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Discoveryhd4

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The DiscoveryHD4 is a high-performance liquid chromatography-mass spectrometry (LC-MS) system designed for metabolomics research. It provides comprehensive metabolite profiling and identification capabilities, enabling researchers to gain insights into biochemical processes and discover novel biomarkers.

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Discovery hd4 platform, by Metabolon (1 mentions)

17 protocols using discoveryhd4

1

Untargeted Metabolomics from Plasma Samples

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We measured untargeted metabolomics using ultra-performance LC–tandem MS on the Metabolon DiscoveryHD4® platform from plasma samples collected at baseline. The measurement of metabolites was performed in 3 subsets in March 2015, January 2016, and March 2017 successively. The data quality control and processing methods have been described previously (16 (link)) and are summarized in the Supplemental Methods. After data quality control and data management, the 3 subsets included 1503, 5992, and 5980 individuals, in which 944, 1168, and 1219 metabolites were measured, respectively, and 781 metabolites were identical across all subsets.
Before analysis, we undertook the following steps for each metabolite within each subset: log-transformation, replacement of outliers with 5 SDs from the mean (Winsorization), and standardization to a mean of 0 and SD of 1. For metabolite concentrations below the limit of detection, we imputed them with the lowest measured value of that metabolite (17 (link)). The different subsets in the exploratory data set were measured at different time points, and we adjusted for measurement time period in the regression analysis.
Li C., Imamura F., Wedekind R., Stewart I.D., Pietzner M., Wheeler E., Forouhi N.G., Langenberg C., Scalbert A, & Wareham N.J. (2022). Development and validation of a metabolite score for red meat intake: an observational cohort study and randomized controlled dietary intervention. The American Journal of Clinical Nutrition, 116(2), 511-522.
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2

Targeted Metabolomics of Raspberry Intervention

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Targeted metabolomics using ultra-performance liquid chromatography–tandem mass spectrometry on the Metabolon DiscoveryHD4® platform (Morrisville, NC, United States) were performed on fasted plasma samples of the 24 participants of the raspberry group collected before (week 0) and after (week 8) the raspberry consumption (16 (link)). The dataset of metabolites consisted of a total of 1,132 biochemicals which included lipids, amino acids, xenobiotics, cofactors and vitamins, nucleotides, carbohydrates and peptides. Data were normalized by dividing the raw values in the experimental batch by the median of those samples in each instrument batch, giving each batch and thus the metabolite a median of one. After batch normalization, data were further imputed by replacing missing values for a given metabolite with its observed minimum. This was done to avoid inflating the false negative rate and weaken the statistical power of the analyses. Normalized and imputed data were then transformed using natural log and filtered based on inter-individual variance. Metabolites presenting no variance (n = 14) or low variance (< 0.1; n = 272) were excluded from further analyses. Data were further filtered to remove unknown compounds (216 unnamed biochemicals).
Barbe V., de Toro-Martín J., San-Cristobal R., Garneau V., Pilon G., Couture P., Roy D., Couillard C., Marette A, & Vohl M.C. (2023). A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption. Frontiers in Nutrition, 10, 1104685.
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3

Untargeted Metabolomics in Plasma Samples

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Untargeted metabolomics were measured using the DiscoveryHD4® platform(97 ) (Metabolon, Inc., Durham, USA), which uses a Hydrophilic Interaction Liquid Chromatographic (HILIC) method in non-fasted citrated plasma samples, in two quasi-randomly selected substudies. Metabolite levels were median-normalized across rundays and no imputation of missing values was performed. All the analyses were performed for each dataset separately. The reported analyses included 9,902 individuals with full covariate information and plasma levels of 977 metabolites. Fixed-effects meta-analysis was used to combine the metabolite results from the two datasets.
Mann J.P., Pietzner M., Wittemans L.B., Rolfe E.D., Kerrison N.D., Imamura F., Forouhi N.G., Fauman E., Allison M.E., Griffin J.L., Koulman A., Wareham N.J, & Langenberg C. (2020). Insights into genetic variants associated with NASH-fibrosis from metabolite profiling. Human Molecular Genetics, 29(20), 3451-3463.
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4

Untargeted Metabolomic Profiling of EPIC-Norfolk

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Genotyping, imputation and untargeted metabolite profiling of baseline non-fasted serum samples from 9,712 unrelated European individuals in the EPIC-Norfolk cohort was performed using the Discovery HD4 platform (Metabolon, Inc.), as previously described32 (link),34 (link).
Lam B.Y., Williamson A., Finer S., Day F.R., Tadross J.A., Soares A.G., Wade K., Sweeney P., Bedenbaugh M.N., Porter D.T., Melvin A., Ellacott K.L., Lippert R.N., Buller S., Rosmaninho-Salgado J., Dowsett G.K., Ridley K.E., Xu Z., Cimino I., Rimmington D., Rainbow K., Duckett K., Holmqvist S., Khan A., Dai X., Bochukova E.G., Trembath R.C., Martin H.C., Coll A.P., Rowitch D.H., Wareham N.J., van Heel D.A., Timpson N., Simerly R.B., Ong K.K., Cone R.D., Langenberg C., Perry J.R., Yeo G.S, & O’Rahilly S. (2021). MC3R links nutritional state to childhood growth and the timing of puberty. Nature, 599(7885), 436-441.
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5

Equine Muscle Metabolomics by UPLC-MS/MS

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Approximately 100 mg of frozen muscle biopsy samples from exercised horses was sent on dry ice to Metabolon®, Inc. for ultra-high performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) analysis using their DiscoveryHD4 comprehensive global metabolomics platform. Samples were prepared and analyzed as per the company’s protocol (Gall et al., 2010 (link)). For metabolite identification, retention time/index (RI), mass-to-charge ratio (m/z), and MS/MS spectra were compared against authenticated standards as part of a library that is curated and maintained by Metabolon®, Inc. (Gall et al., 2010 (link)).
Klein D.J., McKeever K.H., Mirek E.T, & Anthony T.G. (2020). Metabolomic Response of Equine Skeletal Muscle to Acute Fatiguing Exercise and Training. Frontiers in Physiology, 11, 110.
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6

Comprehensive Metabolomics Profiling of Fasted Plasma

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Non-targeted metabolomics profiling was performed at Metabolon, Inc. (Morrisville, NC, USA) on EDTA plasma samples obtained after an overnight fast from 8679 participants, as previously described in detail [51 (link),52 (link),53 (link)]. The Metabolon DiscoveryHD4 platform was applied to assay named metabolites. All samples were processed together for peak quantification and data scaling. We quantified raw mass spectrometry peaks for each metabolite using the area under the curve and evaluated overall process variability by the median relative standard deviation for endogenous metabolites present in all 20 technical replicates in each batch. We adjusted for variation caused by day-to-day instrument tuning differences and columns used for biochemical extraction by scaling the raw peak quantifications to the median for each metabolite by the Metabolon batch. We included 1098 metabolites in the statistical analysis.
Ravi R., Fernandes Silva L., Vangipurapu J., Maria M., Raivo J., Helisalmi S, & Laakso M. (2022). Metabolite Signature in the Carriers of Pathogenic Genetic Variants for Cardiomyopathy: A Population-Based METSIM Study. Metabolites, 12(5), 437.
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7

Metabolomic Profiling of Participant Plasma

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Metabolon Inc. conducted the metabolomics assays on participant plasma
samples at three time points for each participant throughout the course of the
study. Metabolon Inc. generated the data using their DiscoveryHD4 platform in
addition to their Fatty Acid Metabolism (FAME) panel that use a combination of
ultra-high-performance liquid chromatography with tandem mass spectrometry (MS)
and gas chromatography (GC) in the identification of metabolites and fatty
acids. The metabolite values were reported relative to their concentrations
among all participants, except for lipids that were measured via GC-FID, which
were reported as molar percentages of each participant’s total fatty
acids. For analysis, the metabolomics data was median scaled, such that the
median value for each metabolite was one and values that fell beneath the range
of detection were imputed to be the minimum observed value. This scaling was
performed across all samples. All time points were run as a single batch. Counts
of metabolites detected using each technology are listed in Supplementary Table 13. See Supplementary Table 1 for
all metabolites detected.
Price N.D., Magis A.T., Earls J.C., Glusman G., Levy R., Lausted C., McDonald D.T., Kusebauch U., Moss C.L., Zhou Y., Qin S., Moritz R.L., Brogaard K., Omenn G.S., Lovejoy J.C, & Hood L. (2017). A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nature biotechnology, 35(8), 747-756.
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8

Non-targeted Metabolomic Profiling of EDTA-Plasma

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Non-targeted metabolomics profiling was performed at Metabolon, Inc. (Durham, North Carolina, USA)61 on EDTA-plasma samples obtained after ≥10-h overnight fast during METSIM baseline visits. Briefly, methanol extraction of biochemicals followed by a non-targeted relative quantitative liquid chromatography–tandem mass spectrometry (LC-MS/MS) Metabolon DiscoveryHD4 platform was applied to assay named (n = 1240) and unnamed (n = 304) metabolites (Supplementary Table 1 and Supplementary Data 1). Samples were randomized across batches. Batches contained ~144 METSIM samples and 20 well-characterized human-EDTA plasma samples for quality control (QC). All 6490 samples were processed together for peak quantification and data scaling. We quantified raw mass spectrometry peaks for each metabolite using the area under the curve. We evaluated overall process variability by the median relative standard deviation for endogenous metabolites present in all 20 technical replicates in each batch. We adjusted for variation caused by day-to-day instrument tuning differences and columns used for biochemical extraction by scaling the raw peak quantification to the median for each metabolite by Metabolon batch.
Yin X., Chan L.S., Bose D., Jackson A.U., VandeHaar P., Locke A.E., Fuchsberger C., Stringham H.M., Welch R., Yu K., Fernandes Silva L., Service S.K., Zhang D., Hector E.C., Young E., Ganel L., Das I., Abel H., Erdos M.R., Bonnycastle L.L., Kuusisto J., Stitziel N.O., Hall I.M., Wagner G.R., Kang J., Morrison J., Burant C.F., Collins F.S., Ripatti S., Palotie A., Freimer N.B., Mohlke K.L., Scott L.J., Wen X., Fauman E.B., Laakso M, & Boehnke M. (2022). Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nature Communications, 13, 1644.
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9

Untargeted Metabolomics Profiling

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Metabolites were measured as part of Metabolon Inc.’s untargeted Discovery HD4 platform (Metabolon, Morrisville, NC, USA), as previously described in detail [23 (link)]. Briefly, methanol extraction of biochemicals followed by a non-targeted relative quantitative liquid chromatography–tandem mass spectrometry (LC-MS/MS) were performed. The Metabolon Discovery HD4 platform was applied to assay named and unnamed metabolites. A total of 1098 unique metabolites were included in statistical analysis. The classification of the metabolites was based on the Human Metabolome Database (http://www.hmdb.ca, accessed on 1 June 2022).
Fernandes Silva L., Ravi R., Vangipurapu J, & Laakso M. (2022). Metabolite Signature of Simvastatin Treatment Involves Multiple Metabolic Pathways. Metabolites, 12(8), 753.
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10

Non-targeted Metabolomic Profiling of EDTA Plasma

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Non-targeted metabolomic profiling was performed at Metabolon, Inc. (Durham, NC, USA) on EDTA plasma samples obtained after an overnight fast. Briefly, methanol extraction of biochemicals was followed by non-targeted relative quantitative liquid chromatography–tandem mass spectrometry (LC-MS/MS). The Metabolon DiscoveryHD4 platform was applied to assay named metabolites. Samples were randomized across batches. Batches contained ~144 METSIM samples and 20 well-characterized human-EDTA plasma samples for quality control. All samples were processed together for peak quantification and data scaling. We quantified raw mass spectrometry peaks for each metabolite using the area under the curve and evaluated overall process variability using the median-relative standard deviation for endogenous metabolites present in all 20 technical replicates in each batch. We adjusted for variation caused by day-to-day instrument tuning differences and columns used for biochemical extraction by scaling the raw peak quantifications to the median for each metabolite by the Metabolon batch. We included in the statistical analysis 1098 metabolites, having data for at least 1000 men.
Fernandes Silva L., Vangipurapu J., Oravilahti A., Männistö V, & Laakso M. (2023). Plasma Metabolite Signatures in Male Carriers of Genetic Variants Associated with Non-Alcoholic Fatty Liver Disease. Metabolites, 13(2), 267.
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