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We compared four data-independent acquisition (DIA) library workflows for plasma proteomics. A novel gas-phase fractionation (GPF) method for DIA with parallel accumulation and serial fragmentation (diaPASEF) improved protein identification compared to traditional methods.

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Area of Science:

  • Proteomics
  • Biochemistry
  • Analytical Chemistry

Background:

  • Traditional data-independent acquisition (DIA) workflows rely on spectral libraries generated by data-dependent acquisition (DDA) for protein identification.
  • Recent advancements have introduced library-independent DIA strategies, impacting plasma proteomics study outcomes.
  • The choice of DIA library generation method is critical for optimizing protein identification and experimental efficiency.

Purpose of the Study:

  • To establish and evaluate a gas-phase fractionation (GPF) workflow for creating DIA libraries compatible with DIA with parallel accumulation and serial fragmentation (diaPASEF).
  • To compare the GPF-diaPASEF workflow against three other DIA library generation methods: fractionated DDA libraries, fractionated DIA libraries, and predicted spectra libraries.
  • To assess the impact of different DIA library workflows on protein identification, quantification, and total experiment time in plasma proteomics.

Main Methods:

  • A novel gas-phase fractionation (GPF) workflow was developed to generate DIA libraries for diaPASEF analysis.
  • Four DIA library workflows were evaluated using 20 plasma samples from non-small cell lung cancer patients.
  • Protein identification and quantification were performed, and total experiment time was recorded for each workflow.

Main Results:

  • The GPF workflow for diaPASEF demonstrated superior performance over the traditional DDA-PASEF workflow in identifying and quantifying proteins.
  • A library-independent approach utilizing predicted spectra identified and quantified the highest number of proteins, albeit with increased computational demands.
  • Differences in DIA library workflows had a minimal impact on the overall plasma proteomics study outcome but influenced protein identification numbers and experiment duration.

Conclusions:

  • The novel GPF workflow offers an effective strategy for generating DIA libraries for diaPASEF, enhancing protein identification in plasma proteomics.
  • Library-independent methods, particularly those using predicted spectra, can maximize protein identification but require significant computational resources.
  • Selecting the appropriate DIA library workflow is crucial for optimizing the balance between protein coverage, experimental efficiency, and computational cost in plasma proteomics research.