The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
High Throughput Gene Expression Measurement with Real Time PCR in a Microfluidic Dynamic Array
Open accessAbstract
We describe a high throughput gene expression platform based on microfluidic dynamic arrays. This system allows 2,304 simultaneous real time PCR gene expression measurements in a single chip, while requiring less pipetting than is required to set up a 96 well plate. We show that one can measure the expression of 45 different genes in 18 tissues with replicates in a single chip. The data have excellent concordance with conventional real time PCR and the microfluidic dynamic arrays show better reproducibility than commercial DNA microarrays.
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Cites
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., & Speleman, F. (2002) · Genome Biology · doi:10.1186/gb-2002-3-7-research0034 · Cited by 20,038
Calcium's Role in Mechanotransduction during Muscle Development
Benavides Damm, T., & Egli, M. (2014) · Cellular Physiology and Biochemistry · doi:10.1159/000356667 · Cited by 14,502
Andersen, C. L., Jensen, J. L., & Ørntoft, T. F. (2004) · Cancer Research · doi:10.1158/0008-5472.can-04-0496 · Cited by 7,127
Mutation of the mouse klotho gene leads to a syndrome resembling ageing
Kuro-o, M., Matsumura, Y., Aizawa, H., Kawaguchi, H., Suga, T., Utsugi, T., Ohyama, Y., Kurabayashi, M., Kaname, T., Kume, E., et al. (1997) · Nature · doi:10.1038/36285 · Cited by 3,837
Identification of ubiquitin ligases required for skeletal muscle atrophy.
Bodine, S. C., Latres, E., Baumhueter, S., Lai, V. K.-M., Nunez, L., Clarke, B. A., Poueymirou, W. T., Panaro, F. J., Na, E., Dharmarajan, K., et al. (2001) · Science · doi:10.1126/science.1065874 · Cited by 3,521
Gene expression correlates of clinical prostate cancer behavior
Singh, D., Febbo, P. G., Ross, K., Jackson, D. G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A. A., D'Amico, A. V., Richie, J. P., et al. (2002) · Cancer Cell · doi:10.1016/s1535-6108(02)00030-2 · Cited by 2,512
El-Ali, J., Sorger, P. K., & Jensen, K. F. (2006) · Nature · doi:10.1038/nature05063 · Cited by 2,102
MyoD and the transcriptional control of myogenesis
Berkes, C. A., & Tapscott, S. J. (2005) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2005.07.006 · Cited by 849
Mosher, D. S., Quignon, P., Bustamante, C. D., Sutter, N. B., Mellersh, C. S., Parker, H. G., & Ostrander, E. A. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030079 · Cited by 826
Cited by
PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update
Wang, X., Spandidos, A., Wang, H., & Seed, B. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1013 · Cited by 720
Nagy, C., Maitra, M., Tanti, A., Suderman, M., Théroux, J.-F., Davoli, M. A., Perlman, K., Yerko, V., Wang, Y. C., Tripathy, S. J., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0621-y · Cited by 532
Dube, S., Qin, J., & Ramakrishnan, R. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002876 · Cited by 302
Self-citationDisentangling neural cell diversity using single-cell transcriptomics
Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R. (2016) · Nature Neuroscience · doi:10.1038/nn.4366 · Cited by 293
Weaver, S., Dube, S., Mir, A., Qin, J., Sun, G., Ramakrishnan, R., Jones, R. C., & Livak, K. J. (2010) · Methods · doi:10.1016/j.ymeth.2010.01.003 · Cited by 220
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Metadata
- Authors
- Spurgeon, S. L., Jones, R. C., & Ramakrishnan, R.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-02-27
- Volume
- 3
- Issue
- 2
- Pages
- e1662
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2008594900
- Cited by
- 386
- References
- 30
- FWCI
- 8.44
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
