FIGS: Featured Ion-Guided Stoichiometry for Data-Independent Proteomics through Dynamic Deconvolution.

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Data-independent acquisition (DIA) mass spectrometry (MS) offers advantages for peptide quantification.
• However, mixed spectra present challenges for precise stoichiometric analysis.

Purpose of the Study:

• To develop a universal method for accurate and robust peptide quantification in DIA-MS data.
• To address challenges posed by mixed spectra in quantitative proteomics.

Main Methods:

• Analysis of library spectra in specific sets, prioritizing local and preferential analysis.
• Definition of featured ions as fragment ions uniquely assigned to precursors via dynamic deconvolution of mixed spectra.
• Introduction of featured ion-guided stoichiometry (FIGS) for quantification.

Main Results:

• FIGS demonstrates high performance in quantification sensitivity, accuracy, and efficiency.
• The method significantly improves quantification accuracy across the full dynamic range.
• Particular improvement observed for low-abundance peptides.

Conclusions:

• FIGS provides a robust and accurate solution for peptide quantification in DIA-MS.
• The method overcomes limitations associated with mixed spectra, enhancing proteomic analysis.
• FIGS is particularly valuable for quantifying low-abundance peptides, expanding the dynamic range of detection.