Combining Chemical Information From Grass Pollen in Multimodal Characterization

The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters.

A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL

Lipidome profile of fluids and tissues is a growing field as the role of lipids as signaling molecules is increasingly understood, relying on an effective and representative extraction of the lipids present. A number of solvent systems suitable for lipid extraction are commonly in use, though no comprehensive investigation of their effectiveness across multiple lipid classes has been carried out. To address this, human LDL from normolipidemic volunteers was used to evaluate five different solvent extraction protocols [Folch, Bligh and Dyer, acidified Bligh and Dyer, methanol (MeOH)-tert-butyl methyl ether (TBME), and hexane-isopropanol] and the extracted lipids were analyzed by LC-MS in a high-resolution instrument equipped with polarity switching. Overall, more than 350 different lipid species from 19 lipid subclasses were identified. Solvent composition had a small effect on the extraction of predominant lipid classes (triacylglycerides, cholesterol esters, and phosphatidylcholines). In contrast, extraction of less abundant lipids (phosphatidylinositols, lyso-lipids, ceramides, and cholesterol sulfates) was greatly influenced by the solvent system used. Overall, the Folch method was most effective for the extraction of a broad range of lipid classes in LDL, although the hexane-isopropanol method was best for apolar lipids and the MeOH-TBME method was suitable for lactosyl ceramides.