METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN’s high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool’s capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN’s MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.

Lipid identification and quantification are essential objectives in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity, and their dynamic range. In this work, we developed a tailored method for profiling and quantification combining (1) isotope dilution, (2) enhanced isomer separation by C30 fused-core reversed-phase material, and (3) parallel Orbitrap and ion trap detection by the Orbitrap Fusion Lumos Tribid mass spectrometer. The combination of parallelizable ion analysis without time loss together with different fragmentation techniques (HCD/CID) and an inclusion list led to higher quality in lipid identifications exemplified in human plasma and yeast samples. Moreover, we used lipidome isotope-labeling of yeast (LILY)-a fast and efficient in vivo labeling strategy in Pichia pastoris-to produce (nonradioactive) isotopically labeled eukaryotic lipid standards in yeast. We integrated the C lipids in the LC-MS workflow to enable relative and absolute compound-specific quantification in yeast and human plasma samples by isotope dilution. Label-free and compound-specific quantification was validated by comparison against a recent international interlaboratory study on human plasma SRM 1950. In this way, we were able to prove that LILY enabled quantification leads to accurate results, even in complex matrices. Excellent analytical figures of merit with enhanced trueness, precision and linearity over 4-5 orders of magnitude were observed applying compound-specific quantification withC-labeled lipids. We strongly believe that lipidomics studies will benefit from incorporating isotope dilution and LC-MSn strategies.

Maleimide-functionalised Pt(IV) complexes with highly selective binding properties to thiol groups were synthesised as precursors for binding of thiol-containing tumour-targeting molecules like human serum albumin.

In this work, simultaneous targeted metabolic profiling by isotope dilution and non-targeted fingerprinting is proposed for cancer cell studies. The novel streamlined metabolomics workflow was established using anion-exchange chromatography (IC) coupled to high-resolution mass spectrometry (MS). The separation time of strong anion-exchange (2 mm column, flow rate 380 μL min, injection volume 5 μL) could be decreased to 25 min for a target list comprising organic acids, sugars, sugar phosphates, and nucleotides. Internal standardization by fully C labeled Pichia pastoris extracts enabled absolute quantification of the primary metabolites in adherent cancer cell models. Limits of detection (LODs) in the low nanomolar range and excellent intermediate precisions of the isotopologue ratios (on average<5%, N = 5, over 40 h) were observed. As a result of internal standardization, linear dynamic ranges over 4 orders of magnitude (5 nM-50 μM, R > 0.99) were obtained. Experiments on drug-sensitive versus resistant SW480 cancer cells showed the feasibility of merging analytical tasks into one analytical run. Comparing fingerprinting with and without internal standard proved that the presence of the C labeled yeast extract required for absolute quantification was not detrimental to non-targeted data evaluation. Several interesting metabolites were discovered by accurate mass and comparing MS2 spectra (acquired in ddMS2 mode) with spectral libraries. Significant differences revealed distinct metabolic phenotypes of drug-sensitive and resistant SW480 cells.

Quantification is an essential task in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity and their dynamic range of the lipidome. In this work, we introduce lipidome isotope labeling of yeast (LILY) in order to produce (non-radioactive) isotopically labeled eukaryotic lipid standards in yeast for normalization and quantification in mass spectrometric assays. More specifically, LILY is a fast and efficient in vivo labeling strategy in Pichia pastoris for the production of C labeled lipid library further paving the way to comprehensive compound-specific internal standardization in quantitative mass spectrometry based assays. More than 200 lipid species (from PA, PC, PE, PG, PI, PS, LysoGP, CL, DAG, TAG, DMPE, Cer, HexCer, IPC, MIPC) were obtained from yeast extracts with an excellentC enrichment >99.5%, as determined by complementary high resolution mass spectrometry based shotgun and high resolution LC-MS/MS analysis. In a first proof of principle study we tested the relative and absolute quantification capabilities of the C enriched lipids obtained by LILY using a parallel reaction monitoring based LC-MS approach. In relative quantification it could be shown that compound specific internal standardization was essential for the accuracy extending the linear dynamic range to four orders of magnitude. Excellent analytical figures of merit were observed for absolute quantification for a selected panel of 5 investigated glycerophospholipids (e.g. LOQs around 5 fmol absolute; typical concentrations ranging between 1 to 10 nmol per 10 yeast cell starting material; RSDs <10% (N = 4)).