Semi-quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics

Genetic mapping of maize metabolites using high-throughput mass profiling

Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.

Ultra-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Direct Infusion-High-Resolution Mass Spectrometry for Combined Exploratory and Targeted Metabolic Profiling of Human Urine

The application of metabolic phenotyping to epidemiological studies involving thousands of biofluid samples presents a challenge for the selection of analytical platforms that meet the requirements of high-throughput precision analysis and cost-effectiveness. Here direct infusion–nanoelectrospray (DI–nESI) was compared with an ultra-performance liquid chromatography (UPLC)–high-resolution mass spectrometry (HRMS) method for metabolic profiling of an exemplary set of 132 human urine samples from a large epidemiological cohort. Both methods were developed and optimized to allow the simultaneous collection of high-resolution urinary metabolic profiles and quantitative data for a selected panel of 35 metabolites. The total run time for measuring the sample set in both polarities by UPLC–HRMS was 5 days compared with 9 h by DI–nESI–HRMS. To compare the classification ability of the two MS methods, we performed exploratory analysis of the full-scan HRMS profiles to detect sex-related differences in biochemical composition. Although metabolite identification is less specific in DI–nESI–HRMS, the significant features responsible for discrimination between sexes were mostly the same in both MS-based platforms. Using the quantitative data, we showed that 10 metabolites have strong correlation (Pearson’s r > 0.9 and Passing–Bablok regression slope of 0.8–1.3) and good agreement assessed by Bland–Altman plots between UPLC–HRMS and DI–nESI–HRMS and thus can be measured using a cheaper and less sample- and time-consuming method. A further twenty metabolites showed acceptable correlation between the two methods with only five metabolites showing weak correlation (Pearson’s r < 0.4) and poor agreement due to the overestimation of the results by DI–nESI–HRMS. 

Low pyrrolizidine alkaloid levels in perennial ryegrass is associated with the absence of a homospermidine synthase gene

BACKGROUND

Pyrrolizidine alkaloids (PAs) are a class of secondary metabolites that function as feeding deterrents in a range of different plant species. In perennial ryegrass (Lolium perenne L.) the only PAs that have been identified are the thesinine-rhamnoside group, which displays significant genetic variation. Homospermidine synthase (HSS) has evolved from deoxyhypusine synthase (DHS) and catalyses the first step in the PA pathway, making it a key candidate for the investigation of genes influencing observed PA trait variation.

RESULTS

During PCR amplification and sequence analysis of DHS we identified two putative HSS genes in perennial ryegrass. One of the genes (LpHSS1) was absent in some perennial ryegrass plants. Thesinine-rhamnoside levels were measured using liquid chromatography coupled with mass spectrometry in a diverse association mapping population, consisting of 693 plants free of fungal endophytic symbionts. Association tests that accounted for population structure identified a significant association of absence of the LpHSS1 gene with lower levels of thesinine-rhamnoside PAs. HSS-like gene sequences were identified for other grass species of the Poaceae, including tall fescue, wheat, maize and sorghum.

CONCLUSION

HSS is situated at the crucial first step in the PA pathway making it an important candidate gene for investigation of involvement in PA phenotypic variation. In this study, PA level in perennial ryegrass was strongly associated with the presence or absence of the LpHSS1 gene. A genetic marker, developed for the presence/absence of LpHSS1, may be used for marker-assisted breeding to either lower or increase PAs in breeding populations of perennial or Italian ryegrass to investigate a potential role in the deterrence of herbivore pests. The presence of HSS-like genes in several other Poaceae species suggests that PA biosynthesis may occur in plant family members beyond perennial ryegrass and tall fescue and identifies a potential route for manipulating PA levels.

Untargeted Metabotyping Lolium perenne Reveals Population-Level Variation in Plant Flavonoids and Alkaloids

Metabolomics provides a powerful platform to characterize plants at the biochemical level, allowing a search for underlying genes and associations with higher level complex traits such as yield and nutritional value. Efficient and reliable methods to characterize metabolic variation in economically important species are considered of high value to the evaluation and prioritization of germplasm and breeding lines. In this investigation, a large-scale metabolomic survey was performed on a collection of diverse perennial ryegrass (Lolium perenne L.) plants. A total of 2,708 data files, derived from liquid chromatography coupled to high resolution mass spectrometry (LCMS), were selected to assess the effectiveness and efficiency of applying high throughput metabolomics to survey chemical diversity in plant populations. The data set was generated from 23 ryegrass populations, with 3-25 genotypes per population, and five clonal replicates per genotype. We demonstrate an integrated approach to rapidly mine and analyze metabolic variation from this large, multi-batch LCMS data set. After performing quality control, statistical data mining and peak annotation, a wide range of variation for flavonoid glycosides and plant alkaloids was discovered among the populations. Structural variation of flavonoids occurs both in aglycone structures and acetylated/malonylated/feruloylated sugar moieties. The discovery of comprehensive metabolic variation among the plant populations offers opportunities to probe into the genetic basis of the variation, and provides a valuable resource to gain insight into biochemical functions and to relate metabolic variation with higher level traits in the species.

Quantitative analysis of biogenic polyamines in distilled drinks by direct electrospray ionization tandem mass spectrometry using a nanocontainer

RATIONALE

Biogenic polyamines in drinks have been implicated in undesirable physiological effects. Methods for their detection and quantification usually involve derivatization, pre-concentration and clean-up. To assist the evaluation of the potential risk of distillates, it was important to develop a simple and fast analytical method, which is described in this study.

METHODS

Biogenic polyamines were selectively encapsulated after addition of a nanocontainer, cucurbit[7]uril (50 μM), to the distilled drink samples, which were acidified with HCl (pH 3) prior addition of the nanocontainer. The quantification of polyamines was achieved by direct infusion electrospray ionization quadrupole ion trap mass spectrometry in the multiple reaction monitoring mode, using encapsulated amantadine as internal standard, and by monitoring signals originating from their host-guest complexes.

RESULTS

Six point calibration curves, ranging from 0.5 μM to 20 μM of polyamines in water and ethanol/water (50:50), were used to establish instrument response. The method was validated by analysis of fortified Arbutus spirits. Samples of Arbutus and grape pomace spirits were also analyzed. Linear responses were observed for all polyamines and were similar in water, hydro-alcoholic solutions and fortified Arbutus spirits. Putrescine the simple polyamine was detected only in grape pomace distillate samples.

CONCLUSIONS

A quantitative method was developed for rapid and simple analysis of biogenic polyamines in distilled drinks. The detection limits depend on the ionization properties of the samples. Encapsulated amantadine can be used to probe these properties and method application. Copyright © 2016 John Wiley & Sons, Ltd.

A QTL analysis of host plant effects on fungal endophyte biomass and alkaloid expression in perennial ryegrass

The association between perennial ryegrass (Loliumperenne L.) and its Epichloë fungal endophyte symbiont, Epichloëfestucae var. lolii, supports the persistence of ryegrass-based pastures principally by producing bioactive alkaloid compounds that deter invertebrate herbivory. The host plant genotype affects endophyte trait expression, and elucidation of the underlying genetic mechanisms would enhance understanding of the symbiosis and support improvement of inplanta endophyte performance through plant breeding. Rapid metabolite profiling and enzyme-linked immunosorbent assay were used to quantify endophyte alkaloids and mycelial mass (MM) in leaves harvested, in consecutive autumns, from an F1 mapping population hosting standard toxic endophyte. Co-aligned quantitative trait loci (QTL) on linkage groups (LG)2, LG4 and LG7 for MM and concentrations of alkaloids peramine and ergovaline confirmed host plant effects on both MM and alkaloid level and inferred the effect on alkaloids was modulated through the quantity of endophyte present in the leaf tissue. For ergovaline, host regulation independent of endophyte concentration was also indicated, by the presence of MM-independent ergovaline QTL on LG4 and LG7. Partitioning of host genetic influence between MM-dependent and MM-independent mechanisms was also observed for the alkaloid N-formylloline (NFL), in a second mapping population harbouring a tall fescue-sourced endophyte. Single-marker analysis on repeated MM and NFL measures identified marker-trait associations at nine genome locations, four affecting both NFL and MM but five influencing NFL concentration alone. Co-occurrence of QTL on LG3, LG4 and LG7 in both mapping populations is evidence for host regulatory loci effective across genetic backgrounds and independent of endophyte variant. Variation at these loci may be exploited using marker-assisted breeding to improve endophyte trait expression in different host population × endophyte combinations.

Metabolic Profiling of Developing Pear Fruits Reveals Dynamic Variation in Primary and Secondary Metabolites, Including Plant Hormones

Metabolites in the fruits of edible plants include sweet sugars, visually appealing pigments, various products with human nutritional value, and biologically active plant hormones. Although quantities of these metabolites vary during fruit development and ripening because of cell division and enlargement, there are few reports describing the actual dynamics of these changes. Therefore, we applied multiple metabolomic techniques to identify the changes in metabolite levels during the development and ripening of pear fruits (Pyrus communis L. ‘La France’). We quantified and classified over 250 metabolites into six groups depending on their specific patterns of variation during development and ripening. Approximately half the total number of metabolites, including histidine and malate, accumulated transiently around the blooming period, during which cells are actively dividing, and then decreased either rapidly or slowly. Furthermore, the amounts of sulfur-containing amino acids also increased in pear fruits around 3–4 months after the blooming period, when fruit cells are enlarging, but virtually disappeared from ripened fruits. Some metabolites, including the plant hormone abscisic acid, accumulated particularly in the receptacle prior to blooming and/or fruit ripening. Our results show several patterns of variation in metabolite levels in developing and ripening pear fruits, and provide fundamental metabolomic data that is useful for understanding pear fruit physiology and enhancing the nutritional traits of new cultivars.

Recent progress in polar metabolite quantification in plants using liquid chromatography–mass spectrometry

Metabolite analysis or metabolomics is an important component of systems biology in the post-genomic era. Although separate liquid chromatography (LC) methods for quantification of the major classes of polar metabolites of plants have been available for decades, a single method that enables simultaneous determination of hundreds of polar metabolites is possible only with gas chromatography–mass spectrometry (GC–MS) techniques. The rapid expansion of new LC stationary phases in the market and the ready access of mass spectrometry in many laboratories provides an excellent opportunity for developing LC–MS based methods for multi-target quantification of polar metabolites. Although various LC–MS methods have been developed over the last 10 years with the aim to quantify one or more classes of polar compounds in different matrices, currently there is no consensus LC–MS method that is widely used in plant metabolomics studies. The most promising methods applicable to plant metabolite analysis will be reviewed in this paper and the major problems encountered highlighted. The aim of this review is to provide plant scientists, with limited to moderate experience in analytical chemistry, with up-to-date and simplified information regarding the current status of polar metabolite analysis using LC–MS techniques.

Computational analyses of spectral trees from electrospray multi-stage mass spectrometry to aid metabolite identification

Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package “iontree” that can capture, store and analyze MS² and MS³ mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS² spectral comparison, and tools for building platform-independent ion tree (MS²/MS³) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS² databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.

Comparative metabolomics in Glycine max and Glycine soja under salt stress to reveal the phenotypes of their offspring

Metabolomics is developing as an important functional genomics tool for understanding plant systems' response to genetic and environmental changes. Here, we characterized the metabolic changes of cultivated soybean C08 (Glycine max L. Merr) and wild soybean W05 (Glycine soja Sieb.et Zucc.) under salt stress using MS-based metabolomics, in order to reveal the phenotypes of their eight hybrid offspring (9H0086, 9H0124, 9H0391, 9H0736, 9H0380, 9H0400, 9H0434, and 9H0590). Total small molecule extracts of soybean seedling leaves were profiled by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-Fourier transform mass spectrometry (LC-FT/MS). We found that wild soybean contained higher amounts of disaccharides, sugar alcohols, and acetylated amino acids than cultivated soybean, but with lower amounts of monosaccharides, carboxylic acids, and unsaturated fatty acids. Further investigations demonstrated that the ability of soybean to tolerate salt was mainly based on synthesis of compatible solutes, induction of reactive oxygen species (ROS) scavengers, cell membrane modifications, and induction of plant hormones. On the basis of metabolic phenotype, the salt-tolerance abilities of 9H0086, 9H0124, 9H0391, 9H0736, 9H0380, 9H0400, 9H0434, and 9H0590 were discriminated. Our results demonstrated that MS-based metabolomics provides a fast and powerful approach to discriminate the salt-tolerance characteristics of soybeans.

An extracellular siderophore is required to maintain the mutualistic interaction of Epichloë festucae with Lolium perenne

We have identified from the mutualistic grass endophyte Epichloë festucae a non-ribosomal peptide synthetase gene (sidN) encoding a siderophore synthetase. The enzymatic product of SidN is shown to be a novel extracellular siderophore designated as epichloënin A, related to ferrirubin from the ferrichrome family. Targeted gene disruption of sidN eliminated biosynthesis of epichloënin A in vitro and in planta. During iron-depleted axenic growth, ΔsidN mutants accumulated the pathway intermediate N(5)-trans-anhydromevalonyl-N(5)-hydroxyornithine (trans-AMHO), displayed sensitivity to oxidative stress and showed deficiencies in both polarized hyphal growth and sporulation. Infection of Lolium perenne (perennial ryegrass) with ΔsidN mutants resulted in perturbations of the endophyte-grass symbioses. Deviations from the characteristic tightly regulated synchronous growth of the fungus with its plant partner were observed and infected plants were stunted. Analysis of these plants by light and transmission electron microscopy revealed abnormalities in the distribution and localization of ΔsidN mutant hyphae as well as deformities in hyphal ultrastructure. We hypothesize that lack of epichloënin A alters iron homeostasis of the symbiotum, changing it from mutually beneficial to antagonistic. Iron itself or epichloënin A may serve as an important molecular/cellular signal for controlling fungal growth and hence the symbiotic interaction.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Quantitative Trait Locus (QTL) meta-analysis and comparative genomics for candidate gene prediction in perennial ryegrass (Lolium perenne L.)

BACKGROUND

In crop species, QTL analysis is commonly used for identification of factors contributing to variation of agronomically important traits. As an important pasture species, a large number of QTLs have been reported for perennial ryegrass based on analysis of biparental mapping populations. Further characterisation of those QTLs is, however, essential for utilisation in varietal improvement programs.

RESULTS

A bibliographic survey of perennial ryegrass trait-dissection studies identified a total of 560 QTLs from previously published papers, of which 189, 270 and 101 were classified as morphology-, physiology- and resistance/tolerance-related loci, respectively. The collected dataset permitted a subsequent meta-QTL study and implementation of a cross-species candidate gene identification approach. A meta-QTL analysis based on use of the BioMercator software was performed to identify two consensus regions for pathogen resistance traits. Genes that are candidates for causal polymorphism underpinning perennial ryegrass QTLs were identified through in silico comparative mapping using rice databases, and 7 genes were assigned to the p150/112 reference map. Markers linked to the LpDGL1, LpPh1 and LpPIPK1 genes were located close to plant size, leaf extension time and heading date-related QTLs, respectively, suggesting that these genes may be functionally associated with important agronomic traits in perennial ryegrass.

CONCLUSIONS

Functional markers are valuable for QTL meta-analysis and comparative genomics. Enrichment of such genetic markers may permit further detailed characterisation of QTLs. The outcomes of QTL meta-analysis and comparative genomics studies may be useful for accelerated development of novel perennial ryegrass cultivars with desirable traits.

Metabolomics of forage plants: a review

BACKGROUND

Forage plant breeding is under increasing pressure to deliver new cultivars with improved yield, quality and persistence to the pastoral industry. New innovations in DNA sequencing technologies mean that quantitative trait loci analysis and marker-assisted selection approaches are becoming faster and cheaper, and are increasingly used in the breeding process with the aim to speed it up and improve its precision. High-throughput phenotyping is currently a major bottle neck and emerging technologies such as metabolomics are being developed to bridge the gap between genotype and phenotype; metabolomics studies on forages are reviewed in this article.

SCOPE

Major challenges for pasture production arise from the reduced availability of resources, mainly water, nitrogen and phosphorus, and metabolomics studies on metabolic responses to these abiotic stresses in Lolium perenne and Lotus species will be discussed here. Many forage plants can be associated with symbiotic microorganisms such as legumes with nitrogen fixing rhizobia, grasses and legumes with phosphorus-solubilizing arbuscular mycorrhizal fungi, and cool temperate grasses with fungal anti-herbivorous alkaloid-producing Neotyphodium endophytes and metabolomics studies have shown that these associations can significantly affect the metabolic composition of forage plants. The combination of genetics and metabolomics, also known as genetical metabolomics can be a powerful tool to identify genetic regions related to specific metabolites or metabolic profiles, but this approach has not been widely adopted for forages yet, and we argue here that more studies are needed to improve our chances of success in forage breeding.

CONCLUSIONS

Metabolomics combined with other '-omics' technologies and genome sequencing can be invaluable tools for large-scale geno- and phenotyping of breeding populations, although the implementation of these approaches in forage breeding programmes still lags behind. The majority of studies using metabolomics approaches have been performed with model species or cereals and findings from these studies are not easily translated to forage species. To be most effective these approaches should be accompanied by whole-plant physiology and proof of concept (modelling) studies. Wider considerations of possible consequences of novel traits on the fitness of new cultivars and symbiotic associations need also to be taken into account.

Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry

The choice of sample-preparation method is extremely important in metabolomic studies because it affects both the observed metabolite content and biological interpretation of the data. An ideal sample-preparation method for global metabolomics should (i) be as non-selective as possible to ensure adequate depth of metabolite coverage; (ii) be simple and fast to prevent metabolite loss and/or degradation during the preparation procedure and enable high-throughput; (iii) be reproducible; and (iv) incorporate a metabolism-quenching step to represent true metabolome composition at the time of sampling. Despite its importance, sample preparation is often an overlooked aspect of metabolomics, so the focus of this review is to explore the role, challenges, and trends in sample preparation specifically within the context of global metabolomics by liquid chromatography-mass spectrometry (LC-MS). This review will cover the most common methods including solvent precipitation and extraction, solid-phase extraction and ultrafiltration, and discuss how to improve analytical quality and metabolite coverage in metabolomic studies of biofluids, tissues, and mammalian cells. Recent developments in this field will also be critically examined, including in vivo methods, turbulent-flow chromatography, and dried blood spot sampling.

Mass spectrometry-based technologies for high-throughput metabolomics

The metabolome is composed of a vast number of small-molecule metabolites that exhibit a diversity of physical and chemical properties and exist over a wide dynamic range in biological samples. Multiple analytical techniques, used in a complementary manner, are required to achieve high coverage of the metabolome. MS is playing a central role in metabolomics research. Herein, we present a brief overview of the MS-based technologies employed for high-throughput metabolomics. These technologies range from chromatography-MS techniques, such as GC-MS and LC-MS, to chromatography-free techniques, such as direct infusion, matrix-assisted and matrix-free laser desorption/ionization, imaging and some new ambient ionization approaches. Chemoinformatics and bioinformatics tools are widely available to facilitate successful metabolomics studies by turning the complex metabolomics data into biological information through streamlined data processing, analysis and interpretation.

Rapid screening and characterization of metabolites from a marine-derived actinomycete by high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry

A rapid and reliable method has been optimized and established for the analysis of the metabolites from a marine actinomycete by high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC/QTOF MS/MS). From MS/MS spectra, the product ions of [M + H](+) were recorded to provide abundant structural information of the mother nucleus and peptide moieties. Using the QTOF MS/MS and in-source collision-induced dissociation (in-source CID) techniques, three main metabolites including actinomycin D, actinomycin V and actinomycin I were determined and characterized by elemental compositions of precursor and product ions (<7 ppm). Additionally, this method provided information about the compositions of the peptide residues and the sequences of the amino acid from a series of fragment ions. It proved useful for the identification of the metabolites in marine samples which have similar structures especially when there were no reference compounds available.

Identification of urinary biomarkers of colon inflammation in IL10-/- mice using Short-Column LCMS metabolomics

The interleukin-10-deficient (IL10^−/−) mouse develops colon inflammation in response to normal intestinal microflora and has been used as a model of Crohn's disease. Short-Column LCMS metabolite profiling of urine from IL10^−/− and wild-type (WT) mice was used, in two independent experiments, to identify mass spectral ions differing in intensity between these two genotypes. Three differential metabolites were identified as xanthurenic acid and as the glucuronides of xanthurenic acid and of α-CEHC (2,5,7,8-tetramethyl-2-(2′-carboxyethyl)-6-hydroxychroman). The significance of several differential metabolites as potential biomarkers of colon inflammation was evaluated in an experiment which compared metabolite concentrations in IL10^−/− and WT mice housed, either under conventional conditions and dosed with intestinal microflora, or maintained under specific pathogen-free (SPF) conditions. Concentrations of xanthurenic acid, α-CEHC glucuronide, and an unidentified metabolite m/z 495⁻/497⁺ were associated with the degree of inflammation in IL10^−/− mice and may prove useful as biomarkers of colon inflammation.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.