Using Gene Expression Noise to Understand Gene Regulation
Open accessAbstract
Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression “noise,” has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and messenger RNA levels, and they have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive “fingerprint” with which to explore fundamental questions of gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.
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Protocols that lyse entire cells measure total mRNA, and consequently they mix the fluctuating nuclear RNA and the more stable cytoplasmic RNA and combine transcriptional and post-transcriptional effects in a single measurement.
“Second, protocols that lyse entire cells measure total mRNA, and consequently they mix the fluctuating nuclear RNA and the more stable cytoplasmic RNA177,178 and combine transcriptional and post-transcriptional effects in a single measurement.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [178]
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Cites
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 9,969 · see citing works
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 · see citing works
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593 · see citing works
Self-citationImaging individual mRNA molecules using multiple singly labeled probes
Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., & Tyagi, S. (2008) · Nature Methods · doi:10.1038/nmeth.1253 · Cited by 2,028 · see citing works
Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S. (2010) · Science · doi:10.1126/science.1188308 · Cited by 1,982 · see citing works
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 · see citing works
Super-resolution fluorescence microscopy
Huang, B., Bates, M., & Zhuang, X. (2009) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.77.061906.092014 · Cited by 1,657 · see citing works
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 · see citing works
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 · see citing works
Functional roles for noise in genetic circuits
Eldar, A., & Elowitz, M. B. (2010) · Nature · doi:10.1038/nature09326 · Cited by 1,440 · see citing works
Intrinsic noise in gene regulatory networks
Thattai, M., & van Oudenaarden, A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.151588598 · Cited by 1,428 · see citing works
Control of Stochasticity in Eukaryotic Gene Expression
Raser, J. M., & O'Shea, E. K. (2004) · Science · doi:10.1126/science.1098641 · Cited by 1,425 · see citing works
Gene Regulation at the Single-Cell Level
Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S., & Elowitz, M. B. (2005) · Science · doi:10.1126/science.1106914 · Cited by 1,104 · see citing works
Cellular Decision Making and Biological Noise: From Microbes to Mammals
Balázsi, G., van Oudenaarden, A., & Collins, J. J. (2011) · Cell · doi:10.1016/j.cell.2011.01.030 · Cited by 1,016 · see citing works
Stochasticity in Transcriptional Regulation: Origins, Consequences, and Mathematical Representations
Kepler, T. B., & Elston, T. C. (2001) · Biophysical Journal · doi:10.1016/s0006-3495(01)75949-8 · Cited by 909 · see citing works
Analytical distributions for stochastic gene expression
Shahrezaei, V., & Swain, P. S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803850105 · Cited by 829 · see citing works
Noise Propagation in Gene Networks
Pedraza, J. M., & van Oudenaarden, A. (2005) · Science · doi:10.1126/science.1109090 · Cited by 796 · see citing works
The finite state projection algorithm for the solution of the chemical master equation
Munsky, B. E., & Khammash, M. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2145882 · Cited by 760 · see citing works
Self-citationMarkovian Modeling of Gene-Product Synthesis
Peccoud, J., & Ycart, B. (1995) · Theoretical Population Biology · doi:10.1006/tpbi.1995.1027 · Cited by 739 · see citing works
Single-cell dissection of transcriptional heterogeneity in human colon tumors
Dalerba, P. D., Tomer Kalisky, C., Sahoo, D., Rajendran, P. S., Rothenberg, M. E., Leyrat, A. A., Sim, S., Okamoto, J., Johnston, D. M., Qian, D., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.2038 · Cited by 693 · see citing works
Single-RNA counting reveals alternative modes of gene expression in yeast
Zenklusen, D., Larson, D. R., & Singer, R. H. (2008) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1514 · Cited by 684 · see citing works
Central dogma at the single-molecule level in living cells
Li, G., & Xie, X. S. (2011) · Nature · doi:10.1038/nature10315 · Cited by 442 · see citing works
General properties of transcriptional time series in Escherichia coli
So, L., Ghosh, A., Zong, C. C., Sepúlveda, L. A., Segev, R., & Golding, I. (2011) · Nature Genetics · doi:10.1038/ng.821 · Cited by 384 · see citing works
Origins of regulated cell-to-cell variability
Snijder, B., & Pelkmans, L. (2011) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3044 · Cited by 346 · see citing works
Single-molecule transcript counting of stem-cell markers in the mouse intestine
Itzkovitz, S., Lyubimova, A., Blat, I. C., Maynard, M. A., van Es, J. H., Lees, J. A., Jacks, T., Clevers, H. C., & van Oudenaarden, A. (2011) · Nature Cell Biology · doi:10.1038/ncb2384 · Cited by 340 · see citing works
The role of physiological heterogeneity in microbial population behavior
Lidstrom, M. E., & Konopka, M. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.436 · Cited by 295 · see citing works
Regulatory activity revealed by dynamic correlations in gene expression noise
Dunlop, M. J., Cox, R. S., Levine, J. H., Murray, R. M., & Elowitz, M. B. (2008) · Nature Genetics · doi:10.1038/ng.281 · Cited by 219 · see citing works
Genomic Analysis at the Single-Cell Level
Tomer Kalisky, C., Blainey, P. C., & Quake, S. R. (2011) · Annual Review of Genetics · doi:10.1146/annurev-genet-102209-163607 · Cited by 203 · see citing works
Stochasticity of gene products from transcriptional pulsing
Iyer‐Biswas, S., Hayot, F., & Jayaprakash, C. (2009) · Physical Review E · doi:10.1103/physreve.79.031911 · Cited by 179 · see citing works
Cellular Noise Regulons Underlie Fluctuations in Saccharomyces cerevisiae
Stewart-Ornstein, J., Weissman, J. S., & El-Samad, H. (2012) · Molecular Cell · doi:10.1016/j.molcel.2011.11.035 · Cited by 177 · see citing works
Transcription of functionally related constitutive genes is not coordinated
Gandhi, S. J., Zenklusen, D., Lionnet, T., & Singer, R. H. (2010) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1934 · Cited by 109 · see citing works
Bumgarner, S. L., Neuert, G., Voight, B. F., Symbor-Nagrabska, A., Grisafi, P. L., van Oudenaarden, A., & Fink, G. R. (2012) · Molecular Cell · doi:10.1016/j.molcel.2011.11.029 · Cited by 108 · see citing works
Self-citationOctavio, L. M., Gedeon, K., & Maheshri, N. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000673 · Cited by 91 · see citing works
Flow cytometry smaller and better
Bonetta, L. P. (2005) · Nature Methods · doi:10.1038/nmeth1005-785 · Cited by 66 · see citing works
Transcript counting in single cells reveals dynamics of rDNA transcription
Tan, R. Z., & van Oudenaarden, A. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.14 · Cited by 49 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [17]Iyer-Biswas S Hayot F Jayaprakash C Phys Rev E 79 031911 2009 19391975
Cited by
Sorted by citations
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state
Rotem, A., Ram, O., Shoresh, N., Sperling, R. A., Goren, A., Weitz, D. A., & Bernstein, B. E. (2015) · Nature Biotechnology · doi:10.1038/nbt.3383 · Cited by 854 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
Self-citationRevealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
Bakken, T. E., Hodge, R. D., Miller, J. A., Yao, Z., Nguyen, T. N., Aevermann, B., Barkan, E., Bertagnolli, D., Casper, T., Dee, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209648 · Cited by 608 · see citing works
Design and Analysis of Single-Cell Sequencing Experiments
Grün, D., & van Oudenaarden, A. (2015) · Cell · doi:10.1016/j.cell.2015.10.039 · Cited by 536 · see citing works
Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K. D., Imai, T., & Ueda, H. R. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r31 · Cited by 455 · see citing works
Hotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267 · see citing works
scSLAM-seq reveals core features of transcription dynamics in single cells
Erhard, F., Baptista, M. A. P., Krammer, T., Hennig, T., Lange, M., Arampatzi, P., Jürges, C. S., Theis, F. J., Saliba, A.-E., & Dölken, L. (2019) · Nature · doi:10.1038/s41586-019-1369-y · Cited by 233 · see citing works
Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells
Darmanis, S., Gallant, C. J., Marinescu, V. D., Niklasson, M., Segerman, A., Flamourakis, G., Fredriksson, S., Assarsson, E., Lundberg, M., Nelander, S., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.021 · Cited by 219 · see citing works
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Quality Control of Orphaned Proteins
Juszkiewicz, S., & Hegde, R. S. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.07.001 · Cited by 165 · see citing works
Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction
Genshaft, A. S., Li, S., Gallant, C. J., Darmanis, S., Prakadan, S. M., Ziegler, C. G. K., Lundberg, M., Fredriksson, S., Hong, J., Regev, A., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-1045-6 · Cited by 175 · see citing works
Surviving apoptosis: life–death signaling in single cells
Flusberg, D. A., & Sorger, P. K. (2015) · Trends in Cell Biology · doi:10.1016/j.tcb.2015.03.003 · Cited by 149 · see citing works
Roberts, E., Stone, J. E., & Luthey‐Schulten, Z. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23130 · Cited by 117 · see citing works
Labhsetwar, P., Cole, J. A., Roberts, E., Price, N. D., & Luthey-Schulten, Z. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222569110 · Cited by 94 · see citing works
Viney, M., & Reece, S. E. (2013) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2013.1104 · Cited by 73 · see citing works
FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
DeTomaso, D., & Yosef, N. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1176-5 · Cited by 58 · see citing works
Identification of a quality-control factor that monitors failures during proteasome assembly
Zavodszky, E., Peak-Chew, S.-Y., Juszkiewicz, S., Narvaez, A. J., & Hegde, R. S. (2021) · Science · doi:10.1126/science.abc6500 · Cited by 45 · see citing works
Population FBA predicts metabolic phenotypes in yeast
Labhsetwar, P., Melo, M. C. R., Cole, J. A., & Luthey-Schulten, Z. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005728 · Cited by 21 · see citing works
Blasi, T., Buettner, F., Strasser, M. K., Marr, C., & Theis, F. J. (2017) · Physical Biology · doi:10.1088/1478-3975/aa609a · Cited by 18 · see citing works
Cell-cycle dependence of transcription dominates noise in gene expression
Zopf, C. J., Quinn, K., Zeidman, J., & Maheshri, N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003161 · Cited by 4 · see citing works
- Authors
- Munsky, B., Neuert, G., & van Oudenaarden, A.
- Affiliations
- Los Alamos National Laboratory (US)
- Massachusetts Institute of Technology (US)
- Departments of Physics and Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
- Corresponding author
- Munsky, Brian
- Neuert, Gregor
- Venue
- Science
- Published
- 2012-04-13
- Volume
- 336
- Issue
- 6078
- Pages
- 183-187
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3358231
- OpenAlex
- W1984429906
- Cited by
- 745
- References
- 35
- Citations by year
- 2026: 19 · 2025: 37 · 2024: 48 · 2023: 44 · 2022: 51 · 2021: 64 · 2020: 51 · 2019: 43 · 2018: 56 · 2017: 57 · 2016: 62 · 2015: 65 · 2014: 54 · 2013: 68 · 2012: 24
- FWCI
- 6.16
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- KNAW Research Portal (The Royal Netherlands Academy of Arts and Sciences) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Institutes of Health (DP1OD003936)
- National Cancer Institute (DP1CA174420)
- Division of Electrical, Communications and Cyber Systems
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- stochastic gene expression, gene expression regulation, phenotypic variability, cell-to-cell variability, mRNA levels, protein abundance, stochastic modeling
- MeSH terms
- Animals, Gene Expression Regulation, Humans, Models, Genetic, Phenotype, RNA, Messenger, Stochastic Processes, Transcription, Genetic, Models, Statistical, Gene Expression
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