Biology subjects

Argentini, A.

Publications and source records attributed to Argentini, A..

6 recordsLinked to original sources

bioRxiv2026

DiaReport: Reproducible Workflow for Differential Expression Analysis and Interactive Reporting in DIA-based Proteomics

SummaryData-independent acquisition (DIA) has become the preferred data acquisition method for mass spectrometry-based proteomics, yet, reproducible workflows for differential expression (DE) analysis and reporting results remain limited. We present DiaReport, an R package that performs precursor- and protein-level DE analysis from DIA-NN output using MSqRob and QFeatures, while generating high-quality, interactive HTML reports through Quarto. DiaReport integrates precursor data, filtering of missing values, normalization, protein summarization and statistical modeling within a single function, supporting both simple pairwise as well as complex experimental designs. The package provides structured outputs and configuration files to ensure computational reproducibility across different studies. To accommodate diverse research needs, DiaReport includes multiple reporting templates tailored to different proteomic applications. Applying DiaReport to an extracellular vesicle (EV) proteomics dataset demonstrates its ability to efficiently analyze DIA data and provide rapid insights into sample quality and protein level differences. Availability and ImplementationDiaReport is an open-source R package available at https://github.com/Gevaert-Lab/diareport. The package is platform-independent and distributed under the MIT license. Reports are generated using Quarto and require only standard R dependencies. Detailed documentation, installation guides and usage vignettes are provided within the repository. The interactive HTML reports discussed in this study, including the UPS2 benchmark and EV case study, are archived on Zenodo (https://doi.org/10.5281/zenodo.18632744 and https://doi.org/10.5281/zenodo.18632731). ContactCorresponding author: Kris Gevaert; kris.gevaert@vib-ugent.be.

bioinformatics↗

bioRxiv2025

Exhaled breath condensate proteomics using amphipols improves protein detection but reveals statistical challenges in respiratory disease biomarker discovery

BackgroundInvasive diagnostic techniques for pleural mesothelioma (PM) delay early detection, underscoring the need for non-invasive biomarkers. Exhaled breath condensate (EBC) is a promising matrix but has extremely low protein concentration. This study applies an amphipol (APol)-based concentration method to enhance detection of potential PM biomarkers in the EBC proteome. MethodsFour replicate EBC samples from a healthy individual were processed using APols or lyophilization. Subsequently, 145 EBC samples from PM patients (n=25), lung cancer patients (n=28), asymptomatic asbestos-exposed individuals (n=60) or healthy controls (n=32) were concentrated with APols and analysed by liquid chromatography coupled with tandem mass spectrometry. Technical variability from batch effects and collection devices was addressed using omicsGMF, for simultaneous imputation and covariates correction. Statistical analysis employed linear models with technical and clinical covariates. ResultsAPols outperformed lyophilization, yielding 1.7-fold higher proteome coverage and improved reproducibility. Although high data missingness was observed, proteome analysis across 145 samples identified 248 unique proteins. OmicsGMF corrected the substantial variability, while maintaining biological signal. Whereas unsupervised clustering did not reveal distinct disease-specific patterns, supervised statistical analysis detected overall subtle differences between clinical groups (PERMANOVA p-value = 0.008), though no individual proteins reached statistical significance after adjustment for technical and clinical covariates and correction for multiple testing. ConclusionAPols outperforms lyophilization for concentrating proteins in low-abundant EBC samples, enabling enhanced proteome coverage for biomarker discovery studies. However, the inherent high variability of EBC proteomes has a strong effect on detecting possible biomarkers across clinical groups. Key messageO_ST_ABSWhat is already known on this topicC_ST_ABSExhaled breath condensate (EBC) is an attractive non-invasive biofluid for respiratory disease biomarker discovery, but no protein biomarkers have reached clinical application despite extensive investigation. What this study addsUsing superior APols-based protein concentration and rigorous analytical and statistical methods in 145 EBC samples, we demonstrate that fundamental limitations - 96% peptide missingness, 80% skin-protein contamination, high variability, low protein identifications and low fold-changes- make EBC unsuitable for reliable protein biomarker discovery, when proper covariate adjustment is applied. How this study might affect research, practice or policyResearchers pursuing EBC proteomics should be cautious, as inadequate handling of missingness and variability, particularly without proper covariate adjustment, may yield findings that do not reflect true biological differences.

pathology↗

bioRxiv2025

Advancing DIA-based Limited Proteolysis Workflows: introducing DIA-LiPA

Limited proteolysis coupled to mass spectrometry (LiP-MS) probes protein conformational dynamics, but interpretation of LiP-MS data is complicated by heterogenous proteolytic cleavage patterns and missing data. Recent advances in data-independent acquisition (DIA) and machine learning-based search engines promise improved sensitivity and reproducibility, yet their performance in LiP-MS workflows remains underexplored. We systematically evaluated selected library-free DIA workflows using a rapamycin-treated human cell lysate and a yeast heat shock dataset, benchmarking DIA-NN and Spectronaut for identification depth, reproducibility and false discovery rate control. Our results show that library-free approaches achieve high sensitivity, eliminating the experimental overhead and sample requirements associated with empirical libraries. Building on these advances, we introduce a DIA-based Limited Proteolysis data Analysis pipeline (DIA-LiPA), a data analysis workflow tailored for LiP-MS data that integrates semi-tryptic- and tryptic-level precursor data and accounts for missingness to enable structural interpretation. Validation across multiple datasets confirmed that DIA-LiPA reproduces known structural signatures and uncovers additional regulatory patterns, providing a robust framework for mechanistic insights into protein dynamics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/687786v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@19fc486org.highwire.dtl.DTLVardef@1f74b7org.highwire.dtl.DTLVardef@127db11org.highwire.dtl.DTLVardef@19f5b88_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Precursor intensity-based label-free quantification software tools for proteomic and multi-omic analysis within the Galaxy Platform.

For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. In LFQ workflows, protein abundance changes are inferred from peptide-level information, including microbial peptides (for metaproteomics) and peptides carrying post-translational modifications (for proteomics) and/or variant sequences (for proteogenomics). Multi-omics studies (such as proteogenomics and metaproteomics) rely on peptide detection and quantification to identify and quantify peptides that map to unique proteoforms and metaproteins. The Galaxy for proteomics (Galaxy-P) platform has proven useful for the development of accessible workflows to identify proteins in these complex multi-omic studies. However, proteomics workflows within the Galaxy platform have lacked well-tested label-free quantification tools. In this study, our main goals were to evaluate two recently published open-source LFQ tools and to implement them within the Galaxy platform, enabling their easy integration with established workflows. These two tools, moFF and FlashLFQ, were selected based on their described peptide quantification capabilities and amenability to Galaxy implementation. Through rigorous testing and communication with the tools developers, we gained insights into the software features necessary for maximizing the performance of each tool. Software features evaluated included: a) match-between-runs (MBR); b) using both Thermo .raw and HUPO standards .mzML file formats as input for improved quantification; c) use of containers and/or conda packages; d) parameters needed for analyzing large input datasets; and e) optimization and validation of software performance. This work 1) establishes a process for software implementation, optimization and validation within Galaxy; and 2) makes powerful new tools for LFQ available which should prove highly useful for a variety of proteomics and multi-omics applications employing the Galaxy platform.

bioinformatics↗

bioRxiv2019

A simple approach for accurate peptide quantification in MS-based proteomics

Despite its growing popularity and use, bottom-up proteomics remains a complex analytical methodology. Its general workflow consists of three main steps: sample preparation, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and computational data analysis. Quality assessment of the different steps and components of this workflow is instrumental to identify technical flaws and to avoid loss of precious measurement time and sample material. However, assessment of the extent of sample losses along the sample preparation protocol, in particular after proteolytic digestion, is not yet routinely implemented because of the lack of an accurate and straightforward method to quantify peptides. Here, we report on the use of a microfluidic UV/visible spectrophotometer to quantify MS-ready peptides directly in MS loading solvent, consuming only 2 l of sample. We determined the optimal peptide amount for LC-MS/MS analysis on a Q Exactive HF mass spectrometer using a dilution series of a commercial K562 cell digest. Careful evaluation of selected LC and MS parameters allowed us to define 3 g as an optimal peptide amount to be injected on this particular LC-MS/MS system. Finally, using tryptic digests from human HEK293T cells, we showed that injecting equal peptide amounts, rather than approximated ones, results into less variable LC-MS/MS and protein quantification data. The obtained quality improvement together with easy implementation of the approach makes it possible to routinely quantify MS-ready peptides as a next step in daily proteomics quality control.

biochemistry↗

bioRxiv2018

Digging deeper into the human proteome: A novel nanoflow LCMS setup using micro pillar array columns (μPAC™)

In bottom-up proteomics, capillaries up to 75 cm long with internal diameters of 50 to 100 {micro}m packed with sub-2-{micro}m C18-functionalized particles are routinely used in combination with high-resolution mass spectrometry. Unlike such conventional liquid chromatography (LC) columns, micro pillar array columns ({micro}PAC) are fabricated using micromachining technology, resulting in perfectly ordered chromatographic separation beds, leading to a minimized analyte dispersion while column permeability is increased by one order of magnitude. This allows using very long columns (up to 200 cm) at only a fraction of the pressure needed to operate packed bed columns. To validate {micro}PAC column performances, different amounts of tryptic digests of HEK293T cell lysates were prepared and separated using a 200 cm {micro}PAC column or a 40 cm long conventional column. Using an Orbitrap Elite instrument, on average 25% more proteins were identified with the {micro}PAC column. Moreover, the rate at which the peak width increases with gradient time is much lower on the {micro}PAC column. For a 10-hour long gradient, average peak widths below 0.5 min were observed, resulting in consistent identification of over 5,000 proteins. Combining long solvent gradients and this new type of LC column, substantial improvements in proteome coverage could be obtained. Finally, we demonstrated high reproducibility and durability of the {micro}PAC column. Data are available via ProteomeXchange with identifiers PXD011547 and PXD013235.

biochemistry↗