Laser Capture Microdissection Coupled with On-Column Extraction

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Laser Capture Microdissection Coupled with On-Column Extraction LC-MSn Enables Lipidomics of Fluorescently Labeled Drosophila Neurons Sarita Hebbar,†,§ Wolf Dieter Schulz,‡ Ulrich Sauer,‡ and Dominik Schwudke*,†,⊥ †

National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India Carl Zeiss Microimaging GmbH, Munich 81379, Germany

‡

S Supporting Information *

ABSTRACT: We have used laser capture microdissection (LCM) and fluorescence microscopy to isolate genetically labeled neurons from the Drosophila melanogaster brain. From native thin sections, regions of interest could be analyzed with a spatial resolution better than 50 μm. To exploit the specificity of LCM for lipidomics, catapulted tissue patches were directly collected on a reversed phase column and analyzed using an oncolumn extraction (OCE) that was directly coupled with liquid chromatography−multistage mass spectrometry (LC-MSn). With this approach, more than 50 membrane lipids belonging to 9 classes were quantified in tissue regions equivalent to a sample amount of 50 cells. Using this method, the limit of quantitation and the extraction efficiency could be estimated enabling a reliable evaluation of acquired lipid profiles. The lipid profiles of cell body- and synapse-enriched regions of the Drosophila brain were determined and found to be distinct. We argue that this workflow represents a tremendous improvement for tissue lipidomics by integrating genetics, fluorescence microscopy, LCM and LC-MSn.

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guidance system for the sample isolation. The sample material can be obtained sufficiently contamination-free for various downstream molecular analyses, including measurements of DNA, RNA, and proteins.17−23 However, the application of laser capture microdissection (LCM) for a mass spectrometry based lipidomics approach faces several obstacles. For example, thin sections are typically fixed with organic solvents and aldehydes that would chemically interact with endogenous lipids and lead to extraction artifacts. Nevertheless, LCM has been successfully applied for the lipid analysis of fatty acids in Brassia napus seeds,24 phosphatidylcholines, and sphingolipids,25,26 as well as cholesterol27 in human Alzheimer’s disease plaques. However, the sample amount requirement for the chemical analysis was comparable high, which is only achievable by pooling LCM-derived specimens and a limited number of lipid classes could be analyzed. Here, we describe several methodological advancements to improve the scope and coverage of lipidomics studies using LCM. An important innovation of our approach was that we employed a reversed-phase cartridge (C18) for the online extraction of LCM isolated material, which enabled us to automate the lipidomics analysis and reduce the time

ipids regulate many fundamental biological processes and to understand their roles we must be able to quantify individual lipid species and determine their spatial distribution. Matrix-assisted laser desorption ionization-imaging mass spectrometry (MALDI-IMS) has been the method of choice to obtain information about lipid distribution in tissues including the brain,1−4 kidney,5 lens of the eye,6 and has indeed revealed that the phospholipids, sphingolipids, and cholesterol are spatially organized within tissues. The spatial resolution of current technology, which is limited by laser spot size and mass spectrometric sensitivity, has reached the level of tissue organization and single cells.7,8 However, an inherent limitation of MALDI-IMS is the hindrance to perform quantitative analyses caused by ion suppression effects and difficulties to apply internal standards.9 To overcome this limitation, spectra of each pixel need to be normalized based on ion intensities and consequently relative intensities are utilized for obtaining differences in lipid abundances.10,11 As an orthogonal approach, LCM allows the decoupling of the mass spectrometric analysis from the isolation of regions of interest (ROI). In this way hyphenated MS approaches can be applied, where internal standards are added and ion suppression effects can be reduced. Using fluorescently labeled proteins, numerous key molecules can be localized to investigate intracellular organization,12,13 physiology,14,15 and membrane organization.16 To gain insight into the molecular composition of specific tissue regions fluorescence microscopy was utilized as © 2014 American Chemical Society

Received: January 21, 2014 Accepted: May 12, 2014 Published: May 12, 2014 5345

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requirements for downstream processing. We have tested the sensitivity, dynamic range, and extraction efficiency of this approach enabling a reliable evaluation of lipid profiles derived from tissue regions at the micrometer scale. Genetic tools such as the GAL4-UAS system in Drosophila melanogaster28 provided the opportunity to trace genetic manipulations in distinct cell populations through the transgenic expression of fluorescent fusion-proteins. We used LCM to isolate such a fluorescently marked subpopulation of neurons within the brain and analyzed its lipid profile. Finally, we applied this method to evaluate lipid profiles in two distinct regions of the brain, the cortical region packed with cell bodies and the neuropil region enriched in neuronal processes and synapses (defined by localization of the synaptic marker, synaptotagmin29,30). This work establishes the principal steps to perform mass spectrometry based lipidomics of LCM derived specimens at the micrometer scale utilizing fluorescence microscopy as guidance system.

Scheme 1. Lipidomics Analysis of Fluorescently Labeled Tissue Region of Interest (ROI) Using Laser Capture Microdissected (LCM) and On-Column Extraction (OCE) Coupled with LC-MSn a

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MATERIAL AND METHODS Animals. Flies were maintained on standard food (yeast, sugar, cornmeal-based; http://flystocks.bio.indiana.edu/Fly_ Work/media-recipes/caltechfood.htm) at 25 °C. Fly lines used to fluorescently label neuronal subsets in the brain were generated as indicated in Supporting Information S-7 and Table S-1. Chemicals. All solvents were of LC-MS quality and purchased from Sigma-Aldrich (Bangalore, India). Analytical grade glycerol was obtained from Merck (Darmstadt, Germany). All lipid standards, except 1,2,3-triheptad TAG (17:0) (Sigma-Aldrich, Bangalore, India), were obtained from Avanti Polar Lipids (Alabaster, USA). Tissue Preparation and Cryosectioning. Adult flies were screened for the presence of GFP (syt-eGFP or mCD8-GFP) or DsRed (RedStinger-nls), and then heads were isolated under CO2 anesthetization. They were carefully introduced in cyromolds with Jung tissue freezing medium (Leica Microsystems, Bangalore, India) avoiding air bubbles and orientating them such that the base of the head was placed on the bottom of the cryo-mold. The molds were snap frozen and used immediately; otherwise they were stored at −80 °C and used within 5 days. The blocks were trimmed with a scalpel and frontal or horizontal head sections of 14 μm thickness were prepared at −22 °C using a cryostat (Hyrax C25, Carl Zeiss, Germany). Cryosections were immediately placed onto charged glass slides (Superfrost Plus Slides, Thermo Fisher Scientific, U.S.A.), dried and stored in the cooled chamber of the cryostat (Scheme 1). Preparation of Capture Cartridges. HySphere C18 HD cartridges with 7 μm particle diameter were utilized for capturing catapulted sample material (Spark Holland, Emmen, Holland). The cartridges had a column length of 10 mm and 1 mm inner diameter. The center of the cartridges was defined using an in-house built marking stamp. In this way, a circular plane was softly pressed onto the metallic end-caps helping to visualize and center the cartridge for the LCM. After the cartridges were flushed with methanol and water, a volume of approximately 1−2 μL of 10% glycerol in methanol was spotted on the surface of the cartridge to create an adhesive film (Scheme 1). The capture surface of the cartridges was imaged before and after catapulted sample material was collected. The cartridges were centered using full-reflected light with the 2.5× objective and afterward imaged at 5× applying the appropriate fluorescent filter. The position of each cartridge center was

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Utilizing Drosophila melanogaster genetics, neurons in the brain were labeled with dsRed for nuclear-enriched region (magenta), while GFP (green) marks regions containing synaptic membranes. The ROI were identified for LCM using the AutoLPC mode to transfer tissue material to the capture cartridge. The C18 cartridge was then analyzed using OCE-LC-MSn consisting of a micro-LC system, a micro SPE system consisting of a high pressure dispenser (HPD) and the automatic cartridge exchanger (ACE), the Nanomate Triversa ionsource, and a LTQ Orbitrap XL mass spectrometer.

saved in the software using the “Delta position” function in the Palm Robo software. A customized holder for the cartridges was constructed to fit into the Palm RoboMover (Carl Zeiss Microscopy, Munich, Germany). Laser Microdissection and Catapulting. Cryosections were equilibrated to room temperature. Subsequently, tissue sections were quickly visualized and photographed on the PALM MicroBeam system (Carl Zeiss Microscopy, Munich, Germany). The areas of interest were identified, saved as elements using the Palm Robo software, and photographed with the AxioCam ICc camera for bright field and for fluorescence microscopy images, the AxioCam MRm (Scheme 1). A small volume of cold MS-grade water was carefully placed on the tissue section for 1 min and subsequently cautiously removed by absorbing onto a sheet of paper (Kimwipes, Kimberly−Clark, Nennah, USA) and air-dried. For the laser capture microdissection, elements were retrieved from the saved list and subsequently the outline of the ROI was cut two times using 20× objective applying the following settings: cutting energy, 68; catapulting energy, 76. Afterward the Automatic Laser Pressure Catapulting (AutoLPC) procedure was started, utilizing a grid of laser shots at a distance of 7 μm from each other and 1 μm from the ROI outline. Images were taken after AutoLPC to ensure that the ROI was appropriately 5346

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processed. Approximately 30 min were required to image, wash off the freezing medium, and catapult the area of interest onto the cartridge surface. To avoid prolonged exposure to room temperature, only ROIs from one section of a single slide were processed for each collection. Quantification of Catapulted Tissue Flakes. Laser dissected material captured on target surfaces was analyzed using ImageJ software.31 Images were manually thresholded and subsequently only particles comprising at least 4 pixels and above the intensity threshold were counted. Only particle counts and areas coinciding within the 1 mm ID of the marked cartridge center were considered for estimating catapulted sample amounts (Figure 1B, Supporting Information Figure S2). OCE-LC-MSn Setup. The HySphere C18 HD cartridges were integrated into a microflow SPE system consisting of the Automatic Cartridge Exchanger (ACE) and the High Pressure Dispenser (HPD) module (Spark Holland, Emmen, Holland). The HPD module was used to wash remaining glycerol and tissue embedding medium from the cartridge using 40% methanol. Afterward the cartridge was equilibrated with methanol containing 0.1% ammonium acetate equivalent to the void volume of the cartridge and connection lines (Supporting Information, Figure S-3A). Liquid chromatography (LC) was performed on a 1200 micro-LC-system (Agilent Technologies, Waldbronn, Germany) applying a flow rate of 1.5 μL/min. After the pre-LC procedures of the SPE-module were finished, a volume of 0.5 μL of the internal standard mix was injected for each OCE-LC-MSn run (Scheme 1, Supporting Information, Table S-2). After the injection the valve at the ACE module was switched to set the cartridge into the LCflow. Subsequently the extraction of the catapulted tissue flakes was started and a LC-gradient (Supporting Information, Figure S-3B, Table S-3) was applied with solvent A (methanol containing 0.1% ammonium acetate) and solvent B (methyl tert-butyl ether, MTBE). The MS measurements were performed on a hybrid LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with Nanomate TriVersa ion source using a chip with 5 μm nozzle ID (Advion BioSciences Ltd., Ithaca NY, USA). One MS acquisition cycle was approximately 3.6s consisting of one high resolution MS1 scan (R = 100,000 at m/z 400) and several MS3 scans measured in the linear ion trap for quantifying sphingolipids. The complete OCE-LC-MSn setup was operated using the Chemstation (Agilent Technologies, Waldbronn, Germany) as the master (Scheme 1). The timings for several steps in the OCE-LC-MSn procedure were synchronized via serial port contact closure signals. In this way a sequence could be programmed to analyze several cartridges of LCM experiments automatically. Prior to each sequence of OCE-LC-MS n acquisitions for the analyses of LCM derived specimen, the response and elution profile of standard lipids were tested (Supporting Information, Table S-2). Xcalibur software was used to determine average spectra and to generate extracted ion chromatograms of MSn experiments (Thermo Fisher Scientific, Bremen, Germany). Lipid Identification and Annotation. The LipidXplorer software32 was used for identification applying a mass accuracy better than 4 ppm in high resolution mass spectra using earlier reported lipids as reference.33,34 The LipidXplorer MFQL files and import settings will be made available through the wiki page (https://wiki.mpi-cbg.de/lipidx/Main_Page). For the

Figure 1. Customization and optimization of the LCM procedure for Drosophila tissue lipidomics. A: Images of two fly heads, one with red pigmentation in retina, and a mutant for red pigmentation (w1118) before (i) and after (ii) washing off the tissue embedding medium. After washing with water, the reddish retinal pigment is smeared over the tissue section as shown within the dotted outline (ii). This redistribution of the pigment results in quenching of the fluorescence (compare orange outline in greyscale GFP images shown below) for the head section indicated with a dotted outline. Arrowheads in these panels indicate autofluorescence of the cuticle and lens that are not affected by the pigment redistribution. B: Images of the capturing cartridge surface in full reflected light (i) and using fluorescence for detecting tissue flakes with (ii) low and (iii) high sample load. The yellow dotted outline indicates the center of the cartridge of 1 mm diameter and the red circle of 2 mm diameter indicates the surface of the C18 cartridge using a 2.5× objective. Representation of an ImageJ processed image (iv) used for counting tissue flakes of panel (iii).

annotation of lipids, we applied naming conventions as reported earlier.34 Briefly, glycerophospholipids were indicated as ⟨lipidclass⟩ ⟨no. of carbons in all fatty acids⟩:⟨no. of double bonds in all fatty acids⟩. For the annotation of phosphatidylethanolamine vinyl ether and alkyl ether we used the abbreviation (PE-O) and state the number of double bonds of the fatty acid and fatty alcohol moiety. Sphingolipids were annotated as ⟨lipid class⟩ ⟨no. of carbons in the long-chain base and fatty acid moieties⟩:⟨no. of double bonds in the long-chain base and fatty acid moieties⟩;⟨no. of hydroxyl groups in the long-chain base and fatty acid⟩. Lipid class abbreviations were utilized throughout the manuscript as follows: phosphatidylethanolamine (PE), phosphatidylethanolamine ether (PE-O), 5347

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Figure 2. Mass spectrometric strategy for quantitation of lipids of LCM isolated tissue regions A: Representative extracted ion chromatogram (XIC) of OCE-LC-MSn of tissue flakes. With help of the internal standards three retention time regions were defined: (1) begin of run until end of peak of PI 31:1; (2) from the LPC 17:1 peak until end of PE-OO 4ME 16:0 peak and (3) from the peak of PE 31:1 until the peak end of TAG 51:0. From these regions averaged high resolution MS1 spectra were computed and analyzed with LipidXplorer software. B: High-resolution MS1 spectrum obtained from retention time region 2 with major abundant endogenous lipids and internal standards annotated. C: Zoom into the m/z region 687− 692 with the annotation of the mass separated isotopic clusters of PE 32:1, PE 32:2 and CerPE 36:1. D: Magnified m/z region 728−734 showing the separation of the isotopic clusters of PC 32:2, PC 32:1 and PE-O 36:2, PE-O 36:1.

with Welch’s correction and boxplots were calculated using Prism 6.04 (GraphPad Software, La Jolla California, USA).

phosphatidylcholine (PC), phosphatidylinostiol (PI), phosphatidylglycerol (PG), ceramides (Cer), phosphatidylserine (PS), ceramide phosphorylethanolamine (CerPE), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), and triacylglyceride (TAG). Preparation of Figures. Microscopic images were pseudocolored in ImageJ, adjusted for brightness/contrast and assembled into panels using Adobe Photoshop and Adobe Illustrator (Adobe Systems, San Jose, USA). Lipid profiles and standard statistics were computed using Excel 2013 (Microsoft, Redmond, USA). Mann−Whitney U test, D’Agostino and Pearson omnibus normality test, and unpaired t tests

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RESULTS AND DISCUSSION Optimization of LCM for Lipidomics. Tissue sections were placed on positively charged glass slides because of the much better adhesion of sections compared to widely used PEN membrane slides. The thin sections were imaged with the inbuilt fluorescence microscope to spot ROIs that were transgenically labeled with either dsRed or GFP fusion proteins. To retain the functionality of the fluorescent probes and to avoid any artifacts from cross-linking membranes (via amine group-containing lipids like PE, CerPE, and PS) use of 5348

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aldehyde fixatives was completely avoided. It was observed that the tissue-freezing medium interfered with the laser cutting (Supporting Information, Video 1, 2). Therefore, a wash with ice-cold water was included in the workflow. For thin sections containing parts of the eye, one peculiarity observed was that the characteristic colored fly retinal pigment35 was mobilized by water washes (Figure 1A). This resulted in nonspecific fluorescence that masked marked ROIs. However, in tissue sections, without retinal pigment, or in the absence of the retina, the water-wash step had little effect on the fluorescence (Supporting Information, Figure S-1). Therefore, the wash steps were performed after marking the ROI and before the catapulting procedure started. The exact region or identified element could be located using the positioning abilities of the LCM-system and navigating according to established morphological features in the thin section. We would like to note here that the washing step is only required when tissue embedding medium is used for embedding the specimen for cryosectioning (Supporting Information, Video 1, 2). For noncontact transfer of the ROIs the AutoLPC function was utilized36 in which first the ROI was cut and afterward a grid of the laser shot positions was positioned within the outline. The cutting process involves making a clear-cut along the outline of the region of interest; the catapulting process involves using a grid of laser shots (AutoLPC function) that will result in the catapulting of smaller bits of the region of interest (called as flakes) into the collection vessel (Supporting Information, Video 3). For the catapulting process, the laser is defocused from the tissue onto the surface of the glass slide; as a consequence impact spots are visible in reflected light images after catapulting (Figure 4C). These settings had to be manually adjusted for different types of tissues and needed to be controlled to avoid interaction of the tissue with the laser during catapulting. We derived the following settings for AutoLPC from charged glass slides: cutting energy, 68; catapulting energy, 76. AutoLPC shots were placed at a distance of 7 μm from each other and 1 μm from the ROI outline. These settings were first tested by depositing tissue flakes on adhesive caps (Supporting Information, Figure S-2, Video 3) and were later used for capturing tissue flakes on the cartridges. The capture surface was inspected in reflected light (Figure 1Bi) to ensure that the center region of the cartridge was indeed utilized such that the tissue flakes were placed within the flow of the LC-eluent. Subsequently, the cartridge images (Figure 1Bii−iii) were used to count fluorescent particles. In our hands, approximately 40 counts were required for a successful OCE-LC-MSn acquisition. With the AutoLPC mode, the tissue section of the ROI was fragmented into micrometer-sized particles that resulted in a much greater interaction surface for the lipid extraction. On a cautionary note, we emphasize that the particle count is an approximate measure for the sample amount collected because tissue flakes size could not be controlled (Supporting Information, Figure S-2B). In our hands LCM was successfully utilized for isolating ROIs from native thin sections of approximate 6000 μm2 that can be extrapolated to sample amounts equivalent to approximately 50 cells. Analytical Properties of OCE-LC-MSn Setup. Next the analytical performance of the on-column extraction (OCE) coupled to LC-MSn was investigated (Scheme 1). After initial tests integrating an analytical LC-column into the setup, we recognized that the C18 SPE cartridge was sufficient for the extraction and elution of lipids (Figure 2A). Furthermore, we could reduce memory effects performing each MS analysis only

on the C18 cartridge (Supporting Information, Figure S-3). However, the analyses of approximately one hundred OCE-LCMSn acquisitions revealed that endogenous membrane lipids did not necessarily elute in integrable chromatographic peaks (Supporting Information, Figure S-5). That might be due to the fact that extraction and chromatography are not decoupled, leading to peak broadening. In this regard we utilized an alternative data analysis strategy similar to direct infusion lipidomics approaches.37 For each OCE-LC-MSn acquisition retention time of internal standards was utilized to define three time ranges for computing averaged spectra that were subsequently used to quantify in (1) lysolipids, (2) phospholipids and sphingolipids and (3) neutral lipids (Figure 2A). The majority of membrane lipids were eluting in what we refer to as time range (2) comprising of the lipid classes PE, PE-O, PC, PI, PG, CerPE, as well as Cer (Figure 2B). The quantities of endogenous lipids could be determined in reference to added internal standards (see details in Supporting Information S10). In this regard we tested the limit of detection and dynamic range of all internal standards (Supporting Information Figure S-4). Here we could show that for all investigated internal standards, except for PS, approximately an amount of ten femtomol injected on the column was detectable. The problem with PS concurs with earlier reports that PS would exhibit poor chromatographic behavior under our chosen elution conditions.38 For all other lipids, we observed that a dynamic range of approximately 2 orders of magnitude was achievable. Furthermore, we could demonstrate that it is possible to quantify ceramides with MS3 in the linear ion trap using XICs of the long-chain base specific fragments (Supporting Information Figures S-3 and S-4D). Next, we estimated the extraction efficiency of major abundant lipids. For that, eight LCM OCE-LC-MSn acquisitions of LCM derived neuronal tissue flakes were repeated a second time utilizing the same amount of internal standard for determining the remaining amount of the four major abundant membrane lipids PE 34:1, PE 36:2, PE-O 34:1, and PC 34:1 (Figure 2B, Table 1). In all cases, we determined extraction efficiency greater than 98%. Table 1. Extraction Efficiency of Major Abundant Lipids Using LCM and OCE-LC-MSn extraction efficiency (%) AVb SDb Nb a b

PE 34:1

PE 36:2

PE-O 34:1

PC 34:1

98.5 0.8 8

99.3 0.5 8

99.4 0.2 4a

99.4 0.4 7a

Lipids were not detectable in the second OCE-LC-MSn acquisition. AV = average, SD = standard deviation, and N = sample size.

Lipidomics of Identified Neurons. We tested the ability of the procedure to obtain membrane lipid profiles of identified subsets of neurons in the adult Drosophila brain. Here, we focused on the antennal lobes, which are the primary olfactory processing centers in the fly brain. There are around 160 projection neurons (PNs) in the antennal lobes and we used a previously identified enhancer trap line (GH-146 Gal439−41), driving membrane-targeted GFP (mCD8GFP; Figure 4A), to demarcate 90 PNs. Within a series of thin sections we performed LCM on GFP labeled PNs across an average area of 9000 μm2, which is equivalent to sample material of 5349

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other subcellular machinery (Figure 4A). In the Drosophila brain, neuronal cell bodies are arranged in the cortical zone around the synapse-rich neuropil area.42,43 It is possible to differentially label and identify the cortex and different neuropils, with targeted transgenic fluorescent probes and well-defined morphological attributes (Supporting Information Figure S-5). Taking advantage of this anatomical feature, we sampled cell bodies-enriched cortical regions and the synapsedense neuropils in the brain (green−neuropil/synaptic; magenta−cortex/cell bodies in Figure 4B). After the LCM process was finished, the impact of the laser can be observed on the glass surface indicating that the laser energy for pressure catapulting was placed onto the glass (Figure 4C). The collected tissue flakes captured on the cartridges were subsequently processed for lipid analyses by OCE-LC-MSn whereby 51 lipids were identified and quantified representing 9 lipid classes (Supporting Information Table S-5). In both regions, a similar lipid distribution of the major abundant lipids was observed with PE and PE-O as the dominant lipid classes (Figure 4D) and with PE 34:1 and PE 36:2 as the most abundant species (Figure 4F). Between both regions the profiles of the accessible lipid classes were generally very similar but statistically significant changes could be detected for CerPE 34:1, PC 34:2, PC 36:2, and PE-O 36:1 (Figure 4E, F). The measurement reproducibility was sufficient to discern significant changes of lipid abundances of approximately ±30% (using 15 individual flies and 22 sections; Supporting Information Tables S-5 and S-6). Descriptions of the internal membrane systems of Drosophila neurons are limited and this study is the first to obtain such detailed information within a tissue, ex-vivo, by exploiting genetic tools and the anatomical organization of the fruitfly brain.

approximately 75 cells (Figure 3A−D and Supporting Information Figures S-5). Subsequently, the OCE-LC-MSn

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CONCLUSION AND PERSPECTIVE This study has shown that LCM greatly expands the scope of lipidomics, making it effective for quantitative investigations of morphologically distinct or fluorescently labeled tissue areas. Our study introduces methodological innovations that allow the analysis of any organism in which distinct cell populations can be genetically traced, such as the fruitfly (Drosophila melanogaster), zebrafish (Danio rerio) and mouse (Mus musculus). This method is also amenable to tissue sections that are exogenously labeled with fluorescent dyes or stains. We modified three key steps of the typical LCM methodology: we used (i) charged glass slides and AutoLPC to catapult ROI, (ii) C18 cartridges as the capture vessels, and (iii) on column extraction of lipids. Using LCM and OCE-LC-MSn enabled us to estimate the analytical performance in terms of sensitivity, dynamic range and extraction efficiency, which is impossible for MALDI imaging. However, our procedure requires slightly more tissue area collected for a quantitative analysis when compared to the achievable laser spot size of state of the art MALDI imaging.7,8 Future analytical developments will focus on the optimization of column chemistry and cartridge design; these might enhance the efficiency of capturing tissue flakes. For improving the mass spectrometric acquisition we emphasize that the application of fast ionization mode switches and a quadrupole based precursor selection and fragmentation will increase the number of quantifiable lipids as indicated before.44−46 In combination with fluorescently labeled cell populations and the manipulation of expression levels of lipidmetabolic enzymes, this approach will provide a quantitative

Figure 3. LCM and lipidomics of fluorescently labeled neurons of the antennal lobes. A, B: Representative images of a flyhead section in which a subset of antennal lobe neurons is labeled with GFP (green; white outlined region) before (A) and after LCM (B). C: Schematic of the head section indicating retina, R; antennal lobe neurons, AL; fat bodies, F. D: Processed image of the cartridge surface for counting tissue flakes obtained from AL presented in panel A and B. E: Major abundant lipids of the antennal lobe neurons determined in 5 independent experiments showing species above 0.2 mol %. Error bars indicate one standard deviation.

enabled us to obtain lipid profiles consisting of 58 lipids belonging to 9 lipid classes (Figure 3E and Supporting Information Table S-4). The major abundant lipid classes were PE, PE-O, and PC and this finding was in agreement with earlier reported data of larval brain tissue.34 To our knowledge this is the first report where a subset of neuronal cells was fluorescently labeled and could be specifically isolated using LCM to perform lipidomics ex vivo. As a consequence of the sensitivity of the OCE-LC-MSn, it is not required to perform extensive pooling of sample material during LCM for lipidomics. Comparative Lipidomics of Neuronal Cell Bodies and Synaptic Membranes. Finally, we used this workflow to compare lipid profiles of two specialized regions of neuronal cells in the brain, namely (i) neuronal processes and synaptic connections and (ii) cell bodies that house the nucleus and 5350

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Figure 4. Comparing lipid profiles of tissue regions that are synapse-enriched and cell body-enriched. A: Image of a flyhead section with cortical (nuclear) and neuropil (synaptic) areas fluorescently labeled with magenta and green. B: A schematic for the head section represented in panel A and C indicating retina, R; mouth parts, MP; nuclei rich cortical areas, N; different synapse-rich neuropils (ML medial lobe, VL vertical lobe, CA calyx constituting the mushroom body neuropil, AL antennal lobe neuropil, and OL optic lobe neuropil). C: Bright field image of the section after performing LCM. The dotted region presented in the inset shows the laser shot pattern on the glass slide surface after utilizing the AutoLPC function. D: Nuclear and synaptic areas exhibited very similar distribution of lipid classes. E: Lipid species significantly changed in abundance between the nuclear and synaptic areas (p < 0.05, details of the statistical evaluation are presented in Supporting Information Table S-6). F: Lipid profiles of the nuclear area (magenta) of 12 independent experiments and of synaptic area (green) of 10 independent experiments representing all major abundant lipids above 0.2 mol %. Error bars indicate one standard deviation and asterisks mark lipids with significantly changed abundances using unpaired t test with Welch’s correction.

Present Addresses

readout system for cell biology and molecular medicine applications.

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§ Sarita Hebbar: Max Planck Institute for Cell Biology and Genetics, Dresden 01307, Germany ⊥ Dominik Schwudke: Research Center Borstel, Borstel 23845, Germany

ASSOCIATED CONTENT

* Supporting Information S

Author Contributions

Additional information as indicated in the text comprising of data tables, figures, videos, and supplementary method details. This material is available free of charge via the Internet at http://pubs.acs.org.

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All authors contributed to the writing of this manuscript and have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Sonia Sen (NCBS, India) and the Drosophila Stock Center at NCBS for sharing fly stocks. We would like to thank Yamuna Krishnan (NCBS-TIFR, Bangalore, India), Ullrich

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. 5351

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Analytical Chemistry

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Schaible (RC Borstel, Germany), Nicole Zehethofer (RC Borstel, Germany), and Volker Hartenstein (UCLA, Los Angeles, USA) for critical reading of the manuscript. This work was supported by a Wellcome Trust/DBT India Alliance senior fellowship to D.S. D.S. is thankful for the financial support of the NCBS−Merck & Co International Investigator Award and core funding from NCBS-TIFR.

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dx.doi.org/10.1021/ac500276r | Anal. Chem. 2014, 86, 5345−5352