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Published on: May 8, 2021
Thin-diaPASEF: diaPASEF for maximizing proteome coverage in single-shot proteomics.
Ryo Konno1, Masaki Ishikawa1, Daisuke Nakajima1
1Department of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.
Researchers optimized the timsTOF HT mass spectrometry platform using Thin-diaPASEF, achieving high proteomic depth. This advanced method identified thousands of proteins in cell and plasma samples, enhancing biomarker discovery potential.
Area of Science:
- Proteomics
- Mass Spectrometry
- Biomarker Discovery
Background:
- Proteomics using mass spectrometry (MS) provides deep insights into complex proteomes.
- The timsTOF MS platform with parallel accumulation-serial fragmentation (PASEF) offers high scan speeds and spectral quality.
- Bruker's timsTOF HT with TIMS-XR technology enhances dynamic range and analysis depth for high sample loadings.
Purpose of the Study:
- To validate and optimize data-independent acquisition (diaPASEF) parameters on the timsTOF HT.
- To develop an improved diaPASEF method for enhanced proteomic analysis.
- To establish a high-throughput proteomic analysis system for biomarker discovery.
Main Methods:
- Utilized Bruker's timsTOF HT mass spectrometry platform.
- Developed and validated a Thin-diaPASEF method by optimizing key parameters.
- Combined Thin-diaPASEF with an improved Lycopersicon esculentum lectin method for plasma analysis.
Main Results:
- Identified 9,400 proteins in a single shot from HEK cells with a false discovery rate <1%.
- Achieved the highest protein identification number for the timsTOF MS series in standard human cultured cells.
- Identified over 5,000 proteins in a 24-sample/day plasma analysis using Thin-diaPASEF and lectin enrichment.
Conclusions:
- The Thin-diaPASEF method offers high quantitative accuracy and consistency for deep proteomic analysis.
- The optimized system demonstrates high proteome coverage and suitability for biomarker discovery.
- This advancement pushes the boundaries of proteomic analysis on the timsTOF MS platform.
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