A versatile platform for sequential glyco-, phospho-, and proteomics with multi-PTMs integration.

MuPPE achieved superior reproducibility compared to conventional parallel processing methods.

• Coefficient of variation (CV) of 12.3% for MuPPE versus 17.6% for conventional methods
• The platform combines protein aggregation capture with on-bead digestion and tandem enrichment
• Analysis is performed from single biological samples rather than requiring sample splitting

MuPPE reduced sample processing time by 87.5% compared to conventional methods.

• MuPPE processing time: 4 hours
• Conventional methods processing time: 32 hours
• This represents an 87.5% reduction in time

MuPPE identified more serum glycopeptides and brain phosphopeptides than other platforms.

• The platform demonstrated enhanced coverage for both glycoproteome and phosphoproteome analyses
• Comparisons were made against other existing platforms for sequential multi-omic analysis
• Both serum and brain tissue sample types showed improved identification

Application of MuPPE to aging mouse cohorts revealed tissue-specific PTMs remodeling and brain barrier dysfunction.

• The platform was applied to aging mouse cohorts
• Tissue-specific post-translational modification remodeling was uncovered
• Brain barrier dysfunction was identified through multi-level PTMs analysis
• Analysis spanned proteome, phosphoproteome, and glycoproteome levels simultaneously

MuPPE revealed PTMs crosstalk between glycosylation and phosphorylation in arsenic mechanisms of action.

• Drug-induced PTMs crosstalk was identified between glycosylation and phosphorylation
• This crosstalk was linked to pathway regulation driven by arsenic
• The platform enabled simultaneous detection of multiple PTM types from the same sample to reveal these interactions

MuPPE integrates protein aggregation capture with on-bead digestion and tandem enrichment for sequential multi-omic analysis.

• The platform enables sequential glycoproteome, phosphoproteome, and proteome analysis
• Processing is performed from a single biological sample
• The approach avoids the laborious and time-consuming parallel processing procedures of conventional methods
• The method addresses inconsistent reproducibility associated with parallel processing