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

Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.

Automated summary

Data-independent acquisition (DIA) offers advantages for peptide quantification in mass spectrometry (MS), but mixed spectra present challenges for precise stoichiometry. This study introduces FIGS, a method for accurate and robust peptide quantification in DIA-MS data, by analyzing library spectra in specific sets and defining featured ions assigned to corresponding precursors. FIGS demonstrates high performance in quantification sensitivity, accuracy, and efficiency, significantly improving accuracy for the full dynamic range, especially low-abundance peptides.