Metabolic Differentiation of Neuronal Phenotypes by Single-cell

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Metabolic Differentiation of Neuronal Phenotypes by Single-cell Capillary Electrophoresis Electrospray Ionization-Mass Spectrometry Peter Nemes, Ann M. Knolhoff, Stanislav S. Rubakhin, and Jonathan V. Sweedler* Department of Chemistry, University of Illinois at Urbana Champaign, 600 South Mathews Avenue, Urbana, Illinois 61801, United States

bS Supporting Information ABSTRACT: Single-cell mass spectrometry (MS) is a rapidly emerging field in metabolic investigations. The inherent chemical complexity of most biological samples poses analytical challenges when using MS platforms to measure sample content without prior chemical separation. Here, a single-cell capillary electrophoresis (CE) system was coupled with electrospray ionization (ESI) MS to enable the simultaneous measurement of a vast array of endogenous compounds in over 50 identified and isolated large neurons from the Aplysia californica central nervous system. More than 300 distinct ion signals (m/z values) were detected from a single neuron in the positive ion mode, 140 of which were selected for chemometric data analysis. Metabolic features were evaluated among six different neuron types (B1, B2, left pleural 1 (LPl1), metacerebral cell (MCC), R2, and R15) chosen for their various physiological functions. The results indicated chemical similarities among some neuron types (B1 to B2 and LPl1 to R2) and distinctive features for others (MCC and R15 cells). The quantitative nature of the MS platform allowed the comparison of metabolite levels for specific neurons. The CE ESI-MS approach for examination of individual nanoliter-volume cells as described herein is readily adaptable to other volume-limited samples.

n the field of biomarker research and discovery, methods to investigate metabolic heterogeneity among selected cells within a larger group of cells can provide unique information on cell function and fate. Although single-cell metabolomics applications are expanding,1 4 the majority of metabolomics assays provide information about average metabolite levels in a cell population rather than levels in individual cells.5 Of course, if the cell population is heterogeneous, the chemical complexity increases when distinct cells are pooled before measurement. These factors often hinder the study of cell-to-cell heterogeneity, which can be surprisingly broad, even for cells having similar phenotypes.6,7 Consequently, there is a great, unmet demand for methodologies capable of investigating and quantifying the single-cell metabolome. There are a number of analytical techniques that deliver impressive capabilities for probing the chemistry of single cells in exquisite spatial and temporal resolution. With the commercial introduction of highly specific chemical labels, optical microscopic techniques (e.g., Raman spectroscopy, far-field superresolution fluorescence microscopy, stochastic optical reconstruction microscopy) have evolved to offer imaging resolutions below 100 nm and can routinely investigate the cellular molecular machinery in two and three dimensions in a nondestructive manner.3,4,8 These and similar optical analytical approaches enable the direct study of chemical processes in functioning cells under nativelike environments but are only applicable to a selected array of compounds.

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Mass spectrometry (MS) has proven to be an indispensible tool for metabolomic investigations, due in part to its high sensitivity and selectivity and ability to perform label-free detection. The number of MS-based approaches capable of single-cell and subcellular investigations has steadily increased,2,9 11 and include secondary ion mass spectrometry (SIMS),12 14 matrixassisted laser desorption/ionization (MALDI),1,15 scanning microprobe MALDI (SMALDI),16 and nanostructure initiator mass spectrometry (NIMS)17 in vacuum, as well as laser ablation electrospray ionization (LAESI)18 20 and live video MS21 in the ambient environment.22 The chemical complexity observed in most living systems poses challenges for broad-range molecular identification when applying MS alone, prompting the addition of orthogonal approaches for molecular characterization. One such integrated methodology incorporates chemical separation prior to MS detection which provides several analytical benefits. Hyphenated approaches not only simplify chemical complexity in the sample but can isolate molecular interferences from signals of interest. In turn, the analytical figures of merit are improved for qualitative and quantitative experiments. For example, data dimensionality is expanded by gaining a compound-dependent piece of information on the amount of time the analyte spends in separation. Gas chromatography, liquid Received: June 21, 2011 Accepted: August 2, 2011 Published: August 02, 2011 6810

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Analytical Chemistry chromatography, and capillary electrophoresis (CE) MS have demonstrated encouraging results in plant, animal, and human metabolomic and peptidomic applications.10,23 26 Because of its compatibility with small sample volumes and tolerance for high salt concentration, CE has been particularly effective for singlecell MS.27 29 Here we report a bioanalytical approach for the metabolic analysis and chemical classification of individual cells in the central nervous system (CNS) of Aplysia californica (sea hare), a widely studied model in neurobiology. Over 50 single neurons with diameters ranging from 80 to 300 μm were identified and isolated from eight animals in order to assess biological variability. A number of technical and methodological improvements were made to our previously described single-cell CE electrospray ionization (ESI)-MS system28 to combine label-free separation via CE with ESI-MS for the detection and quantitation of metabolites in these neurons. Metabolite concentrations were measured in single cells to probe the chemistry of neuronal diversity. Furthermore, multivariate data analysis was integrated to identify unique chemical components and patterns that correlated with cellular type and/or heterogeneity. We demonstrate that this combined analytical approach facilitates investigations of small-volume samples, including the differentiation of neuron phenotypes based on cellular chemistry.

’ EXPERIMENTAL SECTION Animals and Single Neuron Isolation. Adult A. californica (175 250 g), purchased from the National Resource for Aplysia (Rosenstiel School of Marine & Atmospheric Science, University of Miami, FL), were maintained in continuously circulated, aerated artificial seawater cooled to 14 °C. Eight animals were anesthetized by injecting 390 mM magnesium chloride into the vascular cavity, equal by mass to approximately one-third of each animal’s body weight. As shown in Figure 1A, ganglia and adjacent nerves were surgically dissected, enzymatically treated to remove connective tissues, and identified neurons manually isolated with a sharp tungsten needle (World Precision Instruments, Inc., Sarasota, FL) in artificial seawater that consisted of 460 mM NaCl, 10 mM KCl, 10 mM CaCl2, 22 mM MgCl2, 26 mM MgSO4, and 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), pH 7.8, mixed with glycerol (v/v, 66.7%/33.3%) to stabilize the cell membrane. For quantitation validation, we isolated a cluster of 11 neurons, the R3-13 cluster and treated the cell cluster as described below. Isolated single cells were quickly rinsed with deionized water to minimize inorganic salts and extracellular metabolites on their surfaces. Neurons were placed into 5 μL of 50% (v/v) methanol solution prepared with 0.5% (v/v) acetic acid to facilitate analyte extraction and quench enzymatic processes ex vivo; samples were stored in the solution at 20 °C until analysis. Control experiments confirmed that this environment minimized metabolite decomposition for a period as long as 2 weeks and also facilitated analyte extraction into the solution (data not shown). The resulting extract solutions were centrifuged for 1 min at 2000g and the aliquots directly analyzed by a CE ESI-MS system. Further details are discussed in the Supporting Information. Chemicals. Methanol was purchased from Fisher Scientific (Fair Lawn, NJ) and formic acid was from Thermo Scientific (Rockford, IL). All other chemicals were obtained at gradient grade or higher from Sigma-Aldrich Co. (St. Louis, MO) and were

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used as received. Sample extracts were kept in polymerase chain reaction vials that were purchased from MidSci (St. Louis, MO). CE ESI-MS Platform and Single-Neuron Analysis. Samples were analyzed by a custom-built CE ESI-MS platform that was partially based on an earlier system.28 Schematics of the experimental setup are shown in Figure 1B, and a detailed protocol of operation is provided in the Supporting Information. A multichannel data acquisition and control card (model USB-6008, National Instruments, Austin, TX) was operated by a personal computer (PC in Figure 1B) that executed a custom-written program (LabVIEW 8.2 interface, National Instruments); the card was programmed to output a time-dependent voltage function. This signal was converted to 20 kV by a high-voltage power supply (Bertan model 30R, Spellman High Voltage Electronics Corp., Hauppauge, NY) and was directly applied across a 90 cmlong fused silica capillary (40 μm inner diameter (i.d.), 110 μm outer diameter (o.d.), Polymicro Technologies, Phoenix, AZ) for electrophoretic separations. This separation capillary was connected to a custom-built coaxial sheath-flow ESI source that eliminated nebulizer gas. A microtee assembly (part P-875, IDEX Health & Science, Oak Harbor, WA) housed a stainless steel metal emitter (130 μm i.d., 260 μm o.d., part 21031A, Hamilton Company, Reno, NV) (see CE ESI in Figure 1B). The sheath liquid (50% methanol with 0.1% formic acid) was supplied through the emitter at a 750 nL/min flow rate using a syringe pump (model 70-2000 or 55-2222, Harvard Apparatus, Holliston, MA) and a fused silica delivery capillary for the sheath flow (S in Figure 1B) (75 μm i.d., 363 μm o.d., Polymicro Technologies). The emitter was grounded and positioned ∼2 mm in front of the skimmer plate of the mass spectrometer. The potential on this plate was adjusted to 1700 V to establish the cone-jet spraying mode.30,31 The separation capillary was coaxially fed through the emitter and protruded ∼50 80 μm into the Taylor cone for stable and efficient ion generation by the microelectrospray source.32,33 A 10 kΩ resistor was connected in series into the electrophoretic circuit to monitor the current exhibited through the CE ESI interface. At 20 kV separation voltage, the separation capillary initially exhibited a typical current of 8.0 ( 0.2 μA for ∼10 15 min and reached a plateau at 8.8 ( 0.1 μA for the remainder of the experiment. These experimental conditions provided optimal chemical separation while minimizing solvent heating and/or electrolysis during the CE experiments for up to an hour. For analysis, a volume of 6 nL from each neuron extract was hydrostatically introduced into the separation capillary using a custom-designed injection platform (see Figure 1B) and analyzed in at least duplicate (analytical replicates). Compounds were converted to gas-phase ions by the CE ESI interface. The generated ions were analyzed by a micrOTOF ESI-time-of-flight (TOF) mass spectrometer or a maXis ESI-Qq-TOF tandem MS (MS/MS) mass spectrometer (Bruker Daltonics, Billerica, MA) with ∼5 ppm mass accuracy and ∼8000 fwhm (full width at halfmaximum) resolution. Mass spectra were internally calibrated with sodium formate clusters that formed in the ESI source as sodium salts eluted during CE separation. Calibrations were carried out by applying an enhanced quadratic equation with DataAnalysis (version 4.0, Bruker Daltonics). MS/MS experiments with 20 35 eV collision energies facilitated the molecular identifications. Chemometric Data Analysis. Chemometric data interpretation and quantitation were performed on signal areas and intensities in selected-ion electropherograms, the former employing 6811

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Analytical Chemistry

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Figure 1. Single-neuron analysis by CE ESI-MS. (A) Optical images show (left) the abdominal ganglion removed from A. californica, (middle) manual isolation of an identified R2 neuron, and (right) the solution-extraction of the intracellular analytes. White scale bars = 1 mm. (B) Single-cell analysis platform included a regulated high voltage power supply (HVPS) remotely controlled by a personal computer (PC), SST sample and buffer vials (SV and BF, respectively) for sample-loading and separation in the fused silica capillary (FSC), a custom-designed coaxial sheath-flow (S) CE ESI interface (CE ESI) hyphenated to a mass spectrometer (MS), and a 6.3 stereomicroscope (CAM) to observe and operate the electrospray in the cone-jet spraying mode. White bars denote the scale.

unsupervised principal component analysis (PCA) (Markerview 1.1, Applied Biosystems, Carlsbad, CA). Data preprocessing weighting and scaling was logarithmic and mean center, respectively. All data were plotted by a scientific visualization software package (Origin 8.0, OriginLab Corp., Northampton, MA). Safety Considerations. All high voltage connections were carefully shielded and electrically conductive parts were grounded to protect from exposure. Standard safety protocols were practiced when handling solvents and samples.

’ RESULTS AND DISCUSSION The present study investigated the chemistry of a number of different neuronal networks (and corresponding neurons) in the

A. californica CNS. With relatively large cell sizes (∼50 μm to 1 mm) and small number of neurons, this mollusk is a remarkable model for investigations of memory and learning as well as developmental biology. The functional roles of its individual neurons have been extensively studied, including their ability to modify feeding (metacerebral cell (MCC) and left pleural 1 neuron (LPl1)), regulate mucus release (LPl1 and R2 neuron) and gut motility (B1 and/or B2 neuron), and affect egg laying (R15 neuron).34 38 Although great progress has been made in understanding A. californica on the genomic, transcriptomic, proteomic, and peptidomic scales, relatively little is known concerning the cellto-cell differences in its metabolome. Here, the metabolite content of individual neurons (80 300 μm in diameter) was assessed for these six genotypes and their chemical differences surveyed. 6812

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Analytical Chemistry Sample preparation involved a number of steps and is depicted in Figure 1A. Adult animals were anesthetized, sacrificed, and their CNS ganglia dissected in artificial seawater (see left image in Figure 1A). Individual neurons were identified based on size, color, and relative location in the CNS in accordance with earlier work28 and surgically isolated (see middle image in Figure 1A). To extract endogenous compounds from these cells, the isolated neurons were placed in 50% methanol solution prepared with 0.5% acetic acid (see right image of Figure 1A). Aliquots of the extracts were directly analyzed by a custom-designed CE ESIMS system that was built upon an MS-based platform recently described for volume-limited samples.28 The CE separation step reduced the chemical complexity of the neuron samples prior to MS detection, enabling the simultaneous measurement of a vast number of compounds, both intracellular and exogenous in origin. The acquired electropherograms were initially surveyed by generating selected-ion electropherograms in the 50 500 m/z-range with bins of 500 mDa. This evaluation revealed molecular features for over ∼450 different accurate m/z-values, each corresponding to a different ionic species separated in time. Careful control experiments were conducted to analytically evaluate the cellular and analytical environments. A number of interfering chemical species were found and ascribed to polymer and plasticizer molecules, likely originating from solvent bottles, impurities present in the solutions used (methanol, water, formic acid, and acetic acid), as well as noncovalent adducts that formed in situ during ESI. These ions were manually excluded, and the remaining ∼300 ions (m/z signals) were high-pass filtered at 5  105 counts/s. Following this criterion, a total of 144 ionic species were then m/z-selected from the remaining ion list for further analysis (tabulated in Table S2 in the Supporting Information). It is worth noting that this signal filtering criterion did not bias among chemical classes (e.g., osmolyte versus neurotransmitter molecules) but exhibited preference for signal intensity. Prospective developments in automation and computer processing are aimed at simultaneously monitoring the hundreds of ion signals detected. Nevertheless, the results obtained here demonstrate the broad applicability of our single-cell analytical platform and methodology. Selected-ion electropherograms were generated for each of these ions with a (50 mDa mass window, and the corresponding electrophoretic peak maxima and area were determined for subsequent analysis. Metabolites in Single Neurons. Several of the at least 300 different metabolites detected from a single neuron were identified in a multifaceted protocol. Accurate mass measurements were combined with online metabolite database searches (Metlin39), and positive m/z-matches were further confirmed in collisioninduced dissociation MS/MS and migration-time comparisons against related chemical standards. A representative example is given for the identification of tyrosine in Figure S1 in the Supporting Information. With the use of this multiplexed approach, 36 ions were ascribed with high analytical confidence (below 5 ppm absolute mass accuracy) to small metabolites, osmolytes, and classic neurotransmitters and are detailed in Table S1 in the Supporting Information. The injection of high salts and cellular debris often affected CE migration rates, at times up to 10% relative error; these effects were reduced using the following procedure. The registered m/zselected electropherograms were temporally aligned for the HEPES buffering agent (protonated C8H18N2O4S ion, m/z 239.1060 theoretical) and endogenous lysine, tryptophan, and glutathione,

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Figure 2. Metabolic differences observed among neuronal types. Representative selected-ion electropherograms are shown for a (5 mDa m/z window (signal multiplied by indicated factor): m/z 146.118 (1, acetylcholine, compound 9); m/z 177.103 (2, serotonin, 13); m/z 267.133 (2, unknown); m/z 147.077 (5, glutamine, 26); and m/z 118.087 (0.2, glycine betaine, 32). Migration times were corrected as detailed in Figure S3 and the Supporting Information.

which provided time markers for nonlinear multiparameter regression. An example is presented for selected MCC, LPl1, R15, and R2 neurons in Figure S3 in the Supporting Information. As a result, the ∼10% relative error in migration time reproducibility was reduced to