Progress in Lipid Research 54 (2014) 32–52
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Progress in Lipid Research journal homepage: www.elsevier.com/locate/plipres
Review
Lipidomics in situ: Insights into plant lipid metabolism from high resolution spatial maps of metabolites Patrick J. Horn 1, Kent D. Chapman ⇑ Center for Plant Lipid Research, Department of Biological Sciences, University of North Texas, Denton, TX 76203-5017, USA
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Article history: Received 26 September 2013 Received in revised form 14 January 2014 Accepted 14 January 2014 Available online 27 January 2014 Keywords: Mass spectrometry imaging Seeds Lipids Triacylglycerols Phospholipids MALDI
a b s t r a c t The emergence of ‘omics’ technologies (i.e. genomics, proteomics, metabolomics, etc.) have revealed new avenues for exploring plant metabolism through data-rich experimentation and integration of complementary methodologies. Over the past decade, the lipidomics field has benefited from advances in instrumentation, especially mass spectrometry (MS)-based approaches that are well-suited for detailed lipid analysis. The broad classification of what constitutes a lipid lends itself to a structurally diverse range of molecules that contribute to a variety of biological processes in plants including membrane structure and transport, primary and secondary metabolism, abiotic and biotic stress tolerances, extracellular and intracellular signaling, and energy-rich storage of carbon. Progress in these research areas has been advanced in part through approaches analyzing chemical compositions of lipids in extracts from cells, tissues and/or whole organisms (e.g. shotgun lipidomics), and through visualization approaches primarily through microscopy-based methodologies (e.g. fluorescence, bright field, electron microscopy, etc.). While these techniques on their own provide rich biochemical and biological information, coordinated analyses of the complexity of lipid composition with the localization of these lipids at a high spatial resolution will help to develop a new level of understanding of lipid metabolism within the context of tissue/cellular compartmentation. This review will elaborate on recent advances of one such approach – mass spectrometry imaging (MSI) – that integrates in situ visualization with chemical-based lipidomics. We will illustrate, with an emphasis on oilseed lipid metabolism, how MS imaging can provide new insights and questions related to the spatial compartmentation of lipid metabolism in plants. Further it will be apparent that this MS imaging approach has broad application in plant metabolic research well beyond that of triacylglycerol biosynthesis in oilseeds. Ó 2014 Elsevier Ltd. All rights reserved.
Contents 1.
Overview of mass spectrometry imaging of lipid metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Secondary ion mass spectrometry (SIMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2. Desorption electrospray ionization-mass spectrometry (DESI-MS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4. Metabolite image reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5. Considerations and precautions for in situ lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1. Sample preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2. Matrix selection and deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3. Metabolite (in-source) fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4. Ion suppression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.5. Accurate mass measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33 33 33 34 35 36 36 36 37 37 37
Abbreviations: MS, mass spectrometry; MSI, mass spectrometry imaging; MALDI, matrix-assisted laser desorption/ionization; ESI, electrospray ionization; NMR, nuclear magnetic resonance; TAG, triacylglycerol; DAG, diacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PA, phosphatidic acid; PI, phosphatidylinositol; DGAT, diacylglycerol acyltransferase; PDAT, phosphatidylcholine: diacylglycerol acyltransferase; FA, fatty acids; P, palmitic (16:0); Ln, linolenic (18:3); L, linoleic (18:2); O, oleic (18:1); S, stearic (18:0); G, gadoleic (20:1); numerical designation of lipids indicates number of carbons in acyl chains, number of double bonds. ⇑ Corresponding author. Address: University of North Texas, 1155 Union Circle #305220, Denton, TX 76203-5017, USA. Tel.: +1 940 565 2969; fax: +1 940 565 4136. E-mail address: [email protected] (K.D. Chapman). 1 Present address: Department of Plant Biology, Michigan State University, East Lansing, MI 48824-1312, USA. 0163-7827/$ - see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.plipres.2014.01.003
P.J. Horn, K.D. Chapman / Progress in Lipid Research 54 (2014) 32–52
2.
3.
4.
1.5.6. Reproducibility and validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning lessons about oilseed metabolism from metabolite location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Does heterogeneity of TAG metabolites suggest differences in localization of enzymes? Yes, no and maybe . . . . . . . . . . . . . . . . . . . . . . . 2.1.1. Cyclopropane fatty acid (CPFA) synthase/desaturase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2. Delta-12 fatty acid desaturase (FAD2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3. Delta-15 fatty acid desaturase (FAD3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4. Fatty acid elongase 1 (FAE1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5. Tissue-specific acyltransferase activities? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Total oil distribution by nuclear magnetic resonance (NMR) approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Spatial information informs metabolic engineering experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imaging phospholipids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Phosphatidylcholine as a TAG metabolite and structural membrane lipid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Phosphatidylethanolamine (PE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Phosphatidic acid (PA) and phosphatidylinositol (PI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Potential for future development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Improvements in spatial resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Metabolite identification and quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Expanding range of metabolites and tissues for imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Co-localization of metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Alternative imaging approaches and correlated imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Combine with transcriptomics and/or proteomics to co-localize metabolites, enzymes and expressed genes. . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Overview of mass spectrometry imaging of lipid metabolites Mass spectrometry imaging (MSI, sometimes also abbreviated IMS) is defined by a group of analytical platforms that map the location and relative abundance of metabolites in situ. Each platform operates with a similar overall process by rastering over a tissue surface and producing a set of ions from endogenous metabolites that can be identified and visualized by measuring their mass-to-charge ratios (Fig. 1). The ability to correlate location and metabolite abundance is the key to MS imaging. It is this attribute and improved instrument accessibility [1] that has resulted in a steady increase in number of publications on MS imaging in the last few years (Fig. 2) both in terms of technical advances as well as addressing specific biological questions. In contrast to conventional chemical extracts where spatial information is minimal or absent, in MSI, prepared tissue samples are analyzed directly retaining crucial spatial information for enhanced biochemical characterization. The three primary MS imaging ionization sources adopted commercially to-date include secondary ion MS (SIMS), desorption electrospray ionization (DESI) MS, and matrix-assisted laser desorption/ionization (MALDI) MS. Each platform varies in its mechanism of ionization and therefore offers different levels of spatial resolution (i.e. smallest distance that offers a distinguishable chemical profile) and available imaging applications. Integration of these ionization sources with high-resolution, accurate mass analyzers has enabled high-confidence identification of the ions generated by MSI. Once the raw data is acquired for each MSI method, a spatial map of an ion or set of ions representing a metabolite, protein, or class of desired molecules, can then be reconstructed and visualized using specialized imaging software. Several recent reviews have been published detailing some of the advances in MS imaging [2–4]. Here we will focus mostly on features, considerations, and applications of MALDI-MSI to plant lipid research.
1.1. Secondary ion mass spectrometry (SIMS) SIMS operates through the emission of secondary metabolite ions from a tissue surface following bombardment by a primary ion beam (1–40 keV) [5,6]. While SIMS was one of first imaging methodologies developed [7–9], recent modifications in SIMS
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37 37 39 40 41 43 43 43 44 44 45 45 46 46 47 47 47 48 48 49 49 49
instrumentation now allow imaging of biomolecules up to m/z 1000 [10] which has widened its applicability for metabolites but not yet adequate for most proteomics approaches. SIMS sampling resolution of 400 nm to 1–2 lm [11] is superior to comparable imaging platforms and allows potential subcellular imaging and at attomolar concentrations [12]. Unfortunately, SIMS also suffers from extensive fragmentation and low ion yields [5]. Since SIMS requires a strong vacuum, sample preparation is critical to maintaining structural integrity. Sample preparation methods for tissue analysis (similar to those also required for MALDI) including stages of freezing, sectioning, and drying have been adapted from histological protocols. Methods are suitable for cellular or subcellular analysis with minor modifications such as eliminating freeze-drying [6], or through frozen hydration where cells are kept frozen throughout analysis without a drying step, which, incidentally, was found to enhance phospholipid signals [13]. Most of the applications to date for lipid imaging by SIMS [6] have been restricted to animal tissues [14–16] including lipid classes such as fatty acyls, glycerophospholipids, sphingolipids, sterol lipids, and prenol lipids. For plant lipid imaging, the applications are fewer due to the availability of instruments and difficulties in sample preparation but do include a recent report of successfully imaging flavonoids in pea (Pisum sativum) and Arabidopsis thaliana seed sections [17].
1.2. Desorption electrospray ionization-mass spectrometry (DESI-MS) DESI operates through the generation of charged secondary droplets containing metabolite ions by directing pneumatically-assisted charged droplets at a tissue surface under atmospheric pressure [18]. DESI requires little sample preparation, offering a simpler alternative to SIMS and MALDI. Tissues are often imprinted on materials such as porous Teflon [19] as an alternative to tissue sections. Unfortunately, due to the ionization mechanism, DESI in most cases is currently limited to spatial resolutions around 100– 250 lm [20,21]. A recent report, imaging lipids in brain tissues with morphologically distinct features, demonstrates that spatial resolutions of 35 lm are possible through optimization of DESI experimental parameters [22]. Due to the nature of DESI, much of the experimentation in plant tissues has focused on the surface analysis of lipids [23] in particular on leaves and petals [24], or on secondary metabolites in various tissues [19,25–27].
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A
B co Y
Laser
co
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ea
ea
C
Laser Phospholipids
Triacylglycerols mass spectrometer
Ions
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E TAG Species: Acyl Chains: Netural Formula: Adduct: Theoretical m/z : Mean Measured m/z: X-Coord Y-Coord
875 850 825 800 775 750 725 700 675 650 625 600
1075 1075 1075 1075 1075 1075 1075 1075 1075 1075 1075 1075
TAG-52:4 TAG-16:0/18:2/18:2 C55H98O6 K+ 893.700 893.701
TAG-54:6 TAG-18:2/18:2/18:2 C57H98O6 K+ 917.700 917.702 Ion Intensity
TAG-56:6 TAG-18:2/18:3/20:1 C59H102O6 K+ 945.731 945.734
0 3.1E+04 2.5E+04 2.0E+05 1.4E+05 1.6E+05 1.1E+05 1.5E+05 2.1E+05 2.3E+05 7.9E+04 6.7E+04
0 4.4E+04 6.6E+04 2.8E+05 1.9E+05 2.3E+05 1.6E+05 2.4E+05 2.9E+05 3.0E+05 9.3E+04 8.6E+04
0 2.8E+04 4.5E+04 2.1E+05 1.6E+05 1.6E+05 1.0E+05 1.3E+05 1.5E+05 2.0E+05 6.0E+04 4.9E+04
Fig. 1. Mass spectrometry imaging (MSI) schematic. (A) Plant tissues to be imaged are prepared and cryosectioned with caution to preserve metabolite quantities and localization (see Section 1.5). The example shown is a Camelina sativa seed representative cross section (30 lm thickness) with labeled embryonic axis (ea) and cotyledons (co). Scale bars = 500 lm. (B) MSI experiments rasterize across an x- and y-coordinate plane at selected spatial resolutions (e.g. 25 lm) generating metabolite ions at each spot selected. (C) At each spot selected, the ionization source (i.e. laser) generates a set of metabolite (and other) ions that are directed into the mass spectrometer where their mass-to-charge ratios are measured. (D) Metabolites can be preliminarily identified using the accurate mass and high resolution capabilities of MS imaging instrumentation. At each spot, the absolute intensities (and relative amounts) can be extracted and annotated for each metabolite detected. (E) Ultimately, the raw metabolite information in part D is analyzed by computational software (see Section 1.4) where MS images of metabolite distribution can be reconstructed and visualized.
1.3. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) A third major type MS imaging methodology, MALDI-MS, has been used to generate spatial maps of plant metabolites especially lipids [28,29]. MALDI is a soft ionization method which relies on a sample embedded with a chemical matrix. This chemical matrix
(e.g. commonly an organic acid such as 2,5-dihydroxybenzoic acid, DHB) promotes ionization of the metabolites and reduces the likelihood of these biomolecules being destroyed upon the ionization/ desorption event. The chemical matrix is typically applied to tissue sections which must be carefully prepared as with SIMS (Section 1.1) due to the ionization event occurring under a strong vacuum. A laser beam is rastered over the matrix-embedded tissue
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0 2005
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Year of Publication Fig. 2. Publications in mass spectrometry imaging field. Number of peer-reviewed scientific publications containing the term ‘‘imaging mass spectrometry’’ or ‘‘mass spectrometry imaging’’ from the Web of KnowledgeSM database.
surface to generate ions at each spot where the laser shot is applied. The diameter of the applied laser beam frequently limits the spatial resolution of MALDI, most often to >5 lm in current instruments. This microprobe style of imaging relies on positioncorrelated image reconstruction; however, MALDI can also be set up as a microscope-style instrument using a defocused laser source and a position-sensitive detector [30]. The utilization of MALDI as an imaging technology [3] has evolved from many years of using it as a soft ionization source for sampling of proteins [31] and lipids [32] in solutions spotted onto metal stages. The ability to interface MALDI with several downstream MS detectors, its excellent spatial resolution, and lower instrumentation cost, all have resulted in MALDI as an attractive and well-utilized platform for MS imaging. Nevertheless, there are still several areas of consideration for lipid imaging by MALDI-MSI and these are discussed in Section 1.5. Over the past decade, as research institutions have started to establish MS imaging facilities, the number of publications on imaging plant lipids has steadily increased. Some of the first MALDI-MS imaging reports described the mapping of free fatty acids within strawberry seeds (Fragaria spp.) and apple (Malus domestica) [33], surface lipids in floral and leaf tissues A. thaliana [34– 36], phosphatidylcholine (PC) species in rice grain (Oryza sativa) [37], and ginsenosides, a class of steroid glycosides, in roots of Panax ginseng [38]. More recently, MALDI-TOF-MS of Capsicum fruits revealed the presence of capsaicin primarily in placental tissue with minor amounts in the pericarp regions and even lower amounts detected in seed tissues [39]. MS imaging of root nodules of Medicago truncatula–Sinorhizobium meliloti during nitrogen fixation [40] demonstrated the identification of several metabolites including flavonoids, organic acids, and carbohydrates that have been implicated in the transfer of carbon, or signals within the nodule-bacteroid symbiotic relationship. Several studies (described in more detail in Section 2) have been reported for visualizing detailed membrane and storage lipid compositions in ‘‘wildtype’’ [41] and transgenic cotton (Gossypium hirsutum) seeds [42], triacylglycerol (TAG) species in wild-type and transgenic Camelina sativa seeds [43], TAG and PC species in engineered tobacco (Nicotiana tabacum) leaves [44], and storage lipids in avocado (Persea americana) mesocarp tissue [45].
1.4. Metabolite image reconstruction Specialized software is often required for processing the complex raw MS imaging spectral data and producing images of
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in situ metabolite distributions [46]. Advances within these tailored computer applications have enhanced the utility of MS imaging instrumentation for addressing biological questions. In a typical MS imaging experiment, data acquired at each tissue location (Fig. 1B) contains several pieces of information, most importantly the location acquired at selected x- and y- (and sometimes z-) coordinates and the absolute intensities of each ion (at selected m/z’s) detected (Fig. 1C and D). This absolute intensity data can be normalized and colorized to represent the absolute or relative distribution of a selected m/z or set of m/z’s at each x- and y-position. This in turn produces a representation of the metabolite distribution within the tissue (Fig. 1). Depending on the experimental design, both at the sample (e.g. a single metabolite standard, a chemical extract containing several metabolites, or a tissue section) and acquisition levels (e.g. full scan analysis over an m/z range or combination scheme of full and tandem MS (MS/MS) scans [47]), the complexity of acquired raw data (e.g. files often contain gigabytes of data comprised of several million data points), even for a single tissue location, can be daunting for the most experienced mass spectrometrist. While most of the software packages (both commercial and academic) available now enable users to generate two-dimensional images for external interpretation, it is only a matter of time before algorithms will be developed for multi-layered, computational interpretation of complex metabolite distributions. Metabolite Imager is one application that was developed recently [46] to enable customized imaging and analysis of lipids in tissue sections [41–45] as well as algorithms for visualizing and analyzing many other types of metabolites. Within the Metabolite Imager program, there are several features to address processes involved with both data extraction and image reconstruction. Most often users use a targeted search approach; i.e., they know the metabolite(s) to be imaged and some idea of how it behaves in the MALDI- MS instrument. This includes taking into consideration if the metabolite forms one or more ions (e.g. [M + H]+, [M + K]+, [M + Na]+, etc.), whether the metabolite fragments within the source, whether the metabolite abundance needs to be normalized/corrected due to suppression, and finally whether this metabolite will be considered as a single image measured in absolute intensities or imaged within class of related metabolites and represented as a mole fraction of the class. Metabolite Imager interfaces with widely-used lipidomics/metabolomics databases, such as LipidMAPS (38,000 metabolites), KEGG (3350), MetaCyc (10,000), NIST Chemistry WebBook (69,000), and Plant Metabolic Network (1400) databases, to facilitate identification and analysis. One area that may provide important new insights about unknown metabolites and processes, is the co-localization of metabolites and searching for untargeted metabolites through frequency-based and/or spatially-based algorithms. There are several other applications available either through open source/freeware such as BioMap (Novartis, Basel, Switzerland), DataCube Explorer (AMOLF), and Mirion (JLU) or proprietary software from instrument vendors such as Bruker Daltonics (flexImaging), Shimadzu Biotechnology (Intensity Mapping), AB-SCIEX (TissueView) and Thermo Fisher Scientific (Thermo ImageQuest™ [48]). Complementary applications such as the comparative analysis of multiple tissues through multivariate analysis and data reduction [49], quantification of metabolites [50], and extension of 2D–3D imaging [51] provide additional imaging support. One area that continues to be addressed is the move to a consistent, raw data storage format such as imZML [52] to enhance the compatibility of available software on the different commercial instruments. Ultimately this would result in less application redundancy and would help to address limitations in comparative analysis between different instruments. Although there are several specialized software applications developed and used by different
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groups, it is not unreasonable to think, similar to the array of software packages available for gene expression analysis, that each can be tailored toward addressing specific instrument/conditions and different potential biological questions (e.g., metabolites that are specifically associated with certain cell/tissue types or functional processes). 1.5. Considerations and precautions for in situ lipidomics Similar to the development and advances of most methodologies, there are several areas where caution should be exercised when designing and executing MS imaging experiments. While each MS imaging apparatus (i.e. MALDI vs. DESI vs. SIMS vs. etc.) has some specialized areas of concerns, the focus here is on considerations for imaging lipids using MALDI-MSI; several of the concerns can be applied to other approaches as well. Foremost, sample preparation is an important key to accurate localization of metabolites in tissues. Based on our experience, we recommend minimizing the number of steps for handling tissue samples before analysis. Once the tissue sample is prepared, matrix selection and mode of deposition can alter metabolite responses. Although MALDI is considered a soft ionization method, several metabolites will still fragment upon ionization. A major limitation to quantification and accurate metabolite localization is the suppression of certain metabolite classes by ions derived from other classes. While most metabolites can be identified using accurate mass measurements, this is not always the case. Finally, evaluating the reproducibility of tissue-to-tissue and technical variation is an area that needs further consideration. Each of these areas of concern are presented below in brief to enable future users to consider available approaches and possible complications when visualizing and interpreting in situ metabolite distributions. 1.5.1. Sample preparation Sample preparation continues to be a major consideration for integrity of metabolite compartmentation. Several excellent reviews provide a broad range of experiences in optimizing and evaluating sample preparation methods [53–55]. Currently there are a wide range of published protocols for preparing tissue samples for lipid imaging in both plant [35,37,43] and animal [56–58] systems. One drawback in standardizing sample preparation across diverse tissue systems comes from the variation in imaging instrumentation capabilities. Nevertheless, as the number of applications for MSI has continued to expand, particular in the biomedical community, several of the issues with specimen preparation are well described and now better understood. Some of the key variables that need to be carefully considered include chemical fixation, embedding medium, tissue freezing, cryosectioning, tissue preservation, and matrix deposition. Since the goal of MS imaging is to visualize an accurate representation of the metabolites in situ one has to consider the initial preservation steps that generates a snap-shot of the metabolite state. This step might include chemical fixation and/or freezing. Our initial evidence in cotton seeds showed that lightly fixed (4% paraformaldehyde) embryo sections visually showed less structural damage than unfixed tissues [41]. However, follow-up experiments demonstrated that unfixed seeds could be sectioned as well as fixed seeds if given an appropriate equilibration transition time from freezer to cryostat [42]. One concern in fixed tissues is that lipids such as phosphatidylethanolamine (PE) with an amine group could become cross-linked. However, PE species were detected in sections of fixed embryos, albeit at lower levels than expected based on quantities measured in chemical extracts. Even with suppression of PE by PC (and possibly other generated ions) aside, it appears that some lipids susceptible to cross-linking are still available for ionization after chemical fixation of tissues. Still, there is a
possibility that fixation may influence the detection of some metabolites, so preparation of tissues by freezing alone may be preferable in most cases. For most tissues snap freezing tissue in liquid nitrogen can minimize proteolytic damage and preserve structural integrity [59]. Recent reports in mammalian brain sections demonstrate possible degradation of transient molecules such as acetylcholine through standard sample preparation protocols [60]. A variation of in situ freezing method of the brain samples was necessary to improve the signal-to-noise ratio for small labile molecules. For oilseeds that are in a more dormant state and have a protective seed coat we have found little differences in flash freezing seeds using a combination of cold ( 78 °C) isopentane and dry ice versus a slow freeze of embedded seeds in a 80 °C freezer (despite the concerns of ice crystal formation). Depending on the nature of plant tissue and metabolites attempting to be visualized, the form of fixation and freezing must be chosen carefully [2]. Alternative methods need to be continually evaluated as different plant tissues are tested, including rapid freezing, floating tissues in aluminum foil ‘‘boats’’ on liquid nitrogen, slow freezing, and/or the use of cryoprotectants such as 30% sucrose solution. Regardless of the protocol chosen, tissues should ideally remain frozen (< 20 °C) during and post-sectioning before lyophilization and MSI analysis. Unfortunately, many of the chemicals used for embedding in traditional histology are incompatible with MALDI-MSI, usually because they ionize well, suppress native compounds, and sometimes require clean-up steps that utilize organic solvents that would remove lipids from plant tissues [61,62]. Currently, there have been several published reports using different embedding medium including optical cutting temperature (OCT) polymer [41], gelatin [40,43,63], ice [64], agarose [65] and carboxymethylcellulose [66]. Formalin-fixed, paraffin-embedded sections (FFPE) still represent an alternative method that has been used with some success for analyzing proteins; however, it still has not been shown to work well with lipids [59], possibly due to the use of organic solvents for dehydration and clean-up. It is difficult to evaluate which of these embedding mediums is best since all of these studies used different tissue systems, varied in sample preparation procedures, and utilized different MS instruments. The sectioning thickness imparted by a cryostat is an important consideration for maintaining structural integrity and achieving maximal ionization efficiency. For larger molecules such as proteins, a direct comparison of tissue thickness determined that thinner sections (
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Progress in Lipid Research journal homepage: www.elsevier.com/locate/plipres
Review
Lipidomics in situ: Insights into plant lipid metabolism from high resolution spatial maps of metabolites Patrick J. Horn 1, Kent D. Chapman ⇑ Center for Plant Lipid Research, Department of Biological Sciences, University of North Texas, Denton, TX 76203-5017, USA
a r t i c l e
i n f o
Article history: Received 26 September 2013 Received in revised form 14 January 2014 Accepted 14 January 2014 Available online 27 January 2014 Keywords: Mass spectrometry imaging Seeds Lipids Triacylglycerols Phospholipids MALDI
a b s t r a c t The emergence of ‘omics’ technologies (i.e. genomics, proteomics, metabolomics, etc.) have revealed new avenues for exploring plant metabolism through data-rich experimentation and integration of complementary methodologies. Over the past decade, the lipidomics field has benefited from advances in instrumentation, especially mass spectrometry (MS)-based approaches that are well-suited for detailed lipid analysis. The broad classification of what constitutes a lipid lends itself to a structurally diverse range of molecules that contribute to a variety of biological processes in plants including membrane structure and transport, primary and secondary metabolism, abiotic and biotic stress tolerances, extracellular and intracellular signaling, and energy-rich storage of carbon. Progress in these research areas has been advanced in part through approaches analyzing chemical compositions of lipids in extracts from cells, tissues and/or whole organisms (e.g. shotgun lipidomics), and through visualization approaches primarily through microscopy-based methodologies (e.g. fluorescence, bright field, electron microscopy, etc.). While these techniques on their own provide rich biochemical and biological information, coordinated analyses of the complexity of lipid composition with the localization of these lipids at a high spatial resolution will help to develop a new level of understanding of lipid metabolism within the context of tissue/cellular compartmentation. This review will elaborate on recent advances of one such approach – mass spectrometry imaging (MSI) – that integrates in situ visualization with chemical-based lipidomics. We will illustrate, with an emphasis on oilseed lipid metabolism, how MS imaging can provide new insights and questions related to the spatial compartmentation of lipid metabolism in plants. Further it will be apparent that this MS imaging approach has broad application in plant metabolic research well beyond that of triacylglycerol biosynthesis in oilseeds. Ó 2014 Elsevier Ltd. All rights reserved.
Contents 1.
Overview of mass spectrometry imaging of lipid metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Secondary ion mass spectrometry (SIMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2. Desorption electrospray ionization-mass spectrometry (DESI-MS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4. Metabolite image reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5. Considerations and precautions for in situ lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1. Sample preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2. Matrix selection and deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3. Metabolite (in-source) fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4. Ion suppression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.5. Accurate mass measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33 33 33 34 35 36 36 36 37 37 37
Abbreviations: MS, mass spectrometry; MSI, mass spectrometry imaging; MALDI, matrix-assisted laser desorption/ionization; ESI, electrospray ionization; NMR, nuclear magnetic resonance; TAG, triacylglycerol; DAG, diacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PA, phosphatidic acid; PI, phosphatidylinositol; DGAT, diacylglycerol acyltransferase; PDAT, phosphatidylcholine: diacylglycerol acyltransferase; FA, fatty acids; P, palmitic (16:0); Ln, linolenic (18:3); L, linoleic (18:2); O, oleic (18:1); S, stearic (18:0); G, gadoleic (20:1); numerical designation of lipids indicates number of carbons in acyl chains, number of double bonds. ⇑ Corresponding author. Address: University of North Texas, 1155 Union Circle #305220, Denton, TX 76203-5017, USA. Tel.: +1 940 565 2969; fax: +1 940 565 4136. E-mail address: [email protected] (K.D. Chapman). 1 Present address: Department of Plant Biology, Michigan State University, East Lansing, MI 48824-1312, USA. 0163-7827/$ - see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.plipres.2014.01.003
P.J. Horn, K.D. Chapman / Progress in Lipid Research 54 (2014) 32–52
2.
3.
4.
1.5.6. Reproducibility and validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning lessons about oilseed metabolism from metabolite location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Does heterogeneity of TAG metabolites suggest differences in localization of enzymes? Yes, no and maybe . . . . . . . . . . . . . . . . . . . . . . . 2.1.1. Cyclopropane fatty acid (CPFA) synthase/desaturase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2. Delta-12 fatty acid desaturase (FAD2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3. Delta-15 fatty acid desaturase (FAD3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4. Fatty acid elongase 1 (FAE1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5. Tissue-specific acyltransferase activities? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Total oil distribution by nuclear magnetic resonance (NMR) approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Spatial information informs metabolic engineering experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imaging phospholipids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Phosphatidylcholine as a TAG metabolite and structural membrane lipid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Phosphatidylethanolamine (PE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Phosphatidic acid (PA) and phosphatidylinositol (PI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Potential for future development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Improvements in spatial resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Metabolite identification and quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Expanding range of metabolites and tissues for imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Co-localization of metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Alternative imaging approaches and correlated imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Combine with transcriptomics and/or proteomics to co-localize metabolites, enzymes and expressed genes. . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Overview of mass spectrometry imaging of lipid metabolites Mass spectrometry imaging (MSI, sometimes also abbreviated IMS) is defined by a group of analytical platforms that map the location and relative abundance of metabolites in situ. Each platform operates with a similar overall process by rastering over a tissue surface and producing a set of ions from endogenous metabolites that can be identified and visualized by measuring their mass-to-charge ratios (Fig. 1). The ability to correlate location and metabolite abundance is the key to MS imaging. It is this attribute and improved instrument accessibility [1] that has resulted in a steady increase in number of publications on MS imaging in the last few years (Fig. 2) both in terms of technical advances as well as addressing specific biological questions. In contrast to conventional chemical extracts where spatial information is minimal or absent, in MSI, prepared tissue samples are analyzed directly retaining crucial spatial information for enhanced biochemical characterization. The three primary MS imaging ionization sources adopted commercially to-date include secondary ion MS (SIMS), desorption electrospray ionization (DESI) MS, and matrix-assisted laser desorption/ionization (MALDI) MS. Each platform varies in its mechanism of ionization and therefore offers different levels of spatial resolution (i.e. smallest distance that offers a distinguishable chemical profile) and available imaging applications. Integration of these ionization sources with high-resolution, accurate mass analyzers has enabled high-confidence identification of the ions generated by MSI. Once the raw data is acquired for each MSI method, a spatial map of an ion or set of ions representing a metabolite, protein, or class of desired molecules, can then be reconstructed and visualized using specialized imaging software. Several recent reviews have been published detailing some of the advances in MS imaging [2–4]. Here we will focus mostly on features, considerations, and applications of MALDI-MSI to plant lipid research.
1.1. Secondary ion mass spectrometry (SIMS) SIMS operates through the emission of secondary metabolite ions from a tissue surface following bombardment by a primary ion beam (1–40 keV) [5,6]. While SIMS was one of first imaging methodologies developed [7–9], recent modifications in SIMS
33
37 37 39 40 41 43 43 43 44 44 45 45 46 46 47 47 47 48 48 49 49 49
instrumentation now allow imaging of biomolecules up to m/z 1000 [10] which has widened its applicability for metabolites but not yet adequate for most proteomics approaches. SIMS sampling resolution of 400 nm to 1–2 lm [11] is superior to comparable imaging platforms and allows potential subcellular imaging and at attomolar concentrations [12]. Unfortunately, SIMS also suffers from extensive fragmentation and low ion yields [5]. Since SIMS requires a strong vacuum, sample preparation is critical to maintaining structural integrity. Sample preparation methods for tissue analysis (similar to those also required for MALDI) including stages of freezing, sectioning, and drying have been adapted from histological protocols. Methods are suitable for cellular or subcellular analysis with minor modifications such as eliminating freeze-drying [6], or through frozen hydration where cells are kept frozen throughout analysis without a drying step, which, incidentally, was found to enhance phospholipid signals [13]. Most of the applications to date for lipid imaging by SIMS [6] have been restricted to animal tissues [14–16] including lipid classes such as fatty acyls, glycerophospholipids, sphingolipids, sterol lipids, and prenol lipids. For plant lipid imaging, the applications are fewer due to the availability of instruments and difficulties in sample preparation but do include a recent report of successfully imaging flavonoids in pea (Pisum sativum) and Arabidopsis thaliana seed sections [17].
1.2. Desorption electrospray ionization-mass spectrometry (DESI-MS) DESI operates through the generation of charged secondary droplets containing metabolite ions by directing pneumatically-assisted charged droplets at a tissue surface under atmospheric pressure [18]. DESI requires little sample preparation, offering a simpler alternative to SIMS and MALDI. Tissues are often imprinted on materials such as porous Teflon [19] as an alternative to tissue sections. Unfortunately, due to the ionization mechanism, DESI in most cases is currently limited to spatial resolutions around 100– 250 lm [20,21]. A recent report, imaging lipids in brain tissues with morphologically distinct features, demonstrates that spatial resolutions of 35 lm are possible through optimization of DESI experimental parameters [22]. Due to the nature of DESI, much of the experimentation in plant tissues has focused on the surface analysis of lipids [23] in particular on leaves and petals [24], or on secondary metabolites in various tissues [19,25–27].
34
P.J. Horn, K.D. Chapman / Progress in Lipid Research 54 (2014) 32–52
A
B co Y
Laser
co
X
ea
ea
C
Laser Phospholipids
Triacylglycerols mass spectrometer
Ions
D
E TAG Species: Acyl Chains: Netural Formula: Adduct: Theoretical m/z : Mean Measured m/z: X-Coord Y-Coord
875 850 825 800 775 750 725 700 675 650 625 600
1075 1075 1075 1075 1075 1075 1075 1075 1075 1075 1075 1075
TAG-52:4 TAG-16:0/18:2/18:2 C55H98O6 K+ 893.700 893.701
TAG-54:6 TAG-18:2/18:2/18:2 C57H98O6 K+ 917.700 917.702 Ion Intensity
TAG-56:6 TAG-18:2/18:3/20:1 C59H102O6 K+ 945.731 945.734
0 3.1E+04 2.5E+04 2.0E+05 1.4E+05 1.6E+05 1.1E+05 1.5E+05 2.1E+05 2.3E+05 7.9E+04 6.7E+04
0 4.4E+04 6.6E+04 2.8E+05 1.9E+05 2.3E+05 1.6E+05 2.4E+05 2.9E+05 3.0E+05 9.3E+04 8.6E+04
0 2.8E+04 4.5E+04 2.1E+05 1.6E+05 1.6E+05 1.0E+05 1.3E+05 1.5E+05 2.0E+05 6.0E+04 4.9E+04
Fig. 1. Mass spectrometry imaging (MSI) schematic. (A) Plant tissues to be imaged are prepared and cryosectioned with caution to preserve metabolite quantities and localization (see Section 1.5). The example shown is a Camelina sativa seed representative cross section (30 lm thickness) with labeled embryonic axis (ea) and cotyledons (co). Scale bars = 500 lm. (B) MSI experiments rasterize across an x- and y-coordinate plane at selected spatial resolutions (e.g. 25 lm) generating metabolite ions at each spot selected. (C) At each spot selected, the ionization source (i.e. laser) generates a set of metabolite (and other) ions that are directed into the mass spectrometer where their mass-to-charge ratios are measured. (D) Metabolites can be preliminarily identified using the accurate mass and high resolution capabilities of MS imaging instrumentation. At each spot, the absolute intensities (and relative amounts) can be extracted and annotated for each metabolite detected. (E) Ultimately, the raw metabolite information in part D is analyzed by computational software (see Section 1.4) where MS images of metabolite distribution can be reconstructed and visualized.
1.3. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) A third major type MS imaging methodology, MALDI-MS, has been used to generate spatial maps of plant metabolites especially lipids [28,29]. MALDI is a soft ionization method which relies on a sample embedded with a chemical matrix. This chemical matrix
(e.g. commonly an organic acid such as 2,5-dihydroxybenzoic acid, DHB) promotes ionization of the metabolites and reduces the likelihood of these biomolecules being destroyed upon the ionization/ desorption event. The chemical matrix is typically applied to tissue sections which must be carefully prepared as with SIMS (Section 1.1) due to the ionization event occurring under a strong vacuum. A laser beam is rastered over the matrix-embedded tissue
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Publications
200
150
100
50
0 2005
2006
2007
2008
2009
2010
2011
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Year of Publication Fig. 2. Publications in mass spectrometry imaging field. Number of peer-reviewed scientific publications containing the term ‘‘imaging mass spectrometry’’ or ‘‘mass spectrometry imaging’’ from the Web of KnowledgeSM database.
surface to generate ions at each spot where the laser shot is applied. The diameter of the applied laser beam frequently limits the spatial resolution of MALDI, most often to >5 lm in current instruments. This microprobe style of imaging relies on positioncorrelated image reconstruction; however, MALDI can also be set up as a microscope-style instrument using a defocused laser source and a position-sensitive detector [30]. The utilization of MALDI as an imaging technology [3] has evolved from many years of using it as a soft ionization source for sampling of proteins [31] and lipids [32] in solutions spotted onto metal stages. The ability to interface MALDI with several downstream MS detectors, its excellent spatial resolution, and lower instrumentation cost, all have resulted in MALDI as an attractive and well-utilized platform for MS imaging. Nevertheless, there are still several areas of consideration for lipid imaging by MALDI-MSI and these are discussed in Section 1.5. Over the past decade, as research institutions have started to establish MS imaging facilities, the number of publications on imaging plant lipids has steadily increased. Some of the first MALDI-MS imaging reports described the mapping of free fatty acids within strawberry seeds (Fragaria spp.) and apple (Malus domestica) [33], surface lipids in floral and leaf tissues A. thaliana [34– 36], phosphatidylcholine (PC) species in rice grain (Oryza sativa) [37], and ginsenosides, a class of steroid glycosides, in roots of Panax ginseng [38]. More recently, MALDI-TOF-MS of Capsicum fruits revealed the presence of capsaicin primarily in placental tissue with minor amounts in the pericarp regions and even lower amounts detected in seed tissues [39]. MS imaging of root nodules of Medicago truncatula–Sinorhizobium meliloti during nitrogen fixation [40] demonstrated the identification of several metabolites including flavonoids, organic acids, and carbohydrates that have been implicated in the transfer of carbon, or signals within the nodule-bacteroid symbiotic relationship. Several studies (described in more detail in Section 2) have been reported for visualizing detailed membrane and storage lipid compositions in ‘‘wildtype’’ [41] and transgenic cotton (Gossypium hirsutum) seeds [42], triacylglycerol (TAG) species in wild-type and transgenic Camelina sativa seeds [43], TAG and PC species in engineered tobacco (Nicotiana tabacum) leaves [44], and storage lipids in avocado (Persea americana) mesocarp tissue [45].
1.4. Metabolite image reconstruction Specialized software is often required for processing the complex raw MS imaging spectral data and producing images of
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in situ metabolite distributions [46]. Advances within these tailored computer applications have enhanced the utility of MS imaging instrumentation for addressing biological questions. In a typical MS imaging experiment, data acquired at each tissue location (Fig. 1B) contains several pieces of information, most importantly the location acquired at selected x- and y- (and sometimes z-) coordinates and the absolute intensities of each ion (at selected m/z’s) detected (Fig. 1C and D). This absolute intensity data can be normalized and colorized to represent the absolute or relative distribution of a selected m/z or set of m/z’s at each x- and y-position. This in turn produces a representation of the metabolite distribution within the tissue (Fig. 1). Depending on the experimental design, both at the sample (e.g. a single metabolite standard, a chemical extract containing several metabolites, or a tissue section) and acquisition levels (e.g. full scan analysis over an m/z range or combination scheme of full and tandem MS (MS/MS) scans [47]), the complexity of acquired raw data (e.g. files often contain gigabytes of data comprised of several million data points), even for a single tissue location, can be daunting for the most experienced mass spectrometrist. While most of the software packages (both commercial and academic) available now enable users to generate two-dimensional images for external interpretation, it is only a matter of time before algorithms will be developed for multi-layered, computational interpretation of complex metabolite distributions. Metabolite Imager is one application that was developed recently [46] to enable customized imaging and analysis of lipids in tissue sections [41–45] as well as algorithms for visualizing and analyzing many other types of metabolites. Within the Metabolite Imager program, there are several features to address processes involved with both data extraction and image reconstruction. Most often users use a targeted search approach; i.e., they know the metabolite(s) to be imaged and some idea of how it behaves in the MALDI- MS instrument. This includes taking into consideration if the metabolite forms one or more ions (e.g. [M + H]+, [M + K]+, [M + Na]+, etc.), whether the metabolite fragments within the source, whether the metabolite abundance needs to be normalized/corrected due to suppression, and finally whether this metabolite will be considered as a single image measured in absolute intensities or imaged within class of related metabolites and represented as a mole fraction of the class. Metabolite Imager interfaces with widely-used lipidomics/metabolomics databases, such as LipidMAPS (38,000 metabolites), KEGG (3350), MetaCyc (10,000), NIST Chemistry WebBook (69,000), and Plant Metabolic Network (1400) databases, to facilitate identification and analysis. One area that may provide important new insights about unknown metabolites and processes, is the co-localization of metabolites and searching for untargeted metabolites through frequency-based and/or spatially-based algorithms. There are several other applications available either through open source/freeware such as BioMap (Novartis, Basel, Switzerland), DataCube Explorer (AMOLF), and Mirion (JLU) or proprietary software from instrument vendors such as Bruker Daltonics (flexImaging), Shimadzu Biotechnology (Intensity Mapping), AB-SCIEX (TissueView) and Thermo Fisher Scientific (Thermo ImageQuest™ [48]). Complementary applications such as the comparative analysis of multiple tissues through multivariate analysis and data reduction [49], quantification of metabolites [50], and extension of 2D–3D imaging [51] provide additional imaging support. One area that continues to be addressed is the move to a consistent, raw data storage format such as imZML [52] to enhance the compatibility of available software on the different commercial instruments. Ultimately this would result in less application redundancy and would help to address limitations in comparative analysis between different instruments. Although there are several specialized software applications developed and used by different
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groups, it is not unreasonable to think, similar to the array of software packages available for gene expression analysis, that each can be tailored toward addressing specific instrument/conditions and different potential biological questions (e.g., metabolites that are specifically associated with certain cell/tissue types or functional processes). 1.5. Considerations and precautions for in situ lipidomics Similar to the development and advances of most methodologies, there are several areas where caution should be exercised when designing and executing MS imaging experiments. While each MS imaging apparatus (i.e. MALDI vs. DESI vs. SIMS vs. etc.) has some specialized areas of concerns, the focus here is on considerations for imaging lipids using MALDI-MSI; several of the concerns can be applied to other approaches as well. Foremost, sample preparation is an important key to accurate localization of metabolites in tissues. Based on our experience, we recommend minimizing the number of steps for handling tissue samples before analysis. Once the tissue sample is prepared, matrix selection and mode of deposition can alter metabolite responses. Although MALDI is considered a soft ionization method, several metabolites will still fragment upon ionization. A major limitation to quantification and accurate metabolite localization is the suppression of certain metabolite classes by ions derived from other classes. While most metabolites can be identified using accurate mass measurements, this is not always the case. Finally, evaluating the reproducibility of tissue-to-tissue and technical variation is an area that needs further consideration. Each of these areas of concern are presented below in brief to enable future users to consider available approaches and possible complications when visualizing and interpreting in situ metabolite distributions. 1.5.1. Sample preparation Sample preparation continues to be a major consideration for integrity of metabolite compartmentation. Several excellent reviews provide a broad range of experiences in optimizing and evaluating sample preparation methods [53–55]. Currently there are a wide range of published protocols for preparing tissue samples for lipid imaging in both plant [35,37,43] and animal [56–58] systems. One drawback in standardizing sample preparation across diverse tissue systems comes from the variation in imaging instrumentation capabilities. Nevertheless, as the number of applications for MSI has continued to expand, particular in the biomedical community, several of the issues with specimen preparation are well described and now better understood. Some of the key variables that need to be carefully considered include chemical fixation, embedding medium, tissue freezing, cryosectioning, tissue preservation, and matrix deposition. Since the goal of MS imaging is to visualize an accurate representation of the metabolites in situ one has to consider the initial preservation steps that generates a snap-shot of the metabolite state. This step might include chemical fixation and/or freezing. Our initial evidence in cotton seeds showed that lightly fixed (4% paraformaldehyde) embryo sections visually showed less structural damage than unfixed tissues [41]. However, follow-up experiments demonstrated that unfixed seeds could be sectioned as well as fixed seeds if given an appropriate equilibration transition time from freezer to cryostat [42]. One concern in fixed tissues is that lipids such as phosphatidylethanolamine (PE) with an amine group could become cross-linked. However, PE species were detected in sections of fixed embryos, albeit at lower levels than expected based on quantities measured in chemical extracts. Even with suppression of PE by PC (and possibly other generated ions) aside, it appears that some lipids susceptible to cross-linking are still available for ionization after chemical fixation of tissues. Still, there is a
possibility that fixation may influence the detection of some metabolites, so preparation of tissues by freezing alone may be preferable in most cases. For most tissues snap freezing tissue in liquid nitrogen can minimize proteolytic damage and preserve structural integrity [59]. Recent reports in mammalian brain sections demonstrate possible degradation of transient molecules such as acetylcholine through standard sample preparation protocols [60]. A variation of in situ freezing method of the brain samples was necessary to improve the signal-to-noise ratio for small labile molecules. For oilseeds that are in a more dormant state and have a protective seed coat we have found little differences in flash freezing seeds using a combination of cold ( 78 °C) isopentane and dry ice versus a slow freeze of embedded seeds in a 80 °C freezer (despite the concerns of ice crystal formation). Depending on the nature of plant tissue and metabolites attempting to be visualized, the form of fixation and freezing must be chosen carefully [2]. Alternative methods need to be continually evaluated as different plant tissues are tested, including rapid freezing, floating tissues in aluminum foil ‘‘boats’’ on liquid nitrogen, slow freezing, and/or the use of cryoprotectants such as 30% sucrose solution. Regardless of the protocol chosen, tissues should ideally remain frozen (< 20 °C) during and post-sectioning before lyophilization and MSI analysis. Unfortunately, many of the chemicals used for embedding in traditional histology are incompatible with MALDI-MSI, usually because they ionize well, suppress native compounds, and sometimes require clean-up steps that utilize organic solvents that would remove lipids from plant tissues [61,62]. Currently, there have been several published reports using different embedding medium including optical cutting temperature (OCT) polymer [41], gelatin [40,43,63], ice [64], agarose [65] and carboxymethylcellulose [66]. Formalin-fixed, paraffin-embedded sections (FFPE) still represent an alternative method that has been used with some success for analyzing proteins; however, it still has not been shown to work well with lipids [59], possibly due to the use of organic solvents for dehydration and clean-up. It is difficult to evaluate which of these embedding mediums is best since all of these studies used different tissue systems, varied in sample preparation procedures, and utilized different MS instruments. The sectioning thickness imparted by a cryostat is an important consideration for maintaining structural integrity and achieving maximal ionization efficiency. For larger molecules such as proteins, a direct comparison of tissue thickness determined that thinner sections (
