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Experimental Procedures

Metabolon Platform

Sample Accessioning

Following receipt, samples are inventoried and immediately stored at -80°C. Each sample received is accessioned into the Metabolon LIMS system and assigned a unique identifier associated only with the original source identifier. This identifier is used to track all sample handling, tasks, results, etc.

The samples (and all derived aliquots) are tracked by the LIMS system using a unique identifier when a new task is created. Parent-child sample relationships are also tracked. All samples are maintained at -80°C until processed.

Sample Preparation

Samples are prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards are added prior to the first step in the extraction process for QC purposes. To precipitate the proteins, dissociate small molecules bound to protein, and to recover chemically diverse metabolites, the samples are mixed with methanol with vigorous shaking for 2 minutes (Glen Mills GenoGrinder 2000) followed by centrifugation.

The resulting extract is divided into multiple fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, and one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI. The remaining fractions are reserved for backup.

Samples are then placed briefly on a TurboVap® (Zymark) to remove the organic solvent. Sample extracts are stored overnight under nitrogen before preparation for analysis.

Quality Control

Several types of controls are analyzed in concert with the experimental samples:

  • A pooled matrix sample generated by taking a small volume of each experimental sample (or alternatively, use of a pool of well-characterized human plasma) serves as a technical replicate throughout the data set
  • Extracted water samples serve as process blanks
  • A cocktail of QC standards carefully chosen not to interfere with the measurement of endogenous compounds is spiked into every analyzed sample, allowing for instrument performance monitoring and to aid with chromatographic alignment.

All QC samples and standards are listed in Tables 1 and 2.

Instrument variability is determined by calculating the median relative standard deviation (RSD) for the standards added to each sample prior to injection into the mass spectrometers. Overall process variability is determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the pooled matrix samples. Experimental samples are randomized across the platform run with QC samples spaced evenly among the injections, as outlined in Figure 1 below.

Type Description Purpose
MTRX Large pool of human plasma maintained by Metabolon that has been characterized extensively. Ensure that all aspects of the Metabolon process are operating within specifications.
CMTRX Pool created by taking a small aliquot from every customer sample. Assess the effect of a non-plasma matrix on the Metabolon process and distinguish biological variability from process variability.
PRCS Aliquot of ultra-pure water Process Blank used to assess the compound signals produced from the process itself.

Table 1: Description of Metabolon QC Samples

Type Description Purpose
RS Recovery Standard Assess variability and verify performance of extraction and instrumentation.
IS Internal Standard Assess instrument variability and performance.

Table 2: Metabolon QC Standards

Figure1 QCprep

Figure 1. Preparation of client-specific technical replicates. A small aliquot of each client sample (colored cylinders) is pooled to create a CMTRX technical replicate sample (multi-colored cylinder), which is then injected periodically throughout the platform run. Variability among consistently detected biochemicals can be used to estimate process and platform variability.

Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS):

All methods utilize a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution (PMID: 32445384).

The dried sample extracts are reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contains a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot is analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds (PosEarly). In this method, the extract is gradient eluted from a C18 column (Waters UPLC BEH C18-2.1×100 mm, 1.7 µm) using water and methanol containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA).

A second aliquot is also analyzed using acidic positive ion conditions; however, this aliquot is chromatographically optimized for more hydrophobic compounds (PosLate). In this method, the extract is gradient eluted from the same Waters C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA,operated at a higher organic content.

A third aliquot is analyzed using basic negative ion optimized conditions using a separate dedicated C18 column (Neg). The basic extracts are gradient eluted from the column using methanol and water with 6.5mM Ammonium Bicarbonate at pH 8.

A fourth aliquot is analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1×150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate, pH 10.8. The MS analysis alternates between MS and data-dependent MSn scans using dynamic exclusion. The scan range varies slightly between methods but covers 70-1000 m/z. Raw data files are archived and extracted as described below.


The informatics system consists of four major components: the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analysts. The hardware and software foundations for these informatics components are the LAN backbone, and a database server runs Oracle Enterprise Edition.


The purpose of the Metabolon LIMS system is to enable fully auditable laboratory automation through a secure, easy to use, and highly specialized system. The scope of the Metabolon LIMS system encompasses sample accessioning, sample preparation, instrument analysis and reporting, and advanced data analysis. All subsequent software systems are grounded in the LIMS data structures. The LIMS has been modified to leverage and interface with in-house information extraction and data visualization systems, as well as with third party instrumentation and data analysis software.

Data Extraction and Compound Identification

Raw data are extracted, peaks are identified, and QC processed using a combination of Metabolon developed software services (applications). Each of these services performs a specific task, and they communicate/coordinate with other services using industry-standard protocols.

Compounds are identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and fragmentation data on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library +/- 10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards.

The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between molecules based on one of these factors, the use of all three data points is utilized to distinguish and differentiate biochemicals.

More than 5,400 commercially available purified or in-house synthesized standard compounds have been acquired and analyzed on all platforms to determine their analytical characteristics. An additional 7000 mass spectral entries have been created for structurally unnamed biochemicals, which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis.

Compound Quality Control

A variety of curation procedures are carried out to ensure that a high-quality data set is made available for statistical analysis and data interpretation. The QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities, and to remove or correct those representing system artifacts, mis-assignments, mis-integration and background noise.

Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification and integration among the various samples.

Metabolite Quantification and Data Normalization

Peaks are quantified using area-under-the-curve. For studies spanning multiple days, a data normalization step is performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound is corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the “block correction”, see Figure 2).

For studies that do not require more than one day of analysis, no normalization is necessary other than that performed for purposes of data visualization. In certain instances, biochemical data may have been normalized to an additional factor (e.g., cell counts, total protein as determined by Bradford assay, osmolality, etc.) to account for differences in metabolite levels due to differences in the amount of material present in each sample.

Figure2 Normalization

Figure 2. Visualization of data normalization steps for a multiday platform run.

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