Imaging lipids with secondary ion mass spectrometry

BBAMCB-57595; No. of pages: 12; 4C: 3, 4, 6, 7, 8 Biochimica et Biophysica Acta xxx (2014) xxx–xxx

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Review

Imaging lipids with secondary ion mass spectrometry☆ Mary L. Kraft a,b,⁎, Haley A. Klitzing b a b

Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

a r t i c l e

i n f o

Article history: Received 17 December 2013 Received in revised form 11 March 2014 Accepted 12 March 2014 Available online xxxx Keywords: NanoSIMS TOF-SIMS Cell membrane Model membrane Sample preparation Lipid distribution

a b s t r a c t This review discusses the application of time-of-flight secondary ion mass spectrometry (TOF-SIMS) and magnetic sector SIMS with high lateral resolution performed on a Cameca NanoSIMS 50(L) to imaging lipids. The similarities between the two SIMS approaches and the differences that impart them with complementary strengths are described, and various strategies for sample preparation and to optimize the quality of the SIMS data are presented. Recent reports that demonstrate the new insight into lipid biochemistry that can be acquired with SIMS are also highlighted. This article is part of a Special Issue entitled Tools to study lipid functions. © 2014 Elsevier B.V. All rights reserved.

1. Introduction The abundances and distributions of various lipid species within tissues and cells are linked to both health and disease [1–8]. In mammalian cells, lipids are not only the building blocks of cellular membranes, but they also function as ligands that selectively bind to and regulate the activity of certain protein components in signaling pathways [9–18]. Insight into lipid metabolism, transport, and function has been acquired by studying their distributions within tissues and cells. The locations and abundances of various lipid species are often probed by using fluorescent lipid analogs, lipophilic dyes, or lipid-specific functionalized antibodies that can be detected with light, fluorescence, or electron microscopy [19–23]. The distributions of various lipid species in biological samples can also be mapped with high chemical specificity and without the use of complex labels, such as fluorophores or antibodies, with imaging mass spectrometry. Matrix-assisted laser desorption ionization (MALDI) is perhaps the most popular imaging mass spectrometry technique that has been used to analyze biological samples. As the name implies, the sample is coated with a matrix that promotes biomolecule desorption and ionization, and a laser is used to ionize the molecules within its focal area. This

☆ This article is part of a Special Issue entitled Tools to study lipid functions. ⁎ Corresponding author at: Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. E-mail address: [email protected] (M.L. Kraft).

ionization process minimizes molecular fragmentation, enabling the detection of molecular ions with high mass-to-charge ratios (m/z 500– 20,000) [24,25]. MALDI has been used to image a wide range of biomolecules, including lipids and proteins, with a lateral resolution that is typically N10 μm and a sampling depth in the micron range [25–30]. This spatial resolution renders MALDI imaging appropriate for analyzing the lipid distributions within tissues. MALDI imaging of biomolecules, including lipids, in tissues has been the subject of recent reviews [24, 26,31–33] and will not be discussed further herein. This review will focus on a complementary imaging mass spectrometry technique, secondary ion mass spectrometry (SIMS). SIMS offers higher spatial resolution than MALDI, but typically at the expense of higher molecular fragmentation, which results in lower chemical specificity. SIMS can achieve the sub-micrometer lateral resolution that enables studying the lipid distribution within a single cell. Moreover, the analysis depth of SIMS is ultimately limited by the escape depth of the secondary ions, which is generally limited to the upper one to two monolayers of the sample [34]. Thus, when performed at the surface of the sample, SIMS has the shallow sampling depth (top ≤ 5 nm) that permits imaging the lipids in the plasma membrane with little interference from intracellular membranes. 2. SIMS techniques SIMS imaging of lipids has been performed using two different types of instrumentation, time-of-flight SIMS (TOF-SIMS) and magnetic

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Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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sector SIMS with high lateral resolution. The basic principles that are common to both approaches will be presented first. Then the differences between the two approaches that impart them with distinctive strengths and weaknesses will be discussed. Both TOF-SIMS and high-resolution magnetic sector SIMS are performed under ultra-high vacuum (UHV), which is the techniques' greatest limitation. During analysis, a focused primary ion beam sputters neutral and ionized molecules and molecular fragments from the surface of the sample. This primary ion beam is scanned across the sample, and the secondary ions that are ejected at each beam position are collected. The intensities of the secondary ions that are characteristic of specific components are then used to create a map of that component's distribution on the surface of the sample. Additionally, because material is sputtered from the surface of the sample during SIMS analysis, maps of component distribution at progressively increasing depth in the sample can be generated by repeatedly acquiring SIMS images at the same sample location. When depth profiling is performed with TOF-SIMS, a sputtering scan that removes the damaged material is often inserted after each imaging scan in order to reduce the fragmentation of the species detected during image acquisition. The intensities of the secondary ions detected at each pixel are affected by the concentration of the parent molecule that produced the secondary ions, as well as by concentration-independent factors [35–43]. The first concentration-independent factor, which is referred to as matrix effects, arises because the local chemical environment affects the probability that a species will become ionized [35–40]. The second concentration-independent factor is sample topography; differences in the incidence angle of the primary ion beam affect both the absolute and relative intensities of the ions [41,42]. These concentration-independent variations in ion intensity complicate quantitatively imaging lipid distribution with either TOF-SIMS or highresolution magnetic sector SIMS. Normalization methods have been developed to minimize these concentration-independent variations and produce a SIMS signal whose intensity is proportional to the concentration of the analyte. The different approaches that have been used to normalize TOF-SIMS and NanoSIMS data are described in the following section.

2.1. Comparison of TOF-SIMS and high-resolution magnetic sector SIMS TOF-SIMS and high-resolution magnetic sector SIMS instruments differ in the configurations of their ion optics, primary ion sources, and mass analyzers (Table 1). These differences impart each approach with complementary strengths and weaknesses. In the following sections, these differences are discussed in the context of lipid imaging capabilities.

2.1.1. TOF-SIMS TOF-SIMS instruments typically employ a pulsed primary ion beam that is oriented with oblique incidence to the plane of the sample [31]. The secondary ions that are ejected from the sample are then collected by a TOF mass analyzer that generates a mass spectrum at each pixel. The spectra can have a mass range of 1 to 1500 m/z, but the ion counts generally decrease with increasing mass due to fragmentation. Variations in the secondary ion signal intensity that are caused by matrix effects and sample topography can be reduced, but not eliminated, by normalizing the counts of the signal of interest to that of an abundant ion or all of the ions detected at the same pixel [44,45]. The collection of a mass spectrum at every pixel is advantageous because it negates the need for labels, as any unlabeled molecule that produces secondary ions with distinctive m/z can be detected and imaged. Other advantages are that multiple components of interest can be imaged in parallel, and the spectra contain information about both known and unknown components that are present in the sample. The lateral resolution of any SIMS technique is ultimately limited by the diameter of the primary ion beam, which can be 200 nm or less, depending on the instrument. However, for imaging lipids with TOF-SIMS, the working lateral resolution is often larger than the beam diameter because the numbers of lipid-specific secondary ions detected at each beam position are insufficient for resolving features in an image [46]. Therefore, TOF-SIMS analyses must be optimized to increase the ejection of secondary ions that are characteristic of each lipid species of interest. Intact molecular ion species are most useful for this purpose. To maximize the detection of intact molecular ions and reduce the ejection of fragment ions from the sample, the amount of chemical damage at the surface of the sample must be minimized. Note that the term chemical damage is used to refer to molecular fragmentation, and does not refer to structural reorganization at length scales that are accessible to detection with SIMS. A common approach for minimizing molecular fragmentation is to limit the primary ion dose so that each primary ion impacts a pristine region on the sample, and not a region where the molecules were already fragmented by collision with a primary ion [47,48]. This is referred to as static SIMS analysis, where the static limit is typically estimated to be below 1013 ions/cm2 [48,49]. Even when TOF-SIMS was performed under static conditions, molecular fragmentation can limit the selectivity and working lateral resolution of lipid imaging. When this occurs, fragment ions that are common to multiple species from a common lipid class, such as the phosphocholine headgroup-containing ions at m/z 184 [C5H15NPO4]+ and 224 [C8H19NPO4]+, can be detected to achieve a working lateral resolution ≤1 μm [45,50,51]. However, lipids from the same class cannot be discriminated by these fragment ions. Several strategies have been developed to increase the detection of secondary ion signals that are useful for lipid identification. The most effective approach to date is to

Table 1 Comparison of SIMS approaches. TOF-SIMS

NanoSIMS

Lateral resolution Information Typical strategy for lipid identification

≥500 nm Molecular m/z of ions is characteristic to the component of interest

Primary ion source Mass analyzer Secondary ion size Number of collected ions Requires a priori selection of target component(s)? Unique capabilities

Cluster ion sources (C60+, Bi3+, Au3+, SF5+) TOF Molecular & high mass fragments Entire spectra No Identification of unknown lipid structures at specific locations in tissues; imaging unlabeled lipids with moderate (≥1 μm) lateral resolution Low yields of intact molecular ions, interpretation of the mass spectra

≥100 nm Elemental & isotopic Isotope labeling is used to encode the lipid species of interest with a distinctive isotope signature Cs or oxygen Magnetic sector Monotomic & diatomic 5–7 different m/z ratios Yes Imaging known lipids of interest within model or cellular membranes with high (~100 nm) lateral resolution Selective incorporation of distinct isotopes or elements into specific lipid species

Major challenge

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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use a cluster ion source instead of atomic primary ions. Cluster ion sources, such as C60+, Bi3+, Au3+, and SF5+, enhance the sputter yields of intact molecular ions and reduce the amount of chemical damage on the surface of biological samples [52–56]. These sources have enabled using molecular ions to image the distributions of specific lipid species in tissues [57,58]. Modeling performed by Barbara Garrison and coworkers has provided an understanding of why cluster ion projectiles produce less chemical damage than atomic ions [54,59–61]. Their simulations show that large cluster ion projectiles deposit their energy closer to the sample's surface, which promotes the ejection of intact molecular ions and reduces the accumulation of fragmented molecular species on the surface of the sample [54,59–62]. The reduction in the amount of fragmented molecules at the site of primary ion impact should permit the collection of intact molecular ions after the static limit has been exceeded, enabling depth profiling experiments in which molecular ions and lipid-specific fragment ions are monitored as a function of sputtering depth [55,63–65]. The detection of component-specific secondary ions can also be increased by changing the way in which the sample is prepared for analysis. MALDI matrices can be applied to the sample to increase the efficiency of ionizing the target molecules [66–69]. Though the precise mechanism is not known, the matrix is thought to indirectly transfer the energy from the primary ion beam to the target molecules, promoting their desorption and ionization over fragmentation [69]. Similar to MALDI, different matrix molecules enhance the ionization efficiency of different types of biomolecules during matrix-enhanced SIMS (ME-SIMS). For example, the common MALDI matrix, 2,5dihydroxybenzoic acid, enhances the detection of nearly intact cholesterol ions at m/z 385 [M − H] + and 369 [M + H − H 2 O] + , but decreases the intensity of the phosphocholine ions at m/z 184

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[67]. Ionic liquid matrices derived from the common MALDI matrix, α-cyano-4-hydroxycinnamic acid, enhance the TOF-SIMS detection of molecular ions from common lipid species, including 1,2-dipalmitoylsn-glycero-3-phosphocholine (DPPC), 1,2-dipalmitoyl-sn-glycero-3phosphoethanolamine (DPPE), and cholesterol [68]. The disadvantage of ME-SIMS is that the application of the matrix may redistribute the molecules on the sample surface over distances that can be detected with SIMS. The detection of component-specific secondary ions by TOF-SIMS has also been improved by coating the sample with a thin layer of metal [39,70–72]. These thin (few nm) metal coatings are produced with the commercial sputter coating systems that are used to prepare organic samples for scanning electron microscopy (SEM). The resulting metal coating enhances the detection of intact molecular ions from lipids without altering the distribution of the lipids on the sample surface [70]. An alternative approach to improve the specificity, sensitivity, and working lateral resolution of imaging lipids with TOF-SIMS is to use multivariate analysis to exploit the nonspecific, low mass fragment ions that are abundant in TOF-SIMS spectra for lipid identification [73–76]. Multivariate analysis techniques are statistical methods that enable distinguishing the spectra of different molecules in the absence of component-specific mass peaks [75–81]. Many multivariate analysis techniques exist, but principal component analysis (PCA) is the most common approach applied to TOF-SIMS data [82–84]. PCA of TOFSIMS data is used to compute a small number of new variables, which are combinations of multiple TOF-SIMS peaks that distinguish the different samples [82,83,85–88]. Similarities and differences between the spectra from different samples are visualized by plotting the samples on a graph, called a scores plot, in which the new variables form the axes. PCA of TOF-SIMS data has enabled discriminating, imaging, and

Fig. 1. Images of a phase-separated supported lipid membrane consisting of 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC) and 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC). A) TOF-SIMS positive ion images of a hydrocarbon chain fragment (m/z 69, [C5H9]+), dodecanoic acid fragment (m/z 183, [C12H12O]+), and phosphocholine fragment (m/z 184, [C5H15NPO4]+). Images were acquired of a 85 μm × 85 μm area with 256 × 256 pixels, downbinned to 128 pixels × 128 pixels, and cropped to show a 65 μm × 65 μm area of the membrane. B) PC1 scores images generated by PCA of the downbinned TOF-SIMS image of the same membrane location. C) The AFM image of the same membrane location shows gel- and fluid-phase domains that are enriched with DSPC and DLPC, respectively. Reprinted with permission from Anal. Chem. (2010) 10006–10014. Copyright 2010 American Chemical Society.

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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identifying various lipids in model lipid membranes [73–76,89]. Vaezian et al. demonstrated that PCA of TOF-SIMS images enables identifying different phosphatidylcholine species that produce identical small fragment ions; the distributions of two different phosphatidylcholines within phase-separated supported lipid membranes could also be imaged with higher contrast and specificity than that in the individual TOFSIMS ion images (Fig. 1) [75]. Multivariate analysis of TOF-SIMS data also enables detecting and visualizing differences in the lipid composition within tissues [58,90,91]. Multivariate analysis techniques have also been used to overcome the challenge of separating the ion intensity variations that are due to the local abundance of the parent molecule from those caused by concentration-independent factors (e.g., topography and matrix effects). Specifically, PCA and correlation coefficient mapping remove the ion intensity variations in TOF-SIMS images that are caused by sample topography [41,42,92]. In addition, the amounts of target biomolecules within samples can be quantified by employing partial least squares regression (PLSR) models constructed using TOF-SIMS data from calibration samples [78,80,93,94]. Our own lab has used this approach to quantify the molar percentage of cholesterol at small regions within supported lipid membranes [94]. Of course, a major limitation of such supervised techniques is their requirement for calibration samples, which presently limits their applicability to well-defined model systems. Given the current capabilities of TOF-SIMS, this approach is most advantageous for detecting, identifying, and imaging multiple unlabeled lipids, including those with unknown structures, within tissues. TOFSIMS imaging of lipids in individual cells is possible, though detection is now typically limited to the most abundant lipid species and a lateral resolution ≥1 μm. Ongoing efforts to optimize and integrate cluster ion sources, sample preparation, and data analyses are expected to further increase the working lateral resolution, sensitivity, and specificity of imaging lipids with TOF-SIMS. 2.1.2. High-resolution magnetic sector SIMS performed with a Cameca NanoSIMS 50 An alternative strategy to overcoming the low yields of componentspecific secondary ions that limit the working lateral resolution is to exploit the higher-yielding monoatomic and diatomic ions for component detection. This strategy is implemented in a commercial magnetic sector SIMS instrument, the Cameca NanoSIMS 50(L). SIMS performed with this instrument is often referred to as high-resolution SIMS or NanoSIMS. The NanoSIMS differs from conventional TOF-SIMS instruments in a few ways. First, it reveals the elemental and isotopic compositions in a sample by detecting monoatomic and diatomic secondary ions, which have higher yields than the molecular ions and high mass fragment ions used for TOF-SIMS analysis. Second, the NanoSIMS employs coaxial optics to direct the primary and secondary ions, which must have opposite polarities. The advantage of the co-axial configuration is it enables a shorter working distance, which simultaneously enhances focusing the reactive primary ions (Cs+ or O−) onto the sample and extracting the resulting secondary ions (Fig. 2). Instead of acquiring a complete mass spectrum, monoatomic and diatomic secondary ions with up to five or seven (depending on the NanoSIMS model) different m/z ratios are detected in parallel by a magnetic sector mass spectrometer that can differentiate isobars of the same nominal mass (i.e., 12C15N−, 26.9996 amu; and 13C14N−, 27.0059 amu can be separated). The intensities of the ions detected at each beam position are used to construct a map of the elemental and isotopic compositions of the sample's surface. The microcesium primary ion source developed by Cameca enables imaging negative secondary ions with a lateral resolution as good as 30 nm [95]. However, the working lateral resolution achieved for imaging lipids in membranes is typically around 100 nm due to the relatively low secondary ion counts obtained when biological samples are analyzed with small diameter primary ion beams with low currents.

Fig. 2. Schematic of the Cameca NanoSIMS 50. An oxygen or cesium primary ion beam is focused onto the sample. Positive or negative secondary ions, respectively, are collected by coaxial optics. Monoatomic and diatomic secondary ions with up to five different m/z ratios can be collected in parallel by the magnetic sector. Reproduced with permission from Annu. Rev. Biophys. 38 (2009) 53–74.

Because monoatomic and diatomic secondary ions are collected by the NanoSIMS, the molecules of interest must contain a distinct element or isotope so that they produce elementally or isotopically distinct secondary ions that can be used for component identification. To detect and image lipids, labeling with distinct stable isotopes is advantageous because the isotope-labeled lipid has the same chemical structure as the unlabeled native lipid molecule. This helps to ensure that the labeling will not alter the molecular interactions or intracellular trafficking that may contribute to lipid distribution in the sample. For studies of model lipid membranes, lipids that contain stable isotope labels can be obtained from commercial sources or synthesized with chemical methods. To image specific lipid species within cellular membranes, metabolic labeling must be used to selectively incorporate distinct stable isotopes into the lipid species of interest within living cells. This process involves culturing the cells in the presence of isotope-labeled precursors that are of sufficient size and complexity to promote their biosynthetic incorporation into primarily the lipid species of interest [96]. The cells should be cultured on conductive (i.e., metal) or semiconductive (silicon) substrates that help prevent sample charging

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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during analysis with a NanoSIMS instrument [96]. Metabolic labeling strategies for the selective incorporation of distinct isotopes into various lipid species in mammalian cells have been reported [97–104]. Metabolic labeling must be optimized for the cell line of interest to ensure that a high fraction of the lipid species of interest contains the distinctive isotope label, but little isotope incorporation into other lipid species occurs. A metabolic labeling protocol that has been optimized for imaging sphingolipids with respect to all membrane lipids in the membranes of fibroblasts was recently reported [96]. When developing a stable isotope labeling strategy, the selection of the distinct stable isotope deserves careful consideration. The highest lateral resolution can be achieved with the cesium primary ion beam, which necessitates the collection of negative secondary ions. Of the negative secondary ions that are produced by lipids, CN− and O− have the highest ionization efficiencies, and thus are most desirable for detection. Although lipids contain numerous carbon and hydrogen atoms, the much lower ionization probabilities of CH− and C− have limited the sensitivity of imaging lipids labeled with carbon-13 or deuterium. However, the detection of C− 2 , which has a higher ionization efficiency of either CH− or C−, is expected to improve the sensitivity of imaging 13 C-labeled lipids. Because matrix effects and sample topography influence the intensities of ions that differ only in isotopic composition (i.e., 15N12C−, 14N13C− and 14N12C−) to relatively the same extent, a signal that is proportional to concentration can be obtained by ratioing the isotopically distinct secondary ions to the corresponding naturally abundant ions detected at the same pixel [96,105,106]. NanoSIMS instruments are optimized for imaging with at high primary ion doses (N1015 ions/cm2) that erode the sample surface. This mode of operation, which is called dynamic SIMS, enables the rapid acquisition of depth profiles. However, for the analysis of lipid distribution in the plasma membrane, the vast majority of secondary ions that are collected from the surface of the cell must be produced by the cell membrane, and few secondary ions should be collected from the underlying cytoplasm. This necessitates restricting the sputtering depth so it is less than the thicknesses of the plasma membrane (7.5 nm [107]). Sputtering depth is a function of the primary ion dose and the sputtering rate, which ranges from 0.9 nm·μm2/pA·s to 2.5 nm·μm2/pA·s for biological samples [96,108]. Therefore, NanoSIMS analysis is restricted to the plasma membrane by limiting the primary ion dose. A series of thorough control experiments have confirmed that this approach enables imaging the lipids in the plasma membrane without detecting intracellular membrane components [106]. The lateral resolution, sensitivity, and need for elemental or distinctive isotope-labels for lipid identification render NanoSIMS most advantageous for imaging a small number of known lipid targets that can be metabolically or chemically labeled with stable isotopes, and high (~ 100 nm) lateral resolution is required. Its primary drawbacks are the need for isotope labels, only a limited number of lipid species can be imaged in parallel, and the cost of the NanoSIMS 50(L) instrument limits its availability. 3. Preparation of lipid samples for SIMS analysis Heterogeneity in the lipid distribution within the cellular plasma membrane is widely believed to be essential for cell function, but the source of this organization is poorly understood. SIMS provides chemically specific compositional information that complements the biophysical and structural data that can be acquired with atomic force microscopy (AFM) and fluorescence microscopy. Of course, the ability to image the distributions of various lipid species within the membranes of actual cells using SIMS is highly desired because this capability would enable evaluating the numerous hypotheses that state lipids are heterogeneously distributed in the plasma membrane. However, model lipid membranes have also been the subject of many SIMS studies, not only because they provide a controllable platform for technique development, but also because characterizing lipid demixing within model

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lipid membranes provides valuable insight into the lipid–lipid interactions that may contribute to biological membrane organization [109–111]. Supported lipid bilayers are formed by fusing vesicles onto a suitable substrate, such as glass or oxidized silicon. The resulting membranes are only stable when they are bathed in aqueous solutions, and they delaminate from the surface when removed from water. Therefore, methods to prepare supported lipid membranes for SIMS analysis often involve the use of flash freezing and freeze-drying to remove the water without altering the lipid distribution [112,113]. To permit visually assessing the quality of the membrane before and after sample dehydration, a small amount of fluorescent lipid may be added to the sample. The use of patterned supported lipid bilayers facilitates assessing the membrane quality after freeze-drying, and then re-analyzing the well-preserved regions with SIMS. For substrate fabrication, a material that does not support lipid bilayer formation is deposited onto the substrate so it forms a grid. Metals, photoresist, and microcontact printed proteins can serve as the barrier material [113]. Experimentally we have found that silicon wafers with a 10-nm-thick oxide layer support lipid bilayer formation and prevent charge buildup during SIMS analysis; sample charging reduces the ion yields and working lateral resolution [75, 112]. When vesicles are fused onto the patterned support, lipid bilayers only form on the exposed substrate, resulting in an array of isolated lipid membrane patches. Next, these lipid bilayers are flash frozen by plunging them into liquid ethane, and then the water is sublimed from the sample under vacuum using a freeze-drying apparatus [112]. A method in which the lipid membranes are flash frozen and imaged while in a cryogenic state has proven to enhance the secondary ion yields obtained with TOF-SIMS [49]. Whether frozen-hydrated samples enhance the detection of lipid-specific secondary ions by a NanoSIMS 50 has not been investigated, as existing NanoSIMS instruments are not equipped with cold stages. For the analysis of cells with SIMS, the cells are often chemically fixed with a method that is analogous to cell preparation for scanning electron microscopy (SEM) imaging. The gold standard for chemical fixation involves irreversibly crosslinking the proteins with glutaraldehyde, crosslinking the lipids with osmium tetroxide, and then airdrying the samples [114]. We have experimentally demonstrated that fixation method does not alter the lipid distribution in the membranes of mammalian cells [106]. For most cell studies involving NanoSIMS imaging [96,106,115] and some involving TOF-SIMS [39,70–72], the fixed cells are coated with a thin (3-nm) metal layer to increase the yield of useful secondary ions that are detected during analysis. Noteworthy, these metal layers do not alter the lipid distribution on the surface of the cell [70]. Prior studies have compared the cell morphologies and TOF-SIMS image quality of cells that were chemically fixed with only glutaraldehyde followed by freeze-drying, and those that were cryofixed and freeze-dried [116]. SEM imaging showed that the fine cell surface structures were better maintained by glutaraldehyde fixation than by cryofixation followed by freeze-drying [116]. TOF-SIMS imaging performed with Bi3+ cluster ions showed no differences in the lipid distributions on the glutaraldehyde-fixed and cryofixed cells [116]. In comparison, glutaraldehyde fixation followed by alcohol dehydration leads to a loss of cell surface lipids, which could be reduced by including an osmium tetroxide fixation step after glutaraldehyde fixation [116]. While chemical fixation with glutaraldehyde and osmium tetroxide is ideal for the detection of small molecular fragments with a NanoSIMS, the reactions between the fixatives and lipids may alter the masses of the lipid ions, thereby complicating lipid detection with TOF-SIMS. Indeed, the intensities of the lipid peaks are reduced in samples fixed with either formalin, which produces less stable crosslinks than glutaraldehyde [114], or osmium tetroxide [64,117]. However, whether the reduction in lipid signals is due to changes in the masses of the lipid ions or a reduction in ionization efficiency from matrix effects has not been thoroughly investigated. As an alternative to chemical fixation, a

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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freeze-fracture methodology that permits TOF-SIMS imaging of unfixed, frozen-hydrated membranes has been developed [118–120]. The TOFSIMS image quality and the distribution of biomolecules in cells fixed by freeze-drying and cryogenic preparation have been compared. The phosphocholine ion at m/z 184.1 was more prominent in the spectra acquired from frozen-hydrated cells than from cells fixed with only formalin and freeze-dried, demonstrating that cryogenic preservation improves lipid detection [64]. Depth profiling experiments revealed that the adenine signal at m/z 136.1 was localized to the nucleus of the frozen-hydrated cells, but distributed throughout the cell compartment in formalin-fixed freeze-dried cells [64]. This difference suggests that cryogenic preservation prevents chemical redistribution, but formalin fixation does not. This finding is consistent with previous reports that formalin does not efficiently crosslink and stabilize nucleic acids [121,122]. However, the possibility that ionization of adenine was enhanced by the chemical environment created by fixation (a matrix effect) cannot be excluded because adenine-containing molecules, such as ATP and RNA, are located outside of the nuclei of mammalian cells. SIMS analyses of hydrated, unfrozen lipid samples may soon be feasible, which would not only simplify sample preparation, but would also help ensure that native component distributions are observed. Yang et al. reported the development of a microfluidic device that permits researchers to analyze hydrated samples with SIMS [123]. In this device, tiny volumes of aqueous solution are pumped through a microchannel that is covered by a silicon nitride film [123]. A small (2–3 μm) hole is sputtered through the silicon nitride film [123], which permits analyzing the hydrated sample with the primary ion beam and collecting the secondary ions while keeping evaporation, sample cooling and vacuum pressure at acceptable levels [124]. Future efforts directed at optimizing this device for the analysis of hydrated biological samples at ambient temperatures with SIMS may ultimately enable acquiring SIMS images of lipids under more physiologically relevant conditions. 4. Imaging lipid distributions with SIMS

and not from hydrogen bonding between the two molecules [50]. This result was especially enlightening because hydrogen bonding between the sphingoid base portion of sphingomyelin and the hydroxyl on cholesterol was widely expected to drive the formation of functionally distinct cholesterol- and sphingolipid-enriched domains within the plasma membranes of mammalian cells [129,130]. Johnston and co-workers combined TOF-SIMS with AFM to further probe the formation of cholesterol- and sphingolipid-enriched domains using four-component model membranes [131]. Their study showed that the addition of ceramide to membranes composed of sphingomyelin, cholesterol, and an unsaturated PC led to the formation of ceramide-rich subdomains within the sphingomyelin- and cholesterol-enriched domains [131]. Though this summary is far from comprehensive, these examples demonstrate that new insight into the molecular interactions that drive lipid organization in membranes can be acquired by imaging of the lipid distribution in model membranes with TOF-SIMS. 4.1.2. TOF-SIMS imaging of lipid distribution in individual cells TOF-SIMS studies of lipid distribution in the plasma membranes of individual cells have been extremely challenging because lipid fragmentation has limited the specificity and working lateral resolution obtained. Nonetheless, in a groundbreaking report published 10 years ago, Winograd, Ewing and coworkers demonstrated that new insight into lipid biology could be obtained from TOF-SIMS analysis of individual cells [45]. By imaging the ions produced by all lipids (m/z 69, [C5H9]+), PC (m/z 184, [C5H15NPO4]+), and 2-aminoethylphosphonolipid (2-AEP, m/z 126, [C2H9NPO3]+) on the surfaces of mating Tetrahymena thermophila, they showed that 2-AEP accumulated and PC decreased at the region between the mating Tetrahymena [45]. In a subsequent study in which the Tetrahymena were imaged at various times during mating, they discovered that structural changes in the membrane induced the decrease in PC detected at the site of pore formation between mating Tetrahymena (Fig. 3) [44]. Progress has been made in extracting quantitative information on lipid abundance in cell membranes from TOF-SIMS images, and

Here, the application of TOF-SIMS and NanoSIMS to imaging lipids in membranes will be discussed, where emphasis is placed on recent studies of lipids using NanoSIMS. Because lipid analysis with TOF-SIMS is the subject of recent reviews [24,31,48], following TOF-SIMS section is not intended to be a comprehensive account of all lipid studies performed with TOF-SIMS. Instead, the aim of the following TOF-SIMS section is to highlight the research that demonstrates the new insight into lipid biochemistry that can be acquired with TOF-SIMS. 4.1. TOF-SIMS studies of lipids 4.1.1. TOF-SIMS studies of lipid distribution in model membranes Winograd and co-workers pioneered the application of TOF-SIMS to lipid analysis. By employing model lipid membranes, they first demonstrated the feasibility of lipid detection, and then used these membranes to acquire an understanding of the molecular interactions that affect lipid domain formation [40,50,51,56,125–128]. Fragment ions with low specificity were often used for lipid identification, especially before the implementation of cluster primary ion beams. By using the phosphocholine ion (m/z 184, [C5H15NPO4]+), a tail group fragment ion (m/z 552, [C35H67O4]+) produced by both dipalmitoylphosphatidylcholine (DPPC) and dipalmitoylphosphatidylethanolamine (DPPE), and cholesterol ions (m/z 385 [M − H]+ and 369 [M + H–H2O]+) to image DPPC/DPPE/ cholesterol membranes, they discovered that DPPE induces the formation of cholesterol- and PC-rich domains [128]. In a subsequent study, Zheng et al. used TOF-SIMS to image model membranes composed of cholesterol, and sphingomyelin, and different PC species [50]. Their studies revealed that the formation of cholesterol- and sphingomyelin-enriched domains in the presence of an unsaturated PC was due to favorable interactions between cholesterol and the saturated sphingomyelin tail groups,

Fig. 3. A) Image of a pair of mating Tetrahymena thermophila acquired with differential inference contrast microscopy. TOF-SIMS images show the intensities of ions produced by B) all lipids (m/z 69), C) PC (m/z 184), and D) 2-AEP (m/z 126). Scale bar is 25 μm. Reproduced with permission from Proc. Natl. Acad. Sci. U.S.A. 107 (2010) 2751–2756. Copyright 2010 National Academy of Sciences, USA.

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

M.L. Kraft, H.A. Klitzing / Biochimica et Biophysica Acta xxx (2014) xxx–xxx

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Fig. 4. NanoSIMS images of a freeze-dried supported membrane composed of 19F-GM1, 13C-labeled cholesterol, 2H-sphingomyelin, and 15N-DOPC. Reprinted with permission from J. Am. Chem. Soc. 135 (2013) 5620–5630. Copyright 2010 American Chemical Society.

visualizing intracellular lipid distribution. Piehowski et al. developed a statistical approach to assess whether differences in the counts of the ion of interest that was detected at a small number of pixels are artifacts of counting statistics or if they signify differences in lipid composition [46]. This approach is especially useful for assessing heterogeneities in the lipid distribution within the plasma membrane because limited

signals are typically detected at small regions within membranes. Methods that permit determining the relative differences in the abundance of cholesterol and lipids between different cell populations have also been developed [132,133]. Lanekoff et al. demonstrated the ability to quantify the relative uptake of isotope-labeled lipids from the culture medium with this approach [133]. Finally, Fletcher and co-workers have

Fig. 5. A) 12C14N− and 12C15N− ion images and the corresponding 12C14N−/12C15N− image acquired with a NanoSIMS 50 of an unlabeled lipid membrane treated with a 15N-labeled model antimicrobial peptide. B) 2C14N−/12C15N− image of a lipid membrane treated with the 15N-labeled model antimicrobial peptide. The color scale encodes for enrichment, where blue and red correspond to terrestrial abundance (0.37%) and 40%, which is more than 100 times higher than terrestrial abundance. C) 12C14N− and 12C15N− ion images and the corresponding 12 14 − 12 15 − C N / C N image acquired with a NanoSIMS 50 of an unlabeled lipid membrane in the absence of the 15N-labeled peptide. Reproduced with permission from Proc. Natl. Acad. Sci. U.S.A. 110 (2013) 8918–8923. Copyright 2013 National Academy of Sciences, USA.

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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Fig. 6. A) SEM image of a mouse fibroblast cell. The outlined region shows the approximate location that was analyzed with a NanoSIMS 50 instrument. B) Mosaic of individual 15 N-enrichment images that were acquired with a NanoSIMS 50 shows the distribution of metabolically incorporated 15N-sphingolipids in the cell membrane. Sphingolipidenriched plasma membrane domains are visible. C) Mosaic of individual 18O-enrichment images that were acquired in parallel with the 15N-enrichment images shows that the metabolically incorporated 18O-cholesterol is evenly distributed in the plasma membrane, and is not enriched in the 15N-sphingolipid domains. Reproduced with permission from J. Biol. Chem. 288 (2013) 16855–16861.

shown that cluster primary ion beams enable mapping the threedimensional distributions of lipids and cholesterol within individual cells [53,55,63,134].

4.1.3. TOF-SIMS imaging of lipid distribution in tissues TOF-SIMS imaging with cluster primary ion beams has yielded much new insight into the distributions of various lipid species within tissues. Many studies have focused on elucidating the distributions of cholesterol and various lipid species, including sulfatides, galactosylceramide, and phosphatidylcholines within brain tissue [57,58,135–137]. TOFSIMS imaging of tissues is also emerging as a new tool for pathology studies [90,138–140] For example, Nygren and coworkers showed that atherosclerotic plaques have regions of elevated cholesterol and diacylglycerol at two distinct locations within the aortic wall [141]. Comparison of TOF-SIMS images of human striated muscle tissue sections from control subjects and those with Duchenne muscular dystrophy revealed very different lipid distributions and abundances in the dystrophic samples [139]. More recently, comparative TOF-SIMS

imaging studies have detected higher levels of cholesterol in portions of the cerebral cortex of Alzheimer disease patients [142]. Finally, a recent report in which TOF-SIMS imaging of mouse intestine tissue was used to study dietary fat absorption further demonstrates the power of integrating TOF-SIMS histology into fundamental biomedical research [140].

4.2. High-resolution SIMS imaging of lipids 4.2.1. High-resolution SIMS imaging of lipid distribution in model membranes Initial work on imaging lipids with NanoSIMS focused on supported lipid membranes, which enabled validating the results of the NanoSIMS imaging with complementary imaging techniques [112,143–146]. Early work by Boxer and coworkers established protocols to prepare supported lipid membranes for analysis under UHV, demonstrated that the NanoSIMS had sufficient sensitivity to image isotope-labeled lipids in membranes, and provided a method for quantifying the molar

Please cite this article as: M.L. Kraft, H.A. Klitzing, Imaging lipids with secondary ion mass spectrometry, Biochim. Biophys. Acta (2014), http:// dx.doi.org/10.1016/j.bbalip.2014.03.003

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percentage of lipids at discrete regions in model membranes [143,144]. In a landmark report, they demonstrated that the distributions of two different isotope-labeled lipids, 15N-dilaurolyphosphatidycholine (15NDLPC) and 13C18-distearoylphosphatiylcholine (13C18-DSPC), could be quantified and imaged within phase-separated supported lipid membranes with a lateral resolution of 100 nm using a NanoSIMS [144]. In subsequent studies, fundamental questions concerning lipid–lipid and lipid–protein interactions were addressed by characterizing supported lipid membranes with a NanoSIMS. Kraft, Weber, and coworkers investigated how cholesterol affected the microstructure and composition of lipid domains within phase-separated membranes composed of cholesterol and two saturated lipid species: 15N-DLPC and deuterated distearoylphosphatiylcholine (D70-DSPC). By using AFM to image membrane topography, and NanoSIMS to image the distributions of 15N-DLPC and D70-DSPC in phase-separated membranes that varied in cholesterol concentration, they deduced that cholesterol addition did not induce the formation of liquid-ordered domains [145]. This finding indicates that interactions between cholesterol and saturated lipids do not produce liquid-ordered domains in membranes [145]. A recent report by Boxer and coworkers used a NanoSIMS to investigate phase separation within model membranes composed of a monofluorinated GM1 ganglioside derivative (19F-GM1), 13C-labeled cholesterol, 2Hsphingomyelin, and 15N-dioleoylphosphatidylcholine (15N-DOPC) [146]. NanoSIMS imaging revealed micrometer-scale domains that were enriched with cholesterol and GM1, while the majority of the sphingomyelin was located in a DOPC-rich and cholesterol-deficient phase, though tiny sphingomyelin-rich subdomains were detected within the cholesterol- and GM1-enriched domains (Fig. 4) [146]. This study suggests that cholesterol-sphingolipid interactions are more complex than previously postulated [129,130]. Finally, Rakowska et al. used NanoSIMS imaging in conjunction with AFM to visualize the expansion of antimicrobial pores within a lipid membrane [147]. NanoSIMS imaging of an unlabeled lipid membrane treated with a 15N-labeled model antimicrobial peptide revealed that the peptide content was highest at the edges of the expanding pores (Fig. 5) [147]. Real-time AFM imaging of the peptide-induced changes in the lipid bilayer showed the formation of small pores that expanded until all of the lipids were completely removed from the substrate, suggesting that membrane poration is a process involving continuous peptide recruitment [147].

4.2.2. NanoSIMS imaging of lipid distribution in individual cells In the first application of NanoSIMS to image lipids within cells, Lechene and coworkers monitored the intracellular concentrations of free fatty acids (FFAs) across cell membranes [148]. By culturing adipocytes in the presence of 13C-labeled oleic acid and using NanoSIMS to assess the levels of 13C-FFA in the intracellular lipid droplets and cytoplasm, Kleinfeld et al. showed that a high level of 13C-FFA was taken up into the lipid droplets, and the FFAs in the cytosol and lipid droplets rapidly equilibriated [148]. After utilizing model membranes to investigate lipid–cholesterol interactions, the Kraft lab and coworkers pursued probing the hypothetical existence of cholesterol- and sphingolipid-enriched domains by using NanoSIMS to image lipids in intact plasma membranes [106,115]. By metabolically incorporating the 15N isotope into cellular sphingolipids and the 13C isotope into all cellular lipids, NanoSIMS imaging revealed the presence of 15N-sphingolipid domains within the plasma membrane [106]. Additional experiments revealed that depletion of cellular cholesterol did not eliminate the sphingolipid domains, but did alter their organization and abundance within the plasma membrane [106]. In a subsequent report, the Kraft lab used NanoSIMS to assess whether the 15 N-sphingolipid domains were enriched with cholesterol [115]. NanoSIMS imaging showed that the metabolically incorporated 18Ocholesterol was evenly distributed in the plasma membrane, and was not enriched within the 15N-sphingolipid domains (Fig. 6) [115]. This finding is in opposition to the longstanding lipid raft hypothesis [129,

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130], and supports alternative models in which the lipid organization in the plasma membrane is dependent on the cytoskeleton [149].

5. Conclusions Because lipids are cell membrane components that also serve as cell signaling molecules, knowledge of the distributions of various lipid species within individual cells and tissues will shed light on their roles in health and disease. TOF-SIMS and high-resolution SIMS performed on a NanoSIMS enable directly characterizing the distributions of lipid species of interest at a range of length scales. Given the complementary strengths of these two SIMS approaches, their integration into lipid research is expected to significantly increase our understanding of the functions of lipids in health and disease.

Acknowledgements The authors thank Peter Weber for the comments and advice. This work was supported by a grant from the U.S. National Science Foundation (CHE-1058809).

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