Measurements of metabolic compounds inside cells or tissues are of high informative potential since they represent the endpoint of biological information flow and a snapshot of the integration of many regulatory processes. However, it requires careful extraction to quantify their abundance. Here we present a comprehensive study using ten extraction protocols on four human sample types (liver tissue, bone marrow, HL60 and HEK cells) targeting 630 metabolites of different chemical classes. We show that the extraction efficiency and stability is highly variable across protocols and tissues by using different quality metrics including the limit of detection and variability between replicates as well as the sum of concentration as a global estimate of extraction stability. The profile of extracted metabolites depends on the used solvents - an observation which has implications for measurements of different sample types and metabolic compounds of interest. To identify the optimal extraction method for future metabolomics studies, the benchmark dataset was implemented in an easy-to-use, interactive and flexible online resource (R/shiny app MetaboExtract).

Background: Amino acids have a central role in cell metabolism, and intracellular changes contribute to the pathogenesis of various diseases, while the role and specific organ distribution of dipeptides is largely unknown. Method: We established a sensitive, rapid and reliable UPLC-MS/MS method for quantification of 36 dipeptides. Dipeptide patterns were analyzed in brown and white adipose tissues, brain, eye, heart, kidney, liver, lung, muscle, sciatic nerve, pancreas, spleen and thymus, serum and urine of C57BL/6N wildtype mice and related to the corresponding amino acid profiles. Results: A total of 30 out of the 36 investigated dipeptides were detected with organ-specific distribution patterns. Carnosine and anserine were most abundant in all organs, with the highest concentrations in muscles. In liver, Asp-Gln and Ala-Gln concentrations were high, in the spleen and thymus, Glu-Ser and Gly-Asp. In serum, dipeptide concentrations were several magnitudes lower than in organ tissues. In all organs, dipeptides with C-terminal proline (Gly-Pro and Leu-Pro) were present at higher concentrations than dipeptides with N-terminal proline (Pro-Gly and Pro-Leu). Organ-specific amino acid profiles were related to the dipeptide profile with several amino acid concentrations being related to the isomeric form of the dipeptides. Aspartate, histidine, proline and serine tissue concentrations correlated with dipeptide concentrations, when the amino acids were present at the C- but not at the N-terminus. Conclusion: Our multi-dipeptide quantification approach demonstrates organ-specific dipeptide distribution. This method allows us to understand more about the dipeptide metabolism in disease or in healthy state.

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.

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer's, Parkinson's disease or COVID-19. Deciphering these intricate pathways in human studies requires large sample sizes as a means of reducing variability. While such broad human studies have discovered new associations between a given disease and certain affected metabolites, i.e. biomarkers, they often provide limited functional insights. To design more standardized experiments, reduce variability in the measurements and better resolve the functional component of such dynamic metabolic profiles, model organisms are frequently used. Standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study. However, further aspects such as the choice of extraction protocol and analytical technique can influence the outcome drastically. Here, we employed a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse and Zebrafish) to find the optimal extraction protocols for various matrices. Focusing on parameters such as metabolite coverage, metabolite yield 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 yields, 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. This will enable scientists to search for their metabolite or metabolite class of interest, compare it across the different tested extraction protocols and sample types as well as find reference concentrations.

Analyses of metabolic compounds inside cells or tissues provide high information content since they represent the endpoint of biological information flow and are a snapshot of the integration of many regulatory processes. However, quantification of the abundance of metabolites requires their careful extraction. We present a comprehensive study comparing ten extraction protocols in four human sample types (liver tissue, bone marrow, HL60, and HEK cells) aiming to detect and quantify up to 630 metabolites of different chemical classes. We show that the extraction efficiency and repeatability are highly variable across protocols, tissues, and chemical classes of metabolites. We used different quality metrics including the limit of detection and variability between replicates as well as the sum of concentrations as a global estimate of analytical repeatability of the extraction. The coverage of extracted metabolites depends on the used solvents, which has implications for the design of measurements of different sample types and metabolic compounds of interest. The benchmark dataset can be explored in an easy-to-use, interactive, and flexible online resource (R/shiny app MetaboExtract: http://www.metaboextract.shiny.dkfz.de) for context-specific selection of the optimal extraction method. Furthermore, data processing and conversion functionality underlying the shiny app are accessible as an R package: https://cran.r-project.org/package=MetAlyzer.

Availability of oxygen plays an important role in tissue organization and cell-type specific metabolism. It is, however, difficult to analyze hypoxia-related adaptations in vitro because of inherent limitations of experimental model systems. In this study, we establish a microfluidic tissue culture protocol to generate hypoxic gradients in vitro, mimicking the conditions found in the liver acinus. To accomplish this, four microfluidic chips, each containing two chambers, were serially connected to obtain eight interconnected chambers. HepG2 hepatocytes were uniformly seeded in each chamber and cultivated under a constant media flow of 50 µL/h for 72 h. HepG2 oxygen consumption under flowing media conditions established a normoxia to hypoxia gradient within the chambers, which was confirmed by oxygen sensors located at the inlet and outlet of the connected microfluidic chips. Expression of Hif1α mRNA and protein was used to indicate hypoxic conditions in the cells and albumin mRNA and protein expression served as a marker for liver acinus-like zonation. Oxygen measurements performed over 72 h showed a change from 17.5% to 15.9% of atmospheric oxygen, which corresponded with a 9.2% oxygen reduction in the medium between chamber1 (inlet) and 8 (outlet) in the connected microfluidic chips after 72 h. Analysis of Hif1α expression and nuclear translocation in HepG2 cells additionally confirmed the hypoxic gradient from chamber1 to chamber8. Moreover, albumin mRNA and protein levels were significantly reduced from chamber1 to chamber8, indicating liver acinus zonation along the oxygen gradient. Taken together, microfluidic cultivation in interconnected chambers provides a new model for analyzing cells in a normoxic to hypoxic gradient in vitro. By using a well-characterized cancer cell line as a homogenous hepatocyte population, we also demonstrate that an approximate 10% reduction in oxygen triggers translocation of Hif1α to the nucleus and reduces albumin production.