Biology subjects

Dorfer, V.

Publications and source records attributed to Dorfer, V..

8 recordsLinked to original sources

bioRxiv2026

Making the Impossible Possible: Uniting TMT and DIA for Scalable Low-Input-Proteomics

Isobaric multiplexing using tandem mass tags (TMT) and data-independent acquisition (DIA) are two powerful techniques in quantitative proteomics, each offering distinct advantages. Combining these strategies provides synergistic benefits for low-input applications such as single-cell proteomics (SCP). However, integrating these strategies has remained challenging due to co-isolation interference and increased data complexity. Here, we present a systematic, identification-linked evaluation of a hybrid DIA-TMTpro 18-plex workflow on the Orbitrap Astral mass spectrometer (MS). Using a two-proteome interference model of human and yeast digests, we benchmarked quantitative accuracy and precision of TMT based DDA and DIA acquisition strategies across a range of isolation windows, injection times and input amounts. We provide READ (Reporter Ion Extractor and Annotation Director), an in-house developed Python-based tool which enables streamlined reporter ion (RI) quantification and co-isolation purity assessment. Using READ, TMT-based quantification becomes possible from DIA-focused search engines like Spectronaut or DIA-NN, which do not natively support TMT. We show that co-isolation interference drives both ratio compression and ratio inflation in TMT RI-based quantification regardless of acquisition mode. As an interference-resistant alternative, we further evaluated quantification via TMT complementary ions, which achieved near-label free quantitative accuracy at the cost of reduced plexing capacity and proteome depth. Together, these results establish practical guidelines for combining DIA and TMTpro on the Orbitrap Astral mass spectrometer and provide a foundation for extending DIA-TMT workflows to low-input applications such as SCP, where sensitivity, accuracy and throughput are critical.

biochemistry↗

bioRxiv2026

A DIA-based quantitative crosslinking mass spectrometryframework for dynamic structural proteomics

Proteins undergo dynamic conformational rearrangements and interactions that are central to their biological functions. Quantitative crosslinking mass spectrometry enables the analysis of those dynamics and molecular interactions, but rigorous confidence assessment and empirical validation strategies for quantitative measurements remain underdeveloped, and integrated analysis of complementary structural features, including monolinks and protein-RNA adducts, remains limited. Here we present a data-independent acquisition (DIA)-based framework for quantitative crosslinking mass spectrometry (DIA-QCLMS) that combines optimized acquisition strategies, crosslink-aware spectral libraries and empirical false-discovery-rate (FDR) validation. The workflow supports crosslinks, monolinks and protein-RNA adducts and integrates spectral-library generation from two crosslinking search engines (xiSEARCH and MS Annika). To enable robust confidence assessment in DIA data, we developed a four-state target-decoy spectral library strategy that explicitly models target-target, target-decoy, decoy-target and decoy-decoy crosslink spectra. Experimental entrapment datasets enabled empirical validation of confidence estimation, whereas benchmarking with PhoX-crosslinked Cas9 demonstrated improved quantitative completeness and reproducibility compared with data-dependent acquisition. Application of the workflow to the ATP-dependent RNA helicase UAP56 (DDX39B) resolved ligand-dependent changes in intramolecular restraints, residue accessibility and candidate RNA-contact sites associated with the transition from an open to a clamped conformation. These results establish DIA-QCLMS as a scalable framework for quantitative structural proteomics and provide practical strategies for confidence-controlled analysis of dynamic protein interactions and conformational states.

biochemistry↗

bioRxiv2026

Beyond Delta Masses: MS Andrea Directly Resolves Combinatorial Peptide Modifications in Open Searches

Open modification search (OMS) strategies have gained popularity in mass spectrometry-based proteomics for identification of peptides carrying unknown or unexpected post-translational modifications. However, most OMS engines report only the overall mass difference between precursor and matched peptide, without explicitly identifying or scoring combinations of modifications at the PSM level. Here, we introduce MS Andrea, a novel OMS search engine that directly identifies and scores combinations of modifications without predefining them. MS Andrea uses a sequence tag-based strategy to filter candidate peptides, evaluated using the MS Amanda scoring function. First fixed modifications only, then combinations of modifications from the Unimod database based on the observed mass shift. We evaluated MS Andrea using a human histone dataset and two phosphopeptide datasets (HeLa cells and Arabidopsis thaliana), comparing its performance with MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1 % FDR using the standard target-decoy approach while achieving higher or comparable numbers using model-based FDR estimation. Importantly, MS Andrea reports modification identities and sites for up to four modifications at the PSM level. Together, these results demonstrate that MS Andrea enables more detailed, interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomics.

molecular biology↗

bioRxiv2025

Unified down-stream analysis of crosslinking mass spectrometry results with pyXLMS

Crosslinking mass spectrometry has become the method of choice for the identification of protein-protein interactions and for gaining insight into the structures of proteins in vivo. However, connecting crosslink search engine results with down-stream analysis tools, and therefore gaining biological insight from crosslink identifications, has remained a manual and cumbersome step in the analysis that often requires expert bioinformatics knowledge. Here we introduce pyXLMS, a python package and public web application which aims to simplify and streamline this intermediate step, enabling researchers even without bioinformatics knowledge to conduct in-depth crosslink analyses. In its current state pyXLMS supports input from seven different crosslink search engines, as well as the mzI-dentML format of the HUPO Proteomics Standards Initiative. Down-stream analysis is facilitated by functionality that is directly available within pyXLMS such as aggregation, validation, annotation, filtering, and visualization. In addition, the data can easily be exported to more than ten supported down-stream analysis tools and formats. We demonstrate the applicability and benefits of pyXLMS by re-analyzing a publicly available crosslink dataset with a variety of different search engines and show how the same data analysis workflow can be applied using pyXLMS. pyXLMs is available via https://github.com/hgb-bin-proteomics/pyXLMS.

bioinformatics↗

bioRxiv2025

ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

Mass spectrometry (MS)-based proteomics is a well-established strategy for analyzing complex biological mixtures. Many MS instruments and data acquisition strategies are available, and the data they acquire differ substantially, thus requiring tailored analysis algorithms. Hence, many dedicated bioinformatics workflows are developed. These are in constant evolution, and the community lacks a centralized platform for comparing their performance. Here, we propose ProteoBench, a single platform that brings together software developers and software users to provide an ever-evolving comparison of state-of-the-art proteomics data processing tools. ProteoBench is an open-source resource that enables the community to evaluate data analysis workflows, develop benchmarking modules dedicated to specific comparisons, and discuss the best methods to compare software tools. The platform ensures that the benchmark evolves alongside advances in proteomics data analysis workflows. ProteoBench guides researchers towards the best-suited tool and parameters for their specific project and data according to their needs, and developers can test their newly developed tools or workflows privately, before adding them as public references. This community-driven effort will increase transparency and reproducibility between MS data analysis workflows, as well as facilitate the development and publication of software workflows in the field.

bioinformatics↗

bioRxiv2024

A journey towards developing a new cleavable crosslinker reagent for in-cell crosslinking

Crosslinking mass spectrometry (XL-MS) is a powerful technology that recently emerged as an essential complementary tool for elucidating protein structures and mapping interactions within a protein network. Crosslinkers which are amenable to post-linking backbone cleavage simplify peptide identification, aid in 3D structure determination and enable system-wide studies of protein-protein interactions (PPIs) in cellular environments. However, state-of-the-art cleavable linkers are fraught with practical limitations, including extensive evaluation of fragmentation energies and fragmentation behaviour of the crosslinker backbone. We herein introduce DiSPASO as a lysine-selective, MS-cleavable cross-linker with an alkyne handle for affinity enrichment. DiSPASO was designed and developed for efficient cell membrane permeability and crosslinking while securing low cellular perturbation. We tested DiSPASO employing three different copper-based enrichment strategies using model systems with increasing complexity (Cas9-Halo, purified ribosomes, live cells). Fluorescence microscopy in-cell crosslinking experiments revealed a rapid uptake of DiSPASO into HEK 293 cells within 5 minutes. While DiSPASO represents progress in cellular PPI analysis, its limitations and low crosslinking yield in cellular environments require careful optimisation of the crosslinker design, highlighting the complexity of developing effective XL-MS tools and the importance of continuous innovation in accurately mapping PPI networks within dynamic cellular environments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/621843v2_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1821ed4org.highwire.dtl.DTLVardef@1b6180dorg.highwire.dtl.DTLVardef@1e50aaborg.highwire.dtl.DTLVardef@1f6185_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex

AbstractThe field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein-complexes and serving as a powerful tool in protein-protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n-squared complexity. We here introduce a novel algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. Application of this new algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex, enhancing our understanding of its assembly and functional dynamics. Our findings provide valuable insights into the intricate regulation of cellular homeostasis and immune responses, which are conserved across species, including humans. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible.

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↗