Focus on Metabolism: Spatially Resolved Plant Metabolomics: Some Potentials and Limitations of Laser-Ablation Electrospray Ionization Mass Spectrometry Metabolite Imaging

Laser-ablation electrospray ionization (LAESI)-mass spectrometry imaging has been applied to contrasting plant organs to assess its potential as a procedure for performing in vivo metabolomics in plants. In a proof-of-concept experiment, purple/white segmented Phalaenopsis spp. petals were first analyzed using standard liquid chromatography-mass spectrometry analyses of separate extracts made specifically from the purple and white regions. Discriminatory compounds were defined and putatively annotated. LAESI analyses were then performed on living tissues, and these metabolites were then relocalized within the LAESI-generated data sets of similar tissues. Maps were made to illustrate their locations across the petals. Results revealed that, as expected, anthocyanins always mapped to the purple regions. Certain other (nonvisible) polyphenols were observed to colocalize with the anthocyanins, whereas others were found specifically within the white tissues. In a contrasting example, control and Cladosporium fulvum-infected tomato (Solanum lycopersicum) leaves were subjected to the same procedures, and it could be observed that the alkaloid tomatine has clear heterogeneous distribution across the tomato leaf lamina. Furthermore, LAESI analyses revealed perturbations in alkaloid content following pathogen infection. These results show the clear potential of LAESI-based imaging approaches as a convenient and rapid way to perform metabolomics analyses on living tissues. However, a range of limitations and factors have also been identified that must be taken into consideration when interpreting LAESI-derived data. Such aspects deserve further evaluation before this approach can be applied in a routine manner.Plants are a tremendously rich source of a myriad of structurally and chemically diverse metabolites (Rao and Ravishankar, 2002; D’Auria and Gershenzon, 2005). Many of these metabolites have a (partly) known function in the plant, although our knowledge of the vast majority of plant secondary metabolites is still sparse, or even nonexistent (Rao and Ravishankar, 2002; D’Auria and Gershenzon, 2005; Fernie, 2007). Plant metabolites are also of considerable importance in a crop context. Indeed, most plant species that have undergone domestication have become crops specifically because they provide us with a source of chemicals. This is not only true for all of our food crops, but also for many other species of genera such as Pyrethrum (insecticides), Jasminium and Santalum (perfumes), Hevea (rubber), Nicotiana and Cannabis (drugs), Linum (oils), Artemisia and Taxus (pharmaceuticals), Cinnamomum (flavors), etc. However, despite the importance of plants as a source of exploitable and essential biochemicals, we often still have remarkably limited knowledge of the relevant biosynthetic pathways, the genetics behind the key enzymes, and indeed when, why, and where these metabolites are produced and stored within the plant in question (Fernie, 2007; Sumner et al., 2011; Kueger et al., 2012).The field of plant metabolomics has grown tremendously since its recent inception earlier this century (Fiehn et al., 2000; Fiehn, 2002). As an untargeted approach to gain a broad overview of the complexity of plant metabolic composition, the technology has, in a short time, made significant inroads into helping expand our knowledge of plant biochemistry (Kueger et al., 2012; Etalo et al., 2013; Hunerdosse and Nomura, 2014; Meret et al., 2014). Typically, rich metabolomics data sets already provide us with a valuable means to generate hypotheses relating to plant metabolism, which then become the focus of further, more direct investigation (Quanbeck et al., 2012). New technologies are being developed, and especially, new data-mining strategies are being designed to allow us to look deep into plant metabolism without having first to rely on preconceptions. However, there are significant limitations to the application of the technology, which still remain the topic of much research effort.Robust sampling approaches for plant biochemical analysis generally entail taking reliably measurable amounts of plant material that will yield detectable levels of the chemical components. Although for metabolomics analyses, samples of just 50 mg can often suffice, obtaining a reliable sample with minimum biological variation generally requires an initial pooling of materials from which a representative sample is then taken. We therefore treat plant tissue as being homogeneous, but this is clearly a gross oversimplification (Fernie, 2007). Plants have been considered to be composed of roughly 40 different cell types, and a plant organ such as a leaf will generally contain up to 15 different cell types (Martin et al., 2001). Different morphologies also parallel different biochemical composition. Even directly neighboring cells within an organ, for example, a leaf epidermis that often comprises pavement, guard, trichome, and glandular hair cells, are formed from cells already known to have distinctly different biochemistries. Making an extract, for any kind of metabolomics or standard biochemical analysis, therefore entails that we immediately lose most intercellular and intertissue resolution. However, our knowledge is growing in that, in addition to known or expected biochemical differences between cell types, metabolite accumulation across organs can be far from uniform; indeed, islands of higher and lower concentrations of particular metabolites have been observed. This is of course immediately visible when the metabolites concerned can be seen by the naked eye; anthocyanins, for example, are often found to be heterogeneously distributed across leaves, fruits, and flower petals, creating clear phenotypic patterns. The same may also be true of other compounds that are invisible to the human eye but that, in contrast, may still be detectable by insects (e.g. through their fluorescence capacity; see http://www.naturfotograf.com/UV_flowers_list.html; Gronquist et al., 2001).In an ideal situation, we would like to be able to look directly into a plant tissue and be able to analyze the biochemical composition at the single cell level. Some so-called metabolite imaging technologies, usually based on mass spectrometric detection (mass spectrometry imaging [MSI]), have recently been introduced as a step toward this optimistic goal. Included here are matrix-assisted laser desorption/ionization (MALDI)-MSI, direct analysis in real time, and desorption electrospray ionization approaches (Cody et al., 2005; Cornett et al., 2007; Ifa et al., 2010). Early examples of MALDI-MSI have shown not only how primary metabolites such as sugars can be strongly localized within plant organs (Rolletschek et al., 2011), but also how the heterogeneous distribution of glucosinolates in Arabidopsis (Arabidopsis thaliana) can potentially determine grazing behavior of caterpillars (Shroff et al., 2008). This technology continues to improve, and recent exciting developments have resulted in cellular and subcellular imaging of metabolites at a resolution of 5 to 9 µm using MALDI (Korte et al., 2015). However, some key practical limitations of MALDI-based approaches are centered around the need to initially have to pretreat/dehydrate the tissue prior to applying the required matrix solution and the requirement of applying a vacuum during the biochemical analysis. Recently, a new technology has been introduced, laser ablation electrospray ionization (LAESI), which can potentially overcome some of these limitations, given that measurements can be made on fresh, living tissue without the need for a vacuum, thus creating the potential for high-resolution in vivo metabolomics.Here, we report on a set of experiments performed to assess both the potential and limitations of using LAESI-based MSI approaches to perform metabolic mapping on living plant tissues. While identifying a number of technological challenges that still need to be tackled, we were able to show that it is possible to use LAESI-MSI to map metabolites directly onto their known location (in this case, by exploiting the visibility of anthocyanins) as well as localize invisible metabolites in the same tissue. Results have revealed that in plants, for both petal and leaf tissue, the distribution of metabolites can be highly heterogeneous, and that this heterogeneity is of potential relevance to our gaining a broader, more detailed understanding of the overall molecular organization and phenotypic features of plant tissues. Furthermore, knowledge of the nature and extent of this heterogeneity has particular relevance and importance when trying to understand how a plant functions as a system, interacting with its environment. We predict that a higher resolution understanding of plant biochemistry will lead to an increasingly discriminatory capacity in our ability to define more accurately the spatial complexity of plant molecular organization.