Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for the untargeted profiling of biological samples. A multiplatform LC-MS-based approach is needed to screen polar metabolites and lipids comprehensively. Different mobile phase modifiers were tested to improve the electrospray ionization process during metabolomic and lipidomic profiling. For polar metabolites, hydrophilic interaction LC using a mobile phase with 10 mM ammonium formate/0.125% formic acid provided the best performance for amino acids, biogenic amines, sugars, nucleotides, acylcarnitines, and sugar phosphate, while reversed-phase LC (RPLC) with 0.1% formic acid outperformed for organic acids. For lipids, RPLC using a mobile phase with 10 mM ammonium formate or 10 mM ammonium formate with 0.1% formic acid permitted the high signal intensity of various lipid classes ionized in ESI(+) and robust retention times. For ESI(−), the mobile phase with 10 mM ammonium acetate with 0.1% acetic acid represented a reasonable compromise regarding the signal intensity of the detected lipids and the stability of retention times compared to 10 mM ammonium acetate alone or 0.02% acetic acid. Collectively, we show that untargeted methods should be evaluated not only on the total number of features but also based on common metabolites detected by a specific platform along with the long-term stability of retention times.

Thermal reactions can significantly alter the metabolomic and lipidomic content of biofluids and tissues during storage. In this study, we investigated the stability of polar metabolites and complex lipids in dry human serum and mouse liver extracts over a three-day period under various temperature conditions. Specifically, we tested temperatures of −80 °C (freezer), −24 °C (freezer), −0.5 °C (polystyrene box with gel-based ice packs), +5 °C (refrigerator), +23 °C (laboratory, room temperature), and +30 °C (thermostat) to simulate the time between sample extraction and analysis, shipping dry extracts to different labs as an alternative to dry ice, and document the impact of higher temperatures on sample integrity. The extracts were analyzed using five fast liquid chromatography-mass spectrometry (LC-MS) methods to screen polar metabolites and complex lipids, and over 600 metabolites were annotated in serum and liver extracts. We found that storing dry extracts at −24 °C and partially at −0.5 °C provided comparable results to −80 °C (reference condition). However, increasing the storage temperatures led to significant changes in oxidized triacylglycerols, phospholipids, and fatty acids within three days. Polar metabolites were mainly affected at storage temperatures of +23 °C and +30 °C.

Here, we present a specific atlas of mouse metabolome and lipidome (MetaboAtlas21) in the context of systemic energy balance (chow diet) and under chronic nutrient stress (high-fat diet). Male mice were fed a control (chow) diet for 2 months or a high-fat diet for 2 months and 10 months. Urine, plasma, feces, and 18 different tissues were collected from each animal for metabolomics and lipidomics analysis. These matrices cover digestive, excretory, respiratory, reproductive, endocrine, muscular, cardiovascular, and nervous systems. Also, chow and high-fat diet feeds were analyzed along with quality control human plasma/serum materials (NIST SRM 1950 plasma, Merck S1-100ML serum, Sigma–Aldrich S7023 serum). In total, 408 samples were included in this study. An “all-in-one” extraction protocol LIMeX using methyl tert-butyl ether, methanol, and water was used to isolate metabolite fractions and analyzed using a multiplatform LC-MS-based approach (7 platforms for non-fat tissues and biofluids; 8 platforms for adipose tissues). Raw data files were processed using MS-DIAL 4. Metabolites were annotated using in-house retention time–m/z library and using MS/MS libraries available from commercial and open sources (NIST20, MassBank, MoNA). Lipids were annotated using LipidBlast in MS-DIAL. Ultimately, we annotated over 3,000 unique polar metabolites and complex lipids. To better understand the structure of generated data, we provide a user-friendly data visualization tool (metaboatlas21.metabolomics.fgu.cas.cz) to easily access and analyze the different combinations of tissues and biofluids in response to the metabolic challenge (NIST20, MassBank, MoNA). Lipids were annotated using LipidBlast in MS-DIAL. Ultimately, we annotated over 3,000 unique polar metabolites and complex lipids. To better understand the structure of generated data, we provide a user-friendly data visualization tool (metaboatlas21.metabolomics.fgu.cas.cz) to easily access and analyze the different combinations of tissues and biofluids in response to the metabolic challenge.