LCGC Asia Pacific
Multidimensional liquid chromatography (MDLC) techniques are essential for the separation of highly complex proteomic samples. Advantages of off-line MDLC techniques over on-line approaches include high flexibility in choice of column dimensions and mobile-phase compositions, and the ability to reanalyse sample fractions. Here we present a fully automated off-line two-dimensional chromatographic approach for the analysis of proteomic samples using an UltiMate 3000 system optimized for proteomics MDLC.
Multidimensional liquid chromatography (MDLC) techniques are essential for the separation of highly complex proteomic samples. Advantages of off-line MDLC techniques over on-line approaches include high flexibility in choice of column dimensions and mobile-phase compositions, and the ability to reanalyse sample fractions. Here we present a fully automated off-line two-dimensional chromatographic approach for the analysis of proteomic samples using an UltiMate 3000 system optimized for proteomics MDLC.
Off-line two-dimensional (2D) chromatographic experiments were performed with an UltiMate 3000 system (Dionex) equipped with a dual gradient pump with membrane degasser (DGP3600), flow manager module with active flow-split technology (FLM-3100), autosampler with integrated micro-fraction collector (WPS-3000), and two UV detectors (VWD-3400). The system was coupled on-line to an ion-trap mass spectrometer (HCTultra)
The workflow for automated off-line 2D-LC is as follows: (1) a first-dimension LC separation with fraction collection, followed by (2) repeated cycles of injection of the collected fractions (second-dimension), LC separations and detection of peptides by tandem mass spectrometry. Figure 1 shows a schematic diagram of the 2D-LC system. The first-dimension separation was performed on a 15 cm × 300 μm i.d. strong cation-exchange column (SCX). After injection of 10 pmol of tryptic peptides from transferrin, bovine serum albumin, β-galactosidase, alcohol dehydrogenase, lysozyme, and cytochrome c, a salt gradient from 0–600 mM NaCl in 5 mM phosphate buffer pH 3 + 5% ACN was applied in 20 min at a flow-rate of 6 μL/min. Detection was achieved with a 45 nL flow cell at 214 nm. Fractions were collected every minute from 10–30 min. The second dimension separation included on-line sample preconcentration and desalting using a monolithic trap column, and separation of peptides on a PepSwift 5 cm, 200 μm i.d. monolithic column applying an acetonitrile gradient from 0–36% in 0.05% TFA in 10 min. Peptides were detected by UV using a 3 nL flow cell and MS–MS detection.
Figure 1
To minimize dilution of the peptide fractions, the salt gradient was optimized such that 84% of the peptides eluted in single or adjacent fractions, resulting in the chromatogram shown in Figure 2(a). Excellent retention time precision was observed with RSD values <0.1% for three consecutive 2D-LC runs. Using a polystyrene-divinylbenzene monolithic column (PepSwift) and a steep gradient (3.6% ACN/min), a peak capacity of 125 was obtained within 10 min for the second-dimension separation. Figure 2(b) shows a second-dimension separation of the same fraction (#4) obtained in three consecutive MDLC runs. The chromatograms were obtained 12 h apart, (the time required to complete one MDLC analysis). Retention time precision measured for 16 peptides varied between 0.0 and 0.20% RSD. The protein sequence coverage determined with MASCOT was highly reproducible and varied between 43 and 75% for the samples originating from different proteins.
Figure 2
As a result of the complex nature of the sample, a special representation was developed that allows easy comparison of the different samples with Chromeleon software. (See Figure 2.) Figure 2(a) shows the first dimension separation; Figure 2(b) shows the consecutive second-dimension separations, using a visualization approach similar to the gel images obtained after 2D-gel electrophoresis (2D-PAGE). A second-dimension separation of a single fraction sampled from the first dimension separation is shown in Figure 2(c).
The UltiMate 3000 MDLC allows automated off-line 2D-LC of complex proteomic samples. The method provides high peak capacity, high resolution and excellent retention time stability.
Pepswift is a trademark and UltiMate and Chromeleon are registered trademarks of Dionex Corporation, Sunnyvale, California, USA.
HCTultra is a trademark of Bruker Daltonics Incorporated, Billerica, Massachusetts, USA.
Dionex Corporation
1228 Titan Way, Sunnyvale, California 94085, USA
tel. +1 408 737 0700 fax +1 408 730 9403
Website: www.dionex.com
Bas Dolman, Robert van Ling, Evert-Jan Sneekes, Sebastiaan Eeltink and Remco Swart, Dionex, Amsterdam, The Netherlands.
Real-Time Measurement of EPA Regulated HON Compounds and Environmental Pollutants Using SIFT-MS
September 6th 2024This application note describes the determination of method detection limits (MDLs) for the newly regulated HON (Hazardous Organic NESHAP) compounds, which validate SIFT-MS as an effective solution for measuring these toxic VOCs and other environmental pollutants in ambient air, whether at the fence line or in a mobile setting.
High-Throughput Analysis of Volatile Compounds in Air, Water, and Soil Using SIFT-MS (Sept 2024)
September 6th 2024This study demonstrates high-throughput analysis of BTEX compounds from several matrices (air, water and soil). Detection limits in the single-digit part-per-billion concentration range (by volume) are readily achievable within seconds using SIFT-MS, because sample analysis is achieved without chromatography, pre-concentration, or drying. We also present a calibration approach that enables speciation of ethylbenzene from the xylenes in real-time.
Real-Time Roadside Monitoring of Unreported VOC Emissions from Road Transport by SIFT-MS (Sept 2024)
September 6th 2024This application note summarizes key SIFT-MS results presented in a peer reviewed article entitled “Unreported VOC Emissions from Road Transport Including from Electric Vehicles.” Learn how the Wolfson Atmospheric Chemistry Laboratory at the University of York used SIFT-MS for VOC analysis in its platform to experimentally verify that motor vehicle screen wash is a significant unreported source of VOC emissions (especially for ethanol and methanol).
Adoption of SIFT-MS for VOC Pollution Monitoring in South Korea (Sept 2024)
September 6th 2024This publication reviews VOC pollutant monitoring applications of SIFT-MS in South Korea. SIFT-MS has been applied to emission source characterization, fenceline monitoring, ambient monitoring, pollution mapping, and incident response (including the use of drone-based sampling) for hazardous air pollutants (HAPs), odor nuisance species, and compounds that have high ozone formation potential (OFP) and/or contribute to secondary aerosol (SOA) formation.