DOI: 10.7910/dvn/e95bo5
title: Untargeted Serum Metabolic Profiling by GC×GC-HRTOF-MS

description: Whilst many laboratories take appropriate care, there are still cases where the performances of untargeted profiling methods suffer from a lack of design, control and articulation of the various steps involved. This is particularly harmful to modern comprehensive analytical instrumentations that otherwise provide an unprecedented coverage of complex matrices. In this work, we present a global analytical workflow based on comprehensive two-dimensional gas chromatography (GC×GC) coupled to high resolution time-of-flight mass spectrometry (HR-TOF-MS). It was optimized for sample preparation and chromatographic separation, and validated on in-house QC samples and NIST SRM 1950 samples. It also includes a QC procedure, a multi-approaches data (pre)processing workflow and an original bias control procedure. Compounds of interest were identified using mass, retention and biological informations. As a proof of concept, 35 serum samples representing 3 subgroups of Crohn’s disease (with high, low and quiescent endoscopic activity) were analyzed along with 33 healthy controls. This led to the selection of 31 unique candidate biomarkers able to classify Crohn’s disease and healthy samples with OPLS-DA Q2 0.48 and ROC AUC 0.85 (100% sensitivity and 82% specificity in cross validation). 15 of these 33 candidates were reliably annotated (MSI level 2).
description: GC Image, 2.5b0
description: EXPERIMENTAL DESIGN.__________

This work aims to perform global profiling of serum samples with GC×GC-HRTOF-MS in a quantitative and qualitative manner. A global analytical method was designed that used multiple approaches and took care of every step. It was also optimized and validated. As a proof of concept, it was applied to serum samples to look for potential biomarkers of Crohn’s disease (CD) endoscopic activity. The CD patients (n = 35) were representative of three groups of endoscopic activity, according to the related Crohn’s disease endoscopic index score (CDEIS). Quiescent patients (n=9) had CDEIS = 0, low endoscopic activity patients (n=14) had 0 &lt; CDEIS &lt; 3, and high endoscopic activity patients (n=12) had CDEIS &gt; 3. The controls (n=33), qualified as “healthy” in the sense that they were negative for IBD or any other known cancer at the time of sample intake, were matched for gender and age.
Internal QC samples consisted in 30 µL aliquots of human serum samples distinct from the study. 15 QC samples were analyzed along with the 68 study samples.
First, the CD patients put together were separated from the healthy controls. Second, the three subgroups of CD were separated from each other. __________ 

EXPERIMENTAL FACTORS __________

Analytical: injection order, hemolysis of the sample, sample extraction and drying.
Clinical: age, gender, body mass index (BMI), disease location, tobacco and alcohol consumptions, medication for hypothyroidy, gastroesophageal reflux and ulcers, anti-tumor necrosis factor α (ani-TNFα) and immunosuppression. __________ 

PROTOCOLS __________

SAMPLE COLLECTION __________ 

For all samples, the sample intake, processing and storage procedures were standardized and followed our biobanking guidelines utilized also for clinical trials. These conditions, originally developed from proteomics studies are quite strict concerning the collection and conservation of samples.
In brief, Venosafe (Terumo, NV, EU) 10 mL dry tubes were used for serum collection. The gel separators belonged to different batches due to the total collection time that lasted longer than the batch validity. Clotting time was set at 30 minutes at room temperature before a centrifugation step at 2,000 RCF for 10 minutes at 4 °C. Aliquoting of sera was performed in polypropylene cryotubes. The delay between sample intake and storage (-80 °C) was kept inferior to 2 hours. All patients recruited for the proof of concept study were controlled, for IBD or any known digestive cancer by an endoscopic examination of the bowel (rectum, colon and, when possible, last of the ileum) performed by a trained endoscopist. When applicable, the diagnosis was confirmed by an anatomopathological examination of the biopsies or specimen resected. __________ 

SAMPLE PREPARATION __________

All samples were stored at -80 °C until use. After thawing at 4 °C for 1h, 30 µL of sample were put in hole-shaped vials to which 3 µL of glycine-2,2-d2 and succinic acid-2,2,3,3-d4 were added as isotopically labeled internal standards. Metabolites were extracted with methanol (3:1) before cooling at -20 °C for 10 min and centrifugation at 9,300 RCF for 10 min. Fumaric acid-2,3-d2 (3 µL) was then added, before the samples were dried under gentle N2 flow while heated at 40 °C. They were stored again at -80 °C until use. Before injection, the metabolites were made volatile with 15 µL of MeOX and 10 µL of MSTFA. Both reactions were performed at 40 °C for 1 hour.
The samples were homogenized after each step by 1 min vortexing. The medium was recovered and 3 µL of 4,4'-dibromooctafluorobiphenyl were finally added. All steps were performed manually. __________

CHROMATOGRAPHY __________ 

Capillary column set Rxi-5/Rxi-17 (non polar/semi-polar) of 30 m and 1 m respectively, both 0.25 mm ID and 0.25 µm film thickness. Helium as carrier gas in constant flow mode (1 ml/min). __________

MASS SPECTROMETRY __________

Instrumentation consisted in a JMS-T100GCVV GC×GC-TOF-MS (Jeol Ltd, Tokyo, Japan), using an Agilent 6890 GC (Santa Clara, California, USA) coupled to a CTC Autosampler (CTC Analytics AG, Zwingen, Switzerland). ZX1 loop cryogenic modulator using liquid nitrogen (Zoex Corporation, Houston, USA). 
Initial temperature of 50 °C held for 1 min. Temperature ramp of 3 °C/min until 240 °C then 15 °C/min to 300 °C held for 5 min. Modulation period of 3.5 s coupled to a 700 ms hot jet and a 250 °C splitless injection of 1 µL using a ‘precision’ liner. Electron ionization (EI) in positive mode, 70 eV, 300 µA. Acquisition rate of 25 Hz at m/z 50-700. Ion source and transfer line temperatures of 250 °C.
For exact mass (metabolite identification) : same conditions using a Pegasus GC-HRT 4D (LECO Corporation, Saint-Joseph, Michigan, USA; resolution of 25,000) with the secondary oven kept 15 °C higher than the main one. __________ 

DATA TRANSFORMATION __________ 

The raw data acquired were preprocessed through centroidization (Data Manager software, JEOL Ltd, Japan; .cdf and .gci data formats). GC Image 2.5 software (LLC, Lincoln, Nebraska, USA) automatically performed a process blank substraction and baseline correction. The multiple chromatograms were aligned and used to automatically create a composite template(44,45) that included all detected features. The template was manually verified for artefacts, degradation zones, co-elutions and multiple peaks of single compounds (amino acids, sugars and wraparounds) that were respectively removed, split and merged. Then it was applied to each individual chromatogram to generate consistent peak lists examined for transcription errors. Only metabolites present in at least half the samples of any class were kept. The remaining missing values were replaced by the lowest signal measured for the metabolite divided by two. Peak volumes were normalized to the internal standard giving the lowest relative standard deviation (RSD) in the QC samples. Outliers were sought for by calculating robust z-scores, according to the median absolute deviation of the variable in all concerned samples. When &gt; 50, the outlier was replaced by the closest value divided or multiplied by two (maximum or minimum outlier). Finally, the peak volumes were corrected for analytical variation measured in the QC samples through a locally estimated scatterplot smoothing (LOESS) procedure(23). Only the metabolites with a corrected analytical variation (RSD) lower than 30% were kept. __________

STATISTICS FOR BIOMARKER RESEARCH __________ 

The first step consisted in the application of univariate testing (Fisher ratios, correlations and ANOVA; according to the normality of the distribution), multivariate feature selection (Stepdisc, Runs and ReliefF filterings) and multivariate modelization (PLS-DA, orthogonal partial least squares discriminant analysis (OPLS-DA), random forests (RF), Naïve Bayes classification, support vector machine (SVM) and neural networks). Individual fold change, two-tailed statistical power through Cohen’s d(80,81) and ROC curves were also determined. Second, multiple testing and overfitting issues were taken into account, respectively through Bonferroni correction for univariate analysis(35) and re-sampling error rates (bootstrapping, cross and leave-one-out validations) for multivariate analysis(79). Third, the metabolites were ranked according to their significance based on p-value, order of selection, weight or variable importance in projection (VIP) score. The most consistently significant were selected as candidate biomarkers(79,82). Fourth, to reduce the risk of missing any discriminant compound(35), the candidates were removed from the data set and PLS-DA, OPLS-DA and RF were applied to it. In the same perspective, we compared the supervised plots drawn on the data set before and after the selection and removal of the candidates.

The statistical tests and methods were conducted with MetaboAnalyst web resource(48), Excel add-in Tanagra(49) and RapidMiner(50) and R programs(51).__________
 
METABOLITE IDENTIFICATION __________

The full mass spectra (unit resolution) were matched to NIST 14 and Wiley 9 libraries. The linear retention indices (LRIs) also called Kovats indices were measured with a standard mix of n-alkanes (C8-C20) added to three of the study samples. And compared to theoretical ones (NIST14, Wiley09 libraries, ChemSpider, NIST Webbook). The exact mass of the highest selective fragments available (usually molecular (M)+• or (M-CH3)+• radical ions) was measured with a mass error &lt; 1 ppm (see Mass Spectrometry protocol).

Acceptance thresholds were set for the three types of information: match factor &gt; 700(23) or &gt; 600 with probability &gt; 50%, ∆LRI &lt; 25(54) and mass error &lt; 1 ppm for any specific fragment. We finally assessed the biological plausibility of the annotated candidates with the Human Metabolome Database (HMDB(55)) and the Kyoto Encyclopedia of Genes and Genomes (KEGG56) as well as by conducting a manual literature review.

MSI-2 level ID was obtained if at least 2 of the three criteria were met.