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In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.
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http://dx.doi.org/10.3390/metabo12020135 | DOI Listing |
PLoS Biol
September 2025
Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments.
View Article and Find Full Text PDFJ Chromatogr A
September 2025
Luoyang R&D Center of Technology, SINOPEC Engineering (Group) Co., Ltd, Luoyang 471003, China. Electronic address:
Conventional one-dimensional gas chromatography methods for gasoline quality monitoring require separate analyses for different component classes, limiting analytical efficiency and unconventional additive detection. This study presents a comprehensive two-dimensional gas chromatography with flame ionization detection (GC × GC-FID) platform enabling simultaneous quantification of regulated components and rapid screening of unconventional additives in a single analytical run. The method achieved excellent agreement with ASTM standards and high repeatability for BTEX (benzene, toluene, ethylbenzene, and xylenes) and oxygenates in gasoline.
View Article and Find Full Text PDFInt J Cosmet Sci
September 2025
BASF GmbH, Grenzach-Wyhlen, Germany.
Objective: In the framework of the Alt-SPF consortium, a large ring study was conducted to evaluate different alternative methods for SPF determination. This paper reports and discusses the results of a new in vitro approach, the Fused Method. The study aimed to determine whether the Fused Method would provide similar SPF results to those obtained using ISO 24444 and whether UVA-PF would be comparable to ISO 24443.
View Article and Find Full Text PDFSci Total Environ
August 2025
Health Sciences North Research Institute, Sudbury, Ontario, Canada.
Reliable and comparable data from multiple laboratories are essential for networks relying on reverse transcription quantitative polymerase chain reaction (RT-qPCR)-based surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic material in wastewater. Large-scale networks, such as those spanning Canada and the United States, depend on multiple laboratories deploying varied methods. However, the comparability of these methods and implications for data interoperability have not been rigorously examined.
View Article and Find Full Text PDFEuro Surveill
August 2025
European Commission, Joint Research Centre (JRC), Geel, Belgium.
BACKGROUNDSince March 2024, cases of highly pathogenic avian influenza (HPAI) caused by A(H5N1) virus of clade 2.3.4.
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