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.