The DOI matches its Crossref record.
Automation of Nanoscale Microcapillary Liquid Chromatography−Tandem Mass Spectrometry with a Vented Column
Abstract
To fully automate the sample introduction step for nanoscale microcapillary liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses, 75 microm i.d. x 14 cm capillary columns were interfaced with a commercial autosampler instrument using a novel procedure which allowed dilute peptide samples to be transferred from the AS loop injector to the nanoscale column at flow rates up to 5 microL min(-1). On-column enrichment and desalting was demonstrated for large sample volumes (>40 microL) by constructing a vent 2 cm after the entrance to the packed bed of 5-microm ODS-AQ modified silica. Salts and nonretained solutes were removed via the vent, which allowed for column washing independent of the continuation of the bed into the electrospray source. Separations of test peptide mixtures demonstrated 50-nL elution peak volumes with low- to subfemtomole detection levels. In addition, a highly complex peptide mixture (outer membrane preparation from Psuedemonas aeruginosa) was efficiently separated with more than 100 proteins identified from a single reversed-phase LC-MS/MS analysis. Finally, the vented column (V-column) was utilized for on-line separations in a multidimensional chromatography/tandem MS experiment where large numbers of strong cation exchange chromatography fractions from a trypsinized yeast lysate were desalted, concentrated, and analyzed in a completely automated fashion. The procedures for constructing and using a V-column require minimal changes in current methods and equipment for nano-LC-MS analyses using columns of 100-microm diameter and smaller.
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
Cited by
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., & Gygi, S. P. (2003) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,647
Self-citationA proteomics approach to understanding protein ubiquitination
Peng, J., Schwartz, D., Elias, J. E., Thoreen, C. C., Cheng, D., Marsischky, G., Roelofs, J., Finley, D., & Gygi, S. P. (2003) · Nature Biotechnology · doi:10.1038/nbt849 · Cited by 1,602
Self-citationProteogenomic characterization of human colon and rectal cancer
CPTAC, T. N., Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., Chambers, M. C., Zimmerman, L. J., Shaddox, K. F., et al. (2014) · Nature · doi:10.1038/nature13438 · Cited by 1,462
Proteomics by Mass Spectrometry: Approaches, Advances, and Applications
Yates, J. R., Ruse, C. I., & Nakorchevsky, A. (2009) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-061008-124934 · Cited by 1,078
Comprehensive proteomic analysis of the human spliceosome
Zhou, Z., Licklider, L. J., Gygi, S. P., & Reed, R. (2002) · Nature · doi:10.1038/nature01031 · Cited by 893
Self-citationGambus, A., Jones, R. C., Sanchez-Diaz, A., Kanemaki, M., van Deursen, F., Edmondson, R. D., & Labib, K. (2006) · Nature Cell Biology · doi:10.1038/ncb1382 · Cited by 768
BAD and glucokinase reside in a mitochondrial complex that integrates glycolysis and apoptosis
Danial, N. N., Gramm, C. F., Scorrano, L., Zhang, C.-Y., Krauss, S., Ranger, A. M., Robert Datta, S., Greenberg, M. E., Licklider, L. J., Lowell, B. B., et al. (2003) · Nature · doi:10.1038/nature01825 · Cited by 705
Self-citationDepletion of Abundant Plasma Proteins and Limitations of Plasma Proteomics
Tu, C., Rudnick, P. A., Martinez, M. Y., Cheek, K. L., Stein, S. E., Slebos, R. J. C., & Liebler, D. C. (2010) · Journal of Proteome Research · doi:10.1021/pr100646w · Cited by 390
Khidekel, N., Ficarro, S. B., Peters, E. C., & Hsieh-Wilson, L. C. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403471101 · Cited by 299
Obesity, hyperphagia and increased metabolic efficiency in Pc1 mutant mice
Lloyd, D. J., Bohan, S., & Gekakis, N. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl111 · Cited by 142
(2019) · doi:10.7554/elife.41740 · Cited by 63
Mulkearns, E. E., & Cooper, J. A. (2012) · Molecular Biology of the Cell · doi:10.1091/mbc.e11-09-0812 · Cited by 48
Metadata
- Authors
- Licklider, L. J., Thoreen, C. C., Peng, J., & Gygi, S. P.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-05-14
- Volume
- 74
- Issue
- 13
- Pages
- 3076-3083
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W1963893739
- Cited by
- 218
- References
- 0
- FWCI
- 6.94
- 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.
