2 research outputs found
DnsID in MyCompoundID for Rapid Identification of Dansylated Amine- and Phenol-Containing Metabolites in LC–MS-Based Metabolomics
High-performance chemical isotope
labeling (CIL) liquid chromatography–mass
spectrometry (LC–MS) is an enabling technology based on rational
design of labeling reagents to target a class of metabolites sharing
the same functional group (e.g., all the amine-containing metabolites
or the amine submetabolome) to provide concomitant improvements in
metabolite separation, detection, and quantification. However, identification
of labeled metabolites remains to be an analytical challenge. In this
work, we describe a library of labeled standards and a search method
for metabolite identification in CIL LC–MS. The current library
consists of 273 unique metabolites, mainly amines and phenols that
are individually labeled by dansylation (Dns). Some of them produced
more than one Dns-derivative (isomers or multiple labeled products),
resulting in a total of 315 dansyl compounds in the library. These
metabolites cover 42 metabolic pathways, allowing the possibility
of probing their changes in metabolomics studies. Each labeled metabolite
contains three searchable parameters: molecular ion mass, MS/MS spectrum,
and retention time (RT). To overcome RT variations caused by experimental
conditions used, we have developed a calibration method to normalize
RTs of labeled metabolites using a mixture of RT calibrants. A search
program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a
sample based on matching one or more of the three parameters with
those of the library standards. Using human urine as an example, we
illustrate the workflow and analytical performance of this method
for metabolite identification. This freely accessible resource is
expandable by adding more amine and phenol standards in the future.
In addition, the same strategy should be applicable for developing
other labeled standards libraries to cover different classes of metabolites
for comprehensive metabolomics using CIL LC–MS
MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites
We report an analytical tool to facilitate
metabolite identification
based on an MS/MS spectral match of an unknown to a library of predicted
MS/MS spectra of possible human metabolites. To construct the spectral
library, the known endogenous human metabolites in the Human Metabolome
Database (HMDB) (8,021 metabolites) and their predicted metabolic
products via one metabolic reaction in the Evidence-based Metabolome
Library (EML) (375,809 predicted metabolites) were subjected to <i>in silico</i> fragmentation to produce the predicted MS/MS spectra.
This spectral library is hosted at the public MCID Web site (www.MyCompoundID.org), and a spectral search program, MCID
MS/MS, has been developed to allow a user to search one or a batch
of experimental MS/MS spectra against the library spectra for possible
match(s). Using MS/MS spectra generated from standard metabolites
and a human urine sample, we demonstrate that this tool is very useful
for putative metabolite identification. It allows a user to narrow
down many possible structures initially found by using an accurate
mass search of an unknown metabolite to only one or a few candidates,
thereby saving time and effort in selecting or synthesizing metabolite
standard(s) for eventual positive metabolite identification