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Untargeted Metabolomics Analysis of Pancreatic
Cancer Using LC/QTOF
High Performance Liquid Chromatograph Mass Spectrometer
LC/QTOF method for untargeted metabolomics with flexible workflows for Data Independent Acquisition (DIA) or Data
Dependent Acquisition (DDA).
Data are compatible with all major third-party tools for data processing and statistical analysis of metabolomics data.
The workflow delivers an end-to-end solution for untargeted metabolomics research.
Introduction
Metabolomics is the study of a metabolic phenotype and its
impact on a biological system. It involves the measurement of
small molecules and can be applied to disease aetiology,
discovering potential disease markers in human medicine, but
also to other disciplines such as translational and precision
medicine, untargeted analyses, nutritional assessment,
fermentative optimisations, and agricultural productivity.
Metabolomics studies are designed to detect and identify
metabolites that are considered to be drivers of a biological
process and understand their role in phenotype modulation.
In this application, an LC/QTOF data acquisition method was
developed for routine, robust metabolomics research requiring
minimal MS expertise which can be applied to the untargeted
metabolomics studies of biological tissues and biofluids. Data
were processed using commonly used open-source software
applications cited in the literature. This study used MS-DIAL
(principally for raw data access to detect and align features in
each sample) and MetaboAnalyst (for advanced statistical
analysis to find features that were differentially expressed in the
study).
Study design
The end-to-end untargeted metabolomics workflow has been
applied to characterise the metabolic profiles of serum from
patients with pancreatic ductal adenocarcinoma (PDAC)
compared to healthy controls to reveal the key metabolic
differences in these phenotypes. The research project was
approved by the Pancreatic Cancer Research Fund Tissue Bank
(PCRFTB) Tissue Access Committee.
Sample preparation and extraction
Serum samples were extracted in methanol (1:3 v/v) for UHPLC-
MS/MS analysis. For untargeted metabolomics analysis in
positive ion mode, samples were diluted 1 in 10.
Data acquisition
High resolution UHPLC-MS/MS data were acquired using TOF
MS and DIA-MS/MS in positive ESI mode to exemplify the
workflow (Table 1).
Fig. 1 A typical metabolomics workflow using LC/QTOF data acquisition
methods specifically developed for metabolomics and data processing
workflows highlighting the flexibility of working with open-source tools such
as MS-DIAL and MetaboAnalyst. (Commonly used open-source data
processing applications also include MZmine, GNPS and XCMS which can be
used in the same data processing workflow).
Data Acquisition
Peak
detection
MS-DIAL to detect features in a sample
Alignment MS-DIAL retention time feature
alignment to account for variance in
batch analysis
Annotation MS-DIAL annotation based on ‘start-up
kit’ .msps for metabolites and ‘in silico
retention time- and MS/MS database
for LC/MS/MS based lipidomics’ for lipid
annotation
Statistical
Analysis
MetaboAnalyst to find meaningful
differences between samples
LC
separation
Reversed-phase LC or HILIC methods;
two methods optimised for metabolite
detection
MS and DIA-
MS/MS
LC/QTOF method designed to support
untargeted metabolomics
Data Processing
UHPLC: Nexera X2
Column C18 ((2.1x100 mm 1.7 µm);
column maintained at 50 ˚C
Flow Rate 0.4 mL/min
Mobile Phase A: Water + 0.1% formic acid
B: Acetonitrile + 0.1% formic acid
Rinse (R0/R1/R2) IPA/MeOH/MeCN/H
2
O (1:1:1:1) + 1% formic
acid
Gradient 2% B (0-1min); 40% B (2 min); 90% B (25 min,
curve -3 ); 100% B (27.5-31 min); 2% B
(31.01-35 min).
Injection 0.5 µL; autosampler temperature 4 ˚C
Mass Spectrometer: LCMS-9030
Interface 300 ˚C, 1.5 kV
Heat block, DL 400 ˚C, 250 ˚C
Gas flow rates Nebulizing gas 3 L/min;
Heating gas 10 L/min;
Drying gas 15 L/min
CID gas pressure 230 kPa
MS TOF-MS (m/z 60-1000)
MS/MS 27 DIA-MS/MS (m/z 40-1000;
precursor isolation 20 Da; collision energy
spread 5-55 V).
Total cycle time
(MS and MS/MS)
1 second
Table 1. Analytical conditions
Emily Armitage
1
, Alan Barnes
1
, Elon Correa
2
, Sén Takeda
3
, Neil Loftus
1
, Wen Chung
4
1 Shimadzu Corporation, Manchester, UK 2 Liverpool John Moores University, Liverpool, UK
3 Teikyo University School of Medicine, Tokyo, Japan
4 Leicester HPB Unit, Glenfield Hospital, Leicester, UK
