Biology subjects

Andresen, C.

Publications and source records attributed to Andresen, C..

3 recordsLinked to original sources

bioRxiv2021

Deep metabolic profiling assessment of tissue extraction protocols for three model organisms

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimers, Parkinsons 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.

biochemistry↗

bioRxiv2021

Comparison of extraction methods for intracellular metabolomics

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).

biochemistry↗

bioRxiv2017

Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers

Metazoans are crucially dependent on multiple layers of gene regulatory mechanisms which allow them to control gene expression across developmental stages, tissues and cell types. Multiple recent research consortia have aimed to generate comprehensive datasets to profile the activity of these cell type- and condition-specific regulatory landscapes across many different cell lines and primary cells. However, extraction of genes or regulatory elements specific to certain entities from these datasets remains challenging. We here propose a novel method based on non-negative matrix factorization for disentangling and associating huge multi-assay datasets including chromatin accessibility and gene expression data. Taking advantage of implementations of NMF algorithms in the GPU CUDA environment full datasets composed of tens of thousands of genes as well as hundreds of samples can be processed without the need for prior feature selection to reduce the input size. Applying this framework to multiple layers of genomic data derived from human blood cells we unravel mechanisms of regulation of cell type-specific expression in T-cells and monocytes.

genomics↗