Orthogonality of separation and sorbent evaluation in offline multidimensional peptide fractionation using automated positive pressure micro solid phase extraction

Micro solid-phase extraction (µSPE) is a simple and efficient method for peptide separation, purification, and fractionation prior to mass spectrometry (MS) in bottom-up proteomics workflows. Here, we introduce a positive-pressure (PP)-µSPE platform for offline multidimensional peptide fractionation. Six one-dimensional (1D) fractionation protocols were optimized at low pH reversed-phase (RP), high pH RP, strong cation exchange (SCX), hydrophilic-lipophilic balance (HLB), quaternary methyl-ammonium (QMA), and mixed strong anion exchange/reversed-phase (MAX) using bovine serum albumin (BSA) tryptic peptides. Each protocol yielded six fractions, which were evaluated by peptide size, isoelectric point, and hydrophilicity. Peptide fractions were separated on nano-C18 RP column and analyzed by nanoESI-QTOF-MS, and fractionation performance was subsequently evaluated for each fractionation mode. The data were then paired to quantify orthogonality in projected multidimensional fractionation by employing information theory. QMA yielded the highest entropy, indicating the greatest peptide dispersion in 1D. Conversely, high pH RP fractionation had the lowest entropy and led to increased peptide modification and aggregation, compromising downstream analysis. Joint entropy and mutual information analysis identified the most orthogonal pairings (QMA–low pH RP, MAX–QMA, HLB–QMA) and highlighted redundancy among methods sharing similar separation mechanisms. Workflow’s practical utility was demonstrated on the fragment antigen-binding part of Cetuximab, where QMA fractionation enabled identification of a previously undetected heavy chain peptide, achieving complete sequence coverage. These results demonstrate that PP-µSPE enables repeatable and combinable peptide fractionation across diverse sorbents and complex proteins, and supports targeted workflows by facilitating selective peptide isolation based on their physicochemical properties, streamlining experimental design in multidimensional proteomic analyses.