Biology subjects

Dhaenens, M.

Publications and source records attributed to Dhaenens, M..

15 recordsLinked to original sources

bioRxiv2026

A generalizable normalization framework to decouple protocol and instrument effects: Application to high-sensitivity proteomics multicentric study (PME13)

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

bioinformatics↗

bioRxiv2026

Palaeoproteomic deconvolution of physical and genetic collagen mixtures

Species identification in palaeoproteomics relies on genome-derived protein sequences which are often poor-quality, and lacks tools to cope with multi-species samples. Here, we address both challenges through the analysis of physical and genetic mixtures. Species that are absent from our database are considered a genetic mixture, i.e. a patchwork of peptides from closely related species. Inversely, various overlapping peptide stretches allow us to resolve complex physical mixtures. This is benchmarked by analysing physical mixtures of modern bone fragments, including genetic mixtures. We illustrate the impact of our approach via a rapid and high-throughput analysis of >2500 bone fragments, revealing the Eemian-era faunal environment around Scladina Cave, including the first Palaeoloxodon antiquus identified at this site. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/732552v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@a33296org.highwire.dtl.DTLVardef@4e4b85org.highwire.dtl.DTLVardef@402290org.highwire.dtl.DTLVardef@9d35b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Focus on the edges: a biomolecular network of histone PTMs, metabolites and proteins unveils functional entanglement in AML

The cell phenotype is not a direct manifestation of the genotype but rather a product of cellular history and the environmental context. However, individual biomolecules cannot change independently and show coordinated behavior. To study this in acute myeloid leukemia (AML), we built a unique multi-omics biomolecular network made from proteins, metabolites and histone posttranslational modifications (hPTMs) sequentially extracted from each cell pellet. Edges between the nodes are measured directly using 400 LC-MSMS runs that cover 18 AML cell lines. We provide a novel conceptual framework to illustrate the different classes of functional entanglement between and within omics layers and present the data in three interactive data browsers to allow full community access. To help navigate the network, we approach it from the perspective of two biomolecular targets, i.e. CD34 and the epigenetic mark Histone H3 lysine 27 trimethylation (H3K27me3). Now, this easily accessible biomolecular network serves as a starting point for building and testing hypotheses and streamlining drug development, in the process positioning biomolecular associations center stage in understanding phenotypic complexity.

cancer biology↗

bioRxiv2026

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Recent advances in liquid chromatography-mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics.

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↗

bioRxiv2025

Palaeoproteomics reveals bovine carcass processing with 6,000-year-old flint tools

Lithic tools are the most abundant cultural artefacts found at prehistoric archaeological sites. Through their study, we can understand essential activities such as hunting, processing of animal carcasses and plant materials, and working of resources such as ochre and bone, as well as broader technological and cultural practices. For the first time, proteomics was applied alongside use-wear and optical microscopy residue analyses, enabling the identification of bovine, plant, and human proteins on faceted tools from the Mesolithic-Neolithic site of Bazel-Sluis, Belgium. This retrieval of identifiable protein from an area with poor organic preservation demonstrates wide-reaching potential for archaeological research, opening a new avenue of investigation into prehistoric lifeways and adding to the growing corpus of evidence accessible through palaeoproteomics. A workflow for future analyses is suggested, based on our integrated sampling strategy requiring no additional tool manipulations.

paleontology↗

bioRxiv2025

Profiling histone post-translational modifications to identify signatures of epigenetic drug response in T-cell acute lymphoblastic leukemia

Epigenetic modifications are dynamic and reversible, making them attractive targets for therapeutic intervention in cancer. Although several epigenetic drugs (epidrugs) have been clinically approved, their application in T-cell acute lymphoblastic leukemia (T-ALL) remains limited, and predictive biomarkers of response are lacking. Here, we present a mass spectrometry (MS)-based pharmacoepigenetic approach to profile histone post-translational modifications (hPTMs) to identify signatures associated with epidrug sensitivity in T-ALL. Baseline hPTM landscapes were previously established by our group for 21 T-ALL cell lines using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Here, we treated these cell lines with a panel of nine epidrugs including anthracyclines, histone deacetylase inhibitors, and DNA methyltransferase inhibitors. Correlation of cell viability data with hPTM levels revealed distinct hPTM signatures linked to sensitivity for each drug class. These signatures were subsequently evaluated in T-ALL patient-derived xenograft (PDX) models. However, we our analysis revealed substantial discepancies in hPTM sensitivity signatures compared to those observed in vitro. Co-variation network analysis highlighted divergence in hPTM-hPTM correlation between the two models, underscoring limitations of cell lines for modeling dynamic epigenetic regulation in vivo. Our findings establish a framework for MS-based hPTM profiling in T-ALL and emphasize the importance of model selection in developing predictive epigenetic biomarkers. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/673463v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1b15092org.highwire.dtl.DTLVardef@20bed5org.highwire.dtl.DTLVardef@1d0a687org.highwire.dtl.DTLVardef@1652999_HPS_FORMAT_FIGEXP M_FIG C_FIG Global hPTM profiling of 21 T-ALL cell lines was performed using LC-MS/MS, as previously published by Provez et al. In parallel, the 21 cell lines were treated with a dilution series of nine epidrugs, categorized into three distinct classes, to determine their IC50 values. Finally, Spearman correlation analysis was performed to assess the relationship between hPTM levels and drug sensitivity. Figure created with Biorender.com.

cancer biology↗

bioRxiv2025

Development of a General Purpose Targeted LC-MS Method for Accurate Quantification of the SARS-CoV-2 Spike Protein Expression

The COVID-19 pandemic has catalyzed interest in immuno-multiple reaction monitoring (immuno-MRM) methods, with the detection of peptides unique to the nucleocapsid protein in nasopharyngeal swabs. While current applications predominantly focus on disease biomarkers, the pandemic has unveiled new opportunities, namely for the quantification of antigen expression following mRNA vaccination. Here, we present an optimized immuno-MRM method for quantifying SARS-CoV-2 spike protein fusion peptide, SFIEDLLFNK, for several practical applications. The method is versatile, applicable to multiple biological matrices, including plasma, and can be extended to nasopharyngeal swabs. It also offers a high-precision tool for assessing protein expression following plasmid and mRNA transfection. Moreover, in parallel to enabling accurate antigen quantification, the flow-through can be used to determine the proteome profile of the infected cells, providing insights into the intracellular immune response. This dual capability supports the rapid optimization of mRNA vaccines, thereby driving advancements in vaccine development strategies.

immunology↗

bioRxiv2024

ClassiCOL: LC-MS/MS analysis for ancient species Classification via Collagen peptide ambiguation

LC-MS/MS extends on the MALDI-TOF ZooMS approach by providing fragmentation spectra for each peptide. However, ancient bone samples generate sparse datasets containing only a few collagen proteins, rendering target-decoy strategies unusable and increasing uncertainty in peptide annotation. ClassiCOL embraces and even extends this ambiguity using a novel isoBLAST approach. The exhaustive set of potential peptide candidates created in this way is then used to retain or reject different potential paths at each taxonomic branching point down to the taxonomic level attainable with the sample information, always allowing for potential mixtures in the process. As an end point, all considered ambiguity is graphically represented with a clear prioritization of the species in the sample. Using public as well as in-house data, we demonstrate the performance of this universal postprocessing approach on different instruments and explore the possibility of identifying genetic as well as sample mixtures. Diet reconstruction from 40,000 year old cave hyena coprolites illustrates the exciting potential of this approach. TeaserClassiCOL is a postprocessing tool that allows for more accurate species classification from LC-MS/MS measurements of collagen.

bioinformatics↗

bioRxiv2024

Mass Spectrometry-based Profiling of Single-cell Histone Post-translational Modifications to Dissect Chromatin Heterogeneity

Single-cell proteomics confidently quantifies cellular heterogeneity, yet precise quantification of post-translational modifications, such as those deposited on histone proteins, has remained elusive. Here, we developed a robust mass spectrometry-based method for the unbiased analysis of single-cell histone post-translational modifications (schPTM). schPTM identifies both single and combinatorial histone post-translational modifications (68 peptidoforms in total), which includes nearly all frequently studied histone post-translational modifications with comparable reproducibility to traditional bulk experiments. As a proof of concept, we treated cells with sodium butyrate, a histone deacetylase inhibitor, and demonstrated that our method can i) distinguish between treated and non-treated cells, ii) identify sub-populations of cells with heterogeneous response to the treatment, and iii) reveal differential co-regulation of histone post-translational modifications in the context of drug treatment. The schPTM method enables comprehensive investigation of chromatin heterogeneity at single-cell resolution and provides further understanding of the histone code.

molecular biology↗

bioRxiv2022

An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia

The holistic nature of omics studies makes them ideally suited to generate hypotheses on health and disease. Sequencing-based genomics and mass spectrometry (MS)-based proteomics are linked through epigenetic regulation mechanisms. However, epigenomics is currently mainly focused on DNA methylation status using sequencing technologies, while studying histone posttranslational modifications (hPTMs) using MS is lagging, partly because reuse of raw data is impractical. Yet, targeting hPTMs using epidrugs is an established promising research avenue in cancer treatment. Therefore, we here present the most comprehensive MS-based preprocessed hPTM atlas to date, including 21 T-cell acute lymphoblastic leukemia (T-ALL) cell lines. We present the data in an intuitive and browsable single licensed Progenesis QIP project and provide all essential quality metrics, allowing users to assess the quality of the data, edit individual peptides, try novel annotation algorithms and export both peptide and protein data for downstream analyses, exemplified by the PeptidoformViz tool. This data resource sets the stage for generalizing MS-based histone analysis and provides the first reusable histone dataset for epidrug development.

cancer biology↗

bioRxiv2021

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

bioinformatics↗

bioRxiv2021

Histone sample preparation for bottom-up mass spectrometry: a roadmap to informed decisions

Histone-based chromatin organization enabled eukaryotic genome complexity. This epigenetic control mechanism allowed for the differentiation of stable gene-expression and thus the very existence of multicellular organisms. This existential role in biology makes histones one of the most complexly modified molecules in the biotic world, which makes these key regulators notoriously hard to analyze. We here provide a roadmap to enable fast and informed selection of a bottom-up mass spectrometry sample preparation protocol that matches a specific research question. We therefore propose a two-step assessment procedure: (i) visualization of the coverage that is attained for a given workflow and (ii) direct alignment-between-runs to assess potential pitfalls at the ion level. To illustrate the applicability, we compare four different sample preparation protocols, while adding a new enzyme to the toolbox, i.e., RgpB (GingisREX(R), Genovis), an endoproteinase that selectively and efficiently cleaves at the c-terminal end of arginine residues. Raw data is available via ProteomeXchange with identifier PXD024423.

molecular biology↗

bioRxiv2019

Analyzing data independent acquisition mass spectrometry with noiseless ion-networks

Data-independent acquisition (DIA) mass spectrometry (MS) has introduced deter-ministic, periodic and simultaneous acquisition of all fragment ions. Despite the chimeric side-effects associated with this unprecedented data integrity, DIA data analysis approaches still use conventional spectra and extracted ion chromatograms (XICs) that represent individual precursors and fragments. Here, we introduce ion-networks, an alternative data format wherein nodes correspond to reproducible fragment ions from multiple runs and edges correspond to consistent co-elution. Each ion-network represents a complete experiment and computationally eliminates chimericy based on reproducibility without sacrificing data integrity.

bioinformatics↗

bioRxiv2019

The future of peptide-centric Data-Independent Acquisition is predicted

Data-Independent Acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. We here show that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison. Significance of the StudyData-independent acquisition (DIA) is quickly developing into the most comprehensive strategy to analyse a sample on a mass spectrometer. Correspondingly, a wave of data analysis strategies has followed suit, improving the yield from DIA experiments with each iteration. As a result, a worldwide wave of investments in DIA is already taking place in anticipation of clinical applications. Yet, there is considerable confusion about the most useful and efficient way to handle DIA data, given the plethora of possible approaches with little regard for compatibility and complementarity. In our manuscript, we outline the currently available peptide-centric DIA data analysis strategies in a unified graphic called the DIAmond DIAgram. This leads us to an innovative and easily adoptable approach based on predicted spectral information. Most importantly, our contribution removes what is arguably the biggest bottleneck in the field: the current need for Data Dependent Acquisition (DDA) prior to DIA analysis. Fractionation, stochastic data acquisition, processing and identification all introduce bias in the library. By generating libraries through data independent, i.e. deterministic acquisition, stochastic sampling in the DIA workflow is now fully omitted. This is a crucial step towards increased standardization. Additionally, our results demonstrate that a proteome-wide predicted spectral library can surrogate an exhaustive DDA Pan-Human library that was built based on 331 prior DDA runs.

bioinformatics↗