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Isolation Window Optimization of Data-Independent Acquisition Using Predicted Libraries for Deep and Accurate
Joerg Doellinger1, Christian Blumenscheit1, Andy Schneider1
1Robert Koch-Institute, Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), 13353 Berlin, Germany.
Analytical Chemistry
|August 26, 2020
Summary
This study optimizes data-independent acquisition (DIA) for proteome profiling using in silico spectral libraries and advanced analysis. This approach enhances protein coverage and quantification performance in mass spectrometry (MS).
Area of Science:
- Proteomics
- Mass Spectrometry (MS)
- Bioinformatics
Background:
- In silico spectral library prediction holds potential for improving proteome profiling in data-independent acquisition (DIA).
- Advancements in sample preparation, peptide separation, and data analysis are key to unlocking the full scope of DIA.
- Optimizing data acquisition is crucial for maximizing the performance of advanced DIA strategies.
Purpose of the Study:
- To uncover the full potential of advanced DIA strategies through data acquisition optimization.
- To integrate high-quality in silico libraries with improved peptide separation and data analysis.
- To assess the impact of optimized DIA on proteome coverage and quantification performance.
Main Methods:
- Utilized high-quality in silico spectral libraries for peptide prediction.
- Employed reproducible, high-resolution peptide separation using micropillar array columns.
- Implemented neural network-supported data analysis and optimized mass spectrometry (MS) scan cycles.
Main Results:
- Achieved mean coefficient of variations of 4% with only 1.5 data points per peak (full width at half-maximum).
- Improved proteome coverage to over 8000 proteins from HeLa cells using empirically corrected libraries.
- Reached over 7000 protein identifications using a whole human in silico predicted library.
Conclusions:
- The combination of in silico libraries, advanced peptide separation, and neural network analysis enables long MS scan cycles without compromising quantification.
- This optimized DIA strategy significantly enhances proteome coverage, even with moderate MS scanning speeds.
- The approach shows high potential for applications in clinical proteomics, microbiology, and molecular biology.
