Please login first
Home > Feed > Timeline
Timeline of Justin Van der Hooft

2019
Feb
11
Published new article






Implementations of the chemical structural and compositional similarity metric in R and Python

Published: 11 February 2019 by Cold Spring Harbor Laboratory in bioRxiv

doi: 10.1101/546150

Tandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information. We provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications.

1 Reads | 0 Citations
2018
Dec
09
Published new article






Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotat...

Published: 09 December 2018 by Cold Spring Harbor Laboratory in bioRxiv

doi: 10.1101/491506

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an API. MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

0 Reads | 0 Citations
2018
Dec
03
Published new article






QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Published: 03 December 2018 by PeerJ

doi: 10.7287/peerj.preprints.27295

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

0 Reads | 0 Citations
2018
Dec
03
Published new article






QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Published: 03 December 2018 by PeerJ

doi: 10.7287/peerj.preprints.27295v2

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

0 Reads | 0 Citations
2018
Nov
19
Published new article




Article

Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography...

Published: 19 November 2018 by American Chemical Society (ACS) in Analytical Chemistry

doi: 10.1021/acs.analchem.8b05112

The rapid innovations in metabolite profiling, bioassays and chemometrics have led to a paradigm shift in natural product (NP) research. Indeed, having partial or full structure information about possibly “all” specialized metabolites and an estimation of their levels in plants or microorganisms provides a way to perform pharmacognostic or chemical ecology investigations from a new and holistic perspective. The increasing amount of accurate metabolome data that can be acquired on massive sample sets, notably through data-dependent LC-HRMS/MS and NMR profiling, allows the mapping of natural extracts at an unprecedented level of precision. Most progress made recently in accelerating metabolite identification has been pushed by the need for metabolomics to have tools that provide a confident annotation of the biomarkers highlighted as the results of data mining through multivariate analysis, often on important datasets of complex samples. Historically, NP chemists have been involved in the unambiguous full de novo identification of unknown compounds from complex natural biological matrices. This process is classically performed by the tedious isolation of pure bioactive NPs through comprehensive bioactivity-guided isolation workflows involving orthogonal chromatographic steps at the preparative level. Increasingly advanced metabolomics metabolite profiling methods are of strategic importance in dereplication workflows in NP research as well as for the full metabolome composition assignment of relevant organisms from both drug discovery and chemical ecology perspectives. In this review, we describe the latest developments in metabolite profiling by both LC-MS and NMR-based methods and related databases from a natural product chemist perspective. We assess the current possibilities and limits of such methods and the workflows for manual and automated NP annotations by equally treating the MS and NMR approaches that are both key for the “as confident as possible” NP annotation in crude natural extracts. We also propose future lines of development in the field that are important for NP research but are also generally needed for metabolite annotation in metabolomics because NPs represent perfect candidate compounds for identification due to their intrinsic structural complexity and chemodiversity across organisms. This review does not aim to provide a comprehensive survey of all metabolite profiling applications made in NP research to date. Typical case studies are discussed, and an update of a selection of the latest advanced original studies and numerous specialized reviews is made with links to tools and DBs regarded as useful for their current or future usage in NP research. Evaluations of what can be readily implemented and what is still required for confident NP structural elucidation are made, especially concerning access to generic structural and spectral DBs as well as the use of orthogonal detection methods for improved confidence...

0 Reads | 0 Citations
2018
Nov
07
Published new article






Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmo...

Published: 07 November 2018 by Cold Spring Harbor Laboratory in bioRxiv

doi: 10.1101/463620

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

0 Reads | 0 Citations
2018
Jun
15
Published new article






Untargeted Mass Spectrometry-Based Metabolomics Tracks Molecular Changes in Raw and Processed Foods and Beverages

Published: 15 June 2018 by Cold Spring Harbor Laboratory in Biochemistry

doi: 10.1101/347716

A major aspect of our daily lives is the need to acquire, store and prepare our food. Storage and preparation can have drastic effects on the compositional chemistry of our foods, but we have a limited understanding of the temporal nature of processes such as storage, spoilage, fermentation and brewing on the chemistry of the foods we eat. Here, we performed a temporal analysis of the chemical changes in foods during common household preparations using untargeted mass spectrometry and novel data analysis approaches. Common treatments of foods such as home fermentation of yogurt, brewing of tea, spoilage of meats and ripening of tomatoes altered the chemical makeup through time, through both chemical and biological processes. For example, brewing tea altered its composition by increasing the diversity of molecules, but this change was halted after 4 min of brewing. The results indicate that this is largely due to differential extraction of the material from the tea and not modification of the molecules during the brewing process. This is in contrast to the preparation of yogurt from milk, spoilage of meat and the ripening of tomatoes where biological transformations directly altered the foods molecular composition. Comprehensive assessment of chemical changes using multivariate statistics showed the varied impacts of the different food treatments, while analysis of individual chemical changes show specific alterations of chemical families in the different food types. The methods developed here represent novel approaches to studying the changes in food chemistry that can reveal global alterations in chemical profiles and specific transformations at the chemical level.

0 Reads | 0 Citations
2018
May
15
Published new article






Did a plant-herbivore arms race drive chemical diversity in Euphorbia?

Published: 15 May 2018 by Cold Spring Harbor Laboratory in Evolutionary Biology

doi: 10.1101/323014

The genus Euphorbia is among the most diverse and species-rich plant genera on Earth, exhibiting a near-cosmopolitan distribution and extraordinary chemical diversity, especially across highly toxic macro- and polycyclic diterpenoids. However, very little is known about drivers and evolutionary origins of chemical diversity within Euphorbia. Here, we investigate 43 Euphorbia species to understand how geographic separation over evolutionary time has impacted chemical differentiation. We show that the structurally highly diverse Euphorbia diterpenoids are significantly reduced in species native to the Americas, compared to the Eurasian and African continents, where the genus originated. The localization of these compounds to young stems and roots suggest ecological relevance in herbivory defense and immunomodulatory defense mechanisms match diterpenoid levels, indicating chemoevolutionary adaptation to reduced herbivory pressure.

9 Reads | 0 Citations
2016
Nov
16
Published new article




Article

Topic modeling for untargeted substructure exploration in metabolomics

Published: 16 November 2016 by Proceedings of the National Academy of Sciences in Proceedings of the National Academy of Sciences

doi: 10.1073/pnas.1608041113

The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures (“Mass2Motifs”) from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.

0 Reads | 15 Citations
2013
Aug
01
Published new article




Article

NON-SMOKY GLYCOSYLTRANSFERASE1 Prevents the Release of Smoky Aroma from Tomato Fruit

Published: 01 August 2013 by American Society of Plant Biologists (ASPB) in The Plant Cell

doi: 10.1105/tpc.113.114231

Phenylpropanoid volatiles are responsible for the key tomato fruit (Solanum lycopersicum) aroma attribute termed "smoky." Release of these volatiles from their glycosylated precursors, rather than their biosynthesis, is the major determinant of smoky aroma in cultivated tomato. using a combinatorial omics approach, we identified the non-smoky glycosyltransferase1 (NSGT1) gene. Expression of NSGT1 is induced during fruit ripening, and the encoded enzyme converts the cleavable diglycosides of the smoky-related phenylpropanoid volatiles into noncleavable triglycosides, thereby preventing their deglycosylation and release from tomato fruit upon tissue disruption. In an nsgt1/nsgt1 background, further glycosylation of phenylpropanoid volatile diglycosides does not occur, thereby enabling their cleavage and the release of corresponding volatiles. Using reverse genetics approaches, the NSGT1-mediated glycosylation was shown to be the molecular mechanism underlying the major quantitative trait locus for smoky aroma. Sensory trials with transgenic fruits, in which the inactive nsgt1 was complemented with the functional NSGT1, showed a significant and perceivable reduction in smoky aroma. NSGT1 may be used in a precision breeding strategy toward development of tomato fruits with distinct flavor phenotypes.

0 Reads | 14 Citations
2013
May
31
Published new article




Article

Automatic Chemical Structure Annotation of an LC–MS n Based Metabolic Profile from Green Tea

Published: 31 May 2013 by American Chemical Society (ACS) in Analytical Chemistry

doi: 10.1021/ac400861a

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.org/magma ) to automatically process and annotate the LC-MS(n) data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MS(n) spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC-MS(n) (n ≤ 3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC-MS(n) run, a total of 116,240 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC-MS(n) component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.

0 Reads | 31 Citations
2013
Mar
24
Published new article




Article

Structural elucidation of low abundant metabolites in complex sample matrices

Published: 24 March 2013 by Springer Nature in Metabolomics

doi: 10.1007/s11306-013-0519-8

Identification of metabolites is a major challenge in biological studies and relies in principle on mass spectrometry (MS) and nuclear magnetic resonance (NMR) methods. The increased sensitivity and stability of both NMR and MS systems have made dereplication of complex biological samples feasible. Metabolic databases can be of help in the identification process. Nonetheless, there is still a lack of adequate spectral databases that contain high quality spectra, but new developments in this area will assist in the (semi-)automated identification process in the near future. Here, we discuss new developments for the structural elucidation of low abundant metabolites present in complex sample matrices. We describe how a recently developed combination of high resolution MS multistage fragmentation (MSn ) and high resolution one dimensional (1D)-proton (1H)-NMR of liquid chromatography coupled to solid phase extraction (LC–SPE) purified metabolites can circumvent the need for isolating extensive amounts of the compounds of interest to elucidate their structures. The LC–MS–SPE–NMR hardware configuration in conjunction with high quality databases facilitates complete structural elucidation of metabolites even at sub-microgram levels of compound in crude extracts. However, progress is still required to optimally exploit the power of an integrated MS and NMR approach. Especially, there is a need to improve and expand both MSn and NMR spectral databases. Adequate and user-friendly software is required to assist in candidate selection based on the comparison of acquired MS and NMR spectral information with reference data. It is foreseen that these focal points will contribute to a better transfer and exploitation of structural information gained from diverse analytical platforms.

0 Reads | 16 Citations
2013
Feb
26
Published new article






The Large Scale Identification and Quantification of Conjugates of Intact and Gut Microbial Bioconversion Products of Po...

Published: 26 February 2013 by Royal Society of Chemistry (RSC) in Environmental Forensics

doi: 10.1039/9781849737531-00177

A human diet containing a significant amount of flavonoids, such as present in tea, red wine, apple, and cocoa has been associated with reduced disease risks. After consumption, a part of these flavonoids can be directly absorbed by the small intestine, but the greatest part passages towards the large intestine where microbes break the flavonoids down into phenolic metabolites. After absorption into the blood, both intact and metabolized flavonoids are subsequently methylated, sulphated, and glucuronidated or a combination thereof. The exact chemical structural elucidation and quantification of these conjugates present in the human body are key to identify potential bioactive components. However, this is still a tedious task due to their relative low abundance in a complex background of other high-abundant metabolites and the many possible isomeric forms. Therefore, we aimed to systematically identify these conjugates by using a combination of pre-concentration and separation by solid phase extraction (SPE) followed by LC-FTMSn and 1D 1H NMR. The combination of LC-FTMSn and HPLC-TOF-MS-SPE-NMR resulted in the efficient identification and quantification of low abundant polyphenol metabolites down to micromolar concentrations and thus opens up new perspectives for in depth studying of the bioavailability and the possible mode of action of flavonoids like flavan-3-ols and their gut-microbial break-down products circulating in the human body.

0 Reads | 0 Citations
2012
Sep
10
Published new article




Article

Substructure-based annotation of high-resolution multistage MS n spectral trees

Published: 10 September 2012 by Wiley in Rapid Communications in Mass Spectrometry

doi: 10.1002/rcm.6364

High-resolution multistage MS(n) data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation.

0 Reads | 34 Citations
2012
Aug
02
Published new article




Article

Structural Elucidation and Quantification of Phenolic Conjugates Present in Human Urine after Tea Intake

Published: 02 August 2012 by American Chemical Society (ACS) in Analytical Chemistry

doi: 10.1021/ac3017339

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. To determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea. A combination of a solid-phase extraction (SPE) preparation step and two high pressure liquid chromatography (HPLC)-based analytical platforms was used, namely, accurate mass fragmentation (HPLC-FTMS(n)) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMS(n) analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MS(n) fragmentation with the one-dimensional (1D)-(1)H NMR spectra of HPLC-TOFMS-SPE-trapped compounds, we elucidated the structures of 36 phenolic conjugates, including the glucuronides of 3',4'-di- and 3',4',5'-trihydroxyphenyl-γ-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 h-quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMS(n) and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of less abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine.

0 Reads | 40 Citations
2012
Jun
22
Published new article




Article

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

Published: 22 June 2012 by American Chemical Society (ACS) in Analytical Chemistry

doi: 10.1021/ac2034216

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

0 Reads | 38 Citations
2012
Apr
12
Published new article




Article

Structural Annotation and Elucidation of Conjugated Phenolic Compounds in Black, Green, and White Tea Extracts

Published: 12 April 2012 by American Chemical Society (ACS) in Journal of Agricultural and Food Chemistry

doi: 10.1021/jf300297y

Advanced analytical approaches consisting of both LC-LTQ-Orbitrap Fourier transformed (FT)-MS and LC-time-of-flight-(TOF)-MS coupled to solid-phase extraction (SPE) NMR were used to obtain more insight into the complex phenolic composition of tea. On the basis of the combined structural information from (i) accurate mass fragmentation spectra, derived by using LC-Orbitrap FTMS(n), and (ii) proton NMR spectra, derived after LC-TOFMS triggered SPE trapping of selected compounds, 177 phenolic compounds were annotated. Most of these phenolics were glycosylated and acetylated derivatives of flavan-3-ols and flavonols. Principal component analysis based on the relative abundance of the annotated phenolic compounds in 17 commercially available black, green, and white tea products separated the black teas from the green and white teas, with epicatechin-3,5-di-O-gallate and prodelphinidin-O-gallate being among the main discriminators. The results indicate that the combined use of LC-LTQ-Orbitrap FTMS and LC-TOFMS-SPE-NMR leads to a more comprehensive metabolite description and comparison of tea and other plant samples.

0 Reads | 18 Citations
2011
Dec
20
Published new article




Article

Inhibitory activity of plumbagin produced by Drosera intermedia on food spoilage fungi

Published: 20 December 2011 by Wiley in Journal of the Science of Food and Agriculture

doi: 10.1002/jsfa.5522

The aim of this study was to investigate the growth-inhibiting efficacy of Drosera intermedia extracts (water, methanol and n-hexane) against four food spoilage yeasts and five filamentous fungi strains responsible for food deterioration and associated with mycotoxin production, in order to identify potential antimycotic agents.

0 Reads | 1 Citations
2011
Sep
28
Published new article




Article

Spectral trees as a robust annotation tool in LC–MS based metabolomics

Published: 28 September 2011 by Springer Nature in Metabolomics

doi: 10.1007/s11306-011-0363-7

The identification of large series of metabolites detectable by mass spectrometry (MS) in crude extracts is a challenging task. In order to test and apply the so-called multistage mass spectrometry (MSn ) spectral tree approach as tool in metabolite identification in complex sample extracts, we firstly performed liquid chromatography (LC) with online electrospray ionization (ESI)–MSn , using crude extracts from both tomato fruit and Arabidopsis leaf. Secondly, the extracts were automatically fractionated by a NanoMate LC-fraction collector/injection robot (Advion) and selected LC-fractions were subsequently analyzed using nanospray-direct infusion to generate offline in-depth MSn spectral trees at high mass resolution. Characterization and subsequent annotation of metabolites was achieved by detailed analysis of the MSn spectral trees, thereby focusing on two major plant secondary metabolite classes: phenolics and glucosinolates. Following this approach, we were able to discriminate all selected flavonoid glycosides, based on their unique MSn fragmentation patterns in either negative or positive ionization mode. As a proof of principle, we report here 127 annotated metabolites in the tomato and Arabidopsis extracts, including 21 novel metabolites. Our results indicate that online LC–MSn fragmentation in combination with databases of in-depth spectral trees generated offline can provide a fast and reliable characterization and annotation of metabolites present in complex crude extracts such as those from plants.

0 Reads | 18 Citations
2011
Feb
01
Published new article




Article

Thermal diffusivity of periderm from tomatoes of different maturity stages as determined by the concept of the frequency...

Published: 01 February 2011 by AIP Publishing in Journal of Applied Physics

doi: 10.1063/1.3530735

0 Reads | 6 Citations
2011
Jan
01
Published new article




Article

Polyphenol Identification Based on Systematic and Robust High-Resolution Accurate Mass Spectrometry Fragmentation

Published: 01 January 2011 by American Chemical Society (ACS) in Analytical Chemistry

doi: 10.1021/ac102546x

High-mass resolution multi-stage mass spectrometry (MS(n)) fragmentation was tested for differentiation and identification of metabolites, using a series of 121 polyphenolic molecules. The MS(n) fragmentation approach is based on the systematic breakdown of compounds, forming a so-called spectral tree. A chip-based nanoelectrospray ionization source was used combined with an ion-trap, providing reproducible fragmentation, and accurate mass read-out in an Orbitrap Fourier transform (FT) MS enabling rapid assignment of elemental formulas to the molecular ions and all fragment ions derived thereof. The used protocol resulted in reproducible MS(n) fragmentation trees up to MS(5). Obtained results were stable over a 5 month time period, a concentration change of 100-fold, and small changes in normalized collision energy, which is key to metabolite annotation and helpful in structure and substructure elucidation. Differences in the hydroxylation and methoxylation patterns of polyphenolic core structures were found to be reflected by the differential fragmentation of the entire molecule, while variation in a glycosylation site displayed reproducible differences in the relative intensities of fragments originating from the same aglycone fragment ion. Accurate MS(n)-based spectral tree data are therefore a powerful tool to distinguish metabolites with similar elemental formula, thereby assisting compound identification in complex biological samples such as crude plant extracts.

0 Reads | 38 Citations
2009
Oct
01
Published new article




Article

Iridoid and caffeoyl phenylethanoid glycosides of the endangered carnivorous plant Pinguicula lusitanica L. (Lentibulari...

Published: 01 October 2009 by Elsevier BV in Biochemical Systematics and Ecology

doi: 10.1016/j.bse.2009.05.003

0 Reads | 4 Citations
2019
Feb
17
Affiliation update




Justin Van der Hooft added a new affiliation: Bioinformatics Group, Wageningen University, Wageningen, The Netherlands

Top