Sensitive and rapid method for amino acid quantitation in malaria biological samples using AccQ.Tag ultra performance liquid chromatography-electrospray ionization-MS/MS with multiple reaction monitoring.

An AccQ*Tag ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry (AccQ*Tag-UPLC-ESI-MS/MS) method for fast, reproducible, and sensitive amino acid quantitation in biological samples, particularly, the malaria parasite Plasmodium falciparum is presented. The Waters Acquity TQD UPLC/MS system equipped with a photodiode array (PDA) detector was used for amino acid separation and detection. The method was developed and validated using amino acid standard mixtures containing acidic, neutral, and basic amino acids. For MS analysis, the optimum cone voltage implemented, based on direct infusion analysis of a few selected AccQ*Tag amino acids with multiple reaction monitoring, varied from 29 to 39 V, whereas the collision energy varied from 15 to 35 V. Calibration curves were built using both internal and external standardization. Typically, a linear response for all amino acids was observed at concentration ranges of 3 x 10(-3)-25 pmol/muL. For some amino acids, concentration limits of detection were as low as 1.65 fmol. The coefficients of variation for retention times were within the range of 0.08-1.08%. The coefficients of variation for amino acid quantitation, determined from triplicate UPLC-MS/MS runs, were below 8% on the average. The developed AccQ*Tag-UPLC-ESI-MS/MS method revealed good technical and biological reproducibility when applied to P. falciparum and human red blood cells samples. This study should provide a valuable insight into the performance of UPLC-ESI-MS/MS for amino acid quantitation using AccQ*Tag derivatization.

Microfluidic chips for protein differential expression profiling.

Biomarker discovery and screening using novel proteomic technologies is an area that is attracting increased attention in the biomedical community. Early detection of abnormal physiological conditions will be highly beneficial for diagnosing various diseases and increasing survivability rates. Clearly, progress in this area will depend on the development of fast, reliable, and highly sensitive and specific sample bioanalysis methods. Microfluidics has emerged as a technology that could become essential in proteomics research as it enables the integration of all sample preparation, separation, and detection steps, with the added benefit of enhanced sample throughput. The combination of these advantages with the sensitivity and capability of MS detection to deliver precise structural information makes microfluidics-MS a very competitive technology for biomarker discovery. The integration of LC microchip devices with MS detection, and specifically their applicability to biomarker screening applications in MCF-7 breast cancer cellular extracts is reported in this manuscript. Loading approximately 0.1-1 microg of crude protein extract tryptic digest on the chip has typically resulted in the reliable identification of approximately 40-100 proteins. The potential of an LC-ESI-MS chip for comparative proteomic analysis of isotopically labeled MCF-7 breast cancer cell extracts is explored for the first time.

Combination of an AccQ•Tag-Ultra-Performance Liquid Chromatographic Method with Tandem Mass Spectrometry for the Analysis of Amino Acids.

Amino acid analysis is a powerful tool in life sciences. Current analytical methods used for the detection and quantitation of low abundance amino acids in complex samples face intrinsic challenges such as insufficient sensitivity, selectivity, and throughput. This chapter describes a protocol that makes use of AccQ•Tag chemical derivatization combined with the exceptional chromatographic resolution of ultra-performance liquid chromatography (UPLC) and the sensitivity and selectivity of tandem mass spectrometry (MS/MS). The method has been fully implemented and validated using different tandem quadrupole detectors and thoroughly tested for a variety of samples such as P. falciparum, human red blood cells, and Arabidopsis thaliana extracts. Compared to currently available methods for amino acid analysis, the AccQ•Tag UPLC-MS/MS method presented here provides enhanced sensitivity and reproducibility and offers excellent performance within a short analysis time and a broad dynamic range of analyte concentration. The focus of this chapter is the application of this improved protocol for the compositional amino acid analysis in Arabidopsis thaliana leaf extracts using the Xevo TQ for mass spectrometric detection.

Coupled affinity-hydrophobic monolithic column for on-line removal of immunoglobulin G, preconcentration of low abundance proteins and separation by capillary zone electrophoresis.

A butyl methacrylate-co-ethylene dimethacrylate (BuMA-co-EDMA) monolith was synthesized by UV initiated polymerization at the inlet end of a 75 microm I.D. fused silica capillary that had been previously coated with a protein compatible polymer, poly(vinyl)alcohol. The monolith was used for on-line preconcentration of proteins followed by capillary electrophoresis (CE) separation. For the analysis of standard proteins (cytochrome c, lysozyme and trypsinogen A) this system proved reproducible. The run-to-run %RSD values for migration time and corrected peak area were less than 5%, which is typical of CE. As measured by frontal analysis using lysozyme as solute, saturation of a 1cm monolith was reached after loading 48 ng of protein. Finally, the BuMA-co-EDMA monolithic preconcentrator was coupled to a protein G monolithic column via a zero dead volume union. The coupled system was used for on-line removal of IgG, preconcentration of standard proteins and CE separation. This system could be a valuable sample preparation tool for the analysis of low abundance proteins in complex samples such as human serum, in which high abundance proteins, e.g., human serum albumin (HSA) and immunoglobulin G (IgG), hinder identification and quantification of low abundance proteins.

Preparation and evaluation of poly(polyethylene glycol methyl ether acrylate-co-polyethylene glycol diacrylate) monolith for protein analysis.

A poly(polyethylene glycol methyl ether acrylate-co-polyethylene glycol diacrylate) monolith was prepared by UV-initiated polymerization. Methanol and ethyl ether were selected as porogens from a variety of organic solvents to achieve the desirable characteristics of the monolith. The preparation of the monolith could be achieved within 10 min. The monolith was macroscopically homogeneous, had low flow resistance, and did not swell or shrink significantly in tetrahydrofuran. Inverse size-exclusion data indicate that the monolith had a total porosity of 75.4% and an internal porosity of 9.1%. The monolith could be used for size-exclusion separation of peptides, although it could not separate proteins with molecular masses between 10 and 100 K due to its unique pore size distribution. It was found to resist adsorption of proteins in capillary liquid chromatography when using 100 mM phosphate buffer (pH 7.0) containing 0.5 M NaCl. Complete recovery of both acidic and basic proteins was achieved. The monolith can be used for applications in which inert materials are required for protein analysis.

Design and evaluation of a coupled monolithic pre-concentrator-capillary zone electrophoresis system for the extraction of immunoglobulin G from human serum.

The analysis of proteins in biological fluids by capillary electrophoresis (CE) is of interest in clinical chemistry. However, due to low analyte concentrations and poor concentration limits of detection (CLOD), protein analysis by this technique is frequently challenging. Coupling preconcentration techniques with CE greatly improves the CLOD. An on-line preconcentration-CE method that can selectively pre-concentrate any protein for which an antibody is available would be very useful for the analysis of low abundance proteins and would establish CE as a major tool in biomarker discovery. To accomplish this, the development of an on-line protein G monolithic pre-concentrator-CE device is proposed. To generate active groups for protein immobilization, glycidyl methacrylate (GMA) was used to prepare polymer monoliths. A 1.5-2 cm monolith was cast inside a 75 microm I.D. fused silica capillary that had previously been coated with alternating layers of negatively (dextran) and positively (polybrene) charged polymers. Protein G was covalently bound to GMA. Monoliths from different formulations were prepared and evaluated for binding capacity to optimize the monolith formulation for protein preconcentration. The physical properties of the column considered best for preconcentration were determined by mercury intrusion porosimetry. The total pore area was 4.8m(2)/g, the average pore diameter was 3.3 microm and the porosity was 82%. The monolith had a low flow resistance and was macroscopically homogeneous. The effectiveness of the monolith to rapidly pre-concentrate proteins at flow rates as high as 10 microL/min was demonstrated using a 1.8 microM IgG solution. This system proved effective for on-line sample extraction, clean-up, preconcentration, and CE of IgG in human serum. IgG from diluted (500 and 65,000 times) human serum samples was successfully analyzed using this system. The approach can be applied to the on-line preconcentration and analysis of any protein for which an antibody is available.

An UPLC-ESI-MS/MS Assay Using 6-Aminoquinolyl-N-Hydroxysuccinimidyl Carbamate Derivatization for Targeted Amino Acid Analysis: Application to Screening of Arabidopsis thaliana Mutants.

In spite of the large arsenal of methodologies developed for amino acid assessment in complex matrices, their implementation in metabolomics studies involving wide-ranging mutant screening is hampered by their lack of high-throughput, sensitivity, reproducibility, and/or wide dynamic range. In response to the challenge of developing amino acid analysis methods that satisfy the criteria required for metabolomic studies, improved reverse-phase high-performance liquid chromatography-mass spectrometry (RPHPLC-MS) methods have been recently reported for large-scale screening of metabolic phenotypes. However, these methods focus on the direct analysis of underivatized amino acids and, therefore, problems associated with insufficient retention and resolution are observed due to the hydrophilic nature of amino acids. It is well known that derivatization methods render amino acids more amenable for reverse phase chromatographic analysis by introducing highly-hydrophobic tags in their carboxylic acid or amino functional group. Therefore, an analytical platform that combines the 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) pre-column derivatization method with ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) is presented in this article. For numerous reasons typical amino acid derivatization methods would be inadequate for large scale metabolic projects. However, AQC derivatization is a simple, rapid and reproducible way of obtaining stable amino acid adducts amenable for UPLC-ESI-MS/MS and the applicability of the method for high-throughput metabolomic analysis in Arabidopsis thaliana is demonstrated in this study. Overall, the major advantages offered by this amino acid analysis method include high-throughput, enhanced sensitivity and selectivity; characteristics that showcase its utility for the rapid screening of the preselected plant metabolites without compromising the quality of the metabolic data. The presented method enabled thirty-eight metabolites (proteinogenic amino acids and related compounds) to be analyzed within 10 min with detection limits down to 1.02 × 10-11 M (i.e., atomole level on column), which represents an improved sensitivity of 1 to 5 orders of magnitude compared to existing methods. Our UPLC-ESI-MS/MS method is one of the seven analytical platforms used by the Arabidopsis Metabolomics Consortium. The amino acid dataset obtained by analysis of Arabidopsis T-DNA mutant stocks with our platform is captured and open to the public in the web portal PlantMetabolomics.org. The analytical platform herein described could find important applications in other studies where the rapid, high-throughput and sensitive assessment of low abundance amino acids in complex biosamples is necessary.

Combination of an AccQ·Tag-ultra performance liquid chromatographic method with tandem mass spectrometry for the analysis of amino acids.

Amino acid analysis is a powerful tool in life sciences. Current analytical methods used for the detection and quantitation of low abundance amino acids in complex samples face intrinsic challenges such as insufficient sensitivity, selectivity, and throughput. This chapter describes a protocol that makes use of AccQ∙Tag chemical derivatization combined with the exceptional chromatographic resolution of ultra performance liquid chromatography (UPLC), and the sensitivity and selectivity of tandem mass spectrometry (MS/MS). The method has been fully implemented and validated using different tandem quadrupole detectors, and thoroughly tested for a variety of samples such as Plasmodium falciparum, human red blood cells, and Arabidopsis thaliana extracts. Compared to currently available methods for amino acid analysis, the AccQ∙Tag UPLC-MS/MS method presented here provides enhanced sensitivity and reproducibility, and offers excellent performance within a short analysis time and a broad dynamic range of analyte concentration. The focus of this chapter is the application of this improved protocol for the compositional amino acid analysis in A. thaliana leaf extracts using the Xevo TQ for mass spectrometric detection.

Plant metabolomics by GC-MS and differential analysis.

Metabolomics is a new genomics approach that aims at measuring all or a subset of metabolites in the cell. Several approaches to plant metabolomics are currently used in plant research. These include targeted analysis, metabolite profiling, and metabolic fingerprinting. Metabolic fingerprinting, unlike metabolite profiling, does not aim at separating or identifying all the metabolites present in the sample, but rather generates a fingerprint that characterizes a specific metabolic state of the plant system under investigation. This chapter describes the implementation of metabolic fingerprinting approach using gas chromatography coupled to mass spectrometry (GC-MS) and discriminant function analysis combined with genetic algorithm (GA-DFA). This approach enables the identification of specific metabolites that are biologically relevant, and which may go undetected if direct infusion-based fingerprinting approaches were used due to the sample complexity and matrix suppression effects.

Fast proteomic protocol for biomarker fingerprinting in cancerous cells.

The advance of novel technologies that will enable the detection of large sets of biomarker proteins, to greatly improve the sensitivity and specificity of an assay, represents a major objective in biomedical research. To demonstrate the power of mass spectrometry (MS) detection for large-scale biomarker screening in cancer research, a simple, one-step approach for fast biomarker fingerprinting in complex cellular extracts is described. MCF-7 breast cancer cells were used as a model system. Fast proteomic profiling of whole cellular extracts was achieved on a linear trap quadrupole (LTQ) mass spectrometer by one of the following techniques: (a) data-dependent liquid chromatography (LC)-MS/MS of un-labeled cell extracts, (b) data-dependent LC-MS/MS with pulsed Q dissociation (PQD) detection of iTRAQ labeled samples, and (c) multiple reaction monitoring (MRM)-MS of low abundant proteins that could not be detected with data-dependent MS/MS. The data-dependent LC-MS/MS analysis of MCF-7 cells enabled the identification of 796 proteins (p<0.001) and the simultaneous detection of 156 previously reported putative cancer biomarkers. PQD detection of iTRAQ labeled cells resulted in the detection of 389 proteins and 64 putative biomarkers. MRM-MS analysis enabled the successful monitoring of a panel of low-abundance proteins in one single experiment, highlighting the utility of this technique for targeted analysis in cancer investigations. These results demonstrate that MS-based technologies relying on a one-step separation protocol have the potential to revolutionize biomarker research and screening applications by enabling fast, sensitive and reliable detection of large panels of putative biomarkers. To further stimulate the exploration of proteins that have been previously reported in the literature to be differentially expressed in a variety of cancers, an extensive list of approximately 1100 candidate biomarkers has been compiled and included in the manuscript.

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology.

An isotope tags for relative and absolute quantitation (iTRAQ)-based reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) method was developed for differential protein expression profiling in complex cellular extracts. The estrogen positive MCF-7 cell line, cultured in the presence of 17beta-estradiol (E2) and tamoxifen (Tam), was used as a model system. MS analysis was performed with a linear trap quadrupole (LTQ) instrument operated by using pulsed Q dissociation (PQD) detection. Optimization experiments were conducted to maximize the iTRAQ labeling efficiency and the number of quantified proteins. MS data filtering criteria were chosen to result in a false positive identification rate of <4%. The reproducibility of protein identifications was approximately 60%-67% between duplicate, and approximately 50% among triplicate LC-MS/MS runs, respectively. The run-to-run reproducibility, in terms of relative standard deviations (RSD) of global mean iTRAQ ratios, was better than 10%. The quantitation accuracy improved with the number of peptides used for protein identification. From a total of 530 identified proteins (P < 0.001) in the E2/Tam treated MCF-7 cells, a list of 255 proteins (quantified by at least two peptides) was generated for differential expression analysis. A method was developed for the selection, normalization, and statistical evaluation of such datasets. An approximate approximately 2-fold change in protein expression levels was necessary for a protein to be selected as a biomarker candidate. According to this data processing strategy, approximately 16 proteins involved in biological processes such as apoptosis, RNA processing/metabolism, DNA replication/transcription/repair, cell proliferation and metastasis, were found to be up- or down-regulated.