Optimization of Field-Amplified Sample Injection for Analysis of Peptides by Capillary Electrophoresis−Mass Spectrometry

A versatile experimental approach is described to achieve very high sensitivity analysis of peptides by capillary electrophoresis−mass spectrometry with sheath flow configuration based on optimization of field-amplified sample injection. Compared to traditional hydrodynamic injection methods, signal...

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Veröffentlicht in:Analytical chemistry (Washington) 2006-07, Vol.78 (14), p.4752-4758
Hauptverfasser: Yang, Yuanzhong, Boysen, Reinhard I, Hearn, Milton T. W
Format: Artikel
Sprache:eng
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Zusammenfassung:A versatile experimental approach is described to achieve very high sensitivity analysis of peptides by capillary electrophoresis−mass spectrometry with sheath flow configuration based on optimization of field-amplified sample injection. Compared to traditional hydrodynamic injection methods, signal enhancement in terms of detection sensitivity of the bioanalytes by more than 3000-fold can be achieved. The effects of injection conditions, composition of the acid and organic solvent in the sample solution, length of the water plug, sample injection time, and voltage on the efficiency of the sample stacking have been systematically investigated, with peptides in the low-nanomolar (10-9 M) range readily detected under the optimized conditions. Linearity of the established stacking method was found to be excellent over 2 orders of magnitude of concentration. The method was further evaluated for the analysis of low concentration bioactive peptide mixtures and tryptic digests of proteins. A distinguishing feature of the described approach is that it can be employed directly for the analysis of low-abundance protein fragments generated by enzymatic digestion and a reversed-phase-based sample-desalting procedure. Thus, rapid identification of protein fragments as low-abundance analytes can be achieved with this new approach by comparison of the actual tandem mass spectra of selected peptides with the predicted fragmentation patterns using online database searching algorithms.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac051735v