The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry
Open accessAbstract
Proteomic characterization of blood plasma is of central importance to clinical proteomics and particularly to biomarker discovery studies. The vast dynamic range and high complexity of the plasma proteome have, however, proven to be serious challenges and have often led to unacceptable tradeoffs between depth of coverage and sample throughput. We present an optimized sample-processing pipeline for analysis of the human plasma proteome that provides greatly increased depth of detection, improved quantitative precision and much higher sample analysis throughput as compared with prior methods. The process includes abundant protein depletion, isobaric labeling at the peptide level for multiplexed relative quantification and ultra-high-performance liquid chromatography coupled to accurate-mass, high-resolution tandem mass spectrometry analysis of peptides fractionated off-line by basic pH reversed-phase (bRP) chromatography. The overall reproducibility of the process, including immunoaffinity depletion, is high, with a process replicate coefficient of variation (CV) of <12%. Using isobaric tags for relative and absolute quantitation (iTRAQ) 4-plex, >4,500 proteins are detected and quantified per patient sample on average, with two or more peptides per protein and starting from as little as 200 μl of plasma. The approach can be multiplexed up to 10-plex using tandem mass tags (TMT) reagents, further increasing throughput, albeit with some decrease in the number of proteins quantified. In addition, we provide a rapid protocol for analysis of nonfractionated depleted plasma samples analyzed in 10-plex. This provides ∼600 quantified proteins for each of the ten samples in ∼5 h of instrument time.
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Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
Proteogenomics connects somatic mutations to signalling in breast cancer
CPTAC, N., Mertins, P., Mani, D. R., Ruggles, K. V., Gillette, M. A., Clauser, K. R., Wang, P., Wang, X., Qiao, J. W., Cao, S., et al. (2016) · Nature · doi:10.1038/nature18003 · Cited by 1,676
Self-citationMS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics
Ting, L., Rad, R., Gygi, S. P., & Haas, W. (2011) · Nature Methods · doi:10.1038/nmeth.1714 · Cited by 1,009
Geiger, T., Wehner, A., Schaab, C., Cox, J., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.014050 · Cited by 797
Increasing the Multiplexing Capacity of TMTs Using Reporter Ion Isotopologues with Isobaric Masses
McAlister, G. C., Huttlin, E. L., Haas, W., Ting, L., Jedrychowski, M. P., Rogers, J. C., Kuhn, K., Pike, I., Grothe, R. A., Blethrow, J. D., et al. (2012) · Analytical Chemistry · doi:10.1021/ac301572t · Cited by 605
Pieper, R., Su, Q., Gatlin, C. L., Huang, S., Anderson, N. L., & Steiner, S. (2003) · PROTEOMICS · doi:10.1002/pmic.200390057 · Cited by 356
Qian, W.-J., Kaleta, D. T., Petritis, B. O., Jiang, H., Liu, T., Zhang, X., Mottaz, H. M., Varnum, S. M., Camp, D. G., Huang, L., et al. (2008) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800008-mcp200 · Cited by 208
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Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona
Blume, J. E., Manning, W. C., Troiano, G., Hornburg, D., Figa, M., Hesterberg, L., Platt, T. L., Zhao, X., Cuaresma, R. A., Everley, P. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17033-7 · Cited by 452
Self-citationA Review on Quantitative Multiplexed Proteomics
Pappireddi, N., Martin, L., & Wühr, M. (2019) · ChemBioChem · doi:10.1002/cbic.201800650 · Cited by 299
Identification and Quantification of Proteoforms by Mass Spectrometry
Schaffer, L. V., Millikin, R. J., Miller, R. M., Anderson, L. C., Fellers, R. T., Ge, Y., Kelleher, N. L., LeDuc, R. D., Liu, X., Payne, S. H., et al. (2019) · PROTEOMICS · doi:10.1002/pmic.201800361 · Cited by 209
Algorithmic assessment of cellular senescence in experimental and clinical specimens
Kohli, J., Wang, B., Brandenburg, S. M., Basisty, N., Evangelou, K., Varela-Eirin, M., Campisi, J., Schilling, B., Gorgoulis, V., & Demaria, M. (2021) · Nature Protocols · doi:10.1038/s41596-021-00505-5 · Cited by 196
Thompson, A., Wölmer, N., Koncarevic, S., Selzer, S., Böhm, G., Legner, H., Schmid, P., Kienle, S., Penning, P., Höhle, C., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b04474 · Cited by 186
Metadata
- Authors
- Keshishian, H., Burgess, M. W., Specht, H., Wallace, L., Clauser, K. R., Gillette, M. A., & Carr, S. A.
- Venue
- Nature Protocols
- Published
- 2017-07-27
- Volume
- 12
- Issue
- 8
- Pages
- 1683-1701
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2736913410
- Cited by
- 190
- References
- 8
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- green
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- Keshishian, H., Burgess, M. W., Specht, H., Wallace, L., Clauser, K. R., Gillette, M. A., & Carr, S. A. (2017). Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nature Protocols. https://doi.org/10.1038/nprot.2017.054
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