Image4_Deep Metabolic Profiling Assessment of Tissue Extraction Protocols for Three Model Organisms.TIF (513.24 kB)
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Image4_Deep Metabolic Profiling Assessment of Tissue Extraction Protocols for Three Model Organisms.TIF

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posted on 25.04.2022, 06:20 authored by Hagen M. Gegner, Nils Mechtel, Elena Heidenreich, Angela Wirth, Fabiola Garcia Cortizo, Katrin Bennewitz, Thomas Fleming, Carolin Andresen, Marc Freichel, Aurelio A. Teleman, Jens Kroll, Rüdiger Hell, Gernot Poschet

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.