DnsID in MyCompoundID for Rapid Identification of Dansylated Amine- and Phenol-Containing Metabolites in LC–MS-Based Metabolomics

Abstract

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.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

“…By searching the 3893 peak pairs against the dansyl standard library consisting of 273 labeled standards33 with the use of mass tolerance of 10 ppm and retention time (RT) tolerance of 60 s, 48 metabolites were positively identified based on mass and RT matches (see Supplemental Table S1A for the list). Using MyCompoundID MS search34 based on accurate mass of peak pairs with mass tolerance of 10 ppm, 1054 pairs were matched to metabolites in the HMDB library (see Supplemental Table S1B) and 1824 pairs matched to metabolites in the predicted human metabolite library (EML) with one reaction (see Supplemental Table S1C).…”

“…PCA and OPLS-DA were used to analyze the data. Metabolite identification was performed based on mass and retention time match to the dansyl standard library using DnsID33. Putative identification was done based on accurate mass match to the metabolites in the human metabolome database (HMDB) (8,021 known human endogenous metabolites) and the Evidence-based Metabolome Library (EML) (375,809 predicted human metabolites with one reaction) using MyCompoundID34.…”

Parallel Metabolomic Profiling of Cerebrospinal Fluid and Serum for Identifying Biomarkers of Injury Severity after Acute Human Spinal Cord Injury

“…Using this method of targeting the amine/phenol-containing metabolites, we were able to detect 1213 and 2316 12 C/ 13 C peak pairs from CSF and serum, respectively. By matching accurate mass and retention time with the dansyl standard library14 (mass error <5 ppm and retention time error <30 s) or with authentic standards, we identified 120 peak pairs which belong to 110 metabolites (see Supplemental Table T1). Among these 110 identified metabolites, 100 were common to the two biofluids, while 2 of them could only be detected in CSF and 8 of them were only detectable in serum (see Note in Table T1).…”

Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer

“…By searching the 4090 peak pairs detected against the Dns-library10, 88 metabolites were positively identified based on the mass and retention time matches (see Supplemental Table T1 for the list). Using MyCompoundID MS search11, 565 metabolites were putatively identified by accurate mass matches to the HMDB library12 (see Supplemental Table T2) and 1196 metabolites matched to the predicted human metabolome library with one reaction11 (see Supplemental Table T3).…”

“…Both training and validation datasets were processed using the same protocol. Definitive metabolite identification was performed by matching retention time (rt) and m/z with the dansyl standards library using the DnsID program10 with a m/z tolerance of 5 ppm and retention time tolerance of 10 s. Putative metabolite identification was performed by matching accurate mass of a peak pair against the human metabolome libraries (with zero or one reaction) using MyCompoundID (MCID)11 with a mass tolerance of 5 ppm.…”