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Micrometric molecular histology of lipids by mass spectrometry imaging
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Micrometric molecular histology of lipids by mass spectrometry imaging David Touboul1, Olivier Lapre´vote1,2 and Alain Brunelle1 Time-Of-Flight Secondary Ion Mass Spectrometry is compared to other mass spectrometry imaging techniques, and recent improvements of the experimental methods, driven by biological and biomedical applications, are described and discussed. This review shows that this method that can be considered as a micrometric molecular histology is particularly efficient for obtaining images of various lipid species at the surface of a tissue sample, without sample preparation, and with a routine spatial resolution of 1 mm or less. Addresses 1 Centre de recherche de Gif, Institut de Chimie des Substances Naturelles, CNRS, avenue de la Terrasse, 91198 Gif-sur-Yvette, France 2 Chimie Toxicologie Analytique et Cellulaire, EA 4463, Faculte´ des Sciences Pharmaceutiques et Biologiques, Universite´ Paris Descartes, 4, avenue de l’Observatoire, 75006 Paris, France Corresponding author: Brunelle, Alain ([email protected])
Current Opinion in Chemical Biology 2011, 15:725–732 This review comes from a themed issue on Analytical Techniques Edited by Morgan Alexander and Ian Glimore Available online 24th May 2011 1367-5931/$ – see front matter # 2011 Elsevier Ltd. All rights reserved. DOI 10.1016/j.cbpa.2011.04.017
Introduction Various ex vivo biological imaging methods, such as immunohistochemistry, staining or autoradiography, have been developed to obtain information on the chemical composition at the surface of tissue sections. Among them, mass spectrometry imaging (MSI) is the only one allowing to locate and identify various chemical compounds without selection a priori of a chemical class or compound [1,2]. Since this method does not require targeting compounds before the analysis, it makes possible to draw anatomical images of any ion detected in the mass spectra in one single experiment. Lipids, which can be defined as fat-soluble molecules, have been for a long time considered only as energy storage and as major constituents of the cell membranes. It is now agreed that these classes of compounds are important cell signaling molecules, neurotransmitters and precursors in the regulation of various cellular functions [3]. The importance of lipids in biological sciences is illustrated by the recognition of ‘lipidomics’ as an emerging field among the www.sciencedirect.com
‘omics’. The eight lipid categories as defined by the Lipid Maps initiative [4], fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids and polyketides, sterol lipids and prenol lipids (see www.lipidmaps.org), can be analyzed with gas/liquid chromatography coupled to modern mass spectrometers, providing precise identification and quantification. Before their analysis, lipids have to be extracted from the sample, leading to the loss of the spatial information whereas MSI offers a direct sampling over the tissue surface [3,5].
Comparison with other mass spectrometry imaging methods Basically, in MSI, a focused beam of photons or energetic ions is used to scan over the surface of a tissue section. The size of this beam defines the size of the pixel that corresponds to each point on which a mass spectrum is recorded. The data acquisition consists in the creation of a volume whose dimensions are x and y, the two geometric dimensions of the sample, and m/z the mass-to-charge ratio of the secondary ions. Any slice of this volume along a given m/z value is an ion density map, the so-called ion image [6]. All mass spectrometry methods based on localized ionization processes at the sample surface can be theoretically used for mass spectrometry imaging. Table 1 summarizes the main characteristics of the MSI techniques, listed from left to right with increasing spatial resolution. At both ends are Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) and Nano-Secondary Ion Mass Spectrometry (Nano-SIMS), which are not applicable to molecular ion localization. These two desorption/ionization methods are too energetic to provide signals of intact lipid ions. Nevertheless, the first one is considered as a method of choice for the quantitative localization of trace metals with a resolution of 120 mm [7,8], while the latter is the best known method for submicrometer localization of elements and/or small fragment ions with a precision reaching 100 nm or less [9]. Desorption electrospray ionization (DESI) uses a pneumatically assisted electrospray source to desorb the analytes from the sample surface. DESI is very sensitive for low molecular weight compounds like lipids. It works on samples at atmospheric pressure, but is limited with the current state-of-the art to a spatial resolution well above 100 mm [10]. Matrix-Assisted Laser Desorption Ionization (MALDI) is by far the most popular mass spectrometry imaging method [2,11]. The sample requires being preliminary coated with a matrix, and depending on the proper choice of the latter, any class Current Opinion in Chemical Biology 2011, 15:725–732
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Table 1 Comparison of some performances and characteristics for tissue mass spectrometry imaging, between MALDI-TOF/TOF, LA-ICP-MS, TOF-SIMS and NANO-SIMS LA-ICP-MS Ultimate spatial resolution Sample preparation Mass range Accessible compounds Mass analyzer
DESI
MALDI
TOF-SIMS
NANO-SIMS
50–200 mm
100–200 mm
5–50 mm
400 nm–2 mm
50–150 nm
Dehydrated, not fixed, no matrix m/z < 250 Metals
No preparation, analysis at atmospheric pressure m/z 1000 Lipids, natural products
Quadrupole, TOF
Q-TOF, FT (Orbitrap#)
Dehydrated, homogeneous matrix coating m/z > 200 Lipids, peptides, proteins, drugs, metabolites,. . . TOF/TOF, Q-TOF, FT (Orbitrap#), ion mobility coupled to Q-TOF, QqQ
Dehydrated, not fixed, no matrix m/z 1500 Lipids, drugs, metabolites, elements,. . . TOF
Fixed and dehydrated m/z < 250 Elements, fragments Magnetic
Q: quadrupole, FT: Fourier Transform, TOF: Time-Of-Flight.
of compound can be mapped at the surface of a tissue section. Initially developed to produce images of peptides and proteins [12,13], it is now more and more applied to lipid imaging [14]. MALDI can be coupled with various mass analyzers such as tandem time-of-flight [15] or high resolution mass spectrometers [16]. The spatial resolution is usually limited to 50 mm or eventually improved to 5–10 mm with special experimental conditions [16]. By contrast, Time-Of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS) enables both a high spatial resolution (400 nm–2 mm) and the analysis of intact molecular ions [5,17] (Figure 1). Until early 2000, this surface analysis method was limited to the semi-conductor industry and material analysis [18] for the detection of low molecular weight fragments of
Figure 1
(a) Tissue section
(b)
(d)
Mass spectrometry images
Tissue analysis (c) Mass spectrum Reflectron
Primary ion source Bi3+
Time of flight Detector
Electron floodgun Sample stage Current Opinion in Chemical Biology
Schematic workflow of TOF-SIMS tissue imaging. Current Opinion in Chemical Biology 2011, 15:725–732
organic compounds [19]. The low efficiency of the primary ion sources, like gallium ones, to desorb and ionize intact molecular ions, was accompanied by extensive in-source fragmentation. The situation dramatically changed with the advent of polyatomic ion sources (Aun, Bin, C60) [20–22]. The enhancement of the secondary ion emission, together with a limited surface damage, provides an increased efficiency, enabling sufficient sensitivity to make ion images of lipids with enough intensity for micrometer pixel sizes [23,24]. With the current stateof-the-art, bismuth cluster ion sources are those enabling the best sensitivity at a resolution of 1 mm, or less [25]. The respective capabilities of TOF-SIMS and MALDITOF have recently been compared for rat brain tissue sections. While the first enables the best spatial resolution, the latter allows in situ structural analyses by tandem mass spectrometry together with sensitivity estimated to be in the nanomolar range [26]. MALDI and TOF-SIMS were also correlated onto the same tissue sample in order to get complementary information [27,28]. Matrix-Enhanced (ME-) and Metal-Assisted (MetA-) SIMS are alternative methods which use matrices or metal coatings to enhance the secondary ion emission. The enhancement in the positive ion mode is equivalent to the one obtained with cluster primary ions, while no clear conclusion could be drawn in the negative ion mode [29]. The deposition of a matrix may degrade the spatial resolution and the enhancement also depends on the class of compounds. These methods remain rarely used compared to TOF-SIMS using clusters, which can be used advantageously with both secondary ion polarities to obtain complementary information. Recent studies have demonstrated that, during the preparation of the tissue sample, lipids migrate towards the sample surface between the cutting step at 20 8C and the analysis performed under vacuum at room temperature [30,31]. Most of the lipids are thus concentrated in the top 200–300 nm layers from which the secondary organic ions are emitted by impact of cluster primary www.sciencedirect.com
Micrometric molecular histology of lipids Touboul, Lapre´vote and Brunelle 727
ions. This migration was not found to be accompanied to lateral delocalization.
Experimental methods When indicated, intensity scales of ion images correspond to a dynamic range. Relative quantification of compounds
showing similar physical and chemical properties (which means detected and desorbed with the same efficiencies) between two different areas over the same sample surface and scanned under the same experimental conditions is consequently possible. The mass spectra and the peak areas extracted from the different histological regions of
Figure 2
(a)
(b)
R: Cholesteryl Surfate: B: C16:1 fatty acid G: C180:0 fatty acid
(c)
2 clusters
(d)
3 clusters
(e)
4 clusters
(f)
5 clusters
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TOF-SIMS images of mouse colonic mucosa model of cystic fibrosis. (a) Three color overlay showing the localization of the following negative ions: red, cholesteryl sulfate, m/z 465.4; blue, palmitoleate (C16:1), m/z 253.2, and green, stearate (C18:0), m/z 283.2. (b) Principal component (PC) analysis of the same data. Overlay of the score images of PC1, PC2 and PC3. The color scale represents the contribution for each pixel on the selected principal axes. Red, green and blue indicate a strong positive contribution of PC1, PC2 and PC3 respectively. (c–f) Partitioning clustering of the same data as (a). Distribution of partitioning clusters. Partitioning clustering was performed using the K-mean algorithm. Images correspond to partitioning into two (c), three (d), four (e) and five (f) clusters. The dark blue area corresponds to pixels excluded from the analysis.This research was originally published in J Lipid Res Reference [40] # the American Society for Biochemistry and Molecular Biology. www.sciencedirect.com
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the sample, usually called Regions of Interest (ROI), need only to be normalized towards the same primary ion counts, or to the same area. This approach can be extended to different samples, that is, tissues from control and patient groups, given that all the data have been acquired under the same experimental conditions [32,33–35]. The so-called ‘matrix effects’, which are so often feared, and which are enhancements or decreases of some secondary ion yields owing to the chemical micro-environment of a compound [36], does not matter too much as long as one compares healthy versus non-healthy samples, for which the tissue environment, or matrix, can be assumed to be similar between one sample to the next [32,37]. A simple method to analyze TOF-SIMS imaging data is the following: several intense ion peaks are first selected to draw their corresponding images, which gives characteristic histological areas of the sample. Then these areas are selected and normalized spectra and ion peak areas are extracted from the corresponding ROIs. In a second step images from ions differentiated from the ROIs can be reconstructed, giving a deeper insight of the molecular histology of the sample surface. The analysis of huge amounts of data can rapidly become an insurmountable task, thus making multivariate analyses mandatory. A lot of efforts have been made in the recent years by several groups to implement these methods, like Principal Com-
ponent Analysis (PCA) and Partitioning Clustering (PC), to TOF-SIMS imaging [38,39]. The authors usually correlate groups of ions from various chemical classes to the sample histology. A recent study made by Brulet et al. [40] with sections of colon from mice model of the cystic fibrosis indicated that these methods can help distinguishing the different histological areas of the sample. Although the comparison between spectra from control and diseased samples did not lead to evident different chemical compositions, groups of ions corresponding to different lipid species and different histological areas were distinguished thanks to multivariate analyses (Figure 2). Nevertheless, some important difficulties remain which limit for the moment an easy use of multivariate analyses. First, a mass spectrometry imaging dataset is usually a very big file of several gigabytes with proprietary file formats, or even several tens of gigabytes with public formats. Handling and processing such big files need computers with fast processors and subsequent RAM. Nevertheless, if it is sometimes useful to bin the dataset for rapid pre-processing, this must be done carefully because it is at the expense of the spatial and/or mass precision. The second difficulty lies in the fact that data may be difficult to export from proprietary file formats to be processed with other software. The common and free format imzml initiated by the COMPUTIS European consortium (www.imzml.org) needs to be mentioned
Figure 3
(a)
(b) 100 μm
Collagen (1300-1180cm-1)
(c)
Ester (1780-1710cm-1)
Max
Min (d)
100 μm
16 (f)
(e)
SM fragments
0
14
PI
0
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Combining synchrotron-FTIR and TOF-SIMS microspectroscopies. The histological serial tissue section of liver cirrhosis stained with HES is shown (a). Synchrotron-FTIR microspectroscopy experiments were performed on liver cirrhosis and the distribution of bands corresponding to collagen (b) or ester (c) were visualized. Mass spectrometry images using TOF-SIMS were subsequently recorded on the same tissue section. The video image is shown (d). This allowed imaging sphingomyelin fragments (e) or phosphatidylinositols (f). TOF-SIMS images recorded with 256 256 pixels (pixel size 2 mm). Intensity scale bars on the right of the TOF-SIMS images in counts. SM, sphingomyelines and PI, phosphoinositols.Reprinted with permission from Reference [41]. Copyright 2011 American Chemical Society. Current Opinion in Chemical Biology 2011, 15:725–732
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Micrometric molecular histology of lipids Touboul, Lapre´vote and Brunelle 729
since it is expected to become a universal file format for mass spectrometry imaging data in the near future. Although TOF-SIMS has already been widely utilized in combination with other spectroscopic methods, such as electron microscopy, atomic force microscopy or Raman spectroscopy, very few examples describe multi-spectroscopic approaches including TOF-SIMS on biological samples. Synchrotron-Fourier Transform-Infrared (FT-IR) and -ultraviolet (UV) imaging were recently used in combination with TOF-SIMS imaging by Petit et al. [41] to analyze human cirrhotic liver biopsies. The same sample was successively studied by the three different methods, thus avoiding small variations of the localization of species between adjacent tissue sections (Figure 3). FT-IR identified collagen enrichment in fibrosis whereas esterified species
were mostly distributed into the cirrhotic nodules. TOF-SIMS demonstrated that sphingomyeline and phosphoinositol were differentially distributed into the fibrosis areas or in the cirrhotic nodules. Spectra extracted from UV microscopy experiments allowed visualizing high autofluorescence from fibrous septa confirming the presence of collagen. Staining methods can in some cases be used together with TOF-SIMS imaging. Osmium tetroxide (OsO4), a commonly used stain for unsaturated lipids in electron and optical microscopy of cells and tissues, has been used in combination with TOF-SIMS imaging of lipids in mouse adipose tissue [42]. Contrary to unsaturated fatty acids C18 and diglycerides, unsaturated fatty acids C16 were not co-localized with osmium oxide. Moreover, significant matrix effect was clearly observed.
Figure 4
(a) 100 μm
(b)
100 μm
(c)
Cholesterol mc:16 tc:9.985e+4 (d)
100 μm
(e)
C14:0 FA mc:17 tc:1.285e+5 (i)
100 μm
Sum of DAG mc:179 tc:2.039e+6
100 μm
(f)
Sum of C16 FA mc:67 tc:1.501e+6 (j)
100 μm
DAG C30 mc:19 tc:1.539e+5
100 μm
Sum of TAG mc:7 tc:2.767e+4 (g)
C18:0 FA mc:39 tc:7.462e+5 (k)
100 μm
DAG C32 mc:50 tc:5.465e+5
100 μm
100 μm
C18:1 FA mc:41 tc:8.833e+5 (l)
100 μm
DAG C34 mc:79 tc:8.778e+5
100 μm
(h)
C18:2 FA mc:36 tc:5.338e+5 (m)
100 μm
DAG C36 mc:64 tc:4.609e+5 Current Opinion in Chemical Biology
Non-alcoholic fatty liver disease. TOF-SIMS imaging of a steatosis area. (a) Video image of the steatotic area. Ion images of several different positive and negative secondary ions from this area. (b) Cholesterol (positive ion mode), (c) sum of triacylglycerol ions (TAG, positive ion mode), (d) C14:0 fatty acid (FA) (negative ion mode), (e) sum of C16 fatty acid carboxylate ions (negative ion mode), (f) C18:0 FA (negative ion mode), (g) C18:1 FA (negative ion mode), (h) C18:2 FA (negative ion mode), (i) sum of diacylglycerol ions (DAG, positive ion mode), (j) sum of DAG with 30 carbon atoms (DAG C30, positive ion mode), (k) sum of DAG with 32 carbon atoms (DAG C32, positive ion mode), (l) sum of DAG with 34 carbon atoms (DAG C34, positive ion mode); (m) sum of DAG with 36 carbon atoms (DAG C36, positive ion mode). Color scale bars, with amplitude in number of counts, are indicated to the right of each ion image. The amplitude of the color scale corresponds to the maximum number of counts mc and could be read as [0, mc]. tc is the total number of counts recorded for the specified m/z (it is the sum of counts in all the pixels). Field of view: 500 mm 500 mm.Reprinted with permission from Reference [35]. Copyright 2011 American Chemical Society. www.sciencedirect.com
Current Opinion in Chemical Biology 2011, 15:725–732
730 Analytical Techniques
With the absence of a tandem or a high resolution mass analyzer, the identification of lipid species from the TOFSIMS spectra recorded at the surface of the tissue samples is not as easy as with other MSI methods. It is mandatory to support the assignments by a careful comparison of the data with reference spectra recorded from pure standards. The high rate of in-source fragmentation is often qualified as a latent defect to decry TOF-SIMS. Notwithstanding, the co-localization of different characteristic fragment ions with the molecular ion can greatly help the identification of the desorbed species. The systematic recording of images and spectra in both secondary ion polarities on the same area also provides complementary and useful information for the assignments. Finally the biological relevance, associated to a good knowledge of the chemical composition of the sample enables to avoid major identification errors.
Applications Localization of lipids in organs of model animals proved useful for studying diseases involving lipid disorders. TOF-SIMS was also applied for imaging lipids in plant samples and, apart from lipids, for investigating the distribution of xenobiotic compounds in tissues. All the studies listed below illustrate the TOF-SIMS imaging capabilities, with spatial resolutions of 1 mm or less. Localization of lipids has been studied in the aortic wall [43] and in rat cerebellum [44]. The accumulation of lipids in adipose tissue of mice models of obesity was also imaged [45]. Atherosclerotic plaque [46], non-alcoholic fatty liver disease (NAFLD) [35], Fabry disease [47], Duchenne muscular dystrophy (DMD) [33,34] and cystic fibrosis [40] have all been successfully studied. In the case of the study of human muscle from a patient suffering from the DMD [34], previous results obtained from model mice [33] were confirmed together with many variations of the lipid content between healthy and diseased cells. In the study of NAFLD, accumulation of triacylglycerols, diacylglycerols, monoacylglycerols and fatty acids were observed in steatosis areas of fatty livers compared to control livers. These ion species were concentrated in small vesicles having a size of a few microns. Moreover, very fine differences in lipid localizations, depending on alkyl acid chain lengths of diacylglycerols and fatty acids, and accumulation of very similar lipids to those detected in areas of the fatty livers were found in areas which were not characterized as steatotic ones by the histological control [35] (Figure 4). Most intense ion signals are usually those of cholesterol, Vitamin E and fatty acids (FA), which can be either free FA of fragments of higher molecular weight species. Phospholipids and sphingolipids are detected with weaker ion intensities and need to be concentrated in well-defined areas to be imaged. Galactoceramides were found to be highly concentrated in specific microstructures of kidney samples from patients suffering from the Fabry disease samples [47]. In all those studies, lipid modifications characteristic of the diseases were observed Current Opinion in Chemical Biology 2011, 15:725–732
in correlation with the histopathology of the samples. In plants, the chemical composition has been associated to the microstructure of the waxy surface of Kalanchoe daigremontiana leaves, showing precise localization of glutinol and friedelin, two major wax triterpenoids [48]. Flavonoids fragments from quercetin and kaempferol derivatives were selectively detected in the coat of Arabidopsis thaliana fresh seeds, showing a different composition between wild and mutant plant species [49]. Following TOF-SIMS studies of the bioaccumulation of the brominated flame retardant deca-bromo-diphenyl ether in organs of dosed rats, this compound was shown to accumulate in the cortical part of the adrenals and in specific corpora lutea of ovaries [50].
Conclusion and future prospects TOF-SIMS bio-imaging mainly suffers from the absence of a true tandem mass analyzer, although a recent development has been made in this direction [51]. It appears that it is with the current state-of-the-art very difficult to obtain molecular mass spectrometric images well below 1 mm [52]. An increase of sensitivity could also help to make this analysis method more popular. This could be obtained with the recent advent of massive cluster ion sources which can not only increase the yields of secondary ion emission but also provide access to the analysis of deeper layers without damaging it by sputtering, thus increasing the sensitivity of the method and giving access to a third dimension of the sample [53,54,55]. It has recently been shown that the maximum of secondary ion emission occurs for primary ion energies below that of the maximum of damage of the sample. Thus an increase of the energy of the primary ions would also be beneficial to the efficiency [56]. Nevertheless, TOF-SIMS imaging of tissue is already a method of choice when very precise localization of compound is needed at the micrometer scale, well below the limits of other molecular mass spectrometry imaging methods. Many biological fields could take benefit from this method with its current know-how that requires none or little sample preparation. Then, in parallel with fundamental investigations in desorption and ionization processes, and with methodological developments, much more work is needed to apply TOF-SIMS imaging to relevant biological problems [57].
Acknowledgements This work was supported by the Agence Nationale de la Recherche (grants ANR-09-PIRI-0012-04 MASDA-EYE and ANR-2010-BLAN-0805-01 MASS-IMAGE).
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32. Malmberg P, Jennische E, Nilsson D, Nygren H: High-resolution, imaging TOF-SIMS: novel applications in medical research. Anal Bioanal Chem 2011, 399:2711-2718. The authors show recent applications highlighting the high resolution capabilities of TOF-SIMS to medical research. They show that TOF-SIMS imaging can be used to quantitatively compare lipid characteristic of different histological areas between different groups of samples. 33. Touboul D, Brunelle A, Halgand F, De La Porte S, Lapre´vote O: Lipid imaging by gold cluster time-of-flight - secondary ion mass spectrometry: application to Duchenne muscular dystrophy. J Lipid Res 2005, 46:1388-1395. 34. Tahallah N, Brunelle A, De La Porte S, Lapre´vote O: Lipid mapping in human dystrophic muscle by cluster-time-of-flight secondary ion mass spectrometry imaging. J Lipid Res 2008, 49:438-454. 35. Debois D, Bralet MP, Le Naour F, Brunelle A, Lapre´vote O: In situ lipidomic analysis of non alcoholic fatty liver by cluster TOFSIMS imaging. Anal Chem 2009, 81:2823-2831. 36. Jones EA, Lockyer NP, Vickerman JC: Mass spectral analysis and imaging of tissue by ToF-SIMS—the role of Current Opinion in Chemical Biology 2011, 15:725–732
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buckminsterfullerene, C60+, primary ions. Int J Mass Spectrom 2007, 260:146-157. 37. Proceedings of the Sixteenth International Conference on Secondary Ion Mass Spectrometry, SIMS XVI: General discussion. Appl Surf Sci 2008, 255:1271–1272. 38. Park JW, Min H, Kim YP, Shon HK, Kim J, Moon DW, Lee TG: Multivariate analysis of ToF-SIMS data for biological applications. Surf Interface Anal 2009, 41:694-703. 39. Smentkowski VS, Ostrowski SG, Keenan MR: A comparison of multivariate statistical analysis protocols for ToF-SIMS spectral images. Surf Interface Anal 2009, 41:88-96. 40. Brulet M, Seyer A, Edelman A, Brunelle A, Fritsch J, Ollero M, Lapre´vote O: Lipid mapping of colonic mucosa by cluster TOFSIMS imaging and multivariate analysis in cftr knockout mice. J Lipid Res 2010, 51:3034-3045. The authors have tested different multivariate analysis methods (PCA, partitioning clustering) to a real biological problem. These statistical analyses have helped the authors to analyze their imaging data. 41. Petit VW, Re´fre´giers M, Guettier C, Jamme F, Sebanayakam K, Brunelle A, Lapre´vote O, Dumas P, Le Naour F: Multimodal spectroscopy combining ToF-SIMS, synchrotron-FTIR and UV microspectroscopies on the same tissue section. Anal Chem 2010, 82:3963-3968. 42. Belazi D, Sole´-Dome`nech S, Johansson B, Schalling M, Sjo¨vall P: Chemical analysis of osmium tetroxide in adipose tissue using imaging ToF-SIMS. Histochem Cell Biol 2009, 132:105-115. 43. Malmberg P, Borner K, Chen Y, Friberg P, Hagenhoff B, Mansson JE, Nygren H: Localization of lipids in the aortic wall with imaging TOF-SIMS. Biochim Biophys Acta Mol Cell Biol Lipids 2007, 1771:185-195. 44. Pernber Z, Richter K, Mansson JE, Nygren H: Sulfatide with different fatty acids has unique distributions in cerebellum as imaged by Time-Of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS). Biochim Biophys Acta Mol Cell Biol Lipids 2007, 1771:202-209. 45. Magnusson YK, Friberg P, Sjo¨vall P, Malm J, Chen Y: TOF–SIMS analysis of lipid accumulation in the skeletal muscle of ob/ob mice. Obesity 2008, 16:2745-2753. 46. Mas S, Touboul D, Brunelle A, Aragoncillo P, Egido J, Lapre´vote O, Vivanco F: Lipid cartography of atherosclerotic plaque by cluster-TOF-SIMS Imaging. Analyst 2007, 132:24-26. 47. Touboul D, Roy S, Germain DP, Chaminade P, Brunelle A, Lapre´vote O: MALDI-TOF and cluster-TOF-SIMS imaging of Fabry disease biomarkers. Int J Mass Spectrom 2007, 260:158-165.
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48. Jetter R, Sodhi R: Chemical composition and microstructure of waxy plant surfaces: triterpenoids and fatty acid derivates on leaves of Kalanchoe daigremontiana. Surf Interface Anal 2011, 43:326-330. 49. Seyer A, Einhorn J, Brunelle A, Lapre´vote O: Localization of flavonoids in seeds by cluster time-of-flight secondary ion mass spectrometry imaging. Anal Chem 2010, 82:2326-2333. 50. Seyer A, Riu A, Debrauwer L, Bourge`s-Abella N, Brunelle A, Lapre´vote O, Zalko D: Time-of-Flight Secondary Ion Mass Spectrometry imaging demonstrates the specific localization of Deca-Bromo-Diphenyl-Ether residues in the ovaries and adrenal glands of exposed rats. J Am Soc Mass Spectrom 2010, 21:1836-1845. 51. Hill R, Blenkinsopp P, Thompson SP, Vickerman JC, Fletcher JS: A new time-of-flight SIMS instrument for 3D imaging and analysis. Surf Interface Anal 2011, 43:506-509. 52. Fletcher JS, Lockyer NP, Vickerman JC: Molecular SIMS imaging; spatial resolution and molecular sensitivity: have we reached the end of the road? Is there light at the end of the tunnel?. Surf Interface Anal 2011, 43:253-256. 53. Della-Negra S, Depauw J, Guillermier C, Schweikert EA: Massive cluster: secondary emission from qkeV to qMeV. New emission processes? New SIMS probe?. Surf Interface Anal 2011, 43:62-65. 54. Lee JLS, Ninomiya S, Matsuo J, Gilmore IS, Seah MP, Shard AG: Organic depth profiling of a nanostructured delta layer reference material using large argon cluster ions. Anal Chem 2010, 82:98-105. The authors show that large argon clusters are a method of choice for organic depth profiling since it sputter surface layers without damaging the underlying ones. 55. Ninomiya S, Ichiki K, Seki T, Aoki T, Matsuo J: The emission process of secondary ions from solids bombarded with large gas cluster ions. Nucl Instrum Methods Phys Res B 2009, 267:2601-2604. 56. Wehbe N, Fallavier M, Della Negra S, Depauw J, Brunelle A, Andersen HH: Cluster size and velocity dependences of sputtering and secondary ion emission under gold cluster impact. Nucl Instrum Methods Phys Res B 2010, 268:2596-2602. 57. Walker AV: Why is SIMS underused in chemical and biological analysis? Challenges and opportunities. Anal Chem 2008, 80:8865-8870.
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Micrometric molecular histology of lipids by mass spectrometry imaging David Touboul1, Olivier Lapre´vote1,2 and Alain Brunelle1 Time-Of-Flight Secondary Ion Mass Spectrometry is compared to other mass spectrometry imaging techniques, and recent improvements of the experimental methods, driven by biological and biomedical applications, are described and discussed. This review shows that this method that can be considered as a micrometric molecular histology is particularly efficient for obtaining images of various lipid species at the surface of a tissue sample, without sample preparation, and with a routine spatial resolution of 1 mm or less. Addresses 1 Centre de recherche de Gif, Institut de Chimie des Substances Naturelles, CNRS, avenue de la Terrasse, 91198 Gif-sur-Yvette, France 2 Chimie Toxicologie Analytique et Cellulaire, EA 4463, Faculte´ des Sciences Pharmaceutiques et Biologiques, Universite´ Paris Descartes, 4, avenue de l’Observatoire, 75006 Paris, France Corresponding author: Brunelle, Alain ([email protected])
Current Opinion in Chemical Biology 2011, 15:725–732 This review comes from a themed issue on Analytical Techniques Edited by Morgan Alexander and Ian Glimore Available online 24th May 2011 1367-5931/$ – see front matter # 2011 Elsevier Ltd. All rights reserved. DOI 10.1016/j.cbpa.2011.04.017
Introduction Various ex vivo biological imaging methods, such as immunohistochemistry, staining or autoradiography, have been developed to obtain information on the chemical composition at the surface of tissue sections. Among them, mass spectrometry imaging (MSI) is the only one allowing to locate and identify various chemical compounds without selection a priori of a chemical class or compound [1,2]. Since this method does not require targeting compounds before the analysis, it makes possible to draw anatomical images of any ion detected in the mass spectra in one single experiment. Lipids, which can be defined as fat-soluble molecules, have been for a long time considered only as energy storage and as major constituents of the cell membranes. It is now agreed that these classes of compounds are important cell signaling molecules, neurotransmitters and precursors in the regulation of various cellular functions [3]. The importance of lipids in biological sciences is illustrated by the recognition of ‘lipidomics’ as an emerging field among the www.sciencedirect.com
‘omics’. The eight lipid categories as defined by the Lipid Maps initiative [4], fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids and polyketides, sterol lipids and prenol lipids (see www.lipidmaps.org), can be analyzed with gas/liquid chromatography coupled to modern mass spectrometers, providing precise identification and quantification. Before their analysis, lipids have to be extracted from the sample, leading to the loss of the spatial information whereas MSI offers a direct sampling over the tissue surface [3,5].
Comparison with other mass spectrometry imaging methods Basically, in MSI, a focused beam of photons or energetic ions is used to scan over the surface of a tissue section. The size of this beam defines the size of the pixel that corresponds to each point on which a mass spectrum is recorded. The data acquisition consists in the creation of a volume whose dimensions are x and y, the two geometric dimensions of the sample, and m/z the mass-to-charge ratio of the secondary ions. Any slice of this volume along a given m/z value is an ion density map, the so-called ion image [6]. All mass spectrometry methods based on localized ionization processes at the sample surface can be theoretically used for mass spectrometry imaging. Table 1 summarizes the main characteristics of the MSI techniques, listed from left to right with increasing spatial resolution. At both ends are Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) and Nano-Secondary Ion Mass Spectrometry (Nano-SIMS), which are not applicable to molecular ion localization. These two desorption/ionization methods are too energetic to provide signals of intact lipid ions. Nevertheless, the first one is considered as a method of choice for the quantitative localization of trace metals with a resolution of 120 mm [7,8], while the latter is the best known method for submicrometer localization of elements and/or small fragment ions with a precision reaching 100 nm or less [9]. Desorption electrospray ionization (DESI) uses a pneumatically assisted electrospray source to desorb the analytes from the sample surface. DESI is very sensitive for low molecular weight compounds like lipids. It works on samples at atmospheric pressure, but is limited with the current state-of-the art to a spatial resolution well above 100 mm [10]. Matrix-Assisted Laser Desorption Ionization (MALDI) is by far the most popular mass spectrometry imaging method [2,11]. The sample requires being preliminary coated with a matrix, and depending on the proper choice of the latter, any class Current Opinion in Chemical Biology 2011, 15:725–732
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Table 1 Comparison of some performances and characteristics for tissue mass spectrometry imaging, between MALDI-TOF/TOF, LA-ICP-MS, TOF-SIMS and NANO-SIMS LA-ICP-MS Ultimate spatial resolution Sample preparation Mass range Accessible compounds Mass analyzer
DESI
MALDI
TOF-SIMS
NANO-SIMS
50–200 mm
100–200 mm
5–50 mm
400 nm–2 mm
50–150 nm
Dehydrated, not fixed, no matrix m/z < 250 Metals
No preparation, analysis at atmospheric pressure m/z 1000 Lipids, natural products
Quadrupole, TOF
Q-TOF, FT (Orbitrap#)
Dehydrated, homogeneous matrix coating m/z > 200 Lipids, peptides, proteins, drugs, metabolites,. . . TOF/TOF, Q-TOF, FT (Orbitrap#), ion mobility coupled to Q-TOF, QqQ
Dehydrated, not fixed, no matrix m/z 1500 Lipids, drugs, metabolites, elements,. . . TOF
Fixed and dehydrated m/z < 250 Elements, fragments Magnetic
Q: quadrupole, FT: Fourier Transform, TOF: Time-Of-Flight.
of compound can be mapped at the surface of a tissue section. Initially developed to produce images of peptides and proteins [12,13], it is now more and more applied to lipid imaging [14]. MALDI can be coupled with various mass analyzers such as tandem time-of-flight [15] or high resolution mass spectrometers [16]. The spatial resolution is usually limited to 50 mm or eventually improved to 5–10 mm with special experimental conditions [16]. By contrast, Time-Of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS) enables both a high spatial resolution (400 nm–2 mm) and the analysis of intact molecular ions [5,17] (Figure 1). Until early 2000, this surface analysis method was limited to the semi-conductor industry and material analysis [18] for the detection of low molecular weight fragments of
Figure 1
(a) Tissue section
(b)
(d)
Mass spectrometry images
Tissue analysis (c) Mass spectrum Reflectron
Primary ion source Bi3+
Time of flight Detector
Electron floodgun Sample stage Current Opinion in Chemical Biology
Schematic workflow of TOF-SIMS tissue imaging. Current Opinion in Chemical Biology 2011, 15:725–732
organic compounds [19]. The low efficiency of the primary ion sources, like gallium ones, to desorb and ionize intact molecular ions, was accompanied by extensive in-source fragmentation. The situation dramatically changed with the advent of polyatomic ion sources (Aun, Bin, C60) [20–22]. The enhancement of the secondary ion emission, together with a limited surface damage, provides an increased efficiency, enabling sufficient sensitivity to make ion images of lipids with enough intensity for micrometer pixel sizes [23,24]. With the current stateof-the-art, bismuth cluster ion sources are those enabling the best sensitivity at a resolution of 1 mm, or less [25]. The respective capabilities of TOF-SIMS and MALDITOF have recently been compared for rat brain tissue sections. While the first enables the best spatial resolution, the latter allows in situ structural analyses by tandem mass spectrometry together with sensitivity estimated to be in the nanomolar range [26]. MALDI and TOF-SIMS were also correlated onto the same tissue sample in order to get complementary information [27,28]. Matrix-Enhanced (ME-) and Metal-Assisted (MetA-) SIMS are alternative methods which use matrices or metal coatings to enhance the secondary ion emission. The enhancement in the positive ion mode is equivalent to the one obtained with cluster primary ions, while no clear conclusion could be drawn in the negative ion mode [29]. The deposition of a matrix may degrade the spatial resolution and the enhancement also depends on the class of compounds. These methods remain rarely used compared to TOF-SIMS using clusters, which can be used advantageously with both secondary ion polarities to obtain complementary information. Recent studies have demonstrated that, during the preparation of the tissue sample, lipids migrate towards the sample surface between the cutting step at 20 8C and the analysis performed under vacuum at room temperature [30,31]. Most of the lipids are thus concentrated in the top 200–300 nm layers from which the secondary organic ions are emitted by impact of cluster primary www.sciencedirect.com
Micrometric molecular histology of lipids Touboul, Lapre´vote and Brunelle 727
ions. This migration was not found to be accompanied to lateral delocalization.
Experimental methods When indicated, intensity scales of ion images correspond to a dynamic range. Relative quantification of compounds
showing similar physical and chemical properties (which means detected and desorbed with the same efficiencies) between two different areas over the same sample surface and scanned under the same experimental conditions is consequently possible. The mass spectra and the peak areas extracted from the different histological regions of
Figure 2
(a)
(b)
R: Cholesteryl Surfate: B: C16:1 fatty acid G: C180:0 fatty acid
(c)
2 clusters
(d)
3 clusters
(e)
4 clusters
(f)
5 clusters
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TOF-SIMS images of mouse colonic mucosa model of cystic fibrosis. (a) Three color overlay showing the localization of the following negative ions: red, cholesteryl sulfate, m/z 465.4; blue, palmitoleate (C16:1), m/z 253.2, and green, stearate (C18:0), m/z 283.2. (b) Principal component (PC) analysis of the same data. Overlay of the score images of PC1, PC2 and PC3. The color scale represents the contribution for each pixel on the selected principal axes. Red, green and blue indicate a strong positive contribution of PC1, PC2 and PC3 respectively. (c–f) Partitioning clustering of the same data as (a). Distribution of partitioning clusters. Partitioning clustering was performed using the K-mean algorithm. Images correspond to partitioning into two (c), three (d), four (e) and five (f) clusters. The dark blue area corresponds to pixels excluded from the analysis.This research was originally published in J Lipid Res Reference [40] # the American Society for Biochemistry and Molecular Biology. www.sciencedirect.com
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the sample, usually called Regions of Interest (ROI), need only to be normalized towards the same primary ion counts, or to the same area. This approach can be extended to different samples, that is, tissues from control and patient groups, given that all the data have been acquired under the same experimental conditions [32,33–35]. The so-called ‘matrix effects’, which are so often feared, and which are enhancements or decreases of some secondary ion yields owing to the chemical micro-environment of a compound [36], does not matter too much as long as one compares healthy versus non-healthy samples, for which the tissue environment, or matrix, can be assumed to be similar between one sample to the next [32,37]. A simple method to analyze TOF-SIMS imaging data is the following: several intense ion peaks are first selected to draw their corresponding images, which gives characteristic histological areas of the sample. Then these areas are selected and normalized spectra and ion peak areas are extracted from the corresponding ROIs. In a second step images from ions differentiated from the ROIs can be reconstructed, giving a deeper insight of the molecular histology of the sample surface. The analysis of huge amounts of data can rapidly become an insurmountable task, thus making multivariate analyses mandatory. A lot of efforts have been made in the recent years by several groups to implement these methods, like Principal Com-
ponent Analysis (PCA) and Partitioning Clustering (PC), to TOF-SIMS imaging [38,39]. The authors usually correlate groups of ions from various chemical classes to the sample histology. A recent study made by Brulet et al. [40] with sections of colon from mice model of the cystic fibrosis indicated that these methods can help distinguishing the different histological areas of the sample. Although the comparison between spectra from control and diseased samples did not lead to evident different chemical compositions, groups of ions corresponding to different lipid species and different histological areas were distinguished thanks to multivariate analyses (Figure 2). Nevertheless, some important difficulties remain which limit for the moment an easy use of multivariate analyses. First, a mass spectrometry imaging dataset is usually a very big file of several gigabytes with proprietary file formats, or even several tens of gigabytes with public formats. Handling and processing such big files need computers with fast processors and subsequent RAM. Nevertheless, if it is sometimes useful to bin the dataset for rapid pre-processing, this must be done carefully because it is at the expense of the spatial and/or mass precision. The second difficulty lies in the fact that data may be difficult to export from proprietary file formats to be processed with other software. The common and free format imzml initiated by the COMPUTIS European consortium (www.imzml.org) needs to be mentioned
Figure 3
(a)
(b) 100 μm
Collagen (1300-1180cm-1)
(c)
Ester (1780-1710cm-1)
Max
Min (d)
100 μm
16 (f)
(e)
SM fragments
0
14
PI
0
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Combining synchrotron-FTIR and TOF-SIMS microspectroscopies. The histological serial tissue section of liver cirrhosis stained with HES is shown (a). Synchrotron-FTIR microspectroscopy experiments were performed on liver cirrhosis and the distribution of bands corresponding to collagen (b) or ester (c) were visualized. Mass spectrometry images using TOF-SIMS were subsequently recorded on the same tissue section. The video image is shown (d). This allowed imaging sphingomyelin fragments (e) or phosphatidylinositols (f). TOF-SIMS images recorded with 256 256 pixels (pixel size 2 mm). Intensity scale bars on the right of the TOF-SIMS images in counts. SM, sphingomyelines and PI, phosphoinositols.Reprinted with permission from Reference [41]. Copyright 2011 American Chemical Society. Current Opinion in Chemical Biology 2011, 15:725–732
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Micrometric molecular histology of lipids Touboul, Lapre´vote and Brunelle 729
since it is expected to become a universal file format for mass spectrometry imaging data in the near future. Although TOF-SIMS has already been widely utilized in combination with other spectroscopic methods, such as electron microscopy, atomic force microscopy or Raman spectroscopy, very few examples describe multi-spectroscopic approaches including TOF-SIMS on biological samples. Synchrotron-Fourier Transform-Infrared (FT-IR) and -ultraviolet (UV) imaging were recently used in combination with TOF-SIMS imaging by Petit et al. [41] to analyze human cirrhotic liver biopsies. The same sample was successively studied by the three different methods, thus avoiding small variations of the localization of species between adjacent tissue sections (Figure 3). FT-IR identified collagen enrichment in fibrosis whereas esterified species
were mostly distributed into the cirrhotic nodules. TOF-SIMS demonstrated that sphingomyeline and phosphoinositol were differentially distributed into the fibrosis areas or in the cirrhotic nodules. Spectra extracted from UV microscopy experiments allowed visualizing high autofluorescence from fibrous septa confirming the presence of collagen. Staining methods can in some cases be used together with TOF-SIMS imaging. Osmium tetroxide (OsO4), a commonly used stain for unsaturated lipids in electron and optical microscopy of cells and tissues, has been used in combination with TOF-SIMS imaging of lipids in mouse adipose tissue [42]. Contrary to unsaturated fatty acids C18 and diglycerides, unsaturated fatty acids C16 were not co-localized with osmium oxide. Moreover, significant matrix effect was clearly observed.
Figure 4
(a) 100 μm
(b)
100 μm
(c)
Cholesterol mc:16 tc:9.985e+4 (d)
100 μm
(e)
C14:0 FA mc:17 tc:1.285e+5 (i)
100 μm
Sum of DAG mc:179 tc:2.039e+6
100 μm
(f)
Sum of C16 FA mc:67 tc:1.501e+6 (j)
100 μm
DAG C30 mc:19 tc:1.539e+5
100 μm
Sum of TAG mc:7 tc:2.767e+4 (g)
C18:0 FA mc:39 tc:7.462e+5 (k)
100 μm
DAG C32 mc:50 tc:5.465e+5
100 μm
100 μm
C18:1 FA mc:41 tc:8.833e+5 (l)
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DAG C34 mc:79 tc:8.778e+5
100 μm
(h)
C18:2 FA mc:36 tc:5.338e+5 (m)
100 μm
DAG C36 mc:64 tc:4.609e+5 Current Opinion in Chemical Biology
Non-alcoholic fatty liver disease. TOF-SIMS imaging of a steatosis area. (a) Video image of the steatotic area. Ion images of several different positive and negative secondary ions from this area. (b) Cholesterol (positive ion mode), (c) sum of triacylglycerol ions (TAG, positive ion mode), (d) C14:0 fatty acid (FA) (negative ion mode), (e) sum of C16 fatty acid carboxylate ions (negative ion mode), (f) C18:0 FA (negative ion mode), (g) C18:1 FA (negative ion mode), (h) C18:2 FA (negative ion mode), (i) sum of diacylglycerol ions (DAG, positive ion mode), (j) sum of DAG with 30 carbon atoms (DAG C30, positive ion mode), (k) sum of DAG with 32 carbon atoms (DAG C32, positive ion mode), (l) sum of DAG with 34 carbon atoms (DAG C34, positive ion mode); (m) sum of DAG with 36 carbon atoms (DAG C36, positive ion mode). Color scale bars, with amplitude in number of counts, are indicated to the right of each ion image. The amplitude of the color scale corresponds to the maximum number of counts mc and could be read as [0, mc]. tc is the total number of counts recorded for the specified m/z (it is the sum of counts in all the pixels). Field of view: 500 mm 500 mm.Reprinted with permission from Reference [35]. Copyright 2011 American Chemical Society. www.sciencedirect.com
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730 Analytical Techniques
With the absence of a tandem or a high resolution mass analyzer, the identification of lipid species from the TOFSIMS spectra recorded at the surface of the tissue samples is not as easy as with other MSI methods. It is mandatory to support the assignments by a careful comparison of the data with reference spectra recorded from pure standards. The high rate of in-source fragmentation is often qualified as a latent defect to decry TOF-SIMS. Notwithstanding, the co-localization of different characteristic fragment ions with the molecular ion can greatly help the identification of the desorbed species. The systematic recording of images and spectra in both secondary ion polarities on the same area also provides complementary and useful information for the assignments. Finally the biological relevance, associated to a good knowledge of the chemical composition of the sample enables to avoid major identification errors.
Applications Localization of lipids in organs of model animals proved useful for studying diseases involving lipid disorders. TOF-SIMS was also applied for imaging lipids in plant samples and, apart from lipids, for investigating the distribution of xenobiotic compounds in tissues. All the studies listed below illustrate the TOF-SIMS imaging capabilities, with spatial resolutions of 1 mm or less. Localization of lipids has been studied in the aortic wall [43] and in rat cerebellum [44]. The accumulation of lipids in adipose tissue of mice models of obesity was also imaged [45]. Atherosclerotic plaque [46], non-alcoholic fatty liver disease (NAFLD) [35], Fabry disease [47], Duchenne muscular dystrophy (DMD) [33,34] and cystic fibrosis [40] have all been successfully studied. In the case of the study of human muscle from a patient suffering from the DMD [34], previous results obtained from model mice [33] were confirmed together with many variations of the lipid content between healthy and diseased cells. In the study of NAFLD, accumulation of triacylglycerols, diacylglycerols, monoacylglycerols and fatty acids were observed in steatosis areas of fatty livers compared to control livers. These ion species were concentrated in small vesicles having a size of a few microns. Moreover, very fine differences in lipid localizations, depending on alkyl acid chain lengths of diacylglycerols and fatty acids, and accumulation of very similar lipids to those detected in areas of the fatty livers were found in areas which were not characterized as steatotic ones by the histological control [35] (Figure 4). Most intense ion signals are usually those of cholesterol, Vitamin E and fatty acids (FA), which can be either free FA of fragments of higher molecular weight species. Phospholipids and sphingolipids are detected with weaker ion intensities and need to be concentrated in well-defined areas to be imaged. Galactoceramides were found to be highly concentrated in specific microstructures of kidney samples from patients suffering from the Fabry disease samples [47]. In all those studies, lipid modifications characteristic of the diseases were observed Current Opinion in Chemical Biology 2011, 15:725–732
in correlation with the histopathology of the samples. In plants, the chemical composition has been associated to the microstructure of the waxy surface of Kalanchoe daigremontiana leaves, showing precise localization of glutinol and friedelin, two major wax triterpenoids [48]. Flavonoids fragments from quercetin and kaempferol derivatives were selectively detected in the coat of Arabidopsis thaliana fresh seeds, showing a different composition between wild and mutant plant species [49]. Following TOF-SIMS studies of the bioaccumulation of the brominated flame retardant deca-bromo-diphenyl ether in organs of dosed rats, this compound was shown to accumulate in the cortical part of the adrenals and in specific corpora lutea of ovaries [50].
Conclusion and future prospects TOF-SIMS bio-imaging mainly suffers from the absence of a true tandem mass analyzer, although a recent development has been made in this direction [51]. It appears that it is with the current state-of-the-art very difficult to obtain molecular mass spectrometric images well below 1 mm [52]. An increase of sensitivity could also help to make this analysis method more popular. This could be obtained with the recent advent of massive cluster ion sources which can not only increase the yields of secondary ion emission but also provide access to the analysis of deeper layers without damaging it by sputtering, thus increasing the sensitivity of the method and giving access to a third dimension of the sample [53,54,55]. It has recently been shown that the maximum of secondary ion emission occurs for primary ion energies below that of the maximum of damage of the sample. Thus an increase of the energy of the primary ions would also be beneficial to the efficiency [56]. Nevertheless, TOF-SIMS imaging of tissue is already a method of choice when very precise localization of compound is needed at the micrometer scale, well below the limits of other molecular mass spectrometry imaging methods. Many biological fields could take benefit from this method with its current know-how that requires none or little sample preparation. Then, in parallel with fundamental investigations in desorption and ionization processes, and with methodological developments, much more work is needed to apply TOF-SIMS imaging to relevant biological problems [57].
Acknowledgements This work was supported by the Agence Nationale de la Recherche (grants ANR-09-PIRI-0012-04 MASDA-EYE and ANR-2010-BLAN-0805-01 MASS-IMAGE).
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19. Belu AM, Graham DJ, Castner DG: Time-of-flight secondary ion mass spectrometry: techniques and applications for the characterization of biomaterial surfaces. Biomaterials 2003, 24:3635-3653. 20. Brunelle A, Della-Negra S, Depauw J, Jacquet D, Le Beyec Y, Pautrat M, Baudin K, Andersen HH: Enhanced secondary-ion emission under gold-cluster bombardment with energies from keV to MeV per atom. Phys Rev A 2001, 63:1-10 022902. 21. Weibel D, Wong S, Lockyer N, Blenkinsopp P, Hill R, Vickerman JC: A C60 primary ion beam system for time of flight secondary ion mass spectrometry: its development and secondary ion yield characteristics. Anal Chem 2003, 75:1754-1764. 22. Kollmer F: Cluster primary ion bombardment of organic materials. Appl Surf Sci 2004, 231–232:153-158. 23. Touboul D, Halgand F, Brunelle A, Kersting R, Tallarek E, Hagenhoff B, Lapre´vote O: Tissue molecular ion imaging by gold cluster ion bombardment. Anal Chem 2004, 76:1159-1550. 24. Sjo¨vall P, Lausmaa J, Johansson B: Mass spectrometric imaging of lipids in brain tissue. Anal Chem 2004, 76:4271-4278. 25. Touboul D, Kollmer F, Niehuis E, Brunelle A, Lapre´vote O: Improvement of biological TOF-SIMS imaging with a bismuth cluster ion source. J Am Soc Mass Spectrom 2005, 16:1608-1618. 26. Benabdellah F, Seyer A, Quinton L, Touboul D, Brunelle A, Lapre´vote O: Mass spectrometry imaging of rat brain sections: nanomolar sensitivity with MALDI versus nanometer resolution by TOF–SIMS. Anal Bioanal Chem 2010, 396:151-162. The authors have compared the high spatial resolution of TOF-SIMS to the high sensitivity of MALDI onto adjacent rat brain tissue sections. 27. Eijkel GB, Kaletas¸ BK, van der Wiel IM, Kros JM, Luider TM, Heeren RMA: Correlating MALDI and SIMS imaging mass spectrometric datasets of biological tissue surfaces. Surf Interface Anal 2009, 41:675-685.
Excellent book describing most of the state-of-the-art in MALDI mass spectrometry imaging.
28. Passarelli MK, Winograd N: Characterizing in situ Glycerophospholipids with SIMS and MALDI Methodologies. Surf Interface Anal 2011, 43:269-271.
12. Spengler B, Hubert M: Scanning microprobe matrix-assisted laser desorption ionization (SMALDI) mass spectrometry: instrumentation for sub-micrometer resolved LDI and MALDI surface analysis. J Am Soc Mass Spectrom 2002, 13:735-748.
29. McDonnell LA, Heeren RMA, de Lange RPJ, Fletcher IW: Higher sensitivity secondary ion mass spectrometry of biological molecules for high resolution, chemically specific imaging. J Am Soc Mass Spectrom 2006, 17:1195-1202.
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30. Sjo¨vall P, Johansson B, Lausmaa J: Localization of lipids in freeze-dried mouse brain sections by imaging TOF-SIMS. Appl Surf Sci 2006, 252:6966-6974.
14. Fernandez JA, Ochoa B, Fresnedo O, Giralt MT, Rodrı´guez Puertas R: Matrix-assisted laser desorption ionization imaging mass spectrometry in lipidomics. Anal Bioanal Chem 2011 doi: 10.1007/s00216-011-4696-x. This review of MALDI mass spectrometry imaging applied to lipids contains a table classifying the eight lipid families, with typical chemical structures and examples of species.
31. Debois D, Brunelle A, Lapre´vote O: Attempts for molecular depth profiling directly on a rat brain tissue section using fullerene and bismuth cluster ion beams. Int J Mass Spectrom 2007, 260:115-120.
15. Sugiura Y, Konishi Y, Zaima N, Kajihara S, Nakanishi H, Taguchi R, Setou M: Visualization of the cell-selective distribution of PUFA-containing phosphatidylcholines in mouse brain by imaging mass spectrometry. J Lipid Res 2009, 50:1776-1788. 16. Guenther S, Ro¨mpp A, Kummerb W, Spengler B: AP-MALDI imaging of neuropeptides in mouse pituitary gland with 5 mm spatial resolution and high mass accuracy. Int J Mass Spectrom 2011 doi: 10.1016/j.ijms.2010.11.011. The authors have been able to produce images of small neuropeptides with a resolution of 5 mm, which is to our knowledge the best of the stateof-the-art in MALDI imaging. 17. Brunelle A, Lapre´vote O: Lipid imaging with cluster time-offlight secondary ion mass spectrometry. Anal Bioanal Chem 2009, 393:31-35. 18. Vickerman JC, Briggs D (Eds): TOF-SIMS: Surface Analysis by Mass Spectrometry. Manchester, Chichester: Surface Spectra and IM Publications; 2001. www.sciencedirect.com
32. Malmberg P, Jennische E, Nilsson D, Nygren H: High-resolution, imaging TOF-SIMS: novel applications in medical research. Anal Bioanal Chem 2011, 399:2711-2718. The authors show recent applications highlighting the high resolution capabilities of TOF-SIMS to medical research. They show that TOF-SIMS imaging can be used to quantitatively compare lipid characteristic of different histological areas between different groups of samples. 33. Touboul D, Brunelle A, Halgand F, De La Porte S, Lapre´vote O: Lipid imaging by gold cluster time-of-flight - secondary ion mass spectrometry: application to Duchenne muscular dystrophy. J Lipid Res 2005, 46:1388-1395. 34. Tahallah N, Brunelle A, De La Porte S, Lapre´vote O: Lipid mapping in human dystrophic muscle by cluster-time-of-flight secondary ion mass spectrometry imaging. J Lipid Res 2008, 49:438-454. 35. Debois D, Bralet MP, Le Naour F, Brunelle A, Lapre´vote O: In situ lipidomic analysis of non alcoholic fatty liver by cluster TOFSIMS imaging. Anal Chem 2009, 81:2823-2831. 36. Jones EA, Lockyer NP, Vickerman JC: Mass spectral analysis and imaging of tissue by ToF-SIMS—the role of Current Opinion in Chemical Biology 2011, 15:725–732
732 Analytical Techniques
buckminsterfullerene, C60+, primary ions. Int J Mass Spectrom 2007, 260:146-157. 37. Proceedings of the Sixteenth International Conference on Secondary Ion Mass Spectrometry, SIMS XVI: General discussion. Appl Surf Sci 2008, 255:1271–1272. 38. Park JW, Min H, Kim YP, Shon HK, Kim J, Moon DW, Lee TG: Multivariate analysis of ToF-SIMS data for biological applications. Surf Interface Anal 2009, 41:694-703. 39. Smentkowski VS, Ostrowski SG, Keenan MR: A comparison of multivariate statistical analysis protocols for ToF-SIMS spectral images. Surf Interface Anal 2009, 41:88-96. 40. Brulet M, Seyer A, Edelman A, Brunelle A, Fritsch J, Ollero M, Lapre´vote O: Lipid mapping of colonic mucosa by cluster TOFSIMS imaging and multivariate analysis in cftr knockout mice. J Lipid Res 2010, 51:3034-3045. The authors have tested different multivariate analysis methods (PCA, partitioning clustering) to a real biological problem. These statistical analyses have helped the authors to analyze their imaging data. 41. Petit VW, Re´fre´giers M, Guettier C, Jamme F, Sebanayakam K, Brunelle A, Lapre´vote O, Dumas P, Le Naour F: Multimodal spectroscopy combining ToF-SIMS, synchrotron-FTIR and UV microspectroscopies on the same tissue section. Anal Chem 2010, 82:3963-3968. 42. Belazi D, Sole´-Dome`nech S, Johansson B, Schalling M, Sjo¨vall P: Chemical analysis of osmium tetroxide in adipose tissue using imaging ToF-SIMS. Histochem Cell Biol 2009, 132:105-115. 43. Malmberg P, Borner K, Chen Y, Friberg P, Hagenhoff B, Mansson JE, Nygren H: Localization of lipids in the aortic wall with imaging TOF-SIMS. Biochim Biophys Acta Mol Cell Biol Lipids 2007, 1771:185-195. 44. Pernber Z, Richter K, Mansson JE, Nygren H: Sulfatide with different fatty acids has unique distributions in cerebellum as imaged by Time-Of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS). Biochim Biophys Acta Mol Cell Biol Lipids 2007, 1771:202-209. 45. Magnusson YK, Friberg P, Sjo¨vall P, Malm J, Chen Y: TOF–SIMS analysis of lipid accumulation in the skeletal muscle of ob/ob mice. Obesity 2008, 16:2745-2753. 46. Mas S, Touboul D, Brunelle A, Aragoncillo P, Egido J, Lapre´vote O, Vivanco F: Lipid cartography of atherosclerotic plaque by cluster-TOF-SIMS Imaging. Analyst 2007, 132:24-26. 47. Touboul D, Roy S, Germain DP, Chaminade P, Brunelle A, Lapre´vote O: MALDI-TOF and cluster-TOF-SIMS imaging of Fabry disease biomarkers. Int J Mass Spectrom 2007, 260:158-165.
Current Opinion in Chemical Biology 2011, 15:725–732
48. Jetter R, Sodhi R: Chemical composition and microstructure of waxy plant surfaces: triterpenoids and fatty acid derivates on leaves of Kalanchoe daigremontiana. Surf Interface Anal 2011, 43:326-330. 49. Seyer A, Einhorn J, Brunelle A, Lapre´vote O: Localization of flavonoids in seeds by cluster time-of-flight secondary ion mass spectrometry imaging. Anal Chem 2010, 82:2326-2333. 50. Seyer A, Riu A, Debrauwer L, Bourge`s-Abella N, Brunelle A, Lapre´vote O, Zalko D: Time-of-Flight Secondary Ion Mass Spectrometry imaging demonstrates the specific localization of Deca-Bromo-Diphenyl-Ether residues in the ovaries and adrenal glands of exposed rats. J Am Soc Mass Spectrom 2010, 21:1836-1845. 51. Hill R, Blenkinsopp P, Thompson SP, Vickerman JC, Fletcher JS: A new time-of-flight SIMS instrument for 3D imaging and analysis. Surf Interface Anal 2011, 43:506-509. 52. Fletcher JS, Lockyer NP, Vickerman JC: Molecular SIMS imaging; spatial resolution and molecular sensitivity: have we reached the end of the road? Is there light at the end of the tunnel?. Surf Interface Anal 2011, 43:253-256. 53. Della-Negra S, Depauw J, Guillermier C, Schweikert EA: Massive cluster: secondary emission from qkeV to qMeV. New emission processes? New SIMS probe?. Surf Interface Anal 2011, 43:62-65. 54. Lee JLS, Ninomiya S, Matsuo J, Gilmore IS, Seah MP, Shard AG: Organic depth profiling of a nanostructured delta layer reference material using large argon cluster ions. Anal Chem 2010, 82:98-105. The authors show that large argon clusters are a method of choice for organic depth profiling since it sputter surface layers without damaging the underlying ones. 55. Ninomiya S, Ichiki K, Seki T, Aoki T, Matsuo J: The emission process of secondary ions from solids bombarded with large gas cluster ions. Nucl Instrum Methods Phys Res B 2009, 267:2601-2604. 56. Wehbe N, Fallavier M, Della Negra S, Depauw J, Brunelle A, Andersen HH: Cluster size and velocity dependences of sputtering and secondary ion emission under gold cluster impact. Nucl Instrum Methods Phys Res B 2010, 268:2596-2602. 57. Walker AV: Why is SIMS underused in chemical and biological analysis? Challenges and opportunities. Anal Chem 2008, 80:8865-8870.
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