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BackgroundMultiple analytical methods have been developed to determine the ratios of monolignol monomers, particularly the syringyl/guaiacyl (S/G) ratio, of lignin biopolymers in plant cell walls. Chemical degradation methods yield monomers that are either selective of certain linkages, such as thioacidolysis, or induce chemical changes rendering it impossible to distinguish and determine the source of specific monomers, such as nitrobenzene oxidation. NMR methods provide powerful tools used to analyze cell walls for lignin monomeric composition and linkage information. Pyrolysis-mass spectrometry methods are also widely used, particularly as a high-throughput method. However, the different techniques used to analyze lignin monolignol ratios frequently yield different results within particular studies, making it difficult to interpret and compare results, and to obtain meaningful insights relating these measurements to other characteristics of plant cell walls that may impact biomass sustainability and conversion metrics for the production of bio-derived fuels and chemicals.ResultsThe authors compared the S/G monolignol ratios of pine, several genotypes of poplar, and corn stover biomass obtained from thioacidolysis, pyrolysis-molecular beam mass spectrometry (py-MBMS), HSQC liquid-state NMR and solid-state (ss) NMR methodologies. An underutilized approach to deconvolute ssNMR spectra was implemented to derive S/G ratios. The S/G ratios obtained for the samples did not agree across the different methods, but trends were similar with the most agreement among the py-MBMS, HSQC NMR and deconvoluted ssNMR methods. The relationship between monolignol S/G, thioacidolysis yields, and linkage analysis determined by HSQC is also addressed.ConclusionsThis work demonstrates that different methods using chemical, thermal, and nondestructive NMR techniques to determine native monolignol S/G ratios in plant cell walls may yield different results depending on species and linkage abundances. Spectral deconvolution likely applies well to many hardwoods that are S and G dominant, but results may not be reliable for some woody and grassy species for which the lignin composition is more diverse. HSQC may be a better method for analyzing lignin in those species given the wealth of information provided on additional aromatic moieties and bond linkages. Careful consideration is required when choosing a method to measure S/G ratios and the benefits and shortcomings of each method discussed here are summarized.

Background Pyrolysis-molecular beam mass spectrometry (py-MBMS) analysis of a pedigree of Populus trichocarpa was performed to study the phenotypic plasticity and heritability of lignin content and lignin monomer composition. Instrumental and microspatial environmental variability were observed in the spectral features and corrected to reveal underlying genetic variance of biomass composition. Results Lignin-derived ions were highly impacted by microspatial environmental variation which demonstrates phenotypic plasticity of lignin composition in Populus trichocarpa biomass. Broad-sense heritability of lignin composition after correcting for microspatial and instrumental variation was determined to be H2 = 0.56 based on py-MBMS based on ions known to derive from lignin. Heritability of lignin monomeric syringyl/guaiacyl ratio (S/G) was H2 = 0.81. Broad-sense heritability was also high (up to H2 = 0.79) for ions derived from other components of the biomass including phenolics (e.g., salicylates) and C5 sugars (e.g., xylose). Lignin and phenolic ion abundances were primarily driven by maternal effects, and paternal effects were either similar or stronger for the most heritable carbohydrate-derived ions. Conclusions We have shown that many biopolymer-derived ions from py-MBMS show substantial phenotypic plasticity in response to microenvironmental variation in plantations. Nevertheless, broad-sense heritability for biomass composition can be quite high after correcting for spatial environmental variation. This work outlines the importance in accounting for instrumental and microspatial environmental variation in biomass composition data for applications in heritability measurements and genomic selection for breeding poplar for renewable fuels and materials.