Towards quality assurance and quality control in untargeted metabolomics studies

Beger, Richard D.; Dunn, Warwick B.; Bandukwala, Abbas; Bethan, Bianca; Broadhurst, David; Clish, Clary B.; Dasari, Surendra; Derr, Leslie; Evans, Annie; Fischer, Steven H.; Flynn, Tom; Hartung, Thomas; Herrington, David M.; Higashi, Richard M.; Hsu, Pei-Chen; Jones, Christina M.; Kachman, Maureen; Karuso, Helen; Kruppa, Gary; Lippa, Katrice A.; Maruvada, Padma; Mosley, Jonathan D.; Ntai, Ioanna; O′Donovan, Claire; Playdon, Mary C.; Raftery, Daniel; Shaughnessy, Daniel T.; Souza, Amanda; Spaeder, Timothy; Spalholz, Barbara A.; Tayyari, Fariba; Ubhi, Baljit K.; Verma, Mukesh; Walk, Tilman; Wilson, Ian D.; Witkin, Keren; Bearden, Daniel W.; Zanetti, Krista A.

doi:10.1007/s11306-018-1460-7

Cited by 104 publications

(85 citation statements)

References 6 publications

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“…The implementation of different QA/QC processes has been reported for MS-based metabolomics and lipidomics yet varies in both fields [58][59][60][61]. The lack of well-established QA/QC procedures for untargeted metabolomics may impede the harmonization across laboratories and multi-center studies [62]. Reducing heterogeneity across all sampling sites involves sample selection and preparation, constant cleaning of instruments, calibration, or maintenance, quality checking, data inspection at predetermined time points, and regular personnel training [48,63].…”

Section: The High Demand For Quality Assurance and Quality Control Stmentioning

confidence: 99%

“…Taken as an example, excess exposure to glucocorticoids, which are drugs that are strictly associated with metabolic control, may affect plasma lipidome [65]. Experts in metabolomics and QA/QC procedures gathered in a meeting entitled "Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies" to identify appropriate reporting standards of QA/QC activities for publications and databases, leading to the foundation of the metabolomics Quality Assurance and Quality Control Consortium [62,66]. Pioneering scientists among those who participated in the Think Tank meeting collaborated to publish guidelines that focused on the use of system suitability and QC samples in MS-based untargeted metabolomics to collect high-quality data [58].…”

Section: The High Demand For Quality Assurance and Quality Control Stmentioning

confidence: 99%

“…Based on the content of five-step laboratory errors suggested by Plebani et al[67] 2. In reference to[49,58,62,[70][71][72][73].…”

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confidence: 99%

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Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

et al. 2020

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Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

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Section: The High Demand For Quality Assurance and Quality Control Stmentioning

confidence: 99%

Section: The High Demand For Quality Assurance and Quality Control Stmentioning

confidence: 99%

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Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

et al. 2020

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“…QC samples play a crucial role in untargeted metabolomics studies, in terms of monitoring system stability and data quality (summarized in Table 2). The QC sample will be injected at the start of the analytical batch in order to condition the column and assess instrument stability; the number of injections required may be sample-and column-dependent, but is often in the region of 10 injections [95,96]. Then, the same QC sample can be injected every 4-10 samples, making up to~10% of the sample injections.…”

Section: The Importance Of Quality Controlmentioning

confidence: 99%

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Ivanišević

Want

2019

Metabolites

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Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

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“…First, we identify various sources of measurement error and then propose methods to detect these errors. Our practical software implementation focuses on Biocrates kits that are widely used [12][13][14][15][16][17][18][19][20] . We introduce MeTaQuaC, an easy to use R-package which generates a comprehensive HTML report with interactive elements.…”

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confidence: 99%

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies – MeTaQuaC

Kuhring

Eisenberger

Schmidt³

et al. 2020

Preprint

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Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data is essential for confidence in analytical results and is particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire dataset, alongside standard analyses of quality control samples can be used to identify outliers and quantify s ystematic trends in order to improve downstream analysis. Finally we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

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Towards quality assurance and quality control in untargeted metabolomics studies

Cited by 104 publications

References 6 publications

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies – MeTaQuaC

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