Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC–MS based untargeted metabolomics practitioners

Abstract

The Metabolomics Quality Assurance and Quality Control Consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. As an initial step, members of the consortium and several non-members who used liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics were asked to voluntarily participate in a collaborative research project and took part by providing the QA and QC practices utilized in their laboratories, via a six-page questionnaire composed of over 120 questions and comment fields. All contributors to this project are authors. Responses were then analyzed to identify common and divergent QA and QC practices among the contributing laboratories. For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). Due to the recruitment method for participants and its voluntary nature, although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, most being current members of mQACC, they are not necessarily representative of the worldwide pool of practitioners. The findings presented here, in addition to other data gathering efforts within mQACC, will be used to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

“…The samples can be obtained by pooling aliquots of each original sample and then following the same sample preparation protocol of the biological samples. Proposed in 2006 for the first time for untargeted metabolomics studies in order to check for data quality [ 100 ], QC samples have been and are used in numerous studies and several publications have highlighted their importance in the metabolomics workflow [ 59 , 61 , 98 , 99 , 101 ]. Broadhurst et al have reported on the different kinds of QC samples, and their respective advantages and limitations [ 98 ].…”

“…This case deals with cohorts ranging from 200 to 500 samples, depending on the kind of sample preparation protocols and instruments used. It is the simplest one because recommendations on how to standardize the different steps of the untargeted metabolomics workflow and quality management practices (from the experimental design of the study to the submission of data into public repositories) are available and have been the subject of many publications [ 30 , 57 – 59 , 61 , 62 , 98 , 99 , 105 , 109 – 113 ] (Table 2 ).…”

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

“…As of 2021, metabolomics is considered a mature discipline with more than two decades of advancements in analytical workflows, software tools, and biological information in different biological systems (e.g., in vitro cells, plants, bacteria, animals) [ 10 , 44 ]. In parallel, guidelines to ensure data reliability and reproducibility have been proposed by the Metabolomics Standards Initiative (MSI) and the metabolomics Quality Assurance and Quality Control Consortium (mQACC) [ 117 , 118 ]. The MEtabolomics standaRds Initiative in Toxicology (MERIT) was created in 2017 to describe practical guidelines and minimal reporting standards for regulators to interpret the quality of metabolomics data in the context of regulatory toxicology [ 119 ].…”

“…The mQACC began to address the lack of guidelines and nomenclature by collecting detailed information on QA and QC practices used by different laboratories using LC-MS-based untargeted analysis [ 117 ]. Pooled QC samples were identified as one of the most commonly applied quality measurements in untargeted LC-MS-based metabolomics studies [ 117 ].…”

“…The mQACC began to address the lack of guidelines and nomenclature by collecting detailed information on QA and QC practices used by different laboratories using LC-MS-based untargeted analysis [ 117 ]. Pooled QC samples were identified as one of the most commonly applied quality measurements in untargeted LC-MS-based metabolomics studies [ 117 ]. The importance of QC pooled samples for instrumental source conditioning, carry-over assessments, data filtering, signal correction, and determination of precision has been shown by recent software developments and applications [ 111 , 121 , 122 ].…”