The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Serum Peptide Profiling by Magnetic Particle-Assisted, Automated Sample Processing and MALDI-TOF Mass Spectrometry
Abstract
Human serum contains a complex array of proteolytically derived peptides (serum peptidome) that may provide a correlate of biological events occurring in the entire organism; for instance, as a diagnostic for solid tumors (Petricoin, E. F.; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. Lancet 2002, 359, 572-577). Here, we describe a novel, automated technology platform for the simultaneous measurement of serum peptides that is simple, scalable, and generates highly reproducible patterns. Peptides are captured and concentrated using reversed-phase (RP) batch processing in a magnetic particle-based format, automated on a liquid handling robot, and followed by a MALDI TOF mass spectrometric readout. The protocol is based on a detailed investigation of serum handling, RP ligand and eluant selection, small-volume robotics design, an optimized spectral acquisition program, and consistent peak extraction plus binning across a study set. The improved sensitivity and resolution allowed detection of 400 polypeptides (0.8-15-kDa range) in a single droplet (approximately 50 microL) of serum, and almost 2000 unique peptides in larger sample sets, which can then be analyzed using common microarray data analysis software. A pilot study indicated that sera from brain tumor patients can be distinguished from controls based on a pattern of 274 peptide masses. This, in turn, served to create a learning algorithm that correctly predicted 96.4% of the samples as either normal or diseased.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
Use of proteomic patterns in serum to identify ovarian cancer
Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07746-2 · Cited by 3,236
Potential for biomolecular imaging with femtosecond X-ray pulses
Neutze, R., Wouts, R., van der Spoel, D., Weckert, E., & Hajdu, J. (2000) · Nature · doi:10.1038/35021099 · Cited by 2,022
Matrix-assisted laser desorption/ionization mass spectrometry of biopolymers.
Hillenkamp, F., Karas, M., Beavis, R. C., & Chait, B. T. (1991) · Analytical Chemistry · doi:10.1021/ac00024a716 · Cited by 1,930
Cited by
Protein biomarker discovery and validation: the long and uncertain path to clinical utility
Rifai, N., Gillette, M. A., & Carr, S. A. (2006) · Nature Biotechnology · doi:10.1038/nbt1235 · Cited by 1,867
Tammen, H., Schulte, I., Hess, R., Menzel, C., Kellmann, M., Mohring, T., & Schulz‐Knappe, P. (2005) · PROTEOMICS · doi:10.1002/pmic.200401219 · Cited by 260
Methods for samples preparation in proteomic research
Bodzon-Kulakowska, A., Bierczynska-Krzysik, A., Dylag, T., Drabik, A., Suder, P., Noga, M., Jarzebinska, J., & Silberring, J. (2007) · Journal of Chromatography B · doi:10.1016/j.jchromb.2006.10.040 · Cited by 254
Hu, L., Boos, K.-S., Ye, M., Wu, R., & Zou, H. (2009) · Journal of Chromatography A · doi:10.1016/j.chroma.2009.05.030 · Cited by 56
Lu, J., Huang, Y., Wang, Y., Li, Y., Zhang, Y., Wu, J., Zhao, F., Meng, S., Yu, X., Ma, Q., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039726 · Cited by 33
Metadata
- Authors
- Villanueva, J., Philip, J., Entenberg, D., Chaparro, C. A., Tanwar, M. K., Holland, E. C., & Tempst, P.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-02-14
- Volume
- 76
- Issue
- 6
- Pages
- 1560-1570
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2051301080
- Cited by
- 449
- References
- 4
- FWCI
- 23.44
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.