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Ion Mobility Spectrometry: Fundamental Concepts, Instrumentation, Applications, and the Road Ahead

Ion mobility spectrometry (IMS) is a rapid separation technique that has experienced exponential growth as a field of study. Interfacing IMS with mass spectrometry (IMS-MS) provides additional analytical power as complementary separations from each technique enable multidimensional characterization of detected analytes. IMS separations occur on a millisecond timescale, and therefore can be readily nested into traditional GC and LC/MS workflows. However, the continual development of novel IMS methods has generated some level of confusion regarding the advantages and disadvantages of each. In this Critical Insight, we aim to clarify some common misconceptions for new users in the community pertaining to the fundamental concepts of the various IMS instrumental platforms ( i.e. DTIMS, TWIMS, TIMS, FAIMS and DMA), while addressing the strengths and shortcomings associated with each. Common IMS-MS applications are also discussed in this review, such as separating isomeric species, performing signal filtering for MS, and incorporating collision cross section (CCS) values into both targeted and untargeted omics-based workflows as additional ion descriptors for chemical annotation. Although many challenges must be addressed by the IMS community before mobility information is collected in a routine fashion, the future is bright with possibilities.

Graphical Abstract

Introduction

Ion mobility spectrometry (IMS) is the study of how ions move in gases under the influence of an electric field, or in other words the electrophoretic mobility of ions in buffer gases. Interestingly, while some may view IMS as a newer technique, its historical origins date back to 1896 in Thomson and Rutherford’s seminal work investigating the relationship between electrical conductivity and gaseous media. 1 Due to its fast screening capabilities and high sensitivity, IMS experienced rapid growth during the 1960’s and into the subsequent decades as an atmospheric pressure device which could rapidly screen chemical vapors for trace quantities of hazardous material. 2 , 3 Over the past century, advances in instrumental design further pushed the popularity of IMS forward by enhancing its sensitivity and selectivity. The resulting portable IMS devices continue to be utilized for routine detection of explosives and chemical warfare agents in military operations, sporting events, and airports. 2 , 4

While standalone IMS devices are very powerful, interfacing IMS with mass spectrometry (IMS-MS) has provided even more resolution of chemical space as the complementary separations in both the mobility and mass dimensions enable exceptional levels of selectivity and sensitivity. 5 – 7 Early IMS-MS instrumentation was generally associated with home-built instruments housed in academic settings. 8 , 9 Routine adoption of IMS-MS as an analytical tool began in 2006 with the commercial introduction of the Waters Synapt HDMS, the first widely marketed IMS-MS platform. 10 – 12 After generating considerable interest in both academic and industrial perspectives with the Synapt HDMS, many instrument developers quickly followed suit and developed their own IMS-MS platforms based on unique mobility separations, thereby adding IMS selectivity to MS for challenging systems in complex mixtures. While the flourishing growth of the IMS-MS field is exciting, a significant amount of confusion has developed among scientists new to the field in terms of understanding the subtle differences between different methods. Since each IMS technique possesses pros and cons in terms of specific applications, knowledge of the underlying fundamentals for each is helpful for designing experiments. In this Critical Insight we aim to succinctly describe the fundamentals of each IMS technique and their respective advantages and disadvantages. For further readings on each technique we direct the readers to more extensive review articles which describe each method in greater detail. 5 , 13 – 16

Core Principles of IMS Devices

The core principle of IMS instrumentation is to separate ions in an inert gas (commonly termed “buffer gas”) under the influence of an electric field. 17 The applied electric field ( E ) forces ions to migrate through the buffer gas with a velocity ( v d ) correlated to the specific analyte’s mobility ( K ), measured by Equation 1 .

In a given IMS experiment, the ions are separated by their differences in mobility through either space or time based on the particular IMS method used. 13 Smaller, more mobile ions travel faster (higher v d ) in a specific electric field strength than larger, less mobile ions (smaller K ). Mobility for each ion, K , is measured as a function of the experimental parameters, ( i.e. temperature and pressure), which are often normalized to standard conditions in order to calculate the reduced mobility, K 0 . For simplicity, we use K to denote the mobility, which is interchangeable with K 0 as shown below.

While the primary measurement in IMS analyses is the mobility, 16 for many analytical applications it has become routine to convert the measured mobility into the calculated collision cross section value (CCS, or Ω). The Mason-Schamp equation ( Equation 3 ) is often used to calculate the analyte’s CCS from the measured mobility, where Ω is the momentum transfer collision integral which describes the collision between the ion and the buffer gas and gives direct information about the conformation of the ion traveling through the drift region. 18 , 19

The parameters of this equation are, e - charge of an electron, z - ion charge, N 0 - buffer gas density, μ - reduced mass of the collision partners, k b - Boltzmann’s constant, and T - the drift region temperature. The specifics of gas composition, operational pressure, temperature in the mobility region, analyte migration path, and applied field strength vary for each respective IMS platform (and depend heavily on experimental objectives). These parameters are represented graphically in Figure 1 . 7 From a simplified perspective, the CCS is a normalized measure of gas phase size, typically denoted in units of square Angströms (Å 2 ). 19 , 20 While the Mason-Schamp equation is not universally accepted, currently it is the central equation used to calculate CCS in the community, and is utilized here accordingly. Although there are several IMS methods to conduct mobility separations, each unique instrument platform has distinct advantages and disadvantages. These differences are of importance when considering specific applications for each method ( e.g. isomer separation, signal filtering and deconvolution, or CCS fingerprinting). In the following section, we describe the specific attributes for each IMS method in order to clarify common misconceptions and highlight the strengths and shortcomings of each technique.

Variations of IMS technology with representative descriptions of applied field and gas dynamics. Bullet points describe the relative parameters of each IMS platform with key attributes reflecting the ability to measure CCS information, ion packet distribution, instrument footprint, and modularity among other relevant descriptors. Also included are the main instrument manufacturers which currently market each IMS method. Instrument diagrams have been adapted/reprinted from Ref. 7 “Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry” 1811, Kliman, M. et. al. 935–945. 2011, with permission from Elsevier.

Differences between IMS Instrumentation

Drift tube ion mobility spectrometry (DTIMS) is often described as the classic IMS model and provides simplicity, relative ease of operation, and the ability to measure mobility (and calculate CCS) as a primary method. 19 , 21 These attributes have enabled the DTIMS platform to be readily adopted by many commercial vendors for IMS-MS research ( e.g. Agilent, TofWerk, and Excellims). 22 , 23 The key component of DTIMS is the uniform electric field that propagates through the drift region. The drift region is a defined separation space in which the buffer gas has no directional flow, and the analytes traverse this pressurized region under the influence of a uniformly applied weak electric field (typically tens of V/cm). This uniform field enables DTIMS to measure K as a primary method, and hence calculate the corresponding CCS values for analytes from the Mason-Schamp equation. The ability to calculate CCS from first principles is perhaps the most significant advantage of DTIMS. Other IMS platforms ( i.e. TWIMS) require calibrant ions with well-characterized CCS values previously obtained on DTIMS instruments to create a calibration curve for calculating CCS of unknown analytes. 24 It also should be noted that many DTIMS experiments are also performed in a CCS-calibrated mode (often referred to as the single-field method) in order to maintain an analytical timescale which can be incorporated into the chromatographic timescale. The single-field method has been shown to provide highly reproducible CCS values, and further detail on it and other modes of acquisition for DTIMS are provided in the recent work by Stow et . al . 25

Additionally, many commercial DTIMS instruments do not require RF confinement to contain ion diffusion in the IMS experiment, though recent findings from Allen and Bush suggest ion heating effects noted from RF confinement are minor. 26 An additional advantage of DTIMS includes comprehensive ion collection, wherein all analyte mobilities are collected in a single pulsed experiment (as opposed to scanning instruments, e.g. FAIMS and DMA). However, because DTIMS analyzes ion pulses, its duty cycle is decreased in comparison to continuous IMS methods. For example, many experiments only trap the ions for 4 ms and then allow them to be separated in the drift region for 60 ms. This results in a duty cycle of 6.7% (4ms/60ms), and all other ion signal is lost during the 54 ms waiting for the previous ion packet to hit the detector. In an effort to address this loss, many instrument vendors are utilizing multiplexing strategies in the pulsing sequence wherein multiple packets are pulsed into the drift region at defined times as the first packet travels. Knowledge of the pulsing times allows the subsequent signals to be deconvoluted to their correct arrival times using schemes such as the Hadamard Transformation. 27 , 28 Recent studies have even shown that this approach can provide up to 50% duty cycle, which is a great increase over the standard 6.7%. 29 , 30

An additional challenge of DTIMS systems is how to increase the resolving power of these devices. This is accomplished by increasing the voltage drop across the drift cell and decreasing temperature. 31 – 33 For precise DTIMS measurement, it is essential to keep the ions in the low field limit, therefore to increase the voltage drop either the length of the drift cell or pressure is increased. 34 , 35 However, without careful focusing an increase in both of these parameters increases ion diffusion and can cause extensive peak broadening and ion losses. There are both commercial low and high pressure DTIMS platforms, typically operated at ca. 4 Torr and 760 Torr. 19 , 23 Low pressure systems are often used because ion focusing and obtaining higher sensitivity is easier at lower pressures, however, fewer collisions with the buffer gas decrease separation capacity. High pressure systems often suffer from ion losses at the higher pressures due to difficulties in ion focusing, so they may not possess the sensitivity of lower pressure systems. However, continual advancements in DTIMS design and further increases in pressure of the mobility region ( e.g. atmospheric pressure and greater) have increased the resolving power for some platforms to between 100 and 250 ( t / Δt ) or even greater. 23 , 36

Traveling wave ion mobility spectrometry (TWIMS) led to the widespread popularity of IMS-MS when it was first commercialized in 2006 with the Synapt HDMS and its successors (Synapt G2 in 2011 and Synapt G2-Si in 2013) by Waters Corporation. 10 , 37 – 39 The drift region of TWIMS is very similar schematically to the DTIMS platform, where a stacked set of ring electrodes provides an applied voltage, driving ion motion through the drift region at reduced pressure ( ca. 2–4 Torr). However, while DTIMS applies a uniform electric field to induce analyte migration, TWIMS utilizes an oscillating electric field to produce a set of voltage waves that push the ions through the drift gas towards the mass analyzer. A description of this oscillating electric field has been explained in detail in several publications. 37 , 40 Also, TWIMS utilizes RF confinement to focus the ion packet while it migrates through the drift region, which provides increased analyte signal resulting from decreased ion diffusion. 41 Again, the ion heating resulting from RF confinement is thought to be a minor contributor, as work from Morsa and coworkers has noted that other ion heating in TWIMS resulting from variations in wave height, wave speed, and changes in pressure may be of more importance. 42 , 43 A caveat of TWIMS devices is that each instrument must be calibrated with ions of known mobility prior to calculating CCS values of unknowns (analogous to the single-field method in DTIMS). Historically, polyalanine has been utilized for CCS calibration in TWIMS, although the specific compounds best suited for this purpose are still heavily debated in the field. For example, instrument calibration to obtain CCS values using peptide ions in lipid analyses have been shown to introduce significant error. 44 – 46

Many advantages and disadvantages of TWIMS are also shared with DTIMS, such as the pulsed ion packet delivery by ion gating and comprehensive analyte detection. Two key advantages of TWIMS are 1) low voltage requirements due to constant wave heights and 2) the ability to manipulate ion motion into long path length separations without significant ion loss. These long path length structures effectively enhance mobility separation by increasing the number of interactions between the analyte and drift gas. 47 The low voltage requirement for TWIMS has been essential in the design of two extremely long path length platforms ( e.g. tens to hundreds of meters in length): the circular IMS device recently released by Waters Corporation 48 and the Structures for Lossless Ion Manipulations (SLIM) platform currently undergoing development in the Smith group at Pacific Northwest National Laboratory (PNNL, Richland, WA). 49 , 50 In a recent publication, the SLIM device used a path length of ca. 1 km to perform ion separations. 51 In this case, if a DTIMS platform was utilized that required a uniform voltage drop of approximately 12 V/cm, a power supply of >120,000 V would be needed to supply the voltage at the beginning of the drift region. Due to safety concerns, this is impractical. However, TWIMS enables these long path length analyses with power supplies providing wave height around 30 V. Furthermore, these long path length devices are showing enormous possibilities as demonstrated by their extremely high IMS resolving power ( i.e. > 400 R p ) and separation of ions that have previously not been possible with the lower resolving power of DTIMS. 48 , 51

Trapped Ion Mobility Spectrometry (TIMS) is one of the newest IMS methods and was recently commercialized by Bruker Daltonics. The TIMS analyzer is comprised of a set of electrodes that form three regions: the entrance funnel, TIMS mobility region (ion mobility analyzer) and exit funnel. Both the entrance and exit control ion deflection and focusing, while the TIMS mobility region is utilized to accumulate, trap and elute ions of interest as a result of the interplay between a parallel gas flow and an opposing electric field. 52 , 53 In the previously described DTIMS and TWIMS methods the gas flow is essentially stationary, but TIMS differs as it has a unidirectional buffer gas flow towards the MS detector. In effect, TIMS operates similar to DTIMS in reverse, wherein ion motion is directed towards the MS by gas flow in opposition to the applied electric field strength ( ca. 70 V/cm). 53 , 54 The field strength in a TIMS experiment is slowly decreased to eject ions of specific mobilities from the mobility region for structural analysis. One main difference between TIMS and the previously described DTIMS and TWIMS techniques is its scanning operation. In DTIMS and TWIMS, all ions can be observed utilizing the same experimental conditions. TIMS requires changes to the experimental parameters to see all ions. Thus, it is only able to analyze each molecule as ejected. This property can be beneficial though causing TIMS to be highly selective in terms of separation efficiency (resolving power, ca. 200–400 K/ΔK ). 54 , 55 Selectivity is coupled with the instrument duty cycle and can be tuned based on experimental needs. For example, the rate at which the electric field is scanned determines the selectivity of an experiment. Slower scans are more selective and are more capable of separating analytes with similar mobilities than faster scans, yet faster scanning may be necessary when TIMS is coupled with LC. 56 Thus, the duty cycle of TIMS dictates the level of resolving power possible for each experiment and enables tunable levels of selectivity which can be modulated based on the application ( i.e. selective vs. untargeted modes). As with DTIMS and TWIMS, TIMS utilizes a pulse of ions for separation, and also experiences some losses in duty cycle due to this pulsing. In addition, while TIMS utilizes RF confinement in the mobility region, there is no axial component to this applied frequency and the application of RF is not thought to affect ion structure or mobility. 54 Although recent literature suggests that TIMS devices can also measure K as a primary method (and hence CCS values), most publications calibrate TIMS with analytes of known mobility prior to analysis (in a similar fashion to TWIMS). 54 , 57 Another distinct advantage of TIMS is its compactness, ca. 5–10 cm. This small size is extremely advantageous in creating smaller instrument footprints or easily modifying standalone MS platforms to gain TIMS capabilities. As a final note, recently the ability of chaining multiple TIMS analyzers (TIMS-TIMS) has been described. These linkages enable versatile experimental design where different components can be placed between the multiple TIMS separations for more specific characterization of each ion detected ( e.g. IMS-CID-IMS-MS). 58

FAIMS/DMS/DIMS

Field asymmetric waveform ion mobility spectrometry (FAIMS), differential mobility spectrometry (DMS) and differential ion mobility spectrometry (DIMS) are atmospheric pressure IMS techniques typically interfaced directly behind the ion source and prior to entering the vacuum region of the mass spectrometer. These devices are extremely small, usually just a few square centimeters in surface area, and can be fabricated in different geometries such as cylindrical, planar, and chips. They are easily implemented on existing MS platforms and have a small aperture to maintain the vacuum of the MS system they are coupled to. FAIMS, DMS and DIMS all operate under the same mechanism from an electronics perspective and only differ in the geometry of their respective electrodes; hence, these techniques are grouped in this manuscript. 59 , 60 FAIMS/DMS/DIMS operate as mobility filters wherein a periodic waveform is applied to separate ions under a parallel gas flow. 61 The voltage application alternates between high and low electric field strengths ( ca. alternating polarity, with field strength often several kV/cm), 62 a process that filters for a particular analyte’s change in mobility with field strength as opposed to absolute mobility. In effect, due to the application of this asymmetric waveform, FAIMS/DMS/DIMS devices cannot provide CCS values. In addition, the ion structure itself may change during the oscillation from low to high field strengths. Varying mobility behavior in FAIMS may result from dipole alignment and the clustering and declustering of the ions, and is described in greater detail in the literature. 61 , 63 , 64 However, due to the differences in separation characteristics, FAIMS/DMS/DIMS devices are able to provide a high degree of selectivity that may not be possible in other low field-only methods ( ie. <100 V/cm). Additionally, FAIMS/DMS/DIMS are filtering devices which can be scanned wherein only analytes with a specific response to the changing electric field and those that match the applied compensation voltage (CV) are able to traverse the drift region and exit through the aperture. In this manner, FAIMS/DMS/DIMS operate in an analogous fashion to quadrupole mass analyzers, utilizing CV scans to transmit ions with various responses to change in mobility over a set period. Furthermore, these devices do not pulse ions into the mobility region like DTIMS, TWIMS, and TIMS, yet they acquire continuous mobility data without loss in duty cycle for molecules capable of exiting the device under the specific parameters applied. This continuous collection of targeted spectra enables FAIMS/DMS/DIMS devices to increase the signal-to-noise ratio for the ion(s) of interest by greatly removing unwanted chemical noise in MS spectra. 65 FAIMS/DMS/DIMS separations can also be enhanced by changing the gas composition in the mobility region. 66 , 67 Currently FAIMS/DMS/DIMS devices are marketed from Thermo Fisher Scientific, Owlstone Medical, Sciex, and Heartland MS.

Differential mobility analyzers (DMA) operate in a similar fashion to DTIMS in that both systems utilize a constant electric field and are able to measure K as a primary method. Three of the main differences are that DMA devices operate at ambient pressure, have a well-characterized unidirectional gas flow, and are scanned for the detection of the molecule of choice. DMA is capable of performing measurements not possible with DTIMS as it is typically utilized to detect very large analytes, such as aerosol particles, 68 antibodies, 69 viruses, and other macromolecules ( ca. tens to hundreds of nm 2 ), 70 and is not heavily applied in small molecule screening applications ( e.g. lipids, metabolites, etc.). Recently, Fernández-García and coworkers have measured the mobilities of liquid nanodrops in air with DMA. 71 In a similar fashion, Ouyang and coworkers utilized DMA to calculate CCS of large metal iodide clusters for comparison with computational modeling approaches. 72 DMA therefore provides an important mobility device for measuring extremely large molecules not possible with other IMS based methods. Because DMA is typically used to analyze macromolecules and not peptides, lipids or metabolites, most CCS values for calibration of other IMS techniques are first collected on DTIMS, as opposed to DMA. DMA devices are currently marketed by SEADM and TSI.

Applications of IMS-MS

IMS methods are typically conducted in three principle application settings: isomer separations, signal filtering, and annotation of untargeted features via CCS database matching. In this section, we describe each of these applications of IMS and highlight specific examples in recent literature.

Isomer Separations

First and foremost, while mass spectrometers are very selective in terms of separating and identifying analytes with different chemical formula, distinction of isomeric species in complex samples requires fragmentation methods or chromatographic techniques in addition to the MS measurements. For structurally similar isomers such as lipids, 73 carbohydrates, 74 or amino acids, 75 fragmentation spectra are often very similar and may fail to provide diagnostic ions of each species; hence, alternative methods of separation for these analytes prior to MS analysis is required. IMS provides complementary separation of isomers by utilizing structural differences in mobility to resolve these analytes. Isomeric compounds have been separated by various IMS methods across a wide scope of biological classes including nucleic acids, 76 carbohydrates, 77 , 78 lipids, 79 and peptides. 10 , 37 For example, Figure 2A and ​ and2B 2B describe a recent separation of diglyceride (DG) isomers from Bowman and coworkers who utilized FAIMS to separate isomeric lipids which differed in their double bond orientation (cis/trans isomers, Fig. 2A ) and chain length ( Fig. 2B ). 80 Another recent study from Hofmann and coworkers demonstrated the ability of TWIMS to separate linkage and stereoisomers in simple carbohydrates resulting in baseline resolution. 77 Prototype IMS devices such as the SLIM platform at Pacific Northwest National Lab (PNNL) have demonstrated that even enantiomers can be separated by IMS when complexed with other selective ions such as various cyclodextrins. 81 Enantiomeric mixtures have also been separated in IMS using copper-complexation strategies and diasteromeric adduction. 82 , 83 Protein conformers have been recently studied by both TIMS and DTIMS. 55 , 84 Thus, while isomeric separations still remain very challenging in the analytical community, recent advances in chromatography and IMS are beginning to provide the necessary selectivity to separate and characterize these compounds, which can then in turn aid in elucidating the role of isomers in biological systems.

Illustrations of common applications of IMS-MS from recent publications. (A) Separation of lipid isomers with variations in cis/trans double bonds and variations in chain length adapted/reprinted from reference 80 with permission from Springer, Journal of the American Society for Mass Spectrometry. Bowman, A. P. et. al. Copyright, 2017. (B) Illustration of signal filtering by IMS for simplification of MS signals in targeted workflows. Adapted/reprinted from reference 85 with permission from Springer, Journal of the American Society for Mass Spectrometry. Levin, D. S. et. al. Copyright, 2007.

Signal Filtering by IMS-MS

Another application of IMS focuses on reducing the complexity of mass spectra, particularly in situations where background ions are in high relative abundance compared to the ion of interest. Though all IMS methods which can significantly increase the signal-to-noise ratio for specific ions and decrease background noise, such as DMS, FAIMS, DIMS or DMA, are well suited for this purpose because they can operate as intrinsic mobility filters. For example, Levin and coworkers utilized DMS to simplify complex spectra of oligosaccharides ( Figure 2C ). 85 Using the mobility filtering characteristics of DMS, the MS signal of the target pentasaccharide dramatically increased in comparison to the abundant background ions noted in full scan without IMS ( Figure 2D ). Mobility filtering is also particularly advantageous in standalone IMS systems deployed in the field for vapor analysis. 86 Although not described in detail in this review, overtone mobility spectrometry (OMS) also acts as a mobility filter, wherein only analytes with an intrinsic mobility matching the resonate frequency applied in OMS are transmitted towards the mass analyzer. 87 The fundamentals of OMS and potential applications have been thoroughly explored in previous publications. 87 – 89 IMS methods have also been used to separate contaminant ions from signals of interest in order to acquire higher quality spectra. FAIMS devices have shown utility in separating 1 + contaminant ions from higher charge state proteins and peptides of interest. 90 This capability has been extremely important in avoiding biases in trap-based mass analyzers, which require automated gain control (AGC). 91 It should be noted that while FAIMS and DMA are physical mobility filters, the mobility dimension of other instruments can be used to provide post-acquisition filtering in data processing for specific analytes of interest. For example, DTIMS and TWIMS have also been extremely important in the proteomic and metabolomic analyses of complex samples such as water, soil, and plant material samples, all of which possess a diverse range of molecular contaminants. 92 By separating peptides of interest to different mobility regions away 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, 92 , 93 the proteome coverage of environmental samples can be greatly improved.

Untargeted Annotations by IMS-MS

The last major application of IMS discussed in this insight focuses on incorporating mobility information into both targeted and untargeted MS workflows. 20 Because IMS separates ions on a millisecond timescale, these separations can be easily nested into pre-existing LC/GC-MS approaches. In global analyses, spectral features are prioritized by statistical analysis ( e.g. volcano plots, PCA, etc.) and are subsequently annotated through a combination of analytical descriptors, including (but not limited to) retention time alignment, accurate mass measurement, isotope ratio analysis and fragmentation pattern matching. 94 , 95 For isomeric compounds these molecular descriptors are often shared between species, specific isomer identification remains challenging. Incorporating mobility information (more specifically, K 0 or CCS values) as additional ion descriptors can alleviate some of these challenges in untargeted approaches and provide additional confidence that the molecule is accurately annotated. Figure 3 illustrates the untargeted annotation process, wherein a prioritized feature is noted at m/z 175.0238. Given a mass error tolerance of 10 ppm for this singly charged m/z , there are 73 possible entries noted in METLIN which are comprised of 9 unique molecular formulae. To obtain further structural specificity, additional methods such as isotope ratio analysis, fragmentation data, retention time separation and CCS database matching (provided a certain tolerance in CCS value) can be used to increase structural confidence in annotation of the prioritized feature. We should note that incorporating CCS values for untargeted methods in this manner would currently be called “known-unknowns” analysis, wherein the analyte being annotated would have been characterized by a previous mobility experiment on a standard and the subsequent data uploaded into a CCS database. Characterization of “unknown-unknowns” is much more challenging, wherein no database match for the CCS and m/z exists. In this scenario, using the ratio of mass to mobility (often termed “mass-mobility trendlines”) can also be useful for characterizing unknowns into a potential biological class ( e.g. peptides, carbohydrates, lipids, etc.). 19 , 96 These trendlines are established by previously calculated CCS values, and pre-existing relationships are extrapolated to characterize unknown-unknowns. Computational approaches may also be utilized in these analyses, but currently there is no centrally accepted workflow for in silico approaches ( e.g. projection super-approximation, trajectory method, or exact hard-spheres scattering) and a great amount of research is being dedicated to using molecular dynamics and machine learning to reduce the error between experimental and theoretical CCS values. 97 – 100

Illustrative workflow of untargeted identifications by IMS-MS with incorporation of additional confidence gained with subsequent chemical information acquired from each analytical technique.

From a small molecule perspective, generation of high confidence mobility measurements for library inclusion remains a key challenge in the IMS community. 101 The schematic workflow for developing unified workflows and interpreting the corresponding data is represented in Figure 4 . In order to generate highly reproducible CCS values ( ca. < 0.5% RSD) for database matching, many standardization challenges persist in the IMS community, such as unified protocols for instrument calibration, preferred calibrant ions, and many more. Recent work by Gabelica et. al. has demonstrated that addressing many of these challenging questions requires communal consensus between both academic and industrial investigators to advance the study and application of IMS-MS technology. 16 To generate highly reproducible mobility data (and corresponding CCS values), we direct our readers further towards the communal knowledge developed in that publication for detailed information regarding specific IMS platform guidelines ( e.g. calibration protocols, instrument settings, and data reporting).

Workflows for generation and interpretation of CCS values in IMS-MS experiments.

Future Directions and Conclusions

IMS is experiencing continual innovation through novel instrument developments, new methods of acquiring and filtering data, and continually developing computational strategies, all of which provide increasing confidence in mobility information acquired both experimentally and in silico . 98 The resolving power of IMS has increased by an order of magnitude in less than a decade, 102 opening up new research opportunities to separate and identify previously indistinguishable chemical isomers and isobars (see Figure 3 ). Recent developments have even interfaced IMS to ultra-high resolution mass analyzers such as the Orbitrap MS through modulation of ion pulsing. 103 , 104 As a research tool in both academia and industry, continual advances in IMS-MS technology are attracting new scientists to the community daily, and the potential applications of these analytical strategies are still being discovered. While many challenges remain for routine incorporation of mobility analysis and CCS information into untargeted workflows, the future of IMS is bright and its role in separation science is only expected to keep climbing.

Acknowledgements.

This research were supported by the NIH National Institute of Environmental Health Sciences (P42 ES027704) and by startup funds from North Carolina State University.

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

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CHAPTER 4: Travelling Wave Ion Mobility Separation: Basics and Calibration

• Published: 11 Nov 2021
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K. Richardson and K. Giles, in Ion Mobility – Mass Spectrometry: Fundamentals and Applications, ed. A. E. Ashcroft and F. Sobott, The Royal Society of Chemistry, 2021, pp. 83-104.

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Travelling wave ion mobility separation (TWIMS) embedded in a quadrupole time-of-flight mass spectrometry system has been commercially available since 2006. TWIMS provides both increased system peak capacity and the capability to measure collision cross-section (CCS) values of analyte ions. The relatively complex separation mechanism in TWIMS has necessitated calibration to derive CCS values. Although calibration has been highly effective in many applications, calibration of large and multiply charged molecules has only been possible under carefully chosen conditions. Here we introduce the basics of TWIMS hardware and then progress from a simple model of the ion motion to a more realistic description. This physics-based approach gives rise to an improved calibration strategy that answers many long-standing questions regarding class dependence and sensitivity to travelling wave conditions.

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• Published: 16 March 2017

Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry

• Giuseppe Paglia 1 , 2 &
• Giuseppe Astarita 3  

Nature Protocols volume  12 ,  pages 797–813 ( 2017 ) Cite this article

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Metabolomics and lipidomics aim to profile the wide range of metabolites and lipids that are present in biological samples. Recently, ion mobility spectrometry (IMS) has been used to support metabolomics and lipidomics applications to facilitate the separation and the identification of complex mixtures of analytes. IMS is a gas-phase electrophoretic technique that enables the separation of ions in the gas phase according to their charge, shape and size. Occurring within milliseconds, IMS separation is compatible with modern mass spectrometry (MS) operating with microsecond scan speeds. Thus, the time required for acquiring IMS data does not affect the overall run time of traditional liquid chromatography (LC)-MS-based metabolomics and lipidomics experiments. The addition of IMS to conventional LC-MS-based metabolomics and lipidomics workflows has been shown to enhance peak capacity, spectral clarity and fragmentation specificity. Moreover, by enabling determination of a collision cross-section (CCS) value—a parameter related to the shape of ions—IMS can improve the accuracy of metabolite identification. In this protocol, we describe how to integrate traveling-wave ion mobility spectrometry (TWIMS) into traditional LC-MS-based metabolomic and lipidomic workflows. In particular, we describe procedures for the following: tuning and calibrating a SYNAPT High-Definition MS (HDMS) System (Waters) specifically for metabolomics and lipidomics applications; extracting polar metabolites and lipids from brain samples; setting up appropriate chromatographic conditions; acquiring simultaneously m/z , retention time and CCS values for each analyte; processing and analyzing data using dedicated software solutions, such as Progenesis QI (Nonlinear Dynamics); and, finally, performing metabolite and lipid identification using CCS databases and TWIMS-derived fragmentation information.

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Acknowledgements

This work was partially supported by the Alzheimer's Association (NIRG-11-203674 to G.A.). We thank D. Grant, A. Armirotti, W. Thompson, M. Kliman, H. Vissers, K. Giles, J. Williams, N. Tomczyk, S. Smarason, A. Foglio, M. Tarquinio, B. Shrestha and S. Dhungana for discussions we found most enlightening. We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona for the provision of human biological materials. The Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026, National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the National Institute on Aging (P30 AG19610, Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002 to the Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson′s Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research.

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Paglia, G., Astarita, G. Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry. Nat Protoc 12 , 797–813 (2017). https://doi.org/10.1038/nprot.2017.013

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High-resolution ion mobility based on traveling wave structures for lossless ion manipulation resolves hidden lipid features

• Research Paper
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• Published: 27 June 2024

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• Allison R. Reardon   ORCID: orcid.org/0000-0001-6583-0134 1 ,
• Jody C. May   ORCID: orcid.org/0000-0003-4871-5024 1 ,
• Katrina L. Leaptrot   ORCID: orcid.org/0000-0002-9266-145X 1 &
• John A. McLean   ORCID: orcid.org/0000-0001-8918-6419 1  

High-resolution ion mobility (resolving power > 200) coupled with mass spectrometry (MS) is a powerful analytical tool for resolving isobars and isomers in complex samples. High-resolution ion mobility is capable of discerning additional structurally distinct features, which are not observed with conventional resolving power ion mobility (IM, resolving power ~ 50) techniques such as traveling wave IM and drift tube ion mobility (DTIM). DTIM in particular is considered to be the “gold standard” IM technique since collision cross section (CCS) values are directly obtained through a first-principles relationship, whereas traveling wave IM techniques require an additional calibration strategy to determine accurate CCS values. In this study, we aim to evaluate the separation capabilities of a traveling wave ion mobility structures for lossless ion manipulation platform integrated with mass spectrometry analysis (SLIM IM-MS) for both lipid isomer standards and complex lipid samples. A cross-platform investigation of seven subclass-specific lipid extracts examined by both DTIM-MS and SLIM IM-MS showed additional features were observed for all lipid extracts when examined under high resolving power IM conditions, with the number of CCS-aligned features that resolve into additional peaks from DTIM-MS to SLIM IM-MS analysis varying between 5 and 50%, depending on the specific lipid sub-class investigated. Lipid CCS values are obtained from SLIM IM ( TW(SLIM) CCS) through a two-step calibration procedure to align these measurements to within 2% average bias to reference values obtained via DTIM ( DT CCS). A total of 225 lipid features from seven lipid extracts are subsequently identified in the high resolving power IM analysis by a combination of accurate mass-to-charge, CCS, retention time, and linear mobility-mass correlations to curate a high-resolution IM lipid structural atlas. These results emphasize the high isomeric complexity present in lipidomic samples and underscore the need for multiple analytical stages of separation operated at high resolution.

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Introduction

Mass spectrometry (MS) is one of the premier analytical techniques for molecular characterization. MS represents several different technologies that discriminate ions based on their intrinsic mass, some of which are considered high-resolution MS (HRMS, resolution > 10,000), which allows for finer mass measurements to be achieved with greater precision. However, isomers possess identical chemical formulae and thus are not able to be resolved with HRMS alone. Operating multiple MS stages in tandem (MS/MS) allows for mass-resolved ion dissociation, which provides additional structural information that in some cases allows for isomeric differentiation, although many isomers have similar fragmentation spectra and thus are indistinguishable with MS/MS.

Lipids are biomolecules that significantly contribute to the structure and function of living organisms. They are known for a prevalence of isomers, which primarily result from variability in their hydrophobic regions. Specificity in the identification of lipid isomers is important, because of their potential as diagnostic biomarkers for disease states and can in some cases lessen the potential severity of a specific disorder [ 1 ]. For example, cardiovascular disease (CVD) risk has been attributed to four specific ceramides: d18:1/16:0, d18:1/18:0, d18:1/24:0, and d18:1/24:1, which are isomeric to numerous other lipids with variations in acyl tail length and double bond location [ 2 ]. While these individual lipids can serve as biomarkers, in many instances groups of similar lipids can also signal the potential onset of a disorder [ 2 ]. Thus, the ability to isolate entire classes of lipids and their isomeric components has the potential to benefit diagnoses in clinical chemistry and promote better patient treatment strategies.

According to a 2015 survey of the molecular coverage of the world’s largest chemical repository, PubChem, there are over 60 million unique compounds that are known to exist within 10 to 1,000 Da. Focusing in on a narrow 1 ppm window (354.165 ± 0.0015 Da), there are estimated to be over 11,000 existing structures, underscoring the importance of high chemical resolution measurements [ 3 ]. An analytical separation technique now routinely integrated with MS is ion mobility (IM-MS). The combination of IM with MS results in a high throughput separation that is structurally selective to size/shape, specifically charge-to-collision cross section ( z/Ω by IM) and mass-to-charge ( m/z by MS) [ 4 ]. The IM dimension also provides a reproducible analytical measurement, the collision cross section (CCS), which is useful for compound characterization and alignment of measurements across different laboratories and techniques. Among the different IM separation approaches, drift tube IM (DTIM) can provide direct measurement of CCS values while accessing modest IM resolutions (single peak resolving power, R p  ~ 50) [ 5 , 6 ]. Recent developments in IM have targeted increases in the resolving power of the separation, which have resulted in the development of high-resolution ion mobility (HRIM) techniques such as cyclic IM (cIM), trapped IM spectrometry (TIMS), and the post processing technique HR demultiplexing (HRdm), all of which can access R p values in excess of 200 [ 7 , 8 ]. A particularly promising technology based on traveling wave structures for lossless ion manipulation (SLIM IM) can achieve high resolving powers (R p  > 200) for all species across the IM range used for analysis, enabling full HRIM-MS spectra to be acquired within the timescale of chromatographic separations, while demonstrating high selectivity in resolving various isomer systems [ 9 , 10 , 11 , 12 ]. This is in contrast to IM strategies that provide high resolving powers for a narrow range of ion mobilities that are selected for high resolving power separation.

A theoretical framework to understand what types of isomers could be resolved by IM with increasing levels of resolving power was proposed by Dodds et al . [ 13 ]. Intuitively, as the percent difference in CCS for a pair of isomers becomes smaller (more structurally similar), higher resolving powers are needed for separation. For example, in order to achieve 50% separation (half height resolved), constitutional isomers were estimated to require a resolving power of ~ 50, whereas cis/trans isomers required a resolving power of approximately 200, and diastereomers exhibited very similar CCS values, necessitating a resolving power of at least 300 for partial resolution [ 13 ]. Lastly, enantiomers (“mirror-image” isomers) were indistinguishable using the conventional resolution capability of the DTIM used in the study, and thus were estimated to need very high IM resolving powers to begin to assess their separation, and these systems may not even exhibit structural differences that are resolvable by IM. The high R p accessed by SLIM IM-MS across a broad range of mass enables complex lipid isomer systems to be distinguished from each other to support more specific structural identifications in lipidomic studies. More specifically, the ability to separate cis/trans isomers, double bond positional isomers, and stereoisomers can be achieved with SLIM IM. However, the high resolving power capabilities of SLIM IM-MS to differentiate isomers in complex, biologically-derived samples has not been fully investigated, nor has the potential of CCS alignment for providing high-confidence structural annotations in these multidimensional workflows been assessed [ 14 , 15 ].

In this work, we investigate the capabilities of HRIM-MS analysis for providing accurate lipid identification. We begin by analyzing mixtures of several commercially-available isomeric lipid standards incorporating multiple isomeric types, to assess the ability to resolve challenging systems and to provide benchmarks as to what specific lipid isomer systems can be resolved by HRIM. We then focus on analyzing more complex lipid samples in the form of total lipid extracts, each consisting primarily of lipids of a specific structural sub-classification and thus, putatively similar structures. Within each lipid extract, we observe features previously reported as lipid species comprised of a single DTIM peak [ 16 ], which are resolved into additional SLIM IM peaks under the higher resolving powers achieved via SLIM IM-MS. These additional features observed under HRIM conditions were then tentatively identified using additional pieces of analytical information (outlined below) and supported by CCS-based mathematical fits to the observed mobility-mass correlations generated for each lipid subclass. A total of 225 calibrated TW(SLIM) CCS values were curated from the HRIM analysis as obtained for seven lipid extracts (Fig.  1 A) in positive ion mode.

A Generalized chemical lipid structures of total lipid extracts studied, including the lipid category backbone and the class headgroups. The source of each lipid extract is indicated with an icon representing egg, pig, or soybean origins. B Schematic of the DTIM-MS instrument used for the reference CCS analyses. C Schematic of the SLIM IM-MS (beta prototype) used for the HRIM analyses

Materials and solvents

High-purity (Optima grade) solvents including methanol, chloroform, water, acetonitrile, 2-propanol (IPA), and formic acid were purchased from Fisher Scientific (Hampton, NH). Ammonium formate was purchased from Sigma-Aldrich and was used as a mobile phase additive. A tuning mixture containing symmetrically branched hexakis(fluoroalkoxy)-phosphazenes (HFAPs, ESI-L low concentration tuning mixture, Agilent Technologies) was used for calibrating mass-to-charge ( m/z ) and CCS for both instruments in this study. Purified TLC fractions of total lipid extracts were purchased from Avanti Polar Lipids (Birmingham, AL) as lyophilized powders, reconstituted in chloroform, and prepared to a final concentration of 10 µg/mL in 1:2 chloroform:methanol for analysis. Total lipid extracts including glycerophosphocholine (PC, both chicken egg and soy), glycerophosphoethanolamine (PE, chicken egg), glycerophosphoserine (PS, porcine brain), glycerophosphoglycerol (PG, chicken egg), glucosylceramide (GlcCer, porcine brain), and sphingomyelin (SM, porcine brain). Lipid isomer standards were purchased from Cayman Chemical (Ann Arbor, MI) and Avanti Polar Lipids. PC and PE standards were prepared at 10 µg/mL in 1:1 acetonitrile:methanol, triglyceride (TG) standards were prepared at 40 µM equimolar concentrations in 1:1 methanol:IPA with 2 mM ammonium acetate, and diglyceride (DG) standards were prepared at 40 µM equimolar concentrations in 1:1 methanol:IPA.

Instrumentation

Samples were analyzed via DTIM-MS (6560 IM-Q-TOF, Agilent Technologies) and SLIM IM (both beta prototype and MOBIE platform, MOBILion Systems, Inc.) interfaced with a HRMS (6546 Q-TOF, Agilent). Conceptual instrument diagrams are shown in Fig.  1 B and C. The main difference between the two SLIM IM platforms (beta prototype vs MOBIE platform) is the orientation of the SLIM board (horizontal vs vertical, respectively). Samples were introduced via flow injection analysis (FIA) using a liquid chromatography system (1290 Infinity II, Agilent) and were ionized by positive electrospray ionization (ESI) (Jet Stream, Agilent). All platforms used ultra-high purity nitrogen drift gas in the mobility stage, yielding nitrogen-based CCS measurements (CCS N2 ) [ 17 ].

Data acquisition

For both DTIM-MS and SLIM IM-MS platforms, the ESI source used was identical and operated in positive ion mode under the following conditions: nebulizer pressure, 20 psi; sheath gas flow rate, 12 L/min; sheath gas temperature, 275 °C; drying gas flow rate, 5 L/min; drying gas temperature, 325 °C; capillary voltage, 4000 V; entrance nozzle voltage, 2000 V [ 9 ].

For DTIM-MS acquisition, total lipid extracts were infused using a 3-min FIA method [ 18 ] with an injection volume of 10 µL and a carrier solvent of 0.1% formic acid in 1:1 methanol:water at a flow rate of 70 µL/min. The drift tube was operated under 3.95 Torr nitrogen gas, along with the following additional parameters: ion trap fill time, 20 ms; ion trap release time, 300 µs; drift tube entrance, 1474 V; drift tube exit, 224 V; rear funnel entrance, 217.5 V; rear funnel RF, 150 V pp ; rear funnel exit, 45 V; and IM hexapole entrance, 41 V [ 19 ]. The Q-TOF was operated in the low mass range ( m/z 50–1700), the ion slicer was operated at high sensitivity, and the digitizer operated at 2 GHz extended dynamic range. Single-field DT CCS measurements were obtained using HFAP drift times calibrated to reference DT CCS values [ 19 ].

For SLIM IM-MS acquisition, total lipid extracts were first separated using reversed-phase liquid chromatography (RPLC) with a 1290 Infinity II LC system (Agilent). Mobile phases consisted of both 10 mM ammonium formate and 0.1% formic acid in (A) H 2 O and (B) 60:36:4 IPA:ACN:H 2 O. RPLC was performed using a C-18 column (HypersilGold 1.9 µm, 2.1 mm × 100 mm, Thermo Fisher) at 40 °C with a flow rate of 250 µL/min over a 30-min gradient (Figure S1 ) [ 20 ]. The SLIM IM chamber was operated at 2.50 Torr. TW-based separation was performed using a wave speed of 180 m/s and a peak-to-peak wave amplitude of 40 V pp , optimized for IM resolution based on work from May et al . [ 9 ]. Specific SLIM IM method parameters were chosen to be optimal for the mass range of lipids observed. Data were acquired using MassHunter Acquisition (v. 9.0, Agilent) and EyeOn software (v. 0.3.1.0, MOBILion).

Lipid identification and nomenclature

Lipids were identified by database matching of m/z (± 5 ppm) to the Unified CCS Compendium from Picache et al . [ 21 , 22 ] and LIPID MAPS [ 23 , 24 ]. The LIPID MAPS nomenclature is used throughout the manuscript. Lipids are annotated by their subclass followed by the total number of carbons and the degree of unsaturation of the acyl tails. For example, PC 36:01 denotes the PC subclass with 36 total tail carbons and one site of unsaturation. A lipid feature refers to signal observed at a unique m/z and CCS, which are extracted from a narrow RT (or FIA) range.

Data processing

Lipid IM-MS features were manually extracted as peak centroids using MassHunter IM-MS Browser (v. 10.0.1, Agilent). TW(SLIM) CCS values were calibrated via a 3rd order polynomial using the HFAP ions, and a subclass-specific linear correction factor (based on the average bias of a group of non-splitting lipid features in HRIM) was utilized to align calibrated TW(SLIM) CCS values to within 2% average bias to reference DT CCS values (PG correction factor shown in Figure S2 ) as described by Rose et al . [ 20 , 25 ]. Note that correction factors must be reevaluated for any changes in instrumental settings ( i.e . for different laboratories). In this work, the correction factors obtained for each subclass were directly multiplied to the initial TW(SLIM) CCS resulting from the 3rd order polynomial to get the final calibrated TW(SLIM) CCS value (reported in Table S1 ). CCS calibration, biases, and SLIM IM correction factors were calculated in Excel (Microsoft).

Results and discussion

Hrim separation of isomeric lipid standard mixes.

Previously, May et al . [ 9 ] evaluated selected lipid isomer systems via SLIM IM-MS. In this study, we focus on direct resolution of mixed lipid standards, which represent three different forms of lipid isomers. In each of these examples, the highest abundance ion form is presented, namely [M+COOH] − , [M+Na] + , and [M+Na] + , in Fig.  2 A–C, respectively. Figure  2 A shows the HRIM analysis of geometric cis/trans isomers. For these two PC and PE lipid isomer standard mixtures, the only difference within each subclass is the geometry of the double bond found on each fatty acid tail, as highlighted on their respective structures in Fig.  2 A. The PC isomers, PC (18:1(9Z) / 18:1(9Z)) and PC (18:1(9E) / 18:1(9E)), display near-baseline separation, whereas the PE isomers, PE (18:1(9Z) / 18:1(9Z)) and PE (18:1(9E) / 18:1(9E)), show a complete baseline separation. For both cases, the cis-configuration adopts a more compact gas-phase structure than the trans-configuration, as indicated by the faster arrival time of the cis-configuration, and these results are in agreement with the analyses of the PC cis/trans isomers by both Kyle et al . and Jeanne Dit Fouque et al . [ 15 , 26 ]. Figure  2 B shows the HRIM arrival time distributions for double bond positional isomers including a mixture of PC (18:1(6Z) / 18:1(6Z)) with PC (18:1(9Z) / 18:1(9Z)) and a mixture of TG (54:9(6Z,9Z,12Z)) with TG (54:9(9Z,12Z,15Z)). Both sets of double bond positional isomers display near baseline resolution when analyzed by HRIM. The PC cis-9 peak had a shorter arrival time than the PC cis-6 peak, indicating a more compact structure when the site of unsaturation is located closer to the end of the acyl tail. This finding supports that of Kyle et al . who reported fatty acids had smaller conformations when the site of unsaturation was located further from the lipid headgroup [ 15 ]. For the triglycerides, we observe TG (54:9(6Z,9Z,12Z)) at an earlier arrival time, and thus a more compact structure, than TG (54:9(9Z,12Z,15Z)). Considering just the positions of the sites of unsaturation, the TG isomer results are the opposite of the PC isomers just described. This suggests that when more than one site of unsaturation is present on an acyl tail, or an additional degree of structural freedom is incorporated (i.e. a third acyl tail), a combination of the headgroup and third acyl tail is able to interact with those sites of unsaturation to yield a more compact structure. However, the third acyl tail likely contributes to the gas-phase compaction to a lesser degree than the headgroup. In Fig.  2 C, we evaluate the separation of geometric sn -position stereoisomers including two sets of diglycerides: DG (18:2(9Z,12Z) / 18:2(9Z,12Z)), sn -1,2 with DG (18:2(9Z,12Z) / 18:2(9Z,12Z)), sn -1,3, and DG (18:0 / 18:0), sn -1,2 with DG (18:0 / 18:0), sn -1,3. Though the single peak resolving power achieved in these two diglyceride separations was higher (R p  ~ 360–365) than those achieved with the previous four isomer mixtures (R p  ~ 230–320), the differences in arrival times were relatively smaller, resulting in approximately half-height separation between both sets of diglyceride peaks. This indicates that the structural difference in geometric sn -position stereoisomers has only a minor impact on their gas-phase conformations, requiring higher resolving power to separate them. For the mixture of the DG 18:2/18:2 isomers, the sn -1,3 isomer appears before the sn -1,2 isomer, suggesting that with multiple double bonds on the acyl tail, the further apart the acyl tails are to each other, the more compact structure the isomer can adopt in the gas phase. However, the opposite arrival time ordering is observed when comparing the saturated DG 18:0/18:0 isomers. Specifically the sn -1,2 isomer appears before the sn -1,3 isomer, suggesting that the absence of double bonds in the acyl tails changes the gas-phase conformations adopted by the diglycerides. The arrival time orderings that are observed experimentally for these lipid isomers are nonetheless challenging to predict from an inspection of the primary structure alone, which underscores the importance of empirical data such as these for interpreting results as well as training prediction algorithms and theoretical gas-phase mobility behavior. Jeanne Dit Fouque et al . also analyzed both the DG (18:2(9Z,12Z) / 18:2(9Z,12Z)), sn-1,2 and DG (18:2(9Z,12Z) / 18:2(9Z,12Z)), sn-1,3 geometric sn -position stereoisomers using TIMS-based HRIM and observed a single IM profile for their corresponding mixtures, whereas our results show two resolved IM features for these DG stereoisomers due to the higher resolving powers achieved with SLIM-based HRIM (R p  ~ 360 for SLIM IM compared to R p  ~ 230 for TIMS) [ 26 ]. In summary, the geometric cis/trans and the double bond positional isomers achieved near or full baseline separation with HRIM via SLIM IM-MS, whereas the geometric sn -positional stereoisomers exhibited approximately half-height peak-to-peak separation. These results are in general agreement with previously estimated IM resolving powers needed to separate differing levels of isomeric complexity, which was initially inferred from amino acid isomers [ 13 ].

HRIM analysis of lipid isomer standard mixtures. A Separation of PC and PE geometric cis/trans isomers. B Separation of PC and TG double bond positional isomers. C Separation of DG geometric sn-positional stereoisomers. Peak assignments were confirmed by analysis of the individual isomers

Evaluating separation power of HRIM vs. DTIM

DTIM is generally able to access resolving powers of ~ 50, which can lead to multiple lipid isomers observed as a single unresolved peak [ 6 ]. The further resolution of these peaks into multiple features can be achieved with HRIM via SLIM IM-MS (R p  > 200), [ 9 ]. To assess the extent of isomeric complexity present in biologically-derived lipid mixtures, seven total sub-class specific lipid extracts were evaluated from the glycerophospholipids (PE, PC egg, PC soy, PS, and PG) and sphingolipids (GlcCer and SM) classes. As shown in Fig.  1 A, glycerophospholipids contain two acyl tails varying in chain length and number of double bonds whereas sphingolipids have a fixed sphingosine backbone and one acyl tail varying in chain length and number of double bonds. Lipid annotations with IM are generally limited to the total numbers of acyl tail carbons and sites of unsaturation. While not generally reported from MS or IM-MS data, additional specificity in lipid characterization such as double bond locations and stereochemistry can be obtained by integrating other techniques, such as ozonolysis and fragmentation techniques with IM-MS [ 27 , 28 ].

Analyses via DTIM-MS yielded a total of 144 identified lipid features, (63 glycerophospholipids and 81 sphingolipids), whereas SLIM IM-MS analysis yielded a total of 188 lipid features (83 glycerophospholipids and 105 sphingolipids). The higher number of lipid features obtained by HRIM is driven by the high resolving power of the platform, but also due to the enhanced sensitivity afforded by the use of LC vs FIA, the former of which reduces ion suppression effects by limiting the amount of sample introduced to the ion source at any given time. This study utilized FIA to obtain the conventional resolution IM profiles, while the rainder of the analyses with SLIM IM-MS utilized LC for sample introduction. It is important to highlight that only lipid features observed with DTIM-MS were investigated with SLIM IM-MS and thus the Fig.  3 summaries preclude features uniquely observed in HRIM analysis. Figure  3 A provides a direct comparison of DTIM-MS and SLIM IM-MS lipid features projected in CCS space to highlight how a single feature detected with DTIM-MS can yield multiple peak features with SLIM IM-MS. These lipid features from PC egg and GlcCer lipid extracts were partially resolved into two features with SLIM IM-MS. Figure  3 B expands the SLIM IM-MS analysis to the PS, PE, SM, PC soy, and PG lipid extracts where one lipid feature from each subclass resolves into multiple peaks under HRIM. Specifically, the PE 34:01 [M + Na] + lipid feature (green shading) exhibits three IM peaks with SLIM IM compared to a single peak observed with DTIM (Figure  S3 ). Figure  3 C highlights the percentage of additional features observed from each lipid class using high resolving power IM (speckled pattern) as a fraction of the original number of features detected with DTIM-MS at conventional resolving power (gray), with a sub-class specific summary provided in Fig.  3 D. For example, PC egg displayed 10 DTIM features, but with SLIM IM, five of those 10 features are now shown to be splitting into more than one feature. About 32% of glycerophospholipids displayed features that yielded multiple IM peaks with SLIM IM-MS whereas sphingolipids were found to have about 30% of lipid features resolve into additional IM peaks. Since glycerophospholipids have two varying acyl tails compared to sphingolipids where one of the two acyl tails is a fixed sphingosine backbone, glycerophospholipids have more degrees of freedom and more potential for isomerism. The SM result, with the lowest percentage of splitting lipid features, coincides with this reasoning with only 5% additional HRIM features. An extensive list of each lipid feature observed including numerous new lipid features observed with SLIM IM-MS is contained in Table S1 .

Summary of the IM analysis using both conventional resolution IM (DTIM) and HRIM (SLIM IM) MS. A PC and GlcCer separations in CCS space. B Examples of lipid IM profiles exhibiting multiple features with HRIM. C Lipid features observed with DTIM-MS (gray) compared to SLIM IM-MS (speckled pattern) per lipid category. Color region shows a comparison of the number of splitting features for each total lipid extract. D Lipid features observed with DTIM-MS (gray) and SLIM IM-MS (color) per lipid subclass. The region of color indicates the number of lipid features that resolve into multiple peaks with HRIM that were observed as a single feature with DTIM-MS

In prior work from Leaptrot et al . , linear mobility-mass correlations for these seven lipid sub-classes were mapped in detail using conventional resolution DTIM-MS analysis, which allowed a conformational atlas of lipids to be constructed [ 16 ]. In this HRIM study, new mobility-mass correlations are described within the higher resolution separation of lipid features observed with SLIM IM-MS. To quantitatively map these empirical correlations, calibrated TW(SLIM) CCS values were first projected as a function of mass-to-charge. Unambiguous lipid identifications were assigned from a combination of accurate mass, DT CCS, and retention time, and known lipids were identified within each linear mobility-mass correlation to promote secondary annotation of ambiguous lipid features residing along the same trend. Figure  4 shows two types of lipid correlation trendlines where lipids of the same ion form are grouped by either (1) shared acyl tail chain length (Fig.  4 A for PS, Fig.  4 C for GlcCer) or (2) shared degree of unsaturation (Fig.  4 B for PS, Fig.  4 D for GlcCer). The PS chain length trendlines in Fig.  4 A include two trends for 38:YY [M+H] + of particular interest. These correlations contain three mass-to-charge values that were observed with single DT CCS measurements, that each resolved into two distinct TW(SLIM) CCS values for PS 38:02, PS 38:03, and PS 38:04, though further analysis would be required to discern greater specificity in the identification of these isomeric lipid pairs. The PS unsaturation trends in Fig.  4 B include three [M+H] + adduct trendlines, all with similar slope. Additional correlation lines are also observed in the HRIM analysis for GlcCer, including newly-resolved chain length (7) and unsaturation (10) correlations. Mobility-mass correlation plots for the other five total lipid extracts investigated are shown in Figure S5 . All lipid extracts exhibited additional measurements that were not fit to linear models due to insufficient numbers of data points ( n  < 3).

Mobility-mass correlations of PS and GlcCer total lipid extracts. A PS chain length linear correlations. B PS unsaturation linear correlations. C GlcCer chain length linear correlations. D GlcCer unsaturation linear correlations. A , C Chain length trendline identities labeled by total carbons on acyl tails:YY where YY indicates the total number of double bonds, with the number of double bonds annotated with the data points. B , D Unsaturation trendline identities are labeled by XX:total number of double bonds on the acyl tails where XX indicates the total number, with the number of carbons annotated with the data points. Trendlines each contain n  ≥ 3 number of lipid features. Adduct type is designated by the shape of the marker

From this work, a total of 225 calibrated TW(SLIM) CCS values are reported across the seven total lipid extracts studied and confirmed with at least one additional replicate, with 37 of these TW(SLIM) CCS measurements corresponding to features newly observed with HRIM-MS. Corresponding mobility-mass correlations are shown in Fig.  4 . The high number of features uncovered by HRIM analysis underscore the isomeric complexity present in biologically-derived lipids, and the resulting lipid mobility-mass correlations can be utilized in nontargeted discovery workflows to increase confidence in potential lipid identifications.

SLIM IM-MS intra-platform comparisons

To validate the HRIM observations and assess the reproducibility of the TW(SLIM) CCS calibration methodology, five total lipid extracts were chosen for analysis on a separate, commercial equivalent MOBIE platform. The same samples and method parameters were duplicated for this follow-up analysis. Nearly identical lipid features were observed between the two platforms, despite a difference of several (10) months between acquisitions. Figure S6 compares the HFAP ions obtained with the same method settings on both SLIM IM platform variants. The HFAP ions were found to have higher arrival times on the MOBIE platform than on the beta prototype, which resulted in a slight 0.6% increase (3–4 Å 2 ) in TW(SLIM) CCS values, thus updated correction factors were required for the second step of the DT CCS alignment.

Figure S7 shows the percent biases for the PC, PE, GlcCer, and PG lipid subclasses both before and after applying a class-specific linear correction factor for each subclass, which minimizes the overall percent bias for each class of lipids. Before applying this correction, the 3rd order polynomial calibration using HFAP ions brings the lipid TW(SLIM) CCS values obtained from SLIM IM to within ca. 4% of the reference DT CCS values obtained from DTIM. Applying this linear correction lowers the overall percent bias effectively to zero for all lipids selected. Whereas this second step in the calibration was not necessary for the intra-platform comparisons, applying the linear correction factors reported in Table S3 allowed TW(SLIM) CCS values to be properly aligned (unified) to the reference DT CCS values. An average of the correction factors for the four subclasses studied with MOBIE platform is suggested as the linear correction factor for the SM and PS subclasses in future analyses.

Conclusions

In this study, we evaluated HRIM measurement capabilities of SLIM IM-MS for accurate lipid annotation. SLIM IM analysis of various lipid isomer standards indicated that the geometric cis/trans isomers and the double bond positional isomers can be resolved at baseline resolution, however the geometric sn-positional stereoisomers exhibit only partial resolution. The analysis of seven total lipid extracts spanning the glycerophospholipid and sphingolipid classes revealed numerous additional features with HRIM that were not observed with conventional resolution IM. When comparing the same extracts, the number of single lipid features, which resolved into additional features varied from 5% (SM) to 50% (PC egg) due to the higher resolving powers achieved with SLIM IM-MS. These observations generally correlated to the number of isomers expected for each lipid sub-class, e.g., more features were observed for glycerophospholipids and glycolipids than for sphingolipids. Of the glycerophospholipid and sphingolipid features observed, an average 30% of each lipid class was resolved into an additional feature under HRIM, which is similar to the number of isomers that were previously observed from a large ( n  = 1246) CCS survey of primary human metabolites (31%) [ 29 ]. When plotting the resulting TW(SLIM) CCS values by their mass-to-charge, empirical lipid mobility-mass correlations observed in the HRIM data corresponded to the summed number of carbon atoms in the alkyl chains and total sites of unsaturation. These correlations are referenced to m/z and standardized DT CCS values and thus can be utilized to aid future lipid identification on a variety of IM-MS platforms. The SLIM IM beta prototype was compared to the MOBIE platform by analyzing five of the same lipid extracts on each platform. Once alignment to DTIM was established, a HRIM database of 225 calibrated lipid TW(SLIM) CCS values was compiled, which corroborated the presence of linear lipid correlations previously observed with DTIM-MS. In instances where high resolving power results in multiple lipid structures for the same species, each resolved feature is annotated with a corresponding CCS value. Thus, a CCS database of HRIM measurements can facilitate the alignment of each resolved feature to a reference CCS, improving the confidence in identifying the corresponding lipid by correlating its assignment to multiple descriptors. These TW(SLIM) CCS measurements and corresponding empirical trends will benefit future untargeted lipidomics analyses on HRIM platforms and will provide a useful resource to support lipid annotation assignments with greater structural specificity.

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Acknowledgements

This work was supported in part using the resources of the Center for Innovative Technology (CIT) at Vanderbilt University. Financial support for Vanderbilt authors was provided by the U.S. Department of Energy, Office of Science (DOE SC) under award no. DE-SC0022207. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.

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Conceptualization: A.R.R., J.C.M., K.L.L., J.A.M. Experiments: A.R.R. and J.C.M. Interpretation: A.R.R., J.C.M., K.L.L., J.A.M. Writing and Revisions: A.R.R., J.C.M., K.L.L., J.A.M. Funding acquisition: J.A.M. and J.C.M.

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Reardon, A.R., May, J.C., Leaptrot, K.L. et al. High-resolution ion mobility based on traveling wave structures for lossless ion manipulation resolves hidden lipid features. Anal Bioanal Chem (2024). https://doi.org/10.1007/s00216-024-05385-8

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DOI : https://doi.org/10.1007/s00216-024-05385-8

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• Ion mobility-mass spectrometry
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• Lipid structure
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• Shallow Underground Laboratory

Fundamentals of Traveling Wave Ion Mobility Spectrometry

Revised: July 22, 2010 | Published: December 15, 2008

Deciphering drift time measurements from travelling wave ion mobility spectrometry-mass spectrometry studies

Affiliation.

• 1 Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK.
• PMID: 19423898
• DOI: 10.1255/ejms.947

Detailed knowledge of the tertiary and quaternary structure of proteins and protein complexes is of immense importance in understanding their functionality. Similarly, variations in the conformational states of proteins form the underlying mechanisms behind many biomolecular processes, numerous of which are disease-related. Thus, the availability of reliable and accurate biophysical techniques that can provide detailed information concerning these issues is of paramount importance. Ion mobility spectrometry (IMS) coupled to mass spectrometry (MS) offers a unique opportunity to separate multi-component biomolecular entities and to measure the molecular mass and collision cross-section of individual components in a single, rapid (</= 2 min) experiment, providing 3D- architectural information directly. Here we report a method of calibrating a commercially available electrospray ionisation (ESI)-travelling wave ion mobility spectrometry (TWIMS)-mass spectrometer using known cross-sectional areas determined for a range of biomolecules by conventional IMS-MS. Using this method of calibration, we have analysed a range of proteins of differing mass and 3D architecture in their native conformations by ESI-TWIMS-MS and found that the cross-sectional areas measured in this way compare extremely favourably with cross-sectional areas calculated using an in-house computing method based on Protein Data Bank NMR-derived co-ordinates. This not only provides a high degree of confidence in the calibration method, but also suggests that the gas phase ESI- TWIMS-MS measurements relate well to solution-based measurements derived from other biophysical techniques. In order to determine which instrumental parameters affect the ESI-TWIMS-MS cross-sectional area calibration, a systematic study of the parameters used to optimise TWIMS drift time separations has been carried out, observing the effect each parameter has on drift times and IMS resolution. Finally, the ESI-TWIMS-MS cross-sectional area calibration has been applied to the analysis of the amyloidogenic protein beta(2)-microglobulin and measurements for three co-populated conformational families, present under denaturing conditions, have been made: the folded, partially unfolded and unfolded states.

Publication types

• Research Support, Non-U.S. Gov't
• Validation Study
• Calibration
• Mass Spectrometry / instrumentation
• Mass Spectrometry / methods*
• Peptides / chemistry*
• Protein Conformation
• Protein Denaturation
• Protein Folding
• Proteins / chemistry*
• Reproducibility of Results
• Spectrometry, Mass, Electrospray Ionization / instrumentation
• Spectrometry, Mass, Electrospray Ionization / methods*
• Time Factors

Grants and funding

• BB/D010284/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom
• BB/E012558/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom
• WT_/Wellcome Trust/United Kingdom