The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Clinical microfluidics for neutrophil genomics and proteomics
Open accessAbstract
Neutrophils have key roles in modulating the immune response. We present a robust methodology for rapidly isolating neutrophils directly from whole blood with 'on-chip' processing for mRNA and protein isolation for genomics and proteomics. We validate this device with an ex vivo stimulation experiment and by comparison with standard bulk isolation methodologies. Last, we implement this tool as part of a near-patient blood processing system within a multi-center clinical study of the immune response to severe trauma and burn injury. The preliminary results from a small cohort of subjects in our study and healthy controls show a unique time-dependent gene expression pattern clearly demonstrating the ability of this tool to discriminate temporal transcriptional events of neutrophils within a clinical setting.
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Citations
Cites
Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection.
Li, C., & Wong, W. H. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.1.31 · Cited by 3,133
Neutrophils and immunity: challenges and opportunities
Nathan, C. (2006) · Nature reviews. Immunology · doi:10.1038/nri1785 · Cited by 2,971
The RIN: an RNA integrity number for assigning integrity values to RNA measurements
Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T. (2006) · BMC Molecular Biology · doi:10.1186/1471-2199-7-3 · Cited by 2,623
Mass spectrometry–based proteomics turns quantitative
Ong, S.-E., & Mann, M. (2005) · Nature Chemical Biology · doi:10.1038/nchembio736 · Cited by 1,572 · cited 1×
“Although microarray technology for gene expression is well established, mass spectrometry–based proteomics is being rapidly developed to quantitatively examine a large number of proteins in cell”
BackgroundA network-based analysis of systemic inflammation in humans
Program, I. A. H. R. T. I. L. S. C. R., Calvano, S. E., Xiao, W., Richards, D. R., Felciano, R. M., Baker, H. V., Cho, R. J., Chen, R. O., Brownstein, B. H., Cobb, J. P., et al. (2005) · Nature · doi:10.1038/nature03985 · Cited by 1,429
Self-citationComprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast
de Godoy, L. M. F., Olsen, J. V., Cox, J., Nielsen, M. L., Hubner, N. C., Fröhlich, F., Walther, T. C., & Mann, M. (2008) · Nature · doi:10.1038/nature07341 · Cited by 873
Berger, T., Togawa, A., Duncan, G. S., Elia, A. J., You-Ten, A., Wakeham, A., Fong, H. E. H., Cheung, C. C., & Mak, T. W. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510847103 · Cited by 460
The transcriptional program of terminal granulocytic differentiation
Theilgaard-Mönch, K., Jacobsen, L. C., Borup, R., Rasmussen, T., Bjerregaard, M. D., Nielsen, F. C., Cowland, J. B., & Borregaard, N. (2005) · Blood · doi:10.1182/blood-2004-08-3346 · Cited by 281
Cited by
The present and future role of microfluidics in biomedical research
Sackmann, E. K., Fulton, A. L., & Beebe, D. J. (2014) · Nature · doi:10.1038/nature13118 · Cited by 2,995
Gentile, L. F., Cuenca, A. G., Efron, P. A., Ang, D., Bihorac, A., McKinley, B. A., Moldawer, L. L., & Moore, F. A. (2012) · Journal of Trauma and Acute Care Surgery · doi:10.1097/ta.0b013e318256e000 · Cited by 801
Self-citationAn expression atlas of human primary cells: inference of gene function from coexpression networks
Mabbott, N. A., Baillie, J., Brown, H., Freeman, T. C., & Hume, D. A. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-632 · Cited by 474
Tsai, M., Kita, A., Leach, J., Rounsevell, R., Huang, J. N., Moake, J., Ware, R. E., Fletcher, D. A., & Lam, W. A. (2012) · Journal of Clinical Investigation · doi:10.1172/jci58753 · Cited by 297
Micro Total Analysis Systems for Cell Biology and Biochemical Assays
Kovarik, M. L., Gach, P. C., Ornoff, D. M., Wang, Y., Balowski, J., Farrag, L., & Allbritton, N. L. (2012) · Analytical Chemistry · doi:10.1021/ac202611x · Cited by 270
Microfluidics: A New Tool for Modeling Cancer–Immune Interactions
Boussommier-Calleja, A., Li, R., Chen, M. B., Wong, S. C., & Kamm, R. D. (2016) · Trends in Cancer · doi:10.1016/j.trecan.2015.12.003 · Cited by 224
“cytokine profiling, transcriptomics and proteomics of immune cells[76] [77]) to diagnose and tailor drug treatment[65], as reviewed elsewhere[45].”
BackgroundHuman transcriptome array for high-throughput clinical studies
Xu, W., Seok, J., Mindrinos, M. N., Schweitzer, A. C., Jiang, H., Wilhelmy, J., Clark, T. A., Kapur, K., Xing, Y., Faham, M., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019753108 · Cited by 124
Self-citation
Metadata
- Authors
- Program, T. I. A. T. H. R. T. I. C. R., Kotz, K. T., Xiao, W., Miller-Graziano, C., Qian, W.-J., Russom, A., Warner, E. A., Moldawer, L. L., De, A., Bankey, P. E., Petritis, B. O., Camp, D. G., Rosenbach, A. E., Goverman, J., Fagan, S. P., Brownstein, B. H., Irimia, D., Xu, W., Wilhelmy, J., Mindrinos, M. N., Smith, R. D., Davis, R. W., Tompkins, R. G., & Toner, M.
- Venue
- Nature Medicine
- Published
- 2010-08-29
- Volume
- 16
- Issue
- 9
- Pages
- 1042-1047
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2087318775
- Cited by
- 161
- References
- 8
- FWCI
- 10.19
- Type
- journal article
- Language
- en
- Open access
- green
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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.
