Modern Mass Spectrometers Research Articles

Numerical Compression Schemes for Proteomics Mass Spectrometry Data

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

Exploiting Thread-Level and Instruction-Level Parallelism to Cluster Mass Spectrometry Data using Multicore Architectures.

Modern mass spectrometers can produce large numbers of peptide spectra from complex biological samples in a short time. A substantial amount of redundancy is observed in these data sets from peptides that may get selected multiple times in Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments. A large number of spectra do not get mapped to specific peptide sequences due to low signal-to-noise (S/N) ratio of the spectra from these machines. Clustering is one way to mitigate the problems of these complex mass spectrometry data sets. Recently we presented a graph theoretic framework, known as CAMS, for clustering of large-scale mass spectrometry data. CAMS utilized a novel metric to exploit the spatial patterns in the mass spectrometry peaks which allowed highly accurate clustering results. However, comparison of each spectrum with every other spectrum makes the clustering problem computationally inefficient. In this paper we present a parallel algorithm, called P-CAMS, that uses thread-level and instruction-level parallelism on multicore architectures to substantially decrease running times. P-CAMS relies on intelligent matrix completion to reduce the number of comparisons, threads to run on each core and Single Instruction Multiple Data (SIMD) paradigm inside each thread to exploit massive parallelism on multicore architectures. A carefully crafted load-balanced scheme that uses spatial locations of the mass spectrometry peaks mapped to nearest level cache and core allows super-linear speedups. We study the scalability of the algorithm with a wide variety of mass spectrometry data and variation in architecture specific parameters. The results show that SIMD style data parallelism combined with thread-level parallelism for multicore architectures is a powerful combination that allows substantial reduction in runtimes even for all-to-all comparison algorithms. The quality assessment is performed using real-world data set and is shown to be consistent with the serial version of the same algorithm.

Analysis of Isotopic Labeling in Peptide Fragments by Tandem Mass Spectrometry

Phenotype in multicellular organisms is the consequence of dynamic metabolic events that occur in a spatially dependent fashion. This spatial and temporal complexity presents challenges for investigating metabolism; creating a need for improved methods that effectively probe biochemical events such as amino acid biosynthesis. Isotopic labeling can provide a temporal-spatial recording of metabolic events through, for example, the description of enriched amino acids in the protein pool. Proteins are therefore an important readout of metabolism and can be assessed with modern mass spectrometers. We compared the measurement of isotopic labeling in MS2 spectra obtained from tandem mass spectrometry under either higher energy collision dissociation (HCD) or collision induced dissociation (CID) at varied energy levels. Developing soybean embryos cultured with or without 13C-labeled substrates, and Escherichia coli MG1655 enriched by feeding 7% uniformly labeled glucose served as a source of biological material for protein evaluation. CID with low energies resulted in a disproportionate amount of heavier isotopologues remaining in the precursor isotopic distribution. HCD resulted in fewer quantifiable products; however deviation from predicted distributions were small relative to the CID-based comparisons. Fragment ions have the potential to provide information on the labeling of amino acids in peptides, but our results indicate that without further development the use of this readout in quantitative methods such as metabolic flux analysis is limited.

Techniques for the Analysis of Cysteine Sulfhydryls and Oxidative Protein Folding

Modification of cysteine thiols dramatically affects protein function and stability. Hence, the abilities to quantify specific protein sulfhydryl groups within complex biological samples and map disulfide bond structures are crucial to gaining greater insights into how proteins operate in human health and disease. Many different molecular probes are now commercially available to label and track cysteine residues at great sensitivity. Coupled with mass spectrometry, stable isotope-labeled sulfhydryl-specific reagents can provide previously unprecedented molecular insights into the dynamics of cysteine modification. Likewise, the combined application of modern mass spectrometers with improved sample preparation techniques and novel data mining algorithms is beginning to routinize the analysis of complex protein disulfide structures. Proper application of these modern tools and techniques, however, still requires fundamental understanding of sulfhydryl chemistry as well as the assumptions that accompany sample preparation and underlie effective data interpretation. The continued development of tools, technical approaches, and corresponding data processing algorithms will, undoubtedly, facilitate site-specific protein sulfhydryl quantification and disulfide structure analysis from within complex biological mixtures with ever-improving accuracy and sensitivity. Fully routinizing disulfide structure analysis will require an equal but balanced focus on sample preparation and corresponding mass spectral dataset reproducibility.

Mass spectrometry and inflammation—MS methods to study oxidation and enzyme-induced changes of phospholipids

Many diseases such as arthritis or atherosclerosis are accompanied by inflammatory processes. Inflammation is characterized by the infiltration of cells such as neutrophilic granulocytes and (a) the release of phospholipases [particularly phospholipase A2 (PLA2)] and (b) the generation of reactive oxygen as well as nitrogen species (ROS and RNS). While PLA2 leads to defined lyso products (lacking one acyl residue), lipid oxidation is characterized by much more complex product patterns, including lipid peroxides, aldehydes (by double bond cleavage), and many others. Nevertheless, oxidation processes are highly important under in vivo conditions because molecules with regulatory functions are generated by the oxidation of lipids and/or free fatty acids. Therefore, lipid oxidation products as well as lysolipids are increasingly assumed to represent important disease (bio)markers. Consequently, there is also increasing interest in methods to characterize these products qualitatively and quantitatively.Mass spectrometry (MS) seems to be the method of choice to study (phospho)lipids changed under inflammatory conditions: nowadays, soft ionization MS methods are regularly used to study oxidative lipid modifications because of their high sensitivities and the tremendous mass resolutions that are achievable by using modern mass spectrometers. However, experimental care is required to be able to detect all relevant products. Although electrospray ionization (ESI) MS is so far most popular, applications of matrix-assisted laser desorption and ionization (MALDI) MS are continuously increasing. This review aims to summarize the so far available data on MS analyses of oxidized lipids as well as lysolipids. In addition to model systems, special attention will be paid to the monitoring of oxidized lipids and lysolipids under in vivo conditions. It is the aim of this review to provide a critical survey of the advantages and drawbacks of the different MS methods, with the focus on MALDI and ESI.

Use of theoretical peptide distributions in phosphoproteome analysis.

The high mass accuracy and resolution of modern mass spectrometers provides new opportunities to employ theoretical peptide distributions in large-scale proteomic studies. We used theoretical distributions to study noise filtering and mass measurement errors and to examine mass-based differentiation of phosphorylated and nonphosphorylated peptides. Only the monoisotopic mass of the experimental precursor ion was necessary for this analysis. We found that peak deviations can be used to characterize the modification states of peptides in a sample. When applied to large-scale proteomic data sets, the peak deviation distribution can be used to filter chemical/electronic noise for singly charged species. Using peak deviation distributions, it is possible to separate the phosphorylated peptides from the nonphosphorylated peptides, enabling evaluation of the phosphoproteome content of a sample. Because this approach is simple, with light computational requirements, the analysis of theoretical peptide distributions has a significant potential for application to phosphoproteome analyses. For our studies we used publicly available data sets from three large-scale proteomic studies.

Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology

The recent development of metaproteomics has enabled the direct identification and quantification of expressed proteins from microbial communities in situ, without the need for microbial enrichment. This became possible by (1) significant increases in quality and quantity of metagenome data and by improvements of (2) accuracy and (3) sensitivity of modern mass spectrometers (MS). The identification of physiologically relevant enzymes can help to understand the role of specific species within a community or an ecological niche. Beside identification, relative and absolute quantitation is also crucial. We will review label-free and label-based methods of quantitation in MS-based proteome analysis and the contribution of quantitative proteome data to microbial ecology. Additionally, approaches of protein-based stable isotope probing (protein-SIP) for deciphering community structures are reviewed. Information on the species-specific metabolic activity can be obtained when substrates or nutrients are labeled with stable isotopes in a protein-SIP approach. The stable isotopes ((13)C, (15)N, (36)S) are incorporated into proteins and the rate of incorporation can be used for assessing the metabolic activity of the corresponding species. We will focus on the relevance of the metabolic and phylogenetic information retrieved with protein-SIP studies and for detecting and quantifying the carbon flux within microbial consortia. Furthermore, the combination of protein-SIP with established tools in microbial ecology such as other stable isotope probing techniques are discussed.

Quantitative analysis of global phosphorylation changes with high-resolution tandem mass spectrometry and stable isotopic labeling

Quantitative measurement of specific protein phosphorylation sites is a primary interest of biologists, as site-specific phosphorylation information provides insights into cell signaling networks and cellular dynamics at a system level. Over the last decade, selective phosphopeptide enrichment methods including IMAC and metal oxides (TiO2 and ZrO2) have been developed and greatly facilitate large scale phosphoproteome analysis of various cells, tissues and living organisms, in combination with modern mass spectrometers featuring high mass accuracy and high mass resolution. Various quantification strategies have been applied to detecting relative changes in expression of proteins, peptides, and specific modifications between samples. The combination of mass spectrometry-based phosphoproteome analysis with quantification strategies provides a straightforward and unbiased method to identify and quantify site-specific phosphorylation. We describe common strategies for mass spectrometric analysis of stable isotope labeled samples, as well as two widely applied phosphopeptide enrichment methods based on IMAC(NTA–Fe3+) and metal oxide (ZrO2). Instrumental configurations for on-line LC–tandem mass spectrometric analysis and parameters of conventional bioinformatic analysis of large data sets are also considered for confident identification, localization, and reliable quantification of site-specific phosphorylation.

Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research.

Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research.

Coffee-ring effects in laser desorption/ionization mass spectrometry

This report focuses on the heterogeneous distribution of small molecules (e.g. metabolites) within dry deposits of suspensions and solutions of inorganic and organic compounds with implications for chemical analysis of small molecules by laser desorption/ionization (LDI) mass spectrometry (MS). Taking advantage of the imaging capabilities of a modern mass spectrometer, we have investigated the occurrence of “coffee rings” in matrix-assisted laser desorption/ionization (MALDI) and surface-assisted laser desorption/ionization (SALDI) sample spots. It is seen that the “coffee-ring effect” in MALDI/SALDI samples can be both beneficial and disadvantageous. For example, formation of the coffee rings gives rise to heterogeneous distribution of analytes and matrices, thus compromising analytical performance and reproducibility of the mass spectrometric analysis. On the other hand, the coffee-ring effect can also be advantageous because it enables partial separation of analytes from some of the interfering molecules present in the sample. We report a “hidden coffee-ring effect” where under certain conditions the sample/matrix deposit appears relatively homogeneous when inspected by optical microscopy. Even in such cases, hidden coffee rings can still be found by implementing the MALDI-MS imaging technique. We have also found that to some extent, the coffee-ring effect can be suppressed during SALDI sample preparation.

Honing in on phenotypes: comprehensive two-dimensional gas chromatography of herbivory-induced volatile emissions and novel opportunities for system-level analyses

Plant volatile organic compound (VOC) production requires a complex network of biochemical pathways, which, although well mapped from a biochemical point of view, remains only partly understood with regard to its physiological and genetic regulation. Additionally, although analytical procedures for plant VOC measurement have become increasingly faster and more sensitive in recent years, pinpointing relevant shifts in VOC production from the thousands of molecular fragments that are generated by modern mass spectrometer instruments remains challenging. Here we discuss novel opportunities for system-wide analysis provided by the implementation of non-targeted data processing and multivariate statistics in VOC analysis. We illustrate the value of implementing non-targeted data processing with examples of recent findings from our group on the interactive control exerted by salivary components of a lepidopteran herbivore, Manduca sexta, on herbivory-induced VOC emissions in the wild tobacco Nicotiana attenuata. Finally, we briefly discuss the use of multi-platform data integration for probing the nature of metabolic and regulatory systems underlying VOC emissions.

Comparison of the LTQ‐Orbitrap Velos and the Q‐Exactive for proteomic analysis of 1–1000 ng RAW 264.7 cell lysate digests

There is interest in extending bottom-up proteomics to the smallest possible sample size. We investigated the performance of two modern mass spectrometers for the analysis of samples ranging from 1 ng to 1 µg of RAW 264.7 cell lysate digests. An ultra-performance liquid chromatography (UPLC) system coupled with either an LTQ-Orbitrap Velos or a Q-Exactive mass spectrometer was used for peptide separation and identification. For 1-1000 ng RAW 264.7 cell lysate digests, the Q-Exactive generated 10-83% more protein groups and 11-109% more peptides than the LTQ-Orbitrap Velos (higher-energy collisional dissociation, HCD) with MASCOT database searching, due to its faster scan rate and higher resolution. In addition, HCD and collision-induced dissociation (CID) modes of the LTQ-Orbitrap Velos were compared. HCD produced higher peptide and protein group IDs than CID for 1-1000 ng RAW 264.7 cell lysate digests with MASCOT database searching. Database searching results from SEQUEST and MASCOT were also compared and comparable protein group IDs were obtained from the two search engines. The Q-Exactive outperformed the LTQ-Orbitrap Velos for shotgun proteomics analysis of 1 to 1000 ng RAW 264.7 cell lysate digests in terms of obtained peptide and protein group IDs.

Application of mass spectrometry to hair analysis for forensic toxicological investigations

The increasing role of hair analysis in forensic toxicological investigations principally owes to recent improvements of mass spectrometric instrumentation. Research achievements during the last 6 years in this distinctive application area of analytical toxicology are reviewed. The earlier state of the art of hair analysis was comprehensively covered by a dedicated book (Kintz, 2007a. Analytical and practical aspects of drug testing in hair. Boca Raton: CRC Press and Taylor & Francis, 382 p) that represents key reference of the present overview. Whereas the traditional organization of analytical methods in forensic toxicology divided target substances into quite homogeneous groups of drugs, with similar structures and chemical properties, the current approach often takes advantage of the rapid expansion of multiclass and multiresidue analytical procedures; the latter is made possible by the fast operation and extreme sensitivity of modern mass spectrometers. This change in the strategy of toxicological analysis is reflected in the presentation of the recent literature material, which is mostly based on a fit-for-purpose logic. Thus, general screening of unknown substances is applied in diverse forensic contexts than drugs of abuse testing, and different instrumentation (triple quadrupoles, time-of-flight analyzers, linear and orbital traps) is utilized to optimally cope with the scope. Other key issues of modern toxicology, such as cost reduction and high sample throughput, are discussed with reference to procedural and instrumental alternatives.

The Problem with Peptide Presumption and the Downfall of Target–Decoy False Discovery Rates

In proteomics, peptide-tandem mass spectrum match scores and target-decoy database derived false discovery rates (FDR) are confidence indicators describing the quality of individual and sets of tandem mass spectrum matches. A user can impose a standard by prescribing a limit to these values, equivalent to drawing a line that separates better from poorer quality matches. As a result of setting narrower parent ion mass tolerances to reflect the better resolution of modern mass spectrometers, target-decoy derived FDRs can diminish. FDRs lowered this way consequently drive down the lower-limit for peptide-spectrum match score acceptance. Hence, data quality confidence appears to improve even while fragmentation evidence for some spectra remains weak. One negative outcome can be the presumed identification of peptides that do not exist. The options researchers have to improve proteomics data confidence are not panaceas, and there may be no satisfying solution as long as peptides are identified from a circumscribed list of proteins scientists wish to find.

A Protocol for the Identification of Protein-protein Interactions Based on <sup>15</sup>N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available1. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment2. Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice3. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown4, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)5 and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants.Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for 15N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein7 (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state8. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE19.

A Protocol for the Identification of Protein-protein Interactions Based on <sup>15</sup>N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available1. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment2. Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice3. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown4, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)5 and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants. Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for 15N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein7 (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state8. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE19.

Tempest: GPU-CPU Computing for High-Throughput Database Spectral Matching

Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

Highly Accurate Chemical Formula Prediction Tool Utilizing High-Resolution Mass Spectra, MS/MS Fragmentation, Heuristic Rules, and Isotope Pattern Matching

Mass spectrometry is commonly applied to qualitatively and quantitatively profile small molecules, such as peptides, metabolites, or lipids. Modern mass spectrometers provide accurate measurements of mass-to-charge ratios of ions, with errors as low as 1 ppm. Even such high mass accuracy, however, is not sufficient to determine the unique chemical formula of each ion, and additional algorithms are necessary. Here we present a universal software tool for predicting chemical formulas from high-resolution mass spectrometry data, developed within the MZmine 2 framework. The tool is based on the use of a combination of heuristic techniques, including MS/MS fragmentation analysis and isotope pattern matching. The performance of the tool was evaluated using a real metabolomic data set obtained with the Orbitrap MS detector. The true formula was correctly determined as the highest-ranking candidate for 79% of the tested compounds. The novel isotope pattern-scoring algorithm outperformed a previously published method in 64% of the tested Orbitrap spectra. The software described in this manuscript is freely available and its source code can be accessed within the MZmine 2 source code repository.

Integrated Quantitative And Qualitative Workflow For In Vivo Bioanalytical Support in Drug Discovery Using Hybrid Q-Tof-Ms

UHPLC coupled with orthogonal acceleration hybrid quadrupole-TOF (Q-TOF)-MS is an emerging technique offering new strategies for the efficient screening of new chemical entities and related molecules at the early discovery stage within the pharmaceutical industry. In the first part of this article, we examine the main instrumental parameters that are critical for the integration of UHPLC-Q-TOF technology to existing bioanalytical workflows, in order to provide simultaneous quantitative and qualitative bioanalysis of samples generated following in vivo studies. Three modern Q-TOF mass spectrometers, including Bruker maXis™, Agilent 6540 and Sciex TripleTOF™ 5600, all interfaced with UHPLC systems, are evaluated in the second part of the article. The scope of this work is to demonstrate the potential of Q-TOF for the analysis of typical small molecules, therapeutic peptides (molecular weight <6000 Da), and enzymatically (i.e., trypsin, chymotrypsin and pepsin) cleaved peptides from larger proteins. This work focuses mainly on full-scan TOF data obtained under ESI conditions, the major mode of TOF operation in discovery bioanalytical research, where the compounds are selected based on their pharmacokinetic/pharmacodynamic behaviors using animal models prior to selecting a few desirable candidates for further development. Finally, important emerging TOF technologies that could potentially benefit bioanalytical research in the semi-quantification of metabolites without synthesized standards are discussed. Particularly, the utility of captive spray ionization coupled with TripleTOF 5600 was evaluated for improving sensitivity and providing normalized MS response for drugs and their metabolites. The workflow proposed compromises neither the efficiency, nor the quality of pharmacokinetic data in support of early drug discovery programs.

A New Single Vacuum Furnace Design for Cosmogenic 3He Dating

Helium‐3 is a stable cosmogenic isotope that can be used to determine the time interval during which a rock sample has been at or close to the Earth’s surface. As a result of the high production rate of ‘cosmogenic’3He (≈ 130 at g−1 year−1) and the low detection limit of modern mass spectrometers, it is possible to date exceptionally young surfaces (≈ 1000 years). The precision and accuracy of cosmogenic 3He measurements depend critically on the passive helium blank (produced by the metalwork of the extraction furnace) which can be significant relative to the sample signals. We have developed and constructed, at the CRPG (Nancy, France), a new high temperature furnace (&lt; 1500 °C) to extract helium in minerals such as apatite, pyroxene and olivine at 1050, 1350 and 1450 °C, respectively. The furnace demonstrated an excellent helium extraction yield (&gt; 99% for olivine and pyroxene for heating times of 20–30 min and temperatures in the range 1050–1450 °C) and low residual helium contributions (the blank, obtained under the same analytical conditions as for sample extraction: 1 × 10−15 mole 4He and &lt; 4 × 10−21 mole 3He). This is approximately an order of magnitude lower than those reported by other laboratories using conventional furnaces.