The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A single molecule view of gene expression
Open accessAbstract
Analyzing the expression of single genes in single cells appears minimalistic in comparison to gene expression studies based on more global approaches. However, stimulated by advances in imaging technologies, single cell studies have become an essential tool in understanding the rules that govern gene expression. This quantitative view of single cell gene expression is based on counting mRNAs in single cells, monitoring transcription in real time, and visualizing single proteins. Parallel advances in mathematical models based on stochastic, discrete descriptions of biochemical processes have provided critical insight into the underlying cellular mechanisms that control expression. The view that has emerged is rooted in a probabilistic understanding of cellular processes and quantitatively explains both the mean and the variation observed in gene expression patterns among single cells. Thus, the close coupling between imaging and mathematical theory has established single cell analysis as an essential branch of systems biology.
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Cited by
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
Mammalian genes are transcribed with widely different bursting kinetics
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Eukaryotic transcriptional dynamics: from single molecules to cell populations
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Metadata
- Authors
- Larson, D. R., Singer, R. H., & Zenklusen, D.
- Venue
- Trends in Cell Biology
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 19
- Issue
- 11
- Pages
- 630-637
- ISSN
- 0962-8924 (ISSN-L)
- OpenAlex
- W1976535231
- Cited by
- 178
- References
- 17
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
