What experimental design would you - Chemistry
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As a recent graduate of RMIT University you have just been employed (3-year contract) by a world-renowned infectious diseases institute located in Victoria, Australia. You are required to investigate the effects of the novel small molecule anti-viral (DAD030981) to determine the global metabolic changes which take place in the tissue of lungs of monkeys infected by SARS- CoV-2 (Infected Group) vs the tissue of lungs of healthy monkeys (Control Group). You have been asked by your laboratory head to design and carry out a “metabolomics experiment.”
Q1. What experimental design would you propose to your lab head? He will first need to approve this and then discuss with you how you are going to prepare samples and analyse them before you begin.
Q2. Congratulations! Your lab head has approved your project and has now asked you to provide details as to how you are going to prepare samples for your metabolomics experiment.
Briefly describe your proposed sample preparation workflow ONLY and provide a justification for each step.
What kind of internal standard do you propose to use and why?
Q3. Your lab head has now asked you to analyse the polar metabolite extract on the laboratory’s GC-MS.
Briefly describe how it may be possible to analyse polar metabolites on the GC-MS. What additional sample should you run with you experiment and why?
Q4. Your lab head is quite pleased that you have acquired all data in such a short amount of time and is now eager for you to analyse the data.
Briefly explain what and why deconvolution is important in appropriately identifying metabolites.
How can your lab head determine which metabolites are up and/or down regulated? How can your lab head determine which metabolites are statistically significant?
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Part 1
Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues or organisms. Collectively, these small molecules and their interactions within a biological system are known as the metabolome. Metabolomics strives to obtain complete metabolic fingerprints, to detect differences between them and to provide hypotheses to explain those differences. This area of research is still rapidly developing into a powerful tool in the study of all types of organisms.
Design of experiment
1. Parallel samples should be six to eight. Those of model animal should be about 10. For clinical specimens, it requires 20 to 30.
2. Samples can not be pooling or repeated freezing and thawing, serum / plasma can not be hemolytic. In addition, if using plasma, researchers should use heparin sodium anticoagulation, for the effect of EDTA anticoagulant is not good enough.
3. For NMR, there should be no alcohol or anesthetic. As for urine, feces, intestinal contents samples, sodium azide is also needed.
The two basic experimental designs used in metabolomics are
(a) highly controlled laboratory studies in animal models or in vitro tissue culture and
(b) clinical epidemiological studies to investigate pathogenesis of diseases, biomarkers, drug efficacy, and toxicity.
The choice between univariate vs. multivariate methods is largely driven by the scientific question of interest. If the primary objective of the study is to identify all metabolites associated with a particular outcome, the univariate methods are preferable. If the primary objective instead is to utilize potentially multiple metabolites to predict the outcome of interest, then multivariate methods will be preferred.
Part 2
Sample preparation
Microbiological and cellular samples: rapid inactivation of metabolic activity (quenching), keeping cells undivided.
Animal body fluids: rapid pretreatment after sampling by adding anticoagulants, preservatives, and immediate freeze (-80 ° C).
Serum samples: 500ul / cases (not less than 200ul / cases), must avoid repeated freeze-thaw.
Urine sample: 1ml / example.
Metabolic flux can occur in seconds to minutes, unlike changes in levels of proteins and transcripts, which by and large turn out over minutes or hours. Therefore, sample collection and preparation are one of the most important steps in metabolomics experiments, as suboptimal handling can consequently reduce the accuracy and precision of the results.
Samples can be in the form of blood, serum, plasma, urine, cerebrospinal fluid (CSF), solid tissues, and cells. In the eye, commonly used tissues or fluids are cornea, lens, retina, vitreous, and aqueous. Typically, >30 mg wet tissue is mentioned for untargeted studies. Different sample types require different collection processes and preparation although the principles remain similar. The objective of this step is to quench metabolic activity in the samples and then to isolate or extract the metabolites in an appropriate solvent for the analytical instrument. Quenching is performed to stop or slow down the metabolic activities so that metabolic flux is minimised or eliminated from the sample as soon as possible after collection. It can be performed by reducing the temperature to sub-zero immediately after collection and storing it at −80 °C until the sample is ready for further processing.
The raw data received from analytical instruments is converted into computer-readable formats congruent with relevant software packages. During the analysis of MS data, it abides a pre-processing step to render order between samples and this usually converts continuous data to segmented data. In a chromatography–MS experiment, a 3D matrix of retention time vs mass vs intensity is converted to a 2D matrix of chromatographic peaks and peak areas or heights. This process is called ‘deconvolution’ and provides alignment of retention time and accurate mass.
If Principal Components Analysis (PCA) plot shows separation between the study group and the controls, and the quality control samples are firmly grouped in comparision to the other classes in the experiment, the biological difference between the classes is valid compared with differences due to technical bend during analysis.
Part 3
The last step is to select a metabolite status to the specific peaks induced from either MS or NMR. Data sets induced from metabolic profiling experiments can be very large and the classifying metabolites on a large scale remains an obstruction in metabolomics. Recent technologies have driven the development of comprehensive databases to solve this problem.
The GC works on the principle that a mixture will separate into individual substances when heated. The heated gases are carried through a column with an inert gas (such as helium). As the separated substances emerge from the column opening, they flow into the MS.
Two classifications of identification: (1) Putative identification, where one or two molecular assets are used for identification but an authentic chemical standard is not used; (2) Definitive identification, which is the more methodical form of identification, employs at least two properties (typically the retention time and accurate mass and/or fragmentation mass spectrum and/or NMR spectrum) and compares these properties with an authentic chemical standard analysed under identical analytical conditions. Typically following putative identification, definitive identification is performed on selected metabolites using the relevant authentic standards.
Part 4
The combination of two-dimensional (2D) GC × GC to a fast detector such as time-of-flight (TOF) working at acquisition rates up to 500 MHz and combined with a apt spectral deconvolution assists to fully design peak metabolites and prepares the detection of up to several thousand peaks in a single GC × GC run.
Up and down regulated metabolites
The lab head determined estimates based on two-dimensional gel electrophoresis of proteins, subtractive hybridization, and differential display of mRNA. It was found that ∼4% of the genes in flesh fly pupae are diapause upregulated. The execution patterns fall into diverse discrete categories: genes unaffected by diapause, genes downregulated throughout diapause, genes upregulated throughout diapause, early diapause genes, late diapause genes, and those expressed sometimes around diapause.
Among the most diapause-upregulated genes in the flesh fly are those that encode the 70 kDa heat shock protein (Hsp70) and Hsp23. Both of these genes are swiftly upregulated at the origin of diapause and hang out upregulated throughout diapause, even at cool out temperatures. When diapause is wrapped with hexane, both genes are downregulated within 6 h.
Determine which metabolites are statistically significant
The Lab head adopted multiple testing repairs, such as the Bonferroni correction for controlling the global type I error rate and the Benjamini–Hochberg correction for controlling the false discovery rate (FDR).
This approach involves M tests, where M is the total number of metabolite variables analyzed severally in relation to an end result. The P value for each separate metabolite test can be considered significant or non-significant based on a P value origin that is corrected to account for the fact that multiple hypotheses are being tested. The approach provided a measure of statistical significance for each covariate that is easy to decode.
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