The Full Picture, the Right Picture – High-Resolution Mass Spectrometry for Metabolomic Profiling
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Metabolomics is an important discovery tool in numerous research areas, including drug discovery, disease research and crop engineering, as well as in many industrial applications like biomanufacturing. The acquisition of confident measurements depends on accurate metabolite identification and reliable, properly controlled data generation, so an effective methodology is key.
High-throughput, large-scale setups are particularly susceptible to variation. So, workflows must be designed with high accuracy and reproducibility in mind to ensure that changes detected are attributable to biology. Recorded spectra are compared to authentic standards for valid compound identification, and multiple quality checks eliminate variation introduced by sample preparation, operator and instrument.
Although the focused analysis of a targeted set of metabolites is informative and reliable, sometimes a wider scope is needed to explain a phenotype or explore beyond what is known. Comprehensive metabolome coverage – detection of all metabolites in a sample – provides that scope but poses high demands on the instrument and setup. First, discriminating the hundreds of metabolites potentially in a sample requires a
The first two demands are met by modern mass spectrometers that offer exceptional measurement resolution and analytical versatility while the third is met by a well-planned experimental design.
Resolution, range and versatility – the triad of metabolomic insights
High-resolution, accurate-mass (HRAM) MS offers the resolution, range and versatility needed for metabolomic profiling with confident identification, accurate quantitation and robust performance from sample to sample and run to run.
Depending on the model, these mass spectrometers can reach a resolution power of up to
Figure 1. High resolution boosts selectivity to distinguish low-level key metabolites. Credit: Thermo Fisher Scientific
The value of good internal controls
For metabolomic profiling, HRAM
The choice of a control is dictated by the objective of a run. In the case of metabolomic profiling, the internal controls should be a mixture of reference compounds to support identification of target metabolites as well as represent a spectrum of chemical types, molecular weights, concentrations and ion charge. These controls are available commercially as isotope-labeled metabolite mixes – figure 2 illustrates the coverage of a commonly used solution.
Figure 2. Spanning the metabolome: internal controls made for metabolomic profiling. Credit: Thermo Fisher Scientific
Built into a run sequence, internal controls serve two purposes. First, the mix is serially diluted to run as a calibrant to obtain absolute concentration of the sample metabolites. Second, the labelled metabolite mix is spiked into samples at a constant concentration. The retention time, mass accuracy and signal response of each internal standard is then measured throughout the run sequence to assess instrument and method performance.
The latter performance evaluation tests for losses or errors in the analytical workflow, ensuring that the instrument and method produce valid data.
HRAM in action: not all fetal bovine
sera are alike
An assessment of biochemical differences among batches of fetal bovine serum (FBS) illustrates the power of metabolomic profiling and the relevance of a good internal control. FBS is commonly used as a supplement in basal cell culture media. However, differences in the constitution of FBS may lead to inconsistent cell culture phenotypes and growth rates. To determine if source and processing methods correlate with biochemical differences among FBS batches, 20 samples sourced from four different vendors in five different countries and treated differently during manufacturing were analyzed. Figure 3 depicts the sequence run, including calibrant, pooled quality control samples, and test samples.
Figure 3. FBS study: sample processing, quality control (QC) and run sequence setup. Credit: Thermo Fisher Scientific
Based on the cross-run and cross-injection analysis of the internal control summarized in figure 4, the method and HRAM system used performed exceedingly well. Controls from all injections and runs showed minimal deviation in retention time (A), mass accuracy (B) and signal response (C).
Figure 4. Achieving high-quality data with a fit-for-purpose HRAM system. Credit: Thermo Fisher Scientific
Principal components analysis of the test sample metabolomic profiles clustered FBS batches that were dialyzed during manufacturing completely apart from batches that were heat inactivated or not treated at all (Figure 5A). The profiles also clustered according to country of origin, though these groupings were not as distinct (data not shown).
By retro-mining the acquired data, urea was found to be at lower concentration in dialyzed FBS (Figure 5B). Uridine, also identified retrospectively by matching data to a spectral library, was more concentrated in heat-inactivated FBS from the US, compared to other US-sourced batches (data not shown).
Figure 5. Metabolomic profiling of FBS with retro-mining of acquired data reveals composition differences related to processing method. Credit: Thermo Fisher Scientific
Focused accuracy and comprehensive breadth
With confident compound identification and reliable quantification, HRAM systems excel at targeted profiling of metabolites and allow data mining to discover novel insights that would otherwise be lost. This two-fold analytical capability unlocks the metabolome to understand biological processes. Known metabolites constitute the first scaffold of biochemical pathways underlying phenotypes, while retro-mining data fills gaps and reveals connections. The key to discoveries from metabolomic analysis is acquiring a complete and accurate picture of the metabolome. With exceptional resolution and flexible data acquisition, HRAM brings this diverse population of molecules into focus.