4 FURTHER DEVELOPMENT OF THE SUGAR PROFILING METHOD 4.1 Background
4.2 Material and methods
Chemicals
Methanol (GC; ≥ 99.9%) was purchased from Carl Roth (Karlsruhe, Germany). Heptane (GC) and acetone (GC) were from Merck (Darmstadt, Germany). Pyridine (anhydrous, 99.8%) was supplied by Sigma-Aldrich (Steinheim, Germany). O- Methoxylamine hydrochloride was obtained from Chemos (Regenstauf, Germany). N- Methyl-N-(trimethylsilyl)trifluoroacetamide (MSFTA) with 1% trimethylchlorosilane (TMCS) was from Macherey-Nagel (Düren, Germany). Supplemental Table S4.1 lists all used standard substances.
Consumables
Consumables utilized in this study are listed in Supplemental Table S4.2. Study design and samples
The samples were generated during the FoodBAll study at the MRI in February 2016. The study was a randomized crossover intervention study with the aim to identify
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potential markers of apple and coca cola consumption, including water as a control food and a reference meal (Keto-Drink, Tavarlin GmbH, Pfungstadt, Germany), which
was ingested together with the test foods (apple (Elstar), Coca Cola® and water) in
three separate arms of the study. For the detailed insight on the study design, see Figure 4.1.
Figure 4.1. Study design of the FoodBAll MRI study.
In total, 11 participants (5 males and 6 females), aged between 21-33 years, BMI 19.7-27.1 kg/m², who gave their written consent, were recruited. Participants were included, if they were healthy male or female, between 18 and 40 years, had a BMI between 18.5 and 30 kg/m², were free from prevalent diseases, had no history of a chronic disease, were non-smokers, did not take any medication (e.g. hormonal contraceptive), or did not take supplements within the last four weeks, did not take antibiotics within the last six months, donated blood within the last three months, in case of women were not pregnant or lactating, did not have an allergy or intolerance for one of the test food or reference meals and were willing and able to perform the study. The ethics committee of the State Medical Chamber of Baden-Württemberg, Stuttgart, Germany, approved the study, which was in accordance with the 1964
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Helsinki declaration and its later amendments. The study was registered at the German Clinical Study Register (DRKS00008787) and has the Universal Trial Number U1111-1177-1536.
For optimization and development of the GC×GC-MS sugar profiling method only the urine samples were used. Participants provided spot urine samples before the intake of the test food, at 48 hours after the intervention and collected their urine during the periods described in Figure 4.1. During collection periods the bottles were kept on ice, until samples were processed. After recording urine volume, samples were centrifuged (10 min, 1850 × g, 20 °C), and aliquoted, frozen at -20 °C and after one day stored at -80 °C.
A QC sample was prepared by mixing urine samples from all participants, all interventions and all time points. Separate aliquots were frozen at -80 °C until analysis. Osmolality of the QC sample and study samples was determined in
duplicate after centrifugation by using an Advanced® Osmometer Modell 2020
(Advanced Instruments, Massachusetts, USA). Sample preparation
First, 40 µL of urine sample (study and QC samples) were diluted with water (dH2O)
to adjust osmolality to 60 mOsmol/kg. 20 µL of IS solution (composition see Supplemental Table S4.1) were pipetted to a screw thread vial with a 300 µL fused insert and 40 µL of the diluted urine sample were added. After mixing, the samples were evaporated in a Speedvac-concentrator (Heto maxi dry plus, Jouan Nordic) for 1 h at 40 °C and maximal vacuum (p < 1 mbar). To remove remaining traces of water, 20 µL of methanol were added followed by an additional evaporation step (30 min). After evaporation, a two-step derivatization protocol was performed. First, methoximation was conducted by adding 15 µL of methoxylamine-hydrochloride solution (20 mg/mL in anhydrous pyridine) at 40 °C for 1 h under shaking (1000 rpm). Second, trimethylsilylation was carried out by adding 50 µL of MSTFA with 1% TMCS at 75 °C for 1.5 h. Each day, a solvent blank was prepared similarly to urine samples, but with water instead of urine. At the end of the reaction, 10 µL of the FAME solution for RI were added (Supplemental Table S4.1). For the sugar compound reference standard samples (Supplemental Table S4.1) measured at the end of the measurement series, 50 µL of the 25 µMol/L standard solutions were evaporated to dryness in a two-step procedure as described before. Derivatization conditions were
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the same as for study samples, however 10 µL methoxylamine-hydrochloride solution and 30 µL MSTFA with 1% TMCS were used.
GC×GC-MS analysis
To correct drift and offset effects during the measurement due to shifts in equipment performance, analyte degradation and also prolonged derivatization reactions or changes in the system (e.g. change of septum), the regular measurement of QC samples is necessary, enabling an analyte-wise correction of these effects. Therefore, each day started with the daily solvent blank sample followed by 11 study samples in three groups of four/three samples framed by six QC samples, with two in the beginning and in the end. In the beginning and in the end of each measurement week, a QC sample was measured in Scan mode (m/z 60-550), this run can later be used for identification based on the full scan. At the end of the measurement series, reference standards of sugar compounds (Supplemental Table S4.1) were measured, so that they could be aligned with the study and QC samples and thus enable an a priori identification of known sugar compounds. Every measurement week, the liner and injector septum were replaced and the qMS was tuned, followed by six equilibration runs with QC samples. Table 4.1 lists the GC×GC-MS system and software utilized in this study. In Table 4.2, the GC, modulation and MS parameters are shown.
Table 4.1. GC×GC-MS system and software. Instrument component/
software
Name Manufacturer
Gas chromatograph GC-2010 Shimadzu Corp, Kyoto, Japan
Mass spectrometer QP2010 Ultra Shimadzu Corp, Kyoto, Japan
Autosampler AOC-20i+s Shimadzu Corp, Kyoto, Japan
PTV Injector OPTIC-4 GL Sciences, Eindhoven, The
Netherlands
Modulator ZX2 ZOEX Corp., Houston, USA
GCMS instrument software GCMSSolution 4.11 Shimadzu Corp, Kyoto, Japan PTV software Evolution Workstation 4.1 GL Sciences, Eindhoven, The
Netherlands
GC×GC visualization software ChromSquare 2.1 Chromaleont Srl, Messina, Italy
FiehnLib database FiehnLib Agilent, Santa Clara, USA
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Table 4.2. Method parameters including consumables.
Parameter Setting/value
Autosampler parameters
Syringe 10 µL, conical
Injection volume 1.2 µL
Pre Clean with Solvent (heptane, acetone) 3 Post Clean with Solvent (heptane, acetone) 6
Pre Rinses with Sample 1
Plunger Speed (Suction) High
Viscosity Comp. Time 0.2 s
Plunger Speed (Injection) High
Syringe Insertion Speed High
Pumping Times 3
Injection Port Dwell Time 0.3 s
GC parameters
Carrier gas Helium
GC mode Constant velocity
Initial column head pressure 210.3 kPa
Liner type Deactivated split liner with quartz wool (CS
chromatography, Langerwehe, Germany)
Injector septum Septa Thermolite® Shimadzu Plug (Restek, Bad
Homburg, Germany)
1D-column Rxi-5SilMS; 1L = 60 m plus 10 m of an integrated pre-
column; 1dc = 0.25 mm; 1df = 0.25 µm (Restek, Bad Homburg, Germany)
2D-column BPX50; 2Ltotal = 2.4 m, including a “separation
segment” of 2Lsep = 1.0 m; 2dc = 0.15 mm, 2df = 0.15 µm (SGE, Milton Keynes, United Kingdom)
Column connector SilTite MiniUnion (SGE, Milton Keynes, United
Kingdom)
GC temperature ramp 80 °C → 8 °C/min → 140 °C → 1.75 °C/min → 220 °C
→ 8 °C/min → 255 °C → 4 °C/min → 300 °C (6.16 min).
Run time: 75 min.
Injection mode Cold split
Split ratio 1:5 → 1:20 (1 min)
PTV temperature ramp 90 °C → 60 °C/s → 280 °C, hold until end of run.
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Table 4.2 continued.
Modulation parameters
Modulator type Cryogenic, air-based, loop-type
Modulation period (PM) 2.4 s
Cold jet temperature -90 °C
Hot jet temperature Programmed stepwise, at least 100 °C above oven
temperature
Hot jet duration 200 ms
MS parameters
Ion source temperature 200 °C
Ionization mode EI (70 eV)
MS Mode SIM
Event time 30 ms
SIM m/z 160.00, 277.00, 292.00, 305.00, 307.00, 318.00,
319.00, 333.00, 361.00, 421.00, 437.00
Data acquisition frequency 33 s-1
Data processing
Data processing is a complex and labor-intensive process involving different steps and different softwares (GCMSSolution, R modules, Microsoft Excel). The original workflow for a peaklet-based generic analysis of untargeted GC×GC-MS data was
developed by Björn Egert and Christoph Weinert [31, 91]. This strategy is based on full
scan data. However, for the sugar profiling, the measurement was performed in SIM mode with 11 selected m/z. To address the differences between full scan and SIM data, the workflow was adjusted with the help of Björn Egert. In the following paragraphs, a short description of the relevant points is given, a flow diagram can be found in Supplemental Figure S4.1:
• Raw data processing:
As a first step, raw data was submitted to automated integration. For this, overall 44 integration methods were created, four for each measured m/z (integration parameters see Supplemental Table S4.3). This was necessary due to the limitation of the GCMSSolution software (designed for the processing of one-dimensional GC-MS data) being able to handle only a maximum of 1000 peaks per run. Therefore, the chromatogram had to be integrated within timeframes for each of the 11 measured m/z with an overlap of 1 min between the time frames (Supplemental Figure S4.1, part A). A batch
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process resulted in one txt-file per run (overall 470 runs: QC samples, study samples, technical replicates and standard substances) including the 44 integration methods. Each retention time can be reported up to 11 times depending on the number and size of peaks for each of the m/z.
• Import and restructuring of data:
Each txt-file consisted of peak lists for four time frames of one m/z followed by the time frames of the next m/z and so on in a block-wise structure; information such as retention times (apex, start and end time), area, height and RI are included. Additionally, after each time frame block, the mass spectra consisting of maximally 11 masses for each listed peak are reported (Supplemental Figure S4.1, part B). The txt-files were imported and reorganized towards a tabular structure. Therefore, the 11 m/z were assigned to their respective block peak list based on a heuristical approach. The overlapping time frames of 1 min were removed based on exactly the same retention time and mass spectra. Additionally, based on the modulation time
the retention time (RT) of the raw data was converted to 1D- and 2D-RT’s. To
compensate RT-shifts for the modulations (Supplemental Figure S4.1, part C), runs were shifted batch-wise for maximally two modulations to the left or right. Which batches or measurement days were shifted and in which direction was decided based on the median RT’s for the highest modulation of three randomly chosen sugar compounds in the QC samples of each day. The result of this step was a first global data matrix with a continuous peak list for each m/z and run.
• Filtering and data reduction:
In the next step, a data reduction was performed based on several filters. All peaks with peak heights below 5000, and less than five data points were deleted. Based on the 2D-chromatogram obvious noise peaks and background peaks were deleted by adding their mass spectra to a noise database. Additionally, all peaks were deleted that could not find a matching peak (RT and mass spectral similarity) in any other run.
• Alignment:
The alignment is performed to combine all modulations in one run and to combine matching peaks over all runs. In a first step, a clustering algorithm
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similarity (ε < 0.015) by comparing peaks pair-wise (Supplemental Figure S4.1, part D). In a second step, each resulting cluster was evaluated again by calculating a hierarchical cluster analysis of the mass spectra within each cluster. If the height cut (HC)-value was above 275, the cluster was cut into two separate clusters (Supplemental Figure S4.1, part E). These steps were first performed for six QC samples and nine study samples comprising all study groups leading to a representative reference alignment. Such a reference alignment was necessary to cope with the huge amount of data due to limits in main memory for the computational calculation. Therefore, the processing of large cohorts with many samples is enabled. Then for each peak in each run, the mass spectral similarity was calculated against this reference alignment. The third and last step was the separation of clusters based on their peak height profile. Here, modulations of two separate peaks were combined due to their mass spectral similarity although being two separate peaks (Supplemental Figure S4.1, part F). To find the optimal split between such peaks, the cumulative heights of all runs were calculated. If adding a modulation results in an increase of less than 1.5%, this modulation was assigned to the first of the two peaks and all other modulations build their own cluster.
• Demodulation:
First, the quantifying ion was determined, which is the m/z with the highest signal intensity in most modulations of a cluster. Then, for each run, the signal intensities of the modulations of the quantifying ion were summed up to receive one signal intensity for one analyte in each run.
• Drift correction:
As a result of long measurement series, drift or batch effects can occur due to increasing contamination of the injector and column by matrix compounds, due to batch-wise derivatization (e.g. instability of derivatives, prolonged derivatization) or due to maintenance (e.g. changing of liner or septum). Representative QC samples were injected six times per batch/measurement day to correct such effects. Therefore, analyte-specific correction functions were calculated based on QC samples, and the signal intensities of study samples were adjusted accordingly (Supplemental Figure S4.1, part G). In this step, analytes were separated in correctable and uncorrectable analytes. If an
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analyte was found with less than 15% of the QC samples per batch/measurement day in more than 50% of all batches/measurement days, then the analyte was defined as uncorrectable. No correction of drift effect was performed because neither a batch-wise nor an analyte-specific correction function is possible with so few QC samples. For measurement days/batches with less than 15% QC samples or where the first and/or last QC samples were missing, only a global batch correction via the mean of the QC samples was performed. For all other batches/measurement days, where the frequency of the analyte in the QC samples was high enough, analyte-specific correction functions were calculated. The result was a final data matrix for each correctable and uncorrectable analytes, where analytes were listed in columns and samples in rows.
• Identification:
For identification purposes two steps were performed. First, at the beginning and at the end of each measurement week QC samples were measured in Scan mode. Thus, it was possible to perform a comparison of the mass spectrum of a compound with mass spectral libraries (in-house database, NIST14, FiehnLib). Second, overall 111 sugar reference standards were measured in 35 runs at the end of the measurement series (Supplemental Table S4.1). Therefore, the alignment of these reference standards with the QC and study samples and thus the identification of known sugar compounds was enabled.
• Evaluation:
After the data processing, an evaluation of the analytes had to be performed with respect to the choice of quantifying ion, with respect to a correct alignment and height profile split, with respect to the repeatability and with respect to the occurrence of partial or complete coelution with matrix compounds, non-sugars or other sugar compounds.