The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Development and Evaluation of a Micro- and Nanoscale Proteomic Sample Preparation Method
Open accessAbstract
Challenges associated with the efficient and effective preparation of micro- and nanoscale (micro- and nanogram) clinical specimens for proteomic applications include the unmitigated sample losses that occur during the processing steps. Herein, we describe a simple "single-tube" preparation protocol appropriate for small proteomic samples using the organic cosolvent, trifluoroethanol (TFE) that circumvents the loss of sample by facilitating both protein extraction and protein denaturation without requiring a separate cleanup step. The performance of the TFE-based method was initially evaluated by comparisons to traditional detergent-based methods on relatively large scale sample processing using human breast cancer cells and mouse brain tissue. The results demonstrated that the TFE-based protocol provided comparable results to the traditional detergent-based protocols for larger, conventionally sized proteomic samples (>100 microg protein content), based on both sample recovery and numbers of peptide/protein identifications. The effectiveness of this protocol for micro- and nanoscale sample processing was then evaluated for the extraction of proteins/peptides and shown effective for small mouse brain tissue samples (approximately 30 microg total protein content) and also for samples of approximately 5000 MCF-7 human breast cancer cells (approximately 500 ng total protein content), where the detergent-based methods were ineffective due to losses during cleanup and transfer steps.
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
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Cited by
Protein Analysis by Shotgun/Bottom-up Proteomics
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491
Zhu, Y., Piehowski, P. D., Zhao, R., Chen, J., Shen, Y., Moore, R. J., Shukla, A. K., Petyuk, V. A., Campbell-Thompson, M., Mathews, C. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03367-w · Cited by 587
Self-citationMass spectrometry-based proteomics: existing capabilities and future directions
Angel, T. E., Aryal, U. K., Hengel, S. M., Baker, E. S., Kelly, R. T., Robinson, E. W., & Smith, R. D. (2012) · Chemical Society Reviews · doi:10.1039/c2cs15331a · Cited by 441
Self-citationProteomics of Integral Membrane ProteinsTheory and Application
Speers, A. E., & Wu, C. C. (2007) · Chemical Reviews · doi:10.1021/cr068286z · Cited by 325
Doellinger, J., Schneider, A., Hoeller, M., & Lasch, P. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir119.001616 · Cited by 246
Metadata
- Authors
- Wang, H., Qian, W.-J., Mottaz, H. M., Clauss, T. R. W., Anderson, D. J., Moore, R. J., Camp, D. G., Khan, A. H., Sforza, D. M., Pallavicini, M., Smith, D. J., & Smith, R. D.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-10-05
- Volume
- 4
- Issue
- 6
- Pages
- 2397-2403
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W2111185320
- Cited by
- 179
- References
- 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Development and Evaluation of a Micro- and Nanoscale Proteomic Sample Preparation Method. https://app.cochat.ai/papers/10.1021/pr050160f
- The paper
- Wang, H., Qian, W.-J., Mottaz, H. M., Clauss, T. R. W., Anderson, D. J., Moore, R. J., Camp, D. G., Khan, A. H., Sforza, D. M., Pallavicini, M., Smith, D. J., & Smith, R. D. (2005). Development and Evaluation of a Micro- and Nanoscale Proteomic Sample Preparation Method. Journal of Proteome Research. https://doi.org/10.1021/pr050160f
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.