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Wu C, Lei J, Meng F, Wang X, Wong CJ, Peng J, Lin G, Gingras AC, Ma J, Zhang S. Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion. Anal Chem 2023; 95:17981-17987. [PMID: 38032138 PMCID: PMC10719888 DOI: 10.1021/acs.analchem.3c03357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023]
Abstract
Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.
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Affiliation(s)
- Ci Wu
- Department
of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Georgetown University, Washington D.C. 20007, United States
| | - Jiao Lei
- Clinical
Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Fei Meng
- Clinical
Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Xingyao Wang
- National
& Local Joint Engineering Laboratory of Animal Peptide Drug Development,
College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China
| | - Cassandra J. Wong
- Lunenfeld-Tanenbaum
Research Institute, Toronto, Ontario M5G 1X5, Canada
| | - Jiaxi Peng
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Ge Lin
- Clinical
Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum
Research Institute, Toronto, Ontario M5G 1X5, Canada
- Department
of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1X8, Canada
| | - Junfeng Ma
- Department
of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Georgetown University, Washington D.C. 20007, United States
| | - Shen Zhang
- Clinical
Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
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