Biology subjects

Schessner, J. P.

Publications and source records attributed to Schessner, J. P..

4 recordsLinked to original sources

bioRxiv2025

Dynamic Organellar Mapping in yeast reveals extensive protein localization changes during ER stress

Sophisticated techniques are available for systematic studies of yeast cell biology. However, it remains challenging to investigate protein subcellular localization changes on a proteome-wide scale. Here, we apply Dynamic Organellar Maps (DOMs) by label-free mass spectrometry to detect localization changes of native, untagged proteins during endoplasmic reticulum (ER) stress. We find that hundreds of proteins shift between cellular compartments. For example, we show that numerous secretory pathway proteins accumulate in the ER, thus defining the extent and selectivity of ER retention of misfolded proteins. Furthermore, we identify candidate cargo proteins of the ER reflux pathway, determine constituents of reticulon clusters that segregate from the remainder of the ER and provide evidence for altered nuclear pore complex composition and nuclear import. These findings uncover protein relocalization as a major aspect of cellular reorganization during ER stress and establish DOMs as a powerful discovery tool in yeast.

cell biology↗

bioRxiv2023

Accurate label-free quantification by directLFQ to compare unlimited numbers of proteomes

Recent advances in mass spectrometry (MS)-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Whereas peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratiobased approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 minutes and 100,000 proteomes in less than two hours - thousand-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is available as an open-source Python package and as a GUI with a one-click installer and can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

bioinformatics↗

bioRxiv2023

Perforin-2 is a pore-forming effector of endocytic escape in cross- presenting dendritic cells

During initiation of antiviral and antitumour T cell-mediated immune responses, dendritic cells (DCs) cross-present exogenous antigens on MHC class I. Cross-presentation relies on the unique leakiness of endocytic compartments in DCs, whereby internalised proteins escape into the cytosol for proteasome-mediated generation of MHC I-binding peptides. Given that type 1 conventional DCs excel at cross-presentation, we searched for cell-type specific effectors of endocytic escape. We devised an escape assay suitable for genetic screening and identified a pore-forming protein, perforin-2, as a dedicated effector exclusive to cross-presenting cells. Perforin-2 is recruited to antigen-containing compartments, where it undergoes maturation, releasing its pore-forming domain. Mpeg1-/- mice fail to efficiently prime CD8+ T cells to cell-associated antigens, revealing an important role of perforin-2 in cytosolic entry of antigens during cross-presentation. One-Sentence SummaryPore-forming protein perforin-2 is a dedicated effector of endocytic escape specific to cross-presenting cells

cell biology↗

bioRxiv2021

Data independent acquisition enables deep and fast label-free dynamic organellar mapping

The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. We leveraged this gain to establish flexible map formats scaling from rapid analyses to ultra-deep coverage. Our fastest format takes only [~]2.5h/map and enables high-throughput experimental designs. Furthermore, we introduce DOM-QC, an open-source software tool for in-depth standardized analysis of DOMs and other profiling data. We then applied DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which revealed a subset of Golgi proteins that cycle through endosomes. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.

biochemistry↗