The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 5 of the 34 sources its claims cite were checked for support.
Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
Open accessAbstract
Devil in the Detail: Genetically identical cells in the same environment can show variation in gene expression that may cause phenotypic variation at the single-cell level. But how noisy are most genes? Taniguchi et al. (p. 533 ; see the Perspective by Tyagi ) now report single-cell global profiling of both messenger RNA (mRNA) and proteins in Escherichia coli using a yellow fluorescent protein fusion library. As well as a common extrinsic noise in high-abundance proteins, large fluctuations were observed in low-abundance proteins. Remarkably, in single-cell experiments, mRNA and protein levels for the same gene were uncorrelated.
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Claims the paper makes
5 of 34 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Such stochasticity is manifested in fluctuations of mRNA and protein copy numbers within a cell lineage over time, and in variations of mRNA and protein copy numbers among a population of genetically identical cells at a particular time (1, 2, 3, 4).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
- Not checked — no full text of the sourcecites [1] source
- Partly supportedcites [2] sourceExperimental — accuracy not yet measured
The Abstract says stochastic gene expression can produce variation among identical cells, and its model predicts cell-to-cell variability in mRNA levels for both eukaryotic and prokaryotic promoters. However, the paper's single-cell protein-reporter measurements were made in yeast, not bacteria; it does not establish that both protein and mRNA copy numbers differ among individual bacterial cells. (LLM, conf=0.83)
- Not checked — no full text of the sourcecites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Single molecule sensitivity was confirmed by abrupt photobleaching of membrane-bound YFPs expressed at low level (Fig. S14) (8, 18, 23).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
What the sources say
- Not checked — no full text of the sourcecites [8] source
- Not checked — no full text of the sourcecites [18] source
- Could not verifycites [23] sourceExperimental — accuracy not yet measured
The Results and discussion passage on deletion-mutant verification describes PCR checks, while Materials and methods > Growth tests describes absorbance measurements; neither addresses single-molecule imaging. Searches for photobleaching, membrane-bound YFP, and fluorescence imaging found no passage confirming the claimed sensitivity test. Page numbers were not available in our copy. (LLM, conf=0.78)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“We found that E. coli proteins generally had larger noise and Fano factors than yeast proteins, even for those present at similar copy numbers (Fig. S6) (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Comparison between E. coli and yeast
What the sources say
- Not checked — no full text of the sourcecites [18] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, we found that in a single cell mRNA and protein levels for the same gene are completely uncorrelated.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The distributions for 1,009 out of the 1,018 strains can be well fit by the gamma distribution, Eq. 2 (Fig. S20) (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Analysis of protein distributions
What the sources say
- Not checked — no full text of the sourcecites [18] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Below ten molecules per cell, ηp2 is inversely proportional to protein abundance, indicative of intrinsic noise.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“mRNA and protein copy numbers in a single cell are not correlated (r = 0.01 ± 0.03, SEM, N = 5,447).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The protein noise (ηp2 ≡ σp2/μp2) exhibits two distinct scaling properties (Fig. 2B).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The average protein abundances of essential genes are higher than those for all genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Average protein abundances span five orders of magnitude, ranging from 10-1 to 104 molecules per cell (Fig 2A).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 13
- Probably not a substantial claim. 3
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“In recent years, single-molecule fluorescence microscopy has been used to count mRNA (12-16) or protein (8, 17) molecules in individual cells, especially in bacteria. However, these methods have only been applied to limited number of specific genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
- Not checked — no full text of the sourcecites [12] source
- Supportedcites [13] source
The Results section (page not available) describes fluorescence microscopy used to count individual mRNA molecules from a specific reporter gene in mammalian cells. This paper supports the mRNA-counting citation as an example; the sentence’s broader reference to bacteria does not describe this study. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [14] source
- Supportedcites [15] source
The passage labeled Abstract describes using fluorescence in situ hybridization and microscopy to count individual comK mRNA molecules in single Bacillus subtilis cells. It also describes counting an engineered comK transcript, making this paper an example of the gene-specific, single-cell mRNA measurements for which it is cited. (LLM, conf=0.91)
- Supportedcites [16] source
The Abstract reports counting individual mRNAs in single yeast cells for different genes. The Discussion describes extending the method to analyze multiple genes simultaneously as future work. This paper supports the mRNA-counting example for which it is cited; the sentence assigns protein counting to other citations. (LLM, conf=0.91)
- Not checked — no full text of the sourcecites [8] source
- Not checked — no full text of the sourcecites [17] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, at higher expression levels (>10 molecules per cell), the noise reaches a plateau of ∼0.1 and does not decrease further, suggesting that each protein has at least 30% variation in its expression level.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“We found that the noise floor does not result from cell size effects, nor did it arise from measurement noise (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
What the sources say
- Not checked — no full text of the sourcecites [18] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We discovered large fluctuations in low abundance proteins as well as a common extrinsic noise in high abundance proteins.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We further demonstrate that the extrinsic noise is global to all high expression genes by analyzing the correlations between expression levels of 13 pairs of randomly selected genes.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The prevalence of proteins with very low copy number suggests that single-molecule experiments are necessary for bacteriology.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This striking result highlights the disconnect between proteome and transcriptome analyses of a single cell, as well as the need for single cell proteome analysis.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“A fluorescent protein reporter library of Saccharomyces cerevisiae (9) has proven to be extremely useful in protein profiling (10, 11).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, our result offers a cautionary note in interpreting single-cell transcriptome analysis and argues for the necessity for single cell proteome analysis.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“We attribute the additional noise to extrinsic noise (3), that is the slow variation of the values of a and b, which we confirm with real time observation of protein levels for four randomly selected high copy library strains.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Global scaling of intrinsic and extrinsic protein noise
What the sources say
- Not checked — no full text of the sourcecites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that some proteins and most mRNAs of functional genes are present at low copy numbers in a bacterial cell, the single molecule sensitivity afforded by our measurements is necessary for the understanding of stochastic gene expression and regulation.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In E. coli, mRNA is typically degraded within minutes (Table S6) (18), whereas most proteins, including fluorescent proteins, have a lifetime longer than the cell cycle (34).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Simultaneous RNA and protein measurements in single cells
What the sources say
- Not checked — no full text of the sourcecites [18] source
- Not matchedcites [34]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 1,400 strains attempted, 1,018 strains were confirmed by sequencing and showed no significant growth defects.”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule imaging of a YFP reporter library
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Because the same probe is used for all strains, the optimized hybridization efficiency is unbiased for every measured gene (18).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Single-molecule RNA counting
What the sources say
- Not checked — no full text of the sourcecites [18] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“While single cell mRNA profiling has been carried out with cDNA microarray (6) and mRNA-seq (7), these studies did not have single molecule sensitivity and are not suitable for bacteria, which express mRNA at low copy numbers (8).”
§ Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells** > Abstract
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
Gene expression dynamics follow a kinetic scheme with transcription, translation, mRNA degradation, and protein degradation rates.
“We consider the kinetic scheme”
The authors state this as an assumption.
The transcription and translation rates a and b have static or slowly varying heterogeneous distributions f(a) and g(b) across cells.
“Assuming that there exist static or slowly varying heterogeneities of a and b, with distributions f (a) and g(b), respectively, the protein distribution is”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Stochastic simulations are necessary when reactions involving few particles such as those in genetic information processing and the charging of the tRNAs can lead to large variations in the state of the cell (Taniguchi et al., 2010).”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Taniguchi et al., 2010
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Although transcriptomic profiling has provided insights into Tex cell biology, mRNA abundance is not always a faithful proxy of protein expression across various organisms18–22.”
From Proteotoxic stress response drives T cell exhaustion and immune evasion (2025) · cites this paper as [22]
In the citing paper
“This finding contrasts with data from bacteria where productive protein expression is achieved with such low mRNA concentrations (Taniguchi et al., 2010).”
From Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells (2012) · cites this paper as Taniguchi et al., 2010
In the citing paper
“These methods hold promise for elucidating the epigenetic controls of cellular identity186,187.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [187]
Citations
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–500 of 2,008
Transcriptional bursting is intrinsically caused by interplay between RNA polymerases on DNA
Fujita, K., Iwaki, M., & Yanagida, T. (2016) · Nature Communications · doi:10.1038/ncomms13788 · Cited by 56
Individuality and slow dynamics in bacterial growth homeostasis
Susman, L., Kohram, M., Vashistha, H., Nechleba, J. T., Salman, H., & Brenner, N. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615526115 · Cited by 60
“Most proteins in bacteria are highly expressed, with a relatively small effect of number fluctuations (30) and with degradation negligible over the timescale of a cell-cycle (31, 32).”
Beyond Capture: Circulating Tumor Cell Release and Single‐Cell Analysis
Wu, L., Xu, X., Sharma, B., Wang, W., Qu, X., Zhu, L., Zhang, H., Song, Y., & Yang, C. J. (2019) · Small Methods · doi:10.1002/smtd.201800544 · Cited by 63
Connecting growth with gene expression: of noise and numbers
Shahrezaei, V., & Marguerat, S. (2015) · Current Opinion in Microbiology · doi:10.1016/j.mib.2015.05.012 · Cited by 61
“Global feedback and noise in gene expression Noise in gene expression scales with the inverse square root of protein copy numbers in bacteria and yeast [30,31].”
BackgroundMicrochip-based single-cell functional proteomics for biomedical applications
Lu, Y., Liu, Y., Wei, W., & Shi, Q. (2017) · Lab on a Chip · doi:10.1039/c7lc00037e · Cited by 61
Making sense of snapshot data: ergodic principle for clonal cell populations
Thomas, P. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0467 · Cited by 60
Delogu, F., Kunath, B. J., Evans, P. N., Arntzen, M. Ø., Hvidsten, T. R., & Pope, P. B. (2020) · Nature Communications · doi:10.1038/s41467-020-18543-0 · Cited by 62
Warne, D., Baker, R., & Simpson, M. (2019) · QUT ePrints (Queensland University of Technology) · Cited by 59
Hsieh, K., Mach, K. E., Zhang, P., et al. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00462 · Cited by 60
Contribution of RNA polymerase concentration variation to protein expression noise
Yang, S., Kim, S.-H., Lim, Y. R., Kim, C., An, H. J., Kim, J., Sung, J., & Lee, N. K. (2014) · Nature Communications · doi:10.1038/ncomms5761 · Cited by 60
Quantitative influence of macromolecular crowding on gene regulation kinetics
Tabaka, M., Kalwarczyk, T., & Hołyst, R. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt907 · Cited by 59
“Abundances of DNA binding proteins [NAPs (35,36), TF (37) and RNA polymerase (RNAP) (38)] for early exponential phase of E.”
BackgroundDirect Cell Lysis for Single-Cell Gene Expression Profiling
Švec, D., Andersson, D., Pekny, M., Sjöback, R., Kubista, M., & Ståhlberg, A. (2013) · Frontiers in Oncology · doi:10.3389/fonc.2013.00274 · Cited by 60
“Another issue in RNA purification protocols is degradation by ribonucleases (RNases) (48, 49).”
BackgroundDirect Correlation between Motile Behavior and Protein Abundance in Single Cells
Dufour, Y. S., Gillet, S., Frankel, N. W., Weibel, D. B., & Emonet, T. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005041 · Cited by 58
“Such fluctuations are expected because the molecular components of signaling pathways are typically present in small numbers [1].”
Toward a Genome-Wide Landscape of Translational ControlPreprint
Larsson, O. S., Tian, B., & Sonenberg, N. (2012) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a012302 · Cited by 67
“Moreover, in a recent single-cell study in Escherichia coli, mean mRNA copies and protein copies showed r2p of 0.29 and 0.59 using deep sequencing of RNA (RNA-seq) and fluorescence in situ hybridization (FISH), respectively (Taniguchi et al. 2010).”
MethodInfluentialRibosome-associated ncRNAs: An emerging class of translation regulators
Pircher, A., Gebetsberger, J., & Polacek, N. (2014) · RNA Biology · doi:10.1080/15476286.2014.996459 · Cited by 59
“The same is also true for prokaryal organisms, where no correlation between mRNA and protein copy numbers could be found using a single cell approach in Escherichia coli.(4) Regarding the observation that the transcriptome does not entirely correlate with the proteome(1,5) the term ‘ribonome’ was proposed.”
BackgroundNew aspects of RNA processing in prokaryotes
Evguenieva‐Hackenberg, E., & Klug, G. (2011) · Current Opinion in Microbiology · doi:10.1016/j.mib.2011.07.025 · Cited by 65
Yang, L., Zhou, Y., Zhu, S., Huang, T., Wu, L., & Yan, X. (2012) · Analytical Chemistry · doi:10.1021/ac2031332 · Cited by 60
Determining absolute protein numbers by quantitative fluorescence microscopy
Verdaasdonk, J. S., Lawrimore, J., & Bloom, K. (2014) · Methods in cell biology · doi:10.1016/b978-0-12-420138-5.00019-7 · Cited by 62
Silva, V. O., Soares, L. O., Júnior, A. S., et al. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.01953-14 · Cited by 59
“One major factor might be a lack of correlation between mRNA levels and functional protein for the same gene (44).”
BackgroundMechanisms of biotin-regulated gene expression in microbes
Satiaputra, J., Shearwin, K. E., Booker, G. W., & Polyak, S. W. (2016) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2016.01.005 · Cited by 59
Strength in numbers: quantitative single‐molecule RNA detection assays
Gáspár, I., & Ephrussi, A. (2015) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.170 · Cited by 58
An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data
Berestovsky, N., & Nakhleh, L. K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066031 · Cited by 58
“To account for stochasticity in gene regulatory systems [6,7], probabilistic Boolean networks (PBNs) were introduced [8,9].”
MethodZhang, H., & Guo, P. (2014) · Methods · doi:10.1016/j.ymeth.2014.01.010 · Cited by 58
Theory in Biology: Figure 1 or Figure 7?
Phillips, R. (2015) · Trends in Cell Biology · doi:10.1016/j.tcb.2015.10.007 · Cited by 57
“Similarly, the ongoing debate over when the statistics of mRNA distributions are characterized by the Poisson distribution is helping to clarify the mechanistic underpinnings of the processes of the central dogma as they unfold inside a cell [11–14].”
Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality
Wang, M., Zhang, J., Xu, H., & Golding, I. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0553-z · Cited by 55
Yan, Y., Maurer‐Alcalá, X. X., Knight, R. D., Pond, S. L. K., & Katz, L. A. (2019) · mBio · doi:10.1128/mbio.02524-19 · Cited by 56
Small protein number effects in stochastic models of autoregulated bursty gene expression
Jia, C., & Grima, R. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5144578 · Cited by 55
“Taking into account the latter is important because the copy number of DNA-binding proteins can be in the single digits [26].”
BackgroundKell, D. B., Potgieter, M., & Pretorius, E. (2015) · F1000Research · doi:10.12688/f1000research.6709.2 · Cited by 59
Single-cell transcriptomics of small microbial eukaryotes: limitations and potential
Liu, Z., Hu, S. K., Campbell, V., Tatters, A. O., Heidelberg, K. B., & Caron, D. A. (2017) · The ISME Journal · doi:10.1038/ismej.2016.190 · Cited by 59
“On the other hand, it is considered not suitable for bacteria (Taniguchi et al., 2010), which have only 200–2000 mRNA molecules per cell (Moran et al., 2013).”
BackgroundSpeed, Sensitivity, and Bistability in Auto-activating Signaling Circuits
Hermsen, R., Erickson, D. W., & Terence Hwa, B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002265 · Cited by 55
“Lastly, we assume that each mRNA transcribed from the promoter is instantly translated b times (the ‘‘burst size’’) [11].”
BackgroundKong, B. C., Lassiter, K., Piekarski, A., Dridi, S., Reverter, A., Hudson, N. J., & Bottje, W. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155679 · Cited by 61
Gómez-Schiavon, M., Chen, L., West, A. E., & Buchler, N. E. G. (2017) · Genome biology · doi:10.1186/s13059-017-1297-9 · Cited by 57
“This phenomenon has been observed in bacteria [5, 6], yeast [7, 8], flies [9, 10], and mammals [11–16].”
BackgroundNature’s recyclers: anaerobic microbial communities drive crude biomass deconstruction
Lillington, S. P., Leggieri, P. A., Heom, K. A., & O’Malley, M. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.08.015 · Cited by 61
The Ongoing Quest to Crack the Genetic Code for Protein Production
Nieuwkoop, T., Finger‐Bou, M., John van der, O. O. S. T., & Claassens, N. J. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.09.014 · Cited by 51
Lin, Y. T., & Doering, C. R. (2016) · Physical review. E · doi:10.1103/physreve.93.022409 · Cited by 50
Emara, S., Amer, S., Ali, A., Abouleila, Y., Oga, A., & Masujima, T. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-47656-8_13 · Cited by 58
Viñuelas, J., Kaneko, G., Coulon, A., Vallin, E., Morin, V., Mejia-Pous, C., Kupiec, J.-J., Beslon, G., & Gandrillon, O. (2013) · BMC Biology · doi:10.1186/1741-7007-11-15 · Cited by 60
Stochastic expression dynamics of a transcription factor revealed by single-molecule noise analysis
Hensel, Z., Feng, H., Han, B., Hatem, C. L., Wang, J., & Xiao, J. (2012) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2336 · Cited by 54
The complex chemical Langevin equation
Schnoerr, D., Sanguinetti, G., & Grima, R. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4885345 · Cited by 57
Single Cell Transcriptome Amplification with MALBAC
Chapman, A. R., He, Z., Lü, S., Yong, J., Tan, L., Tang, F., & Xie, X. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0120889 · Cited by 55
“In a single cell, gene expression is intrinsically stochastic and cannot be synchronized among cells, which leads to cell-to-cell variations in mRNA expression levels[2,4,12].”
BackgroundSelf-citationStopka, S. A., Khattar, R., Agtuca, B. J., Anderton, C., Paša‐Tolić, L., Stacey, G. S., & Vértes, Á. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01646 · Cited by 57
“The intrinsic noise is associated with the transcription and translation processes, and it becomes dominant for low copy number proteins (below 10 copies/cell) as it follows a trend close to the inverse of the copy number (Taniguchi et al., 2010).”
BackgroundMethodInfluentialSingle-Cell RNA Sequencing to Understand Host–Pathogen Interactions
Peñaranda, C., & Hung, D. T. (2019) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.8b00369 · Cited by 58
Stochastic modelling reveals mechanisms of metabolic heterogeneity
Tonn, M. K., Thomas, P., Barahona, M., & Oyarzún, D. A. (2019) · Communications Biology · doi:10.1038/s42003-019-0347-0 · Cited by 58
“Other models may be used, either by using closed-form expressions for P ( n etot ), or by inferring the enzyme distribution directly from single-cell protein expression data such as fl ow cytometry or single-cell microscopy 1,17 .”
InfluentialDadiani, M., van Dijk, D., Segal, B., Field, Y., Ben-Artzi, G., Raveh-Sadka, T., Levo, M., Kaplow, I. M., Weinberger, A., & Segal, E. (2013) · Genome Research · doi:10.1101/gr.149096.112 · Cited by 55
“Under certain assumptions (Friedman et al. 2006; Taniguchi et al. 2010), these two parameters correspond to the burst frequency (left graph) and burst size (right graph).”
BackgroundResultKaushik, A. M., Hsieh, K., Mach, K. E., Lewis, S., Puleo, C., Carroll, K. C., Liao, J. C., & Wang, T. (2021) · Advanced Science · doi:10.1002/advs.202003419 · Cited by 55
Small RNA-mediated regulation of host–pathogen interactions
Harris, J. F., Micheva‐Viteva, S., Li, N., & Hong‐Geller, E. (2013) · Virulence · doi:10.4161/viru.26119 · Cited by 55
“coli proteins are present at one copy number or less, supporting a model in which low copy numbers of biomolecules are sufficient to maintain cellular functions.(24) Other sRNAs have been previously identified in pathogenic Yersinia based on sequence homology with known E.”
BackgroundIntrinsically disordered proteins and conformational noise
Mahmoudabadi, G., Rajagopalan, K., Getzenberg, R. H., Hannenhalli, S. S., Govindan Rangarajan, C., & Kulkarni, P. (2012) · Cell Cycle · doi:10.4161/cc.23178 · Cited by 54
“Recent evidence indicates that the information transduced in cellular signaling pathways is significantly affected by noise.(6,7) In fact, it has been proposed that noise in these pathways maybe generated by the interconnected and promiscuous nature of protein interactions that are necessary to transduce signals.”
BackgroundDesponds, J., Tran, H., Ferraro, T., Lucas, T., Perez-Romero, C. A., Guillou, A., Fradin, C., Coppey, M., Dostatni, N., & Walczak, A. M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005256 · Cited by 52
Ultra-sensitive digital quantification of proteins and mRNA in single cells
Lin, J., Jordi, C., Son, M., Phan, H. V., Drayman, N., Abasiyanik, M. F., Vistain, L. F., Tu, H., & Tay, S. (2019) · Nature Communications · doi:10.1038/s41467-019-11531-z · Cited by 54
Cellular organization of the transfer of genetic information
Campos, M., & Jacobs, C. (2013) · Current Opinion in Microbiology · doi:10.1016/j.mib.2013.01.007 · Cited by 55
“Third, the lifetime of mRNAs is typically long in comparison to the time of their synthesis [37,54,55].”
BackgroundDynamics of the translational machinery
Petrov, A. N., Kornberg, G., O’Leary, S. E., Tsai, A., Uemura, S., & Puglisi, J. D. (2011) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2010.11.007 · Cited by 54
“On the other hand, the correlation of the protein production in random gene pairs within a single cell demonstrated that relative protein abundance depends on global epigenetic factors such as global transcriptional and translational regulation [44].”
BackgroundSpatial proteomics for understanding the tissue microenvironment
Mao, Y., Wang, X., Huang, P., & Tian, R. (2021) · The Analyst · doi:10.1039/d1an00472g · Cited by 55
Spo0A∼P Imposes a Temporal Gate for the Bimodal Expression of Competence in Bacillus subtilis
Mirouze, N., Desai, Y. B., Raj, A., & Dubnau, D. A. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002586 · Cited by 54
“A relative unimportance of intrinsic noise would be expected if Spo0A were abundant in these cells [37] and in fact it has been estimated that there are about 2000 molecules of this protein per cell during growth [38].”
Heterogeneity of intracellular replication of bacterial pathogens
Hélaine, S., & Holden, D. W. (2013) · Current Opinion in Microbiology · doi:10.1016/j.mib.2012.12.004 · Cited by 54
“coli cells revealed that half of the total pool of proteins is present at less than 10 copies per cell [48].”
BackgroundDana, A., & Tuller, T. (2014) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.015099 · Cited by 55
ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics
Hasenauer, J., Hasenauer, C., Hucho, T. B., & Theis, F. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003686 · Cited by 56
Compartmentalization and spatiotemporal organization of macromolecules in bacteria
Govindarajan, S., Nevo‐Dinur, K., & Amster‐Choder, O. (2012) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2012.00348.x · Cited by 54
“…technologies and the development of better fluorescent reagents, both methods have been recently applied to detect RNA in bacterial cells, despite the inherent limitations, that is, the small cell size and the low copy number of mRNAs per cell (five copies or fewer) (Taniguchi et al., 2010).”
Lenn, T., & Leake, M. C. (2012) · Open Biology · doi:10.1098/rsob.120090 · Cited by 47
Park, A. J., Murphy, K., Surette, M. D., Bandoro, C., Krieger, J. R., Taylor, P. P., & Khursigara, C. M. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00262 · Cited by 57
Advances and challenges in single-cell RNA-seq of microbial communities
Imdahl, F., & Saliba, A. (2020) · Current Opinion in Microbiology · doi:10.1016/j.mib.2020.10.001 · Cited by 54
Approximating solutions of the Chemical Master equation using neural networks
Sukys, A., Öcal, K., & Grima, R. (2022) · iScience · doi:10.1016/j.isci.2022.105010 · Cited by 52
Workability of mRNA Sequencing for Predicting Protein Abundance
Ponomarenko, E. A., Krasnov, G. S., Kiseleva, O. I., Kryukova, P. A., Arzumanian, V. A., Dolgalev, G. V., Ilgisonis, E. V., Lisitsa, A., & Poverennaya, E. V. (2023) · Genes · doi:10.3390/genes14112065 · Cited by 59
Lin, X., Ding, H., & Zeng, Q. (2015) · Environmental Microbiology · doi:10.1111/1462-2920.13104 · Cited by 53
“05 to 5 transcripts per cell (Taniguchi et al., 2010).”
Generation and filtering of gene expression noise by the bacterial cell cycle
Walker, N., Nghe, P., & Tans, S. J. (2016) · BMC Biology · doi:10.1186/s12915-016-0231-z · Cited by 53
“Background Single-cell experiments have shown gene expression to fluctuate randomly under constant conditions [1–7], which can have key consequences for the fidelity of signal propagation [8], cell fate decisions [9, 10], and fitness [4, 11–16].”
BackgroundGene transcription in bursting: a unified mode for realizing accuracy and stochasticity
Wang, Y., Ni, T., Wang, W., & Liu, F. (2018) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12452 · Cited by 51
“Nevertheless, it was confirmed that mRNA production is not a Poisson process but can be well fitted by the two-state model (Golding et al., 2005; Taniguchi et al., 2010; So et al., 2011; Sanchez & Golding, 2013; Skinner et al., 2013; Chong et al., 2014).”
BackgroundMethodSamarah, L. Z., Khattar, R., Tran, T. H., Stopka, S. A., Brantner, C. A., Parlanti, P., Veličković, D., Shaw, J., Agtuca, B. J., Stacey, G. S., et al. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c00936 · Cited by 58
Haas, B. L., Matson, J. S., DiRita, V. J., & Biteen, J. S. (2014) · Molecules · doi:10.3390/molecules190812116 · Cited by 52
“In some cases, the protein of interest has a low native expression level [114].”
Thomas, P., Grima, R., & Straube, A. V. (2012) · Physical Review E · doi:10.1103/physreve.86.041110 · Cited by 50
Expression patterns of elemental cycling genes in the Amazon River Plume
Satinsky, B. M., Smith, C. B., Sharma, S., et al. (2017) · The ISME Journal · doi:10.1038/ismej.2017.46 · Cited by 52
“Even for model bacteria growing exponentially, cells contain fewer transcripts than genes (~0.4 transcripts gene copy−1 for exponential phase Escherichia coli and ~0.5 transcripts gene copy− 1 for Vibrio alginolyticus; Kramer and Singleton, 1992; Taniguchi et al., 2010) (Table 2).”
BackgroundInfluentialNoise in the Vertebrate Segmentation Clock Is Boosted by Time Delays but Tamed by Notch Signaling
Keskin, S., Devakanmalai, G. S., Kwon, S. B., Vu, H., Hong, Q., Lee, Y. Y., Soltani, M., Singh, A., Ay, A. A., & Özbudak, E. M. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.04.069 · Cited by 46
“The total clock gene expression noise (Figure 2G) is as high as the levels observed in single-cell systems (Golding et al., 2005; Raj et al., 2006; Taniguchi et al., 2010) but much higher than those of developmental genes in Drosophila (Boettiger and Levine, 2013; Little et al., 2013), likely due…”
Jia, C., & Grima, R. (2021) · Physical Review X · doi:10.1103/physrevx.11.021032 · Cited by 51
Fulcher, J. M., Markillie, L. M., Mitchell, H. D., Williams, S., Engbrecht, K. M., Degnan, D. J., Bramer, L., Moore, R., Chrisler, W., Cantlon-Bruce, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54099-z · Cited by 76
“All of the figures generated and associated code are included in R markdown files at the nanoSPLITS GitHub repository (https://github.com/Cajun-data/nanoSPLITS).”
A Single-Cell Perspective on Non-Growing but Metabolically Active (NGMA) Bacteria
Manina, G., & McKinney, J. D. (2013) · Current topics in microbiology and immunology · doi:10.1007/82_2013_333 · Cited by 51
A novel method for cross-species gene expression analysis
Kristiansson, E., Österlund, T., Gunnarsson, L. M., Arne, G., Larsson, D. G. J., & Nerman, O. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-70 · Cited by 48
“The abundance of mRNA is stochastic by nature, both on a cellular and multicellular level [3,4], and there are often large variability between gene expression patterns from different organisms [5].”
BackgroundJia, C. (2017) · Physical review. E · doi:10.1103/physreve.96.032402 · Cited by 46
“4 (see Supplementary Table S6 in [45] for details).”
BackgroundBeyond the bulk: disclosing the life of single microbial cells
Rosenthal, K., Oehling, V., Dusny, C., & Schmid, A. (2017) · FEMS Microbiology Reviews · doi:10.1093/femsre/fux044 · Cited by 51
“coli cells, although transcriptome and proteome are dependent on the same gene expression mechanisms (Taniguchi et al. 2010).”
BackgroundThe emergence of metabolic heterogeneity and diverse growth responses in isogenic bacterial cells
Şimşek, E., & Kim, M. (2018) · The ISME Journal · doi:10.1038/s41396-017-0036-2 · Cited by 50
Transimulation - Protein Biosynthesis Web Service
Siwiak, M., & Zielenkiewicz, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073943 · Cited by 50
“coli expresses ,600 genes at a time from the pool of ,4000 [15] and transcript turnover is very rapid [16,17].”
BackgroundAbundance and distribution of RNA polymerase II in Arabidopsis interphase nuclei
Schubert, V., & Weißhart, K. (2015) · Journal of Experimental Botany · doi:10.1093/jxb/erv091 · Cited by 50
Deneke, C., Lipowsky, R., & Valleriani, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055442 · Cited by 50
Dynamics of simple gene-network motifs subject to extrinsic fluctuations
Roberts, E., Be’er, S., Bohrer, C. H., SHARMA, R. R., & Assaf, M. (2015) · Physical Review E · doi:10.1103/physreve.92.062717 · Cited by 50
Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback
Jia, C., Wang, L. Y., Yin, G. G., & Zhang, M. Q. (2019) · Physical review. E · doi:10.1103/physreve.100.052406 · Cited by 47
“In some previous papers, the parameter K is chosen to be the average cell volume [53, 67].”
Phenotypic heterogeneity in mycobacterial stringent response
Ghosh, S., Sureka, K., Ghosh, B., Bose, I., Basu, J., & Kundu, M. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-18 · Cited by 53
“While experimental support for gamma distribution has been obtained earlier [44], a recent exhaustive study [45] of the E.”
MethodPruś, G., Satpathy, S., Weinert, B. T., Narita, T., & Choudhary, C. (2024) · Cell · doi:10.1016/j.cell.2024.03.024 · Cited by 55
“9 However, we know little about the site-specific abundance and dynamics of PTMs.”
Direct comparison of small RNA and transcription factor signaling
Hussein, R., & Lim, H. N. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks439 · Cited by 53
“sRNA concentrations of 1500–3000 molecules/cell have been reported (37,38), whereas TF mRNAs are typically less than two molecules/cell (39).”
Multiscale stochastic modelling of gene expression
Bokes, P., King, J. R., Wood, A. T. A., & Loose, M. (2011) · Journal of Mathematical Biology · doi:10.1007/s00285-011-0468-7 · Cited by 45
Park, A. J., Surette, M. D., & Khursigara, C. M. (2014) · Frontiers in Microbiology · doi:10.3389/fmicb.2014.00464 · Cited by 52
“…(Schwanhäusser et al., 2011); (ii) the correlation between mRNA and protein abundance within a single sample type is often low (Nie et al., 2006; Taniguchi et al., 2010; Maier et al., 2011; Walley et al., 2013); and (iii) proteins are more stable and have a greater dynamic range than mRNA…”
BackgroundAn ancient Chinese wisdom for metabolic engineering: Yin-Yang
Wu, S. G., He, L., Wang, Q., & Tang, Y. (2015) · Microbial Cell Factories · doi:10.1186/s12934-015-0219-3 · Cited by 52
“coli cell may only have dozens of molecule copies and places minimal biosynthesis burden on cell metabolism) [20].”
BackgroundKoutsoumanis, K. P., & Aspridou, Z. (2016) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2016.06.021 · Cited by 54
The Evolution of Gene-Specific Transcriptional Noise Is Driven by Selection at the Pathway Level
Barroso, G. V., Puzović, N., & Dutheil, J. Y. (2017) · Genetics · doi:10.1534/genetics.117.300467 · Cited by 46
“Noise is inversely related to mean expression (Figure 2A), in agreement with previous observations at the protein level (Bar-Even et al. 2006; Taniguchi et al. 2011).”
BackgroundResultCell fate potentials and switching kinetics uncovered in a classic bistable genetic switch
Fang, X., Liu, Q., Bohrer, C. H., Hensel, Z., Han, W., Wang, J., & Xiao, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05071-1 · Cited by 47
Probing Cell-Free Gene Expression Noise in Femtoliter Volumes
Karig, D., Jung, S., Srijanto, B., Collier, C. P., & Simpson, M. L. (2013) · ACS Synthetic Biology · doi:10.1021/sb400028c · Cited by 46
Rydenfelt, M., Cox, R. S., García, H. G., & Phillips, R. (2014) · Physical Review E · doi:10.1103/physreve.89.012702 · Cited by 43
A single-cell drug efflux assay in bacteria by using a directly accessible femtoliter droplet array
Iino, R., Hayama, K., Amezawa, H., Sakakihara, S., Kim, S. H., Matsumono, Y., Nishino, K., Yamaguchi, A., & Noji, H. (2012) · Lab on a Chip · doi:10.1039/c2lc40394c · Cited by 48
Loutherback, K., Birarda, G., Chen, L., & Holman, H. N. (2016) · Protein and Peptide Letters · doi:10.2174/0929866523666160106154035 · Cited by 48
BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization
de Boer, C. G., & Regev, A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2255-6 · Cited by 47
Viable cell culture in PDMS-based microfluidic devices
Tanyeri, M., & Tay, S. (2018) · Methods in cell biology · doi:10.1016/bs.mcb.2018.09.007 · Cited by 54
“Detection, imaging, and tracking at single molecule and single cell level have been realized using PDMS-based microfluidic devices (Taniguchi et al., 2010; Tay et al., 2010; Taylor et al., 2009).”
MethodByeon, H., Go, T., & Lee, S. J. (2018) · Optics & Laser Technology · doi:10.1016/j.optlastec.2018.12.014 · Cited by 48
Micro/nanofluidics-enabled single-cell biochemical analysis
Lin, L., Chen, Q., & Sun, J. (2017) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2017.11.017 · Cited by 47
Single-cell multimodal imaging uncovers energy conversion pathways in biohybrids
Fu, B., Mao, X., Park, Y., Zhao, Z., Yan, T., Jung, W. J., Francis, D. H., Li, W., Pian, B., Salimijazi, F., et al. (2023) · Nature Chemistry · doi:10.1038/s41557-023-01285-z · Cited by 47
Metadata
- Authors
- Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S.
- Affiliations
- Harvard University (US)
- University of Toronto (CA)
- Corresponding author
- Taniguchi, Yuichi
- Choi, Paul J.
- Li, Gene-Wei
- Chen, Huiyi
- Xie, X. Sunney
- Venue
- Science
- Published
- 2010-07-30
- Volume
- 329
- Issue
- 5991
- Pages
- 533-538
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC2922915
- OpenAlex
- W2053460598
- Cited by
- 1,982
- References
- 33
- Citations by year
- 2026: 60 · 2025: 77 · 2024: 85 · 2023: 91 · 2022: 102 · 2021: 110 · 2020: 122 · 2019: 127 · 2018: 153 · 2017: 117 · 2016: 158 · 2015: 157 · 2014: 156 · 2013: 171 · 2012: 161
- FWCI
- 60.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP1OD000277)
- Canadian Institutes of Health Research (77639_1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- Escherichia coli, single-cell proteomics, single-cell transcriptomics, gamma distribution, transcription rate, extrinsic noise, intrinsic noise, mRNA-protein correlation, gene expression variation
- MeSH terms
- Escherichia coli, Luminescent Proteins, Microscopy, Fluorescence, RNA, Bacterial, RNA, Messenger, Saccharomyces cerevisiae, Transcription, Genetic, Protein Biosynthesis, Gene Library, Gene Expression, In Situ Hybridization, Fluorescence, Proteome, Gene Expression Profiling, RNA Stability, Escherichia coli Proteins, Microfluidic Analytical Techniques
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