Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit.

The AbsoluteIDQ p400 HR kit is a commercial product for targeted metabolomics. While the kit has been validated for human plasma and serum, adherent cell lysates have not yet been evaluated. We have optimized the detection of polar and lipid metabolites in cell lysates using the kit to enable robust and repeatable analysis of the detected metabolites. Parameters optimized include total cell mass, loading volume and extraction solvent. We present a cell preparation and analytical method and report on the performance of the kit with regard to detectability of the targeted metabolites and their repeatability. The kit can be successfully used for a relative quantification analysis of cell lysates from adherent cells although validated only for human plasma and serum. Most metabolites are below the limit of the Biocrates' set quantification limits and we confirmed that this relative quantification can be used for further statistical analysis. Using this approach, up to 45% of the total metabolites in the kit can be detected with a reasonable analytical performance (lowest median RSD 9% and 13% for LC and FIA, respectively) dependent on the method used. We recommend using ethanol as the extraction solvent for cell lysates of osteosarcoma cell lines for the broadest metabolite coverage and 25 mg of cell mass with a loading volume of 20 µL per sample.

Optimized Workflow for On-Line Derivatization for Targeted Metabolomics Approach by Gas Chromatography-Mass Spectrometry.

Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available.

Progression-Dependent Altered Metabolism in Osteosarcoma Resulting in Different Nutrient Source Dependencies.

Osteosarcoma (OS) is a primary malignant bone tumor and OS metastases are mostly found in the lung. The limited understanding of the biology of metastatic processes in OS limits the ability for effective treatment. Alterations to the metabolome and its transformation during metastasis aids the understanding of the mechanism and provides information on treatment and prognosis. The current study intended to identify metabolic alterations during OS progression by using a targeted gas chromatography mass spectrometry approach. Using a female OS cell line model, malignant and metastatic cells increased their energy metabolism compared to benign OS cells. The metastatic cell line showed a faster metabolic flux compared to the malignant cell line, leading to reduced metabolite pools. However, inhibiting both glycolysis and glutaminolysis resulted in a reduced proliferation. In contrast, malignant but non-metastatic OS cells showed a resistance to glycolytic inhibition but a strong dependency on glutamine as an energy source. Our in vivo metabolic approach hinted at a potential sex-dependent metabolic alteration in OS patients with lung metastases (LM), although this will require validation with larger sample sizes. In line with the in vitro results, we found that female LM patients showed a decreased central carbon metabolism compared to metastases from male patients.

Modified Protocol of Harvesting, Extraction, and Normalization Approaches for Gas Chromatography Mass Spectrometry-Based Metabolomics Analysis of Adherent Cells Grown Under High Fetal Calf Serum Conditions.

A gas chromatography mass spectrometry (GC-MS) metabolomics protocol was modified for quenching, harvesting, and extraction of metabolites from adherent cells grown under high (20%) fetal calf serum conditions. The reproducibility of using either 50% or 80% methanol for quenching of cells was compared for sample harvest. To investigate the efficiency and reproducibility of intracellular metabolite extraction, different volumes and ratios of chloroform were tested. Additionally, we compared the use of total protein amount versus cell mass as normalization parameters. We demonstrate that the method involving 50% methanol as quenching buffer followed by an extraction step using an equal ratio of methanol:chloroform:water (1:1:1, v/v/v) followed by the collection of 6 mL polar phase for GC-MS measurement was superior to the other methods tested. Especially for large sample sets, its comparative ease of measurement leads us to recommend normalization to protein amount for the investigation of intracellular metabolites of adherent human cells grown under high (or standard) fetal calf serum conditions. To avoid bias, care should be taken beforehand to ensure that the ratio of total protein to cell number are consistent among the groups tested. For this reason, it may not be suitable where culture conditions or cell types have very different protein outputs (e.g., hypoxia vs. normoxia). The full modified protocol is available in the Supplementary Materials.

Sex-specific metabolic and functional differences in human umbilical vein endothelial cells from twin pairs.

BACKGROUND AND AIMS: Gonadal hormones are mainly thought to account for sex and gender differences in the incidence, clinical manifestation and therapy of many cardiovascular diseases. However, intrinsic sex differences at the cellular level are mostly overlooked. Here, we assessed sex-specific metabolic and functional differences between male and female human umbilical vein endothelial cells (HUVECs). METHODS: Cellular metabolism was investigated by bioenergetic studies (Seahorse Analyser) and a metabolomic approach. Protein levels were determined by Western blots and proteome analysis. Vascular endothelial growth factor (VEGF)-stimulated cellular migration was assessed by gap closure. HUVECs from dizygotic twin pairs were used for most experiments. RESULTS: No sex differences were observed in untreated cells. However, sexual dimorphisms appeared after stressing the cells by serum starvation and treatment with VEGF. Under both conditions, female cells had higher intracellular ATP and metabolite levels. A significant decline in ATP levels was observed in male cells after serum starvation. After VEGF, the ratio of glycolysis/mitochondrial respiration was higher in female cells and migration was more pronounced. CONCLUSIONS: These results point to an increased stress tolerance of female cells. We therefore propose that female cells have an energetic advantage over male cells under conditions of diminished nutrient supply. A more favourable energy balance of female HUVECs after serum starvation and VEGF could potentially explain their stronger migratory capacity.

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.