Integrating ion mobility spectrometry into MS-based exposome measurements Perspective


approach, CCSs are computationally predicted from the chemical structures of the molecules themselves, as captured using molecular modeling and quantum chemical calculations. In addition, unlike chromato- graphic retention times, the CCS parameter recorded under low drift field conditions is influenced by far fewer


physicochemical parameters, meaning that


excellent correspondence between measured values and library entries can be expected across different instruments. To broadly predict CCS values, an automated com-


ChemAxon) to predict protonation/deprotonation states and adduct sites [105]. Initial geometry optimi- zations are performed using the Merck molecular force field [106] implemented in Avogadro (v.1.1.1) [107] and final geometry optimizations are performed using a density functional theory implemented in NWChem [108] at the B3LYP/6–31g* level [109–111]. Finally, the theoretical CCS values are calculated based on the geometry-optimized structures using the N2


putational pipeline is required as depicted in Figure 4. First, International Chemical Identifiers (InChIs; [104]) are converted into 2D structures, and then analyzed using the Marvin pKa


plugin (Marvin 15.9.14, 2015, -optimized trajectory method implemented in the


MOBCAL software [112–114]. In this way, the genera- tion of accurate predicted CCS values can facilitate the broad identification of detected molecules in combi- nation with accurate mass and MS/MS spectra, when available, and possibly with just accurate mass. The power of this approach is that metabolites can be anno- tated rapidly since only a chemical structure (e.g., in InChI format) obtained from either one of the many databases (e.g., HMDB, CHEBI, Chemspider, Pub- Chem) or drawn by hand in chemical structure soft- ware is required for predicting theoretical CCS values to be matched with experimental numbers, together with other metrics, such as accurate mass and/or MS/MS spectra. By utilizing a theoretical pipeline such as defined


total of 11,046 unique ionized structures. To improve throughput for predicting so many structures, CCS was calculated using the IMPACT software [105,115], parameterized for helium as the drift gas. On aver- age, it required approximately 0.5 s to calculate the CCS of a molecule on a windows desktop (with 16 GB RAM, Intel®


Xeon® and the construction of the ionized s tructures required to 2D/3D structure conversions, the pKa future science group


1.6 GHz CPU); while the InChI calculations


above, the feasibility of broadly predicting CCS for small molecules was determined by calculating theo- retical CCSs for 5068 metabolites from the HMDB, in their protonated, deprotonated and sodiated forms based on pKa


analysis, yielding predicted CCSs for a


lighting the fact that annotation and probable identifi- cation based on a single property (i.e., m/z) is not suf- ficient. Figure 5A shows the predicted CCS values for 12 different isobaric small molecules with the formula C6


H16


ing accurate masses. As the searched databases grow in size (i.e., by including ChemSpider or others), and as novel undocumented molecules are considered, this problem will only increase in magnitude (Figure 5C). In terms of the required agreement between pre-


H13 NO2 (Figure 5B) plotted versus their correspond-


dicted and measured CCS values, the combination of instrumental measurement precision and accuracy of CCS prediction is critical to the success of the described workflow. As discussed above, extremely good instrumental precision (<0.5%) is now readily possible on individual instruments, and interlabora- tory exercises indicate that DTIMS-MS instrumen- tation can provide reproducible consensus CCS val- ues with an RSD of less than 1% [93,94] for a wide range of biologically relevant molecules, which is a marked improvement over raw chromatographic retention time matching across different laboratories. This latter point is particularly critical to the success of the proposed approach, as a sound understand- ing of the uncertainty of CCS measurements must be ascertained in order to improve the identification potential. In an initial evaluation, experimental and theoretical CCS values of selected ionized structures (protonated, deprotonated and sodiated) of 11 metab- olites were compared, resulting in an average error of approximately 2% (Figure 5D) and indicating the potential of predicted CCS as a metric for supporting chemical identification in exposomic analysis. The approach of using IMS to compare experi-


mental predicted CCS values is not new and dates back to the early 1990s [116]. More recently, the com- munity has been moving in the direction of routine implementation of IMS and CCS in their methods for chemical analysis on a larger scale. Paglia et al. integrated TWIMS into an LC-MS method and established a retention time, CCS and accurate mass database of 125 common metabolites; CCSs were


www.future-science.com 36


about 3.3, 2.9 and 26.3 s per molecule, respectively, for 1465 molecules. The DFT calculations required approximately 1 h per molecule to run on a single computer node of a 3.4 petaflop linux cluster that has 1440 compute nodes and 16 cores per node. From these results, the chemical search space for possible molecules with a given accurate monoisotopic mass could be reduced by a minimum of 79% using CCS with a standard deviation (SD) of 2% (compared with a 0% reduction if using only m/z to find a match) within the HMDB library. For example, 76 different metabolites had the molecular formula C10


O, high-


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