Exploring the plant lipidome: techniques, challenges, and prospects

Abstract

Plant lipids are a diverse group of biomolecules that play essential roles in plant architecture, physiology, and signaling. To advance our understanding of plant biology and facilitate innovations in plant-based product development, we must have precise methods for the comprehensive analysis of plant lipids. Here, we present a comprehensive overview of current research investigating plant lipids, including their structures, metabolism, and functions. We explore major lipid classes, i.e. fatty acids, glyceroglycolipids, glycerophospholipids, sphingolipids, and phytosterols, and discuss their subcellular distributions. Furthermore, we emphasize the significance of lipidomics research techniques, particularly chromatography-mass spectrometry, for accurate lipid analysis. Special attention is given to lipids as crucial signal receptors and signaling molecules that influence plant growth and responses to environmental challenges. We address research challenges in lipidomics, such as in identifying and quantifying lipids, separating isomers, and avoiding batch effects and ion suppression. Finally, we delve into the practical applications of lipidomics, including its integration with other omics methodologies, lipid visualization, and innovative analytical approaches. This review thus provides valuable insights into the field of plant lipidomics and its potential contributions to plant biology.

Keywords: Biomolecules; Environmental stress; Lipidomics; Mass spectrometry; Plant lipids.

Fig. 1

Structures of major functional lipids. a Chemical structures of common fatty acids, showing the hydrocarbon chain and terminal carboxylic group. b Examples of glyceroglycolipid categories. Glyceroglycolipids are neutral lipids consisting of a diacylglycerol and a hexose group. MGDG, monogalactosyl diacylglycerol, DGDG, digalactosyl diacylglycerol, SQDG, sulfoquinovosyl diacylglycerol. c Phytosterol consists of a tetracyclic ring backbone, with a hydroxyl group at the 3rd carbon atom and an aliphatic side chain containing eight to ten carbon atoms at the 17th carbon atom. d A phospholipid molecule consists of a polar phosphate head, which is hydrophilic, and a non-polar, hydrophobic lipid tail. e The chemical structures of representative sphingolipids. Sphingolipids consist of a sphingoid long-chain base, a fatty acid chain, and a head group

Fig. 2

Validated fragmentation rules of various lipids. a Product-ion mass spectra of glyceroglycolipid DG (16:0/16:0/0:0) at 40V voltage in positive ion mode. Three types of smaller molecules could be produced, C₁₆H₃₁O₂Na, C₁₉H₃₅O₃Na, and C₁₆H₃₂O₂, because Na⁺ could be connected at different positions in the original molecule. b Mapping the fragmentation of ceramide (d18:0/16:0) at 40V voltage in positive ion mode. The resulting spectral information indicates that three groups of fragments could be produced: two -OH and one C₁₆H₃₀O. c-e Typical fragmentation patterns of glycerides (c), sphingolipids (d), and methylated fatty acids (e). The one-way arrows indicate where fragmentation of the lipid molecule occurs

Fig. 3

The lipidomics workflow. Lipidomics involves collecting samples of various biological materials, such as whole organisms, different tissues, or cells, and extracting their lipids. The lipid extracts are then combined with the appropriate internal standards for semi- or relative-quantification of the biological lipidomes, primarily using mass spectrometry (MS). Targeted or non-targeted approaches can be used in quantitative MS-based lipidomics. The use of MS-imaging to map the spatial distribution of different lipids in tissue sections is also increasing. After the lipidome dataset is obtained, phenotypic validation and pathway mapping can be performed using various bioinformatics techniques

Fig. 4

Current challenges in lipidomics and potential solutions. The challenges of lipidomics (inner circle) at various stages along with possible solutions (outer circle). For challenges in sampling and extraction, the use of inert gas for lipid extraction can improve bioavailability, which can be limited due to instability, susceptibility to oxidation, and poor water solubility (Züllig et al. 2020). For challenges in LC–MS, methods such as computational lipidomics and machine learning can be used to address challenges such as ion characteristics information dependence and data interpretation in lipidomics (Peyraud et al. ; Colantonio et al. ; Shen et al. 2023). Using labeling, isotope labeling or IM-MS techniques can distinguish isomers (Zhou et al. 2019). Ion suppression impairs the accuracy of quantification and identification by LC–MS, especially for low-abundance lipids (Annesley 2003). Researchers can use internal standards or control samples to reduce the impact of ion suppression. Quality control of samples can reduce the batch effect (Sanchez-Illana et al. ; Alseekh et al. 2021). Reproducibility, reusability, and transparency of data are vital concerns of lipidomics and other omics integration (Shen et al. 2023). Single-cell lipidomics requires high coverage, accuracy and advanced data analysis methods (Wang et al. 2023). It is important to establish standards and consensus for the standardization of lipidomics, the nontargeted lipidome atlas MS-DIAL 4 offers a one-stop solution for lipidome data standardization (Tsugawa et al. ; Shen et al. 2023)