The overall goal of this procedure is to perform robust non-targeted metabolite profiling of serum in a large scale experiment. First, extract the metabolites in a plate format. Establish a robust QC sample and method to ensure analytical stability over time.
Then acquire mass spectral data on QC and randomized serum samples in replicate, proceed to data, pre-processing, statistical analysis, and metabolite identification. The results of non-targeted metabolite profiling by U-P-L-C-M-S can be utilized to identify small molecule biomarkers in serum. The protocol is presented here for the analysis of serum.
However, it can easily be extended to the analysis of other biological fluids, such as urine, tissue, and cell culture extracts. To create a plate map for sample preparation, open a spreadsheet In the first column, enter the sample list in order of loading. In the second column, enter the 96 well plate locations using correct nomenclature for your autos sampler software.
Save one well in each plate for the QC sample. If your lc autos sampler can handle two plates at a time, separate the sample list into batches of 190 and save this information in a second worksheet. Within each of these batches, randomize the injection order.
Now copy and paste your injection Order into the LCMS sample queue software system. Reserve the first three rows of the sample queue for column conditioning injections. Prepare a 10 milliliter stock of QC sample containing a minimum of four compounds of known mass and retention time that span the chromatographic elution of the experiment.
Thaw the serum samples on ice and gently vortex for about five seconds. Pre-wet the tips of a 12 channel pipetter with methanol to help prevent tip dripping during sample dispensing. Then add 370 microliters of cold methanol to each well of a 96 Well plate transfer, 100 microliters of each sample to the corresponding plate location from the plate maps.
Cover the plates and incubate at minus 80 degrees Celsius for 30 minutes. To precipitate the proteins, centrifuge the plates at 3, 200 Gs and four degrees Celsius for 30 minutes. Then transfer 250 microliters of the supernatant to a new 96 well plate and repeat the centrifugation step.
Now transfer 60 microliter aliquots of the snat to 3 96 well plates suitable for the UPLC or lc autos sampler. Add 100 microliters of QC sample mix to the reserved well position on the plate. Seal each plate with adhesive film.
Set the autos sampler to four degrees Celsius. Place the first two plates into the autos sampler. Set up the acquisition method using one of the two alternative reverse phase gradients.
Gradient A is biased towards non-polar molecules. While gradient B will provide improved coverage of moderately polar molecules. Set the mass spectrometer to collect data in positive mode, scanning from 50 to 1200 mass charge ratio.
Additional specific instrument conditions will vary according to the system used at the beginning of each two. Plate patch of samples Manually inject the QC samples five times. Use the third through fifth QC injections to monitor for peak area of less than 25%RSD retention time of plus or minus 0.05 minutes, and mass accuracy of plus or minus three parts per million.
These parameters may need to be adjusted to fit the specifications of the instrument being used for data acquisition. If the QC samples pass, acquire data on serum samples for those two plates when acquisition of the entire dataset is complete. Perform feature detection, alignment, and normalization of the mass spectrometry data for the entire dataset.
Then perform statistical analysis to determine molecular features of biological interest. These steps are described in the text protocol. The following workflow is in a conceptual overview of the metabolite identification process.
An alternative strategy is provided in the text protocol. Use software to predict a molecular formula from the accurate mass and isotopic distribution of the significant molecular features. Next, search the accurate mass measurements against in-house or public metabolite.
Databases filter the candidate metabolite, identifications by mass error predicted molecular formula, retention time, insource fragmentation, and biological relevance in the trends observed with respect to experimental design. This will result in a putative metabolite identification. Absolute identification requires matching accurate mass retention, time and fragmentation for the experimental compound with an authentic standard of the putative metabolite.
Finally, assign identification confidence to all reported metabolite identifications based on the metabolomic standards initiative Recommendations. This workflow indicates the basic analytical steps of performing a non-targeted metabolite profiling experiment by U-P-L-C-M-S. The raw data can be visualized as a base peak chromatogram.
This example shows a serum sample that was analyzed by gradient a subsequent to statistical analysis. Metabolite identification is attempted for all biologically important and statistically significant molecular features.Here. Caffeine was identified in human serum by matching the chromatographic retention time accurate mass and fragmentation pattern between the experimental molecular feature and an authentic standard of the putative metabolite.
This is an example of a level one metabolite identification At after watching this video, you should have a good understanding of how to ensure analytical stability during large scale, non-targeted metabolite profiling of serum using UPLC ms. However, analytical stability is only one important aspect of a successful metabolite profiling, experiment. Experimental design and downstream data analysis and interpretation are also critical.
But outside the scope of this video tutorial, these topics are introduced and discussed in the text protocol.
