Why Metabolon
Mass spectrometry is an inherently semi-quantitative, highly sensitive technology that measures the relative quantity differences of an individual metabolite as expressed by the metabolite’s peak intensity variations in comparative samples. Quantitation can be relative (analyzed relative to a reference sample) or absolute (analyzed using a standard curve method).
Sample normalization in metabolomics is key to deriving accurate biological insight, but care must be taken because of the diversity of metabolite structures and behaviors. There are different normalization approaches, including methods that adjust:
In untargeted metabolomics, there is no standard method for measuring the total amount of metabolites directly, however, Metabolon has performed extensive analyses supported by publications and found that the abovementioned second approach greatly outperformed other methods.1
“When performing normalization to metabolomics data, it is important that the method appropriately corrects for the systematic variation but preserves the biological variation,” says Greg Michelotti, Senior Director of Scientific and Translational Strategy at Metabolon.
In a 2018 study, we determined the best way to normalize metabolomics data based on analysis of plasma samples obtained from participants in the Insulin Resistance Atherosclerosis Study (IRAS).1 From this cohort, 1,716 samples were analyzed using the Metabolon Global Discovery Panel. Accommodating this many samples required between 13 and 15 instrument runs per arm of the platform.
The resulting analysis measured 1,274 metabolites. Untargeted metabolomics profiling was compared to a separate targeted panel for a subset of metabolites representative of multiple biochemical classes. In this study, we showed that the normalization methods that rely on metabolite-specific adjustments significantly outperformed the methods that make adjustments across each sample, such as total ion count (TIC) normalization.
In many cases, the sample-based normalizations performed worse than performing no normalization. Correcting by the median batch value from the experimental samples (MED) can work well in various applications: for each metabolite, divide the raw peak areas for a sample by the median of the raw peak areas for all samples in the same instrument batch.
However, suppose one wants to run a very small set and merge it into previous data sets or compare the values in two different data sets. In that case, it is typically better to normalize by bridging control samples (BRDG): for each metabolite, divide the raw peak areas for a given sample by the median of the raw peak areas of the bridging control samples. The main drawback of BRDG is that metabolites that are not present in the bridge samples cannot be normalized.
Learn more about various normalization methods for LC/MS metabolomics data
1. Wulff, Jacob E., and Matthew W. Mitchell. “A comparison of various normalization methods for LC/MS metabolomics data.” Advances in Bioscience and Biotechnology 9.08 (2018): 339.
Targeted metabolomics can take advantage of absolute quantitation since the panel or assay can be optimized for specific compounds. Optimization improves sensitivity and specificity but sacrifices broad analyte coverage. Absolute quantitation means that the metabolites can be quantitated based on a known quantity using the standard curve method.
A standard curve or calibration curve is a general method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration. The quantity of metabolites in a sample is reported as a concentration (eg, 21.5 ng/mL). You may want to use absolute quantitation when you want to compare data over time. This type of quantitation is helpful when your biomarker data extends across various studies and batches or comprises diagnostic test data.
Once you see the full value of metabolomics, the only remaining question is: who does it best? While many laboratories have metabolite profiling or analytical chemistry capabilities, comprehensive metabolomics technologies are extremely rare.
Accurate, unbiased metabolite identification across the entire metabolome introduces signal-to-noise challenges that very few labs are equipped to handle. Also, translating massive quantities of data into actionable information is slow, if not impossible, for most because proper interpretation takes two things that are in short supply: experience and a comprehensive database.
Using our robust platform and visualization tools, our experts are uniquely able to tell you more about your molecule and develop assay panels to help you zero in on the results you need.
Coverage
Ability to interrogate thousands of metabolites across diverse biochemical space, revealing new insights and opportunities
Comparability
Ability to integrate the data from different studies into the same dataset, in different geographies, among different patients over time
Competency
Ability to inform on proper study design, generate high‐quality data, derive biological insights, and make actionable recommendations
Capacity
Ability to process hundreds of thousands of samples quickly and cost‐efficiently to service rapidly growing demand
Request a quote for our services, get more information on sample types and handling procedures, request a letter of support, or submit a question about how metabolomics can advance your research.
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