Ion mobility spectrometry and the omics: Distinguishing isomers, molecular classes and contaminant ions in complex samples

Trends in Analytical Chemistry 116 (2019) 292e299

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Ion mobility spectrometry and the omics: Distinguishing isomers, molecular classes and contaminant ions in complex samples Kristin E. Burnum-Johnson a, Xueyun Zheng b, James N. Dodds c, Jeremy Ash c, Denis Fourches c, Carrie D. Nicora a, Jason P. Wendler a, Thomas O. Metz a, Katrina M. Waters a, Janet K. Jansson a, Richard D. Smith a, Erin S. Baker c, * a b c

Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA Department of Chemistry, Texas A &M University, College Station, TX, USA Department of Chemistry, NC State University, Raleigh, NC, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 29 April 2019

Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), lowering the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions. © 2019 Elsevier B.V. All rights reserved.

Keywords: Ion mobility spectrometry Mass spectrometry Omics Proteomics Lipidomics Metabolomics Glycomics Exposomics

1. Introduction Mass spectrometry (MS)-based analyses have become one of the most informative methods for studying complex mixtures in the 21st century. However, evaluating biological and environmental systems with MS-based measurements can be extremely challenging due to the many molecule types (e.g. proteins, lipids, small molecules, metals, etc.) present in the different samples and the wide range of concentrations they exist at. To address these challenges and enable more effective MS measurements, advanced sample preparation techniques are often employed to separate or fractionate the diverse molecular classes. These methods include biphasic and triphasic extractions, depletion of the most abundant proteins in blood plasma and serum analyses, and extensive solid phase extractions to remove salts and clean up the samples prior to their injection into the MS

* Corresponding author. 2620 Yarbrough Dr. Campus Box 8204 Dabney Hall, Rm 726 Raleigh, NC 27695, USA. E-mail address: [email protected] (E.S. Baker). https://doi.org/10.1016/j.trac.2019.04.022 0165-9936/© 2019 Elsevier B.V. All rights reserved.

platform. Unfortunately, even the most advanced procedures are not always able to completely clean up the samples and limit the detector suppression that can occur when highly concentrated species are present concurrently with those at much lower abundance. Additionally, isomeric molecules of the same elemental composition and similar structure cause even further measurement challenges since they often cannot be distinguished by their fragmentation patterns or with chromatography. Ion mobility spectrometry (IMS) is therefore being increasingly utilized as a way to address some of these analytical challenges. Over the last decade, the use of IMS in analytical measurements has rapidly increased with applications in national security-related analyses, patient screening, environmental monitoring and numerous other areas [1e5]. IMS is a gas phase technique that provides rapid structural separations based on the balance of two forces that impact the movement of an ion, the electric field and the drag force from the collision with buffer gas molecules [6]. In IMS, compounds of different sizes, shapes, and charges are separated as they travel through a buffer gas. Variations on the electric field and stationary state of the buffer gas have given rise to multiple IMS-

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based platforms. Drift tube IMS (DTIMS) [7e9], traveling wave IMS (TWIMS) [10], trapped IMS (TIMS) [11], field asymmetric IMS (FAIMS; also called differential mobility spectrometry (DMS) or differential ion mobility spectrometry (DIMS)) [12,13] and differential mobility analyzers (DMA) [14,15] are five of the most common forms of IMS used in current analyses of complex samples. DTIMS and TWIMS perform comprehensive analyses and allow all ions to be analyzed simultaneously under the same conditions, while TIMS, FAIMS and DMA devices are scanned to evaluate specific ions or classes separately. Additionally, the measured mobilities for molecular species in DTIMS, TWIMS, TIMS and DMA can be converted into rotationally averaged collision cross sections (CCS) [16,17], which provide useful descriptors of the corresponding ions' 3-dimensional gas-phase structures under the given experimental conditions. In DTIMS and DMA, CCS values are calculated directly from the measured arrival times to provide structural insight and remove pressure, temperature and length dependencies. However, calibration procedures are required when measuring CCS values with TWIMS and TIMS. Due to the alternating high and low fields used in FAIMS, no current approach exists to allow CCS values to be obtained, but this unique separation mechanism allows FAIMS to distinguish species that presently challenge other IMS methods. DTIMS, TWIMS, TIMS, DMA and FAIMS have all shown great utility in separating molecular classes and interfering ions of various forms. Additionally, they can be interfaced with mass spectrometers [18] to permit simultaneous acquisition of IMS structural and MS m/z information on a rapid millisecond to second timescale. The CCS values from IMS separations are then used to support molecular identifications from the IMS-MS analyses as they provide structural insight. Although CCS and m/z are not truly orthogonal identifiers due to some correlation between the two characteristics, different molecular classes (e.g. peptides and lipids) still exhibit distinct m/z versus CCS trend lines due to backbone characteristics. This allows potential insight to be garnered about unknown analytes based on their position in the m/z versus CCS trend line space [19]. Molecular dynamics and density functional theory (DFT) candidate structures have also been utilized for comparison of the experimental CCS values and have shown excellent agreement with errors often <2%. This agreement is because actual molecule properties such as bond types, charge position and molecular compaction can be used theoretically to predict structures and explain the different trend lines. Since IMS is able to quickly separate distinct chemical species such as carbohydrates, peptides, lipids, and organic material from other components, it enables better analyses for the molecule of choice in complex samples (Fig. 1) [19,20]. These measurements can then be used to quickly screen conditions while gathering information about the molecular species present, and differentiating isomers and separating contaminates from the compounds of interest. Furthermore, these IMS separations can be coupled with liquid chromatography (LC) separations, enabling multi-dimensional measurements that address highly complex samples without increasing the analysis time [21e23]. The additional IMS separation can also be used to significantly reduce the LC separation times while maintaining the depth of coverage characteristic of much longer traditional LC-MS analyses [24]. Examples of how IMS has been used to separate molecular structures, isomers, chemical classes and reduce detector suppression caused by contaminant ions are illustrated in this review to show its utility in omic measurements. 2. IMS-MS in proteomic analyses In proteomics, measurements with high throughput and sensitivity are essential for improving the characterization of complex mixtures in both discovery and validation studies. Proteomic measurements are typically divided into bottom-up, middle-down

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Fig. 1. The various molecule types present in environmental and biological samples greatly hinder comprehensive MS measurements. IMS provides a way of separating molecule types such as glycans, nucleotides, peptides, organic material and lipids based on their structures to reduce detector suppression and enable better identifications. In this IMS evaluation, glycans traversing the IMS drift cell travel fastest due to their ring-based structures, while lipids are slowest due to their rigid linear backbone.

and top-down approaches. In top-down approaches, intact proteins are separated, detected and identified; whereas both bottom-up and middle-down strategies utilize digestion, where the intact proteins are broken down and identified by their characteristic peptides. Middle-down and bottom-up approaches are differentiated based on the endopeptidase used in the digestion, as middledown analyses often utilize endopeptidase that results in larger peptides (e.g. Lys-C or OMP-T) than bottom-up techniques which commonly use trypsin or pepsin. Since IMS-MS provides structural information not easily obtained using MS alone, it has been utilized in top down proteomic studies since the 1990s to investigate protein structural changes that could differentiate diseases (e.g. misfolding and aggregation) [25e27]. IMS-MS has been specifically used to study intact proteins related to intrinsically disordered proteins implicated in neurodegenerative diseases like Alzheimer's (amyloid-b protein) and Parkinson's (a-synuclein protein) [28e30]. The need to understand how protein structures change upon stress is also promoting collision induced unfolding (CIU) studies, where energy is imparted on proteins or protein complexes and IMS is used to measure the structural changes [31]. In most CIU analyses, proteins tend to adopt compact conformations at the lowest energies, however, as the energy is increased, the proteins and complexes slowly begin to unfold. Recent examples of CIU have probed the conformational

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stability of protein complexes and protein-ligand interactions [32,33], serving as an invaluable tool in the repertoire of structural biology techniques. In both bottom-up and middle-down approaches, extensive LC separation times (often >100 min) and high-resolution mass spectrometers with tandem MS capabilities are used to distinguish the multiply charged isomeric species. However, even these LC-MS/MS methodologies still lack the ability to fully resolve all peptide components, especially when many are at low abundance. Performing LC-MS analyses provides obvious benefits in improving the resolution of proteomic measurements without sacrificing analysis time and even has the capability to speed up the LC times [24,34]. Furthermore, IMS enables a way of separating peptide and contaminant ions if they are extracted together and cannot be removed using cleanup methods, such as when DTIMS was utilized to separate CHAPS and SDS from peptides of interest [35,36]. FAIMS devices have also shown utility in separating 1 þ contaminant ions from higher charge state proteins and peptides of interest, allowing more accumulation time in the linear ion trap or orbitrap [37]. This capability has been extremely important in avoiding biases in trap-based mass analyzers, which require automated gain control (AGC). Specifically, AGC is used in linear ion traps and orbitraps to limit the number of ion charges accumulated to prevent excessive space charge effects and allow higher measurement accuracy. However, any high concentration contaminant causes the AGC to greatly reduce trapping time so that the lower abundance ions do not have enough time to collect in the trap and provide a reasonable signal for detection. LC-IMSMS measurements have therefore greatly aided the analysis of complex environmental water, soil, and plant material samples which have many diverse types of molecular contaminants [36]. In processing these environmental samples, organic material is typically extracted with the proteins. While LC helps reduce ionization suppression, the IMS separation is particularly useful for separating the peptides from the high concentrations of organic material (e.g., humic acid substances in soil and polyphenols in plants), natural contaminants (e.g., abundant salts or polymers), and detergents prior to detection [35,36] (Fig. 2). These LC-IMS-MS proteomic measurements have also been useful for reducing the number of false positives resulting from complex samples due to the extra characterization dimension [38]. Advantages such as higher sensitivity, lower detection limits and more rapid throughput than LC-MS measurements have also been noted in LC-IMS-MS studies [39,40]. The ability of LC-IMS-MS to provide high throughput and sensitive analyses, while separating peptides from contaminant ions that can arise from glassware and the multiple proteomic extraction steps, is also extremely important in biological and environmental studies. In one biological study [41], plasma was collected from Ebola patients in a field biosafety level-4 laboratory (BSL-4) in Sierra Leone. During the immunodepletion and viral inactivation steps of Ebola infected plasma samples, polymers were unavoidably introduced causing problems for ion trap-based MS platforms. In the analyses, the AGC function of the QExactive quickly terminated the accumulation of the ions due to signal from the polymers dominating the samples. However, during the corresponding LCIMS-MS analyses of the same samples, the IMS separation moved the polymers to a different spectral region and since TOF instruments do not require an AGC all ions were analyzed equally. Thus, a drastic increase in the number of proteins detected with LCIMS-MS versus the LC-QExactive was observed (Fig. 3A). The number of observed enzymes that mapped to the KEGG atlas also greatly increased with the LC-IMS-MS analysis (Fig. 3B), allowing additional information to be uncovered about the mechanistic role these proteins play in the Ebola disease [41].

Fig. 2. LC and IMS separation of organic material contaminant from peptides in an Arabidopsis leaf (top) and soil sample (bottom). The IMS separation was able to move most of the organic material to a later arrival time space than the peptide ions due to the different molecular structures. All molecular species are normalized to the highest concentration molecules in these plots, illustrating that the leaf peptides did not have as much interference from the organic material as the soil did.

High throughput proteomic analyses have also been of great interest for analyzing the numerous patients necessary to address human biodiversity. Recently, IMS was even used to combine both the discovery and validation steps to simultaneously increase coverage and throughput. In this discovery and targeted monitoring (DTM) approach [42,43], heavy-labeled peptide standards were spiked into a tryptic digest for highly sensitive and precise relative quantitation of the selected target peptides. Global analyses were also performed in these studies so that unknown peptides could be characterized based on their LC elution times, CCS values, and accurate masses. The discovery-based analyses leveraging DTM provided better overall protein sequence coverage than most discovery LC-MS methods and also detection of lower abundance peptides [42, 43].

3. IMS-MS in small molecule metabolomic and exposomic studies The evaluation of small molecules with IMS-MS is also of great interest due to the many isomers and nominal mass isobars that coelute and fragment together when selection windows of 0.5 Da or greater are utilized in MS/MS evaluations. In metabolomic measurements, small molecule metabolites, intermediates and products of metabolism are studied in environmental and biological systems [44]. The presence of xenobiotic molecules in these metabolomic evaluations is also possible as they can be introduced through various sources such as chemical manufacturing plants, consumption of foods and drugs, or contact with personal-care products. The study of xenobiotic molecules is often called exposomics, but can be coupled with metabolomic measurements since many are

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Fig. 3. Proteins identified in plasma samples contaminated with polymers from Ebola patients were compared for a LC-QExactive MS (noted as LC-MS [41]) and LC-IMS-QTOF MS (noted as LC-IMS-MS). A) Venn diagrams are used to illustrate the number of Uniprot protein IDs (left) and proteins that mapped to human metabolic pathways in the KEGG database (right). LC-MS only identifications are shown in red, LC-IMS-MS only in blue, and the overlap of both in green. All proteins were identified with at least two peptides and a requirement that one peptide must be unique (i.e., the case of a single peptide matching only one protein in the reference database). B) The specific enzymes that mapped to the KEGG atlas using the color scheme from A), illustrating the much higher coverage with the LC-IMS-MS analysis.

metabolized in the body and observed in normal metabolomic assays. However, it is important to consider the polarity of the xenobiotics of interest, extraction used, and ion source needed for detection as many xenobiotics need special conditions for analysis (e.g. polyaromatic hydrocarbons will not be found in polar exactions and analyses with electrospray ionization). The complexity of the different biological and environmental matrices and the possible occurrence of thousands of different small molecules therefore requires analytical methods able to screen and identify many compounds in a single run. LC-IMS-MS has therefore shown great promise for the untargeted analysis of small molecules in biological and environmental samples due to its speed and multidimensional analysis dimensions. IMS-MS and LC-IMS-MS have been utilized in numerous studies to improve both selectivity and coverage of the metabolome by providing additional structural information compared to routine MS or LC-MS-based methods [45]. Since different molecular classes fall on distinct trend lines based on their CCS and m/z values, IMS has been extremely valuable for separating out many different types of small molecules (Fig. 4). To date there have been various CCS databases released for different substance classes such as peptides [46e48], N-glycans [49], drug-like compounds [48], metabolites [50,51], lipids [52e54], environmental toxins [55] and various biomolecules [56], ultimately aiding in their classification. There is also great effort going into the standardization of the CCS values from the same type of instruments as well as different IMS techniques [57e59]. Standardization considerations are mainly related to the type of ion mobility spectrometry performed, buffer gas composition utilized, calibration procedures required, and

instrumental parameters used such as electric fields, temperature and pressure. Recent reviews have demonstrated that addressing these questions requires communal consensus between both academic and industrial researchers to advance the application of IMSMS technology and provide a frame of reference for reporting CCS measurements [60]. Previous LC-IMS-MS small molecule evaluations have been performed for biological matrices and environmental samples such as plasma and wastewater [48]. Wastewater, in particular, provides many challenges for analytical measurements as anthropogenic compounds such as pharmaceuticals, pesticides, or personal-care products can be found in the water cycle. The application of LCIMS-MS for the untargeted analysis of water samples was found to be a very useful tool due to its speed and multiple analysis dimensions, which provided better identifications than LC-MS alone. Two-dimensional LC systems have even been combined with IMSMS to perform four dimensional analyses and increase peak capacity even further [61]. Additionally, IMS provides added value in the evaluation of unknown molecules as the combination of the CCS value and exact mass for each feature (and LC information when available) limits the number of possible candidates. Molecular modeling [62] and machine learning [63,64] also allow the evaluation of theoretical and experimental CCS values to predict possible identities or molecular classification for unknown molecules. These theoretical approaches have the potential to enable the annotation and identification of many small molecules, even though in current small molecule databases only ~30,000 have measured MS/MS values.

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Fig. 4. The distinct CCS verses m/z trend lines for the different classes of small molecules examined with DTIMS and N2 as the drift gas. The deprotonated values are shown for all molecules except the phosphatidylcholines ([MþH]þ) and PBDEs ([M-BrþO]).

4. IMS-MS in the analysis of lipidomic and glycomic isomers Both lipidomic and glycomics measurements are also proving increasingly important for understanding biological and environmental systems. However, both molecule types challenge current techniques due to their many possible isomers. Lipid isomers often occur from different fatty acyl positions (sn-1/sn-2 vs. sn-2/sn-1), sn-backbones connectivities (sn-1/sn-2 vs. sn-1/sn-3), double bond positions (positional and conformational), similar fatty acyl groups (14:0/14:0 vs. 16:0/12:0), S versus R orientations, and distinct but structurally similar headgroups. Glycan evaluations are also exceedingly difficult due to the high abundance of isomeric species resulting from isomeric monosaccharides (glucose, galactose, mannose, etc.), anomericity (a-versus b-linkages), glycosidic linkage position (e.g. a(1e4) linkage, a(1e6) linkage, or b(1e4)), and branching versus linear glycan forms in which the monosaccharide composition is identical but the connectivity along the monosaccharide chain differs. Because of the abundance of structural variability in the isomers for both lipids and glycans, IMS-MS has emerged as a powerful technique for their evaluations. While some of the isomeric glycan and lipid groups can be separated using specific chromatographic methods, the large variability in chemical properties such as polarity can greatly complicate global lipidomic and glycomic analyses. Furthermore, LC parameter optimization can be very time consuming and costly. Since obtaining better resolving power with IMS only consists of buffer gas optimization (e.g. lower temperatures or addition of mobile phase modifiers) and it is amenable to a range of pre-separation techniques and ionization methods, IMS is an ideal complement to existing methods [65]. To date, numerous instances of IMS-MS have been published demonstrating improvements in lipid identification. In one study IMS-MS/MS analyses were utilized to provide a high level of structural information for phosphatidylcholines (PCs), including differentiation of fatty acyl substituents at the sn-1 versus sn-2 position, and location of fatty acyl double bond(s) for PCs in plasma [66]. A different evaluation showed that glycero- and phospholipid isomers can be separated using high resolution FAIMS-MS/MS with ~75% success when forming silver adducts of the target lipids [67,68]. To increase lipid identifications, many research groups are compiling their own CCS libraries for lipids, similar to what has been done for small molecules/metabolites

[56,69]. The work to date has illustrated greatly improved selectivity in various lipidomics approaches, including LC-IMS-MS shotgun approaches [58]. Machine-learning algorithm-based CCS prediction has also been implemented to generate large-scale CCS libraries in support of lipidomics [69]. These values are also being utilized with LC elution times, MS vales and MS/MS fragmentation patterns for identifications, where the multiple characteristics greatly reduce the number of false positives [52]. To date, elucidating detailed information about glycans and glycoconjugates has been limited due to their structural complexity [70]. The earliest IMS glycan applications involved the separation and CCS assessment of small oligosaccharides such as tetrasaccharides, hexasaccharides, and cyclic oligosaccharides (cyclodextrins) [26,71]. More challenging glycans were then successfully resolved using IMS-MS include anomeric trisaccharides [72], high mannose N-glycans [73], and even glycopeptides [74e76]. Most IMS-MS studies of glycans to date have mainly focused on positive ion mode characterization due to its higher ionization efficiency [77e80], but recent reports on negative ion mode are also showing great promise [81]. Additionally, various salt and metal ions adducts have shown enhanced isomeric separations and provided further opportunities for better glycan analyses [81,82]. Thus, future evaluations of lipids and glycans are expected to rely heavily on IMS analyses for the rapid and sensitive separation their isomers, greatly improving their characterization and identification [83]. 5. Conclusions While IMS-MS was once characterized as an emerging technique in the omics field, it is being increasingly integrated into modern omic measurements. Its utility in separating peptides and proteins with only minor structural differences has shown to be extremely powerful in bottom-up, middle-down and top-down studies. It has also greatly enabled the evaluation of isomers, which were previously inseparable or required long chromatography. Additionally, the ability of IMS-MS to distinguishing contaminant ions and molecular classes is proving to be essential in all omic analyses of complex environmental and biological samples. The CCS values from IMS measurements are also proving to be important in many omic areas. By measuring CCS values for

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available standards and compiling databases, rapid targeted analyses and identification of small molecules, lipids, glycans and peptides are possible. These CCS values are also being incorporated into computational methods such as theoretical modeling of structures and machine-based approaches, enabling molecular insight when no standards are available. Undoubtedly, the increasing utility of IMS-MS hinges on enhancing its resolving power and successfully coupling it with other pre-separation, fragmentation and software tools. Fortunately, developments in all these areas are taking place to increase the capabilities of IMS-MS measurements. Higher resolution IMS and MS instrument platforms such as those possible with structures for lossless ion manipulations (SLIM) are becoming available, providing even more confidence in global measurements of complex samples [84]. Additionally, fragmentation approaches such as ECD are being added to various IMS platforms for extended molecular classification capabilities and software tools enabling rapid data analyses are being developed to push the technology even further. Since the analysis of the multi-dimensional LC-IMS-MS data has proven quite difficult due to the many dimensions (LC, IMS, MS and DIA or DDA MS/MS), the future quite seems promising with its recent incorporation into numerous software packages including the open source Skyline software [85]. Thus, IMS-MS capabilities are only expected to increase in the future, making it an essential tool for high quality omic analyses. Acknowledgements The authors would like to thank Nathan Johnson for help with the figures. Portions of this research were supported by grants from the NIH National Institute of Environmental Health Sciences (R01 ES022190 and P42 ES027704), National Institute of General Medical Sciences (P41 GM103493), National Institute of Allergy and Infectious Diseases (U19 AI106772), National Institute of Child Health and Human Development (R21 HD084788) and the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory. This research used capabilities developed by the Pan-omics program (funded by the U.S. Department of Energy Office of Biological and Environmental Research Genome Sciences Program). This work was performed in the W. R. Wiley Environmental Molecular Sciences Laboratory (EMSL), a DOE national scientific user facility at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle for the DOE under contract DE-AC05-76RL0 1830. References [1] H. Borsdorf, G.A. Eiceman, Ion mobility spectrometry: principles and applications, Appl. Spectrosc. Rev. 41 (4) (2006) 323e375. https://doi.org/10.1080/ 05704920600663469. [2] C. Lapthorn, F. Pullen, B.Z. Chowdhry, Ion mobility spectrometry-mass spectrometry (IMS-MS) of small molecules: separating and assigning structures to ions, Mass Spectrom. Rev. 32 (1) (2013) 43e71. https://doi.org/10.1002/ mas.21349. [3] F. Lanucara, S.W. Holman, C.J. Gray, C.E. Eyers, The power of ion mobility-mass spectrometry for structural characterization and the study of conformational dynamics, Nat. Chem. 6 (4) (2014) 281e294. https://doi.org/10.1038/ nchem.1889. [4] C.D. Chouinard, M.S. Wei, C.R. Beekman, R.H.J. Kemperman, R.A. Yost, Ion mobility in clinical analysis: current progress and future perspectives, Clin. Chem. 62 (1) (2016) 124e133. https://doi.org/10.1373/clinchem.2015.238840. [5] J.C. May, R.L. Gant-Branum, J.A. McLean, Targeting the untargeted in molecular phenomics with structurally-selective ion mobility-mass spectrometry, Curr. Opin. Biotechnol. 39 (2016) 192e197. https://doi.org/10.1016/j.copbio.2016. 04.013. [6] E. Mason, E. McDaniel, Transport Properties of Ions in Gases, Wiley, New York, 1988. [7] M.J. Cohen, F.W. Karasek, Plasma chromatography™da new dimension for gas chromatography and mass spectrometry, J. Chromatogr. Sci. 8 (6) (1970) 330e337. https://doi.org/10.1093/chromsci/8.6.330.

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