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 1,501–1,600 of 2,008
Son, J. B., Kim, S.-H., Yang, S., Ahn, Y., & Lee, N. K. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c01816 · Cited by 4
Characterizing the nonmonotonic behavior of mutual information along biochemical reaction cascades
Fan, R., & Hilfinger, A. (2024) · Physical review. E · doi:10.1103/physreve.110.034309 · Cited by 3
Gene syntaxes modulate gene expression and circuit behavior on plasmids
Deng, Y., Maurais, H. E., Etheridge, K., & Sarpeshkar, R. (2025) · Journal of Biological Engineering · doi:10.1186/s13036-025-00493-0 · Cited by 3
Activating Cancer Hallmarks through Changes in mRNA/Protein Regulation
Chavez, J. H. G., Barton, N., Lindgren, C. M., Mendenhall, B., Kimball, B., & Payne, S. H. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00284 · Cited by 2
François, J., Jambhekar, A., & Lahav, G. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169436 · Cited by 4
Cyclo-Stationary Distributions of mRNA and Protein Counts for Random Cell Division Times
Ali, S. Y., Saran, A., Prasad, A., Singh, A., & Das, D. (2025) · PRX Life · doi:10.1103/ysj3-gkrr · Cited by 3
Dependency of Cellular Decision Making in Physiology and Influence of Preceding Growth Conditions
Puranik, S., & Purohit, H. J. (2014) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-014-1167-9 · Cited by 3
Space-induced bifurcation in repression-based transcriptional circuits
Van, A. L., Soula, H. A., & Berry, H. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0125-z · Cited by 2
“1 and 5 [31] or even up to 50 for strongly expressed promoters like the Plac promoter under strong induction [32].”
BackgroundInfluentialPropagation and control of gene expression noise with non-linear translation kinetics
Dyken, J. D. V. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.07.006 · Cited by 3
(Membrane) Protein Production in Context
Schavemaker, P. E., & Poolman, B. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.08.009 · Cited by 3
Synergy and Redundancy in a Signaling Cascade with Different Feedback Mechanisms*
王, L.-F. 莉. W., Xu, Y., Ma, J., & Jia, Y. (2018) · Communications in Theoretical Physics · doi:10.1088/0253-6102/70/4/485 · Cited by 3
Inferring distributions from observed mRNA and protein copy counts in genetic circuits
Atitey, K., Loskot, P., & Rees, P. C. T. (2018) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/aaef5c · Cited by 2
Elucidating effects of reaction rates on dynamics of the lac circuit in Escherichia coli
Atitey, K., Loskot, P., & Rees, P. C. T. (2018) · Biosystems · doi:10.1016/j.biosystems.2018.11.003 · Cited by 2
“Correlations between protein production statistics and mRNA statistics were obtained in [54].”
MethodSimultaneous Targeted Detection of Proteins and RNAs in Single Cells
Ooi, A. T., & Ruff, D. W. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9240-9_22 · Cited by 3
Phenotypic Heterogeneity inMycobacterium tuberculosis
Dhar, N., McKinney, J. D., & Manina, G. (2017) · ASM Press eBooks · doi:10.1128/9781555819569.ch32 · Cited by 2
Modulation of stochastic gene expression by nuclear export processes
Smith, M. E., Soltani, M., Kulkarni, R., & Singh, A. (2021) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc45484.2021.9683294 · Cited by 3
Gelber, I. (2022) · Physical Biology · doi:10.1088/1478-3975/ac5e0f · Cited by 3
“An experimental ensemble of cells will have a distribution of initial fluorescence intensities [21].”
BackgroundDynamic mechanism of phase variation in bacteria based on multistable gene regulatory networks
Galimzyanov, A. V., & Tchuraev, R. N. (2022) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2022.111212 · Cited by 3
Design and Analysis of Massively Parallel Reporter Assays Using FORECAST
Gilliot, P.-A., & Gorochowski, T. E. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2617-7_3 · Cited by 3
Sakurai, Y., & Hori, Y. (2022) · Automatica · doi:10.1016/j.automatica.2022.110647 · Cited by 3
“Despite these advancements, one limitation of these general frameworks is that they focus only on the analysis of intrinsic noise while experimental observations suggest that the stochastic cellular response is the result of the combined effects of intrinsic and extrinsic noise (Taniguchi et al. 2010).”
BackgroundA quantitative interpretation of oxidative protein folding activity in Escherichia coli
Rettenbacher, L. A., & von der Haar, T. (2022) · Microbial Cell Factories · doi:10.1186/s12934-022-01982-3 · Cited by 2
Variations in protein expression associated with oral cancer
Jiang, W., He, Z., Zhang, Y., Ran, S., Sun, Z., & Chen, W. (2023) · Technology and Health Care · doi:10.3233/thc-236014 · Cited by 3
“These differences may be due to the fact that transcript mRNA levels correspond to only 29–55% of the protein levels in prokaryotes [36], especially in the presence of perturbation-induced changes.”
BackgroundGene expression and wildlife health: varied interpretations based on perspective
Bowen, L., Yee, J. L., Bodkin, J. L., Waters, S. C., Murray, M. J., Coletti, H. A., Ballachey, B. E., Monson, D. H., & Miles, A. K. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1157700 · Cited by 3
“For example, variability in expression in unicellular organisms is anti-correlated to population average gene expression, which in turn is tightly coupled to growth rate (Tyson et al., 1979; Ingraham et al., 1983; Bar-Even et al., 2006; Newman et al., 2006; Brauer et al., 2008; Klumpp et al., 2009; Taniguchi et al., 2…”
BackgroundMaxCal can infer models from coupled stochastic trajectories of gene expression and cell division
Torres, A., Cockerell, S., Phillips, M. W., Balázsi, G., & Ghosh, K. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.05.017 · Cited by 3
Kale, T., Khatri, D., & Athale, C. A. (2023) · Physical Biology · doi:10.1088/1478-3975/acdcda · Cited by 3
“However, protein expression levels in single E. coli cells were seen to fluctuate with a period longer than the bacterial cell cycle with most of the noise ‘extrinsic’ (Taniguchi et al 2010).”
The ability to sense the environment is heterogeneously distributed in cell populations
Goetz, A., Akl, H., & Dixit, P. D. (2024) · eLife · doi:10.7554/elife.87747.3 · Cited by 3
Guzella, T., Barreto, V. M., & Carneiro, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007910 · Cited by 2
“Although transcript copy number distributions are generally assumed to be described by a negative binomial distribution at any range of expression levels, they are well-approximated by a log-normal distribution at high copy numbers per cell [45].”
BackgroundStelzer, C., & Benenson, Y. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008389 · Cited by 3
“Simulations that employ Poisson, Gamma or lognormal distributions show a strikingly similar effect (S5 Fig).”
Ngo, G., Girbas, M. G., Schätzle, H., Hammer, A., Safarian, S., Hübinger, M., & Schleiff, E. (2020) · Journal of Bacteriology · doi:10.1128/jb.00568-20 · Cited by 3
Postponing production exponentially enhances the molecular memory of a stochastic switch
Bokes, P. (2021) · European Journal of Applied Mathematics · doi:10.1017/s0956792520000479 · Cited by 3
Micro/Nano fluidics Enabled Single-Cell Biochemical Analysis
Lin, L. (2019) · Integrated analytical systems · doi:10.1007/978-981-32-9729-6_8 · Cited by 3
Evolving kinetics of gene expression in stochastic environments
Häkkinen, A., & Ribeiro, A. S. (2012) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2012.02.002 · Cited by 3
Single-molecule methods for studying gene regulation in vivo
Hensel, Z., & Xiao, J. (2013) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-013-1243-y · Cited by 3
“For example, a genetic regulatory circuit may consist of one or two DNA copies of a gene and a few tens of DNA-binding transcription factor molecules, with random production of relatively short-lived mRNA transcripts that are each a template for the translation of a handful of protein molecules [24, 30, 33, 34, 75, 85…”
BackgroundMethodInfluentialCounting Molecules Within Cells
Coffman, V. C., Lee, I., & Wu, J. (2014) · Colloquium Series on Quantitative Cell Biology · doi:10.4199/c00109ed1v01y201406qcb001 · Cited by 3
Feedback Regulation and Its Efficiency in Biochemical Networks
Kobayashi, T. J., Yokota, R., & Aihara, K. (2016) · Journal of Statistical Physics · doi:10.1007/s10955-015-1443-2 · Cited by 3
“Now these strategies play the fundamental role to design single-cell experiments and to derive information on the anatomy of fluctuation from the experimental observations [8,15,29,34,37,44].”
BackgroundMapping and differential expression analysis from short‐read RNA‐Seq data in model organisms
Zhao, Q., Gratten, J., Restuadi, R., & Li, X. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0060-7 · Cited by 5
“Previous studies have provided important insights into the dynamics of transcription and cell-to-cell variation in gene expression [111–113].”
BackgroundA Bio-Physical Model for the Kinetoplast DNA
Michieletto, D. (2016) · Springer theses · doi:10.1007/978-3-319-41042-5_5 · Cited by 3
Lenn, T., & Leake, M. C. (2012) · Cited by 3
Recent advancement of light‐based single‐molecule approaches for studying biomolecules
Croop, B., Zhang, C., Lim, Y., Gelfand, R. M., & Han, K. Y. (2019) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1445 · Cited by 4
“…phenomena, such as the assembly of macromolecular complexes (Hoskins et al., 2011) or transcription (Larson, Zenklusen, Wu, Chao, & Singer, 2011; Taniguchi et al., 2010) and translation (Morisaki et al., 2016; C. Wang, Han, Zhou, & Zhuang, 2016; Wu, Eliscovich, Yoon, & Singer, 2016; Yan, Hoek,…”
Thermotogales origin scenario of eukaryogenesis
Kuwabara, T., & Igarashi, K. (2020) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2020.110192 · Cited by 3
Bohrer, C. H., & Roberts, E. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2016.11.3078 · Cited by 3
Ding, D., & Sun, X. S. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2996295 · Cited by 3
“A comparison of these differentially translated proteins to those differentially regulated ones from the transcriptome study [8] and the proteome study [9] in Shewanella oneidensis MR-1was performed (Fig.”
BackgroundInfluentialJahanshahi, S., & Li, Y. (2020) · ChemBioChem · doi:10.1002/cbic.202000280 · Cited by 3
Hansberg, W. (2022) · Differentiation · doi:10.1016/j.diff.2022.02.005 · Cited by 4
Stopka, S. A., & Vértes, Á. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9831-9_11 · Cited by 3
The Effects of Ribosome Autocatalysis and Negative Feedback in Resource CompetitionPreprint
Chandra, F. A., & Vecchio, D. D. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/042127 · Cited by 3
“Assuming an average of 2-3 mRNA transcripts per gene copy for highly expressed genes (30), the external demand given by a high copy plasmid would be around 1000-2100 mRNA copies.”
BackgroundStochastic Biochemical Networks in Random Environments: Probabilistic Modeling and Inference
Zechner, C. (2014) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010256930 · Cited by 2
BROCKMAN: Deciphering variance in epigenomic regulators by k -mer factorizationPreprint
de Boer, C. G., & Regev, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/129247 · Cited by 4
“Consistent with this possibility, low mRNA expression levels generally result in more variable (noisier) protein levels (35) since transcription or decay of a single mRNA results in greater fold differences in low-abundance genes.”
BackgroundDiverse spatial expression patterns emerge from common transcription bursting kineticsPreprint
Zoller, B., Little, S. C., & Gregor, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.08215 · Cited by 4
“First, unlike many other systems (bacteria, yeast, mammalian culture cells), there is no clearly identifiable noise floor at high expression [7, 21, 47].”
BackgroundLepore, A., Taylor, H. N., Landgraf, D., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/237248 · Cited by 3
Wang, X., Teng, L., Zhang, H., Zhou, Q., Su, X., Cui, X., Bu, D., Gong, X., Poetsch, A., & Ning, K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/270397 · Cited by 3
García‐Timermans, C., Rubbens, P., Heyse, J., Kerckhof, F., Props, R., Skirtach, A. G., Waegeman, W., & Boon, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/545681 · Cited by 3
Savulescu, A. F., Brackin, R., Bouilhol, E., Dartigues, B., Warrell, J., Pimentel, M. R., Dallongeville, S., Schmoranzer, J., Olivo‐Marín, J., Gomes, E. R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/536383 · Cited by 5
Havugimana, P. C., Hu, P., & Emili, A. (2019) · Wiley eBooks · doi:10.1002/9781119081661.ch8 · Cited by 4
Self-citationAnalysis of Biological Noise in an Organelle Size Control SystemPreprint
Bauer, D., Ishikawa, H., Wemmer, K. A., Kondev, J., & Marshall, W. F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.31.276428 · Cited by 4
An Hfq-dependent post-transcriptional mechanism fine tunes RecB expression in Escherichia coliPreprint
Kalita, I., Iosub, I. A., McLaren, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.23.465540 · Cited by 3
Positive supercoiling buildup is a trigger of E. coli ’s short-term response to cold shockPreprint
Dash, S., Palma, C. S. D., Baptista, I. S. C., Bahrudeen, M. N. M., Almeida, B. L. B., Chauhan, V., Jagadeesan, R., & Ribeiro, A. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.22.473827 · Cited by 3
“Next, we used a YFP fusion library [Taniguchi et al., 2010] to measure CS effects on the single-cell protein levels of 30 CSR genes (Figure 1, step 2).”
BackgroundMethodInfluentialRobust and resource-optimal dynamic pattern formation of Min proteins in vivoPreprint
Ren, Z., Weyer, H., Würthner, L., Li, D., Sou, C., Villarreal, D., Frey, E., & Jun, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.15.553443 · Cited by 2
Kleiner, M., Polerecký, L., Lott, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.25.568684 · Cited by 3
An Hfq-dependent post-transcriptional mechanism fine tunes RecB expression in Escherichia coliPreprint
Kalita, I., Iosub, I. A., McLaren, L., Goossens, L., Granneman, S., & Karoui, M. E. (2024) · eLife · doi:10.7554/elife.94918.1 · Cited by 3
An Hfq-dependent post-transcriptional mechanism fine tunes RecB expression in Escherichia coli
Kalita, I., Iosub, I. A., McLaren, L., Goossens, L., Granneman, S., & Karoui, M. E. (2024) · eLife · doi:10.7554/elife.94918 · Cited by 5
Soil meta-omics: Current status, challenges, and applications
Kumar, V., Jaiswal, D. K., Sahu, P. K., Chaurasia, R., Kasoundhan, S., Patel, A., & Verma, D. (2025) · Ecological Genetics and Genomics · doi:10.1016/j.egg.2025.100411 · Cited by 2
Demographic noise induced patterns in space and time
Patti, F. D., Arbel‐Goren, R., Fanelli, D., & Stavans, J. (2026) · Physics Reports · doi:10.1016/j.physrep.2026.03.002 · Cited by 4
Single-cell phenotypic heterogeneity shapes quorum signaling dynamics in Pseudomonas aeruginosa
Lange, D., Litvinov, V., & Dar, D. (2026) · Nature Communications · doi:10.1038/s41467-026-71109-4 · Cited by 5
“We therefore plotted both CV and TER against mean expression for each gene-condition pair, fi tting regression lines to model the baseline expression noise (Methods).”
Arad, D., Fargeon, O., Levin, L., Svenningsen, S. L., Aspit, L., & Bar-Yaacov, D. (2026) · mBio · doi:10.1128/mbio.00551-26 · Cited by 2
“In contrast, the estimated average number of mRNA molecules per gene per E. coli cell is 0.4–3 (28, 29).”
InfluentialSingle-bacterium metabolome revealing heterogeneous cellular states in bacterial populations
Zhan, L., Liu, H., Chang, W., Cao, K., Zhang, S., Gao, Y. N., Chu, B., Yin, H., He, H., Zhou, R., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72373-0 · Cited by 2
Wong, J. V., Yao, G., Nevins, J. R., & You, L. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385075-1.00010-x · Cited by 2
Sources of stochasticity in constitutive and autoregulated gene expression
Marathe, R., Gomez, D., & Klumpp, S. (2012) · Physica Scripta · doi:10.1088/0031-8949/2012/t151/014068 · Cited by 2
Li, R., Xu, L., & Shi, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.11.002 · Cited by 2
Taniguchi, Y. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2987-0_5 · Cited by 2
Self-citationDistribution of population-averaged observables in stochastic gene expression
Bhattacharyya, B., & Kalay, Z. (2014) · Physical Review E · doi:10.1103/physreve.89.012715 · Cited by 2
Heras, A. D. L., Xiao, W., Sren, V., & Elfick, A. P. D. (2016) · SLAS TECHNOLOGY · doi:10.1177/2211068216655151 · Cited by 3
Glycosaminoglycans (GAGs) in Cardiovascular Disease: Searching for the Sweet Spot
Lucas, A., & Wakefield, D. N. (2016) · Journal of Clinical & Experimental Cardiology · doi:10.4172/2155-9880.1000e141 · Cited by 2
“For example, it has been demonstrated that a single cell’s mRNA copy numbers and protein are uncorrelated for any given gene [2].”
BackgroundProteomics in Personalized Medicine
Goswami, S. (2017) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-2607-0.ch004 · Cited by 2
Synthetic Biology of Microbial Biofuel Production
Bokinsky, G., Groff, D., & Keasling, J. D. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-394430-6.00011-x · Cited by 2
Tuning extrinsic noise effects on a small genetic circuit
Bahrudeen, M. N. M., & Ribeiro, A. S. (2017) · doi:10.7551/ecal_a_075 · Cited by 2
Structural identification of biochemical reaction networks from population snapshot data
Eugenio Cinquemani, B. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.2227 · Cited by 2
“…the advent of single-cell monitoring techniques such as flow-cytometry and videomicroscopy and their coupling with microfluidic devices and barcoding (Taniguchi et al., 2010; Klein et al., 2016; Zechner et al., 2012), randomness of single-cell responses can be observed and statistically quantified.”
BackgroundOliveira, S. M. D., Bahrudeen, M. N. M., Startceva, S., et al. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-78658-2_14 · Cited by 2
Ab initio scaling laws between noise and mean of gene expression
Rodrigo, G. (2019) · Physical review. E · doi:10.1103/physreve.100.032415 · Cited by 2
Molecular Origins of Transcriptional Heterogeneity in Diazotrophic Klebsiella oxytoca
Bashir, T., Brackston, R. D., Waite, C., Kotta‐Loizou, I., Carey, M. R., Engl, C., Buck, M., & Schumacher, J. (2022) · mSystems · doi:10.1128/msystems.00596-22 · Cited by 2
Recent progress in co-detection of single-cell transcripts and proteins
Huang, S., Fan, Q., Wang, Y., Huang, Z., Shi, W., Gong, Y., Yang, T., Wang, J., Wu, L., & Yang, C. J. (2023) · Nano Research · doi:10.1007/s12274-023-5924-3 · Cited by 2
Multi-Omics Methods Applied to Flower Development
Álvarez-Urdiola, R., Matus, J. T., & Riechmann, J. L. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3299-4_23 · Cited by 2
Universal scaling relation and criticality in metabolism and growth of Escherichia coli
Guan, S., Zhang, Z., Zhang, Z., & Shi, H. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013035 · Cited by 2
“With the significant developments of single-cell technologies, it is feasible to track protein expression, metabolic fluxes, cellular growth and division at the single-cell level [3–5].”
BackgroundA High-Speed Microscopy System Based on Deep Learning to Detect Yeast-Like Fungi Cells in Blood
Liu, R., Li, X., Liu, Y., Du, L., Zhu, Y., Wu, L., & Hu, B. H. (2024) · Bioanalysis · doi:10.4155/bio-2023-0193 · Cited by 2
Engineering biology fundamental for plant-derived bioactive compounds: challenges and prospects
Chen, R., & Zhang, L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15558-1.00001-1 · Cited by 2
Enzyme kinetics simulation at the scale of individual particles
Kearney, T., & Flegg, M. B. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0216285 · Cited by 2
Ali, S. Y., Prasad, A., & Das, D. (2025) · Physical Review E · doi:10.1103/physreve.111.014405 · Cited by 2
Determining interaction directionality in complex biochemical networks from stationary measurements
Leibovich, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-86332-0 · Cited by 2
Albocher, N., Heidenreich, M., Fadel, A., Sirotkin, E., Goldberger, O., Nussbaum‐Shochat, A., Levy, E. D., Schueler‐Furman, O., Schuldiner, M., & Amster‐Choder, O. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115436 · Cited by 2
High-content enhancement strategies for fluorescence in situ hybridization imaging
He, Y., Du, Z., Zhu, L., Dong, Y., Huang, K., & Xu, W. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118354 · Cited by 2
High-frequency Noise Attenuation of a Two-component System Responding to Short-pulse Input
Nishida, A., Sekine, R., Kiga, D., et al. (2016) · doi:10.1145/3029375.3029377 · Cited by 2
“Among the identified parameters shown in Table 2, the OmpR concentration is consistent with the result of single-molecule measurements [39].”
BackgroundResultInfluentialRoussel, M. R. (2020) · Algorithms · doi:10.3390/a13090209 · Cited by 2
“Proteins typically have much longer lifetimes than their mRNAs [66,67].”
BackgroundRombouts, S., & Nöllmann, M. (2020) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaa051 · Cited by 4
Analytical Techniques for Single-Cell Studies in Microbiology
Puchkov, E. O. (2020) · Springer eBooks · doi:10.1007/978-981-10-4857-9_17-3 · Cited by 2
Active Degradation Explains the Distribution of Nuclear Proteins during Cellular Senescence
Giampieri, E., Cecco, M. D., Remondini, D., Sedivy, J. M., & Castellani, G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118442 · Cited by 2
On-Chip Living-Cell Microarrays for Network Biology
Willaert, R., & Hichem Sahli, B. (2011) · InTech eBooks · doi:10.5772/20499 · Cited by 2
Use of Microarray Technology to Improve DNA Vaccines in Fish Aquaculture - The Rhabdoviral Model
Encinas, P. A., Gómez-Casado, E., Estepa, A., & Coll, J. M. (2012) · InTech eBooks · doi:10.5772/28591 · Cited by 2
“Although correlation of gene and protein expression has been found in some plants (Gallardo et al., 2007; Joosen et al., 2007), most studies found no correlation, including a recent report on individual E. coli cells (Taniguchi et al., 2010).”
Computing mRNA and protein statistical moments for a renewal model of stochastic gene-expression
Antunes, D., & Singh, A. (2013) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2013.6761031 · Cited by 2
Enhanced 3D localization of individual RNA transcripts via astigmatic imaging
Perillo, E. P., Haro, L. D., Phipps, M. E., Martinez, J. S., Yeh, H., Dunn, A. K., Shepherd, D. P., & Werner, J. H. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2038197 · Cited by 2
Stochastic Simulations of Cellular Processes: From Single Cells to Colonies
Luthey-Schulten, Z. A. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2013.11.077 · Cited by 2
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
