Structurally Selective Imaging Mass Spectrometry by Imaging Ion Mobility-Mass Spectrometry

  • John A. McLean
  • Larissa S. Fenn
  • Jeffrey R. Enders
Part of the Methods in Molecular Biology book series (MIMB, volume 656)

Abstract

This chapter describes the utility of structurally based separations combined with imaging mass spectrometry (MS) by ion mobility-MS (IM-MS) approaches. The unique capabilities of combining rapid (μs-ms) IM separations with imaging MS are detailed for an audience ranging from new to potential practitioners in IM-MS technology. Importantly, imaging IM-MS provides the ability to rapidly separate and elucidate various types of endogenous and exogenous biomolecules (e.g., nucleotides, carbohydrates, peptides, and lipids), including isobaric species. Drift tube and traveling wave IM-MS instrumentation are described and specific protocols are presented for calculating ion–neutral collision cross sections (i.e., apparent ion surface area or structure) from experimentally obtained IM-MS data. Special emphasis is placed on the use of imaging IM-MS for the analysis of samples in life sciences research (e.g., thin tissue sections), including selective imaging for peptide/protein and lipid distributions. Future directions for rapid and multiplexed imaging IM-MS/MS are detailed.

Key words

Ion mobility ion mobility-mass spectrometry IM-MS imaging mass spectrometry IMS MSI imaging ion mobility-mass spectrometry structural separations MALDI IM-MS/MS 

1 Introduction

One of the recent advances in mass spectrometry (MS) instrumentation is the incorporation of post-ionization separations on the basis of ion mobility (IM) combined with subsequent MS analysis (IM-MS). Importantly, IM-MS adds an additional dimension of separations on the basis of analyte structure to facilitate interpretation of MS spectra directly from complex biological samples. Typically separations in the IM dimension are completed in 100 s of microseconds to milliseconds, thus imaging matrix-assisted laser desorption/ionization (MALDI)-IM-MS is performed on the same timescale as contemporary imaging MALDI-MS experiments. In practice, the combination of imaging IM-MS can be thought of as rapid gas-phase electrophoresis at each spatial dimension yielding a 5D data set (i.e., mobility, m/z, relative abundance, and x, y spatial coordinates). This information can be supplemented by performing fragmentation studies at each spatial position in imaging IM-MS/MS experiments (Section1.4.1). Furthermore, IM gas-phase separations yield direct structural information (ion–neutral collision cross sections, or apparent ion surface area (Å2)) for analytes that can be interpreted by complementary molecular simulations using either ab initio and/or molecular dynamic techniques.

The main focus of this chapter is to illustrate the advantages of merging IM-MS with imaging MALDI-MS, which is aimed primarily at new or potential users of imaging MALDI-IM-MS. The ability to separate molecules based on collision cross section allows for the simplification and validation of complex spectra, such as those commonly encountered in biological imaging MS. The recent commercial availability of IM-MS instruments should ultimately result in this technology becoming a staple in many structural MS and biological laboratories. In order to avoid repetition with other works in this edition, this chapter focuses mainly on the theory of IM-MS separations and the benefits of using this technique for biological imaging experiments. The selected examples described and illustrated herein are intended to underscore the advantages and limitations of imaging MALDI-IM-MS rather than being a comprehensive review of IM-MS research. The reader is directed to several recent reviews for a more detailed description of IM-MS (1, 2, 3, 4, 5).

1.1 Ion Mobility Applications to the Life Sciences

Although gas-phase IM separations have existed for well over a century (6) and coupling IM–MS has existed since the early 1960s (7, 8), the utility of IM-MS for biomolecular separations was not fully realized until combined with soft ionization techniques, such as electrospray ionization (ESI) and MALDI (9, 10). The first applications of IM-MS to determine peptide and protein structures were performed in the late 1990s (11, 12, 13). Subsequent to these pioneering studies, research over the past decade has extended IM-MS techniques to the study of complex biological samples, such as whole cell lysates (14), plasma (15, 16, 17, 18), homogenized tissue (14,19,20), non-covalent complexes (21, 22, 23), or directly from thin tissue sections (24, 25). However, until very recently, IM-MS was essentially available in only a limited number of laboratories where custom instruments were constructed. The recent introduction of commercially available IM-MS instrumentation, in several forms, has further fueled the integration of IM-MS techniques into life sciences research programs. The following sections describe the theory of IM separations (Section1.2), an overview of IM-MS instrumentation (Section1.3), and data interpretation in IM-MS conformation space (Section1.4). Materials, methods, and protocols for performing imaging IM-MS of complex biological samples are then detailed (Sections2 and 3).

1.2 Overview and Theory of Ion Mobility Separations

Ion mobility-mass spectrometers are composed of an ion source, a mobility separation cell, a mass analyzer, and a detector as depicted in Fig.21.1a. There are many variations to the general design such as different ion sources (i.e. ESI, MALDI) and types of ion mobility separation cells used (i.e., whether the ions are dispersed in space or time). For imaging IM-MS applications typically time-of-flight (TOF) mass analyzers are used for timescale considerations as described below (seeSection1.3). This chapter focuses on temporal ion dispersion through the use of drift tube or traveling wave ion mobility (DTIM and TWIM, respectively). In contrast with high-energy ion–neutral gas-phase collisions used in collision-induced dissociation (CID), both DTIM and TWIM separations utilize low-energy gas-phase collisions to separate ions on the basis of predominantly molecular surface areas. Briefly, ions are injected into a drift tube filled with a neutral drift gas and migrate under the influence of a weak electrostatic field gradient (Fig.21.1b). Larger ions have a lower mobility than smaller ions which result in longer drift times versus shorter drift times, respectively. This field is electrostatic for drift tube and electrodynamic for traveling wave separations, respectively. The migration of these ions is impeded by collisions with the neutral drift gas to a degree that is proportional to apparent surface area or collision cross section. Although the experimental parameter obtained from IM separations is the ion arrival time distribution (tatd) or the time between ion injection and ion detection, it can be converted to collision cross section or apparent surface area as illustrated in Fig.21.1c. The following description details how this conversion is performed based on the kinetic theory of gases for drift tube separations. For a derivation of ion–neutral collision cross-section theory, the reader is directed to several excellent texts and reviews (26, 27, 28). Procedures to estimate collision cross section using traveling wave IM are described elsewhere (29, 30). This section summarizes several of the key equations and practical considerations for determining ion–neutral collision cross sections in uniform electrostatic field IM instrumentation. Further details for experimental implementation will follow in the methods section.
Fig. 21.1.

(a) A block diagram of the primary components of biological IM-MS instrumentation. (b) A conceptual depiction of an IM drift cell. A stack of ring electrodes are connected via resistors in series to form a voltage divider, which is typically designed to generate a relatively uniform electrostatic field along the axis of ion propagation. Ions of larger apparent surface area experience more collisions with the neutral drift gas and therefore elute slower than ions of smaller apparent surface area. (c) A hypothetical IM separation for peptide ions exhibiting two distinct structural sub-populations corresponding to globular (left) and to helical (right) conformations. The arrival time distribution data (top axis), or what is measured, can be transformed to a collision cross-section profile (bottom axis) via equation [4] and described in Section3.1. Adapted with kind permission from Springer Science+Business Media (1) Fig. 1.

1.2.1 Transforming Drift Time to Collision Cross Section

The separation of ions in a weak electrostatic field (E) is measured as the ion drift velocity (vd) and is related by the proportionality constant, K, which is the mobility of the ion in a particular neutral gas:
$$v_{\rm{d}} = KE$$
([1])
The drift cell is of a fixed length (L), and the velocity of the ion packet is determined by measuring the drift time (td) of the packet across the drift cell. In evaluating K, the drift velocity of the ion packet depends not only on the electrostatic field strength but also on the pressure (p, torr) of the neutral drift gas and the temperature (T, kelvin) of separation. Therefore, it is conventional practice to report K as the standard or reduced mobility (K0), which normalizes the results to standard temperature and pressure conditions (i.e., 0°C and 760 torr):
$$K_0 = K\frac{p}{{760}}\frac{{273}}{T}$$
([2])
For applications where IM is used to obtain structural information about the ion, such as those in structural proteomics and biophysics, the IM separations are performed using weak electrostatic fields (ca. 20–30 V cm−1 torr−1). Provided the field strength is sufficiently weak, or under so-called low-field conditions, a closed equation for the ion–neutral collision cross section can be expressed from the kinetic theory of gases (seeNote 1). When the IM separations are performed in low-field conditions, i.e., constant K, the mobility is related to the collision cross section of the ion–neutral pair:
$$K_0 = \frac{{(18\pi )^{1/2} }}{{16}}\frac{{ze}}{{(k_{\rm{B}} T)^{1/2} }}\left[ {\frac{1}{{m_{\rm{i}} }} + \frac{1}{{m_{\rm{n}} }}} \right]^{1/2} \frac{{760}}{p}\frac{T}{{273}}\frac{1}{{N_0 }}\frac{1}{\Omega }$$
([3])
where these parameters include the charge of the ion (ze), the number density of the drift gas at STP (N0, 2.69×1019 cm−3), the reduced mass of the ion–neutral collision pair (ion and neutral masses of mi and mn, respectively), Boltzmann’s constant (kB), and the ion–neutral collision cross section (Ω). Inspection of equation [3] shows that the mobility of an ion is inversely related to its collision cross section or apparent surface area. Substituting for K0 in equation [3] and rearranging to solve for the collision cross section yields:
$$\Omega = \frac{{(18\pi )^{1/2} }}{{16}}\frac{{ze}}{{(k_{\rm{B}} T)^{1/2} }}\left[ {\frac{1}{{m_{\rm{i}} }} + \frac{1}{{m_{\rm{n}} }}} \right]^{1/2} \frac{{t_{\rm{d}} E}}{L}\frac{{760}}{p}\frac{T}{{273}}\frac{1}{{N_0 }}$$
([4])
which is the typical functional form of the equation used to solve for collision cross sections from IM data (seeSection3). Conceptually, the ion–neutral collision cross section can be thought of as the radius of the orientationally averaged projection of the ion in combination with the drift gas, i.e., Ω=π(ri+rHe)2, as depicted in Fig.21.2 (27).
Fig. 21.2.

Visual representation of the ion–neutral collision cross section measured using DTIM strategies. The radii of the ion (ri) and the drift gas helium atom (rHe) can be used to approximate the collision cross section (Ω) using the simplified equation Ω = π(ri+rHe)2 (27).

In both equations [3] and [4], the collisions of ions with neutrals are considered to be a completely elastic process. Thus, the collision cross section obtained is termed the hard-sphere collision cross section. When compared to molecular simulations, these collision cross section measurements can provide detailed structural information about the analyte (31, 32, 33, 34).

1.3 Instrumentation Overview

The two time-dispersive methods of IM separation are DTIM and TWIM. Drift tube IM facilitates absolute collision cross section calculations (35, 36, 37, 38). This data can then be compared to molecular simulation results to interpret analyte structural and conformational details. Traveling wave IM utilizes electrodynamic fields, which only provides estimated collision cross sections when measurements are compared to internal standards with previously measured DTIM absolute collision cross sections (29, 30). This is because gas-phase theory is insufficiently developed for the fundamental physical processes in TWIM separations, although recent efforts in this regard have been reported (39). Nevertheless, both DTIM and TWIM instrumentations are increasingly used for imaging IM-MS applications.

1.3.1 Drift Tube Ion Mobility

The first ion mobility instruments were developed on a drift tube design (40). As described in Section1.2, DTIM-MS instruments are conceptually analogous to typical imaging MALDI-MS instruments with the exception of inserting an IM drift cell between the MALDI source and mass analyzer. Specifically for imaging applications it is important to consider the timescales of separation, which are largely affected by drift cell pressure and length. The fundamental limit to throughput in imaging applications is dependent on the slowest component, which in this case is either the duration of IM separation or the pulse-to-pulse repetition rate of the MALDI laser. Although atmospheric pressure drift cells and reduced pressure (1–10 torr) drift cells have been constructed for IM-MS (41) the latter are much better suited for imaging applications. For example, atmospheric pressure drift cells typically separate ions on a timescale of 10 s of ms (42), whereas reduced pressure drift cells can provide separations in <1 ms for relatively short drift cells (ca. 10 cm (2)). This is significant because it dictates the fastest rate at which the MALDI laser can be operated without losing time correlation in signals arising from different MALDI events, i.e., 10 Hz for 100 ms versus 1,000 Hz for 1 ms drift times, respectively (43).Thus by having short drift times one can perform imaging IM-MS without compromising imaging rates in traditional imaging MALDI-MS experiments. MALDI repetition rates using DTIM in imaging IM-MS have ranged from 300 to 1,000 Hz (24, 25, 44). Even with fast separation times, IM resolution typically ranges from 30 to 50 (r=tt at FWHM), whereas longer, cryogenically cooled, or higher pressure drift tubes have been reported with resolutions exceeding 100 (45, 46, 47). Furthermore, drift tube instruments can also be equipped for imaging IM-MS/MS experiments as described in Section1.4.1.

1.3.2 Traveling Wave Ion Mobility

The recent commercial availability of traveling wave ion mobility (TWIM) instrumentation (Waters Corp.) has made imaging IM-MS accessible to a large number of users. Similar to drift tube instruments, TWIM separates ions by time dispersion through collisions with a background buffer gas, but in contrast, it uses electrodynamic fields rather than electrostatic fields (1, 48). This is accomplished by transmitting voltage pulses sequentially across a stack of ring electrodes (similar to Fig.21.1b), which creates the so-called traveling wave (49). Conceptually, TWIM separations are performed based on the susceptibility of different ions to the influence of the specific wave characteristics and have been described as the ability of ions to “surf ” on waves (48). Adjustable wave parameters include traveling wave pulse height, wave velocity, and ramping either of these variables. The commercial platform (Synapt HDMS) is comprised of a MALDI (200-Hz pulse repetition rate) or ESI source, a mass-resolving quadrupole, a trapping region for injecting pulses of ions into the TWIM, the TWIM drift cell, an ion transfer region, and an orthogonal TOFMS (r=mm at FWHM of >17,500). As described in Section1.4.1, CID can be performed in the regions before and after the TWIM drift cell (seeNote 2). Generally resolution in the TWIM is <15, but this is sufficient for the separation of many molecular classes of interest (e.g., isobaric peptides from lipids) in the imaging community (seeSection1.4). For example TWIM has been used to separate biomolecular signals from complex samples (50) and to study the structure of peptides following CID in the trapping region (51). Although protocols have been proposed to approximate collision cross-section values using TWIM experimental data, the calculations still rely on absolute values obtained using drift tube instruments (29, 30).

1.4 Data Interpretation in Conformation Space

Typical data for a 2D IM-MS separation are presented in Fig.21.3 for the separation of lipids and oligonucleotides. Conformation space data (Fig.21.3a) are termed such because they represent biomolecular structure, or conformation, as a function of m/z (seeNote 3). An integrated mass spectrum over all arrival time distributions is shown in Fig.21.3b, which is what would be observed in the absence of IM. An integrated IM arrival time distribution is illustrated by the white curve of Fig.21.3c which would be obtained by placing the detector directly after the IM drift cell. By plotting the data in 2D conformation space two distinct correlations are observed, one for lipids and one for oligonucleotides, respectively. Note that either extracted mass spectra or arrival time distributions can be derived from conformation space data. For example, an extracted arrival time distribution over the m/z range of 860–870 is represented by the gray curve in Fig.21.3c. The latter illustrates baseline resolution for a sodium-coordinated lipid (sphingomyelin 44:1, m/z = 866.7 Da) and a protonated oligonucleotide (CGT, m/z = 860.2 Da) of nearly the same m/z.
Fig. 21.3.

(a) A 2D IM-MS conformation space plot for several lipid and oligonucleotide standards. This data illustrate the variation of gas-phase packing efficiencies for different types of biomolecules. (b) An integrated mass spectrum over all arrival time distributions. (c) The white curve represents the integrated arrival time distribution over the mass range of 500−1,200 m/z, whereas the gray curve represents an extracted arrival time distribution over the mass range of 860–870 m/z. The latter illustrates baseline resolution of two peaks with similar masses but different mobilities (lipid-[sphingomyelin 44:1+Na]+, m/z = 866.7 Da; oligonucleotide-[CGT+H]+, m/z = 860.2 Da).

One of the main challenges in the analysis of complex biological samples, such as those encountered in biomolecular imaging MS, is the large diversity of molecular species with the high probability for isobaric molecules at a particular m/z. Structural separations on the basis of ion mobility allow isobaric species belonging to different biomolecular classes to be easily resolved (Fig.21.4), resulting in more confident peak assignments. Although biomolecules are generally composed of a limited combination of elements (e.g., C, H, O, N, P, and S), different biomolecular classes preferentially adopt structures at a given m/z correspondent to the prevailing intermolecular folding forces for that class. A hypothetical plot delineating regions of conformation space for which different biomolecular classes (e.g., nucleotides, carbohydrates, peptides, lipids) are predicted to occur is presented in Fig.21.4a. This occurs as a result of different average densities for different molecular classes (nucleotides>carbohydrates>peptides>lipids (1)). Although more pronounced for an m/z range over 2,000, at lower ranges as that depicted in Fig.21.4b separations are still feasible but the correlations begin to overlap. Nevertheless, imaging IM-MS allows for obtaining more informative images on the basis of structure and m/z whereby isobaric chemical noise is selectively rejected on the basis of structure.
Fig. 21.4.

(a) A hypothetical plot illustrating the differences in IM-MS conformation space for different molecular classes based on different gas-phase packing efficiencies. (b) A plot of collision cross section as a function of m/z for different biologically relevant molecular classes, including oligonucleotides (n = 96), carbohydrates (n = 192), peptides (n = 610), and lipids (53). All species correspond to singly charged ions generated by using MALDI, where error ±1σ is generally within the data point. Values for peptides species are from (35). (a) is adapted with kind permission from Springer Science+Business Media: (1) 2a. (b) is adapted with kind permission from Springer Science+Business Media: (59), 1a.

The first examples in the literature of combining imaging IM-MS was performed using DTIM and demonstrated two important advantages over imaging MS strategies, namely (i) the ability to separate isobaric species on the basis of structure and m/z and (ii) enhanced signal-to-noise ratios by the separation of chemical noise (24, 25). The former is demonstrated through the selective differentiation of nominally isobaric peptide and lipid species as illustrated in Fig.21.5a. In proof-of-concept experiments, both the lipid and the peptide were spotted onto a thin tissue section of mouse liver (12 μm) using a reagent spotter in either a “\” direction for the peptide or a “/” direction for the lipid (Fig.21.5a, top left). In the 2D IM-MS plot (Fig.21.5a, top right), the signal for the protonated forms of the lipid, phosphotidylcholine 34:2, and the peptide, RPPGFSP, overlaps in the m/z spectrum, but is baseline resolved in the IM arrival time distribution with the peptide and lipid centered at 449 and 504 μs, respectively. In Fig.21.5abottom, the × represents what would be obtained using conventional imaging MS in the absence of IM, which is a convolution of both the peptide and lipid signals. The right two images are for the same 1 Da mass range (759–760 m/z), but selectively for structures corresponding to putative peptides “\” and lipids “/,” respectively (25). Thus, structurally selective imaging on the basis of molecular class results in more accurate images in contrast with conventional imaging MS alone. In Fig.21.5b, imaging IM-MS is demonstrated for a coronal rat brain section (16 μm) where the image to the right corresponds structurally to lipids and specifically to the sodium-coordinated cerebroside 24:0 OH (m/z = 850.7 Da). This results in enhanced signal to noise for species of interest through the separation of chemical noise and contaminants with IM (24).
Fig. 21.5.

(a) Imaging DTIM-MS of a nominally isobaric peptide (RPPGFSP) and lipid (PC 34:2) deposited onto a mouse liver thin tissue section (12 μm) in the pattern of an “×”. The “\” line is RPPGFSP, while the “/” line is a phosphotidylcholine extract, respectively (top left). An optical image of the patterned matrix/analyte spots deposited on the tissue section (top right). A zoomed view in the region of PC 34:2 and RPPGFSP for a representative 2D IM-MS conformation space plot of a mixture of the two analytes. IM-MS signal intensity is indicated by false coloring, where purple and yellow corresponds to the least and most intense signals, respectively. (bottom left) An extracted ion intensity map over the mass range of 759–760 Da representing what would be obtained using conventional imaging MALDI-MS. (bottommiddle and right) Extracted ion intensity maps for imaging DTIM-MS of the peptide and lipid over the mass range of 759–760 Da and DTIM drift times of 447–451 μs and 502–506 μs, respectively. (b, left) An integrated mass spectrum of cerebrosides directly from rat brain tissue. (b, right) Imaging DTIM-MS of the sodium-coordinated cerebroside 24:0 OH (m/z = 850.7). (b, far right) An optical image of an adjacent rat brain section. Histological abbreviations are Cx – cortex; fmi – forceps minor of the corpus callosum; CPu – caudate putamen (striatum); Acb – nucleus accumbens; ac – anterior commissure; and lo – lateral olfactory tract. Figures (a) and (b) are adapted from (25) and (24), respectively, with permission. Copyright© 2007 Wiley-Liss, Inc.

More recently, imaging IM-MS using a TWIM drift cell has been demonstrated (Fig.21.6). Although the IM resolution is more limited using TWIM, it is sufficient for the separation of lipids from peptides as illustrated for a coronal thin tissue rat brain section (Fig.21.6i, seeNote 4). The utility of imaging TWIM-MS for mapping the distribution of a drug, vinblastine, in a kidney from a whole body section is shown in Fig. 21.6ii, b. The accuracy of the obtained image is increased through the addition of TWIM due to the removal of isobaric interferences common in highly complex biological samples, such as tissue (52). Since commercial TWIM instrumentation equipped for imaging applications was released in 2008, the present number of reports is rather limited but expected to increase substantially in the near future.
Fig. 21.6.

(i) Imaging TWIM-MS data of a rat brain thin tissue section illustrating selective imaging of peptides and lipids on the basis of structure. (ii) Imaging TWIM-MS data obtained in the analysis of a small drug molecule, common name vinblastine, in thin tissue kidney sections of vinblastine-dosed rats. (a) An optical image of the kidney from the whole body section dosed at 6 mg kg−1 IV vinblastine before matrix application. (b) The same tissue section as shown in (a) but imaged by TWIM-MS showing the distribution of vinblastine within the kidney, with the highest intensity (white) showing a broken ring of intensity between the cortex and the medulla. (c) Optical image of the kidney within the whole body section dosed with 3H vinblastine. (d) Whole body autoradiography of the tissue section shown in (c) indicated is the broken ring of slightly higher intensity (white) between the cortex and the medulla. Reproduced with permission from (52). Copyright 2008 American Chemical Society.

1.4.1 Imaging IM-MS/MS Measurements

When performing imaging experiments, additional confirmation of an unidentified peak is often required. A common practice for increasing confidence in peak assignments is to image in a selected reaction monitoring mode for a fragmentation channel characteristic of the analyte of interest (e.g., using CID (53)). Like traditional MS/MS, the coupled arrangement of IM and MS yields the ability to obtain structure information in the MS dimension by performing IM-MS/MS. In IM-MS/MS mode, MS1 can be accomplished in two ways: (i) time dispersion in the drift tube can perform parent ion selection (2) or (ii) a mass analyzer can be used (49). By placing an ion activation region between the drift tube and mass analyzer ions may be selected for fragmentation according to drift time. Performing the ion selection in this manner provides a multiplex advantage in that all fragment ions will possess the same drift time as the parent ion. This is significant in imaging applications because the sample is limited to the spatial coordinates of a particular pixel. In imaging IM-MS/MS experiments, multiple parent ions can be fragmented whereby fragment ions are correlated to their respective parent ions by drift time. A demonstration of the potential utility of IM-MS/MS is illustrated in Fig.21.7 for a carbohydrate, lacto-N-fucopentaose 1 (LNFP1). In-source decay fragmentation for this carbohydrate is illustrated in Fig.21.7a where fragment ions occur at different times in the IM separation. Correlated fragmentation spectra can also be obtained as illustrated in Fig.21.7b by using both in-source fragmentation and post-IM CID. The CID fragmentation results in ions correlated to the drift time of the parent. Importantly, this results in redundancy of the fragment ions that are observed for higher confidence that particular fragment ions arise from the parent ion of interest. For example, the integrated mass spectra for in-source and CID fragmentation (i.e., pre- and post-IM separation) are illustrated in Fig.21.7c, d, respectively. When applied to multiple ions, this operation allows for multiple reaction monitoring (MRM) in a single scan. This application is a highly promising yet virtually untapped resource for biomolecular imaging MS, where limited sample exists at each pixel location.
Fig. 21.7.

ESI-TWIM-MS/MS of the carbohydrate, lacto-N-fucopentaose 1 (LNFP1), illustrating two modes of IM-MS/MS. (a) In-source decay fragmentation of LNFP1 followed by TWIM analysis of the fragment ions. (b) In-source decay prior to TWIM separation and collision-induced dissociation following TWIM separation for LNFP1. The latter results in fragment ions to be observed at the same drift time as the parent leading to the possibility for simultaneous CID for various ions at the same time. In both CID and ISD, cross-ring cleavages were seen, but glycosidic bond cleavages were the most abundant type of fragmentation. (c) In-source decay fragmentation spectrum that was extracted from the IM-MS plot above. Along with a zoom in view of the region from ∼500 to 850 Da. Nomenclature for the fragmentation pattern of carbohydrates was first used by Domon and Costello (54). All in-source fragmentation and collision-induced dissociation peaks are labeled utilizing this nomenclature. (d) CID spectrum of the top dotted line for LNFP1 extracted from (b). (c) and (d) can be compared to examine the difference between the two different means for fragmenting carbohydrates. Dotted lines are for illustration purposes of the fragmentation peaks.

2 Materials

  1. 1.

    Sample prepared for MALDI analysis (thin tissue section washed, fixed to MALDI plate, and MALDI matrix applied, seeChapters 4, 7, 11, 16, 20, and 21 for detailed methodologies).

     
  2. 2.

    Mass and drift tube IM standards/calibrants. Mass standards correspond to peptides and proteins bracketing the mass range of interest. Ion mobility structural standards for DTIM are typically C60 and C70 fullerenes, because they exist in one structural form. These can be used for evaluating DTIM resolution and for day-to-day evaluation of instrument performance. Additionally, fullerenes can be used as mass standards as they are structurally separated from biomolecules in conformation space (seeFig.21.4) and provide a wide range of gas-phase reaction products resulting in peaks spanning a large mass range in increments of 24 Da. To validate gas pressure in DTIM, typically the peptide bradykinin (RPPGFSPFR) is used to compare collision cross-section measurement with the accepted value of 242±2 Å2 (12). Bradykinin can be mixed with matrix of choice or a 1 mg/ml standard solution in H2O can be combined 1:1 v/v with 20 mg/ml α-cyano-4-hydroxycinnamic acid in 50% methanol. Both calibrants can be applied to MALDI plate using the dried droplet method (55).

     
  3. 3.

    Traveling wave IM standards/calibrants. As discussed in Section1.3, estimated collision cross sections obtained by TWIM require internal standards with corresponding absolute collision cross-section values obtained using DTIM. Published absolute collision cross sections can be obtained from several published databases, including (i) peptide collision cross sections determined by ESI (56, 57), (ii) intact protein collision cross sections determined by ESI (58), (iii) peptide collision cross sections determined by MALDI (36), and (iv) biologically relevant carbohydrate, lipid, and oligonucleotide collision cross sections determined by MALDI (59).

     

3 Methods

3.1 Performing Collision Cross-Section Measurements Using DTIM

  1. 1.

    In order to take measurements, the samples for imaging (tissue, etc.) should be prepared the same as for conventional imaging MALDI-MS (seeNote 5).

     
  2. 2.

    Following insertion of the sample target into the instrument, mass and ion mobility standard/calibrants are measured. In particular, to MALDI-IM-MS methods the laser pulse serves as the start signal (t0) for measuring the IM arrival time distribution (tatd). These time distinctions are necessary for the calculations in Step 4.

     
  3. 3.

    Following separation in the IM drift cell filled with an inert gas (1–10 torr, seeNote 6), ions are directed through a skimming and differential pumping region where the pressure is reduced from 1–10 to ∼10−8 torr for mass analysis in the orthogonal TOFMS. The stop time for tatd corresponds to the ion injection time for the TOFMS measurement.

     
  4. 4.
    To perform the calculations as described in Section1.2.1 (e.g., equation [4]) the arrival time distribution must be corrected for time spent in regions outside of the drift cell (i.e., time spent traversing from the MALDI plate into the drift cell, in skimming and differential pumping regions, and ion optic regions prior to the source of the TOFMS). This will result in the drift time (td) of the ions within the IM drift cell used in the calculation of collision cross section:
    $$t_{\rm{d}} = t_{{\rm{atd}}} - t_{{\rm{dtc}}}$$
    ([5])
     
  5. 5.

    To determine the value of tdtc, IM separations are performed by varying the voltage across the drift cell while maintaining all other experimental parameters constant. The arrival time distribution measured at each drift voltage is then plotted versus the inverse of drift voltage (1/V). Provided the range of voltages used maintains ion separations under low-field conditions, this plot will result in a linear correlation. If non-linearity is observed, a calculation of the low-field limit should be performed (seeNote 1), because curvature in this plot indicates that mobility is not constant over the voltage range used. A linear regression of this data results in a y-intercept corresponding to tdtc (seeNote 7). Preferably at least five voltages should be used to define this line although for high-precision measurements as many voltages as is practical should be used (seeNote 8).

     
  6. 6.

    After the td has been determined from the tatd, it can now be used to calculate the collision cross section, Ω, of the ion of interest through the equation [5] (seeNotes 9 and 10 (26)).

     
  7. 7.

    After the collision cross section has been calculated, this can be further related to the structure using molecular dynamic simulations. More information about these computational methods can be found in more detail in other resources (31, 32, 33, 34). An excellent overview and tutorial of these strategies can be found elsewhere (60).

     
  8. 8.

    For calculating relative collision cross sections using traveling wave ion mobility-MS, there are two main procedures used which can be found in the literature (29, 30).

     

4 Notes

  1. 1.

    For electrostatic fields higher than the low-field limit, the ion velocity distribution depends less strongly on the temperature of the separation and the mean ion energy increases as it traverses the drift region. Consequently, K is no longer constant and depends on the specific ratio of the electrostatic field to the gas number density (E/N) (see (26) for a derivation of calculating the low-field limit for a particular analyte).

     
  2. 2.

    In principle, the Synapt has sufficient activation/ dissociation regions to perform up to MS5, although typically up to MS3 is practically feasible.

     
  3. 3.

    Presentation of 3D conformation space data (IM arrival time distribution, m/z, signal intensity) is typically projected with false coloring or gray scale representing signal intensity to project 3D data in a 2D plot.

     
  4. 4.

    There is presently no consensus on the reporting of IM-MS conformation space data, i.e., TWIM-MS data are generated with arrival time distribution on the abscissa and m/z on the ordinate axes, but it is either reported using this convention or where the axes are inverted. This reporting of conformation space data parallels historical preferences in the reporting of DTIM-MS data (e.g., seeFigs.21.3, 21.4, and 21.5).

     
  5. 5.

    Typically ionization is performed at the pressure of the DTIM (e.g., 1–10 torr), which results in moderate pressure MALDI. Thus some collisional cooling typically takes place after ionization and can result in matrix-adducted and cluster species. These can be dissociated prior to DTIM by performing injected ion experiments. Furthermore, matrix optimization may be required. One effect of moderate pressure MALDI that we have observed is that higher ion currents can be achieved at slightly lower matrix-to-analyte ratios (i.e., 1,000–100:1) than those used in high-vacuum MALDI (i.e., 10,000–1,000:1).

     
  6. 6.

    As developed in more detail in the theory section, typically He is used because of its low mass and low polarizability relative to other inert gases. However, other drift gases or drift gas additives can be used to promote long-range interactions between the ion and drift gas. This is analogous to tuning selectivity in HPLC by changing the mobile or stationary phase that is used.

     
  7. 7.

    The y-intercept of this plot corresponds to tdtc because it represents the limit of td→0 at infinite drift cell voltage. Also note that the accuracy with which this correction should be made is more important for shorter drift times and its significance is less important at longer drift times. For example, in fast separations as described for imaging IM-MS experiments the values of tdtc can approach the relative magnitude of td.

     
  8. 8.

    For the most accurate results, the drift time correction should be evaluated for each component in the IM profile. The motivation for evaluating individual drift time corrections arises from additional ion–neutral collisions in the differential pumping regions at the entrance and/or exit of the IM drift cell. In these regions the gas dynamics typically transition from viscous to molecular flow, e.g., at the exit aperture of the drift cell at 1–10 torr to the high vacuum (∼10−8 torr) of the mass spectrometer, respectively.

     
  9. 9.

    Note that the equation for calculating collision cross section is derived from classical electrodynamics, and as such, great care should be exercised in the dimensionality of the units used. Specifically, the units for E should be expressed in CGS Gaussian units, i.e., statvolts cm−1, where 1 statvolt equals 299.79 V. Note that statvolts cm−1 is equivalent to statcoulombs cm−2 and that elementary charge, e, is 4.80×10−10 statcoulombs.

     
  10. 10.

    By comparing empirically determined cross sections with theoretical results, it has been shown that the hard-sphere approximation is best suited for analytes larger than ca. 1,000 Da, which is typically the size range in which many biological measurements are made. However, as the size of the analyte approaches the size scale of the drift gases used for separation, long-range interaction potential between the ion and neutral must be considered for accurate results (33, 61, 62).

     

Notes

Acknowledgments

We thank Whitney B. Ridenour and Richard M. Caprioli (Vanderbilt University) for assistance and use of the Synapt HDMS (data shown in Figs.21.6 and 21.7), which is supported by the Vanderbilt University Mass Spectrometry Research Core. Financial support for this work was provided by the National Institutes of Health-NIDA (#HHSN271200700012C), Vanderbilt University College of Arts and Sciences, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrated Biosystems Research and Education, the American Society for Mass Spectrometry (Research award to J.A.M), the Spectroscopy Society of Pittsburgh, Waters Corp., and Ionwerks Inc.

References

  1. 1.
    Fenn, L. S., McLean, J. A. (2008) Biomolecular structural separations by ion mobility-mass spectrometry. Anal Bioanal Chem, 391, 905–909.PubMedCrossRefGoogle Scholar
  2. 2.
    McLean, J. A., Ruotolo, B. T., Gillig, K. J., Russell, D. H. (2005) Ion mobility-mass spectrometry: a new paradigm for proteomics. Int J Mass Spectrom, 240, 301–315.CrossRefGoogle Scholar
  3. 3.
    Wyttenbach, T., Bowers, M. T. (2003) Gas-phase conformations: the ion mobility/ion chromatography method. Modern Mass Spectrom, 225, 207–232.CrossRefGoogle Scholar
  4. 4.
    Jarrold, M. F. (2000) Peptides and proteins in the vapor phase, Annu Rev Phys Chem, 51, 179–207.PubMedCrossRefGoogle Scholar
  5. 5.
    Clemmer, D. E. Jarrold, M. Fd. (1997) Ion mobility measurements and their applications to clusters and biomolecules. J Mass Spectrom, 32, 577–592.CrossRefGoogle Scholar
  6. 6.
    Eiceman, E. A., Karpas, Z. (2004) Ion Mobility Spectrometry, 2nd Ed., CRC Press, Boca Raton, FL, http://Chapter 1.Google Scholar
  7. 7.
    McAfee, K. B., Jr., Edelson, D. (1963) Identification and mobility of ions in a townsend discharge by time-resolved mass spectrometry, Proc Phys Soc Lond, 81, 382–384.CrossRefGoogle Scholar
  8. 8.
    Barnes, W. S., Martin, D. W., McDaniel, E. W. (1961) Mass spectrographic identification of the ion observed in hydrogen mobility experiments, Phys Rev Lett, 6, 110–111.CrossRefGoogle Scholar
  9. 9.
    Gieniec, J., Mack, L. L., Nakamae, K., Gupta, C., Kumar, V., Dole, M. (1984) Electrospray mass spectroscopy of macromolecules: application of an ion-drift spectrometer. Biomed Mass Spectrom, 11, 259–268.CrossRefGoogle Scholar
  10. 10.
    Von Helden, G., Wyttenbach, T., Bowers, M. T. (1995) Inclusion of a MALDI ion source in the ion chromatography technique: conformation information on polymer and biomolecular ions. Int J Mass Spectrom Ion Process, 146/147, 349–364.CrossRefGoogle Scholar
  11. 11.
    Shelimov, K. B., Clemmer, D. E., Hudgins, R. R., Jarrold, M. F. (1997) Protein structure in vacuo: the gas phase conformations of BPTI and cytochrome c, J Am Chem Soc, 119, 2240–2248.CrossRefGoogle Scholar
  12. 12.
    Wyttenbach, T., Von Helden, G., Bowers, M. T. (1996) Gas-phase conformation of biological molecules: bradykinin. J Am Chem Soc, 118, 8355–8364.CrossRefGoogle Scholar
  13. 13.
    Von Helden, G., Wyttenbach, T., Bowers, M. T. (1995) Conformation of macromolecules in the gas phase: use of matrix-assisted laser desorption methods in ion chromatography. Science, 267, 1483–1485.CrossRefGoogle Scholar
  14. 14.
    Myung, S., Lee, Y. J., Moon, M. H., Taraszka, J., Sowell, R., Koeniger, S., Hilderbrand, A. E., Valentine, S. J., Cherbas, L., Cherbas, P., Kaufmann, T. C., Miller, D. F., Mechref, Y., Novotny, M. V., Ewing, M. A., Sporleder C. R., Clemmer, D. E. (2003) Development of high-sensitivity ion trap ion mobility spectrometry time-of-flight techniques: a high-throughput nano-LC-IMS-TOF separation of peptides arising from a Drosophila protein extract. Anal Chem, 75, 5137–5145.PubMedCrossRefGoogle Scholar
  15. 15.
    Isailovic, D., Kurulugama, R. T., Plasencia, M. D., Stokes, S. T., Kyselova, Z., Goldman, R., Mechref, Y., Novotny, M. V. and Clemmer, D. E. (2008) Profiling of human serum glycans associated with liver cancer and cirrhosis by IMS-MS. J Proteome Res, 7, 1109–1117.PubMedCrossRefGoogle Scholar
  16. 16.
    Liu, X., Valentine, S. J., Plasencia, M. D., Trimpin, S., Naylor, S., Clemmer, D. E. (2007) Mapping the human plasma proteome by SCX-LC-IMS-MS. J Am Soc Mass Spectrom, 18, 1249–1264.PubMedCrossRefGoogle Scholar
  17. 17.
    Valentine, S. J., Plasencia, M. D., Liu, X., Krishnan, M., Naylor, S., Udseth, H. R., Smith, R. D., Clemmer, D. E. (2006) Toward plasma proteome profiling with ion mobility-mass spectrometry. J Proteome Res, 5, 2977–2984.PubMedCrossRefGoogle Scholar
  18. 18.
    Liu, X., Plasencia, M., Ragg, S., Valentine, S. J., Clemmer, D. E. (2004) Development of high throughput dispersive LC–ion mobility–TOFMS techniques for analysing the human plasma proteome. Brief Funct Genomic Proteomic, 3, 177–186.PubMedCrossRefGoogle Scholar
  19. 19.
    Liu, X., Miller, B. R., Rebec, G. V., Clemmer, D. E. (2007) Protein expression in the striatum and cortex regions of the brain for a mouse model of Huntington’s disease.J Proteome Res, 6, 3134–3142.PubMedCrossRefGoogle Scholar
  20. 20.
    Taraszka, J. A., Kurulugama, R., Sowell, R. A., Valentine, S. J., Koeniger, S. L., Arnold, R. J., Miller, D. F., Kaufman, T. C., Clemmer, D. E. (2005) Mapping the proteome of Drosophila melanogaster: analysis of embryos and adult heads by LC-IMS-MS methods. J Proteome Res, 4, 1223–1237.PubMedCrossRefGoogle Scholar
  21. 21.
    Benesch, J. L. P., Ruotolo, B. T., Simmons, D. A., Robinson, C. V. (2007) Protein complexes in the gas phase: technology for the structural genomics and proteomics. Chem Rev, 107, 3544–3567.PubMedCrossRefGoogle Scholar
  22. 22.
    Ruotolo, B. T., Hyung, S.-J., Robinson, P. M., Giles, K., Bateman, R. H., Robinson, C. V. (2007) Ion mobility-mass spectrometry reveals long-lived, unfolded intermediates in the dissociation of protein complexes. Angew Chem Int Ed, 46, 8001–8004.CrossRefGoogle Scholar
  23. 23.
    Ruotolo, B. T., Giles, K., Campuzano, I., Sandercock, A. M., Bateman, R. H., Robinson, C. V. (2005) Evidence for macromolecular rings in the absence of bulk water. Science, 310, 1658–1661.PubMedCrossRefGoogle Scholar
  24. 24.
    Jackson, S. N., Ugarov, M., Egan, T., Post, J. D., Langlais, D., Schultz, J. A., Woods, A. S. (2007) MALDI-ion mobility-TOFMS imaging of lipids in rat brain tissue. J Mass Spectrom, 42, 1093–1098.PubMedCrossRefGoogle Scholar
  25. 25.
    McLean, J. A., Ridenour, W. B., Caprioli, R. M. (2007) Profiling and imaging of tissues by imaging ion mobility-mass spectrometry. J Mass Spectrom, 42, 1099–1105.PubMedCrossRefGoogle Scholar
  26. 26.
    Mason, E. A., McDaniel, E. W. (1988) Transport Properties of Ions in Gases. John Wiley & Sons, New York, NY.Google Scholar
  27. 27.
    Mason, E. A. (1984) Ion mobility: its role in plasma chromatography, in Plasma Chromatography, (Carr, T. W. ed.), Plenum Press, New York, NY, 43–93.Google Scholar
  28. 28.
    Revercomb, H. E. and Mason, E. A. (1975) Theory of plasma chromatography/gaseous electrophoresis – a review. Anal Chem, 47, 970–983.CrossRefGoogle Scholar
  29. 29.
    Ruotolo, B. T., Benesch, J. L. P., Sandercock, A. M., Hyung, S.-J. Robinson, C. V. (2008) Ion mobility-mass spectrometry analysis of large protein complexes. Nat Protoc, 3, 1139–1152.PubMedCrossRefGoogle Scholar
  30. 30.
    Williams, J. P., Scrivens, J. H. (2008) Coupling desorption electrospray ionization and neutral desorption/extractive electrospray ionization with a travelling-wave based ion mobility mass spectrometer for the analysis of drugs. Rapid Commun Mass Spectrom, 22, 187–196.PubMedCrossRefGoogle Scholar
  31. 31.
    Gidden, J., Bowers, M. T. (2003) Gas-phase conformations of deprotonated and protonated mononucleotides determined by ion mobility and theoretical modeling. J Phys Chem B, 107, 12829–12837.CrossRefGoogle Scholar
  32. 32.
    Gidden, J., Bowers, M. T. (2003) Gas-phase conformations of deprotonated trinucleotides (dGTT(–), dTGT(–), and dTTG (–)): the question of zwitterion formation. J Am Soc Mass Spectrom, 14, 161–170.PubMedCrossRefGoogle Scholar
  33. 33.
    Wyttenbach, T., Witt, M., Bowers, M. T. (2000) On the stability of amino acid zwitterions in the gas phase: the influence of derivatization, proton affinity, and alkali ion addition. J Am Chem Soc, 122, 3458–3464.CrossRefGoogle Scholar
  34. 34.
    Shvartsburg, A. A., Jarrold, M. F. (1996) An exact hard-spheres scattering model for the mobilities of polyatomic ions. Chem Phys Lett, 261, 86–91.CrossRefGoogle Scholar
  35. 35.
    Dwivedi, P., Wu, P., Klopsch, S. J., Puzon, G. J., Xun, L., Hill, H. H. (2008) Metabolic profiling by ion mobility mass spectrometry (IMMS). Metabolomics, 4, 63–80.CrossRefGoogle Scholar
  36. 36.
    Tao, L., McLean, J. R., McLean, J. A., Russell, D. H. (2007) A collision cross-section database of singly-charged peptide ions. J Am Soc Mass Spectrom, 18, 1232–1238.PubMedCrossRefGoogle Scholar
  37. 37.
    Ruotolo, B. T., Verbeck, G. F., Thomson, L. M., Woods, A. S., Gillig, K. J., Russell, D. H. (2002) Distinguishing between phosphorylated and nonphosphorylated peptides with ion mobility-mass spectrometry. J Proteome Res, 1, 303–306.PubMedCrossRefGoogle Scholar
  38. 38.
    Furche, F., Ahlrichs, R., Weis, P., Jacob, C., Gilb, S., Bierweiler, T., Kappes, M. M. (2002) The structures of small gold cluster anions as determined by a combination of ion mobility measurements and density functional calculations. J Chem Phys, 117, 6982–6990.CrossRefGoogle Scholar
  39. 39.
    Shvartsburg, A. A., Smith, R. D. (2008) Fundamentals of traveling wave ion mobility spectrometry. Anal Chem, 80, 9689–9699.PubMedCrossRefGoogle Scholar
  40. 40.
    Mason, E. A., McDaniel, E. W. (1988) Measurement of drift velocities and longitudinal diffusion coefficients, in Transport Properties of Ions in Gases, John Wiley & Sons, New York, NY, 31–102. Google Scholar
  41. 41.
    Kanu, A. B., Dwivedi, P., Tam, M., Matz, L., Hill, H. H., Jr. (2008) Ion mobility-mass spectrometry, J Mass Spectrom, 43, 1–22.PubMedCrossRefGoogle Scholar
  42. 42.
    Steiner, W. E., Clowers, B. H., English, W. A., Hill, H. H., Jr. (2004) Atmospheric pressure matrix-assisted laser desorption/ionization with analysis by ion mobility-mass spectrometry. Rapid Commun Mass Spectrom, 18, 882–888.PubMedCrossRefGoogle Scholar
  43. 43.
    McLean, J. A., Russell, D. H. (2003) Sub-femtomole peptide detection in ion-mobility-time-of-flight mass spectrometry measurements. J Proteome Res, 2, 427–430.PubMedCrossRefGoogle Scholar
  44. 44.
    McLean, J. A., Ridenour, W. B., Caprioli, R. M. (2007) Imaging ion mobility mass spectrometry: advantages, challenges, and future prospects. Proceedings of the 55th Annual Meeting of the American Society for Mass Spectrometry, Indianapolis, IN.Google Scholar
  45. 45.
    Merenbloom, S. I., Glaskin, R. S., Clemmer, D. E. (2009) High resolution ion cyclotron mobility spectrometry. Anal Chem, 81, 1482–1487.Google Scholar
  46. 46.
    Wyttenbach, T., Kemper, P. R., Bowers, M. T. (2001) Design of a new electrospray ion mobility mass spectrometer. Int J Mass Spectrom, 212, 13–23.CrossRefGoogle Scholar
  47. 47.
    Dugourd, Ph., Hudgins, R. R., Clemmer, D. E., Jarrold, M. F. (1997) High-resolution ion mobility measurements. Rev Sci Instrum, 68, 1122–1129.CrossRefGoogle Scholar
  48. 48.
    Giles, K., Pringle, S. D., Worthington, D. L., Wildgoose, J. L., Bateman, R. H. (2004) Applications of travelling wave-based radio-frequency-only stacked ring ion guide. Rapid Commun Mass Spectrom, 18, 2401–2414.PubMedCrossRefGoogle Scholar
  49. 49.
    Pringle, S. D., Giles, K., Wildgoose, J. L., Williams, J. P., Slade, S. E., Thalassinos, K., Bateman, R. H., Bowers, M. T., Scrivens, J. H. (2007) An investigation of the mobility separation of some peptide and protein ions using a new hybrid quadrupole/travelling wave IMS/oa-ToF instrument. Int J Mass Spectrom, 261, 1–12.CrossRefGoogle Scholar
  50. 50.
    Vakhrushev, S. Y., Langridge, J., Campuzano, I., Hughes, C., Peter-Katalinic, J. (2008) Ion mobility mass spectrometry analysis of human glycourinome. Anal Chem, 80, 2506–2513.PubMedCrossRefGoogle Scholar
  51. 51.
    Riba-Garcia, I., Giles, K., Bateman, R.H., Gaskell, S.J. (2008) Evidence for structural variants of a- and b- type peptide fragment ions using combined ion mobility/mass spectrometry. J Am Soc Mass Spectrom, 19, 609–613.PubMedCrossRefGoogle Scholar
  52. 52.
    Trim, P. J., Henson, C. M., Avery, J. L., McEwen, A., Snel, M. F., Claude, E., Marshall, P. S., West, A., Princivalle, A. P., Clench, M. R. (2008) Matrix-assisted laser desorption/ionization-ion mobility separation-mass spectrometry imaging of vinblastine in whole body tissue sections. Anal Chem, 80, 8628–8634.PubMedCrossRefGoogle Scholar
  53. 53.
    Reyzer, M. L., Hsieh, Y., Ng, K., Korfmacher, W .A., Caprioli, R. M. (2003) Direct analysis of drug candidates in tissue by matrix-assisted laser desorption/ionization mass spectrometry. J Mass Spectrom, 38, 1081–1092.PubMedCrossRefGoogle Scholar
  54. 54.
    Domon, B., Costello, C. E. (1988) A systematic nomenclature for carbohydrate fragmentations in FAB-MS/MS spectra of glycoconjugates. Glycoconjugate J, 5, 397–409.CrossRefGoogle Scholar
  55. 55.
    Karas, M., Hillenkamp, F. (1988) Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal Chem, 60, 2299–2301.PubMedCrossRefGoogle Scholar
  56. 56.
    Henderson, S. C., Valentine, S. J., Counterman, A. E., Clemmer, D. E. (1999) ESI/ion trap/ion mobility/time-of-flight mass spectrometry for rapid and sensitive analysis of biomolecular mixtures. Anal Chem, 71, 291–301.PubMedCrossRefGoogle Scholar
  57. 57.
    Hoaglund, C. S., Valentine, S. J., Sporleder, C. R., Reilly, J. P., Clemmer, D. E. (1998) Three-dimensional ion mobility/TOFMS analysis of electrosprayed biomolecules. Anal Chem, 70, 2236–2242.PubMedCrossRefGoogle Scholar
  58. 58.
    Clemmer cross section database (2006) (Assessed January 28th, 2009 at http://www.indiana.edu/∼clemmer/Research/crosssection database/cs database.htm)
  59. 59.
    Fenn, L. S., Kliman, M., Mahsutt, A., Zhao, S. R., McLean, J. A. (2009) Characterizing ion mobility-mass spectrometry conformation space for the analysis of complex biological samples. Anal Bioanal Chem, 394, 235–244.Google Scholar
  60. 60.
    Theories and analysis of IM-MS data (Assessed January 28th, 2009 at http://bowers.chem.ucsb.edu/theory_analysis/index.shtml).
  61. 61.
    Wyttenbach, T., von Helden, G., Batka, J. J., Jr., Carlat, D., Bowers, M. T. (1997) Effect of the long-range interaction potential on ion mobility measurements. J Am Soc Mass Spectrom, 8, 275–282.CrossRefGoogle Scholar
  62. 62.
    Mesleh, M. F., Hunter, J. M., Shvartsburg, A. A., Schatz, G. C., Jarrold, M. F. (1996) Structural information from ion mobility measurements: effects of the long-range potential. J Phys Chem, 100, 16082–16086.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • John A. McLean
    • 1
  • Larissa S. Fenn
    • 1
  • Jeffrey R. Enders
    • 1
  1. 1.Department of ChemistryVanderbilt Institute of Chemical Biology, and Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt UniversityNashvilleUSA

Personalised recommendations