Michael R La Frano1,2,3, Suzan L Carmichael4, Chen Ma4, Macy Hardley4, Tong Shen1, Ron Wong4, Lorenzo Rosales3, Kamil Borkowski1,5, Theresa L Pedersen6, Gary M Shaw4, David K Stevenson4, Oliver Fiehn1,7, John W Newman8,9,10,11. 1. West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, CA, USA. 2. Department of Nutrition, University of California Davis, Davis, CA, USA. 3. Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA, USA. 4. Department of Pediatrics, Stanford University, Stanford, CA, 94305, USA. 5. USDA-ARS Western Human Nutrition Research Center, Davis, CA, USA. 6. Advanced Analytics, Woodland, CA, USA. 7. Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia. 8. West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, CA, USA. John.newman@ars.usda.gov. 9. Department of Nutrition, University of California Davis, Davis, CA, USA. John.newman@ars.usda.gov. 10. USDA-ARS Western Human Nutrition Research Center, Davis, CA, USA. John.newman@ars.usda.gov. 11. Obesity and Metabolism Research Unit, USDA-ARS-WHNRC, 430 West Health Sciences Drive, Davis, CA, 95616, USA. John.newman@ars.usda.gov.
Abstract
BACKGROUND: Population-based biorepositories are important resources, but sample handling can affect data quality. OBJECTIVE: Identify metabolites of value for clinical investigations despite extended postcollection freezing delays, using protocols representing a California mid-term pregnancy biobank. METHODS: Blood collected from non-pregnant healthy female volunteers (n = 20) underwent three handling protocols after 30 min clotting at room temperature: (1) ideal-samples frozen (- 80 °C) within 2 h of collection; (2) delayed freezing-samples held at room temperature for 3 days, then 4 °C for 9 days, the median times for biobank samples, and then frozen; (3) delayed freezing with freeze-thaw-the delayed freezing protocol with a freeze-thaw cycle simulating retrieved sample sub-aliquoting. Mass spectrometry-based untargeted metabolomic analyses of primary metabolism and complex lipids and targeted profiling of oxylipins, endocannabinoids, ceramides/sphingoid-bases, and bile acids were performed. Metabolite concentrations and intraclass correlation coefficients (ICC) were compared, with the ideal protocol as the reference. RESULTS: Sixty-two percent of 428 identified compounds had good to excellent ICCs, a metric of concordance between measurements of samples handled with the different protocols. Sphingomyelins, phosphatidylcholines, cholesteryl esters, triacylglycerols, bile acids and fatty acid diols were the least affected by non-ideal handling, while sugars, organic acids, amino acids, monoacylglycerols, lysophospholipids, N-acylethanolamides, polyunsaturated fatty acids, and numerous oxylipins were altered by delayed freezing. Freeze-thaw effects were assay-specific with lipids being most stable. CONCLUSIONS: Despite extended post-collection freezing delays characteristic of some biobanks of opportunistically collected clinical samples, numerous metabolomic compounds had both stable levels and good concordance.
BACKGROUND: Population-based biorepositories are important resources, but sample handling can affect data quality. OBJECTIVE: Identify metabolites of value for clinical investigations despite extended postcollection freezing delays, using protocols representing a California mid-term pregnancy biobank. METHODS: Blood collected from non-pregnant healthy female volunteers (n = 20) underwent three handling protocols after 30 min clotting at room temperature: (1) ideal-samples frozen (- 80 °C) within 2 h of collection; (2) delayed freezing-samples held at room temperature for 3 days, then 4 °C for 9 days, the median times for biobank samples, and then frozen; (3) delayed freezing with freeze-thaw-the delayed freezing protocol with a freeze-thaw cycle simulating retrieved sample sub-aliquoting. Mass spectrometry-based untargeted metabolomic analyses of primary metabolism and complex lipids and targeted profiling of oxylipins, endocannabinoids, ceramides/sphingoid-bases, and bile acids were performed. Metabolite concentrations and intraclass correlation coefficients (ICC) were compared, with the ideal protocol as the reference. RESULTS: Sixty-two percent of 428 identified compounds had good to excellent ICCs, a metric of concordance between measurements of samples handled with the different protocols. Sphingomyelins, phosphatidylcholines, cholesteryl esters, triacylglycerols, bile acids and fatty acid diols were the least affected by non-ideal handling, while sugars, organic acids, amino acids, monoacylglycerols, lysophospholipids, N-acylethanolamides, polyunsaturated fatty acids, and numerous oxylipins were altered by delayed freezing. Freeze-thaw effects were assay-specific with lipids being most stable. CONCLUSIONS: Despite extended post-collection freezing delays characteristic of some biobanks of opportunistically collected clinical samples, numerous metabolomic compounds had both stable levels and good concordance.
Entities:
Keywords:
Biorepositories; Data quality; Delayed freezing; Metabolite stability; Metabolomics
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