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J Proteome Res. 2014 Aug 1;13(8):3826-36. doi: 10.1021/pr500530e. Epub 2014 Jul 21.

Off-Line Multidimensional Liquid Chromatography and Auto Sampling Result in Sample Loss in LC/LC-MS/MS.

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†Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, SR11, La Jolla, California 92037, United States.
‡Department of Structural Pathology, Institute of Nephrology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757 Asahimachi-dori, Niigata 951-8510, Japan.
§Department of Physiology, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt.


Large-scale proteomics often employs two orthogonal separation methods to fractionate complex peptide mixtures. Fractionation can involve ion exchange separation coupled to reversed-phase separation or, more recently, two reversed-phase separations performed at different pH values. When multidimensional separations are combined with tandem mass spectrometry for protein identification, the strategy is often referred to as multidimensional protein identification technology (MudPIT). MudPIT has been used in either an automated (online) or manual (offline) format. In this study, we evaluated the performance of different MudPIT strategies by both label-free and tandem mass tag (TMT) isobaric tagging. Our findings revealed that online MudPIT provided more peptide/protein identifications and higher sequence coverage than offline platforms. When employing an off-line fractionation method with direct loading of samples onto the column from an eppendorf tube via a high-pressure device, a 5.3% loss in protein identifications is observed. When off-line fractionated samples are loaded via an autosampler, a 44.5% loss in protein identifications is observed compared with direct loading of samples onto a triphasic capillary column. Moreover, peptide recovery was significantly lower after offline fractionation than in online fractionation. Signal-to-noise (S/N) ratio, however, was not significantly altered between experimental groups. It is likely that offline sample collection results in stochastic peptide loss due to noncovalent adsorption to solid surfaces. Therefore, the use of the offline approaches should be considered carefully when processing minute quantities of valuable samples.


HEK293; MudPIT; TMT; isobaric; label-free; offline; online

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