Biology subjects

Pietz, T.

Publications and source records attributed to Pietz, T..

2 recordsLinked to original sources

bioRxiv2026

iSBEM: An Open-Source Workflow for Automated ROI Targeting in Volume Electron Microscopy

Serial Block Face - Scanning Electron Microscopy (SBF-SEM) is a volume EM method suited to investigate the 3D architecture of tissues and even entire organisms at high resolution. However, imaging large volumes in their entirety is time-consuming and not always necessary. Many research projects have a focused interest in well-defined sub-regions of the samples. The targeting and acquisition of such regions of interest (ROIs) are however currently conducted in a manual way and require heavy involvement of experienced operators. We present a workflow and an original open-source software tool (iSBEM), which allow automated targeting of ROIs in a large tissue sample, based on X-ray microscopy (XRM) maps. After an initial ROI identification and registration of the XRM map with the sample mounted on the SBF-SEM stage, iSBEM takes over the control of the microscope, triggering high resolution acquisitions at defined ROI positions, with minimal user intervention. We demonstrate the approach on two biologically distinct specimens -- malarial oocysts in infected mosquito midgut tissue, and immune cells in human kidney biopsies -- achieving significant improvement in acquisition throughput relative to manual operations, without compromising targeting precision. We also showcase the workflow in a correlative light-Xray-electron microscopy setup, which allowed us to further improve the correct target definition.

cell biology↗

bioRxiv2024

PEPerMINT: Peptide Abundance Imputation in Mass Spectrometry-based Proteomics using Graph Neural Networks

MotivationAccurate quantitative information about the protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry protocols. Here, proteins are digested into peptides before quantification via mass spectrometry. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. ResultsTo impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against eleven common imputation methods on six diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the users needs based on the reliability of imputed values. Availability and implementationThe code is available at https://github.com/DILiS-lab/pepermint.

bioinformatics↗