Toward global metabolomics analysis with hydrophilic interaction liquid chromatography-mass spectrometry: improved metabolite identification by retention time prediction.

TLDR: It is demonstrated that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography-high-resolution mass spectrometry metabolomics platform, allowing identified metabolites to be mapped onto an organism-wide metabolic network, providing opportunities for future studies of cellular metabolism from a global systems biology perspective.

Abstract: Metabolomics is an emerging field of postgenomic biology concerned with comprehensive analysis of small molecules in biological systems. However, difficulties associated with the identification of detected metabolites currently limit its application. Here we demonstrate that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography (HILIC)–high-resolution mass spectrometry metabolomics platform. A quantitative structure retention relationship (QSRR) model, incorporating six physicochemical variables in a multiple-linear regression based on 120 authentic standard metabolites, shows good predictive ability for retention times of a range of metabolites (cross-validated R2 = 0.82 and mean squared error = 0.14). The predicted retention times improved metabolite identification by removing 40% of the false identifications that occurred with identification by accurate mass alone. The importance of this procedure was demonstrated by putative identification ...

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University of Groningen
Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography-
Mass Spectrometry
Creek, Darren J.; Jankevics, Andris; Breitling, Rainer; Watson, David G.; Barrett, Michael P.;
Burgess, Karl E. V.
Published in:
Analytical Chemistry
DOI:
10.1021/ac2021823
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
2011
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Creek, D. J., Jankevics, A., Breitling, R., Watson, D. G., Barrett, M. P., & Burgess, K. E. V. (2011). Toward
Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry:
Improved Metabolite Identification by Retention Time Prediction.
Analytical Chemistry
,
83
(22), 8703-8710.
https://doi.org/10.1021/ac2021823
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1
Supporting information 4: (A) Distribution of 3,133 putatively identified peaks from the
mixtures of metabolite standards. 627 peaks were putatively identified; 1,314 peaks
rejected based on predicted retention time; 455 were classified as noise if they had very
small peaks (< 10,000) or were present in blank samples; 247 were annotated as duplicate
or shoulder peaks based on mass and retention time; 667 were common MS artefacts such
as isotopes, adducts and fragments (B). (B) Common related peaks observed on HILIC-
Orbitrap platform for automated removal in data processing by matching the mass
difference (within 3 ppm) and retention time (within 9 seconds) if the peak has a lower
intensity than the base peak.
A B
a) Only if peak intensity >50-fold lower than the base peak
Related peak
Mass difference
13
C isotopes
+1.003355
15
N isotopes
+0.997035
18
O isotopes
+2.004245
34
S isotopes
+1.995796
37
Cl isotopes
+1.997050
Double charge
Mass/2
Triple charge
Mass/3
Sodium adduct
+21.98194
Potassium adduct
+37.95588
Sodium–potassium
exchange
+15.97394
Sodium–ammonium
exchange
–4.95540
Sodium formate
adduct
+67.98740
Acetonitrile adduct
+41.02655
Acetonitrile and
sodium adduct
+63.00849
Ammonium adduct
+17.02655
Water loss
–18.01057
CO
2
loss
–43.98983
Formic acid loss
–46.00548
Ammonium loss
–17.02655
Centroid/
apodisation artefact
+/– <0.9
a