Investigating larger population come with a machine-driven variation, which by this the actual variation originating from natural diversity. Here we demonstrate a method to reduce the technical variation for downstream integration analysis. The advantages of this technique include a face separation-based extraction, providing the analysis of various metabolites on the respective analytical platform, while removing the systemic error and maintaining the biological variance.
Once the genotypic and phenotypic information is obtained, this method can be applied to any diverse natural population in any kingdom of life. To begin take 20-milliliter harvesting tubes, add two five millimeter, and two eight millimeter diameter metal beads for homogenizing and label the tubes. Then, fill up a dewar with liquid nitrogen, immediately after cutting harvest fresh leaf and root tissue samples in liquid nitrogen by flash freezing.
Store the harvested biological samples at 80 degrees celsius for further processing. Pre-cool the tube holders in liquid nitrogen. Next, grind the tissues at 25 hertz for one minute to obtain a homogeneous powder.
Repeat freezing, and grinding if the tissue is not homogeneously ground. Weigh 50 milligrams of fresh plant material in a pre-cooled labeled two millimeter safe lock microcentrifuge tubes ensuring that plant material stays frozen during the weighing process. Add one millilitre of pre-cooled extraction mixture 1 to 50 milligrams aliquot of the sample, and vortex the tube briefly before keeping on ice.
Incubate the samples on an orbital shaker at 800 times G for 10 minutes at four degrees celsius, followed by sonication in an ice-cooled sonication bath for 10 minutes. Use a multi-channel pipette to add 500 microliters of extraction mixture too to the samples and mix the extracts by brief vortexing, then centrifuge the mixture at 11, 200 times G for 5 minutes at four degrees celsius. After centrifugation, transfer 500 microliters of the separated upper lipid-containing phase to a pre-labeled 1.5 milliliter safe lock microcentrifuge tube, removed the rest of the upper phase.
In separate, 1.5-milliliter safe lock microcentrifuge tubes transfer 150 microliters of the lower semi-polar phase four GCMS and 300 microliters of the semi-polar phase four U-H-P-L-C MS analysis. Use a vacuum concentrator for solvent evaporation to concentrate all the extracted fractions without heating and store the dried fractions at 20 degrees celsius. To prepare the sample for ultra-high performance, liquid chromatography, mass spectrometry, or H-P-L-C MS re-suspend the dried semi-polar fractions in 180 microliters of a mixture of methanol and water.
Then sonicate the semi-polar phase for 2 minutes, followed by centrifugation at 11, 200 times G for one minute. After centrifugation, transfer 90 microliters of the supernatant to a glass vial and inject two microliters of the extracts into the LCMS sample poured. Perform the metabolite fractionation on a reversed Phase C18 column held at 40 degrees celsius, and a flow of 400 microliters per minute with gradual changes of effluent A and B, acquire the mass spectra of the test blank and quality control samples in negative ionization mode with a mass range of 102 1500 mass to charge ratio.
For the semi-polar metabolites samples, run a pooled QC sample in data-dependent tandem mass spectrometry in negative and positive ionization modes. Use the obtained mass spectra for the annotation. Perform the normalization of the data set by checking the distribution of the internal standard.
Normalize the data by correcting for the signal of single or multiple internal standards. Next, correct the peak intensities obtained from the chromatogram over the exact sample weight. For correcting the intensity drift across multi-batch series, perform QC-based correction methods such as locally estimated scatterplot smoothing using R.Before performing genome-wide association studies or GWAS, filter the genotypic data for minor allele frequencies less than 5%and a missing rate of more than 10%using tassel.
This will avoid low-frequency bias. To eliminate the bias originating from the environmental factors use the R package LME four and calculate the best linear, unbiased predictions or blops for each normalized feature over the experimental repetitions. Use blops of each feature individually to perform GWAS with the rMVP package in R.The lipidomic data across several common bean species is shown based on the raw base peak chromatograms of two QC samples from different batches.
A variation in the signal intensities was observed for certain lipid classes. The systemic error was more evident when the principal component analysis was performed on the raw data. The normalization procedure, including the locally estimated scatterplot smoothing, led to the clustering of the QC samples.
When dissecting into the individual clusters, machine-driven errors in batch seven and eight became clear prior to normalization, which was corrected out. The post normalized and transformed individual metabolomic clusters were used for GWAS, yielding in several trait marker associations. In the representative analysis, different compound classes are highlighted in different colors, while the markers associated with multiple compound classes are indicated with its nearest gene.
Data normalization and transformation are one of the most important steps. Ensure that proper correction has been performed to correct for systemic error, and ensure normality in the distribution. By performing the correction for the analytical variation, several integration approaches can be performed, such as metabolic correlation analysis and integration into phenomic data to shed light on complex traits.
