We would like to thank J.P. Chanton, J.E. Kostka, and M.M. Kolton for assistance with collecting peat samples. Portions of this work were conducted at the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research. PNNL is operated by Battelle for the DOE under Contract DE-AC05-76RL01830. This work was supported by the U.S. Department of Energy, Office of Science, and Office of Biological and Environmental research (grants: DE-AC05-00OR22725, DE-SC0004632, DESC0010580, DE-SC0012088, and DE-SC0014416).

1. Sequential Extraction of Organic Matter from Soil, Sediments, or Peat
<ol>
	<li>Collect soil, sediments, or peat via coring and divide cores according to the hypothesis being tested (e.g.,depth). Store samples in polytetrafluoroethylene coated containers and freeze at -80 &#176;C for storage prior to analysis. 
	NOTE: Approximately 25 mg C is needed for this protocol. For peat (typically 45% C), 50 mg of dried peat is required. Larger amounts of sample may be needed for low organic samples lik...

<ol>
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The authors have nothing to disclose.

The single-throughput, fully-coupled analysis stream used to characterize metabolites and the proteome provides insights into the pathways by which C cycling is occurring in a complex ecosystem. Soil and peat are heterogeneous matrices, and therefore, one of the critical steps of this method occurs in the earliest steps in ensuring that the starting peat or soil material is highly homogenous. It is preferable to grind the sample well as aggregates can reduce extraction efficiency. This is a particular problem for aggrega...

Globally, wetlands are estimated to contain 529 Pg of carbon (C), mostly as organic C buried in peat deposits1. Currently, such peatlands act as a net C sink, sequestering 29 Tg C y-1 in North America alone1. However, environmental disturbance such as draining, fires, drought, and warmer temperatures can offset this C sink by increasing organic matter decomposition resulting in increased C losses via greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production1,2. Climate change may contribute to C loss if warmer temperatures or dryer conditions stimulate faster C decomposition by microorganisms. Alternatively, higher temperatures and air CO2 concentrations may stimulate primary production to sequester more CO2 as organic carbon (OC). To what extent and how fast that OC is then decomposed into CO2 and CH4 depends on the complex interactions between the electron donor substrates, the availability of electron acceptors, and the microorganisms that mediate the transformation. In many cases, the mechanisms are not well-characterized, thus their response to environmental perturbations is not well-constrained and it remains unclear what the net result of climate change will be on carbon balance in peatland ecosystems.
The complex nature of natural organic matter (NOM) has made even identifying the organic compounds present in the NOM mixtures historically difficult. Recent advances have greatly improved our ability to characterize compounds that traditionally and, to some extent continue to be, regarded as recalcitrant humic or fulvic compounds3,4,5. We now understand that many of these compounds are actually microbially available and may be decomposed if a suitable terminal electron acceptor (TEA) is made available6,7. Calculating the nominal oxidation state of the carbon (NOSC) for a compound provides a metric for predicting the potential for decomposition and the energy yield of the TEA required. However, it requires a molecular-level characterization of the organic matter7. NOSC is calculated from the molecular formula via the following equation7: NOSC = &#8722; ((&#8722;z + 4(#C) + (#H) &#8722; 3(#N) &#8722; 2(#O) + 5(#P) &#8722; 2(#S)) / (#C)) + 4, where z is the net charge. NOSC is correlated with the thermodynamic driving force8, wherein compounds with higher NOSC are easier to degrade, while compounds with lower NOSC require increasingly energetic TEAs in order to be reduced. Compounds with NOSC less than &#8722;2 require a high energy yielding TEAs such as O2, nitrate or MnIV, and cannot be degraded by commonly occurring lower energy yielding TEAs such as FeIII or sulfate7. This is an important consideration in the waterlogged anoxic conditions found in wetlands where O2 and other high energy yielding TEAs are scarce9 and therefore the degradation of lower NOSC compounds under these conditions are thermodynamically limited. Environmental perturbation can influence the thermodynamic state of the ecosystem through hydrologic changes that influence O2 (the most energetic electron acceptor), changes in organic substrates and electron acceptors made available by primary production, and to a smaller extent by temperature. An important example of the temperature effects in wetland systems occurs with regard to the trade-off that occurs between homoacetogenesis (i.e., acetate production from CO2 and H2) and hydrogenotrophic methanogenesis (i.e., CH4 production from CO2 and H2). At low temperatures it appears that homoacetogenesis is slightly favored, while warmer temperatures favor CH4 production10. This temperature effect may have important implications for the response of ecosystems to changing climate, as CH4 is a much stronger greenhouse gas than CO211 and thus increasing production of CH4 at the expense of CO2 at warmer temperatures may contribute to a positive feedback with climate warming.
Peatlands produce globally significant quantities of CO2 and CH46 via microbial respiration of naturally occurring organic matter. The NOSC of the organic carbon substrates determines the relative proportion of CO2:CH4 produced which is a critical parameter because of the higher radiative forcing of CH4 compared to CO211, but also because modeling efforts have identified this ratio as a critical parameter for estimating C flux in peatlands12. In the absence of terminal electron acceptors other than CO2, it can be shown by electron balance that organic C substrates with NOSC &#62; 0 will produce CO2:CH4 &#62; 1, organic C with NOSC = 0 produces CO2 and CH4 in equimolar ratio, and organic C with NOSC &#60; 1 will produce CO2:CH4 &#60; 113. Decomposition of OC in natural ecosystems is mediated by microorganisms, so that even when degradation of a specific compound is thermodynamically feasible, it is kinetically limited by the activity of microbial enzymes and, under anoxic conditions, by the thermodynamic driving force (i.e., NOSC)7. Until now it has been challenging to fully characterize the organic matter because the diversity of compounds present requires different complementary techniques for their characterization. Recent advances have closed the gap; using a suite of analytical techniques we can analyze a large range of organic compounds providing molecular-level characterization and, in some cases quantification, from small primary metabolites like glucose up to 800 Da poly-heterocycles. Previously such large complex molecules would have been characterized simply as lignin-like or tannin-like and assumed to have been recalcitrant.&#160;Molecular-level characterization, however, allows the calculation of NOSC for even these large complex molecules. These NOSC values are linearly correlated with the thermodynamic driving force allowing for an assessment of the quality of organic matter available for decomposition, which in many cases reveals that these complex molecules may actually be microbially degradable even under the anoxic conditions that prevail in wetlands.
Since introduction of O2 allows organic matter of nearly all naturally observed NOSC values to be decomposed, herein we focus on changes in the organic matter and microbial proteomics which are likely to be the primary drivers in wetland (i.e., limited O2) systems. However, all the techniques that we will discuss can be applied to organic matter from any ecosystem. Commonly, bulk measurements based on optical and fluorescence analyses have been used to assess organic matter quality3,14. When using bulk measurements such as these, however, fine details are lost as large numbers of molecules are categorized together under generic terms like humics or fulvics. The definitions of these categories are not well-constrained and, in fact, may vary from study to study making comparisons impossible. Further, bulk measurements do not provide the molecular detail necessary for calculating the thermodynamics governing the system and therefore fall short of truly assessing organic matter quality15.
Individual techniques such as Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), nuclear magnetic resonance (NMR) spectroscopy, gas chromatography mass spectrometry (GC-MS), and liquid chromatography mass spectrometry (LC-MS) do provide such molecular-level detail. While each of these techniques presents its own limitations, they also bring their own strengths that can be leveraged in an integrated approach to achieve the fine molecular detail necessary for quantifying organic matter quality in a rigorous thermodynamic sense. GC-MS is useful for identifying critical small metabolites that are likely to have proximal influence on CO2 and CH4 production (e.g.,&#160;glucose, acetate, etc.); however, GC-MS requires verification against a standard and is therefore limited to already known compounds present in the database preventing identification of novel compounds. Furthermore, GC-MS is a semi-qualitative technique allowing inference about the changes in relative concentrations, but not providing the actual concentration information necessary for calculating Gibb&#39;s free energies for example. Finally, GC-MS requires derivatization of molecules prior to analysis which limits resolution to compounds smaller than ~400 Da and volatile alcohols are lost during the drying step.
One-dimensional (1D) 1H liquid state NMR allows highly quantitative characterization of small metabolites (including primary small molecular weight metabolites and volatiles like alcohols, acetate, acetone, formate, pyruvate, succinate, short-chained fatty acids, as well as a range of carbohydrates notoriously absent or compromised from MS-based methods) and their concentrations are particularly useful for calculating thermodynamic parameters. Yet, like GC-MS, 1D NMR of complex mixtures requires standardization relative to a database and therefore does not alone allow easy identification of novel compounds that are likely to be abundant in complex natural and changing ecosystems. Additionally, NMR is less sensitive than the MS-based techniques and therefore, quantitative metabolite profiling is achieved only above 1 &#181;M using NMR systems equipped with helium-cooled cold-probes. Not widely appreciated, some NMR cold-probes are salt-tolerant and allow environmental mixture analysis in the presence of millimolar salt concentrations when used in smaller diameter (&#60; 3 mm outer diameter) sample tubes16. However, a further complication of NMR is that high amounts of paramagnetic metals and minerals (e.g.,&#160;Fe and Mn above 1 - 3 wt%), which can be abundant in upland soils, can broaden spectral features and complicate interpretation of the NMR spectra. Using solid phase extraction (SPE) can aide in the interpretation of both NMR and MS-based metabolomics methods by reducing the mineral salts and increasing spectral quality.
FTICR-MS by direct injection is a highly sensitive technique capable of detecting tens of thousands of metabolites from a single sample, but it does not capture the critical small metabolites such as acetate, pyruvate, and succinate and is notoriously difficult to use for sugars and other carbohydrates17, nor does it provide quantitative information. However, unlike the other techniques, FTICR-MS excels at identifying and assigning molecular formula to novel compounds and therefore identifies the largest number of compounds providing more molecular information than any of the other described techniques. This is useful, because the molecular information provided by FTICR-MS (and other techniques) can be used to calculate NOSC which is related to the thermodynamic driving force governing the likelihood of certain reactions8 and their rates under certain conditions7. Furthermore, by coupling FTICR-MS with separation techniques, such as LC together with tandem MS, quantitative structural information can be attained, offsetting some of the disadvantages of this technique. LC-MS is useful for identifying lipid-like compounds and other metabolites that are not well-characterized by any of the other methods. Coupling LC FTICR-MS or LC-MS with a fraction collector and collecting fractions of specific unknowns of interest for structural elucidation by two-dimensional (2D) liquid state NMR is the ideal situation for identifying and quantifying unknown compounds18,19. However, this is a very time-consuming step that could be used if and when needed. Taken individually, each of these techniques provide a different snapshot of the organic matter, and by integrating them, we can achieve a more complete understanding than using any of the techniques in isolation.
While the thermodynamic considerations set the ultimate constraints on what transformations are possible in a system, organic matter decomposition is mediated by microorganisms whose enzyme activities control reaction rates. Thus, fully understanding the controls on organic matter decomposition and ultimately greenhouse gas (CO2 and CH4) production from wetlands requires an integrated omics approach to characterizing the microbial enzyme activities as well as the metabolites. In this article, we describe a method for achieving such a comprehensive analysis from a single sample using a sequential approach that results in a fully paired analysis. This approach expands on the metabolite, protein, and lipid extraction (MPLex) protocol in which proteomics was coupled with GC-MS and LC-MS20 to identify small metabolites, proteins, and lipids by incorporating quantitative metabolite information via NMR and identification of larger secondary metabolites via FTICR-MS. Slightly different to MPLex, we begin the protocol with a water extraction and then use sequential extraction with increasingly non-polar solvents. All extractions are done on a single sample which conserves sample when volumes are limited or difficult to obtain and decreases experimental error introduced through variation among aliquots from heterogeneous sample matrices (e.g.,&#160;soil and peat) or differences in storage conditions and duration.
Finally, by coupling the OM analyses with proteomics analyses of the microbial community, we can build metabolic networks that describe the pathways and transformation of organic matter decomposition. This allows us to test specific hypotheses about how perturbations to the system will influence ultimate CO2 and CH4 production through alteration to the available organic substrates, electron acceptors, and the microbial communities mediating the reactions via the activity of enzyme catalysts.
The overall goal of this method is to provide a single throughput protocol for analyzing metabolites, lipids, and microbial proteins from a single sample thereby creating a fully paired dataset for building metabolic networks while constraining analytical errors.

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	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:617px;">methoxyamine hydrochloride</td>
			<td style="width:73px;">Sigma Aldrich</td>
			<td style="width:123px;">226904</td>
			<td style="width:253px;">derivitization agent</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:617px;">5 mm triple resonance salt-tolerant cold probe&#160;</td>
			<td style="width:73px;">Bruker</td>
			<td style="width:123px;"></td>
			<td>instrumentation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:617px;">capillary GC column HP-5MS column (30 m &#215; 0.25 mm &#215; 0.25 &#956;m)</td>
			<td style="width:73px;">Agilent</td>
			<td style="width:123px;">AG19091S-433</td>
			<td>instrumentation</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">reversed phase charged surface hybrid column (3.0 mm &#215; 150 mm &#215; 1.7 &#956;m particle size)</td>
			<td style="width:73px;">ThermoFisher</td>
			<td style="width:123px;"></td>
			<td>instrumentation</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">2 mL glass vials</td>
			<td style="width:73px;">VWR International</td>
			<td style="width:123px;">46610-722</td>
			<td>sample vials</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">autosampler vials</td>
			<td style="width:73px;">VWR International</td>
			<td style="width:123px;">97055-324; 9467671</td>
			<td>sample vials</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">Chloroform</td>
			<td style="width:73px;">VWR International</td>
			<td>JT9174-3</td>
			<td>solvent</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">Ethanol</td>
			<td style="width:73px;">VWR International</td>
			<td style="width:123px;">BDH67002.400</td>
			<td>solvent</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">methanol</td>
			<td style="width:73px;">VWR International</td>
			<td style="width:123px;">BDH85681.400</td>
			<td>solvent</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">pyridine</td>
			<td style="width:73px;">VWR International</td>
			<td style="width:123px;">BDH67007.400</td>
			<td>solvent</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">2,2-dimethyl-2-silapentane-5-sulfonate-d6</td>
			<td style="width:73px;">Sigma Aldrich</td>
			<td style="width:123px;">178837</td>
			<td>standard</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:617px;">C8-C24 fatty acid methyl ester</td>
			<td style="width:73px;">Sigma Aldrich</td>
			<td style="width:123px;">CRM18918</td>
			<td>standard</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">N-methyl-N- (trimethylsilyl)trifluoroacetamide</td>
			<td style="width:73px;">Sigma Aldrich</td>
			<td style="width:123px;">24589-78-4</td>
			<td>standard</td>
		</tr>
		<tr height="104">
			<td height="104" style="height:104px;width:617px;">Suwanee River Fulvic Acid standard</td>
			<td style="width:73px;">International Humic Substances Society</td>
			<td style="width:123px;">2S101F</td>
			<td>standard</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">trimethylchlorosilane</td>
			<td style="width:73px;">Sigma Aldrich</td>
			<td align="right" style="width:123px;">89595</td>
			<td>standard</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:617px;">Tuning Solution</td>
			<td style="width:73px;">Agilent</td>
			<td style="width:123px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:617px;">FTICR-MS analysis software</td>
			<td style="width:73px;">Bruker</td>
			<td colspan="2">Compass DataAnalysis 4.1</td>
		</tr>
		<tr height="125">
			<td height="125" style="height:125px;width:617px;">Formularity Software</td>
			<td style="width:73px;">Pacific Northwest National Laboratory</td>
			<td style="width:123px;">Formularity</td>
			<td style="width:253px;">available for download at: https://omics.pnl.gov/software/formularity</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:617px;">GC-MS</td>
			<td style="width:73px;">Agilent</td>
			<td colspan="2">Agilent GC 7890A with MSD 5975C</td>
		</tr>
		<tr height="83">
			<td height="83" style="height:83px;width:617px;">silica-based sorbent</td>
			<td style="width:73px;">Phenomenex (Torrance, CA)</td>
			<td colspan="2">Strata C18-E (PN 8E-S001-DAK)</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:617px;">NMR TUBE 3MM 8 150 CS5</td>
			<td style="width:73px;">VWR International</td>
			<td>KT897820-0008</td>
			<td>NMR tube</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:617px;">Varian Direct Drive 600-MHz NMR spectrometer&#160;</td>
			<td style="width:73px;">Varian Inova</td>
			<td>Varian Direct Drive 600-MHz</td>
			<td>NMR spectrometer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Chenomx NMR Suite 8.3</td>
			<td style="width:73px;">Chenomx</td>
			<td>Chenomx NMR Suite</td>
			<td>NMR software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">ultra-performance liquid chromatograph&#160;</td>
			<td style="width:73px;">waters</td>
			<td>Aquity UPLC H&#160;</td>
			<td>liquid chromatograph&#160;</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">Velos-ETD Orbitrap mass spectrometer&#160;</td>
			<td style="width:73px;">ThermoFisher</td>
			<td>Thermo Scientific LTQ Orbitrap Velos</td>
			<td>mass spectrometer&#160;</td>
		</tr>
	</tbody>
</table>

single-throughput complementary high-resolution analytical techniques for characterizing complex natural organic matter mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

We performed the described complementary analysis protocol and compared peat with depth in the S1 bog in the Spruce and Peatlands Response Under Changing Environments (SPRUCE) site in Minnesota, USA. These results are compared to those from a permafrost bog and fen from northern Sweden to show how sites may vary in metabolite and enzyme activities. We identified 3,312 enzymes in the proteomics analysis. An analysis of the enzymes activities with depth reveals that the number of enzymes declines sharply between 15 cm and ...

Watch this Scientific Journal Video about Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures at JoVE.com

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

This protocol describes the method for comprehensive, unbiased molecular characterization of a single sample using both mass spectrometry, using metabolomics, proteomics and lipidomics, and nuclear magnetic resonance spectroscopy platforms. The significance of this approach is that we can characterize a biological system downstream of the genome, through identifying different types of molecules, allowing us to infer metabolic and decomposition pathways. Sequential Extraction Technique, allow extraction of bioavailable, polar compounds using water, followed by MPLEx to capture any additional polar compounds or metabolites as well as proteins and lipids from a single sample.Visual demonstration of this method is critical because it guides the scientist through separating the three layers during the second extraction step, and splitting the extracts between different analysis instruments. Integrative multi-omic analyses empowers more effective investigations and complete understanding of complex biological systems. This robust method extracts a wide diversity of molecules and is applicable to diverse sample types.Begin this procedure with collection and preparation of the samples as described in the text protocol. Using an ethanol washed, stainless steel utensil, aliquot 50 milligrams of each of the dried samples into individual two milliliter glass tubes. Add one milliliter of distilled, degassed water to each sample.Then cap the vials and shake for two hours on a shaker table. Centrifuge the samples at 15 thousand times gravity for 30 minutes. And then allow the solutions to stand at room temperature for 20 minutes.Decant and save the supernatant from each sample. Conduct an MPLEx Extraction on the now water extracted residues by repeating these extraction steps. Except to substitute a minus 20 degree celsius four to three chloroform to methanol mixture for the distilled, degassed water.Carefully separate the two resulting solvent layers, which will be visually distinguishable through removing the top layer by careful pipetting. Dry down the lower non polar layer in the freeze dryer. As soon as it is dry, add five microliters of chloroform and 195 microliters of methanol if analyzing within the next couple of days.To improve the electrospray ionization efficiency for fourier-transform ion cyclotron resonance mass spectrometry, abbreviated FTICR-MS by direct injection. Dilute the chloroform extract one to one in methanol, and the water extract two to one in methanol. Calibrate the FTICR spectrometer by directly injecting 100 microliters of a tuning solution, spanning a mass range of approximately 100 to 1, 300 daltons, into the FTICR-MS.Direct inject 100 microliters of the Suwannee River Fulvic Acid standard to the electrospray ionization source. Coupled to the FTICR spectrometer through a syringe pump set to a Flow Rate of 3.0 microliters per minute. Set the needle voltage to positive 4.4 kilovolts.Queue one to 100 mass to charge ratio and the glass capillary at 180 degrees celsius. Inspect the resulting spectra using the analysis software to confirm the quality of the data. Now, introduce 100 microliters of each extract via direct injection to the electrospray ionization source, coupled to the FTICR spectrometer through a syringe pump set to a Flow Rate of 3.0 microliters per minute.Set the parameters as before. Adjust the ion accumulation time for each sample or group of samples to account for variation in carbon concentration. Collect 144 scans for each sample, average the scans and then conduct an internal calibration using a homologous CH2 series.Dry the extracts using a concentrator and save the remainder of the extracts for subsequent gas chromatography mass spectrometry, liquid chromatography mass spectrometry and nuclear magnetic resonance spectroscopy analysis. To prepare for GC-MS, first prepare blank control samples as detailed in the text protocol. To protect carbinol groups add 20 microliters of 30 milligrams per milliliter methoxamine hydrochloride in Paradyne to each of the samples, including the methanol extracts, the water extracts, the blanks and the FAME calibration samples.Seal the vials with caps. Vortex the extracts for 20 seconds, then sonicate the extracts for 60 seconds. Centrifuge the extracts at 37 degrees celsius for 90 minutes at 100 times gravity.Add 80 microliters of MSTFA with 1%trimethylchlorosilane to each sample. After vortexing and sonicating the extracts as before, centrifuge the extracts again at 37 degrees celsius for 30 minutes at 100 times gravity. After cooling extracts to room temperature, transfer into GC-MS autosampler vials.Proceed to GC-MS analysis and data processing as described in the text protocol. To prepare for liquid state NMR analysis, dilute the remainder of the water extracts by 10%with a five millimolar DSS internal standard. Transfer the mixture into a high quality, three millimeter outer diameter borosilicate glass NMR tube.Proceed to NMR analysis and data processing as described in the text protocol. To perform the LC-MS lipidomics analysis, inject 10 microliters of each extract into an ultra performance liquid chromatography system, coupled to an Orbitrap mass spectrometer using a reversed phase charged surface hybrid column. Set a 34 minute gradient as listed in the text protocol at a Flow Rate of 250 microliters per minute.Use both negative and positive ionization modes with higher energy collision dissociation and collision-induced dissociation. To perform proteomics analysis, first extract proteins according to the MPLEx protocol for the remainder of the methanol phase as outlined in the text protocol. Peat was compared with depth in the S1 bog at the Spruce and Peatlands Response Under Changing Environments or SPRUCE site in Minnesota, USA.3, 312 enzymes were identified in the proteomics analysis. An analysis of the enzyme activities with depth reveals that the number of enzymes declines sharply between 15 centimeters and 45 centimeters in the SPRUCE Bog. To show how sites may vary in metabolite and enzyme activities, SPRUCE results are compared to those from a Permafrost Bog and Fen in northern Sweden.Overall 67, 040 metabolites were identified in all of the SPRUCE Peat samples from the combination of FTICR-MS, NMR, GC-MS and LC-MS analyses. Shown here is the relative fraction of different chemical classes identified via various techniques in the different depths. While amino acids and sugars decline with depth, this is not observed for lipids.By cross validating metabolites identified in all analyses against the KEGG Database it was determined that the identified compounds are involved in common metabolic pathways such as tricarboxylic acid cycle, glycolysis and sugar metabolism. Throughout this procedure it is critical to be aware of potential sources of contamination. Beginning at sample collection through analysis, samples should not come in contact with pegged plastics which can negatively affect ionization.Many of the solvents used during the extraction procedure are hazardous or flammable. Always wear appropriate PPE to avoid skin and eye contact, as well as to avoid contaminating samples.

single-throughput-complementary-high-resolution-analytical-techniques

Research

JoVE Journal

Environment

8.5K Görüntüleme.  Pacific Northwest National Laboratory. Bu protokol, metabolomik, proteomik ve lipidomik ve nükleer manyetik rezonans spektroskopi platformları kullanılarak tek bir numunenin kapsamlı, tarafsız moleküler karakterizasyonu yöntemini açıklamaktadır. Bu yaklaşımın önemi, farklı molekül türlerini tanımlayarak, metabolik ve ayrışma yollarını çıkarabilmek için genomun aşağısındaki biyolojik sistemi karakterize edebilmemizdir. Sıralı Ekstraksiyon Tekniği, biyomevcut ekstraksiyon sağlar, su kullanarak kutup ...