The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Single cell analysis: the new frontier in ‘omics’
Open accessAbstract
Cellular heterogeneity arising from stochastic expression of genes, proteins and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capabilities of ‘Omics’ technologies. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third- and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in Omics, and single cell Omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity.
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Cites
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
The origins and the future of microfluidics
Whitesides, G. M. (2006) · Nature · doi:10.1038/nature05058 · Cited by 9,687
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
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
Direct multiplexed measurement of gene expression with color-coded probe pairs
Geiss, G. K., Bumgarner, R. E., Birditt, B., Dahl, T., Dowidar, N., Dunaway, D. L., Fell, H. P., Ferree, S., George, R. D., Grogan, T., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1385 · Cited by 2,066
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
Human Genome Sequencing Using Unchained Base Reads on Self-Assembling DNA Nanoarrays
Drmanac, R., Sparks, A. B., Callow, M. J., Halpern, A. L., Burns, N. L., Kermani, B. G., Carnevali, P., Nazarenko, I., Nilsen, G. B., Yeung, G., et al. (2010) · Science · doi:10.1126/science.1181498 · Cited by 1,212
Stochastic protein expression in individual cells at the single molecule level
Cai, L., Friedman, N., & Xie, X. S. (2006) · Nature · doi:10.1038/nature04599 · Cited by 1,145
Systems Biology and New Technologies Enable Predictive and Preventative Medicine
Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004) · Science · doi:10.1126/science.1104635 · Cited by 1,108
Probing Gene Expression in Live Cells, One Protein Molecule at a Time
Yu, J., Xiao, J., Ren, X., Lao, K., & Xie, X. S. (2006) · Science · doi:10.1126/science.1119623 · Cited by 918
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CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231
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Paxdb, a database of protein abundance averages across all three domains of life
Wang, M., Weiss, M., Simonovic, M., Haertinger, G., Schrimpf, S. P., Hengartner, M. O., & von Mering, C. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.014704 · Cited by 466
Bioaugmentation and its application in wastewater treatment: A review
Herrero, M., & Stuckey, D. C. (2015) · Chemosphere · doi:10.1016/j.chemosphere.2014.10.033 · Cited by 461
Huang, S. (2011) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2011.0008 · Cited by 94
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de Bekker, C., Bruning, O., Jonker, M. J., Breit, T. M., & Wösten, H. A. (2011) · Genome Biology · doi:10.1186/gb-2011-12-8-r71 · Cited by 81
A blueprint for human whole-cell modeling
Szigeti, B., Roth, Y. D., Sekar, J. A. P., Goldberg, A. P., Pochiraju, S. C., & Karr, J. R. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.10.005 · Cited by 72
Brooks, L. R. K., & Mias, G. I. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00392 · Cited by 32
Microbiome modeling: a beginner's guide
Lange, E., Kranert, L., Krüger, J., Benndorf, D., & Heyer, R. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1368377 · Cited by 21
Blasi, T., Buettner, F., Strasser, M. K., Marr, C., & Theis, F. J. (2017) · Physical Biology · doi:10.1088/1478-3975/aa609a · Cited by 18
Single-cell systems analysis: decision geometry in outliers
Abrahams, L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa1078 · Cited by 3
Metadata
- Authors
- Wang, D., & Bodovitz, S.
- Venue
- Trends in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2010-06
- Volume
- 28
- Issue
- 6
- Pages
- 281-290
- ISSN
- 0167-7799 (ISSN-L)
- OpenAlex
- W1540655326
- Cited by
- 673
- References
- 10
- FWCI
- 2.50
- 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.
