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 like mineral or forested uplands soils depending on the C content ( up to 5 g). Because extraction with organic solvents will pull any polyethylene glycol (PEG) into the extracts, which will negatively affect ionization during step 2.4, it is important to avoid allowing samples to contact plastic at any point during collection, storage or extraction.</li>
	<li>When ready to analyze the samples, freeze dry to constant weight, then grind the samples in a high-speed ball mill using stainless steel grinding balls to homogenize and break up any aggregates. 
	NOTE: The protocol can be paused at this point and the material stored at -80 &#176;C.</li>
	<li>Using an ethanol-washed stainless-steel utensil, aliquot 50 mg of each of the dried samples into individual 2 mL glass vials. These samples will be sequentially extracted using a series of solvents to consecutively extract increasingly non-polar metabolites from each sample. Add 1 mL of distilled, degassed water (H2O) to each sample, cap the vials and shake for 2 h on a shaker table.</li>
	<li>After shaking allow the solutions to stand at room temperature (RT) for 20 min, then centrifuge at 15,000 x g for 30 min, decant and save the supernatant from each. 
	NOTE: These solutions will be injected into the FTICR-MS by direct injection.</li>
	<li>Conduct a Folch extraction21 (also known as MPLEx20) on the now water extracted residues by repeating steps 1.3 and 1.4 substituting 1 mL of a -20 &#176;C 4:3&#160;chloroform:methanol mixture for the water in step 1.3. 
	CAUTION: Chloroform and methanol are both highly flammable and toxic. Use appropriate personal protective equipment (PPE) to avoid skin contact and avoid open flame.</li>
	<li>Carefully separate the two resulting solvent layers, which will be visually distinguishable, for separate analysis by FTICR-MS by using a separation funnel or simply remove the top layer by careful pipetting. 
	NOTE: The chloroform-containing fraction will be at the bottom while the methanol-containing fraction is less dense and will be on the top.</li>
	<li>Dilute the chloroform extract (step 1.6) 1:1 in methanol and the water extract (step 1.4) 2:1 in methanol to improve the electrospray ionization (ESI) efficiency for FTICR-MS by direct injection. 
	NOTE: The methanol-containing fraction from step 1.7 does not need to be diluted further in methanol. The methanol layer will be run by direct injection on the FTICR-MS.</li>
</ol>
2.FTICR-MS Analysis
<ol>
	<li>Calibrate the FTICR spectrometer by directly injecting 100 &#181;L of a tuning solution (see Table of Materials) spanning a mass range of approximately 100 - 1,300 Da into the FTICR-MS.</li>
	<li>Prepare the Suwannee River Fulvic Acid standard (see Table of Materials) by making a 1 mg mL-1 solution in ultrapure filtered water and then diluting the resulting solution to 20 &#181;g mL-1 in methanol.&#160;</li>
	<li>Direct inject 23 &#181;L of this final solution to the ESI source coupled to the FTICR spectrometer through a syringe pump set to a flow rate of 3.0 &#956;L min-1. Set needle voltage to +4.4 kV, Q1 to 150 m/z and glass capillary at 180 &#176;C. Inspect resulting spectra using the analysis software (see Table of Materials) to confirm the quality of the data.</li>
	<li>Use HPLC grade methanol before running samples and throughout the sampling to monitor carryover. Introduce 23 &#956;L of each extract via direct injection to the ESI source coupled to the FTICR spectrometer through a syringe pump set to a flow rate of 3.0 &#956;L min-1. Set needle voltage to +4.4 kV, Q1 to 150 m/z and glass capillary at 180 &#176;C.</li>
	<li>Adjust ion accumulation time (IAT) for each sample or group of samples to account for variation in C concentration. Typical values are between 0.1 to 0.3 s. Collect 144 scans for each sample, average the scans and then conduct an internal calibration using homologous CH2 (i.e., 14 Da separation) series.&#160; 
	NOTE: After FTICR-MS analysis, the decision can be made to either combine the water and methanol extracted fractions in order to streamline the remaining steps or the fractions can be kept separate throughout subsequent steps. The advantages and disadvantages of each approach are described at length in the Discussion. If doing so, combine the water and methanol-extracted fractions.&#160;</li>
	<li>Dry extracts using a concentrator and save the remainder of the extracts (~ 1 mL) for subsequent GC-MS (water, methanol or water + methanol), LC-MS (chloroform) and NMR (water + methanol) analysis.&#160; 
	NOTE: The protocol can be paused at this point and the material stored at -20 &#176;C.</li>
</ol>
3. FTICR-MS Data Processing
<ol>
	<li>Co-align all sample peak lists for the entire dataset to reduce mass shifts and standardize peak assignments using Formularity software22 ahead of formula assignment.</li>
	<li>Use the Formularity software22 to assign molecular formula using a signal to noise ratio greater than 7, mass measurement error &#60; 1 ppm, and allowing C, H, O, N, S, and P while excluding all other elements.</li>
	<li>If multiple candidate formulas are returned for a given mass (frequent above 500 Da) impose constraints consistent with the material being sampled. For example, in peat, typical constraints include: lowest mass error, lowest number of heteroatoms (N, S, P), and when present, P must be in oxidized form (i.e., there must be at least 4 O atoms for each P atom in the formula).&#160;</li>
</ol>
4. Chemical Derivatization for GC-MS
<ol>
	<li>Prepare blank control samples of HPLC-grade hexane in GC-MS autosampler vials23. Dissolve 100 mg fatty acid methyl esters (FAMEs: C8 - C28) mixture retention time standard in 200 &#181;L of hexane.</li>
	<li>To protect carbonyl groups, add 20 &#956;L of 30 mg mL-1 methoxyamine hydrochloride in pyridine to each of the methanol extracts and water extracts (or combined methanol/water extracts if using) from step 2.6, blanks and FAME calibration samples23. Seal vials with caps. 
	CAUTION: Pyridine is both highly toxic and flammable. Wear appropriate PPE to prevent skin or eye contact and avoid open flame. In addition, pyridine volatilizes at RT causing harmful air contamination. Work only in well-ventilated spaces under a fume hood.</li>
	<li>Vortex extracts for 20 s. Sonicate extracts for 60 s. Then, incubate extracts in a centrifuge at 37 &#176;C for 90 min at 100 x g. 
	NOTE: An excessive amount of carbohydrates or salts can cause metabolites to crystalize after being dried down. Sonicating the samples will help to reconstitute the crystalized metabolites into the derivatizing reagent.</li>
	<li>After incubation, add 80 &#956;L of N-methyl-N-(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane to each sample, vortex extracts for 20 s. Sonicate extracts for 60 s and incubate extracts again in a centrifuge at 37 &#176;C for 30 min at 100 x g.</li>
	<li>Cool extracts to RT (20 - 24 &#176;C), then transfer into GC-MS autosampler vials.</li>
</ol>
5. GC-MS Analysis
<ol>
	<li>Tune and calibrate MS according to vendor recommendations (see Table of Materials) before analysis to make sure the machine read MS data correctly. Check that helium gas pressure is within specified tolerance.</li>
	<li>Separate polar metabolites on a GC column (30 m &#215; 0.25 mm &#215; 0.25 &#956;m). Set the oven temperature protocol as follows: (1) initial temperature of 60 &#176;C for 1 min, (2) ramp to 325 &#176;C at a rate of 10 &#176;C min-1, and (3) hold at 325 &#176;C for 5 min.</li>
	<li>Analyze extracts using a GC coupled to a single quadrupole MS. Set injection port temperature to a constant 250 &#176;C. Inject 1 &#956;L of each derivatized extract in the splitless mode.</li>
</ol>
6. GC-MS Data Processing
<ol>
	<li>Inspect all the data files to ensure that they were correctly captured. Pay attention to potential shifts with regards to internal standard retention times and intensities to confirm that that the data was captured consistently throughout the analysis.</li>
	<li>Convert the vendor specific MS data format to a general MS format if required. Process raw data files using MetaboliteDetector24 calibrating retention indices based on the FAME internal standards. After aligning the retention times of all data files, continue with deconvolution and finally metabolite identification by matching retention indices and GC-MS spectra against the FiehnLib polar metabolite library25.</li>
	<li>Cross-check remaining unidentified metabolites against the NIST14 GC-MS library using spectral matching. Validate identifications individually to eliminate false identifications and reduce deconvolution errors.</li>
</ol>
7. Liquid State NMR Analysis
<ol>
	<li>Dilute the remainder of the water extracts (~ 300 &#181;L) by 10% (vol/vol) with a 5 mM 2,2-dimethyl-2-silapentane-5-sulfonate-d6 internal standard. Alternatively, combine water and methanol extracts from step 1 then resubstitute in water. By doing so however, some of the volatile compounds may be lost during the freeze-drying step. Typical final sample volumes are in the range 180 - 300 &#181;L.</li>
	<li>Transfer the mixture into a high-quality 3 mm outer diameter (O.D.) borosilicate glass NMR tube.</li>
	<li>Collect spectra using an NMR spectrometer (ideally at least 600 MHz) equipped with a 5 mm triple resonance salt-tolerant cold probe and a cold-carbon pre-amplifier.</li>
	<li>Collect 1D 1H spectra using a 1D nuclear Overhauser effect spectroscopy (NOESY) presaturation experiment at 298 K with a 4 s acquisition time, 1.5 s recycle delay, 65,536 complex points, and 512 scans.</li>
	<li>Collect 2D 1H-13C heteronuclear single-quantum correlation (HSQC) and 1H-1H total correlation (TOCSY) spectra to help in assigning metabolites and validation.</li>
	<li>Process, assign, and analyze all spectra using the NMR analysis software (see Table of Materials) to quantify intensities relative to the internal standard. Identify metabolites by matching chemical shift, J-coupling and intensity information in the samples against the library.&#160; 
	NOTE: The library is further enhanced by custom targeted metabolite standards and metabolite additions made on a regular basis. The protocol can be paused at this point and the material stored at -20 &#176;C.</li>
</ol>
8. LC-MS Lipidomics Analysis
<ol>
	<li>Rewet the dried chloroform extract generated during step 2.5 with 200 &#956;L of methanol.</li>
	<li>Inject 10 &#956;L of each extract into an ultra-performance liquid chromatograph coupled to an Orbitrap mass spectrometer using a reversed phase charged surface hybrid column (3.0 mm &#215; 150 mm &#215; 1.7 &#956;m particle size). Set a 34-min gradient (mobile phase A: acetonitrile/H2O (40:60) containing 10 mM ammonium acetate; mobile phase B: acetonitrile/isopropanol (10:90) containing 10 mM ammonium acetate) at a flow rate of 250 &#181;L/min. Use both negative and positive ionization modes with higher-energy collision dissociation and collision induced dissociation. 
	CAUTION: Acetonitrile is an eye, skin, and respiratory irritant. Wear appropriate PPE. In addition, acetonitrile is combustible. Do not allow to contact oxidizing agents, toxic fumes may be produced during burning.</li>
	<li>Upload the raw LC-MS/MS data files into LIQUID&#160;along with the target file (i.e., list of &#62; 25,000 lipid species) for the respective ionization mode (positive or negative). Process the raw file. Manually validate the resulting identifications by examining the MS/MS spectra for presence of diagnostic ions, if applicable, matching fragment ions (e.g., fatty acyl chains), isotopic separation, mass ppm error of the precursors, and the retention time. Export resulting list of confidently identified lipids as a .tsv file. 
	NOTE: The protocol can be paused at this point and store the material at -20 &#176;C.</li>
</ol>
9. Proteomics Analysis
<ol>
	<li>Extract proteins according to the MPLEx protocol20 from the remainder of the methanol phase resulting from step 2.4, by washing the extract with 20 times the extract volume of additional cold (-20 &#176;C) methanol.</li>
	<li>Use a 1 mL/50 mg silica-based sorbent (see Table of Materials) to condition C18 SPE columns with 3 mL methanol, 2 mL of 0.1% trifluoroacetic acid (TFA) in water, followed by the addition of the extract from step 9.1 at a rate no greater than 1 mL/min.</li>
	<li>Following the addition of the sample, wash the column with 4 mL of 95:4.9:0.1 water:acetonitrile:TFA, then allow to dry. Place a 1.5 mL collection tube under the SPE column, and elute the sample with 1 mL of 80:19.9:0.1 methanol:water:TFA.</li>
	<li>Concentrate the extracts to 100 &#181;L under vacuum-assisted freeze dryer, then measure the protein concentration by bicinchoninic acid (BCA) colorimetric assay26&#160;at a wavelength of 562 nm.</li>
	<li>Centrifuge extracts at 10,000 x g for 10 min at 4 &#176;C. Discard the resulting supernatant and dry the remaining pellet under vacuum for 5 min. Resuspend the protein pellet in water to a final concentration of 0.1 &#181;g peptide per &#181;L.</li>
	<li>Add dithiothreitol to a final concentration of 5 mM and incubate at 60 &#176;C for 30 min. Dilute 10-fold with 100 mM NH4HCO3/8M urea solution and incubate at 37 &#176;C for 3 h in the presence of 1 mM CaCl2 and porcine trypsin at a 1:50 enzyme to protein ratio. 
	NOTE: Protocol may be paused at this point and store material at -20 &#176;C.</li>
	<li>Separate extracts via liquid chromatography using an exponential gradient of a 0.1% formic acid in water mobile phase (A) and a 0.1% formic acid in acetonitrile mobile phase (B) at 10 kpsi and 500 nL min-1.</li>
	<li>Introduce resulting eluent into an ESI-coupled mass spectrometer collecting spectra from 400-2,000 m/z with 100,000 resolution at m/z 400 in a linear ion trap (LTQ) Orbitrap mass spectrometer.</li>
	<li>Convert RAW spectra files to the mzML format using msConvert or ProteoWizard accepting all default parameters. Use the universal database search tool MSGFPlus to search resulting proteomes against a targeted protein database of protein sequences predicted from relevant metagenome assembled genomes.</li>
	<li>Append common contaminants (e.g., trypsin, keratin, albumin) and remove all exactly duplicated protein sequences to improve peptide-to-mass-spectrum match statistics. Evaluate the resulting MSGF spectral probability scores to determine which peptide-to-mass-spectrum match is best. Use the Q-Value from MSGFPlus to filter the entire data pile to 1% false discovery rate (FDR).</li>
</ol>
10. Metabolomics Analysis and Metabolic Network Building
<ol>
	<li>Compile all metabolite molecular formula identified in steps 3, 6, 7, and 8 into a single database of metabolites present in the samples. Combine these metabolites with the Enzyme Commission (EC) or KEGG Orthology (KO) numbers of enzymes identified in step 9. Search this combined dataset against the KEGG27&#160;database, metabolic pathways section.</li>
	<li>Manually evaluate results to identify most probable pathways and integrate into a metabolic model.&#160;</li>
</ol>

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E., 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=LIQUID:+an-open+source+software+for+identifying+lipids+in+LC-MS/MS-based+lipidomics+data.">LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data.</a> Bioinformatics. 33 (11), 1744-1746 (2017).</li><li>Kanehisa, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enzyme+annotation+and+metabolic+reconstruction+using+KEGG.">Enzyme annotation and metabolic reconstruction using KEGG.</a> Protein Function Prediction: Methods and Protocols. 1611, 135-145 (2017).</li><li>Van Krevelen, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Graphical-statistical+method+for+the+study+of+structure+and+reaction+processes+of+coal.">Graphical-statistical method for the study of structure and reaction processes of coal.</a> Fuel. 29, 269-284 (1950).</li><li>Hodgkins, S. B., 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=Changes+in+peat+chemistry+associated+with+permafrost+thaw+increase+greenhouse+gas+production.">Changes in peat chemistry associated with permafrost thaw increase greenhouse gas production.</a> Proceedings of the National Academy of Sciences. 111 (16), 5819-5824 (2014).</li></ol>

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 ...

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

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Environment

8.5K Views.  Pacific Northwest National Laboratory. 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.

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

Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is highly sensitive to disturbance. It is also the primary substrate for soil microorganisms, which is fundamental to nutrient cycling. Due to these attributes, labile organic carbon (LOC) has been identified as an indicator parameter for soil health. Quantifying the turnover rate of LOC also aids in understanding changes in soil nutrient cycling processes. A sequential fumigation incubation method has been developed to estimate soil LOC and potential C turnover rate. The method requires fumigating soil samples and quantifying CO2-C respired during a 10 day incubation period over a series of fumigation-incubation cycles. Labile organic C and potential C turnover rate are then extrapolated from accumulated CO2 with a negative exponential model. Procedures for conducting this method are described.

Labile organic carbon (LOC) and the potential carbon turnover rate are sensitive indicators of changes in soil nutrient cycling processes. Details are provided for a method based on fumigating and incubating soil in a series of cycles and using the CO2 accumulated during the incubation periods to estimate these parameters.

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is ...

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Dissolved organic matter (DOM) is a highly diverse mixture of molecules providing one of the largest sources of energy and nutrients to stream ecosystems. Yet the in situ study of DOM is difficult as the molecular complexity of the DOM pool cannot be easily reproduced for experimental purposes. Nutrient additions to streams however, have been shown to repeatedly alter the in situ and ambient DOM pool. Here we demonstrate an easily replicable field-based method for manipulating the ambient pool of DOM at the ecosystem scale. During nutrient pulse experiments changes in the concentration of both dissolved organic carbon and dissolved organic nitrogen can be examined across a wide-range of nutrient concentrations. This method allows researchers to examine the controls on the DOM pool and make inferences regarding the role and function that certain fractions of the DOM pool play within ecosystems. We advocate the use of this method as a technique to help develop a deeper understanding of DOM biogeochemistry and how it interacts with nutrients. With further development this method may help elucidate the dynamics of DOM in other ecosystems.

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Dissolved organic matter provides an important source of energy and nutrients to stream ecosystems. Here we demonstrate a field-based method to manipulate the ambient pool of dissolved organic matter in situ through easily replicable nutrient pulses.

Dissolved organic matter (DOM) is a highly diverse mixture of molecules providing one of the largest sources of energy and nutrients to stream ecosystems. ...

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence the oxidizing capacity of the atmosphere through interactions with ozone and hydroxyl radicals. The main sink of NOx is the formation and deposition of nitric acid, a component of acid rain and a bioavailable nutrient. NOx is emitted from a mixture of natural and anthropogenic sources, which vary in space and time. The collocation of multiple sources and the short lifetime of NOx make it challenging to quantitatively constrain the influence of different emission sources and their impacts on the environment. Nitrogen isotopes of NOx have been suggested to vary amongst different sources, representing a potentially powerful tool to understand the sources and transport of NOx. However, previous methods of collecting atmospheric NOx integrate over long (week to month) time spans and are not validated for the efficient collection of NOx in relevant, diverse field conditions. We report on a new, highly efficient field-based system that collects atmospheric NOx for isotope analysis at a time resolution between 30 min and 2 hr. This method collects gaseous NOx in solution as nitrate with 100% efficiency under a variety of conditions. Protocols are presented for collecting air in urban settings under both stationary and mobile conditions. We detail the advantages and limitations of the method and demonstrate its application in the field. Data from several deployments are shown to 1) evaluate field-based collection efficiency by comparisons with in situ NOx concentration measurements, 2) test the stability of stored solutions before processing, 3) quantify in situ reproducibility in a variety of urban settings, and 4) demonstrate the range of N isotopes of NOx detected in ambient urban air and on heavily traveled roadways.

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Previous work suggests that the nitrogen isotopic composition of atmospheric nitrogen oxides might distinguish the influence of different sources in the environment. We report on an automated, mobile, field-based method for the high collection efficiency of atmospheric NOx for N isotopic analysis at an hourly time resolution.

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence ...

Production and Measurement of Organic Particulate Matter in a Flow Tube Reactor

Organic particulate matter (PM) is increasingly recognized as important to the Earth's climate system as well as public health in urban regions, and the production of synthetic PM for laboratory studies have become a widespread necessity. Herein, experimental protocols demonstrate approaches to produce aerosolized organic PM by &#945;-pinene ozonolysis in a flow tube reactor. Methods are described for measuring the size distributions and morphology of the aerosol particles. The video demonstrates basic operations of the flow tube reactor and related instrumentation. The first part of the video shows the procedure for preparing gas-phase reactants, ozonolysis, and production of organic PM. The second part of the video shows the procedures for determining the properties of the produced particle population. The particle number-diameter distributions show different stages of particle growth, namely condensation, coagulation, or a combination of both, depending on reaction conditions. The particle morphology is characterized by an aerosol particle mass analyzer (APM) and a scanning electron microscope (SEM). The results confirm the existence of non-spherical particles that have grown from coagulation for specific reaction conditions. The experimental results also indicate that the flow tube reactor can be used to study the physical and chemical properties of organic PM for relatively high concentrations and short time frames.

This paper describes the operation procedure for the flow tube reactor and related data collection. It shows the protocols for setting the experiments, recording data and generating the number-diameter distribution as well as the particle mass information, which gives useful information about chemical and physical properties of the organic aerosols.

Organic particulate matter (PM) is increasingly recognized as important to the Earth's climate system as well as public health in urban regions, and the ...

Production and Measurement of Organic Particulate Matter in the Harvard Environmental Chamber

The production and the evolution of atmospheric organic particulate matter (PM) are insufficiently understood for accurate simulations of atmospheric chemistry and climate. The complex production mechanisms and reaction pathways make this a challenging research topic. To address these issues, an environmental chamber, providing enough residence time and close-to-ambient concentrations of precursors for secondary organic materials, is needed. The Harvard Environmental Chamber (HEC) was built to serve this need, simulating the production of gas and particle phase species from volatile organic compounds (VOCs). The HEC has a volume of 4.7 m3 and a mean residence time of 3.4 h under typical operating conditions. It is operated as a completely mixed flow reactor (CMFR), providing the possibility of indefinite steady-state operation across days for sample collection and data analysis. The operation procedures are described in detail in this article. Several types of instrumentation are used to characterize the produced gas and particles. A High-Resolution Time-of-Fight Aerosol Mass Spectrometer (HR-ToF-AMS) is used to characterize particles. A Proton-Transfer-Reaction Mass Spectrometer (PTR-MS) is used for gaseous analysis. Example results are presented to show the use of the environmental chamber in a wide variety of applications related to the physicochemical properties and reaction mechanisms of organic atmospheric particulate matter.

This paper describes operation procedures for the Harvard Environmental Chamber (HEC) and related instrumentation for measuring gaseous and particle species. The environmental chamber is used to produce and study secondary organic species produced from the organic precursors, especially related to atmospheric organic particulate matter.

The production and the evolution of atmospheric organic particulate matter (PM) are insufficiently understood for accurate simulations of atmospheric ...

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic bonds that constitute the organic component of soils. However, the relatively low organic matter content of soils (commonly &#60; 5% by mass) and absorbance overlap of mineral and organic functional groups in the mid-infrared (MIR) region (4,000-400 cm-1) engenders substantial interference by dominant mineral absorbances, challenging or even preventing interpretation of spectra for SOM characterization. Spectral subtractions, a post-hoc mathematical treatment of spectra, can reduce mineral interference and enhance resolution of spectral regions corresponding to organic functional groups by mathematically removing mineral absorbances. This requires a mineral-enriched reference spectrum, which can be empirically obtained for a given soil sample by removing SOM. The mineral-enriched reference spectrum is subtracted from the original (untreated) spectrum of the soil sample to produce a spectrum representing SOM absorbances. Common SOM removal methods include high-temperature combustion (&#39;ashing&#39;) and chemical oxidation. Selection of the SOM removal method carries two considerations: (1) the amount of SOM removed, and (2) absorbance artifacts in the mineral reference spectrum and thus the resulting subtraction spectrum. These potential issues can, and should, be identified and quantified in order to avoid fallacious or biased interpretations of spectra for organic functional group composition of SOM. Following SOM removal, the resulting mineral-enriched sample is used to collect a mineral reference spectrum. Several strategies exist to perform subtractions depending on the experimental goals and sample characteristics, most notably the determination of the subtraction factor. The resulting subtraction spectrum requires careful interpretation based on the aforementioned methodology. For many soil and other environmental samples containing substantial mineral components, subtractions have strong potential to improve FTIR spectroscopic characterization of organic matter composition.

SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

Isolation of Mandibular Gland Reservoir Contents from Bornean 'Exploding Ants' (Formicidae) for Volatilome Analysis by GC-MS and MetaboliteDetector

The aim of this manuscript is to present a protocol describing the metabolomic analysis of Bornean &#39;exploding ants&#39; belonging to the Colobopsis cylindrica (COCY) group. For this purpose, the model species C. explodens is used. Ants belonging to the minor worker caste possess distinctive hypertrophied mandibular glands (MGs). In territorial combat, they use the viscous contents of their enlarged mandibular gland reservoirs (MGRs) to kill rival arthropods in characteristic suicidal &#39;explosions&#39; by voluntary rupture of the gastral integument (autothysis). We show the dissection of worker ants of this species for the isolation of the gastral portion of the wax-like MGR contents as well as listing the necessary steps required for solvent-extraction of the therein contained volatile compounds with subsequent gas chromatography-mass spectrometry (GC-MS) analysis and putative identification of metabolites contained in the extract. The dissection procedure is performed under cooled conditions and without the use of any dissection buffer solution to minimize the changes in the chemical composition of the MGR contents. After solvent-based extraction of volatile metabolites contained therein, the necessary steps for analyzing the samples via liquid-injection-GC-MS are presented. Lastly, data processing and putative metabolite identification with the use of the open-source software MetaboliteDetector is shown. With this approach, the profiling and identification of volatile metabolites in MGRs of ants belonging to the COCY group via GC-MS and the MetaboliteDetector software become possible.

Isolation of Mandibular Gland Reservoir Contents from Bornean &#039;Exploding Ants&#039; Formicidae for Volatilome Analysis by GC-MS and MetaboliteDetector

Minor workers of the ant species Colobopsis explodens are dissected to isolate the wax-like content stored in their hypertrophied mandibular gland reservoirs for subsequent solvent-extraction and analysis by gas chromatography-mass spectrometry. The&#160;annotation and identification of volatile constituents using the open-source software MetaboliteDetector is also described.

The aim of this manuscript is to present a protocol describing the metabolomic analysis of Bornean &#39;exploding ants&#39; belonging to the Colobopsis ...

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Based on the conventional cleaning methods (water cleaning (WC) + brush cleaning (BC)), this study evaluated the influence of ultrasonic cleaning (UC) on collecting various sized particulate matter (PM) retained on leaf surfaces. We further characterized the retention efficiency of leaves to various sized PM, which will help to assess the abilities of urban trees to remove PM from ambient air quantitatively. 
Taking three broadleaf tree species (Ginkgo biloba, Sophora japonica, and Salix babylonica) and two needleleaf tree species (Pinus tabuliformis and Sabina chinensis) as the research objects, leaf samples were collected 4 days (short PM retention period) and 14 days (long PM retention period) after the latest rainfall. PM retained on the leaf surfaces was collected by means of WC, BC, and UC in sequence. Then, retention efficiencies of leaves (AEleaf) to three types of the various sized PM, including easily removable PM (ERP), difficult-to-remove PM (DRP), and totally removable PM (TRP), were calculated. Only around 23%-45% of the total PM retained on leaves could be cleaned off and collected by WC. When the leaves were cleaned through WC+BC, the underestimation of the PM retention capacity of different tree species was in the range of 29%-46% for various sized PM. Almost all PM retained on leaves could be removed if UC was supplemented to WC+BC. 
In conclusion, if the UC was complemented after the conventional cleaning methods, more PM on leaf surfaces could be eluted and collected. The procedure developed in this study can be used for assessing the PM removal abilities of different tree species.

The ultrasonic cleaning method was applied to elute the particulate matter (PM) retained on leaf surfaces after PM was eluted by the conventional cleaning methods (water cleaning only or water cleaning plus brush cleaning). The methodology can help to improve the estimation accuracy for PM retention capacity of leaves.

Based on the conventional cleaning methods (water cleaning (WC) + brush cleaning (BC)), this study evaluated the influence of ultrasonic cleaning (UC) on ...

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Characterizing Microbiome Dynamics &amp;#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.

Here, we present a protocol for the sequential targeted quantification and non-targeted analysis of fluorinated compounds in water by mass spectrometry. This methodology provides quantitative levels of known fluorochemical compounds and identifies unknown chemicals in related samples with semi-quantitative estimates of their abundance.

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the ...