Deep Metabolic Profiling Assessment of Tissue Extraction Protocols for Three Model Organisms

Abstract

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer’s, Parkinson’s disease or COVID-19. To better understand such diseases and their intricate metabolic pathways in human studies, model animals are regularly used. There, standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study that can be achieved through model organisms. Although metabolomic approaches have been employed on model organisms before, no systematic assessment of different conditions to optimize metabolite extraction across several organisms and sample types has been conducted. We address this issue using a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse, and zebrafish) to find an optimal extraction protocol for various matrices. Focusing on parameters such as metabolite coverage, concentration and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest concentrations, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app “MetaboExtract”. Hereby, scientists can search for metabolites or compound classes of interest, compare them across the different tested extraction protocols and sample types as well as find reference concentration values.

Comparison of extraction methods for intracellular metabolomics of human tissues

“…Importantly, isopropanol-based monophasic extractions require fewer solvents and are easier to scale up due to their rapid protocol. While MTBE and chloroform extraction (Bligh & Dryer) are comparable in their coverage (see MetaboExtract), MTBE may be seen as a non-toxic alternative that also provides less technical variance (Calderón et al, 2019;Gegner et al, 2022).…”

Spatially Resolved Metabolomics Method for Mapping the Global Molecular Landscape of Whole-Body Zebrafish (Danio rerio) Using Ambient Mass Spectrometry Imaging

“…6−9 In recent years, research on the combination of the zebrafish model and metabolomics has increased. 10,11 However, the majority focused on lipid metabolites due to the significant connection between lipid metabolism and disease processes. And for adult zebrafish, only individually dissected tissues were analyzed.…”

“…In view of the promising application of zebrafish models, it is essential to understand their molecular information systematically. The genome reference sequence of zebrafish has been acquired, providing a high-quality sequence assembly generated following methods used for human and mouse genome sequencing. , Several studies have also mapped the global proteomic profiles of zebrafish adults and embryos, providing an important proteome resource for understanding developmental processes and recapitulating disease mechanisms. − In recent years, research on the combination of the zebrafish model and metabolomics has increased. , However, the majority focused on lipid metabolites due to the significant connection between lipid metabolism and disease processes. And for adult zebrafish, only individually dissected tissues were analyzed. − In addition, metabolomics applying platforms such as liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) do not provide in situ distribution information of metabolites.…”

Chromatomass-Spectrometric Method for the Quantitative Determination of Amino- and Carboxylic Acids in Biological Samples

“…It should be noted that the data on technical repetitions demonstrate an excellent result in terms of the accuracy of the presented analysis, where most of the measurements (up to 90%) had a variability below 5%. According to Gegner et al, CV ranges from 0 to 10% are considered excellent, 11–20% are considered good, 21–30% are considered acceptable, and >30% are not acceptable [ 49 ]. Other authors, regarding data on the accuracy of measurements of amino acids in biological samples, mentioned that the precision of similar measurements was in the range of 15% [ 50 ].…”