Advancements in technology over the years have propelled omics analysis to the level of single cell resolution. Following the breakthroughs in single cell transcriptomics and genomics, single cell proteomics has recently rapidly progressed, aided by highly sensitive mass spectrometry instrumentation. However, there is currently a paucity of studies and methodologies for single cell lipidomics, aside from imaging-based approaches. Profiling lipids at the single cell level holds promise for providing novel insights into the complex heterogeneity of cells in various human disorders. Further, by integrating single cell lipidomics with other single cell omics including proteomics, it becomes possible to achieve single cell multiomics, enabling the discovery of novel molecular signatures. We developed untargeted single cell lipidomics using nanoflow liquid chromatography-ion mobility spectrometry-mass spectrometry. To enhance lipid coverage at the single cell level, the method was conducted in both positive and negative ion modes. We identified an average of 161 lipids spanning phospholipids, sphingolipids, cholesteryl esters, and glycerides in positive ion mode from single cells of human cholangiocarcinoma cells based on a rule-based lipid annotation. Additionally, an average of 20 species of phospholipids was identified in the negative ion mode. These preliminary data demonstrate a new methodology to profile lipids at a single or low input of cells.

The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipid species for which no reference spectra are available is tedious and often involves a lot of manual work. Integrating high-resolution mass spectrometry with enhancements from chromatographic and ion mobility separation enables the in-depth investigation of intact lipids. We investigated phosphorylated glycosphingolipids from the nematode Caenorhabditis elegans, a biomedical model organism, and aimed to identify different species from this class of lipids, which have been described in one particular publication only. We checked if these lipids can be detected in lipid extracts of C. elegans. We used UHPLC-UHR-TOF-MS and UHPLC-TIMS-TOF-MS in combination with dedicated data analysis to check for the presence of phosphorylated glycosphingolipids. Specifically, candidate features were identified in two datasets using Mass Spec Query Language (MassQL) to search fragmentation data. The additional use of retention time (RT) and collisional cross section (CCS) information allowed to filter false positive annotations. As a result, we detected all previously described phosphorylated glycosphingolipids and novel species as well as their biosynthetic precursors in two different lipidomics datasets. MassQL significantly speeds up the process by saving time that would otherwise be spent on manual data investigations. In total over 20 sphingolipids could be described. MassQL allowed us to search for phosphorylated glycosphingolipids and their potential biosynthetic precursors systematically. Using orthogonal information such as RT and CCS helped filter false positive results. With the detection in two different datasets, we demonstrate that these sphingolipids are a general part of the C. elegans lipidome.

Ultrahigh resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Interlaboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceuticals), large-scale environmental studies, or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of 17 laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics, showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.

A correction to this paper has been published: https://doi.org/10.1007/s11306-021-01784-5

Metabolomics—the endpoint of the omics cascade—is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

The utility of metabolomics is well documented; however, its full scientific promise has not yet been realized due to multiple technical challenges. These grand challenges include accurate chemical identification of all observable metabolites and the limiting depth-of-coverage of current metabolomics methods. Here, we report a combinatorial solution to aid in both grand challenges using UHPLC-trapped ion mobility spectrometry coupled to tandem mass spectrometry (UHPLC-TIMS-TOF-MS). TIMS offers additional depth-of-coverage through increased peak capacities realized with the multi-dimensional UHPLC-TIMS separations. Metabolite identification confidence is simultaneously enhanced by incorporating orthogonal collision cross section (CCS) data matching. To facilitate metabolite identifications, we created a CCS library of 146 plant natural products. This library was generated using TIMS with N2 drift gas to record the TIMSCCSN2 of plant natural products with a high degree of reproducibility; i.e., average RSD = 0.10%. The robustness of TIMSCCSN2 data matching was tested using authentic standards spiked into complex plant extracts, and the precision of CCS measurements were determined to be independent of matrix affects. The utility of the UHPLC-TIMS-TOF-MS/MS in metabolomics was then demonstrated using extracts from the model legume Medicago truncatula and metabolites were confidently identified based on retention time, accurate mass, molecular formula, and CCS.

Edible vegetable oils comprise integral components of humans' daily diet during the lifetime. Therefore, they constitute a central part of dietary-exposome, which among other factors regulates human health. In particular, the regular consumption of olive oil (OO) has been largely accepted as a healthy dietary pattern. Responsible for its recognition as a superior edible oil is its exceptional aroma and flavor. Its unique composition is characterized by high levels of monounsaturated fatty acids and the presence of minor constituents with important biological properties, such as the so-called OO polyphenols. Being a high added value product, OO suffers from extensive fraud and adulteration phenomena. However, its great chemical complexity, variability, and the plethora of parameters affecting OO composition hamper significantly the selection of the absolute criteria defining quality and authenticity, and a reliable and robust methodology is still unavailable. In the current study, Flow Injection Analysis-Magnetic Resonance Mass Spectrometry (FIA-MRMS) was investigated under a metabolic profiling concept for the analysis of Greek Extra Virgin Olive Oils (EVOO). More than 200 monovarietal (Koroneiki) EVOO samples were collected from the main Greek OO producing regions and investigated. Both intact oil and the corresponding polyphenols were analyzed in fast analysis time of 2 and 8 min, respectively. In parallel, an LC-Orbitrap MS platform was used to verify the efficiency of the method as well as a tool to increase the identification confidence of the proposed markers. Based on the results, with FIA-MRMS, comparable and improved projection and prediction models were generated in comparison to those of the more established LC-MS methodology. With FIA-MRMS more statistically significant compounds and chemical classes were identified as quality and authenticity markers, associated with specific parameters, i.e. geographical region, cultivation practice, and production procedure. Furthermore, it was possible to monitor both lipophilic and hydrophilic compounds with a single analysis. To our knowledge, this approach is among the few studies in which two FT-MS platforms combining LC and FIA methods were integrated to provide solutions to quality control aspects of OO. Moreover, both lipophilic and hydrophilic components are analyzed together, providing a holistic quality control workflow for OO.

A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation–serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions.

Lipids form a highly diverse group of biomolecules fulfilling central biological functions, ranging from structural components to intercellular signaling. Yet, a comprehensive characterization of the lipidome from limited starting material, for example in tissue biopsies, remains very challenging. Here, we develop a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry. Taking advantage of the PASEF principle (Meier et al. , PMID: 26538118), we fragmented on average nine precursors in each 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The very high acquisition speed of about 100 Hz allowed us to obtain MS/MS spectra of the vast majority of detected isotope patterns for automated lipid identification. Analyzing 1 uL of human plasma, PASEF almost doubled the number of identified lipids over standard TIMS-MS/MS and allowed us to reduce the analysis time by a factor of three without loss of coverage. Our single-extraction workflow surpasses the plasma lipid coverage of extensive multi-step protocols in common lipid classes and achieves attomole sensitivity. Building on the high precision and accuracy of TIMS collisional cross section measurements (median CV 0.2%), we compiled 1,327 lipid CCS values from human plasma, mouse liver and human cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions.

Sinorhizobium meliloti establishes a symbiosis with Medicago species where the bacterium fixes atmospheric nitrogen for plant nutrition. To achieve a successful symbiosis, however, both partners need to withstand biotic and abiotic stresses within the soil, especially that of excess acid, to which the Medicago-Sinorhizobium symbiotic system is widely recognized as being highly sensitive.

The redox imbalanced 6 mutant (rimb6) of Arabidopsis thaliana was isolated in a genetic screening approach for mutants with defects in chloroplast-to-nucleus redox signaling. It has an atypically low activation status of the 2-Cys peroxiredoxin-A promoter in the seedling stage. rimb6 shows wildtype-like germination, seedling development and greening, but slower growth and reduced biomass in the rosette stage. Mapping of the casual mutation revealed that rimb6 carries a single nucleotide polymorphism in the gene encoding CONSTITUTIVE EXPRESSER OF PATHOGENESIS RELATED (PR) GENES 1, CPR1 (At4g12560), leading to a premature stop codon. CPR1 is known as a repressor of pathogen signaling and regulator of microtubule organization. Allelism of rimb6 and cpr1 revealed a function of CPR1 in chloroplast stress protection. Expression studies in pathogen signaling mutants demonstrated that CPR1-mediated activation of genes for photosynthesis and chloroplast antioxidant protection is, in contrast to activation of pathogen responses, regulated independently from PAD4-controlled salicylic acid (SA) accumulation. We conclude that the support of plastid function is a basic, SA-independent function of CPR1.

Abstract Offspring of type 2 diabetes (T2D) patients have increased risk to develop diabetes, due to inherited genetic susceptibility that directly interferes with the individual adaption to environmental conditions. We characterise T2D offspring (OSP) to identify metabolic risk markers for early disease prediction. Plasma of metabolically healthy OSP individuals (n = 43) was investigated after an oral lipid tolerance test (oLTT) by an untargeted mass spectrometric approach for holistic metabolome analyses. Two subgroups of OSP probands can be separated by oLTT, although not differing in general clinical parameters. Analyses of the plasma metabolome revealed mainly medium-chain acylcarnitines and very long-chain fatty acids with differential abundance in the subgroups. The study presented indicates that metabolically healthy OSP of T2D patients differ upon metabolic challenging in serum metabolite composition, especially medium-chain acylcarnitines. The difference suggest that postprandial lipid induced glucose intolerance (LGIT) may serve as a further valuable marker for early diabetes prediction.

C. glutamicum is a bacterium used for biotechnological production of amino acids and other metabolites. Arginine is of commercial importance in cosmetic and pharmaceutical industries and as food additive. Here metabolomics and proteomics data were acquired from three C. glutamicum mutant strains and compared to wildtype extracts to gain insights into changes introduced by the rational strain design with the aim to increase arginine production in the bacterial workhorse. Several known compounds in the arginine biosynthetic pathway could automatically be identified using the MetaboScape software. Label-free proteomics data evaluated by MaxQuant revealed significant changes of proteins involved in the arginine biosynthesis pathway. Mapping alterations detected by both OMICS approaches on biochemical pathway maps enabled quick formulation of hypotheses for the observed changes in the biological context. Our results demonstrate that combination of non-targeted omics techniques enables in-depth investigation of changes in C. glutamicum caused by rational strain design to increase production of desired metabolites. Additionally, non-targeted metabolomics data evaluation resulted in the tentative identification of several unknown compounds — significantly changed in mutant strains — as gamma-glutamyl dipeptides.

Exploring the diversity of plant secondary metabolism requires efficient methods to obtain sufficient structural insights to discriminate previously known from unknown metabolites. De novo structure elucidation and confirmation of known metabolites (dereplication) remain a major bottleneck for mass spectrometry-based metabolomic workflows, and few systematic dereplication strategies have been developed for the analysis of entire compound classes across plant families, partly due to the complexity of plant metabolic profiles that complicates cross-species comparisons. 17-hydroxygeranyllinalool diterpene glycosides (HGL-DTGs) are abundant defensive secondary metabolites whose malonyl and glycosyl decorations are induced by jasmonate signaling in the ecological model plant Nicotiana attenuata. The multiple labile glycosidic bonds of HGL-DTGs result in extensive in-source fragmentation (IS-CID) during ionization. To reconstruct these IS-CID clusters from profiling data and identify precursor ions, we applied a deconvolution algorithm and created an MS/MS library from positive-ion spectra of purified HGL-DTGs. From this library, 251 non-redundant fragments were annotated, and a workflow to characterize leaf, flower and fruit extracts of 35 solanaceous species was established. These analyses predicted 105 novel HGL-DTGs that were restricted to Nicotiana, Capsicum and Lycium species. Interestingly, malonylation is a highly conserved step in HGL-DTG metabolism, but is differentially affected by jasmonate signaling among Nicotiana species. This MS-based workflow is readily applicable for cross-species re-identification/annotation of other compound classes with sufficient fragmentation knowledge, and therefore has the potential to support hypotheses regarding secondary metabolism diversification.

Metabolomics comprises of the monitoring of small molecules present in a biological system as a function of time and space. Coupled with emerging modeling approaches, it facilitates predictions of reaction sequences. Here, we explore the potential of metabolomic tools for analyzing the complex chemical systems in a model reaction, the hydrothermal reforming (HTR) of glycine. The profiles for more than 20 monitored compounds were used to reconstruct the glycine reaction network. The mechanism of glycine conversion into serine and alanine was validated, where new carbon-carbon (C-C) bonds are formed from the C2-position of glycine. We thus demonstrated that metabolomic methods are useful for the analysis of complex combinatorial problems in chemistry.