Untargeted Metabolomics

Untargeted Metabolomics and Lipidomics Services for Plants, Microbes, Animal and human Biofluids samples

Mass spectrometry–based profiling across diverse biological systems with expert data analysis and fast turnover

MetasSysX is a metabolomics and lipidomics company which offers a high–throughput mass spectrometry based untargeted metabolomic analysis. The advanced MS platforms enable in-depth, unbiased profiling of thousands of metabolites and lipids across various types of samples, including:

Plant tissues and parts: leaves, roots seeds, flowers, phloem, xylem, fruits, whole seedlings

Plant cell compartments: purified chloroplasts, enriched plasma membrane, cytoplasm

Animal tissues: liver, heart, brain, serum, plasma, skin, kidneys, testicles,

Animal biofluids: milk, urine, snakes and spiders' venoms, CSF

Whole animals: Caenorhabditis elegans, mosquitos, Drosophila melanogaster, rotifer, pests, spidermites,

Food samples: wine, oils, fermentation products (yogurts)

Human biofluids, tissues and biopsies: serum, plasma, urine, CSF, saliva, urine, dry blood spots from finger pricking and blood studies, skin biopsies, skin stripping discs,

Samples to control processes: fermentation, media, cell cultures

At metaSysX, our untargeted metabolomic analysis pipeline includes extraction of metabolites and lipids, mass spectrometric measurements, processing and analysis of data.

At metaSysX, our untargeted metabolomic analysis pipeline includes extraction of metabolites and lipids, mass spectrometric measurements, processing and analysis of data. This workflow supports untargeted profiling as well as annotation of metabolic features facilitating comparative studies. The extracts of metabolites are analyzed through complementary platforms gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) — to maximize metabolite coverage. This procedure is used for metabolite and lipids profiling of divers biological matrices like plant, fungi, bacteria, animals' tissues as well as clinical samples from human tissues and biofluids.

Comprehensive plant metabolome analysis and robust bioinformatic approaches are used in breeding strategies, resolving mode of action of herbicide, fungicide or insecticide, identifying a biomarker or gene function. Metabolomics and lipidomics can be applied in research of new drug discovery, resolving toxic effects, natural product research. Our platforms enable unbiased metabolite and lipids profiling of thousands of samples in a short time. MetaSysX spectral and chromatographic database contains a wide range of biochemical classes from plants, bacteria, fungi as well as animal and human specific analytes.

The major biochemical classes:

  • Amino acids and derivatives (proteinogenic & non-proteinogenic)
  • Carbohydrates and sugar derivatives
  • Lipids, fatty acids, and sterols
  • Nucleotides, nucleosides, and nucleobases
  • Phenolic compounds, flavonoids and polyphenols
  • Organic acids and metabolic intermediates
  • Vitamins and cofactors
  • Alkaloids and nitrogen heterocycles
  • Polyamines and miscellaneous bioactives
  • Carnitine and acylcarnitines
  • Nitrogen heterocycles/pharmaceuticals

Our database cover well parts or complete biochemical pathways amongst all:

  • Signaling pathways including cyclic nucleotides (cAMP, cGMP)
  • Protein biosynthesis (amino acids)
  • Polyamine biosynthesis (N-methylputrescine, putrescine)
  • Peptide metabolism
  • Amino acids biosynthesis and degradation pathway
  • Alkaloids biosynthesis from amino acids like tryptophan, tyrosine, phenylalanine (e.g tropane, Isoquinoline)
  • Secondary metabolites biosynthesis in plants like nicotine, caffeine
  • Kynurenine pathway
  • Neurotransmitters-related pathways (natural inhibitors of ion-channels and receptors, neurotransmitters)
  • Glutathione oxidization/reduction
  • Flavonoids biosynthesis
  • Vitamin biosynthesis and metabolism
  • Hormones biosynthesis and metabolism
  • Synthesis and degradation of nucleotides (purine and pyrimidine metabolism) and B12 cofactor biosynthesis
  • TCA cycle, glycolysis / gluconeogenesis, glyoxylate cycle, pentose phosphate pathway and shikimate biosynthesis
  • Energy metabolism

This workflow supports relative quantitation and annotation of metabolic features enabling to discriminate genotypes, treatments and other conditions and identifying statistically relevant metabolites.

Untargeted metabolomics enables relative quantitation of compounds detected over samples. It allows us to compare the same metabolite across samples or conditions, but not different metabolites within the same sample. This type of analysis informs how much of an analyte is detected relative to the same analyte in different samples.

It allows to assess

  • The effect of a treatment on biological samples
  • Metabolome differences of samples with different genetic background
  • Discovery of biomarkers or discriminative features

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