Using size exclusion chromatography‐RPLC and RPLC‐CIEF as two‐dimensional separation strategies for protein profiling

Abstract

Bottom-up proteomics (analyzing peptides that result from protein digestion) has demonstrated capability for broad proteome coverage and good throughput. However, due to incomplete sequence coverage, this approach is not ideally suited to the study of modified proteins. The modification complement of a protein can best be elucidated by analyzing the intact protein. 2-DE, typically coupled with the analysis of peptides that result from in-gel digestion, is the most frequently applied protein separation technique in MS-based proteomics. As an alternative, numerous column-based liquid phase techniques, which are generally more amenable to automation, are being investigated. In this work, the combination of size-exclusion chromatography (SEC) fractionation with RPLC-Fourier-transform ion cyclotron resonance (FTICR)-MS is compared with the combination of RPLC fractionation with CIEF-FTICR-MS for the analysis of the Shewanella oneidensis proteome. SEC-RPLC-FTICR-MS allowed the detection of 297 proteins, as opposed to 166 using RPLC-CIEF-FTICR-MS, indicating that approaches based on LC-MS provide better coverage. However, there were significant differences in the sets of proteins detected and both approaches provide a basis for accurately quantifying changes in protein and modified protein abundances.

Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy

“…36 While these studies demonstrate the power of multi-dimensional separation platforms for the deep profiling of proteomes by top-down MS, none of these works achieved top-down MS analysis of proteins larger than 80 kDa. 21,37,38 Zhang et al employed isoelectric focusing and superficially porous silica LC for the separation of mouse cardiac tissue extract, and were able to detect myosin heavy chain variants. 39 However, the coverage of the cardiac proteome was limited.…”

Progress in Top-Down Proteomics and the Analysis of Proteoforms

“…The benefit of increasing the depth of proteome coverage with more extensive front-end fractionation seemed to outweigh the cost of higher complexity of the experimental design and increased variability for these studies, as platform development was primarily gauged on the basis of the number of IDs that were yielded. For example, in a comparison of two different two-dimensional approaches, Simpson et al (27) reported 297 IDs by size-exclusion chromatography fractionation, with RPLC-FTICR-MS providing better coverage than the 166 IDs determined from an RPLC-CIEF-FTICR-MS experiment. Interestingly, however, the IDs yielded from either strategy were markedly different, which makes a case for the complementarity of front-end prefractionation approaches.…”

Sensitive and Specific Identification of Wild Type and Variant Proteins from 8 to 669 kDa Using Top-down Mass Spectrometry

“…Top-down proteomics is hampered by the lack of a sample preparation technique as generally applicable as in-gel digestion (25), the lack of a fragmentation technique as robust as collisionally activated dissociation (CAD) of peptides (26), and the lack of compatibility of top-down MS data with all popular bottom-up software (27), including Mascot (28), Sequest (29), Protein Prospector (30), and Peptide Search (31). Recent improvements in these areas include the following: 1) separation techniques compatible with top-down analysis (18,32); 2) fragmentation techniques, including nozzle-skimmer dissociation (3,(33)(34)(35), electron transfer dissociation (7,8), prefolding dissociation (9), and activated ion electron capture dissociation (ECD) (5), which extended the protein upper mass limit to 229 kDa for top-down methods (9); and 3) a search engine as advanced as any bottom-up software; ProSight PTM (11,12,36) was developed and integrated with a comprehensive MS database within the Xcalibur software suite (Thermo Finnigan, San José , CA). Our objectives here are (i) to help develop an extended version of Mascot that incorporates top-down MS 2 search ability and (ii) to infer its search sensitivity and selectivity.…”