Biology subjects

Kockmann, T.

Publications and source records attributed to Kockmann, T..

6 recordsLinked to original sources

bioRxiv2024

Characterization of the Glutathione Redox State in the Golgi Apparatus

Redox homeostasis is crucial for cell function, and, in eukaryotic cells, studying it in a compartmentalized way is essential due to the redox variations between different organelles. The redox state of organelles is largely determined by the redox potential of glutathione, EGSH, and the concentration of its reduced and oxidized species, [GS]. The Golgi apparatus is an essential component of the secretory pathway, yet little is known about the concentration or redox state of GSH in this organelle. Here, we characterized the redox state of GSH in the Golgi apparatus using a combination of microscopy and proteomics methods. Our results prove that the Golgi apparatus is a highly oxidizing organelle with a strikingly low GSH concentration (EGSH = - 157 mV, 1-5 mM). These results fill an important gap in our knowledge of redox homeostasis in subcellular organelles. Moreover, the new Golgi-targeted GSH sensors allow us to observe dynamic changes in the GSH redox state in the organelle and pave the way for robust characterization of the Golgi redox state under various physiological and pathological conditions.

cell biology↗

bioRxiv2024

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

bioinformatics↗

bioRxiv2021

Molecular roadmap of the healthy stress response in the mouse hippocampus

The acute stress response mobilizes energy to meet situational demands and re-establish homeostasis. However, little is known about the nature and dynamics of the underlying molecular cascades. We used a brief forced swim exposure to trigger a strong stress response in mice, which transiently increases anxiety, but does not lead to lasting maladaptive behavioral changes. Using multiomic profiling we characterize the stress-induced molecular events in the hippocampus over time, across molecular scales, and down to the level of cell types and single cells. Our results unveil the complexity and specificity of the healthy stress response, with widespread changes in protein phosphorylation and gene transcription, but tightly regulated protein translation. All observed molecular events resolve efficiently within 4 hours after initiation of stress. The data are accessible through an interactive web portal, creating an extensive molecular resource that will help understand how stress impacts brain function in health and disease.

neuroscience↗

bioRxiv2020

rawR - Direct access to raw mass spectrometry data in R

The Bioconductor project has shown that the R statistical environment is a highly valuable tool for genomics data analysis1, but with respect to proteomics we are still missing low level infrastructure to enable performant and robust analysis workflows in R. Fundamentally important are libraries that provide raw data access. Our R package rawDiag has provided the proof-of-principle how access to mass spectromerty raw files can be realized by wrapping vendor-provided APIs, but rather focused on meta data analysis and visualization2. Our novel package rawR now provides complete, OS independent access to all spectral data logged in Thermo Fisher Scientific raw files. In this technical note we present implementation details and describe the main functionality provided by the rawR package. In addition, we report two use cases inspired by real-word research task that demonstrate the application of the package. Availabilityhttps://github.com/fgcz/rawR

bioinformatics↗

bioRxiv2020

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

Here we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can either be provided manually by the user (table format), or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other websites (https://github.com/kusterlab/universal_spectrum_explorer).

bioinformatics↗

bioRxiv2018

rawDiag - an R package supporting rational LC-MS method optimization for bottom-up proteomics

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a non-trivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan level metadata. rawDiag is implemented as R package and can be executed on the command line, or through a graphical user interface (GUI) for less experienced users. The code runs platform independent and can process a hundred raw files in less than three minutes on current consumer hardware as we show by our benchmark. In order to demonstrate the functionality of our package, we included a real-world example taken from our daily core facility business.

bioinformatics↗