1
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Broeckling CD, Beger RD, Cheng LL, Cumeras R, Cuthbertson DJ, Dasari S, Davis WC, Dunn WB, Evans AM, Fernández-Ochoa A, Gika H, Goodacre R, Goodman KD, Gouveia GJ, Hsu PC, Kirwan JA, Kodra D, Kuligowski J, Lan RSL, Monge M, Moussa LW, Nair SG, Reisdorph N, Sherrod SD, Ulmer Holland C, Vuckovic D, Yu LR, Zhang B, Theodoridis G, Mosley JD. Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples. Anal Chem 2023; 95:18645-18654. [PMID: 38055671 PMCID: PMC10753522 DOI: 10.1021/acs.analchem.3c02924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/02/2023] [Accepted: 11/10/2023] [Indexed: 12/08/2023]
Abstract
Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.
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Affiliation(s)
- Corey D. Broeckling
- Analytical
Resources Core: Bioanalysis and Omics Center; Department of Agricultural
Biology, Colorado State University, Fort Collins, Colorado 80525, United States
| | - Richard D. Beger
- Division
of Systems Biology, National Center for
Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Leo L. Cheng
- Departments
of Radiology and Pathology, Massachusetts
General Hospital, Harvard Medical School, Boston, Massachusetts 02114, United States
| | - Raquel Cumeras
- Department
of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili
(IISPV), URV, CERCA, 43204 Reus, Spain
| | - Daniel J. Cuthbertson
- Agilent
Technologies Inc., 5301 Stevens Creek Blvd, Santa Clara, California 95051, United States
| | - Surendra Dasari
- Department
of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - W. Clay Davis
- National
Institute of Standards and Technology, Chemical
Sciences Division, 331
Fort Johnson Road, Charleston, South Carolina 29412, United States
| | - Warwick B. Dunn
- Centre
for Metabolomics Research, Department of Biochemistry and Systems
Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown St., Liverpool L69 7ZB,U.K.
| | - Anne Marie Evans
- Metabolon,
Inc. 617 Davis Drive, Suite 100, Morrisville, North Carolina 27560, United States
| | | | - Helen Gika
- School
of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Royston Goodacre
- Centre
for Metabolomics Research, Department of Biochemistry and Systems
Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown St., Liverpool L69 7ZB,U.K.
| | - Kelli D. Goodman
- Metabolon, Inc., 617 Davis Drive, Suite 100, Morrisville, North Carolina 27560, United States
| | - Goncalo J. Gouveia
- Institute for Bioscience
and Biotechnology Research, National Institute
of Standards and Technology, University
of Maryland, Gudelsky
Drive, Rockville, Maryland 20850, United States
| | - Ping-Ching Hsu
- Department
of Environmental Health Sciences, University
of Arkansas for Medical Sciences, Little Rock, Arkansas 72205-7190, United States
| | - Jennifer A. Kirwan
- Metabolomics, Berlin Institute of Health at Charite, Anna-Louisa-Karsch-Str. 2, 10178 Berlin, Germany
| | - Dritan Kodra
- Department
of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Julia Kuligowski
- Neonatal
Research Group, Health Research Institute
La Fe, Avenida Fernando
Abril Martorell 106, 46026 Valencia, Spain
| | - Renny Shang-Lun Lan
- Arkansas Children’s Nutrition Center, Little Rock, Arkansas 72202-3591, United States
| | - María
Eugenia Monge
- Centro
de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas
(CONICET), Godoy Cruz
2390, C1425FQD Ciudad
de Buenos Aires, Argentina
| | - Laura W. Moussa
- Center
for Veterinary Medicine, Office of New Animal Drug Evaluation, U.S. Food and Drug Administration, Rockville, Maryland 20855, United States
| | - Sindhu G. Nair
- Department
of Biological Sciences, University of Alberta, Edmonton, AB T6G 2G2, Canada
| | - Nichole Reisdorph
- Department
of Pharmaceutical Sciences, University of
Colorado−Anschutz Medical Campus, Aurora, Colorado 80045, United States
| | - Stacy D. Sherrod
- Department
of Chemistry and Center for Innovative Technology, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - Candice Ulmer Holland
- Chemistry
Branch, Eastern Laboratory, Office of Public
Health Science, USDA-FSIS, Athens, Georgia 30605, United States
| | - Dajana Vuckovic
- Department
of Chemistry and Biochemistry, Concordia
University, 7141 Sherbrooke
Street West, Montreal, QC H4B 1R6, Canada
| | - Li-Rong Yu
- Division
of Systems Biology, National Center for
Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Bo Zhang
- Olaris, Inc., 175 Crossing
Blvd Suite 410, Framingham, Massachusetts 01702, United States
| | - Georgios Theodoridis
- Department
of Chemistry, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Jonathan D. Mosley
- Center
for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, Georgia 30605, United States
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Heyman HM, McCulloch SD, Karoly ED, Mitchell MW, Goodman KD, Evans AM. Metabolomics can
spot
the difference:
Dried Blood Spot (DBS)
coming of age in a metabolomics era. FASEB J 2022. [DOI: 10.1096/fasebj.2022.36.s1.r5469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Tobin NH, Murphy A, Li F, Brummel SS, Taha TE, Saidi F, Owor M, Violari A, Moodley D, Chi B, Goodman KD, Koos B, Aldrovandi GM. Comparison of dried blood spot and plasma sampling for untargeted metabolomics. Metabolomics 2021; 17:62. [PMID: 34164733 PMCID: PMC8340475 DOI: 10.1007/s11306-021-01813-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/11/2021] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Untargeted metabolomics holds significant promise for biomarker detection and development. In resource-limited settings, a dried blood spot (DBS)-based platform would offer significant advantages over plasma-based approaches that require a cold supply chain. OBJECTIVES The primary goal of this study was to compare the ability of DBS- and plasma-based assays to characterize maternal metabolites. Utility of the two assays was also assessed in the context of a case-control predictive model in pregnant women living with HIV. METHODS Untargeted metabolomics was performed on archived paired maternal plasma and DBS from n = 79 women enrolled in a large clinical trial. RESULTS A total of 984 named biochemicals were detected across both plasma and DBS samples, of which 627 (63.7%), 260 (26.4%), and 97 (9.9%) were detected in both plasma and DBS, plasma alone, and DBS alone, respectively. Variation attributable to study individual (R2 = 0.54, p < 0.001) exceeded that of the sample type (R2 = 0.21, p < 0.001), suggesting that both plasma and DBS were capable of differentiating individual metabolomic profiles. Log-transformed metabolite abundances were strongly correlated (mean Spearman rho = 0.51) but showed low agreement (mean intraclass correlation of 0.15). However, following standardization, DBS and plasma metabolite profiles were strongly concordant (mean intraclass correlation of 0.52). Random forests classification models for cases versus controls identified distinct feature sets with comparable performance in plasma and DBS (86.5% versus 91.2% mean accuracy, respectively). CONCLUSION Maternal plasma and DBS samples yield distinct metabolite profiles highly predictive of the individual subject. In our case study, classification models showed similar performance albeit with distinct feature sets. Appropriate normalization and standardization methods are critical to leverage data from both sample types. Ultimately, the choice of sample type will likely depend on the compounds of interest as well as logistical demands.
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Affiliation(s)
- Nicole H Tobin
- Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, California, USA
| | - Aisling Murphy
- Department of Obstetrics and Gynecology, David Geffen School of Medicine at the University of California, Los Angeles, California, USA
| | - Fan Li
- Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, California, USA
| | - Sean S Brummel
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Taha E Taha
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Friday Saidi
- UNC Project-Malawi, Kamuzu Central Hospital, Lilongwe, Malawi
| | - Maxie Owor
- MU-JHU Research Collaboration (MUJHU CARE LTD) CRS, Kampala, Uganda
| | - Avy Violari
- Perinatal HIV Research Unit, Chris Hani Baragwanath Hospital, Soweto, South Africa
| | - Dhayendre Moodley
- Centre for AIDS Research in South Africa, Durban, South Africa
- Department of Obstetrics and Gynecology, School of Clinical Medicine, University of KwaZulu Natal, Durban, South Africa
| | - Benjamin Chi
- Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Brian Koos
- Department of Obstetrics and Gynecology, David Geffen School of Medicine at the University of California, Los Angeles, California, USA
| | - Grace M Aldrovandi
- Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, California, USA.
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4
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Ford L, Kennedy AD, Goodman KD, Pappan KL, Evans AM, Miller LAD, Wulff JE, Wiggs BR, Lennon JJ, Elsea S, Toal DR. Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism. J Appl Lab Med 2021; 5:342-356. [PMID: 32445384 DOI: 10.1093/jalm/jfz026] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 09/09/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways. METHODS We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms. RESULTS The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches. CONCLUSIONS This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Sarah Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
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5
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Freed TA, Coresh J, Inker LA, Toal DR, Perichon R, Chen J, Goodman KD, Zhang Q, Conner JK, Hauser DM, Vroom KET, Oyaski ML, Wulff JE, Eiríksdóttir G, Gudnason V, Torres VE, Ford LA, Levey AS. Validation of a Metabolite Panel for a More Accurate Estimation of Glomerular Filtration Rate Using Quantitative LC-MS/MS. Clin Chem 2019; 65:406-418. [PMID: 30647123 PMCID: PMC6646882 DOI: 10.1373/clinchem.2018.288092] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 12/11/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Clinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables. METHODS An ultra-performance liquid chromatography-tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 - P30); goal <10%]. RESULTS The assay had a mean imprecision (≤10% intraassay, ≤6.9% interassay), linearity over the quantitative range (r 2 > 0.98), and analyte recovery (98.5%-113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 - P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups. CONCLUSIONS The new eGFRmet equation could serve as a confirmatory test for GFR estimation.
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Affiliation(s)
| | - Josef Coresh
- Departments of Epidemiology, Medicine and Biostatistics, Johns Hopkins University, Bloomberg School of Public Health and School of Medicine, Baltimore, MD
| | - Lesley A Inker
- Division of Nephrology, Tufts Medical Center, Boston, MA
| | | | | | - Jingsha Chen
- Departments of Epidemiology, Medicine and Biostatistics, Johns Hopkins University, Bloomberg School of Public Health and School of Medicine, Baltimore, MD
| | | | | | | | | | | | | | | | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Vicente E Torres
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN
| | | | - Andrew S Levey
- Division of Nephrology, Tufts Medical Center, Boston, MA;
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6
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Zhang Q, Ford LA, Goodman KD, Freed TA, Hauser DM, Conner JK, Vroom KET, Toal DR. LC-MS/MS method for quantitation of seven biomarkers in human plasma for the assessment of insulin resistance and impaired glucose tolerance. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1038:S1570-0232(16)30598-0. [PMID: 28029544 DOI: 10.1016/j.jchromb.2016.10.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 10/17/2016] [Accepted: 10/22/2016] [Indexed: 10/20/2022]
Abstract
Early detection of insulin resistance (IR) and/or impaired glucose tolerance (IGT) is crucial for delaying and preventing the progression toward type 2 diabetes. We recently developed and validated a straightforward metabolite-based test for the assessment of IR and IGT in a single LC-MS/MS method. Plasma samples were diluted with isotopically-labeled internal standards and extracted by simple protein precipitation. The extracts were analyzed by LC-MS/MS for the quantitation of 2-hydroxybutyric acid (0.500-40.0μg/mL), 3-hydroxybutyric acid (1.00-80.0μg/mL), 4-methyl-2-oxopentanoic acid (0.500-20.0μg/mL), 1-linoleoyl-2-hydroxy-sn-glycero-3-phosphocholine (2.50-100μg/mL), oleic acid (10.0-400μg/mL), pantothenic acid (0.0100-0.800μg/mL), and serine (2.50-100μg/mL). Liquid chromatography was carried out on a reversed phase column with a run time of 3.1min and the mass spectrometer operated in negative MRM mode. Method validation was performed on three identical LC-MS/MS systems with five runs each. Sufficient linearity (R2>0.99) was observed for all the analytes over the ranges. The imprecision (CVs) was found to be less than 5.5% for intra-run and less than 5.8% for inter-run for the seven analytes. The analytical recovery was determined to be between 96.3 and 103% for the seven analytes. This fast and robust method has subsequently been used for patient sample analysis for the assessment of IR and IGT.
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Affiliation(s)
- Qibo Zhang
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States.
| | - Lisa A Ford
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
| | - Kelli D Goodman
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
| | - Tiffany A Freed
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
| | - Deirdre M Hauser
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
| | - Jessie K Conner
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
| | - Kate E T Vroom
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
| | - Douglas R Toal
- Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, United States
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