Full length articleAdditional band broadening of peptides in the first size-exclusion chromatographic dimension of an automated stop-flow two-dimensional high performance liquid chromatography

- A fully automated stop-flow 2D-LC system was established for peptide separation.
- Effects of stop-flow operation on band broadening were studied theoretically.
- Shorter time and lower 1st D temperature were suggested for stop-flow 2D analysis.
- Stop-flow 2D-LC was proved to be a promising approach for complex sample analysis.

The need to improve the peak capacity of liquid chromatography motivates the development of two-dimensional analysis systems. This paper presented a fully automated stop-flow two-dimensional liquid chromatography system with size exclusion chromatography followed by reversed phase liquid chromatography (SEC × RPLC) to efficiently separate peptides. The effects of different stop-flow operational parameters (stop-flow time, peak parking position, number of stop-flow periods and column temperature) on band broadening in the first dimension (1st D) SEC column were quantitatively evaluated by using commercial small proteins and peptides. Results showed that the effects of peak parking position and the number of stop-flow periods on band broadening were relatively small. Unlike stop-flow analysis of large molecules with a long running time, additional band broadening was evidently observed for small molecule analytes due to the relatively high effective diffusion coefficient (Deff). Therefore, shorter analysis time and lower 1st D column temperature were suggested for analyzing small molecules. The stop-flow two-dimensional liquid chromatography (2D-LC) system was further tested on peanut peptides and an evidently improved resolution was observed for both stop-flow heart-cutting and comprehensive 2D-LC analysis (in spite of additional band broadening in SEC). The stop-flow SEC × RPLC, especially heart-cutting analysis with shorter analysis time and higher 1st D resolution for selected fractions, offers a promising approach for efficient analysis of complex samples.