This work was funded in part by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC02-07ER64494) and DOE OBP Office of Energy Efficiency and Renewable Energy (DE-AC05-76RL01830). The authors would like to acknowledge Anders Gurda for his patient and skillful contribution to videography and editing.

NOTE: Always wear appropriate personal protective equipment (PPE) throughout the procedure. To avoid potential sample contamination, do not touch glassware with bare hands. Wear appropriate gloves when running protocol steps that require handling of chloroform.
1. Preparations (2 Days for ~40 Samples)
<ol>
	<li>Collect soil into sterile bags and transport from the field in a cooler containing ice. If it is not possible to sieve fresh soil and freeze dry immediately, store the samples in a -80 &#176;C freezer until ready to start the analysis.</li>
	<li>Prepare soil by homogenizing. Remove roots and stones and break up clods by coarse sieving (e.g. 2 mm).</li>
	<li>Prepare for freeze drying by putting subsamples in an appropriate container as per instructions of the freeze dryer manual and freeze dry as soon as possible. Once soils are freeze dried, store in a sealed container with desiccant until extraction. It is best to store freeze dried soil at a minimum of -20 &#176;C but preferably at -80 &#176;C.</li>
	<li>In preparation for extraction, remove freeze dried soils from storage and grind to a flour-like consistency. Methods for grinding include ball mill, ball beater, and mortar and pestle. After grinding soil, store in a freezer (see 1.3). 
	NOTE: The amount of sample used for extraction depends on its organic matter content. A general guideline is to use 0.5 to 1 g of a soil that is 12 to 18% carbon by weight, and 3 to 5 g for a soil 1 to 3% carbon by weight.</li>
	<li>Pre-rinse 30 mL centrifuge tubes with hexane. Add approximately 2 to 3 mL of hexane to tubes and vortex for 5 sec. Decant hexane to another tube, and vortex. Hexane (2 to 3 mL) can be used to serially rinse six tubes. Store hexane-rinsed tubes inverted in the fume hood and dispose of used hexane in an appropriate waste container.</li>
	<li>Wrap the glassware in 2 to 3 layers of aluminum foil and place in the muffle furnace. Bake (muffle) glassware at 450 &#176;C for 4.5 h.</li>
	<li>Prepare reagents.
	<ol>
		<li>For the soil amount used, add chemicals in a 0.8:1:2 volume ratio for phosphate-buffer (P-buffer):CHCl3:MeOH.</li>
		<li>Prepare P-buffer solution: Phosphate-Buffer: 0.1 M, pH 7.0.
		<ol>
			<li>Add 61 mL of 1M K2HPO4 stock, sterile (chemical should be ACS certified grade or better). Add 39 mL of 1 M KH2PO4 stock, sterile (chemical should be ACS certified grade or better). Fill to 1000 mL&#160;with Type 1 water. Adjust pH to 7.0 with NaOH or HCl. Store unused solution for up to 7 d at ambient temperature or 30 days in a refrigerator.</li>
			<li>Alternatively, weigh out 10.62 g of K2HPO4 and 5.31 g of KH2PO4; dilute to 1 L with Type 1 water. Check pH, adjust if necessary.</li>
		</ol>
		</li>
		<li>Prepare Reagent 1, Saponification Reagent.
		<ol>
			<li>Dispense 300 mL of Type 1 water. Add 300 mL of CH3OH (HPLC grade or higher). Add 90.00 g of NaOH (certified ACS or better). Add NaOH pellets to the solution while stirring. Stir until the pellets dissolve. It is recommended to store this solution no longer than 30 d.</li>
		</ol>
		</li>
		<li>Prepare Reagent 2, Methylation Reagent.
		<ol>
			<li>Dispense 275 mL of CH3OH (HPLC grade or higher). Add 325 mL of certified 6.00 N HCL while stirring. It is recommended to store this solution no longer than 30 d.</li>
		</ol>
		</li>
		<li>Prepare Reagent 3, Extraction Reagent: 50 % C6H14 (Hexane), 50% C5H12O (MTBE).
		<ol>
			<li>In the fume hood, combine 200 mL of C6H14 (HPLC grade or higher) and 200 mL of C5H12O (HPLC grade or higher). Mix well; cap tightly. Store in flammables cabinet or fume hood for no longer than 1 year.</li>
		</ol>
		</li>
		<li>Prepare Reagent 4, Base wash Reagent.
		<ol>
			<li>Dispense 900 mL of Type 1 water to beaker. Add 10.80 g of NaOH (Certified ACS or better) while stirring. Stir until the pellets dissolve. It is recommended to store this solution no longer than 30 d.</li>
		</ol>
		</li>
		<li>Prepare stock solution of internal standard.
		<ol>
			<li>Weigh out 100 mg of 19:0 EE standard. Add to 50 mL of hexane-rinsed volumetric flask. Add ~15 mL of Hexane-MTBE solution (reagent 3) to flask, swirl to dissolve. Top off to 50 mL with Hexane-MTBE solution (reagent 3). Cap and swirl to mix. Transfer to hexane-rinsed glass tubes with PTFE lined caps and store at -20 &#176;C.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. DAY 1 - Extraction of Fatty Acids from Soil (4 to 5 h for ~40 Samples)
<ol>
	<li>Prepare three 5 to 10 mL dispensing pipettes for P-buffer, chloroform, and methanol. 
	NOTE: Less hazardous alternatives to chloroform have been proposed as extractants. These may result in equivalent lipid recovery rates 16,17, but are not evaluated in this protocol.</li>
	<li>Weigh freeze-dried soils into 30 mL centrifuge tubes and record soil mass.</li>
	<li>Make two blanks and include a check standard (a soil that has been extracted previously).</li>
	<li>In the fume hood, add the reagents to the soil in the centrifuge tube in the following order:&#160;P-buffer, CHCl3, and MeOH (Table 1). Allow the soils time to wet after the P-buffer addition before adding the CHCl3. Cap the centrifuge tubes tightly and cover to protect from light.</li>
	<li>Place them on the shaker horizontally making sure they are well-secured. With the speed setting on 280 rpm, shake for 1 h18.</li>
	<li>Prepare two 16 mm x 150 mm glass tubes for each sample as follows: Label the tube and add the same volume of CHCl3 and an equal volume of P-buffer.</li>
	<li>Remove the centrifuge tubes from the shaker and centrifuge for 10 min at 1,430 x g and 25 &#176;C. Phase separation should be visible in the glass tube.</li>
	<li>In the fume hood, decant the supernatant from the centrifuge tube into one of tubes prepared in step 2.6. Repeat steps 2.4 through 2.5 and decant the supernatant into the second tube.</li>
	<li>Securely cap all the16 mm &#215; 150 mm glass tubes with PTFE-lined caps and invert 10 x to mix.</li>
	<li>Allow the samples to stand undisturbed overnight to complete separation of the two phases. To do this, keep the samples in a dark cabinet/space or covered in aluminum foil at room temperature. It is acceptable to allow the extracts to separate over the weekend.
	<ol>
		<li>Alternatively, if one desires to move directly to the next step, or if samples get disturbed the next day, centrifuge samples for 10 min at 1,000 x g and 25 &#176;C. 
		NOTE: Subject all samples within a comparison group to the same phase separation standing time.</li>
	</ol>
	</li>
</ol>
3. DAY 2 - Isolation of Lipids (3 to 4 h for ~40 Samples)
<ol>
	<li>Turn on evaporation apparatus and solvent trap. Set evaporation apparatus to 33 &#176;C and preheat.</li>
	<li>Set up a vacuum aspirator in the hood: a side-arm flask connected to a vacuum pump, a length of high purity tubing and a glass pipet.</li>
	<li>Aspirate the top layer and interface (approximately 2/3 of the way down) of the two glass tubes, retaining the aqueous layer. Repeat the process&#160;using a clean pipet for each sample.</li>
	<li>Combine the aqueous layers of extract from the second tube with that in the first tube by carefully decanting. Ensure that the tube being decanting is free of scratches or cracks.</li>
	<li>Once the CHCl3 extracts are combined, inspect the liquid - it should be clear. If not add MeOH until clear.</li>
	<li>Dry down all the samples using the vacuum evaporation apparatus. Start with settings of temperature 33 &#176;C, vortex speed 26%, and pressure 400 mbar.</li>
	<li>Place the samples in the evaporation system and after 10 min slowly lower the pressure to 350 mbar.</li>
	<li>Monitor the progress and when the liquid level in the tube has reached the level of the heating block, decrease the pressure to 300 mbar.</li>
	<li>Continue to monitor the height of the remaining liquid and when it is approximately 1 cm in depth, decrease the pressure to 200 mbar.</li>
	<li>After samples have dried, remove samples and run the pump for 10 additional min to clear vapors.</li>
	<li>Cap the tubes tightly and store the lipids in a freezer at -80 &#176;C.</li>
	<li>Dispose of the aspirated liquid in accordance with applicable chemical safety regulations.</li>
</ol>
4. DAY 3 - Saponification and Methylation (6 to 7 h for ~40 Samples)
<ol>
	<li>Turn on the water baths. Set bath 1 to 95 &#176;C and bath 2 to 80 &#176;C.</li>
	<li>Set the pipet dispensers and ensure they are dispensing the correct volume. 
	Reagent 1: 1 mL per sample 
	Reagent 2: 2 mL per sample 
	Reagent 3: 1.25 mL per sample 
	Reagent 4: 3 mL per sample 
	NOTE: The heating process will build pressure in the tube. Do not use scratched, cracked, or chipped tubes.</li>
	<li>Using a pipet dispenser add 1.0 mL of Reagent 1 to the dried lipids. Cap tightly, vortex for 5 s and place in a rack.</li>
	<li>Place the rack of sample tubes into the 95 &#176;C water bath for 5min. Remove the rack of tubes from the bath and check the tubes for leaks, indicated by bubbles rising in the tube. Retighten or replace the caps of leaking tubes, and check again for cracks or chips. Continue heating the tubes in the water bath for an additional 10 min.</li>
	<li>Remove samples from bath 1 and place in bath 2 (80 &#176;C) and continue the incubation for another 15 min.</li>
	<li>Remove the tubes and cool the samples by placing the rack into a pan of tap water for cooling.</li>
	<li>Add 2 mL of Reagent 2 to each sample. Cap tightly and vortex for 5 to 10s.</li>
	<li>Place the rack into the 80 &#176;C water bath and incubate for 10 min.</li>
	<li>Remove the rack of tubes from the water bath and put into a pan of tap water for cooling. Agitate the rack of tubes to accelerate the cooling process. 
	NOTE: Do not exceed time and temperature. Too much heating could degrade FAMEs.</li>
	<li>Using the pipet dispenser add 1.25 mL of Reagent 3 to each sample tube to extract the fatty acid methyl esters. Cap tightly and put the tubes on the shaker for 10 min.</li>
	<li>After shaking, allow the rack of tubes sit for 10 min for the phases to separate. Transfer the organic phase (top layer) to a 16 mm x 100 mm glass test tube using a glass pipet.</li>
	<li>Repeat the extraction by again adding Reagent 3, shaking, allowing phases to separate, and transferring the top phase. 
	NOTE Do not transfer any of the bottom-phase. It is fine to leave a small amount of top phase in the tube.</li>
	<li>Using the pipet dispenser, add 3 mL of Reagent 4 to the 16 mm x 100 mm tubes.</li>
	<li>Cap the test tubes tightly and vortex for 20-30 s.</li>
	<li>After vortexing, centrifuge for 3 min at 1,000 &#215; g.</li>
	<li>Using a clean glass pipet, aspirate the top organic phase and transfer to a 4- mL amber vial.</li>
	<li>Turn on evaporation apparatus and solvent trap. Set evaporation apparatus to 30 &#176;C, the vortex speed to 26%, the vacuum to 200 mbar, and press preheat.</li>
	<li>Monitor the progress. After samples have dried, remove samples and run the pump for 10 additional min to clear vapors.</li>
	<li>Remove pipet from the reagent bottles 1 and 4 and pump through some dilute acid (e.g. 1% HCl), followed by D.I. water. Rinse the pipet used for Reagent 2 by pumping through D.I. water. In the hood, drain the solvent from the pipet used for Reagent 3 into an appropriate container and keep it in the hood until the solvent residues have evaporated.</li>
	<li>Store the pipets inverted, with the plungers removed to prevent sticking of the check valves.</li>
	<li>Use glass pipets once and then dispose in an appropriate container.</li>
	<li>In the hood, allow solvent residues on glassware to evaporate.</li>
	<li>Rinse glassware with clean water and detergent solution.</li>
</ol>
5. DAY 4 - Preparation of the Working Solution and Transfer of FAMEs to the GC Vials (2-3 h for ~40 Samples)
<ol>
	<li>Gather materials: 4 mL amber vials with dried samples; amber 2 mL GC vials;&#160;400 &#181;L flat bottom glass inserts and caps; 500 &#181;L glass syringe; 50 mL volumetric flask; Stock solution - Ethyl nonadecanoate (19:0 EE internal standard) in 50/50 hexane/MTBE (Reagent 3).</li>
	<li>Using the glass syringe, add 500 &#181;L of ethyl nonadecanoate (19:0 EE) stock solution to a 50-mL volumetric flask.</li>
	<li>Fill flask to volumetric score with Reagent 3.</li>
	<li>Cap flask and invert 5x to mix.</li>
	<li>Transfer 3 mL to a clean 4 mL vial for a working solution reservoir.</li>
	<li>Using the glass syringe, add 300 &#181;L of the working solution to each of the 4 mL vials containing the dried fatty acid methyl esters and cap.</li>
	<li>Vortex the sample for 15 s and set aside to stand for 15 min.</li>
	<li>Using a glass Pasteur pipet, carefully transfer the suspended fatty acid methyl esters to a 2 mL GC vial containing the 400 &#181;L insert.</li>
	<li>Store sealed GC vials in a -20 &#176;C freezer prior to analysis.</li>
	<li>Submit samples for GC analysis. 
	NOTE: The analysis must be carried out using a specific GC column and conditions which are described in Supplementary File S1. It is best that the GC analysis be completed within 2 weeks of methylation.</li>
</ol>

The authors have nothing to disclose.

For the examination of multiple samples from experiments with many replicates and/or experimental units, researchers may find phospholipid fatty-acid analysis (PLFA) to be prohibitive in terms of time and materials25. With the PLFA method, cell membrane phospholipids are extracted, purified, and identified using the modified Bligh and Dyer26 two-phase aqueous-organic extraction. This is followed by solid-phase silica chromatography to separate lipids by polarity, and an alkaline methylation of phospholipid fatty acids into fatty acid methyl esters. In PLFA profiling lipid yield can be low but of a very high purity. Microbial ID introduced an alternate method, the fatty acid methyl-ester procedure (MIDI-FA). In the MIDI-FA method, all lipids are extracted directly from pure cultures or soil/sediment samples11,12,27 through saponification. This method has lower lipid loss and is rapid because it has none of the concentration or purification steps of the PLFA method. However, while the MIDI-FA method is quick and less expensive, because it was originally designed for identifying organisms in pure culture, there are no initial extraction or purification steps. Thus, it can include lipid-like compounds co-extracted from soil organic matter that distort the community signature27,28,29. Because this inclusion can also distort biomass measures, MIDI-FA has typically been used only to coarsely qualitatively describe soil lipids13.
The procedure we describe here combines the best of the two separate extraction procedures: 1) an extraction and concentration of lipids using the modified Bligh and Dyer26 method, and 2) the fatty acid methyl ester saponification, methylation, extraction, and base wash procedure developed commercially. This method was developed to achieve the benefits of both of these protocols while minimizing the disadvantages15. By performing the initial extraction and isolating the organic-soluble components (e.g. lipids) prior to performing MIDI-FA, and completing this with a purification step, this protocol offers a balance between speed and precision. Although this method may not be suitable when high purity is required (i.e., for 13C PLFA analysis) or when analyzing phospholipids and neutral lipids separately, in many cases it allows detection of microbial community responses to environmental conditions with greater sensitivity than DNA-based methods30,31,32,33. Membrane lipids decompose rapidly after cell death allowing them to reflect the living microbial community at the time of sampling5,7, in contrast with environmental DNA in which much of the information comes from dead or inactive organisms34. Given the high rate of dormancy observed among soil microorganisms35, the characterization of live biomass can be used to understand temporal plant-microbe interactions at a relatively fine temporal scale and lipid biomarkers can be used to assay the physiological status of the microbial community7. It has been shown that high throughput methods are required to assess microbial response in large field settings25, and while the method we propose here does not replicate the accuracy of PLFA biomarker profiling, it increases throughput while minimizing the variability realized with the MIDI-FA procedure. The method has proven to be an effective tool in addressing questions related to microbial community dynamics on a wide range of soils in large-scale agricultural and ecosystem studies36,37,38,42,43,44,45,46,47,48,49,50.
Lipid classes are combined with this method and there may be a loss of the information contained in those separate classes22,39, but combining the lipid classes may strengthen the power to detect the arbuscular mycorrhizal fungi origin of the 16:1 &#969;5c from both phospho- and neutral-lipids40. In addition, while the number of unknown fatty acids (which could be derived from non-living organic matter) may be higher with this method, it was shown to be lower than MIDI-FA and allows for treatment comparison of lipid profiles from studies with many samples where sample throughput capacity is a concern15. The neutral fraction is generally thought to derive primarily from storage lipids produced by fungi, though there may also be some smaller contribution from soil fauna41. In light of this, the method described here may yield results showing a greater contribution of the fungal lipids 18:2 &#969; 6,9c and 18:1 &#969; 9c than PLFA. The other lipids that tend to show up in the neutral fraction include some of the saturated fatty acids, e.g. 16:0, 18:0, 20:0.
There are different ways in which lipid data can be expressed and analyzed. The most common representations are abundance (nmol g-1 soil), mole fraction (nmol individual lipid nmol-1 total lipid), and mole percent (mole fraction*100). Normalized by the total lipids in a sample, mole fraction and mole percent are measures of the relative abundance of a given lipid. After an appropriate transformation, e.g., arcsine square-root, mole fraction is appropriate for use in analysis by principal components or redundancy analysis ordination. Abundance is the absolute amount of a given lipid extracted per gram of soil. Because the quantity of lipid per cell is reasonably constant, and the lipid extraction is highly efficient and comprehensive, total abundance is a good estimate of total lipids and the abundance of key indicators reflects the biomass of the ecological group it represents17. Finally, a good way to look at microbial community composition is to use multivariate analysis methods16, e.g., ordination methods such as nonmetric multidimensional scaling (NMDS - which does not need data transformation) or principal components analysis (PCA), can be useful for comparing the relative abundance of all lipid biomarkers.

Given the key role of microorganisms in nutrient cycling 1, modification of plant community composition 2, regulation of plant productivity 3, and decomposition of organic matter 4, understanding soil microbial communities is a vital to understanding terrestrial ecosystems.
Because of their relatively high abundance in soil, and their chemical signature, lipid biomarkers can be used to profile the dominant ecological groups comprising soil microbial communities 5. By quantifying lipid biomarkers that are characteristic of different microbial groups, we can estimate total lipids, and then separate these lipids into ecologically relevant groups such as Gram positive (Gm+) and Gram negative (Gm-) bacteria, arbuscular mycorrhizal (AM) and saprotrophic fungi, and actinomycetes 5,6,7,8.
There are many methods for characterizing aspects of microbial communities. The PLFA method is one commonly used to understand basic microbial community structure. It is an effective way to assess the relative abundance of microbial groups as well as total microbial biomass. Due to rapid lipid turnover, PLFA profiling also allows relatively fast detection of changes in the soil microbial community and gives information that allows comparison of ecosystem function, e.g., fungal:bacterial ratios to assess rates of nutrient cycling 1,9,10. However, while the PLFA extraction method is time honored and well respected, it is also time consuming and does not lend itself well to ecosystem-scale studies that require a large number of samples from field scale replicates11,12.
In contrast, the fatty acid methyl ester extraction method (MIDI-FA) has the potential to allow rapid throughput. In this method, samples are saponified, converted to FAMEs, extracted, and then analyzed. The MIDI-FA method is rapid but less discriminating than PLFA, which combines extraction of lipids with separation of different lipid classes13 (phospholipids, neutral lipids, and glycolipids).
In this protocol, we describe a method that combines elements of both PLFA and MIDI-FA lipid profiling. It employs extraction of lipids using the initial chloroform extraction steps of the modified Bligh and Dyer method, and then saponification and conversion to FAMEs. This provides a robust way to detect microbial community structure while excluding much of the background noise from non-microbial material 5,14. This method was developed to achieve a balance between both the PLFA and MIDI-FA protocols, i.e., retain most of the accuracy while increasing throughput to make it logistically and economically feasible to analyze lipids from large-scale studies with many samples15. By performing the initial extraction and isolating the organic-soluble components (e.g. lipids) prior to performing MIDI-FA, and completing this with a purification step, the protocol offers a balance between speed and precision.

<table border="0" cellpadding="0" cellspacing="0" width="934">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:376px;">RapidVap</td>
			<td style="width:117px;">Labconco</td>
			<td style="width:119px;">7900000</td>
			<td style="width:323px;">Evaporative system</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">RapidVap</td>
			<td>Labconco</td>
			<td>7491400</td>
			<td>Sample block</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Chem-resistant vacuum pump</td>
			<td>Labconco</td>
			<td>7584000</td>
			<td>Vacuum pump</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Chemical trap cannister</td>
			<td>Labconco</td>
			<td>7815300</td>
			<td>Trap cannister</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Solvent insert</td>
			<td>Labconco</td>
			<td>7515200</td>
			<td>Solvent trap insert</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Diaphragm pump</td>
			<td>Welch</td>
			<td>7398000</td>
			<td>DryFast vacuum pump</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Water bath</td>
			<td>Fisher</td>
			<td>15-462-15Q</td>
			<td>2 well water bath</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Gas chromatograph</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For sample analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Centrifuge</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For sample separation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Freeze Dryer</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For sample lyophilization</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Repipet (6)</td>
			<td>BrandTech</td>
			<td>4720440</td>
			<td>For dispensing reagents</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Vortex</td>
			<td>Fisher</td>
			<td>02-215-365</td>
			<td>Analog vortex mixer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Teflon centrifuge tubes</td>
			<td>Thermo/Nalgene</td>
			<td>3114-0030</td>
			<td>Teflon sample tubes</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Caps</td>
			<td>Thermo/Nalgene</td>
			<td>DS3131-0020</td>
			<td>Caps for teflon tubes</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Test tube</td>
			<td>Corning</td>
			<td>9825-16</td>
			<td>16x100mm tubes</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Test tube</td>
			<td>Corning</td>
			<td>9825-16xx</td>
			<td>16x150mm tubes</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Caps</td>
			<td>Corning</td>
			<td>9998-15</td>
			<td>15-415 thread black phenolic caps w/PTFE liner</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Pasteur pipets</td>
			<td>Fisher</td>
			<td>13-678-20B</td>
			<td>14.6cm</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">500 uL glass syringe</td>
			<td>Fisher</td>
			<td>13684106LC</td>
			<td>Hamilton 81217</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Amber vials</td>
			<td>Agilent</td>
			<td>5182-0716</td>
			<td>4mL Amber vials</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Caps</td>
			<td>Agilent</td>
			<td>5182-0717</td>
			<td>Blue screw caps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Inserts</td>
			<td>Agilent</td>
			<td>5181-3377</td>
			<td>400uL flat bottom glass inserts</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Standards</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">19:0 ethyl nonadecanoate (Ethyl nonadecanoate)</td>
			<td>VWR</td>
			<td>TCN0459-5G</td>
			<td>Analytical standard</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Chemicals</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Dipotassium phosphate (K2HPO4 - ACS grade or better)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Phosphate-Buffer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Potassium phosphate monobasic (KH2PO4 - ACS grade or better)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Phosphate-Buffer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Methanol (CH3OH - HPLC grade or better)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Reagents 1 &#38; 2</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Sodium hydroxide (NaOH - ACS grade or better)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Reagents 1 &#38; 4</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Hydrochloric acid (6N HCL)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Reagent 2</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Hexane (HPLC grade or better)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Reagent 3</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MTBE (Methyl tert-butyl ether - HPLC grade or better)</td>
			<td>Various</td>
			<td>N/A</td>
			<td>For making Reagent 3</td>
		</tr>
	</tbody>
</table>

a lipid extraction and analysis method for characterizing soil microbes in experiments with many samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The data tables from the reports can be collated into a spreadsheet or database. After adjusting for response factor (a correction factor that normalizes the response for different chain lengths), peak areas can be compared with the peak area of external or internal standards to arrive at a concentration in the extract. By dividing through by the mass of soil extracted, the data can be expressed as mass of FAME per gram of soil or, by using the molecular weight of each FAME, the more commonly reported nmol per gram of soil. The sum of microbial FAMEs is indicative of total microbial biomass, and can be compared among treatments (Figure 1). Certain FAMEs can also be associated with particular microbial groups such as Gram-positive or Gram-negative bacteria, actinomycetes, arbuscular mycorrhizal fungi, and saprotrophic fungi 19,20,21,22,23,24. Ratios of the mass of specific biomarkers can reflect the relative abundance of these groups (Figure 2). Overall FAME abundance patterns create community-level fingerprints, which allow comparison of microbial community dissimilarity through multivariate techniques such as ordination (Figure 3). In contrast with most DNA-based approaches, community-level lipid data can be analyzed either as relative or absolute abundances. If total biomass differs substantially among samples, these two approaches will give very different results; the ecological questions underlying the experiment should determine which approach is used.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55310/55310fig1.jpg" /> 
Figure 1: Total FAME biomass. Comparison of total FAME biomarker biomass (nmol g-1 soil) from continuous corn, fertilized prairie, and unfertilized prairie. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55310/55310fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55310/55310fig2.jpg" /> 
Figure 2: FAME biomass of bacteria and fungi. Treatment comparison of bacterial and fungal FAME biomarker biomass (nmol g-1 soil) from continuous corn, fertilized prairie, and unfertilized prairie.Fungal and bacterial mass from absolute abundance. Average (sum f/sum b). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55310/55310fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55310/55310fig3.jpg" /> 
Figure 3: Non-metric Dimensional Scaling of FAME lipid biomarkers. Comparison of overall microbial community using absolute abundance profiles of all microbial FAME lipid biomarkers. In this example, continuous corn and unfertilized prairie communities separated and are very far apart while some fertilized prairie samples have microbial communities that resemble those from corn, and others resemble the unfertilized prairie. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55310/55310fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" src="/files/ftp_upload/55310/55310table1.jpg" /> 
Table 1: The solvent type, proportions added g-1 soil, and their order of addition. This is important for proper extraction and separation of the organic and aqueous phases.
Supplementary File S1: <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55310/S1_GC_analysis.docx" target="_blank">Please click here to download.</a>

Watch this Scientific Journal Video about A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples at JoVE.com

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

a-lipid-extraction-analysis-method-for-characterizing-soil-microbes

Nanyang Technological University

Research

JoVE Journal

Environment

19.7K Views.  University of Wisconsin - Madison. The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Video: A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes and soil organic matter formation to small-scale stable-isotope biomarker probing. Coupling multiple stable isotopes such as 13C with 15N, 18O or&#160;2H has the potential to reveal even more information about complex stoichiometric relationships during biogeochemical transformations. Isotope labeled plant material has been used in various studies of litter decomposition and soil organic matter formation1-4. From these and other studies, however, it has become apparent that structural components of plant material behave differently than metabolic components (i.e. leachable low molecular weight compounds) in terms of microbial utilization and long-term carbon storage5-7. The ability to study structural and metabolic components separately provides a powerful new tool for advancing the forefront of ecosystem biogeochemical studies. Here we describe a method for producing 13C and 15N labeled plant material that is either uniformly labeled throughout the plant or differentially labeled in structural and metabolic plant components.
Here, we present the construction and operation of a continuous 13C and 15N labeling chamber that can be modified to meet various research needs. Uniformly labeled plant material is produced by continuous labeling from seedling to harvest, while differential labeling is achieved by removing the growing plants from the chamber weeks prior to harvest. Representative results from growing Andropogon gerardii Kaw demonstrate the system&#39;s ability to efficiently label plant material at the targeted levels. Through this method we have produced plant material with a 4.4 atom%13C and 6.7 atom%15N uniform plant label, or material that is differentially labeled by up to 1.29 atom%13C and 0.56 atom%15N in its metabolic and structural components (hot water extractable and hot water residual components, respectively). Challenges lie in maintaining proper temperature, humidity, CO2 concentration,&#160;and light levels in an airtight 13C-CO2 atmosphere for successful plant production. This chamber description represents a useful research tool to effectively produce uniformly or differentially multi-isotope labeled plant material for use in experiments on ecosystem biogeochemical cycling.

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

This method explains how to build and operate a continuous 13C and 15N isotope labeling chamber for uniform or differential plant tissue labeling. Representative results from metabolic and structural labeling of Andropogon gerardii are discussed.

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes ...

A Simple Method for Automated Solid Phase Extraction of Water Samples for Immunological Analysis of Small Pollutants

A new method for solid phase extraction (SPE) of environmental water samples is proposed. The developed prototype is cost-efficient and user friendly, and enables to perform rapid, automated and simple SPE. The pre-concentrated solution is compatible with analysis by immunoassay, with a low organic solvent content. A method is described for the extraction and pre-concentration of natural hormone 17&#946;-estradiol in 100 ml water samples. Reverse phase SPE is performed with octadecyl-silica sorbent and elution is done with 200 &#181;l of methanol 50% v/v. Eluent is diluted by adding di-water to lower the amount of methanol. After preparing manually the SPE column, the overall procedure is performed automatically within 1 hr. At the end of the process, estradiol concentration is measured by using a commercial enzyme-linked immune-sorbent assay (ELISA). 100-fold pre-concentration is achieved and the methanol content in only 10% v/v. Full recoveries of the molecule are achieved with 1 ng/L spiked de-ionized and synthetic sea water samples.

A protocol for the extraction and pre-concentration of estradiol from water samples by using an automated and miniaturized system is presented.

A new method for solid phase extraction (SPE) of environmental water samples is proposed. The developed prototype is cost-efficient and user friendly, and ...

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about the overall structure of terrestrial microbial communities. PLFA profiling has been extensively used in a range of ecosystems as a biological index of overall soil quality, and as a quantitative indicator of soil response to land management and other environmental stressors.
The standard method presented here outlines four key steps: 1. lipid extraction from soil samples with a single-phase chloroform mixture, 2. fractionation using solid phase extraction columns to isolate phospholipids from other extracted lipids, 3. methanolysis of phospholipids to produce fatty acid methyl esters (FAMEs), and 4. FAME analysis by capillary gas chromatography using a flame ionization detector (GC-FID). Two standards are used, including 1,2-dinonadecanoyl-sn-glycero-3-phosphocholine (PC(19:0/19:0)) to assess the overall recovery of the extraction method, and methyl decanoate (MeC10:0) as an internal standard (ISTD) for the GC analysis.

Phospholipid fatty acids provide information about the structure of soil microbial communities. We present methods for extraction from soil samples with a single-phase chloroform mixture, fractionation of extracted lipids using solid phase extraction columns, and methanolysis to produce fatty acid methyl esters, which are analyzed by capillary gas chromatography.

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about ...

Use of a Filter Cartridge for Filtration of Water Samples and Extraction of Environmental DNA

Recent studies demonstrated the use of environmental DNA (eDNA) from fishes to be appropriate as a non-invasive monitoring tool. Most of these studies employed disk fiber filters to collect eDNA from water samples, although a number of microbial studies in aquatic environments have employed filter cartridges, because the cartridge has the advantage of accommodating large water volumes and of overall ease of use. Here we provide a protocol for filtration of water samples using the filter cartridge and extraction of eDNA from the filter without having to cut open the housing. The main portions of this protocol consists of 1) filtration of water samples (water volumes &#8804;4 L or &gt;4 L); (2) extraction of DNA on the filter using a roller shaker placed in a preheated incubator; and (3) purification of DNA using a commercial kit. With the use of this and previously-used protocols, we perform metabarcoding analysis of eDNA taken from a huge aquarium tank (7,500 m3) with known species composition, and show the number of detected species per library from the two protocols as the representative results. This protocol has been developed for metabarcoding eDNA from fishes, but is also applicable to eDNA from other organisms.

We describe a protocol for filtration of water samples with a filter cartridge and extraction of environmental DNA (eDNA) without having to cut open the housing to remove the filter. This protocol is developed for metabarcoding eDNA from fishes, but is also applicable to eDNA from other organisms.

Recent studies demonstrated the use of environmental DNA (eDNA) from fishes to be appropriate as a non-invasive monitoring tool. Most of these studies ...

Nanopore DNA Sequencing for Metagenomic Soil Analysis

This article describes the steps for construction of a DNA library from soil, preparation and use of the nanopore flow cell, and analysis of the DNA sequences identified using computer software. Nanopore DNA sequencing is a flexible technique that allows for rapid microbial genome sequencing to identify bacterial and viral species, to characterize bacterial strains, and to detect genetic mutations that confer resistance to antibiotics. The advantages of nanopore sequencing (NS) for life sciences include its low complexity, reduced cost, and rapid real-time sequencing of purified genomic DNA, PCR amplicons, cDNA samples, or RNA. NS is an example of "strand sequencing" which involves sequencing DNA by guiding a single stranded DNA molecule through a nanopore that is inserted into a synthetic polymer membrane. The membrane has an electrical current applied across it, so as the individual bases pass through the nanopore the electrical current is disrupted to varying degrees by the four nucleotide bases. The identification of each nucleotide occurs by detecting the characteristic modulation of the electrical current by the different bases as they pass through the nanopore. The NS system consists of a handheld, USB powered portable device and a disposable flow cell that contains a nanopore array. The portable device plugs into a standard laptop computer that reads and records the DNA sequence using computer software.

Nanopore technology for sequencing biomolecules has wide applications in the life sciences, including identification of pathogens, food safety monitoring, genomic analysis, metagenomic environmental monitoring, and characterization of bacterial antibiotic resistance. In this article, the procedure for metagenomic soil DNA sequencing for species identification using the nanopore sequencing technology is demonstrated.

This article describes the steps for construction of a DNA library from soil, preparation and use of the nanopore flow cell, and analysis of the DNA ...

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes ...

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Traditional methods of identifying fungal exposures in occupational environments, such as culture and microscopy-based approaches, have several limitations that have resulted in the exclusion of many species. Advances in the field over the last two decades have led occupational health researchers to turn to molecular-based approaches for identifying fungal hazards. These methods have resulted in the detection of many species within indoor and occupational environments that have not been detected using traditional methods. This protocol details an approach for determining fungal diversity within air samples through genomic DNA extraction, amplification, sequencing, and taxonomic identification of fungal internal transcribed spacer (ITS) regions. ITS sequencing results in the detection of many fungal species that are either not detected or difficult to identify to species level using culture or microscopy. While these methods do not provide quantitative measures of fungal burden, they offer a new approach to hazard identification and can be used to determine overall species richness and diversity within an occupational environment.

Determining the fungal diversity within an environment is a method utilized in occupational health studies to identify health hazards. This protocol describes DNA extraction from occupational air samples for amplification and sequencing of fungal ITS regions. This approach detects many fungal species that can be overlooked by traditional assessment methods.

Traditional methods of identifying fungal exposures in occupational environments, such as culture and microscopy-based approaches, have several ...

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Excavation of plant roots from the field as well as processing of samples into endosphere, rhizosphere, and soil are described in detail, including DNA extraction and data analysis methods. This paper is designed to enable other laboratories to use these techniques for the study of soil, endosphere, and rhizosphere microbiomes.

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The ...

High-throughput Siderophore Screening from Environmental Samples: Plant Tissues, Bulk Soils, and Rhizosphere Soils

Siderophores (low-molecular weight metal chelating compounds) are important in various ecological phenomenon ranging from iron (Fe) biogeochemical cycling in soils, to pathogen competition, plant growth promotion, and cross-kingdom signaling. Furthermore, siderophores are also of commercial interest in bioleaching and bioweathering of metal-bearing minerals and ores. A rapid, cost effective, and robust means of quantitatively assessing siderophore production in complex samples is key to identifying important aspects of the ecological ramifications of siderophore activity, including, novel siderophore producing microbes. The method presented here was developed to assess siderophore activity of in-tact microbiome communities, in environmental samples, such as soil or plant tissues. The samples were homogenized and diluted in a modified M9 medium (without Fe), and enrichment cultures were incubated for 3 days. Siderophore production was assessed in samples at 24, 48, and 72 hours (h) using a novel 96-well microplate CAS (Chrome azurol sulphonate)-Fe agar assay, an adaptation of the traditionally tedious and time-consuming colorimetric method of assessing siderophore activity, performed on individual cultivated microbial isolates. We applied our method to 4 different genotypes/Lines of wheat (Triticum aestivum L.), including Lewjain, Madsen, and PI561725, and PI561727 commonly grown in the inland Pacific Northwest. Siderophore production was clearly impacted by the genotype of wheat, and in the specific types of plant tissues observed. We successfully used our method to rapidly screen for the influence of plant genotype on siderophore production, a key function in terrestrial and aquatic ecosystems. We produced many technical replicates, yielding very reliable statistical differences in soils and within plant tissues. Importantly, the results show the proposed method can be used to rapidly examine siderophore production in complex samples with a high degree of reliability, in a manner that allows communities to be preserved for later work to identify taxa and functional genes.

We present a protocol for rapid screening of environmental samples for siderophore potential contributing to micronutrient bioavailability and turnover in terrestrial systems.

Siderophores (low-molecular weight metal chelating compounds) are important in various ecological phenomenon ranging from iron (Fe) biogeochemical cycling ...