Decoding Plant Stress Responses: LC–MSⁿ Reveals GIPC Dynamics in Barley Grain Development

Glycosyl inositol phospho ceramides (GIPCs)are the most abundant sphingolipids in plants, forming a major structural component of the plasma membrane in both dicotyledonous and monocotyledonous species. Despite their ubiquity, the structural complexity and functional diversity of GIPCs have made them analytically challenging to study. These lipids play essential roles in membrane organization, signal transduction, plant development, immune responses, and adaptation to environmental stress. GIPC biosynthesis occurs in the Golgi apparatus through a conserved pathway involving multiple glycosyltransferases.

In this study, a team of researchers from the University of Vienna developed an advanced liquid chromatography–mass spectrometry (LC–MS) method incorporating multistage fragmentation (MSⁿ) and automated annotation to enable high-throughput structural profiling of GIPC series A–F. Using barley (Hordeum vulgare) grains as a monocot model system, they applied this method to characterize GIPC profiles during grain development under natural and heat stress conditions. The results revealed the accumulation of B-series GIPC specifically associated with early development and heat stress response, supporting their role in membrane remodeling and stress adaptation. In this, the first of a two-part series, LCGC International spoke to Evelyn Rampler of the Department of Analytical Chemistry at the University of Vienna and corresponding author of the paper that resulted from this research that was published in Plant Journal (1), about the insights yielded regarding the role of glycosphingolipids in plant physiology and environmental resilience.

Your study introduces a robust reversed-phase high-resolution mass spectrometry and multistage fragmentation (RP-HRMSⁿ) assay for GIPC profiling. What were the key technical hurdles in separating and characterizing highly glycosylated GIPC structures using reversed-phase chromatography?

One of the main challenges with GIPC is the lack of standardized analytical methods for in-depth structural characterization, despite being the most abundant sphingolipids in plant and algal plasma membranes. Key technical bottle necks were the need for a specialized extraction protocol to isolate GIPC from barley seeds, the absence of commercially available GIPC standards, and the fact that annotation—such as spectral library development—had to be done entirely manually.

Fortunately, reversed-phase chromatography itself was relatively straightforward in our hands. We utilized a UPLC HSS T3 Acquity column (Waters), consistent with our established lipidomics workflows (23 min gradient), using ACN:H₂O (3:2, v/v) as solvent A and IPA:ACN (9:1, v/v) as solvent B, both containing 0.1% formic acid and 10 mM ammonium formate. To enhance separation, we adapted the chromatography for glycolipids and extended the run to 30 min. Further, we applied the equivalent carbon number (ECN) model which helps identifying false positive hits not eluting according fatty acid chain lengths and number of double bonds.

Why was MSⁿ necessary for GIPC characterization, and how did it help resolve isobaric species or determine branching patterns in glycan head groups?

MSⁿ was crucial for achieving molecular-level annotation of GIPC and resolving structural features in both the lipid and glycan moieties, including ceramide composition, fatty acid chains and glycan branching. In our previous study (2), we managed to annotate simple A-series GIPC using MS² with higher-energy collisional dissociation (HCD) on a Q Exactive HF, but this approach wasn’t sufficient for higher glycosylated GIPCs. For the published Plant Journal article, we used the Orbitrap ID-X Tribrid mass spectrometer (Thermo Fisher Scientific), which combines Orbitrap and ion trap mass analyzers to enable MSⁿ.

MSⁿ helped us to characterize the sphingoid base and fatty acid (molecular species level) of GIPC and therefore distinguish isomers such as 18:0/24:1 and 18:1/24:0, which is most of the time not achieved using only higher-energy collisional dissociation.

Although we expected B-series GIPCs in barley (as a monocot), our initial analyses failed to detect the negative mode inositol phosphate fragments (m/z 259 and 241 for IP and IP-H₂O), which we had previously used as mandatory markers in our decision rules within the lipid data analyzer. The key insight came from Marlene Pürhringer, the first author and PhD student in my lab, who discovered that B-series GIPC with branched glycans instead produced characteristic fragments at m/z 403 and 421 (IP+Hex) in negative mode. After integrating these new diagnostic fragments into the decision rules—together with the Group of Jürgen Hartler at the University of Graz—we enabled automated annotation of these GIPC species using the freely accessible Lipid Data Analyzer (LDA).

Given the amphiphilic nature of GIPC, how did you optimize the extraction protocol for high-throughput LC–MS analysis, particularly in barley grains with high starch content?

We based our extraction strategy on findings from Lisa Panzenböck’s master thesis, which provided a comprehensive review of GIPC extraction protocols. Due to the amphiphilic nature of GIPC—with a hydrophilic glycan head and a hydrophobic ceramide moiety—conventional protocols like Bligh and Dyer or Folch often didn’t suceed, particularly with very long chain fatty acid containing species that tended to remain insoluble or partition into the aqueous phase. Panzenböck’s work highlighted the effectiveness of the IPA:hexane:H₂O (55:20:25, v/v/v) solvent system, which we had also successfully applied in our 2020 Metabolites paper. This method reliably extracted over 95 mol% of total sphingolipids, including GIPC. To further optimize the protocol for barley—a matrix with high starch content—we included a one-phase alkaline hydrolysis step (0.1 M KOH in MeOH), which helped remove interfering glycerophospholipids and, as shown in Nina Thür’s thesis, also reduced starch-related gelatinization issues during sample prep. In collaboration with Verena Ibl’s group, we addressed potential enzymatic degradation by adding a protease inhibitor cocktail immediately after ball mill homogenization. For less mature barley grains, desludging during extraction led to particular challenges, which we could solve by adding KOH during the extraction step. Altogether, the applied extraction protocol based on previous experience and testing in different plant materials enriched GIPC relative to bulk lipids and allowed for robust, high-throughput LC–MS analysis—even in complex, starch-rich matrices like barley.

Can you elaborate on how decision rule-based annotation and the ECN model were used to enhance confidence in structural assignments, especially without commercial GIPC standards?

Decision rules are especially valuable in cases like GIPC analysis, where no commercial standards or comprehensive compound libraries are available, and only certain structural elements—such as inositol phosphate (IP), its water loss, or characteristic losses of hexoses or fatty acids—are known. We developed a set of in-house decision rules based on these known structural features and implemented them in the open-access software LDA, in collaboration with the group of Jürgen Hartler. This enabled automated annotation of 102 GIPC species in our samples. Our structural assignments were based on a combination of accurate m/z data, diagnostic multistage (MSⁿ) fragments, retention time matching between positive and negative ion modes, and the equivalent carbon number (ECN) model, which helped confirming the expected elution order in reversed-phase chromatography.

Decicion rules were developed for each theoretical GIPC series. For example, A-series GIPC consistently showed predictable glycan (B and C ions) and lipid (Y, Z, V, W ions) fragments. Branched B- and C-series GIPC produced unique fragments at m/z 403 and 421 (IP + hexose and IP + hexose–H₂O), indicating glycan branching directly on the inositol. The known branching fragment at m/z 535, previously described by Buré and associates, was also detected in our data. Together, this rule-based, multi-criteria approach allowed us to confidently annotate GIPC species at the molecular level, even in the complete absence of authentic standards.

What advantages does your RP-HRMSⁿ method offer over traditional MRM-based workflows or gas chromatography-mass spectrometry (GC–MS) approaches when profiling higher GIPC series (C and D) and glycan branching?

Our RP-HRMSⁿ method provides several important advantages over traditional MRM-based or GC–MS approaches, particularly when it comes to profiling complex GIPC species—such as C- and D-series—and resolving glycan branching patterns. Traditional methods like MRM or combined GC–MS/LC–MS workflows are targeted, meaning they can only detect compounds predefined in the method setup. As a result, they often miss higher-order GIPC (for example, Hex-Hex-Hex or more complex glycan branches) and lack the structural depth required to distinguish modifications like hydroxylation, amination, or acetylation.

In contrast, our data-dependent RP-HRMSⁿ workflow is untargeted and allows for in-depth structural characterization of both the glycan and lipid moieties. It’s based on a tailored extraction strategy specific to GIPC and excludes interfering bulk lipids, enhancing GIPC-specific detection and sensitivity. We combined this with a decision rule-based annotation system in the open-source LDA, developed with the group of Jürgen Hartler. Crucially, because the decision rules are built on known structural motifs, our method doesn’t rely on commercial standards. This allows us not only to annotate known GIPC but also to automatically detect new GIPC species, as long as they conform to the structural logic defined in the rules. Overall, this approach offers greater structural resolution, broader class coverage (A- through D-series), automated annotation, and the flexibility to detect previously unknown GIPC variants—capabilities that traditional targeted workflows simply can't match.

References

1. Pühringer, M.; Thür, N.; Schnurer, M. et al. Automated Mass Spectrometry-Based Profiling of Multi-Glycosylated Glycosyl Inositol Phospho Ceramides (GIPC) Reveals Specific Series GIPC Rearrangements During Barley Grain Development and Heat Stress Response. Plant J. 2025, 122 (6), e70279. DOI: 10.1111/tpj.70279
2. Panzenboeck, L.; Troppmair, N.; Schlachter, S. et al. Chasing the Major Sphingolipids on Earth: Automated Annotation of Plant Glycosyl Inositol Phospho Ceramides by Glycolipidomics. Metabolites 2020, 10 (9), 375. DOI: 10.3390/metabo10090375

Join us Wednesday as we include our interview with Evelyn Rampler. What if you could track stress signals and sugar branching in plant lipids—without commercial standards? Get ready for a next-generation LC–MSⁿ platform that does just that.