Theoretical and experimental comparison of one dimensional versus on-line comprehensive two dimensional liquid chromatography for optimized sub-hour separations of complex peptide samples

This study was devoted to the search for conditions leading to highly efficient sub-hour separations of complex peptide samples with the objective of coupling to mass spectrometry. In this context, conditions for one dimensional reversed phase liquid chromatography (1D-RPLC) were optimized on the basis of a kinetic approach while conditions for on-line comprehensive two-dimensional liquid chromatography using reversed phase in both dimensions (on-line RPLCxRPLC) were optimized on the basis of a Pareto-optimal approach. Maximizing the peak capacity while minimizing the dilution factor for different analysis times (down to 5 min) were the two objectives under consideration. For gradient times between 5 and 60 min, 15 cm was found to be the best column length in RPLC with sub-2 mu m particles under 800 bar as system pressure. In RPLCxRPLC, for less than one hour as first dimension gradient time, the sampling rate was found to be a key parameter in addition to conventional parameters including column dimension, particle size, flow-rate and gradient conditions in both dimensions. It was shown that the optimum sampling rate was as low as one fraction per peak for very short gradient times (i.e. below 10 min). The quality descriptors obtained under optimized RPLCxRPLC conditions were compared to those obtained under optimized RPLC conditions. Our experimental results for peptides, obtained with state of the art instrumentation, showed that RPLCxRPLC could outperform 1D-RPLC for gradient times longer than 5 min. In 60 min, the same peak intensity (same dilution) was observed with both techniques but with a 3-fold lower injected amount in RPLCxRPLC. A significant increase of the signal-to-noise ratio mainly due to a strong noise reduction was observed in RPLCxRPLC-MS compared to the one in 1D-RPLC-MS making RPLCxRPLC-MS a promising technique for peptide identification in complex matrices. (C) 2017 Elsevier B.V. All rights reserved.