 · Cited by 17
“This relationship holds in yeast (s(2)/m), bacteria (s(2)/m) [24] and human T-cells (s(2)/m) [34], suggesting the processes that determine the power-law are common across different species.”
BackgroundWu, Y., Park, K. J., Deighan, C., et al. (2016) · Frontiers in Oncology · doi:10.3389/fonc.2016.00234 · Cited by 18
“), the probe sequence and target sequence of RNA ISH are precisely known and have only 20 bp oligonucleotides in the most recently reported versions (21, 22).”
BackgroundDeterminants for Efficient Editing with Cas9-Mediated Recombineering in Escherichia coli
Choudhury, A., Fankhauser, R. G., Freed, E. F., et al. (2020) · ACS Synthetic Biology · doi:10.1021/acssynbio.9b00440 · Cited by 18
Gehin, G., Carraro, N., Gurfield, K., van der Meer, J. R., & Peña, J. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c00255 · Cited by 17
Recent advances in single-cell studies of gene regulation
Selimkhanov, J., Hasty, J., & Tsimring, L. S. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2011.11.006 · Cited by 17
“com [53 ] expanded on this study by looking at a library of YFP-tagged proteins and their corresponding mRNA in E.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
