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Limited amount and extreme concentration variability of proteomic-related samples require efficient analyte preconcentration and purification prior to the mass spectrometry (MS)-based analysis. Preferably, these steps should be coupled on-line with chosen fractionation and detection techniques for the minimization of the sample loss. To realize such sample pretreatment, herein, an on-chip solid-phase extraction – gradient elution – tandem mass spectrometry (SPE-GEMS/MS) is introduced. This technique combines in a microfluidic format on-line sample preconcentration/purification on SPE sorbent with further fractionation and MS/MS analysis. C8-functionalized mesoporous magnetic microspheres are chosen as a sorbent, spatially-confined with an applied magnetic field. They assure a selective enrichment and analysis of large hydrophobic peptides (2.5-7 kDa), matching the desired mass bin of the extended bottom-up proteomic (eBUP, 3-7 kDa) approach. Within less than 35 min and without additional sample purification, SPE-GEMS/MS provided 66.5 % of protein sequence coverage from 75 fmol of BSA tryptic digest. Analysis of only 33 fmol of a single monoclonal antibody, digested with secreted aspartic protease 9 (Sap9) to large peptides, yielded 80 % of its sequence coverage. More complex equimolar mixture of six antibodies (55 fmol each), submitted to Sap9 proteolysis, was also successfully processed by SPE-GEMS/MS, resulting in 50-67 % of the total antibody sequence coverage. Importantly, for all antibodies, unique peptides containing complementarity determining regions were detected for both heavy and light chains, leading to a correct identification of mixture components despite their high sequence homology. Moreover, SPE-GEMS/MS microchip and chosen magnetic sorbent are cost-effective, can be produced and operated in a disposable manner. Therefore, present technique could be potentially suitable for a high throughput sequencing of monoclonal antibodies and rapid eBUP-based structural protein analysis, especially when only limited sample amount is available.
Tamar Kohn, Xavier Fernandez Cassi, Timothy R. Julian
Bart Deplancke, Vincent Roland Julien Gardeux, Riccardo Dainese, Daniel Alpern