Support by Centre National de la Recherche Scientifique (CNRS, UMR 6612 and 7339) is gratefully acknowledged.

<ol>
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T., Pietropaolo, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Behavioral Genetics of the Mouse Genetics of Behavioral Phenotypes. Vol. 1, (2013).</li><li>Viola, A., Saywell, V., Villard, L., Cozzone, P. J., Lutz, N. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolic+fingerprints+of+altered+brain+growth,+osmoregulation+and+neurotransmission+in+a+Rett+syndrome+model.">Metabolic fingerprints of altered brain growth, osmoregulation and neurotransmission in a Rett syndrome model.</a> PLoS ONE. 2, e157 (2007).</li><li>Lutz, N. W., Sweedler, J. V., Wevers, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methodologies for Metabolomics. , (2013).</li><li>Rabinowitz, J. D., Purdy, J. G., Vastag, L., Shenk, T., Koyuncu, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolomics+in+drug+target+discovery.">Metabolomics in drug target discovery.</a> Cold Spring Harb Symp Quant Biol. 76, 235-246 (2011).</li><li>Wishart, D. S., Wevers, R. A., Lutz, N. W., Sweedler, J. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methodologies for Metabolomics. , (2013).</li><li>Ponten, U., Ratcheson, R. A., Salford, L. G., Siesjo, B. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+freezing+conditions+for+cerebral+metabolites+in+rats.">Optimal freezing conditions for cerebral metabolites in rats.</a> Journal of Neurochemistry. 21, 1127-1138 (1973).</li><li>Henry, P. G., Oz, G., Provencher, S., Gruetter, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+dynamic+isotopomer+analysis+in+the+rat+brain+in+vivo+automatic+quantitation+of+13C+NMR+spectra+using+LCModel.">Toward dynamic isotopomer analysis in the rat brain in vivo automatic quantitation of 13C NMR spectra using LCModel.</a> NMR Biomed. 16, 400-412 (2003).</li><li>Lutz, N. W., Cozzone, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiparametric+optimization+of+(31)P+NMR+spectroscopic+analysis+of+phospholipids+in+crude+tissue+extracts+2+Line+width+and+spectral+resolution.">Multiparametric optimization of (31)P NMR spectroscopic analysis of phospholipids in crude tissue extracts 2 Line width and spectral resolution.</a> Anal Chem. 82, 5441-5446 (2010).</li><li>Lutz, N. W., Cozzone, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiparametric+optimization+of+(31)P+NMR+spectroscopic+analysis+of+phospholipids+in+crude+tissue+extracts.+1.+Chemical+shift+and+signal+separation.">Multiparametric optimization of (31)P NMR spectroscopic analysis of phospholipids in crude tissue extracts. 1. Chemical shift and signal separation.</a> Anal Chem. 82, 5433-5440 (2010).</li><li>Lutz, N. W., Cozzone, P. J., Lutz, N. W., Sweedler, J. V., Wevers, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methodologies for Metabolomics. , (2013).</li><li>Lutz, N. W., Fernandez, C., Pellissier, J. F., Cozzone, P. J., Beraud, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cerebral+biochemical+pathways+in+experimental+autoimmune+encephalomyelitis+and+adjuvant+arthritis+a+comparative+metabolomic+study.">Cerebral biochemical pathways in experimental autoimmune encephalomyelitis and adjuvant arthritis a comparative metabolomic study.</a> PLoS ONE. 8, e56101 (2013).</li><li>Lutz, N. W., Cozzone, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Principles+of+multiparametric+optimization+for+phospholipidomics+by+31P+NMR+spectroscopy.">Principles of multiparametric optimization for phospholipidomics by 31P NMR spectroscopy.</a> Biophys Rev. 5, 295-304 (2013).</li><li>Pearson, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Shimming an NMR magnet. , (1991).</li><li>Lane, A. N., Fan, T. W. M., Higashi, R. M., Correia, J. J., Detrich, H. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Biophysical tools for biologists. Vol. 1, In vitro techniques, (2008).</li><li>Peeling, J., Wong, D., Sutherland, G. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nuclear+magnetic+resonance+study+of+regional+metabolism+after+forebrain+ischemia+in+rats.">Nuclear magnetic resonance study of regional metabolism after forebrain ischemia in rats.</a> Stroke. 20, 633-640 (1989).</li></ol>

The authors have nothing to disclose.

NMR spectroscopy is an efficient method for measuring concentrations of chemical compounds in solution in a very reproducible and accurate manner. However, to obtain high-quality data it is necessary to adhere to certain rules concerning sample preparation and analysis. In the determination of metabolite concentrations by NMR spectroscopy, neither the generation nor the reception of the NMR signal dominates the quantitation error, unless the intensity of an observed signal approaches the detection threshold (particularly weak signal). In all other cases biological variability, the sample preparation technique (extraction efficiency, physical and chemical stability of compounds, pipetting and weighing errors etc.), and/or the choice of signal acquisition and processing parameters will determine precision and accuracy of results. Obviously, any metabolic changes occurring during tissue harvest will also be reflected in the metabolite concentrations obtained. Therefore, it is very important to complete tissue harvest as fast as possible, and to apply particular tissue freezing techniques such as funnel freezing if accurate in vivo concentrations of rapidly metabolizing compounds are important (notably glucose, lactate, ATP and its catabolites, ADP and AMP).
Even at the intermediate magnetic-field strength (9.4 T) of a routine high-resolution NMR spectrometer excellent spectra can be obtained. For instance, in 1H NMR spectra of the aqueous phase of rat brain extracts, signals whose difference in chemical shift, &#916;&#948;, amounts to ca. 0.005&#160;ppm (corresponding to 2.0&#160;Hz at 400&#160;MHz resonance frequency) can be discerned and quantitated separately. This is illustrated by the partial separation of (i) the lactate and threonine doublets, and (ii) the alanine doublet from the small peaks at its base (lactate 13C satellite doublet; Figure 1, top). Spectrometers operating at higher fields (14.1 or even 18.8&#160;T) would increase resolution and sensitivity further, although these instruments are less common in NMR laboratories due to their high purchasing cost.
Using 1H NMR spectroscopy, a broad range of metabolites can be analyzed simultaneously, i.e. in one single acquisition. The sensitivity of the experiment can be improved by increasing the number of accumulated transients, although this choice increases measurement time proportionally. (The signal-to-noise ratio is proportional to the square root of the number of transients.) However, the maximum total acquisition time given in the above protocol (20 - 30&#160;min) should be sufficient for virtually all purposes, unless the amount of tissue available for extraction is very limited. Besides making use of the most NMR-sensitive nucleus (proton), metabolomic 1H NMR spectroscopy has the advantage of comprehensive databases being available for different field strengths and sample pH values10. Furthermore, software for automatic or semi-automatic spectrum evaluation has become available10.
While 1H NMR spectra of tissue extracts can be well resolved without addition of chelating agents, this is no longer true for 31P NMR spectra. In fact, the concentration of the chelating agent used (e.g., EDTA or CDTA) has a significant influence on both line width and chemical shift of 31P NMR signals stemming from phosphorylated metabolites. Increasing CDTA concentration in an extract decreases 31P NMR line widths, but this advantage may be outweighed by smaller differences between chemical shifts, &#916;&#948;, of neighboring peaks. Therefore, the amount of chelating agent used has to be optimized for both line width and chemical shift together. In lipid extracts, these two spectral parameters are significantly influenced by the overall sample concentration, i.e. the amount of tissue extracted, but also by the pH value and the temperature of the sample during the NMR measurement. Consequently, any optimization of measurement conditions must consider the combined effects of all experimental parameters involved, some of these not being additive. The complexity of this situation is illustrated by the behavior of PL 31P NMR spectra shown in Figure 2. Although the lowest tissue concentration (118&#160;mg/ml), the lowest temperature (277&#160;K) and the highest CDTA concentration (1,000&#160;mM) tested would result in lowest line width, the suggested protocol recommends the use of moderate tissue concentration (236&#160;mg/ml), intermediate CDTA concentration (200&#160;mM) and room temperature (297&#160;K) to avoid signal overlap.
Although the conditions of sample preparation and spectrum acquisition are of utmost importance, the role of spectrum processing parameters in extracting a maximum of information from the acquired raw data should not be underestimated. A particular set of processing parameters my be ideal for detecting and quantitating PLs that occur at low concentrations; however, the same set of parameters may obscure individual peaks in &#39;crowded&#39; spectral regions (Figure 3, top right). Conversely, processing parameters providing optimal resolution enhancement in regions of strongly overlapping resonances (Figure 3, bottom) would render low-intensity peaks undetectable. Recent developments, including also a comprehensive PL 31P NMR database13,14, greatly facilitate the analysis of NMR spectra13,14.
Ultimately, the objectives of the underlying application must define the criteria for optimization, i.e. the desired balance of spectral resolution, sensitivity, precision of metabolite quantification, speed, and other factors. For instance, the recommended one-phase system for PL analysis permits more reliable and efficient PL quantification than two-phase systems (Figure 3, top left), and provides high spectral resolution as well as sufficient sensitivity for the quantitation of less-abundant PLs. However, in cases where best signal separation is of much higher priority than efficient and precise quantitation, the use of a higher percentage of chloroform-d in the solvent mixture may be attempted, although this easily leads to phase separation in the sample. If this is the case, the volume of the lower phase must be determined to obtain absolute PL amounts (mg PL, per g tissue or mg total protein).
In proton-decoupled heteronuclear NMR experiments, signal intensities may be altered by nuclear Overhauser effects (NOE), in addition to saturation due to rapid acquisition schemes. If uncorrected these effects result in metabolite quantitation errors. There are two alternative strategies to deal with this challenge. In the first approach, individual transients of an acquisition are separated by long delays. This method avoids any saturation or NOE but results in relatively long experiments; it should be used if precise and accurate results are needed. In some cases, priority may have to be given to fast spectrum acquisition, in particular for very dilute samples that can only be measured using a high number of transients. In such cases, addition of paramagnetic relaxation agents to the sample would allow rapid data acquisition without saturation or NOE. Alternatively, fast data acquisition without addition of paramagnetic agents can be employed. The latter method requires correction of signal areas by way of correction factors that have to be determined for each NMR resonance, whereas the presence of relaxation agents causes some broadening of NMR signals that may reduce the precision of metabolite quantitation for crowded spectral regions. Generally, the optimal choice of experimental conditions is not only dictated by the sample type, but also by the information to be extracted from an experiment13. If high priority has to be given to fast analysis of rather abundant metabolites, without the need for high precision and accuracy, it may be adequate to measure highly concentrated samples and employ short repetition times, possibly with paramagnetic relaxation agent added to the sample. By contrast, if accurate quantitation of a large number of metabolites is more important than speed, it is preferable to use less concentrated extracts and accept long NMR acquisition times, as demonstrated by the protocol presented here. Furthermore, if a particular research project emphasizes specific metabolites, experimental conditions can be adjusted to achieve optimal spectral resolution for the spectral regions in question. The protocol and discussion presented here may serve as a guide for the optimization of metabolomic analysis of crude tissue extracts by NMR spectroscopy.

Murine models have been utilized extensively in brain research1. Genotype-phenotype correlations have been investigated in mouse and rat brains by studying gene expression at the RNA and/or protein levels on the one hand, and morphological, functional, electrophysiological and/or behavioral phenotypes on the other2-6. However, to completely understand the mechanisms linking phenotype to genotype, it is imperative to investigate the molecular events downstream of protein expression, i.e. the metabolism of the biochemical substrates upon which enzymes act7. This requirement led, over the past 10 to 15 years, to a renaissance of metabolic research in many branches of biology8,9. While classical metabolic studies have often been focused on details of specific pathways, the new metabolomic approach is geared towards an all-encompassing investigation of the global metabolic profile of the tissue under consideration. One consequence of this concept is an obvious need for analytical tools that minimize bias towards specific metabolic pathways and/or classes of compounds. However, a classical biochemical assay is based on a particular chemical reaction of a specific analyte that needs to be specified before the assay is performed. By contrast, spectroscopic techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) (i) are based on particular molecular (physical) properties of biochemical compounds, each of which gives rise to one or several distinct signals in a spectrum detected in the course of one experiment; and (ii) detect a large number of different compounds per experiment.
Thus, each spectrum contains the combined information of a whole range of metabolites. For this reason, spectroscopic methods are adequate tools for metabolomics, as no prior selection needs to be made regarding the nature of the analyte to be measured8. As a consequence, these techniques naturally lend themselves to exploratory studies because they greatly facilitate the detection of unexpected metabolic changes.
Although NMR spectroscopy and MS can be used interchangeably for the analysis of many metabolites, each method possesses specific advantages and disadvantages that have recently been reviewed10. Briefly, NMR spectroscopy can usually be performed from crude extracts and does not require chromatographic separation of sample compounds before analysis. By contrast, MS works with gas or liquid chromatography (GC or LC) separation, except for particular recent developments such as mass spectrometry imaging. In a few special cases such as the analysis of sugars, LC separation may become a necessity for NMR spectroscopy as well, because the resonance lines of different sugars overlap significantly in proton (1H) NMR spectra. Nevertheless, 1H NMR spectroscopy without chromatographic separation remains the most popular, almost universally applied metabolomic NMR method. Generally, sample preparation is more time-consuming and complex for MS than it is for NMR spectroscopy. Serious problems due to matrix effects are much less common in NMR spectroscopy than in MS where they may lead to considerably attenuated signals. Metabolite quantitation can be achieved with either method. However, multiple standard compounds are needed for MS due to variations in matrix effects and ionization efficiencies between metabolites. By contrast, only one standard per sample is needed for an NMR spectroscopic analysis because under appropriate measuring conditions, the latter method is intrinsically quantitative thanks to the strictly linear NMR response by the observed nuclei. A major drawback of NMR is its relatively low sensitivity. MS, in particular LC-MS, is more sensitive than NMR by several orders of magnitude; for this reason, MS is to be preferred over NMR for the analysis of compounds occurring at very low concentrations. On the other hand, the nondestructive nature of the NMR experiment is a clear advantage over MS; in this way, NMR can be performed repeatedly on the same sample, e.g., for different NMR-active nuclei such as 1H, phosphorus-31 (31P), carbon-13 (13C), fluorine-19 (19F) etc., as no material is consumed by NMR as opposed to MS measurements.
Both NMR and MS can be employed in different modes, each one being optimal for the detection of compounds with particular chemical characteristics. For instance, 31P NMR is often better suited than 1H NMR for the analysis of moderately concentrated phosphorylated compounds, although almost all phosphorylated metabolites also contain protons. However, their 1H NMR signals may be obscured by 1H NMR signals from other, non-phosphorylated compounds, while the latter obviously do not cause background signals in 31P NMR spectra. In an analog situation, 19F NMR analysis is to be preferred for fluorinated compounds, e.g., fluorinated drugs (no background signals from endogenous metabolites), while the special case of 13C NMR is of interest almost exclusively if the fate of 13C-labeled exogenous metabolic precursors needs to be followed, due to the extremely low natural abundance of the 13C isotope (ca. 1%). Many mass spectrometers work in either negative ion mode or positive ion mode. Therefore, it is important to know ahead of the analysis whether the ions to be observed are negatively or positively charged. We focus here on a protocol for the analysis of the brain tissue metabolome by 1H and 31P NMR spectroscopy because this method yields a large number of important metabolite concentrations at low cost in terms of (i) time needed for sample preparation and (ii) effort required for metabolite quantitation. All experiments can be performed using the equipment of a standard wet-chemistry laboratory and a high-resolution NMR spectroscopy facility. Further requirements are described in the Protocol section below.

<table><tbody><tr><td>Isoflurane</td><td>Virbac</td><td>Vetflurane</td><td>Anesthetic for animals</td></tr><tr><td>Isoflurane vaporizer</td><td>Ohmeda</td><td>Isotec 3</td><td>Newer model available: Isotec 4</td></tr><tr><td>Scalpel, scissors, forceps, clamps</td><td>Harvard Apparatus&lt;br/&gt; Fisher Scientific</td><td>various&lt;br/&gt; various</td><td>Surgical equipment for animals</td></tr><tr><td>Freeze-clamp tool</td><td>homebuilt</td><td>n/a</td><td>Tong with aluminium plates, to be inserted&lt;br/&gt; in liquid nitrogen for cooling</td></tr><tr><td>Dewar</td><td>Nalgene</td><td>4150-4000</td><td></td></tr><tr><td>Liquid nitrogen</td><td>Air Liquide</td><td>n/a</td><td></td></tr><tr><td>Nitrogen gas</td><td>Air Liquide</td><td>n/a</td><td></td></tr><tr><td>Nitrogen evaporator</td><td>Organomation Associates</td><td>N-EVAP 111</td><td>Can be replaced by homebuilt device</td></tr><tr><td>Mortar</td><td>Sigma-Aldrich</td><td>Z247472</td><td></td></tr><tr><td>Pestle</td><td>Sigma-Aldrich</td><td>Z247510</td><td></td></tr><tr><td>Tissue homogenizer</td><td>Kinematica</td><td>Polytron</td><td>With test tubes fitting homogenizer shaft</td></tr><tr><td>Electronic scale</td><td>Sartorius</td><td>n/a</td><td></td></tr><tr><td>Methanol</td><td>Sigma-Aldrich</td><td>M3641</td><td></td></tr><tr><td>Chloroform</td><td>Sigma-Aldrich</td><td>366910</td><td></td></tr><tr><td>Glass centrifuge tubes</td><td>Kimble</td><td>45500-15, 45500-30</td><td>Kimax 15 ml, 30 ml tube</td></tr><tr><td>Microcentrifuge tubes</td><td>Kimble</td><td>45150-2</td><td>Kimax 2 ml tube; should replace &quot;Eppendorf&quot; tube if compatible with centrifuge rotor</td></tr><tr><td>Polystyrene pipettes</td><td>Costar Corning</td><td>Stripettes</td><td>5 and 10 ml volumes</td></tr><tr><td>Deuterochloroform</td><td>Sigma-Aldrich</td><td>431915</td><td>99.96% deuterated</td></tr><tr><td>Deuterium oxide</td><td>Sigma-Aldrich</td><td>423459</td><td>99.96% deuterated</td></tr><tr><td>Deuterium chloride</td><td>Alpha Aesar</td><td>42406</td><td>20% in deuterium oxide</td></tr><tr><td>Sodium deuteroxide</td><td>Sigma-Aldrich</td><td>164488</td><td>30% in deuterium oxide</td></tr><tr><td>Lyophilizer</td><td>Christ</td><td>Alpha 1-2</td><td></td></tr><tr><td>Cold centrifuge</td><td>Heraeus</td><td>Megafuge 16R</td><td></td></tr><tr><td>pH meter</td><td>Eutech Cybernetics</td><td>Cyberscan</td><td></td></tr><tr><td>CDTA</td><td>Sigma-Aldrich</td><td>D0922</td><td></td></tr><tr><td>Cesium hydroxide</td><td>Sigma-Aldrich</td><td>516988</td><td></td></tr><tr><td>NMR tubes</td><td>Wilmad</td><td>528-PP</td><td></td></tr><tr><td>NMR stem coaxial insert</td><td>Sigma-Aldrich</td><td>Z278513</td><td>By Wilmad</td></tr><tr><td>NMR pipettes</td><td>Sigma-Aldrich</td><td>Z255688</td><td></td></tr><tr><td>Pipettes</td><td>Eppendorf</td><td>Research</td><td>With tips for volumes from 0.5 to 1,000 &amp;mu;l</td></tr><tr><td>Pipet-Aid</td><td>Drummond</td><td>XP</td><td></td></tr><tr><td>NMR spectrometer</td><td>Bruker</td><td>AVANCE 400</td><td>including probe and other accessories</td></tr><tr><td>NMR software</td><td>Bruker</td><td>TopSpin 1.3</td><td>newer version available: Topspin 3.2</td></tr><tr><td>Water-soluble standard compounds</td><td>Sigma-Aldrich</td><td>various</td><td></td></tr><tr><td>Phospholipid standard compounds</td><td>Avanti Polar Lipids&lt;br/&gt; Doosan Serdary&lt;br/&gt; Sigma-Aldrich</td><td>various&lt;br/&gt; various&lt;br/&gt; various</td><td>Source for plasmalogens, but may be &amp;lt;70 - 80% purity</td></tr><tr><td>Methylenediphosphonate</td><td>Sigma-Aldrich</td><td>M9508</td><td></td></tr><tr><td>TSP-d4</td><td>Sigma-Aldrich</td><td>269913</td><td></td></tr></tbody></table>

metabolomic analysis of rat brain by high resolution nuclear magnetic resonance spectroscopy of tissue extracts

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.

To obtain best resolution in metabolic NMR spectra of brain and other tissue extracts, it has long been common practice to remove or mask metal ions (most importantly: paramagnetic ions) present in extract solutions. This has been achieved either by adding a chelating agent such as EDTA or CDTA to the extract19, or by passing the extract through an ion exchange resin such as Chelex-10020. The results presented in Figure 1 demonstrate that this step is not necessary for 1H NMR spectroscopic analysis if brain extracts are carefully prepared according to the above protocol. Here, extremely narrow spectral lines were obtained for all spectral regions analyzed. Even in very crowded regions, e.g., for glutamine/glutamate and myo-inositol/glycine, peaks at a distance of &#60;0.01&#160;ppm are almost completely separable at 400&#160;MHz (Figure 1, bottom). As a consequence, qualitative, and quantitative analysis of the numerous small, but currently unassigned, peaks visible in the center and bottom panels of Figure 1 can be envisaged in the future.
<img alt="Figure 1" fo:content-width="3in" src="/files/ftp_upload/51829/51829fig1highres.jpg" width="300" /> 
Figure 1. Subregions of a typical 1H NMR spectrum (400&#160;MHz) of the aqueous phase of a brain tissue extract from a female Lewis rat. All three panels demonstrate the extremely high resolution obtainable using the protocol presented in this paper. Neither chelating agent nor ion exchange resin has been used during sample preparation. Center and bottom panels show the existence of many unassigned low-intensity peaks that hint at the huge dynamic range covered by high-resolution 1H NMR spectroscopy of tissue extracts if performed using optimized experimental parameters. These weak but well detectable signals can potentially be identified and quantified in the future. Several detected metabolites are specific of brain tissue, e.g., the neuron marker NAA or the neurotransmitter GABA; however, most compounds are involved in a broad spectrum of metabolic pathways that are common to mammalian cells, such as amino acid, branched-chain organic acid, polyol, (phospho)lipid and energy metabolism as well as in glycolysis and glutaminolysis, and in functions such as osmoregulation, cell growth and proliferation. The asterisk denotes the methyl resonance stemming from a methanol impurity. Abbreviations: ala, alanine; lac, lactate; threo, threonine; BHB, &#946;-hydroxybutyrate; val, valine; ile, isoleucine; leu, leucine; AAB, &#945;-aminobutyrate; AHB, &#945;-hydroxybutyrate; tau, taurine; scy-Ins, scyllo-inositol; myo-Ins, myo-inositol; GPC, glycerophosphocholine; PC, phosphocholine; cho, choline; crn, creatinine; Cr, creatine; GABA, &#947;-aminobutyrate; asp, aspartate; NAA, N-acetylaspartate; gln, glutamine; glu, glutamate; suc, succinate; NANA, N-acetylneuraminate; ac, acetate; gly, glycine. The small peaks at the base of the ala doublet stem from the lactate 13C satellite doublet (top panel). Reprinted under the Creative Commons Attribution License (CCAL) terms from Lutz N.W. et al. (2013) Cerebral biochemical pathways in experimental autoimmune encephalomyelitis and adjuvant arthritis: a comparative metabolomic study. PLOS ONE 8(2): e56101. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51829/51829fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In 31P NMR spectroscopy, masking or removing paramagnetic cations (mostly iron) is unquestionably a necessity because phosphates easily form complexes with divalent and trivalent ions. Addition of CDTA to extracts affects both spectral line widths and chemical shifts; compare Figure 2, top and bottom left. Line narrowing by choosing 1,000&#160;mM (bottom left) instead of 200&#160;mM (top left) CDTA was desired, but resulted in superposition of PL signals that should be quantified separately (top left). Therefore, 200&#160;mM CDTA is recommended for the aqueous component of the PL solvent, all other conditions being equal. Moreover, the sample temperature during measurement affects spectral line widths and chemical shifts; compare Figure 2, top and bottom right. Line narrowing by choosing 277&#160;K (bottom right) instead of 297&#160;K (top right) was desired, but resulted in superposition of PL signals that should be quantified separately (top right). Therefore, 297&#160;K is recommended as measurement temperature, all other conditions being equal. Comparison between the two top panels shows that a decrease in CDTA concentration from 200&#160;mM (top left) to 50&#160;mM (top right) only results in a modest increase in line width, and in almost no change in chemical shifts. However, note that the tissue concentration in the 50&#160;mM CDTA extract is half as high as it is in the 200&#160;mM CDTA extract, explaining the relatively narrow lines in the top right panel13.
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51829/51829fig2highres.jpg" width="500" /> 
Figure 2. Phosphatidylethanolamine (PtdE) regions of phospholipid 31P NMR spectra (162&#160;MHz) of brain tissue extracts from female Lewis rats. (Top left panel) Brain tissue concentration, 236&#160;mg/ml; CDTA concentration and pH in the aqueous component of the solvent, 200&#160;mM and 7.33, respectively; measurement temperature, 297&#160;K. PtdEplasm and SM signals are well resolved. (Bottom left) Brain tissue concentration, 236&#160;mg/ml; CDTA concentration and pH in the aqueous component of the solvent, 1,000&#160;mM and 7.36, respectively; measurement temperature, 297&#160;K. PtdEplasm and SM signals overlap entirely; they cannot be resolved despite reduced line width, compared with the top left spectrum. (Top right) Brain tissue concentration, 118&#160;mg/ml; CDTA concentration and pH in the aqueous component of the solvent, 50&#160;mM and 7.14, respectively; measurement temperature, 297&#160;K. PtdE and SM signals are well-resolved. (Bottom right) Brain tissue concentration, 118&#160;mg/ml; CDTA concentration and pH in the aqueous component of the solvent, 50&#160;mM and 7.14, respectively; measurement temperature, 277&#160;K. PtdE and SM signals overlap entirely; they cannot be resolved, despite reduced line width compared with the top right spectrum. Abbreviations: PtdEplasm, ethanolamine plasmalogen; PtdE, phosphatidylethanolamine; SM, sphingomyelin; PtdS, phosphatidylserine; PtdC, phosphatidylcholine. Reprinted with permission from Lutz N.W. et al. (2010) Multiparametric optimization of 31P NMR spectroscopic analysis of phospholipids in crude tissue extracts. 1. Chemical shift and signal separation. Anal Chem 82 (13): 5433-5440. Copyright 2010 American Chemical Society. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51829/51829fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Using the above protocol (one-phase system14; Figure 3, top left), a large number of quantifiable PLs were detected (Figure 3, top right), covering a considerable concentration range (note truncated high-intensity signals). Some of these signals are as yet unassigned (U1, U2, U6). If sample preparation, NMR measurement and spectrum processing are performed as indicated in this protocol, spectral resolution is even sufficient to routinely detect partial splitting of certain peaks (PtdS, PtdE, PtdEplasm, AAPtdE; bottom left). As a consequence, qualitative and quantitative analysis of further PL subgroups can be envisaged in the future. Figure 3, bottom right, illustrates the power of judiciously chosen processing parameters to partially separate signals of low-concentration compounds (here: PtdC1u, a PL derived from PtdC that is not fully identified, and PtdCplasm) resonating close to very strong signals (here: PtdC).
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51829/51829fig3highres.jpg" width="500" /> 
Figure 3. Phospholipid 31P NMR spectroscopy (162&#160;MHz) of brain tissue extracts from female Lewis rats. (Top left panel) A one-phase system (left) was preferred over a two-phase system (right). The commonly used two-phase system hampers correct PL quantitation because most of the upper phase is located outside the sensitive volume of the coil. (Top right panel) Complete 31P NMR PL spectrum of rat brain. For better visibility of weak signals (PtdIP, PtdG), exponential line broadening (LB = 3&#160;Hz) was applied. In this representation, several PL signals are not well-resolved, notably in the PtdC and PtdE regions. For PLs generating more than one 31P NMR signal, observed nuclei are underlined (PtdIP, PtdIP, PtdIP2, PtdIP2). Currently unassigned signals are denoted by &#8220;Un&#8221; (where n = 1, 2, ...). (Bottom left panel) PtdE and PtdS regions of the same spectrum. For better peak resolution, Lorentzian-Gaussian line shape transformation was applied (LB = -1&#160;Hz, GB = 0.3). Because of these processing parameters, many very weak PL signals are difficult to detect. However, at least two peaks can be discerned for each PtdE, PtdEplasm, AAPtdE, and PtdS. (Bottom right panel) PtdC region obtained with the same processing parameters as the PtdE region. Several signals at the base of the dominating PtdC resonance were detected unambiguously, while they cannot be discerned in the upper spectrum generated with exponential line broadening (AAPtdC, PtdCplasm, PtdC1u). Besides the currently unassigned PtdC analog, PtdC1u, further minor resonances may be present upfield from PtdC. Abbreviations: PtdIP, phosphatidylinositol phosphate; PtdIP2, phosphatidylinositol diphosphate; PtdA, phosphatidic acid; PtdG, phosphatidylglycerol; CL, cardiolipin; PtdE.., sum of PtdE, PtdEplasm and AAPtdE; PtdI, phosphatidylinositol. For further abbreviations see legend to Figure 2. Reprinted with permission from Lutz N.W. et al. (2010) Multiparametric optimization of 31P NMR spectroscopic analysis of phospholipids in crude tissue extracts. 1. Chemical shift and signal separation. Anal Chem 82 (13): 5433-5440. Copyright 2010 American Chemical Society. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51829/51829fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td colspan="2">Harvesting and Freezing Rat Brain</td>
		</tr>
		<tr>
			<td>Dewar for N2liq.; freezing clamp (e.g., homemade Wollenberger tongs, refs. 1 and 2, bottom of table); anesthesia chamber; surgical scissors; scalpel; 500 ml bottle with cleaning alcohol</td>
			<td>1 of each</td>
		</tr>
		<tr>
			<td>Forceps</td>
			<td>2</td>
		</tr>
		<tr>
			<td>1 ml syringe, 10 ml syringe</td>
			<td>1 of each per animal</td>
		</tr>
		<tr>
			<td>25 G needles&#160;</td>
			<td>2 per animal</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Sample Preparation</td>
		</tr>
		<tr>
			<td>Typical amount of brain tissue per extraction</td>
			<td>250-350 mg</td>
		</tr>
		<tr>
			<td>MeOH volume used in homogenizing 250 - 350 mg brain tissue</td>
			<td>4 ml</td>
		</tr>
		<tr>
			<td>5 ml plastic pipette</td>
			<td>3 per extract</td>
		</tr>
		<tr>
			<td>10 ml plastic pipette</td>
			<td>1 per extract</td>
		</tr>
		<tr>
			<td>Glass vial (&#8805;20 ml volume)</td>
			<td>1 per extract</td>
		</tr>
		<tr>
			<td>Chloroform-resistant centrifuge tube</td>
			<td>1 per extract</td>
		</tr>
		<tr>
			<td>Waiting period after tissue homogenization in MeOH (mixture at 4 &#176;C)</td>
			<td>15 min</td>
		</tr>
		<tr>
			<td>Water and CHCl3 volume added to brain tissue homogenized in 4 ml MeOH</td>
			<td>4 ml each</td>
		</tr>
		<tr>
			<td>Waiting period after mixing homogenized tissue with water and CHCl3 (mixture at -20 &#176;C)</td>
			<td>overnight</td>
		</tr>
		<tr>
			<td>Centrifugation for separation of aqueous from organic extract phase (glass centrifuge tube)</td>
			<td>13,000 &#215; g, for 40 min at 4 &#176;C</td>
		</tr>
		<tr>
			<td>Concentration of CDTA solution (aqueous phase of solvent mixture for dissolving extracted lipids)</td>
			<td>200 mM</td>
		</tr>
		<tr>
			<td>pH of aqueous phase of solvent mixture for dissolving extracted lipids</td>
			<td>7.4</td>
		</tr>
		<tr>
			<td>Volume ratio in lipid solvent A used for 31P PL analysis (CDCl3 : MeOH : CDTA solution)</td>
			<td>5:4:1</td>
		</tr>
		<tr>
			<td>Amount of solvent A used to dissolve lipids extracted from 250 - 350 mg brain tissue</td>
			<td>700 &#956;l</td>
		</tr>
		<tr>
			<td>Centrifugation for spinning down solid particles in NMR sample (microcentrifuge tube)</td>
			<td>11,000 x g, for 30 min at 4 &#176;C</td>
		</tr>
		<tr>
			<td>pH of NMR sample containing water-soluble metabolites</td>
			<td>7.3</td>
		</tr>
		<tr>
			<td>Sample volume transferred to NMR tube, after centrifugation</td>
			<td>600 &#956;l</td>
		</tr>
		<tr>
			<td>MDP concentration in coaxial insert stem for 31P NMR</td>
			<td>20 mM</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">NMR Experiments</td>
		</tr>
		<tr>
			<td>Sample temperature during NMR experiment (to be adjusted for minimizing peak overlap)</td>
			<td>25 &#176;C</td>
		</tr>
		<tr>
			<td>Spinning frequency during NMR experiment</td>
			<td>15-20 Hz</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">&#160;&#160;&#160;1H NMR Acquisition Parameters</td>
		</tr>
		<tr>
			<td>Solvent providing 2H lock signal&#160;</td>
			<td>D2O</td>
		</tr>
		<tr>
			<td>Excitation pulse width, P1</td>
			<td>11 &#181;sec</td>
		</tr>
		<tr>
			<td>Excitation pulse, amplifier attenuation, PL1</td>
			<td>0 dB</td>
		</tr>
		<tr>
			<td>FID size, TD</td>
			<td>32 k</td>
		</tr>
		<tr>
			<td>FID acquisition time, AQ</td>
			<td>3.283 sec</td>
		</tr>
		<tr>
			<td>Relaxation delay, D1</td>
			<td>15 sec</td>
		</tr>
		<tr>
			<td>Solvent suppression delay, D4</td>
			<td>6 sec</td>
		</tr>
		<tr>
			<td>Total repetition time, TR</td>
			<td>24.3 sec</td>
		</tr>
		<tr>
			<td>Spectral width in ppm, SWP</td>
			<td>12.47 ppm</td>
		</tr>
		<tr>
			<td>Spectral width in Hz, SW</td>
			<td>4,990 Hz</td>
		</tr>
		<tr>
			<td>Receiver gain, RG</td>
			<td>512</td>
		</tr>
		<tr>
			<td>Solvent suppression (continuous wave)</td>
			<td>CW</td>
		</tr>
		<tr>
			<td>Solvent suppression, amplifier attenuation, PL9</td>
			<td>50 dB</td>
		</tr>
		<tr>
			<td>Number of transients, NS</td>
			<td>32 or 64</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">&#160;&#160;&#160;31P NMR Acquisition Parameters</td>
		</tr>
		<tr>
			<td>Solvent providing 2H lock signal&#160;</td>
			<td>CDCl3</td>
		</tr>
		<tr>
			<td>Excitation pulse width, P1</td>
			<td>9.5 &#181;sec</td>
		</tr>
		<tr>
			<td>Excitation pulse, amplifier attenuation, PL1</td>
			<td>4 dB</td>
		</tr>
		<tr>
			<td>FID size, TD</td>
			<td>16 k</td>
		</tr>
		<tr>
			<td>FID acquisition time, AQ</td>
			<td>2.019 sec</td>
		</tr>
		<tr>
			<td>Relaxation delay, D1</td>
			<td>15 sec</td>
		</tr>
		<tr>
			<td>Total repetition time, TR</td>
			<td>17 sec</td>
		</tr>
		<tr>
			<td>Spectral width in ppm, SWP</td>
			<td>25 ppm</td>
		</tr>
		<tr>
			<td>Spectral width in Hz, SW</td>
			<td>4,058 Hz</td>
		</tr>
		<tr>
			<td>Receiver gain, RG (maximum)</td>
			<td>16,384</td>
		</tr>
		<tr>
			<td>Proton decoupling (composite pulse decoupling)</td>
			<td>CPD</td>
		</tr>
		<tr>
			<td>Proton decoupling, amplifier attenuation, PL13</td>
			<td>19 dB</td>
		</tr>
		<tr>
			<td>Number of transients, NS</td>
			<td>1,500 or 2,000</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">NMR Data Processing</td>
		</tr>
		<tr>
			<td colspan="2">&#160;&#160;&#160;1H Resolution Enhancement, Gaussian-Lorentzian Lineshape Transformation</td>
		</tr>
		<tr>
			<td>Overall evaluation of spectrum (if required, optimize separately for individual spectral regions, using 0.1 &#60; GB &#60; 0.4, and -0.6 &#60; LB &#60;&#160; -0.2 Hz)</td>
			<td>GB = 0.15, LB = -0.2 Hz</td>
		</tr>
		<tr>
			<td>Very weak signals, e.g., aromatic acids (alternatively to apodization with LB &#8805; 0.5 Hz)</td>
			<td>GB = 0.015, LB = -0.3 Hz</td>
		</tr>
		<tr>
			<td>Areas under peaks: use lineshape fits for overlapping resonances</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">&#160;&#160;&#160;31P resolution enhancement, Gaussian-Lorentzian lineshape transformation</td>
		</tr>
		<tr>
			<td>Overall evaluation of spectrum (optimize separately for individual spectral regions, using 0.05 &#60; GB &#60; 0.2, and -3.0 &#60; LB &#60;&#160; -1.0 Hz)</td>
			<td>GB = 0.05, LB = -1.0 Hz</td>
		</tr>
		<tr>
			<td>Very weak signals, e.g., PtdIP2, PtdA: apodization</td>
			<td>LB = 3 Hz</td>
		</tr>
		<tr>
			<td>Areas under peaks: use lineshape fits for overlapping resonances</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">References</td>
		</tr>
		<tr>
			<td colspan="2">1. Palladino GW, Wood JJ, Proctor HJ. Modified freeze clamp technique for tissue assay. The Journal of surgical research 289, 188-190 (1980).</td>
		</tr>
		<tr>
			<td colspan="2">2. Wollenberger A, Ristau O, Schoffa G. [A simple technic for extremely rapid freezing of large pieces of tissue]. Pflugers Archiv fur die gesamte Physiologie des Menschen und der Tiere 270, 399-412 (1960).</td>
		</tr>
	</tbody>
</table>
Table 1. Experimental parameters for high-resolution 1H and 31P NMR spectroscopy of brain tissue extracts. Typical values for volume ratios and concentrations of solvents and reagents used in brain tissue extraction and NMR sample preparation are presented. Further recommended values relate to sample pH and measurement temperature, as well as NMR acquisition and processing parameters. Minor adjustments may be necessary, in particular if protocol is applied to tissues other than brain. NMR parameters have been optimized for measurements at 9.4 T, and should be adjusted as needed for spectrometers operating at different magnetic-field strengths.

Watch this Scientific Journal Video about Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts at JoVE.com

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

The neurochemistry of mammalian brain is changed in many neurological and systemic diseases. Characteristic profiles of cerebral metabolites can be efficiently obtained based on crude extracts of brain tissue. To this end, high-resolution NMR spectroscopy is employed, enabling detailed quantitative analysis of metabolite concentrations (metabolomics).

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics ...

metabolomic-analysis-rat-brain-high-resolution-nuclear-magnetic

Nanyang Technological University

Research

JoVE Journal

Neuroscience

14.7K Views.  Aix-Marseille Université. The neurochemistry of mammalian brain is changed in many neurological and systemic diseases. Characteristic profiles of cerebral metabolites can be efficiently obtained based on crude extracts of brain tissue. To this end, high-resolution NMR spectroscopy is employed, enabling detailed quantitative analysis of metabolite concentrations (metabolomics).

Video: Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD

Identification of biomarkers representing the evolution of the pathophysiology of Post Traumatic Stress Disorder (PTSD) is vitally important, not only for objective diagnosis but also for the evaluation of therapeutic efficacy and resilience to trauma. Ongoing research is directed at identifying molecular biomarkers for PTSD, including traumatic stress induced proteins, transcriptomes, genomic variances and genetic modulators, using biologic samples from subjects' blood, saliva, urine, and postmortem brain tissues. However, the correlation of these biomarker molecules in peripheral or postmortem samples to altered brain functions associated with psychiatric symptoms in PTSD remains unresolved. Here, we present an animal model of PTSD in which both peripheral blood and central brain biomarkers, as well as behavioral phenotype, can be collected and measured, thus providing the needed correlation of the central biomarkers of PTSD, which are mechanistic and pathognomonic but cannot be collected from people, with the peripheral biomarkers and behavioral phenotypes, which can.
Our animal model of PTSD employs restraint and tail shocks repeated for three continuous days - the inescapable tail-shock model (ITS) in rats. This ITS model mimics the pathophysiology of PTSD 17, 7, 4, 10. We and others have verified that the ITS model induces behavioral and neurobiological alterations similar to those found in PTSD subjects 17, 7, 10, 9. Specifically, these stressed rats exhibit (1) a delayed and exaggerated startle response appearing several days after stressor cessation, which given the compressed time scale of the rat's life compared to a humans, corresponds to the one to three months delay of symptoms in PTSD patients (DSM-IV-TR PTSD Criterian D/E 13), (2) enhanced plasma corticosterone (CORT) for several days, indicating compromise of the hypothalamopituitary axis (HPA), and (3) retarded body weight gain after stressor cessation, indicating dysfunction of metabolic regulation.
The experimental paradigms employed for this model are: (1) a learned helplessness paradigm in the rat assayed by measurement of acoustic startle response (ASR) and a charting of body mass; (2) microdissection of the rat brain into regions and nuclei; (3) enzyme-linked immunosorbent assay (ELISA) for blood levels of CORT; (4) a gene expression microarray plus related bioinformatics tools 18. This microarray, dubbed rMNChip, focuses on mitochondrial and mitochondria-related nuclear genes in the rat so as to specifically address the neuronal bioenergetics hypothesized to be involved in PTSD.

We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.

Identification of biomarkers representing the evolution of the pathophysiology of Post Traumatic Stress Disorder (PTSD) is vitally important, not only for ...

Medicine

Assessing Hepatic Metabolic Changes During Progressive Colonization of Germ-free Mouse by 1H NMR Spectroscopy

It is well known that gut bacteria contribute significantly to the host homeostasis, providing a range of benefits such as immune protection and vitamin synthesis. They also supply the host with a considerable amount of nutrients, making this ecosystem an essential metabolic organ. In the context of increasing evidence of the link between the gut flora and the metabolic syndrome, understanding the metabolic interaction between the host and its gut microbiota is becoming an important challenge of modern biology.1-4
 Colonization (also referred to as normalization process) designates the establishment of micro-organisms in a former germ-free animal. While it is a natural process occurring at birth, it is also used in adult germ-free animals to control the gut floral ecosystem and further determine its impact on the host metabolism. A common procedure to control the colonization process is to use the gavage method with a single or a mixture of micro-organisms. This method results in a very quick colonization and presents the disadvantage of being extremely stressful5. It is therefore useful to minimize the stress and to obtain a slower colonization process to observe gradually the impact of bacterial establishment on the host metabolism.
 In this manuscript, we describe a procedure to assess the modification of hepatic metabolism during a gradual colonization process using a non-destructive metabolic profiling technique. We propose to monitor gut microbial colonization by assessing the gut microbial metabolic activity reflected by the urinary excretion of microbial co-metabolites by 1H NMR-based metabolic profiling. This allows an appreciation of the stability of gut microbial activity beyond the stable establishment of the gut microbial ecosystem usually assessed by monitoring fecal bacteria by DGGE (denaturing gradient gel electrophoresis).6 The colonization takes place in a conventional open environment and is initiated by a dirty litter soiled by conventional animals, which will serve as controls. Rodents being coprophagous animals, this ensures a homogenous colonization as previously described.7
 Hepatic metabolic profiling is measured directly from an intact liver biopsy using 1H High Resolution Magic Angle Spinning NMR spectroscopy. This semi-quantitative technique offers a quick way to assess, without damaging the cell structure, the major metabolites such as triglycerides, glucose and glycogen in order to further estimate the complex interaction between the colonization process and the hepatic metabolism7-10. This method can also be applied to any tissue biopsy11,12.

Assessing Hepatic Metabolic Changes During Progressive Colonization of Germ-free Mouse by 1H NMR Spectroscopy

A progressive colonization procedure is described to further assess its impact on the host hepatic metabolism. Colonization is monitored non invasively by evaluating the urinary excretion of microbial co-metabolites by NMR-based metabolic profiling while hepatic metabolism is assessed by High Resolution Magic Angle Spinning (HR MAS) NMR profiling of intact biopsy.

It is well known that gut bacteria contribute significantly to the host homeostasis, providing a range of benefits such as immune protection and vitamin ...

Immunology and Infection

A Dual Tracer PET-MRI Protocol for the Quantitative Measure of Regional Brain Energy Substrates Uptake in the Rat

We present a method for comparing the uptake of the brain&#39;s two key energy substrates: glucose and ketones (acetoacetate [AcAc] in this case) in the rat. The developed method is a small-animal positron emission tomography (PET) protocol, in which&#160;11C-AcAc and 18F-fluorodeoxyglucose (18F-FDG) are injected sequentially in each animal. This dual tracer PET acquisition is possible because of the short half-life of 11C (20.4 min). The rats also undergo a magnetic resonance imaging (MRI) acquisition seven days before the PET protocol. Prior to image analysis, PET and MRI images are coregistered to allow the measurement of regional cerebral uptake (cortex, hippocampus, striatum, and cerebellum). A quantitative measure of 11C-AcAc and 18F-FDG brain uptake (cerebral metabolic rate; &#956;mol/100 g/min) is determined by kinetic modeling using the image-derived input function (IDIF) method. Our new dual tracer PET protocol is robust and flexible; the two tracers used can be replaced by different radiotracers to evaluate other processes in the brain. Moreover, our protocol is applicable to the study of brain fuel supply in multiple conditions such as normal aging and neurodegenerative pathologies such as Alzheimer&#39;s and Parkinson&#39;s diseases.

Small-animal positron emission tomography enables the assessment of the brain&#39;s two main energy substrates:&#160;glucose and ketones. In the present method, 11C-acetoacetate and 18F-fluorodeoxyglucose are injected sequentially in each animal, and their uptake is measured quantitatively in specific brain regions determined from the magnetic resonance images.

We present a method for comparing the uptake of the brain&#39;s two key energy substrates: glucose and ketones (acetoacetate [AcAc] in this case) in the ...

Pure Shift Nuclear Magnetic Resonance: a New Tool for Plant Metabolomics

Nuclear Magnetic Resonance (NMR) is one of the most powerful tools used in metabolomics. It stands as a highly accurate and reproducible method that not only provides quantitative data but also permits structural identification of the metabolites present in complex mixtures.
Metabolic profiling by 1H NMR has proven useful in the study of various types of plant scenarios, which include the evaluation of crop&#160;conditions, harvest and post- harvest treatments, metabolic phenotyping, metabolic pathways, gene regulation, identification of biomarkers, chemotaxonomy, quality control, denomination of origin, among others. However, signal overlapping of the large number of resonances with expanded J-coupling multiplicities complicates the spectra analysis and its interpretation, and represents a limitation for classical 1H NMR profiling.
In the last decade, novel NMR broadband homonuclear decoupling techniques through which multiplet signals collapse into single resonance lines - commonly called Pure Shift methods - have been developed to overcome the spectra resolution problem inherent to 1H NMR classical spectra.
Here a step-by-step protocol of the plant extract preparation and the procedure to record optimal Pure Shift PSYCHE and SAPPHIRE-PSYCHE spectra in three different plant matrices - Vanilla plant&#160;leaves, potato tubers (S. tuberosum), and Cape gooseberries (P. peruviana) - is presented. The effect of the gain in resolution in metabolic identification, correlation analysis and multivariate analyses, as compared against classical spectra, is discussed.

This paper presents the use of PSYCHE and SAPPHIRE-PSYCHE in the metabolic profiling of plants and includes detailed procedures for sample preparation and optimal Pure Shift NMR spectra recording. Examples through which the gain in resolution achieved by homonuclear decoupling allows a more comprehensive understanding of the system are discussed.

Nuclear Magnetic Resonance (NMR) is one of the most powerful tools used in metabolomics. It stands as a highly accurate and reproducible method that not ...

Biochemistry

High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

The molecular synaptic mechanisms underlying auditory learning and memory remain largely unknown. Here, the workflow of a proteomic study on auditory discrimination learning in mice is described. In this learning paradigm, mice are trained in a shuttle box Go/NoGo-task to discriminate between rising and falling frequency-modulated tones in order to avoid a mild electric foot-shock. The protocol involves the enrichment of synaptosomes from four brain areas, namely the auditory cortex, frontal cortex, hippocampus, and striatum, at different stages of training. Synaptic protein expression patterns obtained from trained mice are compared to na&#239;ve controls using a proteomic approach. To achieve sufficient analytical depth, samples are fractionated in three different ways prior to mass spectrometry, namely 1D SDS-PAGE/in-gel digestion, in-solution digestion and phospho-peptide enrichment. 
High-resolution proteomic analysis on a mass spectrometer and label-free quantification are used to examine synaptic protein profiles in phospho-peptide-depleted and phospho-peptide-enriched fractions of synaptosomal protein samples. A commercial software package is utilized to reveal proteins and phospho-peptides with significantly regulated relative synaptic abundance levels (trained/na&#239;ve controls). Common and differential regulation modes for the synaptic proteome in the investigated brain regions of mice after training were observed. Subsequently, meta-analyses utilizing several databases are employed to identify underlying cellular functions and biological pathways.

The identification of molecules and pathways controlling synaptic plasticity and memory is still a major challenge in neuroscience. Here, a workflow is described addressing the relative quantification of synaptic proteins supposedly involved in the molecular reorganization of synapses during learning and memory consolidation in an auditory learning paradigm.

The molecular synaptic mechanisms underlying auditory learning and memory remain largely unknown. Here, the workflow of a proteomic study on auditory ...

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each other at the biochemical level1. Nuclear magnetic resonance (NMR) spectroscopy is one of several technologies, including gas chromatography&#x2013;mass spectrometry (GC-MS), with considerable promise for such studies. Advantages of NMR are that it is suitable for untargeted analyses, provides structural information and spectra can be queried in quantitative and statistical manners against recently available databases of individual metabolite spectra2,3. In addition, NMR spectral data can be combined with data from other omics levels (e.g. transcriptomics, genomics) to provide a more comprehensive understanding of the physiological responses of taxa to each other and the environment4,5,6. However, NMR is less sensitive than other metabolomic techniques, making it difficult to apply to natural microbial systems where sample populations can be low-density and metabolite concentrations low compared to metabolites from well-defined and readily extractable sources such as whole tissues, biofluids or cell-cultures. Consequently, the few direct environmental metabolomic studies of microbes performed to date have been limited to culture-based or easily defined high-density ecosystems such as host-symbiont systems, constructed co-cultures or manipulations of the gut environment where stable isotope labeling can be additionally used to enhance NMR signals7,8,9,10,11,12. Methods that facilitate the concentration and collection of environmental metabolites at concentrations suitable for NMR are lacking. Since recent attention has been given to the environmental metabolomics of organisms within the aquatic environment, where much of the energy and material flow is mediated by the planktonic community13,14, we have developed a method for the concentration and extraction of whole-community metabolites from planktonic microbial systems by filtration. Commercially available hydrophilic poly-1,1-difluoroethene (PVDF) filters are specially treated to completely remove extractables, which can otherwise appear as contaminants in subsequent analyses. These treated filters are then used to filter environmental or experimental samples of interest. Filters containing the wet sample material are lyophilized and aqueous-soluble metabolites are extracted directly for conventional NMR spectroscopy using a standardized potassium phosphate extraction buffer2. Data derived from these methods can be analyzed statistically to identify meaningful patterns, or integrated with other omics levels for comprehensive understanding of community and ecosystem function.

A method for metabolite extraction from microbial planktonic communities is presented. Whole community sampling is achieved by filtration onto specially prepared filters. After lyophilization, aqueous-soluble metabolites are extracted. This approach allows for application of environmental metabolomics to trans-omics investigations of natural or experimental microbial communities.

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each ...

Biology

Functional Magnetic Resonance Spectroscopy at 7 T in the Rat Barrel Cortex During Whisker Activation

Nuclear magnetic resonance (NMR) spectroscopy offers the opportunity to measure cerebral metabolite contents in vivo and noninvasively. Thanks to technological developments over the last decade and the increase in magnetic field strength, it is now possible to obtain good resolution spectra in vivo in the rat brain. Neuroenergetics (i.e., the study of brain metabolism) and, especially, metabolic interactions between the different cell types have attracted more and more interest in recent years. Among these metabolic interactions, the existence of a lactate shuttle between neurons and astrocytes is still debated. It is, thus, of great interest to perform functional proton magnetic resonance spectroscopy (1H-MRS) in a rat model of brain activation and monitor lactate. However, the methyl lactate peak overlaps lipid resonance peaks and is difficult to quantify. The protocol described below allows metabolic and lactate fluctuations to be monitored in an activated brain area. Cerebral activation is obtained by whisker stimulation and 1H-MRS is performed in the corresponding activated barrel cortex, whose area is detected using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI). All steps are fully described: the choice of anesthetics, coils, and sequences, achieving efficient whisker stimulation directly in the magnet, and data processing.

After checking by blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) that the corresponding somatosensory barrel field cortex area (called S1BF) is correctly activated, the main goal of this study is to quantify lactate content fluctuations in the activated rat brains by localized proton magnetic resonance spectroscopy (1H-MRS) at 7 T.

Nuclear magnetic resonance (NMR) spectroscopy offers the opportunity to measure cerebral metabolite contents in vivo and noninvasively. Thanks to ...

Using Near-infrared Fluorescence and High-resolution Scanning to Measure Protein Expression in the Rodent Brain

Neuroscience is the study of how cells in the brain mediate various functions. Measuring protein expression in neurons and glia is critical for the study of neuroscience as cellular function is determined by the composition and activity of cellular proteins. In this article, we describe how immunocytochemistry can be combined with near-infrared high-resolution scanning to provide a semi-quantitative measure of protein expression in distinct brain regions. This technique can be used for single or double protein expression in the same brain region. Measuring proteins in this fashion can be used to obtain a relative change in protein expression with an experimental manipulation, molecular signature of learning and memory, activity in molecular pathways, and neural activity in multiple brain regions. Using the correct proteins and statistical analysis, functional connectivity among brain regions can be determined as well. Given the ease of implementing immunocytochemistry in a laboratory, using immunocytochemistry with near-infrared high-resolution scanning can expand the ability of the neuroscientist to examine neurobiological processes at a systems level.

Here, we present a protocol that uses near-infrared dyes in conjunction with immunohistochemistry and high-resolution scanning to assay proteins in brain regions.

Neuroscience is the study of how cells in the brain mediate various functions. Measuring protein expression in neurons and glia is critical for the study ...

Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular method to study brain function in a resting, non-task state. This protocol describes a preclinical survival method for obtaining rs-fMRI data. Combining low dose isoflurane with continuous infusion of the &#945;2 adrenergic receptor agonist dexmedetomidine provides a robust option for stable, high-quality data acquisition while preserving brain network function. Furthermore, this procedure allows for spontaneous breathing and near-normal physiology in the rat. Additional imaging sequences can be combined with resting-state acquisition creating experimental protocols with anesthetic stability of up to 5 h using this method. This protocol describes the setup of equipment, monitoring of rat physiology during four distinct phases of anesthesia, acquisition of resting-state scans, quality assessment of data, recovery of the animal, and a brief discussion of post-processing data analysis. This protocol can be used across a wide variety of preclinical rodent models to help reveal the resulting brain network changes that occur at rest.

This protocol describes a method for obtaining stable resting-state functional magnetic resonance imaging (rs-fMRI) data from a rat using low dose isoflurane in combination with low dose dexmedetomidine.

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular method to study brain function in a resting, non-task ...

Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level

To this day, metabolic brain connectivity is mostly studied on a group level through the acquisition of static positron emission tomography (PET) data of multiple subjects. Our research groups are currently studying changes in metabolic connectivity across multiple time points following an intracerebral hemorrhage on an intrasubject level in rats. To investigate intrasubject metabolic brain connectivity, temporal information of the tracer uptake in different brain regions is required, which can be achieved through dynamic PET. In this publication, we give a detailed description of our data acquisition and analysis protocol.
Dynamic PET data of the rat brain are acquired on a dedicated preclinical PET system using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) as tracer. The tracer is injected intravenously as a bolus at the start of the PET scan. During the 60 min acquisition, animals are sedated with medetomidine.
After acquisition, the PET data are reconstructed into thirty 2 min time frames using an iterative reconstruction algorithm (Maximum-Likelihood Expectation-Maximization). A parcellated atlas consisting of multiple volumes of interest (VOIs) is used to extract time-activity curves of each VOI, which are then used to calculate the Pearson correlation coefficient between each pair of VOIs.
This dynamic PET protocol enables the assessment of metabolic connectivity differences between two single scans, rather than between groups of scans. This approach allows for the study of changes in metabolic connectivity within a single subject across different time points, or for the comparison of an individual&#39;s metabolic connectivity to a normal database. Such comparisons could be useful for tracking disease progression or aiding in the diagnosis of neurological disorders characterized by disrupted communication between brain regions, such as epilepsy or dementia.

The acquisition of dynamic positron emission tomography (PET) data and reconstruction into time frames allows for metabolic brain connectivity analysis at the single-subject level. We describe a method to acquire [18F]FDG dynamic PET data of the rat brain and obtain a connectivity matrix through the extraction of time-activity curves of volumes of interest.

To this day, metabolic brain connectivity is mostly studied on a group level through the acquisition of static positron emission tomography (PET) data of ...

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

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