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(GC–GC/MS and GC/MS) Workflows G Model CHROMA-358514; No. of Pages 10 ARTICLE IN PRESS Journal of Chromatography A, xxx (2017) xxx–xxx Contents lists available at ScienceDirect Journal of Chromatography A j ournal homepage: www.elsevier.com/locate/chroma Full length article Optimizing targeted/untargeted metabolomics by automating gas chromatography/mass spectrometry (GC–GC/MS and GC/MS) workflows a,∗ a b b Albert Robbat Jr. , Nicole Kfoury , Eugene Baydakov , Yuriy Gankin a Department of Chemistry, Tufts University, 200 Boston Ave, Suite G700, Medford, MA, 02155, United States b EPAM Systems, 41 University Drive, Newtown, PA 18940, United States a r t i c l e i n f o a b s t r a c t Article history: New database building and MS subtraction algorithms have been developed for automated, sequential Received 26 January 2017 two-dimensional gas chromatography/mass spectrometry (GC–GC/MS). This paper reports the first use Received in revised form 20 April 2017 of a database building tool, with full mass spectrum subtraction, that does not rely on high resolution Accepted 5 May 2017 MS data. The software was used to automatically inspect GC–GC/MS data of high elevation tea from Available online xxx Yunnan, China, to build a database of 350 target compounds. The database was then used with spectral deconvolution to identify 285 compounds by GC/MS of the same tea. Targeted analysis of low elevation Keywords: tea by GC/MS resulted in the detection of 275 compounds. Non-targeted analysis, using MS subtraction, Annotated database building yielded an additional eight metabolites, unique to low elevation tea. MS subtraction © 2017 Elsevier B.V. All rights reserved. Spectral deconvolution GC–GC/MS GC/MS 1. Introduction increases in frequency and duration of extreme weather events [4,5]. Botanicals and their essential oils are highly complex prod- Toward this end, we developed new data analysis software ucts that contain thousands of volatile organic compounds (VOCs). to analyze 2-dimensional, automated, sequential gas chromatog- These plant-based flavorants are used in foods, beverages, and raphy/mass spectrometry (GC–GC/MS) data to obtain retention pharmaceuticals and personal care, cleaning supply and other con- time and mass spectral constituent information in complex sam- sumer products as odorants. When and where these products are ples [6,7]. The same software can be used to track their presence sourced as well as fruits, vegetables, tea, coffee, cocoa, herbs and by GC/MS [8–10]. Although GC/MS is quantitative, the technique spices is becoming increasingly important. For example, recently by itself cannot quantify the number of metabolites identified by we showed that under extreme weather conditions such as the East GC–GC/MS or differentiate them in samples exposed to extreme Asian monsoon rains the quality of tea changes significantly [1–3]. weather nor track them in the manufacture of finished goods with- Not only is tea less flavorful after the heavy rains, which farmers and out employing spectral deconvolution algorithms [11]. buyers know well, the concentrations of antioxidant, anticancer, While GC–GC/MS is excellent at producing retention time and anti-inflammatory, antifungal, and antibacterial compounds also mass spectral data, it is extremely time-consuming. For example, change drastically 5-days after the start of the monsoon rains. if the 1st column separation employs a 40-min temperature pro- Although some metabolite concentrations changed little, other gram and 1-min sample portions are transferred from the 1st to the sensory and nutraceutical compounds increased or decreased hun- 2nd column, a total of 40, 2nd dimension data files are produced. dreds of percent. These findings make evident that concentration If the 2nd column is a 50-min separation, the analysis of a single changes are unrelated to leaf growth and are of great importance sample takes days. In addition, despite the increase in separation to industry and consumers alike given the current and expected space GC–GC/MS offers, coelution still occurs due to the complex- ity of natural products. Also, high concentration analytes such as limonene in citrus oils will appear in multiple data files due to flow switch imprecision, which means the same compound must be rec- onciled to eliminate redundancies in the database. The total time ∗ Corresponding author. we spend creating one library takes months to accomplish. To over- E-mail address: [email protected] (A. Robbat Jr.). http://dx.doi.org/10.1016/j.chroma.2017.05.017 0021-9673/© 2017 Elsevier B.V. All rights reserved. Please cite this article in press as: A. Robbat Jr., et al., Optimizing targeted/untargeted metabolomics by automating gas chromatogra- phy/mass spectrometry (GC–GC/MS and GC/MS) workflows, J. Chromatogr. A (2017), http://dx.doi.org/10.1016/j.chroma.2017.05.017 G Model CHROMA-358514; No. of Pages 10 ARTICLE IN PRESS 2 A. Robbat Jr. et al. / J. Chromatogr. A xxx (2017) xxx–xxx come these deficiencies, we developed new data analysis software for 2 h with VOCs collected in the organic phase. Anhydrous sodium that automatically inspects each peak in the data file, subtracts the sulfate was added to the distillate and concentrated to 500 ␮L under mass spectrum of a compound from the total ion current (TIC) chro- a stream of purified nitrogen. matogram, and evaluates whether the residual signal approximates background noise. When this occurs, compound identity, retention 2.2. Automated, sequential 2D GC–GC/MS time, mass spectrum, and deconvolution ions are uploaded to the software. The instrument configuration and heartcutting process have Rasmussen and Isenhour [12] first assessed the efficiency of been described in detail elsewhere [1]. Briefly, the first GC (Agi- library search algorithms to identify unknowns, followed by Stein lent model 6890, Santa Clara, CA) housed the 1st column (C1, and Scott [13] and McLafferty et al. [14] Recently, Stein [15] 30 m × 250 ␮m × 0.25 ␮m Rtx-Wax, Restek, Bellefonte, PA) and reviewed the basic principles and factors that affect compound was equipped with a flame ionization detector (FID). Operat- ◦ ◦ identification using mass spectral reference libraries while Spark- ing conditions were: 40 C for 1 min, then ramped to 240 C at ◦ man [16], Koo [17] and Samokhin [18] compared the performance 5 C/min. C1 was connected to a CIS4 inlet (Gerstel, Mülheim an of newer library-matching algorithms [19–22] to those of Ras- der Ruhr, Germany), operating in splitless mode, on one end and mussen, Stein, and McLafferty. The development of early mass to a 5-port crosspiece (Gerstel) on the other. The 2nd column spectral deconvolution software aimed at untangling spectra of (C2, 30 m × 250 ␮m × 0.25 ␮m Rxi-5MS, Restek) was housed in GC coeluting compounds was investigated by Champan [23] and Likic 2 (Agilent model 6890), which was connected to the crosspiece [24]. More recent deconvolution software was reviewed by Putri through a cryogenic freeze trap (CTS1, Gerstel) on one end and to an [25], Du [26] and Yi [27], including vendor-specific software such as Agilent mass spectrometer (model 5975) on the other. C2 operating ◦ ◦ ◦ ChromaTOF (LECO), MassHunter Profinder (Agilent), and MassLynx conditions were: 40 C for 1 min, and ramped to 280 C at 5 C/min. (Waters) as well as ADAP-GC 2.0 [28], AutoDecon [29], AMDIS [30], Both columns operated at 1.2 mL/min of constant helium flow. A MetaboliteDetector [31], MetaboAnalyst [32], MetabolomeExpress multi-column switching device (MCS, Gerstel) supplied counter- Project [33], MetAlign [34], mMass [35], MZmine [36], OpenChrom current flow to the crosspiece. Based on 1 min sample portions, a [37], PyMS [38], PYQAN [39], SpectConnect [40], and TagFinder [41]. total of 40 heartcut data files were obtained. Because each heart- The latter group operate on a wide range of data files. All of these cut was an independent analysis, subsequent injections were made solutions provide spectral matching between library and sample after each preceding sample portion eluted from both columns. data. Until BinBase none of the aforementioned software included As a result, total analysis time was 3.5 days for each sample. MS ◦ ◦ database functions that allowed analysts to add new information, operating conditions were: 230 C and 150 C for the ion source compare sample outputs, or track compounds across multiple sam- and quadrupole, respectively, 70 eV electron impact voltage, and ples [42]. Although BinBase and Mass Profiler (Agilent) can compare 50–350 mass range, 12 scans/sec. A standard mixture of C7–C30 n- data sets, they rely on high resolution MS data to differentiate sam- alkanes (Sigma-Aldrich, St. Louis, MO) was used to calculate the ples. In addition, BinBase is reliant on LECO’s ChromaTOF software retention index (RI) for each compound. to deconvolve spectra, limiting its application to LECO instruments. To our knowledge no software program exists that can differenti- 2.3. Tea analysis ate MS fragmentation patterns and automatically subtract a full MS spectrum from the TIC signal to reveal and identify coeluting GC/MS operating conditions were as described in system 2. Con- compounds. centrations were calculated as relative peak areas (RPA) except In this paper, we present new data analysis software, which for four compounds. Naphthalene-d8 (Restek) served as the inter- works on all instrument data files that produce an industry standard nal standard. Calibration curves were produced for pentanol .cdf extension [43–45]. The Ion Analytics software automatically and terpinolene (TCI, Nihonbashi-honco, Japan), trans-linalool investigates each GC–GC/MS peak, determines mass spectral con- oxide (Sigma-Aldrich), and toluene (Supelco, Bellefonte, PA) from stancy at each scan across the peak. If invariant, uploads the 0.5 ug/ml to 50 ug/ml. Response factors were calculated for each retention time, mass spectrum, and relative abundance of three to compound as follows: six fragmentation ions used for deconvolution as well as the iden- tity of the compound after searching the analyst’s library, NIST, AiCIS RF = Wiley, Adams or any other spectral library that can be saved in AISCi NIST format.
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