Lipidomics studies on NIDDK / NIST human plasma samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Human Homo Sapiens
TISSUE(S): Blood
SUBMITTER: 
Eoin Fahy
PROVIDER: ST000004 | MetabolomicsWorkbench | Wed Feb 20 00:00:00 GMT 2013
REPOSITORIES: MetabolomicsWorkbench
Similar Datasets
Project description:Lipids in the reference material NIST SRM 1950 (50 uL) were extracted according to the Matyash protocol. The sample was analysed in 5 technical replicates by ESI(-)-HILIC-TIMS-MS with PASEF enabled with 100 ms.
2022-12-09 | MSV000090873 | MassIVE
Project description:We present a multi-platform assessment and a global resource for epigenetics research from the FDA's Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus on 5-methylcytosine modifications.
2023-04-21 | GSE230132 | GEO
Project description:We present a multi-platform assessment and a global resource for epigenetics research from the FDA's Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus on 5-methylcytosine modifications.
2021-10-26 | GSE186383 | GEO
Project description:Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography-mass spectrometry (LC-MS) involves the removal of unwanted features (retention time; m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces a package for the Python programming language for pre-processing LC-MS data for quality control procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC-MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are showcased with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of plasma candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies. in addition to NIST SRM 1950 – Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.
2020-11-21 | MTBLS1919 | MetaboLights
Project description:Proper QA/QC is an essential component of any analytical procedure. The variability in platforms and approaches used by the untargeted cores requires that each lab develop specific QA/QC protocols, making cross-laboratory assessments challenging. To address the difference in measures across the untargeted cores, the untargeted quality assurance working group (UQAWG) has identified the importance of using the same biological reference sample across the different laboratories. To reach this goal, Dr. Jones and Mr. Walker of Emory University volunteered to take the lead in identifying vendors, requesting quotes, purchasing the pooled material, delivering the specified volumes to each of the lab hubs and storing additional material on-site at Emory University. Based upon discussion with the UQAWG, it was decided that each participating laboratory (five total) will receive 500 mL of plasma and 2000 mL of urine, providing sufficient volume for daily analysis over the next five years. Initially, NIST standard reference materials were considered; however, limited supply and cost makes the use of NIST materials impractical. Vendors, including BioreclamationIVT, Lee Biosolutions Inc and Gemini Bio-Products were contacted to request quotes for preparing pooled plasma and urine meeting the following requirements: K2EDTA recovered whole blood (plasma), equal distribution male/female, equal distribution of Black/Hispanic/Caucasian donors and be collected from greater than 40 donors. In addition, we requested all blood samples be collected in FDA inspected facilities, provide collection details, provide general demographics for each donor and be delivered in 500 mL aliquots. BioreclamationIVT, who also provided the biological samples for the NIH metabolomics ring trial, was selected as the most appropriate vendor due to the ability to provide the requested materials, lead-time to deliver and price. To date, both pools have been purchased. The urine pool was received on July 21, 2016 and the plasma pool will be received July 26, 2016. Upon receipt, both pools will be stored at -80°C. Summary demographics were provided by BioreclamationIVT, the urine pool was created by combining urine samples from 60 males and 60 females with an average age of 38 (range 18-60) and equal distribution of races. The plasma pool was created by combining recovered plasma obtained from 50 males and 50 females with an average age of 38.4 (range 19-72) and equal distribution of races. Both urine and plasma pools will be delivered to the lab hubs frozen in units of 500 mL. Upon receipt, plasma and urine pools should be aliquoted into volumes that provide sufficient material so that freeze-thaw of the pooled material is minimized. For unambiguous identification, the urine pool has been named UrineRef_20160721 and the plasma pool has been named PlasmaRef_20160726. The purpose of providing each untargeted lab hub with the pooled material is two-fold. Through analysis of UrineRef_20160721 (if study samples are urine) or PlasmaRef_20160726 (if study samples are plasma) in each batch of samples analyzed for CHEAR, it will be possible to have a common reference across the different lab hubs. Thus, the ability to compare analyses on different platforms will be greatly improved. The use of a consistent reference material in each batch will also facilitate reference standardization, a concept developed for retrospective quantification of high-resolution metabolomics (Go, Walker et al. 2015). Using reference standardization, quantification post-data acquisition is possible by referencing the pooled material analyzed with each series of samples. The pooled material can be characterized and analytes quantified using traditional analytical chemistry techniques, such as quantification by methods of addition or standardization with a certified reference material, such as NIST SRM 1950. Known concentrations within the pooled material can be used to determine an analyte response factor and calculate sample concentrations based on single-point calibration. The benefit of this approach is that targeted quantification is only required in the pooled sample (i.e. UrineRef_20160721 or PlasmaRef_20160726), chemicals selected for quantification do not need to be selected a priori, and population wide estimates of plasma chemical concentrations can be determined without having to re-analyze samples using a targeted approach. Current Clinical Biomarker Laboratory protocol requires three analytical replicates of pooled reference material per day (three technical replicates per analytical replicate per column configuration, or 18 injections/instrument-day), resulting in analyses of the reference material every 10 samples. Results from untargeted LC-HRMS analyses of pooled CHEAR reference materials. PlasmaRef_20160726 was analyzed with additional NIST reference materials: SRM 1950, SRM 1957 and SRM 1958. UrineRef_20160721 was analyzed with NIST reference materials SRM 3672 and 3673.
2017-02-21 | ST000595 | MetabolomicsWorkbench
Project description:Proper QA/QC is an essential component of any analytical procedure. The variability in platforms and approaches used by the untargeted cores requires that each lab develop specific QA/QC protocols, making cross-laboratory assessments challenging. To address the difference in measures across the untargeted cores, the untargeted quality assurance working group (UQAWG) has identified the importance of using the same biological reference sample across the different laboratories. To reach this goal, Dr. Jones and Mr. Walker of Emory University volunteered to take the lead in identifying vendors, requesting quotes, purchasing the pooled material, delivering the specified volumes to each of the lab hubs and storing additional material on-site at Emory University. Based upon discussion with the UQAWG, it was decided that each participating laboratory (five total) will receive 500 mL of plasma and 2000 mL of urine, providing sufficient volume for daily analysis over the next five years. Initially, NIST standard reference materials were considered; however, limited supply and cost makes the use of NIST materials impractical. Vendors, including BioreclamationIVT, Lee Biosolutions Inc and Gemini Bio-Products were contacted to request quotes for preparing pooled plasma and urine meeting the following requirements: K2EDTA recovered whole blood (plasma), equal distribution male/female, equal distribution of Black/Hispanic/Caucasian donors and be collected from greater than 40 donors. In addition, we requested all blood samples be collected in FDA inspected facilities, provide collection details, provide general demographics for each donor and be delivered in 500 mL aliquots. BioreclamationIVT, who also provided the biological samples for the NIH metabolomics ring trial, was selected as the most appropriate vendor due to the ability to provide the requested materials, lead-time to deliver and price. To date, both pools have been purchased. The urine pool was received on July 21, 2016 and the plasma pool will be received July 26, 2016. Upon receipt, both pools will be stored at -80°C. Summary demographics were provided by BioreclamationIVT, the urine pool was created by combining urine samples from 60 males and 60 females with an average age of 38 (range 18-60) and equal distribution of races. The plasma pool was created by combining recovered plasma obtained from 50 males and 50 females with an average age of 38.4 (range 19-72) and equal distribution of races. Both urine and plasma pools will be delivered to the lab hubs frozen in units of 500 mL. Upon receipt, plasma and urine pools should be aliquoted into volumes that provide sufficient material so that freeze-thaw of the pooled material is minimized. For unambiguous identification, the urine pool has been named UrineRef_20160721 and the plasma pool has been named PlasmaRef_20160726. The purpose of providing each untargeted lab hub with the pooled material is two-fold. Through analysis of UrineRef_20160721 (if study samples are urine) or PlasmaRef_20160726 (if study samples are plasma) in each batch of samples analyzed for CHEAR, it will be possible to have a common reference across the different lab hubs. Thus, the ability to compare analyses on different platforms will be greatly improved. The use of a consistent reference material in each batch will also facilitate reference standardization, a concept developed for retrospective quantification of high-resolution metabolomics (Go, Walker et al. 2015). Using reference standardization, quantification post-data acquisition is possible by referencing the pooled material analyzed with each series of samples. The pooled material can be characterized and analytes quantified using traditional analytical chemistry techniques, such as quantification by methods of addition or standardization with a certified reference material, such as NIST SRM 1950. Known concentrations within the pooled material can be used to determine an analyte response factor and calculate sample concentrations based on single-point calibration. The benefit of this approach is that targeted quantification is only required in the pooled sample (i.e. UrineRef_20160721 or PlasmaRef_20160726), chemicals selected for quantification do not need to be selected a priori, and population wide estimates of plasma chemical concentrations can be determined without having to re-analyze samples using a targeted approach. Current Clinical Biomarker Laboratory protocol requires three analytical replicates of pooled reference material per day (three technical replicates per analytical replicate per column configuration, or 18 injections/instrument-day), resulting in analyses of the reference material every 10 samples. Results from untargeted LC-HRMS analyses of pooled CHEAR reference materials. PlasmaRef_20160726 was analyzed with additional NIST reference materials: SRM 1950, SRM 1957 and SRM 1958. UrineRef_20160721 was analyzed with NIST reference materials SRM 3672 and 3673.
2017-02-17 | ST000596 | MetabolomicsWorkbench
Project description:we determined the sensitivity of the Lumos mass spectrometer using the NIST monoclonal antibody reference material at various concentrations to detect its peptides in a background of S. cerevisiae whole cell lysate, which was kept at a constant concentration.
2018-09-12 | PXD009441 | Pride
Project description:Lipids in the reference material NIST 1950 (50 uL) were extracted accodring to the Matyash protocol. The sample was analysed in 5 technical replicates by ESI(-)-HILIC-TIMS-MS with PASEF enabled with 100 ms and 500 ms.
2022-11-30 | MSV000090817 | MassIVE
Project description:Implementation of operational workflows using untargeted high-resolution ion mobility mass spectrometry in clinical lipidomics require reproducible, high-throughput lipid extraction, high-quality lipid annotation, absolute quantification, and cross-validation. We present a high-throughput 4D-PASEF MS platform suitable for clinical plasma lipidomic profiling encompassing automated extraction, accurate lipid annotation, and reproducible quantification. Newly generated 4D-PASEF lipid descriptors (m/z, RT, CCS, MS2) of 200 lipid standards and of 493 lipid signals curated from reference plasma, along with qualification of reproducible features in replicate lipid extracts and dilution analyses enabled highly confident annotation, reaching 100% confidence, of 370 lipids from NIST SRM 1950 plasma and 364 lipids from NIST SRM 1951 serum. 359 plasma lipids were reproducibly quantified using absolute quantification, cross-validated by inter-instrument studies, and supported by inter-laboratory data. The high-throughput 4D-PASEF lipidomics platform was demonstrated by reproducible identification of intra-individual multidien lipidome phenotype in plasma, serum, blood, venous, and finger-prick dried blood spots.
2022-12-07 | ST002402 | MetabolomicsWorkbench
Project description:Brief description of the data submitted: Three LC-MS/MS files acquired using Data Dependent Acquisition (DDA) for
(1) NIST 1950 Frozen Human Plasma standard reference material subjected to reversed-phase chromatographic (RPC) metabolomic assay tailored for complex lipid separation;
(2) plasma Long-Term Reference (LTR) sample, routinely integrated in profiling studies at the National Phenome Centre for study-independent monitoring of precision, analysed by hydrophilic interaction liquid chromatography (HILIC) metabolomic assay tailored for small molecule polar metabolites;
(3) pooled Long-Term Reference (LTR) urine sample, routinely integrated in profiling studies at the National Phenome Centre for study-independent monitoring of precision, analysed by reversed-phase chromatographic (RPC) metabolomic assay tailored for small molecule metabolites.
Paper title: MS2Query: Reliable and Scalable MS2 Mass Spectral-based Analogue Search
Author list: Niek F. de Jonge, Joris R. Louwen, Elena Chekmeneva, Stephane Camuzeaux, Femke J. Vermeir, Robert S. Jansen, Florian Huber, Justin J.J. van der Hooft
Citation: bioRxiv 2022.07.22.501125; doi: https://doi.org/10.1101/2022.07.22.501125
2022-11-04 | MSV000090642 | MassIVE