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 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.
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.
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)
2. DAY 1 – Extraction of Fatty Acids from Soil (4 to 5 h for ~40 Samples)
3. DAY 2 – Isolation of Lipids (3 to 4 h for ~40 Samples)
4. DAY 3 – Saponification and Methylation (6 to 7 h for ~40 Samples)
5. DAY 4 – Preparation of the Working Solution and Transfer of FAMEs to the GC Vials (2-3 h for ~40 Samples)
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.
Figure 1: Total FAME biomass. Comparison of total FAME biomarker biomass (nmol g-1 soil) from continuous corn, fertilized prairie, and unfertilized prairie. Please click here to view a larger version of this figure.
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). Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
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: Please click here to download.
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 ω5c 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 ω 6,9c and 18:1 ω 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.
The authors have nothing to disclose.
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.
RapidVap | Labconco | 7900000 | Evaporative system |
RapidVap | Labconco | 7491400 | Sample block |
Chem-resistant vacuum pump | Labconco | 7584000 | Vacuum pump |
Chemical trap cannister | Labconco | 7815300 | Trap cannister |
Solvent insert | Labconco | 7515200 | Solvent trap insert |
Diaphragm pump | Welch | 7398000 | DryFast vacuum pump |
Water bath | Fisher | 15-462-15Q | 2 well water bath |
Gas chromatograph | Various | N/A | For sample analysis |
Centrifuge | Various | N/A | For sample separation |
Freeze Dryer | Various | N/A | For sample lyophilization |
Repipet (6) | BrandTech | 4720440 | For dispensing reagents |
Vortex | Fisher | 02-215-365 | Analog vortex mixer |
Teflon centrifuge tubes | Thermo/Nalgene | 3114-0030 | Teflon sample tubes |
Caps | Thermo/Nalgene | DS3131-0020 | Caps for teflon tubes |
Test tube | Corning | 9825-16 | 16x100mm tubes |
Test tube | Corning | 9825-16xx | 16x150mm tubes |
Caps | Corning | 9998-15 | 15-415 thread black phenolic caps w/PTFE liner |
Pasteur pipets | Fisher | 13-678-20B | 14.6cm |
500 uL glass syringe | Fisher | 13684106LC | Hamilton 81217 |
Amber vials | Agilent | 5182-0716 | 4mL Amber vials |
Caps | Agilent | 5182-0717 | Blue screw caps |
Inserts | Agilent | 5181-3377 | 400uL flat bottom glass inserts |
Standards | |||
19:0 ethyl nonadecanoate (Ethyl nonadecanoate) | VWR | TCN0459-5G | Analytical standard |
Chemicals | |||
Dipotassium phosphate (K2HPO4 – ACS grade or better) | Various | N/A | For making Phosphate-Buffer |
Potassium phosphate monobasic (KH2PO4 – ACS grade or better) | Various | N/A | For making Phosphate-Buffer |
Methanol (CH3OH – HPLC grade or better) | Various | N/A | For making Reagents 1 & 2 |
Sodium hydroxide (NaOH – ACS grade or better) | Various | N/A | For making Reagents 1 & 4 |
Hydrochloric acid (6N HCL) | Various | N/A | For making Reagent 2 |
Hexane (HPLC grade or better) | Various | N/A | For making Reagent 3 |
MTBE (Methyl tert-butyl ether – HPLC grade or better) | Various | N/A | For making Reagent 3 |