Ultrahigh‐pressure dual online solid phase extraction/capillary reverse‐phase liquid chromatography/tandem mass spectrometry (DO‐SPE/cRPLC/MS/MS): A versatile separation platform for high‐throughput and highly sensitive proteomic analyses

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Integrated top-down bottom-up proteomics combined with on-line digestion has great potential to improve the characterization of protein isoforms in biological systems and is amendable to high throughput proteomics experiments. Bottom-up proteomics ultimately provides the peptide sequences derived from the tandem MS analyses of peptides after the proteome has been digested. Topdown proteomics conversely entails the MS analyses of intact proteins for more effective characterization of genetic variations and/or post-translational modifications. Herein, we describe recent efforts toward efficient integration of bottom-up and top-down LC-MS-based proteomics strategies. Since most proteomics separations utilize acidic conditions, we exploited the compatibility of pepsin (where the optimal digestion conditions are at low pH) for integration into bottom-up and top-down proteomics work flows. Pressure-enhanced pepsin digestions were successfully performed and characterized with several standard proteins in either an off-line mode using a Barocycler or an on-line mode using a modified high pressure LC system referred to as a fast on-line digestion system (FOLDS). FOLDS was tested using pepsin and a whole microbial proteome, and the results were compared against traditional trypsin digestions on the same platform. Additionally, FOLDS was integrated with a RePlay configuration to demonstrate an ultrarapid integrated bottom-up top-down proteomics strategy using a standard mixture of proteins and a monkey pox virus proteome. Molecular & Cellular Proteomics 10: 10.1074/ mcp.M110.001479, 1-11, 2011.In-depth characterization and quantitation of protein isoforms, including post-translationally modified proteins, are challenging goals of contemporary proteomics. Traditionally, top-down (1, 2) and bottom-up (3, 4) proteomics have been two distinct analytical paths for liquid-based proteomics analysis. Top-down proteomics is the mass spectrometry (MS)-based characterization of intact proteins, whereas bottom-up proteomics requires a chemical or enzymatic proteolytic digestion of all proteins into peptides prior to MS analysis. Both strategies have their own strengths and challenges and can be thought of as complementary rather than competing analytical techniques.In a top-down proteomics approach, proteins are usually separated by one-or two-dimensional liquid chromatography (LC) and identified using high performance MS (5, 6). This approach is very attractive because it allows the identification of protein isoforms arising from various amino acid modifications, genetic variants (e.g. single nucleotide polymorphisms), mRNA splice variants, and multisite modifications (7) (e.g. specific histone modifications) as well as characterization of proteolytic processing events. However, there are several challenges that have limited the broad application of the approach. Typically, intact proteins are less soluble than their peptide complement, which effectively results in greater losses during various stages of sample handling (i.e. limited...

Section: Folds On Dual On-line Solid Phase Extraction/reversed Phase mentioning

confidence: 99%

Pressurized Pepsin Digestion in Proteomics

López-Ferrer

Pétritis

Robinson

et al. 2011

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“…LC-MS/MS Analysis-The peptide samples were separated using a homebuilt ultrahigh-pressure dual on-line solid phase extraction/capillary reverse-phase liquid chromatography system, having a maximum operating pressure of 10,000 psi, as previously described (32). Briefly, the system is equipped with two reverse-phase liquid chromatography capillary columns (75 m ID ϫ 360 m OD ϫ 80 cm length, C18-bonded particles, 3 m, 300 Å pore size, Jupiter) (Phenomenex, Torrance, CA), which were manufactured in-house through slurry packing.…”

Section: Methodsmentioning

confidence: 99%

A Serum Protein Profile Predictive of the Resistance to Neoadjuvant Chemotherapy in Advanced Breast Cancers

Hyung

Lee

et al. 2011

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aPrediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies. Molecular & Cellular Proteomics

“…LC/MS/MS Experiments-The tryptic yeast peptides were separated using a modified version of our ultrahigh pressure dual on-line solid phase extraction/capillary reverse-phase liquid chromatography (DO-SPE/cRPLC) system, with a maximum operating pressure of 10,000 p.s.i., that is described in detail elsewhere (13). Briefly we incorporated SCX separation (operated using an Agilent 1100 nano-LC system) on line with the DO-SPE/cRPLC system (operated using two ISCO pumps, 100 DM) to construct an on-line two-dimensional LC system.…”

Section: Methodsmentioning

confidence: 99%

Postexperiment Monoisotopic Mass Filtering and Refinement (PE-MMR) of Tandem Mass Spectrometric Data Increases Accuracy of Peptide Identification in LC/MS/MS

Shin

Jung

Hyung

et al. 2008

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Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes ( MS has evolved into a robust and powerful tool for analyzing biomolecules. For example, MS is presently a preferred analytical tool for proteomics experiments in which dynamic populations of proteins are to be identified and quantified. In shotgun proteomics, hundreds or thousands of proteins are routinely identified through LC/MS/MS experiments using the proteolytic digests of proteome samples. During these LC/ MS/MS experiments, peptides are dynamically selected for fragmentation; the resultant tandem mass spectra (usually on the order of 10 5 spectra) are subsequently subjected to database searching (e.g. SEQUEST or Mascot) to best describe or annotate the peptide.Searching a large set of tandem mass spectra against a protein sequence database is a computationally expensive process; the resultant peptide spectral matches (PSMs) 1 are prone to false-positive (FP) identifications, which have led to the development of several validation methods (1, 2). Acquisition of high quality tandem mass spectra is critical to the whole process of protein identification through the shotgun approach because the measured fragment ion masses are compared with the theoretical values predicted by the search algorithms; any factors that adversely affect the acquisition of tandem mass spectra would undoubtedly lead to incorrect peptide assignments. Venable and Yates (3) systematically studied the effects that variability in tandem mass spectral quality had on peptide identification (i.e. peptide scores) and suggested an approach involving both presearch spectral averaging and postsearch XCorr averaging.Large portions (up to 90%) of tandem mass spectral data are not assigned to peptide sequences (4, 5) for many reasons, such as the presence of unknown protein sequences, the possibility of chemical (or post-translational) modification having occurred, insufficient spectral quality for high confi-