Methodologies for Metabolomics

EXPERIMENTAL STRATEGIES AND TECHNIQUES

Edited by

Norbert W. Lutz University of Aix-Marseille, France

Jonathan V. Sweedler University of Illinois, Urbana-Champaign, USA

Ron A. Wevers University Medical Centre Nijmegen, the Netherlands

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Methodologies for metabolomics: experimental strategies and techniques I [edited by] Norbert W. Lutz, Jonathan V. Sweedler, Ron A. Wevers.

p. ; cm. Includes bibliographica l references and index. ISBN 978-0-521-76590-9 (hardback) I. Lutz, Norbert W., 1952- II. Sweed I er, Jonathan V. Ill. Wevers, Ron A., 1951-[DNLM: 1. Metabolomics - methods. 2. Mass Spectrometry - methods. 3. Metabolic Diseases - diagnosis. 4. Metabolome. OU 120) 543'.65-dc23 2012013907

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140

Direct Metabolomics from Tissues and Cells: Laser Ablation Electrospray Ionization for Small Molecule and Lipid Characterization

Akos Verles, Bindesh Shrestha, and Peter Nemes

The systematic study of metabolites and their related pathways in various organisms is experiencing a renaissance. In the first half of the twentieth century, studying select metabolic processes played a central role in biochemistry and brought about major discoveries, such as the ornithinc and the citric acid cycle (Kornberg, 2000). From the middle of che century, increasingly powerful tools used in metabolism research included efficient separation methods (gas chromatography fGCJ and high­performance liquid chromatography) combined with spectrometers aimed at small molecules (nuclear magnetic resonance [NMRI anJ mass spectrometry [MS]). In the 1970s and early 1980s, genetics took center stage, aided by the introduction of DNA sequencing, polymerase chain reaction (PCR), and recombinant DNA technologies.

ln the late 1980s, with the introduction of soft ionization methods in MS. the sys­tematic investigation of peptides and proteins became possible, and proteomics was born. During the 1990s and the first decade of the twenty-first century. researchers in gcnomics accomplished the sequencing of genomes in multiple species. The devel­opment of microarray techniques gave us powerful tools to study the transcriptome. and. with the help of new techniques in MS. investigators in proteomics began the challenging task of mapping the corresponding proteomes.

Metabolomics - the systematic investigation of the metabolites and metabolic processes in an organism - is lagging behind owing to multiple challenges. In con­trast to nucleic acids and proteins, metabolites are structurally diverse. As a con­sequence, their separation and spectroscopic identification require a wide variety of protocols. Metabolite concentrations also vary extensively in space and time; for example, the temporal changes are more rapid than in the case of biopolymcrs. Typi­cal changes in the transcriptome can take minutes, hours, or longer (Ye et al., 2009), whereas protein and metabolite concentrations vary in the range of minutes and seconds. Conventional metabolomic protocols rely on rapid sampling that incl udes quenching of the biochemical processes and often derivatization before separation and spectrometric analysis (Smart et al.. 2010). Because of these complex steps, it is always questionable how close to its native state the studied system has remained.

To explore the native metabolomc, new me thods are needed that respond to various small organic molecules on a time scale of seconds. Ideally, these techniques have to be capable of local analysis and exhibit sufficiently high sensitivity. Beca use metabolite molecules are much smaller than nucle ic acids or proteins, they arc more

1. Direct Tissue Analysis and Imaging

prone to spatial redistribution during sample preparation. The local analysis and molecular imaging of metabolites in tissues and cells also require special consid­erations (see Chapters 18, 21, and 22 for noninvasive and nondestructive in vivo metabolic imaging by NMR techniques).

The advent of atmospheric pressure ionization methods for MS has enabled the direct analysis of tissues and cells with little or no sample preparation. This rapidly expanding group of technologies ha!> been used extensively to analyze biological ,amples directly and includes ambient desorption electrospray ionization (DESI) , Takats et al., 2004), direct analysis in real time (DART) (Cody ct al., 2005), extrac­tive clcctrospray ionization (EESI) (Chen ct al.. 2006a, 2007), atmospheric pres­<>ure infrared matrix-assisted laser desorption ionization (AP IR-MALDI) (Li et al., 2007; Yertes ct al., 2008), electrospray-assistcd laser desorption ionization (ELDI) Shiea et al., 2005), and laser ablation electrospray ionization (LAESI) (Nemes and

\'ertes, 2007). Many of these techniques can be characterized as in situ or. in some cases, in vivo methods (Nemes and Vertcs, 2012). Although the analysis sacrifices -.ome cells of the studied organism, it is less obtrusive than biopsies. Ultimately, sam­pling can be arranged so that a living organism can be viewed unperturbed, as long a the sampled volume is significantly smaller than the volume of the organism. This chapter focuses on the applications of LAESI MS for the direct analysis of tissues and cells with two-dimensional and three-dimensional imaging and depth profiling of small metabolites and lipids.

1. Direct Tissue Analysis and Imaging with Laser Ablation Electrospray Ionization

In the field of biomedical analysis, I .A EST MS has exhibited considerable success n interrogating the small molecule composition of biological fluids, such as blood

9lasma and urine as well as plant, animal, and human tissues and single cells. There are numerous fundamental and unique aspects that place LAESI MS among the enabling methodologies in direct metabolomic investigations.

In LAESJ MS, the sampling step is spatially independent from that of ion gen­eration. First, the native water molecules of the sample arc excited by a focused mid-infrared laser light. At a wavelength of 2,940 nm, rapid energy deposition xcurs through the excitation of the vibrational modes of water molecules. Pro­\ided that sufficient energy is coupled into the sample. the result of the proccs!> · phase explosion-driven ablation that expels particulate matter within a few hun­dred microseconds (Apitz and Vogel, 2005; Chen et al., 2006b; Chen and Yertes, ~008), and the momentum gained by the cjectiles transports them to several tens of millimeters above the sample surface (Nemes and Yertcs, 2007).

An electrospray source, preferably operated in the cone-jct mode, efficiently ~enerates a cloud of small charged droplets (Nemes et al., 2007) that intercepts the particles ablated from the sample (Nemes and Ycrtcs, 2007). As the droplets coa­e ce with the particulate matter, the metabolites of the sample are transferred into a

charged solution-phased microenvironmcnt. Ultimately, metabolites are converted to soft ions via processes similar to ion production during conventional electrospray ionization (ESJ) (Nemes et al., 2012). Correspondingly, with LAESJ MS, samples can be analyzed within seconds, providing a snapshot of the fast-evolving metabolite

141

142 Direct Metabolomics from Tissues and Cells

composition. This forms the basis of high-throughput and large-scale investigations using LAESI MS.

1.1. Label-Free Metabolite Identification

The ions generated in LAESI can be analyzed by any mass spectrometer that is equipped with an atmospheric pressure interface. Most commonly, a commercial ESl or atmospheric pressure chemical ionization source is replaced by a custom­built LAESI ion source. More recently, the introduction of commercial LAESJ ion sources has been announced (Protea Biosciences, Inc., Morgantown, WV). The LAESI system has been successfully implemented on time-of-llight and ion trap instruments that feature quadrupole and hexapole ion guides for enhanced ion transfer and collisional activation. The mass spectrometer can be operated in single­stage or tandem modes to facilitate chemical identification (Nemes and Vertes, 2007) .

The application of LAESI MS in metabolomics follows different workllows for metabolite discovery than for the structural identification of known compounds. The laltcr typically employs a mullistep MS-based approach . Accurate mass-to­charge ratio (m/z) measurement is the starting point in the structure identification by LAESI MS. Ions are measured with m/z accuracies such as less than 5 ppm, although for targeted compounds lower accuracies can also yield satisfactory results. The measured m/z values are compared against metabolomic databases for prokaryotic. plant (e.g., http://www.plantcyc.org/), and animal species as well as human samples (e.g., hltp://www.hmdb.ca/). There is a growing number of multiorganism pathway databases integrated with genomic and proteomic data (e.g., http://metacyc.org/, http://www.gcnome.jp/kegg/pathway.html) that can also help identification.

lsotopt: distribution patterns can also be determined to narrow the list of puta­tive compounds further. The remaining positive matches are evaluated in tandem MS experiments, whereby the fragmentation behavior of the unknown ion is com­pared with that of chemical standards. In certain cases, a reactant can be added to the electrosprayed solution to transform the analytes chemically. Here the objective is to improve the ion yield or facilitate structure-specific fragmentation. The latter can be beneficial for lipids that are known to yield a very limited number of frag­ments in collision-activated dissociation from protonated precursors. Introducing lithium ions via the electrosprayed solution leads to the generation of lithiated lipid species that readi ly fragment into numerous characteristic product ions that help lo decipher the structure of the parent lipid molecule (Shrestha el al., 2010a). This mul­tistep elucidation scheme for known metabolites usually yields identifications with high analytical confidence for biomedical samples.

In the metabolite discovery mode (i.e., in cases where a particular metabolite has not been described in the literature for a given species), the use of databases is of limited help. In these situations, tandem MS-based techniques arc manda­tory, and orthogonal analytical techniques are needed to enhance the confidence of identification further. The latter include offlinc experiments using classic separation methods (e.g., liquid chromatography with MS detection). An appealing yet unused alternative is coupling LAESI with ion mobility spectrometry (Bohrer et al., 2008), which cou ld separate structural isomers. In this experiment, the time of LAESI ion

1. Direct Tissue Analysis and Imaging

Figure 6.1. Schematic of the LAESI MS apparatus for metabolic investigations directly from tissues. The sample (S), positioned on a sample holde r (SH), is ablated by a mid-infrared laser focused by a focusing lens (FL), and the abla­tion plume (red dots) is intercepted by charged droplets (green dots) generated by an electrospray source (ES). The charged droplets are seeded with the neutral ablated ma terial, which results in the production of sample-specific ions that a rc analyzed by a mass spectrom­eter (MS). The sample is optionally cooled or kept frozen by a Peltier stage (PT) equipped with a heat sink ( HS) and fans.

generation would be synchronized with the injection of ions into the ion mobil­ity cell. Combining LAESI with ion mobility spectrometry raises the prospect of improving confidence in metabolite identification while maintaining fast sample analysis.

1.2. Metabolic Analysis

The flexibility that results from decoupling sampling from ion generation has far­reaching benefits in practical metabolomic investigations. Perhaps one of the most attractive feat ures of LAESI MS is that the biomedical analysis is facilitated by :he inherent water content of the sample. As shown in Figure 6.1, LAESI MS allows direct metabolic analysis because it el iminates the requirement to modify :he biological or medical sample. For example, certain metabolite and xenobiotic molecules have been analyzed in water-containing samples, such as bodily fluids, ,;thin seconds without any sample preparation. Examples show that small metabo-

lites (e.g., carnitine, phosphocholine, tetradccenoylcarnitine) and lipid molecules c: g., glycerophosphocholines [PCsJ; the lipid nomenclature accepted by the LIPID

MAPS consortium [http://www.lipidmaps.org/] is used throughout this chapter) can ~ readily detected , and the excretion of drug molecules (e.g., the antihistamine f~:'l:ofenadine) can be monitored directly from whole blood and urine (Nemes and \ c:rtes, 2007). LAESI mass spectra often include multiply charged ions, a phe­c menon typically observed in EST. Deconvolution of the ion charge states reveals

:.mdant proteins in human blood, including the serum albumin and the a and :> chains of hemoglobin. Thus, the molecular mass range of LAESI MS analysis '!'Xlends from small metabolites to macromolecules greater than 66 kDa.

Local analysis is another important feat ure of the LAESI MS method. The anal­-' area of the laser beam is typically approximately 200 µm in diameter and can

reduced to 50 µm without significant signal loss. The ablation dimensions can carefully adjusted for various tissue and cell types to support in situ, ex vivo .

.. rl in vivo experimen ts. The latter can be observed in Figure 6.2, which shows the

143

144 Direct Metabolomics from Tissues and Cells

10mm

Figure 6.2. In vivo analysis of a French marigold seedling by LAESI MS. (left panel) In these profiling experiments, mass spectra. acq uired on 350-µm-diameter areas of the leaf, stem and root, reveal that (right panel) the organs exhibit dramatically different primary and secondary metabolite composition. (Adapted with permission from emes, P., and Vertes, A. [2007]. La~er ablation electrospray ionization for atmospheric pressure, in vivo, and imaging mas~ spectrometry. Anal. Chem. 79, 8098-8106. Copyright 2007 American Chemical Society.)

metabolic profiling of the root, stem, and leaf regions of a live French marigold (Tagetes patula) seedling (Nemes and Vertes, 2007). The observed ions have been assigned to primary and secondary metabolites. With superficial damage to the live tissue. local analysis enables intensity profiles to be recorded across the various regions of the seedling. For example, the root primarily generates ions with m/z less than 250, the stem yields sodiated kaempfcrol 3-0-(211 ,3"-di-p-coumaroyl)-gluco ide or some of its structural isomers with an exceptionally high abundance, whereas the leaf produces various ions assigned to photosynthetic products.

Quantitative LAESI analysis facilitates metabolomic investigations. LAESI ion signa l intensities from solutions of drug standards indicate that they follow a linear correlation with analyte concentrations over a four-decade dynamic range (Nemes ct al. , 2007) . Metabolites in biological samples are present in a complex matrix of constituen ts, each of which can compete for the available charge within the clec­trosprayed droplets. In some cases, this results in narrower dynamic ranges or lim­its quantitation to the determination of relative concentrations within the sample. Using deuterated analogues as internal standards, the absolute concentrations of small metabolites, including neurotransmitters and osmolytes (e.g., choline, carni­tine. and bctaine), have been identified in the electrical organ tissue of Torpedo cal­ifornica (Sripadi et a l., 2009). Other reports have established quantitation of lipids in the mouse brain, allowing the measurement of the absolute concentration of PC (34:1) with good analytical performance (Shrestha et al., 2010a). The analyte levels determined by LAESI MS have agreed with levels reported by traditional analyt­ical methods. The abi lity to correlate ion signal intensities with native metabolite

CEM cells

1. Direct Tissue Analysis and Imaging

C81 cells .,,. """ C<eatlne ,. \ 4 Phosphocreabne mlz 132 c_...._

~-H-lnt'.I~,....,....., ...

145

Glycine,. 1uanldlnoacc1a10 ~ ... Arginine ~ L·Ornlthlne 1 ., Pu1resclene

(urea cycle) - «I. mlz 89 mlz 175

""""''"''""""'t Spermine Spermid1ne

mlz203 _.,,.,..,..._ m1Z148

-~-Glycerol .. .. c.lycerol-3-phosphat•

_...,~:...r- Choline....._\ =::::::..•• ADP/ -Iii' ~HO

Phoaphochoi1ne Glycerophosp~~~ne ~ CTP ....J rn'z 184 r-1ny lad

~..,..,. .... pp1 .0

COP-Cho~ne Lyso PhoSfl!\al1dytchofone

l .20loctloll'..,ol~ nr!F•lly•ad c,...,...,.._.,. ..... ~ ._,.,_ CMP Phosphatidyk:holl H ~Mo\lAl

•g mlz780 Folds increase

Figure 6.3. (left panel) Metabolic differences between control (CEM) and virally transformed T lymphocytes {C81) identified by LAES l MS. The most affected metabolites were{/) Lhioac­etamide, (2) pulrescine or pyrrolidine, (3) choline. ( 4) praline, (5) ta urine, (6) crealine, (7) permidine, (8) p-aminoben10ic acid, (9) iminoasparlic acid, (10) arginine, (/I) dopamine.

1/2) phosphocholine, (/ 3) carbamoyl-phosphate, (/ 4) spermine, (15) methoxytyramine, (/ 6) \-acetyl aspartic acid or N-formyl glutamic acid, (/7) homovanillic acid, (18) glyccrophos­phocholine, (19) glutathione, (20) 8-hydroxyguanosine, and (21) adenosine monophosphate. These changes pointed to significant perturbations in the (top right panel) creatine and polyamine biosynthesis pathways and the (boflom right panel) lipid metabolism pathway. 1 .\dapted from Sripadi, P., Shrestha. B .• Easley, R. L., Carpio, L., Kehn-Hall , K ., Cheva­lier, S., el al. l201 OJ. Direct detection of diverse metabolic changes in virally transformed and Tax-expressing cells by mass spectrometry. PLoS ONE 5. e 12590.)

concen trations in tissues and cells is essential in the investigation of metabolic changes in an organism.

Human metabolic pathways and their changes can also be explored by LAESI \IS. Virally transformed and Tax-expressing human cells were profiled more "."ccently in high-throughput experiments (Sripadi ct al., 2010). Figure 6.3 presents :woof several biosynthetic pathways significantly alte red by the viral transforma­: • Cln of host cells. Such measurements can be performed in a few minutes and require •nly small cell populations. With LAESI MS, several virus type- specific (HTLVl vs.

HTLV3). expression-specific (Taxl vs. Tax3), and cell type-specific (T lymphocytes .... kidney epithelial cells) changes were observed in the metabolite profiles. These

fr..:atures indicate tha t LAESI MS can be used in biomarker discovery and patient ~.onitoring. More exte nsive investigations addressing how disease states a lter cellu­lar metabolic pathways can a lso e nable new treatment strategies.

1.3. Metabolic Imaging

f .\.ESI MS offers a means to achieve local analysis with spatial resolution to 50 µm. 1 nder appropriate experimental conditions (e.g. , temperature and humidity), the

146 Direct Metabolomics from Tissues and Cells

structural and chemical integrity of biological tissues and cells is essentially retained around the ablated spot. Assuming proportionality between the LAESI MS ignal and the metabolite concentration in the tissues, spatial variations in the chemical composition of these systems can be interrogated. The ion signal measured across the tissues allows the reconstruction of the corresponding molecular image. To per­form LAESI mass spectrometry imaging (MSI), the sample is mounted onto a sam­ple holder (Figure 6.1). Using two computer-controlled independent translation stages (X and Y directions), the sample is positioned at the foca l point of the ablat­ing mid-infrared light. The generated ions are simultaneously mass-analyzed, and the data arc stored for each X- Y coordinate of the interrogated area. Detailed pro­tocols of lateral imaging by LAESI MS and data evaluation can be found elsewhere (Nemes and Yertes, 2010a. 2010b). The molecular image of the sample for a partic­ular species is reconstructed by representing the intensity of the related ion signal on a false color scale and correlating it with the coordinates for every pixel of the interrogated area.

Most plant tissues are structurally different from tissues found in animals and humans. In plants, the waxy cuticle and the rigid cell walls act as natural barriers for unwanted water loss in an MSI experiment. In contrast, when animal tissues are dissected, evaporative water loss is relatively rapid. Because water serves as the energy coupling medium in LAESI experiments, animal tissue sections arc generally kept cold or fro1en during analyses (Nemes et al., 2010; Shrestha et al.. 2010a). A Peltier-cooling stage equipped with a heat sink and a fan, as shown in Figure 6.1, can maintain the appropriate conditions for several hours (Nemes and Ycrtes, 2010b) and ensure that the water content of tissues does not appreciably change during the LAESI MSI time frame.

Direct molecular imaging of thin animal tissue sections allows the appreciation of their metabolic organization a t atmospheric conditions. In a more recent exam­ple. two-dimensional distributions of more than 200 distinctive ionic species were simultaneously measured from a 100-µm-thick coronal rat (Rattus norvegicus) brain section (Nemes ct al., 2010). Among the monitored metabolites were neurotrans­mitter molecules, such as 'Y-aminobutyric acid (GABA) and choline; polyamines, such as spermidine and spermine; and metabolites essential for chemical energy transfer, including adenosine and adenosine monophosphate. Many lipid specie were a lso detected, and severa l of them were identified as PCs and glyccrophos­phoethanolamincs (PEs).

Using high mass-resolution time-of-flight MS in conjunction with LAESI. it becomes feasible to deconvolutc the spatial distribution of metabolites that pos­sess an identical nominal mass. Figure 6.4 presents the ion images of GABA and choline, which differ only by 38 mDa in their monoisotopic mass and have been found to populate the tissue section in a vastly different manner. Similar distribu­tions can be obtained for any detected m/z.

The massive data sets generated in LAESI MSI can be evaluated via Pearson co-localization maps (Nemes et al. , 2010). Figure 6.4 shows the tissue distribution of the unassigned lipid ion m/z 702.537 and the PC (37:6) and PE (40:6) on a false-color scale. The calculated Pearson co-localiLation map highlights the areas where these two ions are found together. Co-localization of metabolites can be exploited in the discovery of metabolic pathways active in certain tissue regions.

GABA ·--··-~'! .''."

. . : ~- ~

Ji llO c:

" ! 100

~ • IO c: !!

,104.070

1. Direct Tissue Analysis and Imaging

±0.010

£ o~--~4-~~--.-~~ ....... ~--.... 104.00 104.05 10•. 10

m/z 104.15 10•.20

Figure 6.4. Metabolic imaging of a I 00-µ m-thick coronal section of a rat brain by LAESI MS. High mass-resolution differentiates among ions with identical nominal masses, including tht: GABA and choline ions, which exhibit dramatically different spatial distributions. Charac­teristic tissue localization is found for several metabolites and lipids. including adcnosine, an unassigned lipid, and a combination of PC (37:6) and PE ( 40:6). The Pearson co-localization map between the unassigned lipid and combination of PC (37:6) and PE (40:6) distributions can help to explore metabolic relations in space. Scale bars denote I mm. (Adapted with permission from Nemes, P., Woods, A. S., and Ycrtes. A. (2010J. Simultaneous imaging of -;malJ metabolites and lipids in rat brain tissues at atmospheric pressure by laser ablation electrospray ionization mass spectrometry. Analytical Chemis1ry 82, 982-988. Copyright 2010 American Chemical Society.)

Various metabolites can also be imaged in plant tissues. Jon signals registered from plants by LAESI MS often correspond to primary and secondary metabolites, and many of them exhibit tissue-specific accumulation patterns. Leaves of the varie­;ated zebra plant (Aphelandra squarrosa) can serve as an example. Some metabo­lites, such as kaempferol-(diacetylcoumaryl-rhamnosidc), are found to be homo­;cneously distributed in the tissue, whereas others. including methoxykaempferol ;Jucoronide and chlorophyll a, exclusively populate the yellow and green sectors, rt: pectively (Nemes et al. , 2008). In-depth investigations reveal that the distribu­. ons of certain secondary metabolites are linked to plant physiology. For example,

i1 Arabidopsis thaliana leaves, LAESI MSI studies revealed that the outer ll mina and the midvein synthesize 4-methylsulfinylbutylglucosinolate, indol-3-mr..:thylglucosinolatc, and 8-methylsulfinyloctylglucosinolate in high concentrations ('emes et al., 2009b). Jn agreement with these LAESI MSI results, independent ·:udies found similar concentration profiles and suggested that they were con-

tent with a natural plant defense mechanism against herbivore attacks (Shroff e~ al., 2008).

1.4. Metabolic Depth Profiling and Three-Dimensional Imaging

If 'llUhiple laser pulses arc delivered to the same spot on the tissue, each pulse ~-naves a consecutive layer of cells. This enables the chemical composition of the ~Jerlying voxels (volumetric pixels) to be probed, producing a depth profile. The ..... --unt of material sampled by each laser pulse is governed by the applied laser ;,t:nce and the tensile strength of the tissue (Nemes ct al., 2008; Nemes and Vertcs,

__ 1b; Vertes et al., 2008). Mid-infrared laser ablation has been demonstrated with --ue removal rates between 30 µm/pulse and 80 µm/pulse (Nemes et al., 2008;

147

148 Direct Metabolomics from Tissues and Cells

, ... .. ... ... .. '" ... 12 114 - .. ,,. ... .. .. 11 10 10

Figure 6.5. Three-dimensional metabolic imaging in leaf tissues by LAESI MS. The epider­mal and palisade mesophyll regions of the Spa1hiphyllw11 lynise leaf tissue accumulate (left panel) cyanidin/kaempferol rhamnoside glucoside (m/z 595.2) and (middle panel) chlorophyll a (mlz 893.5) at high levels. (right panel) In the variegated leaves of A . squarrosa , acacctin (m/z 285.0) is abundant in the second and third layers of the tissue with a homogeneous dis­tribution in the other . (Adapted with permission from emes, P., Barton, A. A., and Vcrtes, A. [2009]. Three-dimensional imaging of metabolites in tissues under ambient conditions by laser ablation electrospray ionization mass spectrometry. Analytical Chemistry 81, 6668-6675. Copyright 2009 American Chemical Society.)

Nemes ct al., 2009a, 2009b). The results of single-cell analysis (Shrestha and Vertes, 2009; Shrestha et al., 2011) suggest that voxel dimensions can be tailored to reach subcellula r levels.

Three-dimensional MSI is realized by the combination of lateral imaging with depth profiling (Nemes et al., 2009a). In practice, the tissue is positioned laterally while consecutive laser pulses explore the depth variations in its chemistry at each (X, Y) coordinate of the interrogated area. Detailed information on instrumentation and data analysis is available elsewhere (Nemes and Yertes, 2010b). In the acquired data sets, the intensity distribution for each m/z is mapped to the absolute position of the voxel , yielding the three-dimensional molecular images of the tissue for every detected m/z signal.

Results of three-dimensional LAESI MSI indicate that primary and secondary metabolites often populate plant tissues in a distinctive manner. For example, in the peace lily (Spathiphyllum lynise) leaflet, cyanidin/kaempferol rhamnoside glu­coside wa!> found in the top 40 µm (Nemes et al., 2009a). The corresponding three­dimensional distribution, seen in the left panel of Figure 6.5, reveals the eleva ted abundance of this species in the epidermal region of the tissue. In agreement with these results , independent studies reported higher concentrations of kaempferol gly­cosides in the upper epidermal layers, likely to serve in part as ultraviolet-screening pigments against the de trimental effects of solar radiation (Aleni us et al., 1995). In contrast, the middle panel of Figure 6.5 shows that chlorophyll a is abundant in the palisade mesophyll layer of the leaf tissue from 40 to 80 µm , in agreement with the known localization of chloroplasts within plant tissues. A different distribution pattern was revealed in the three-dimensional MSI of A. squarrosa leaves (Nemes e t al.. 2009a). The right panel of Figure 6.5 presents the spatia l distribution of

a"""' th~

---- --==-- - -

2. Metabolic Analysis of Single Cells

acacetin and indicates that this secondary metabolite follows the yellow sectors of the variegated tissue in the buried second and third layers but does not exhibit het­erogeneity in the lop and bottom epidermal layers.

The in-depth information gained by three-dimensional LAESI MSI enables metabolomic studies on a new level. For example, two-dimensional MSI shows that the distribution of kaempfcrol-(diacctylcoumaryl-rhamnoside) is homogeneous in the A. squarrosa leaf tissue (Nemes ct al., 2008). However, the three-dimensional •On images indicate that this secondary metabolite accumulates only in the mes­ophyll layers (Nemes c t al., 2009a), a piece of information that is lost in all lateral lOlaging experiments that fail to report on the absolute depth of analysis. The abi lity to interrogate biological systems in three dimensions has far-reaching implications in metabolomics and is likely to boost research in the life sciences.

2. Metabolic Analysis of Single Cells

- .ngle-cell gene expression profiling using PCR has shown cell-to-cell variability ... "en within the same cell types (Stahlberg and Bcngtsson, 2010). Classical ana­:· acal tools, such as laser capture microdissection coupled with microarrays, have ., wn heterogeneity for the gene expression among single neurons in a rat hip-

pccampus (Kamme el al., 2003). However, standard metabolomic methods usually an.dyze numerous cells and provide information on the average mctabolome, disre­_a .. ding cell-lo-cell heterogeneity. Direct single-cell metabolomics requires the anal­',· of chemically diverse metabolites in a small sample volume defined by the cell s:z .. Ideally, analytical methods for direct single-cell mctabolornic analysis require o=- mal sample preparation and exhibit high sensitivity and acute selectivity. Based er _pectroscopy, chromatography, and MS techniques, a diverse array of analytical _ "ruques have been used for studying a varying number of metabolites in sin­-~ ..:ells. ExceJJent review on this topic arc avai lable (Amantonico et al., 2010a; d-"'ltd et al.. 2010; Wang and Bodovitz, 2010). In this section, we briefly present - "'10 t common approaches used for the metabolic analysis of single cells with

.al emphasis on MS techniques. One classical single-cell detection and sorting technique, flow cytometry, can

_ .... ployed for the identification and separation of cells that exhibit a particular pe.rty, such as a disease state. In some cases. flow cytometry with antibody-based

or genetically encoded fluorescence can be used to characterize a small num­of biochemicals in single cells (Borland e l al., 2008; Cohen et al., 2008). El~ctrochemical detection of metabolites in single cells requires the u c of

.ectrochemical detectors (Ewing ct al..1992). The method has high sensitivity re ults in label-free detection but is limited to a few easily oxidized biochemi­k electrochemical measurements, a microclectrode can be positioned inside or a 'ingle cell to detect easily oxidized biochemical species in the cell (Arbault et 9Q-: Ewing et al., 1992: Schroeder et al., 1992; Schulte and Schuhmann. 2007) . • _ fR is a well-established analytical technique for metabolic profiling of tis­

-:J cell extracts (Beckonert et al. . 2007) (see also Chapter 18). More than two ago, NMR spectroscopy allowed the analysis of metabolites in human ery-

cyte cell populations. and NMR imaging was used to map the lipid content of a ,\.-nopus laevis egg (Aguayo et al., 1986; Brown ct al., 1977). Osmolytes and

149

150 Direct Metabolomics from Tissues and Cells

metabolites were studied more recently in single neurons isolated from the sea slug Aplysia californica employing NMR (Grant et al., 2000; Schoeniger et al., 1994). This spectroscopic technique can be used for the label-free, nondestructive, and in vivo interrogation of metabolites in these large cells. but it has insufficient sensitiv­ity to work with single animal cells of more common sizes (Griffin, 2006; Kim ct al., 2010). Developments in microstrip technology have enabled the NMR detection of 100 pmol of analyte and can provide enough sensitivity to study metabolites in small cell populations (Krojanski el al., 2008).

Fluorescence-based analytical methods in combination with microscopy have been particularly successful in studying the chemistry of single cells and subcellular structures (Muzzey and van Oudenaarden, 2009; Watson, 1987; Wu et al., 2008; Yip and Kurtz, 2002). These techniques are nondestructive, have impressive detection limits (down to the single molecule level), are capable of high-throughput analy­ses, and are quantitative in nature. Fluorescence-based metabolomics requires the introduction or presence of fluorophorcs inside a cell. For example, using coupled fluorescent proteins, the maltose levels in the cytosol of live yeast cells and the glu­cose concentrations in immortalized kidney cells were examined in single-cell fluo­rescence resonance energy transfer (FRET) experiments (Fehr ct al., 2002, 2003). Some metabolites are natively fluorescent and can be directly monitored without chemica l tagging. For example, the amount of carotcnoids in yeast cells was calcu­lated based on autofluorescence (An et al.. 2000). Alternatively, genetic encoding for the production of the fluorophore (e.g., a green fluorescent protein (GFP]) is possible.

Capillary electrophoresis (CE) is another powerful technique and has been cou­pled lo various detectors to characterize biochemical species in single cells (Arciba! et al. , 2007). The direct analysis of single cells by CE is typically performed by inserting a microcapillary into the cell for sampling. The extracted material can be analyzed using electrochemical detection, laser-induced fluorescence, or MS (Cruz ct al., 1997; Hofstadter et al., 1995; Hogan and Yeung, 1992; Kennedy ct al., 1989; Lillard ct al., 1996; Olefirowic7 and Ewing, 1990; Sims et al.. 1998; Yeung, 1999). The utility of CE combined with ES I MS for analyzing metabolites in single cells has been demonstrated on neurons of A. californica (Lapainis c t al., 2009; Nemes Cl al.. 2011 ).

The analysis of single cells using MS results in the simultaneous detection of multiple species. MS techniques operate without chemical labeling, can elucidate structural information, and, in many cases, have adequate sensi tivity for single­ccll experiments. With MS, multiple metabolites can be detected without select­ing them before the analysis (Monroe ct a l. , 2007). However, MS is a destructive technique and usually requires careful sample preparation. Metabolic profi.ling has been achieved for extremely large cells, the X. /aevis oocytes, by GC-MS using in­lincr si lylat ion (Kock ct al., 2009). Because of the high spatial resolution of the ion spullering process, secondary ion mass spectrometry (SIMS) has also been used to ana lyze metabolites within a single cell and in subccllular structures (Chandra ct a l. , 2000; Colliver ct al., 1997; Ostrowski et al., 2004).

Matrix-assisted laser desorption ionization (MALDI) MS has been used to ana lyze neuropcptidcs in single neurons (Rubakhin et al. , 2003) and peptides in mammalian cells such as single rat intermediate pituitary cells (Rubakhin et al.,

Figure 6.1 tra obtai, 400, and: (blue), i (red), an• (black) d· sitivity an N IMS. T image of 1 ysis. The with pcrn Yanes, C ucci, 0., l al. [2007]. mass spe< U3. Cop) Group.)

2006; Ru (NIMS) I ing euka1 cells (An shows m 231) by 2007). TI nous pho ing mcth ize metal Closteriu MS has a placenta. A more 1

on silicor lites in si1 meta bole

One incombi1 this appn croscopy. material. and the s enabled t chemical

2. Metabolic Analysis of Single Cells 151

Laser-NIMS

Figure 6.6. Comparison of mass spec­tra obtained by the ana lysis of I, IOO, 400, a nd 500 cancer ce lls by laser IMS (blue). ion NIMS (green), MALO! (red), and nanoelectrospray ionization (black) demonstrates the supe rior sen­sitivity and metabolite coverage of laser NIMS. The inse1 shows a fluorescent image o f the cell after laser N IMS anal ­y is. The scale bar is 28 µm. (Adapted \\.ith permission Crom orthen, T. R .. Yanes, 0., Northen, M. T.. Marrin­ucci. D ., Uritboonthai, W., Apon, J., cl

al. [20071. Clathrate nanostructures for mass spectrome try. Nature 449, I 033-t.:3. Copyright 2007 Nature Publishing Group.)

MALDI

400 mlz

~006: Rubakhin and Sweedler. 2007). Nanostructure-initiator mass spectrometry "IMS) with laser excitation has shown sensi tivity at the single-cell level for analyz­

;..ng eukaryotic microorga nisms, yeast (Saccharomyces cereuisiae), and single human cells (Amantonico c t al., 2009: Andrea e t al., 2008: Northen c l al. , 2007). Figure 6.6 ,hows mass spectra obtained from 1, 100, 400, and 500 cancer cells (MDA-MB­_]l} by laser-NIMS, ion-NIMS, MALDI, and nanoelectrospray (Northen el al., : 07}. These mass spectra seemed to indicate higher efficiency to ionize e ndoge­r u phospholipids and metabolites in laser NIMS experime nts than by the compet­l"; methods. A similar MALDf-based tech nique has been e mployed to character­IZ metabolic heterogeneity within a cell population of unicellular eukaryotic alga. ::· \Cerium acerosum (Amantonico ct al., 2010b). Direct laser desorption ionization .{ has also been used to study strongly absorbing secondary metabo lites in leaves,

- ....lCenta, stamens, and styli in plants with single-cell resolution (Dirk et a l. , 2009) . ..,ore recently introduced matrix-free laser desorption ionization method based

ilicon nanopost arrays (NAPA) has shown the ability to analyze the metabo­~ in single yeast cells and very small cell populations with a high coverage of the ~ tabolome (Walker et al., 2011 ) in vacuum.

One direct me thod to analyze metabolites in single cells uses videomicroscopy a mbination with MS (Mizuno e t a l. , 2008); a schematic is shown in Figure 6.7. In

..1pproach, contents of single cells are drawn into a nanospray tip under videomi­npy, followed by the addition of an electrospray solution to the sampled cell

• .. nal. At this point, the nanospray tip is converted into an e lectrospray emitter, ·he prayed cell content is analyzed by a mass spectrometer. This approach has ~d the detection of serotonin and histamine in granules of mast cells, known

:emical mediators during allergy stimulation.

ESI

900

152 Direct Metabolomics from Tissues and Cells

(a) (c)

cells n

Video-

... Figure 6.7. Analysis of a single cell by video MS. (a) T he cells are observed by vidcomi­croscopy. (b) Contents of a cell arc extracted into a nanospray ionization tip. The ESI solvent b combined wi th the cell content in the t ip and (c) the mixture is electrosprayed into the mass spectrometer Lo obtain a (d) mass spectrum. (Adapted with permission from Mizuno, H., Tsuyama, ., Harada, T., and Masujima, T. [2008]. Live single-cell video-ma~s spectrom­etry for cellular and subcellular molecular detection and cell classification. Joumal of Mass Spectrometry 43, 1692-1700. Copyright 2008 John Wiley and Sons, Ltd.}

Si ngle-cell metabo lomics is a particularly promising applica tion o f LA ESI MS. Several opera ting parame ters allow fine -tuning of the amount of material sampled d uring mid-infra red a blation for such p urposes. The p ulse e nergy and focusing prop­e rties of the laser beam, the optical a nd mechanical proper ties of the sample, and the geometry of the LAE SI setup a rc just a few underlying variables tha t can help tai lor the analyzed area a nd volume to the physical dime nsions o f ind ividua l cells. The related instrumentation extends beyond the scope o f this chapter, and the reader is referred elsewhere (Nemes and Ve rtes, 2010a, 2010b ; Shrestha and Vertes, 2009, 2010). Amo ng the available techniques for single-cell me tabolomics by LAESI is the use of a sharpened optical fi ber to deliver the laser pulse to abla te the selected cells. The selection of a ingle cell and the coupl ing of the laser pulse to the cell are assisted by two long-d istance microscopes. Figure 6.8 shows such an arrange­ment that is used to ana lyze individ ual epidermal cells of A Ilium cepa (Shrcstha and Vcrtes, 2009). Each ce ll yielded signals for more than seventy che mically different

==-FERENC

A.:: ~-o-J. 8 , "l;ucfear mi

mus. C. ~ ntau no

plgment: lll

Aoantonko, nanostruc1t leuibolom

Amantoruco., metabolorru ~~93-25

References

100 203.0520

~ 80 219.0258

3651067 ·u; c ~ 60 = QI > ::= 40

"' Qi 0::: 20

0 150 300 450 600 750

mlz

Figure 6.8. Single-cell metabolic analysis of A. cepa epidermal tissue by LAESI MS. (left panel) A chemically etched optical fiber is used to select and ablate the cells of interest. (right pane() MS analysis of the generated ions reveals phenotype-specific secondary metabolite composition , including cyanidin glucoside that is abundant in the purple cells of the tissue inset). (Adapted with permission fro m Shrestha, B., and Vcrtes, A. [2009]. In situ metabolic

profiling of single cells by laser ablation electrospray ionization mass spectrometry. Anal. Chem. 8 1, 8265-8271. Copyright 2009 American C hemical Society.)

on . By targeting numerous cells, the metabolite content of neighboring cells can be ~nalyzed. For example, it was found that purple cells contained significant levels of .nthocyanidins, other flavonoids , and their glucosides, including cyanidin glucoside Shrestha e t al., 2011; Shrestha and Yertes, 2009). In agreement with these results,

·'le latter is a known purple pigment and is contained in the cell vacuoles in purple

rlants. Extending single-cell analysis to small cell populations offers new insight

r ... garding cellular heterogeneity. In a more recent development, thirteen neighbor­ng cells in the epidermal tissue of A. cepa were sampled for their metabolic com po­

, ·100 (Shrestha et al., 2010b). The variance in these data can shed light on cellular ~ ... terogeneity on a metabolic leve l under native-like experimental conditions.

As discussed previously, the direct metabolomic analysis of single cells using \1$ is a quickly emerging field. We anticipate rapid growth in the development of ~ ~ \\ tools and methods for the ambient MS analysis of single cells and small cell p pulations.

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