A novel mass spectrometry cluster for high-throughput quantitative proteomics

Proteomic studies related to genetic determinants of variability in protein concentrations

Genetic variation has multiple effects on the proteome. It may influence the expression level of proteins, modify their sequences through single nucleotide polymorphisms, the occurrence of allelic variants, or alternative splicing (ASP) events. This perspective paper summarizes the major effects of genetic variability on protein expression and isoforms and provides an overview of proteomics techniques and methods that allow studying the effects of genetic variability at different levels of the proteome. The paper provides an overview of recent quantitative trait loci studies performed to explore the effect of genetic variation on protein expression (pQTL). Finally it gives a perspective view on advances in proteomics technology and the role of the Chromosome-Centric Human Proteome Project (C-HPP) by creating large-scale resources that may facilitate performing more comprehensive pQTL experiments in the future.

Parallel deep transcriptome and proteome analysis of zebrafish larvae

Background

Sensitivity and throughput of transcriptomic and proteomic technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNA-seq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and for any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in embryos of zebrafish, which is an upcoming model in disease studies.

Results

We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes. Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed.

Conclusions

By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies.

Identification of genetic variants influencing the human plasma proteome

Genetic variants influencing the transcriptome have been extensively studied. However, the impact of the genetic factors on the human proteome is largely unexplored, mainly due to lack of suitable high-throughput methods. Here we present unique and comprehensive identification of genetic variants affecting the human plasma protein profile by combining high-throughput and high-resolution mass spectrometry (MS) with genome-wide SNP data. We identified and quantified the abundance of 1,056 tryptic-digested peptides, representing 163 proteins in the plasma of 1,060 individuals from two population-based cohorts. The abundance level of almost one-fifth (19%) of the peptides was found to be heritable, with heritability ranging from 0.08 to 0.43. The levels of 60 peptides from 25 proteins, 15% of the proteins studied, were influenced by cis-acting SNPs. We identified and replicated individual cis-acting SNPs (combined P value ranging from 3.1 × 10(-52) to 2.9 × 10(-12)) influencing 11 peptides from 5 individual proteins. These SNPs represent both regulatory SNPs and nonsynonymous changes defining well-studied disease alleles such as the ε4 allele of apolipoprotein E (APOE), which has been shown to increase risk of Alzheimer's disease. Our results show that high-throughput mass spectrometry represents a promising method for large-scale characterization of the human proteome, allowing for both quantification and sequencing of individual proteins. Abundance and peptide composition of a protein plays an important role in the etiology, diagnosis, and treatment of a number of diseases. A better understanding of the genetic impact on the plasma proteome is therefore important for evaluating potential biomarkers and therapeutic agents for common diseases.

Standardized and automated solid-phase extraction procedures for high-throughput proteomics of body fluids

In order to balance the speed of analytical sample preparation procedures with mass spectrometry (MS)-based clinical proteomics the application of high-throughput robotic systems for body fluid workup is essential. In this paper we describe the implementation of various solid-phase extraction (SPE) sample preparation protocols on two different platforms, namely: 1) Magnetic bead-based SPE of peptides and proteins from body fluids on a Hamilton liquid handling workstation; 2) Cartridge-based SPE on a SPARK Symbiosis system. All SPE protocols were optimized for MS-based proteomics and compared with respect to obtained peptide- and protein profiles. Throughput numbers that were achieved in a 24 hour time frame for the sample workup procedures were more than 700 samples for the magnetic bead-based method and over 1000 samples for the cartridge-based method.

Identification of human serum peptides in fourier transform ion cyclotron resonance precision profiles

The continuous efforts to find new prognostic or diagnostic biomarkers have stimulated the use of mass spectrometry (MS) profiles in a clinical setting. In the early days (about one decade ago), a single low-resolution mass spectrum derived from an individual's body fluid was used for comparative studies. However, a peptide profile of a complex mixture is most informative when recorded on an ultrahigh resolution instrument such as a Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. In this study we show the benefits of the ultrahigh resolving power and the high mass accuracy and precision provided by an FTICR mass spectrometer equipped with a 15-tesla magnet. The ultrahigh-resolution data not only allow assignment of fragment ions with high charge states (4+, 5+) but also enhance confidence of human serum peptide identifications from tandem MS experiments. This is exemplified with collision-induced dissociation (CID) and electron transfer dissociation (ETD) data of middle-down-sized endogenous or protein-breakdown peptides that are of interest in biomarker discovery studies.

Scientific workflow management in proteomics

Data processing in proteomics can be a challenging endeavor, requiring extensive knowledge of many different software packages, all with different algorithms, data format requirements, and user interfaces. In this article we describe the integration of a number of existing programs and tools in Taverna Workbench, a scientific workflow manager currently being developed in the bioinformatics community. We demonstrate how a workflow manager provides a single, visually clear and intuitive interface to complex data analysis tasks in proteomics, from raw mass spectrometry data to protein identifications and beyond.

Precision profiling and identification of human serum peptides using Fourier transform ion cyclotron resonance mass spectrometry

Many biomarker discovery studies are based on matrix-assisted laser desorption/ionisation (MALDI) peptide profiles. In this study, 96 human serum samples were analysed on a Bruker solariX(TM) MALDI Fourier transform ion cyclotron resonance (FTICR) system equipped with a 15 tesla magnet. Isotopically resolved peptides were observed in ultrahigh resolution FTICR profiles up to m/z 6500 with mass measurement errors (MMEs) of previously identified peptides at a sub-ppm level. For comparison with our previous platform for peptide profile mass analysis (i.e. Ultraflex II) the corresponding time-of-flight (TOF) spectra were obtained with isotopically resolved peptides up to m/z 3500. The FTICR and TOF systems performed rather similar with respect to the repeatability of the signal intensities. However, the mass measurement precision improved at least 10-fold in ultrahigh resolution data and thus simplified spectral alignment necessary for robust and quantitatively precise comparisons of profiles in large-scale clinical studies. From each single MALDI-FTICR spectrum an m/z-list was obtained with sub-ppm precision for all different species, which is beneficial for identification purposes and interlaboratory comparisons. Furthermore, the FTICR system allowed new peptide identifications from collision-induced dissociation (CID) spectra using direct infusion of reversed-phase (RP) C(18)-fractionated serum samples on an electrospray ionisation (ESI) source.

Protein expression dynamics during Escherichia coli glucose-lactose diauxie

Background

Escherichia coli is a well-studied anaerobic bacteria which is able to regulate metabolic pathways depending on the type of sugar presented in the medium. We have studied the glucose-lactose shift in E. coli at the protein level using a recently developed mass spectrometry platform.

Method

Cells were grown in minimal medium containing two sugars (glucose and lactose) and analyzed using novel mass spectrometry cluster. The cluster combines the high resolving power and dynamic range of Fourier transform ion cyclotron resonance (FTICR) for accurate mass measurement and quantitation with multiple ion traps for fast and sensitive tandem mass spectrometry. The protein expression profile was followed in time across the glucose-lactose diauxic shift using label-free quantitation from the FTICR data.

Results and Conclusion

The entire dataset was interrogated by KEGG pathway analysis, mapping measured changes in protein abundance onto known metabolic pathways. The obtained results were consistent with previously published gene expression data, with β-galactosidase being the most strongly induced protein during the diauxic shift.