Biology subjects

Mechtel, N.

Publications and source records attributed to Mechtel, N..

4 recordsLinked to original sources

bioRxiv2026

BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces

Foundation models and curated repositories have transformed bioimage AI, yet most biologists cannot readily run, adapt, or extend them on available hardware. BioEngine lls this gap as the execution and adaptation layer between curated AI and scalable compute, deployable on a laptop, workstation, or cluster. Scientists then screen models, ne-tune from the browser, enable real-time smart microscopy, and deploy analysis applications, all by describing their goal to an AI agent.

bioinformatics↗

bioRxiv2025

MetAlyzer to Perform Streamlined, Interactive, and Pathway-Mapping Analysis of Targeted Metabolomics Data from the biocrates Platform

Mass spectrometry (MS)-based metabolomics has emerged as a powerful tool to address multifaceted biological questions. Commercial solutions like the ones developed at biocrates allow reliable and quantitative targeted metabolic profiling, including the conversion of the raw MS spectra into absolute concentrations of metabolites. These results can be exported for further analysis under several formats with varying levels of human-vs. machine-readability. The default output format is an Excel spreadsheet that favours human readability and therefore requires extra preparation steps for downstream bioinformatic analysis and data exploration. To streamline this next step for users of this platform, we developed MetAlyzer, an R package (https://github.com/Lu-Group-UKHD/MetAlyzer) specifically designed to handle the spreadsheets generated by WebIDQ, the biocrates workflow manager software. MetAlyzer converts WebIDQ-generated spreadsheets into flexible SummarizedExperiment objects and provides functions for data preprocessing, statistical testing, and visualization of differential metabolites. To further support data exploration and hypothesis generation by users without coding experience, we also developed an interactive and intuitive Shiny app (https://metalyzer.shinyapps.io/MetAlyzer_ShinyApp/) that interfaces with MetAlyzers core functionality, enabling users to execute the complete analysis workflow without writing code. This combination can help scientists deepen their understanding of metabolomics results, supporting the broader adoption of metabolomics in the life sciences community.

bioinformatics↗

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↗