Metabolomics Flashcards
What is metabolomics?
Metabolomics is the study of metabolites in cells/organisms which represents their molecular phenotype as it directly reflects the activity and the state of cells by studying the net interaction of the cell/organism and its external environment which causes phenotypic changes. Metabolomics tells you what is happening right now but not why.
What are metabolites?
Metabolites are biomolecules <1000 m/z and are intermediates of metabolic reactions.
Mammals<microbes<plants
Primary metabolites: are synthesized by the cell as they are indispensable for growth and maintenance. Includes amino acids, alcohols, vitamins (B2 & B12), polyols, organic acids, and nucleotides.
Secondary metabolites: are organic compounds produced by bacteria, fungi, or plants which are not directly involved in their growth, development, or reproduction. The absence of secondary metabolites results in the long-term impairment of the organism’s survivability. Includes phenolics, alkaloids, and glycosides.
What is the Warburg effect?
The Warburg effect hypothesizes that cancer is due to tumour cells generating energy through non-oxidative glycolysis.
What are the main steps in an untargeted metabolomic experiment?
Experiment design, sample preparation, analysis with MS and NMR, data analysis, metabolite identification, hypothesis generation, and experimental validation.
What are the steps in the discovery section of a metabolomic experiment?
Sample extract, sample preparation, MS analysis, and data interpretation to obtain the primary information.
What are the steps in the validation section of a metabolomic experiment?
metabolite detection with MS, validate the analytical protocol with a standard reference, sample preparation and analysis, identify and quantify, and interpret the information.
What spectroscopic techniques can be used in metabolomics?
Fourier-transform infrared spectroscopy (FTIR), Raman Spectroscopy (RS), Surface-enhanced Raman scattering (SERS), Surface-enhanced Resonant Raman scattering (SERRS), Nuclear magnetic resonance spectroscopy (NMR).
What mass spectrometer can be used in metabolomics?
Discovery: QTOF, TOF/TOF, ion mobility MS, orbitraps MS.
Validation: single quadrupole, ion trap MS, triple QTOF.
What are MS and MS/MS profiling?
MS1 profiling is a survey scan that determines the mass of the precursor ion and its abundance. MS/MS (MS2) profiling obtains the structural information.
What is a quadrupole mass filter (QMF)?
A quadrupole mass filter (QMF) consists of 4 parallel rods spaced equidistantly, where a quadrupolar field is generated between the spaces of the electrodes using a specifically applied voltage gradient to scan the ions as they enter the mass analyser/spectrometer. The specifically applied voltage is 180 degrees out of phase with each electrode and is related to ions of a specific mass. This allows the voltage to scan/filter a specific mass range.
What is the difference between GC and LC?
GC: Low mass and metabolite range, Low chemical waste generation, Low-pressure system. The chemical identification is based on fragmentation patterns and retention times.
LC: higher metabolite and mass range, higher chemical waste production, higher rate of instrumentation failure and downtime. The chemical identification is based on DIA/DDA analysis with mass spectrometry.
What are the factors to consider while designing a metabolomics experiment?
Sample blanks should account for instrumental noise and background contamination.
Consider that PLASTICIZERS can be easily detected and should be avoided.
Consider time management to avoid the high costs of running samples.
Consider how many metabolites are being detected.
Consider the method of analysis and if it can handle the number of data points.
Consider if the sample’s chemistry can undergo metabolite analysis.
Consider if the sample replication and class structure are sufficient for data analysis to answer the question posed.
What is unsupervised (principal components) analysis?
A dimensionality reduction technique that remodels the data and its underlying structure to use fewer features to describe it. This increases the interpretability of data whilst preserving important information.
What is supervised (partial least squares) analysis?
A chemometrics technique that associates the inputs with targets in linear regression, by minimising the error between the input data and the model response from the output data.
