The direct infusion-shotgun proteome analysis (DI-SPA) alongside data-independent acquisition mass spectrometry achieved fast proteome identification and quantification without chromatographic separation. However, robust peptide identification and quantification (label and label-free) for the DI-SPA data is still insufficient. We find that in the absence of chromatography, the identification of DI-SPA can be boosted by extending acquisition cycles repeatedly and maximizing the utilization of the featured repetition characteristics, combined with the machine learning-based automatic peptide scoring strategy. Here, we present the repeat-enhancing featured ion-guided stoichiometry (RE-FIGS), a complete and compact solution to (repeated) DI-SPA data. Using our strategy, the peptide identification can be improved above 30% with high reproducibility (70.0%). Notably, the label-free quantification of repeated DI-SPA can be successfully obtained with high accuracy (mean median error, 0.108) and high reproducibility (median error, 0.001). We believe our RE-FIGS method could boost the broad application of the (repeated) DI-SPA method and offer a new choice for proteomic analysis.
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