Biomedical subjects

Lennart Martens

Publications and source records attributed to Lennart Martens.

At least 19 recordsLinked to original sources

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2026

narrowPASEF: A Sample-Aware diaPASEF Method Optimization Strategy Improving Differential Proteomics Performance on Low-Abundance Proteins.

Recent instrumental and computational innovations in mass-spectrometry-based proteomics offer new promise in biomarker discovery, thanks to unprecedented proteome coverage and depth. Data-independent acquisition (DIA) methods are very promising in this context as they allow improved proteome coverage, reduced missing value rates, and enhanced quantification precision. However, DIA methods also suffer from their own challenges, such as increased data complexity, cycle times, and background noise. In this work, we propose a sample-aware diaPASEF method optimization strategy for a timsTOF platform. Thorough method optimizations have first been conducted on standard HeLa lysates. Then, a ground-truth calibrated sample series, consisting of a range of UPS amounts spiked into a complex Arabidopsis background, was used to mimic differential analyses under controlled conditions. These benchmark experiments demonstrate clear benefits of using narrowPASEF for differential protein discovery. Finally, our strategy was applied to real use case biological samples to conduct a differential analysis of purified mouse astrocyte cells across two different conditions. narrowPASEF improved the proteome depth by 13%, considering proteins quantified with a coefficient of variation (CV) of <20%, and led to a 68% (435 vs 729) increase in differentially expressed proteins. These results provide an opportunity for a more precise and comprehensive analysis of the biological functions of biomarkers, offering a more profound understanding of the disease mechanisms. The benefits of our sample-aware narrowPASEF strategy demonstrated the most substantial impact on low-abundance proteins. Overall, these results show promise for more valuable and robust biomarker discoveries in the future.

Proteomics↗

PubMed2026

The need for standardization and improved open (meta)data practices in metaproteomics.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. Video Abstract.

Proteomics↗

PubMed2026

Project ODIN: advancing environmental genomic surveillance for public health across sub-Saharan Africa.

Persistent SARS-CoV-2 transmission, ongoing mpox outbreaks, and the continued spread of endemic diseases such as typhoid fever and cholera underscore the urgent need for global, multiomics surveillance. In this Personal View, we present Project ODIN, a consortium of European and African partners launched in 2023 that aims to meet this challenge by deploying innovative systems for near real-time pathogen detection and actionable public health insights. The project is a collaboration between high-income and low-income countries in northern Europe and sub-Saharan Africa. Focusing on low-income and middle-income countries, ODIN integrates metagenomics with mobile laboratory systems for comprehensive pathogen monitoring across diverse environments. ODIN emphasises standardised sampling, bioinformatics pipelines, and data-sharing protocols to ensure reliable, interoperable results while addressing infrastructure and resource limitations. By bridging gaps in genomic surveillance, these initiatives seek to strengthen outbreak preparedness, improve pathogen detection, monitor antimicrobial resistance, and provide a holistic approach to One Health challenges. Together, these innovations could advance global surveillance capacity-particularly in under-resourced regions-paving the way for effective disease control and evidence-based policy making.

Humans↗

PubMed2026

Large-scale proteomics profiling of peripheral blood of DM1 patients identifies biomarkers for disease severity and functional capacity.

BackgroundMyotonic Dystrophy Type 1 (DM1), the most common genetic neuromuscular disorder in adults, poses significant challenges for drug development due to its multisystem nature and high clinical variability in symptoms and disease progression. With a growing number of therapies entering clinical trials, this study addresses the urgent need for biomarkers that can serve as surrogate endpoints.MethodsWe profiled 437 serum samples from adult DM1 patients collected at two timepoints of the OPTIMISTIC trial using bottom-up mass spectrometry with data-independent acquisition. Associations between protein expression, the disease-causing CTG-repeat and 25 clinical outcome measures were studied using linear mixed-effect models. All key study findings were validated in an independent cohort of 69 DM1 patients and 10 healthy controls.ResultsOf the 259 identified proteins, 161 showed significant associations with the CTG-repeat length (FDR < 5%). Hypogammaglobulinemia was confirmed and shown to be worse in severely affected patients. A strong proteomic signature was associated with clinical measures of functional capacity, with the 6-Minute Walk Test showing the strongest signal (70 associations, FDR < 5%). These novel associations reveal a compelling link between chronic inflammation and reduced functional capacity. A machine learning algorithm identified a minimal set of 13 proteins robustly reflecting both the underlying genetic defect and functional capacity.ConclusionsDM1 induces a broad disease fingerprint in the serum proteome, predominantly affecting proteins of the immune system. A carefully selected panel of proteins showed the greatest potential to meet the statistical criteria required for surrogate endpoints in clinical trials.

Humans↗

PubMed2007

MascotDatfile: an open-source library to fully parse and analyse MASCOT MS/MS search results.

MS-based protein identification is an important part of both gel-based and gel-free proteome studies. The MASCOT search engine (http://www.matrixscience.com) provides one of the most popular automated algorithms for this task. Here we present an open-source software library written in Java that parses raw MASCOT results into an easily accessible and fully functional object model (http://genesis.ugent.be/MascotDatfile). Several scripts based on this library are provided as examples, allowing direct automation of important routine tasks involved in processing peptide identifications.

Algorithms↗

PubMed2006

Annotating the human proteome: beyond establishing a parts list.

The completion of the human genome has shifted the attention from deciphering the sequence to the identification and characterisation of the functional components, including genes. Improved gene prediction algorithms, together with the existing transcript and protein information, have enabled the identification of most exons in a genome. Availability of the 'parts list' has fostered the development of experimental approaches to systematically interrogate gene function on the genome, transcriptome and proteome level. Studying gene function at the protein level is vital to the understanding of how cells perform their functions as variations in protein isoforms and protein quantity which may underlie a change in phenotype can often not be deduced from sequence or transcript level genomics experiments alone. Recent advancements in proteomics have afforded technologies capable of measuring protein expression, post-translational modifications of these proteins, their subcellular localisation and assembly into complexes and pathways. Although an enormous amount of data already exists on the function of many human proteins, much of it is scattered over multiple resources. Public domain databases are therefore required to manage and collate this information and present it to the user community in both a human and machine readable manner. Of special importance here is the integration of heterogeneous data to facilitate the creation of resources that go beyond a mere parts list.

Humans↗

PubMed2006

Cell_motility: a cross-platform, open source application for the study of cell motion paths.

BACKGROUND: Migration is an important aspect of cellular behaviour and is therefore widely studied in cell biology. Numerous components are known to participate in this process in a highly dynamic manner. In order to obtain a better insight in cell migration, mutants or drugs are used and their motive phenotype is then linked with the disturbing factors. One of the typical approaches to study motion paths of individual cells relies on fitting mean square displacements to a persistent random walk function. Since the numerous calculations involved often rely on diverse commercial software packages, the analysis can be expensive, labour-intensive and error-prone work. Additionally, due to the nature of algorithms employed the calculations involved are not readily reproducible without access to the exact software package(s) used. RESULTS: We here present the cell_motility software, an open source Java application under the GNU-GPL license that provides a clear and concise analysis workbench for large amounts of cell motion data. Apart from performing the necessary calculations, the software also visualizes the original motion paths as well as the results of the calculations to help the user interpret the data. The application features an intuitive graphical user interface as well as full user and developer documentation and both source and binary files can be freely downloaded from the project website at http://genesis.UGent.be/cell_motility . CONCLUSION: In providing a free, open source software solution for the automated processing of cell motion data, we aim to achieve two important goals: labs can greatly simplify their data analysis pipeline as switching between different computational software packages becomes obsolete (thus reducing the chances for human error during data manipulation and transfer) and secondly, to provide scientists in the field with a freely available common platform to perform their analyses, enabling more efficient data quality control through peer reviewing.

Animals↗

PubMed2006

PRIDE: a public repository of protein and peptide identifications for the proteomics community.

PRIDE, the 'PRoteomics IDEntifications database' (http://www.ebi.ac.uk/pride) is a database of protein and peptide identifications that have been described in the scientific literature. These identifications will typically be from specific species, tissues and sub-cellular locations, perhaps under specific disease conditions. Any post-translational modifications that have been identified on individual peptides can be described. These identifications may be annotated with supporting mass spectra. At the time of writing, PRIDE includes the full set of identifications as submitted by individual laboratories participating in the HUPO Plasma Proteome Project and a profile of the human platelet proteome submitted by the University of Ghent in Belgium. By late 2005 PRIDE is expected to contain the identifications and spectra generated by the HUPO Brain Proteome Project. Proteomics laboratories are encouraged to submit their identifications and spectra to PRIDE to support their manuscript submissions to proteomics journals. Data can be submitted in PRIDE XML format if identifications are included or mzData format if the submitter is depositing mass spectra without identifications. PRIDE is a web application, so submission, searching and data retrieval can all be performed using an internet browser. PRIDE can be searched by experiment accession number, protein accession number, literature reference and sample parameters including species, tissue, sub-cellular location and disease state. Data can be retrieved as machine-readable PRIDE or mzData XML (the latter for mass spectra without identifications), or as human-readable HTML.

Databases, Protein↗

PubMed2006

Improving the reliability and throughput of mass spectrometry-based proteomics by spectrum quality filtering.

In contemporary peptide-centric or non-gel proteome studies, vast amounts of peptide fragmentation data are generated of which only a small part leads to peptide or protein identification. This motivates the development and use of a filtering algorithm that removes spectra that contribute little to protein identification. Removal of unidentifiable spectra reduced both the amount of computational and human time spent on analyzing spectra as well as the chances of obtaining false identifications. Thorough testing on various proteome datasets from different instruments showed that the best suggested machine-learning classifier is, on average, able to recognize half of the unidentified spectra as bad spectra. Further analyses showed that several unidentified spectra classified as good were derived from peptides carrying unanticipated amino acid modifications or contained sequence tags that allowed peptide identification using homology searches. The implementation of the classifiers is available under the GNU General Public License at http://www.bioinfo.no/software/spectrumquality.

Adult↗

PubMed2006

The HUPO Brain Proteome Project jamboree: centralised summary of the pilot studies.

The Bioinformatics Committee of the HUPO Brain Proteome Project (HUPO BPP) meets regularly to execute the post-lab analyses of the data produced in the HUPO BPP pilot studies. On January 9-11, 2006 the members as well as invited analysts came together at the European Bioinformatics Institute in Hinxton, UK for the pilot studies jamboree. The results of the reprocessing were presented and tasks forces were initiated to compile, to interpret and to summarise the data obtained.

Animals↗

PubMed2006

Functional annotation of proteins identified in human brain during the HUPO Brain Proteome Project pilot study.

The HUPO Brain Proteome Project is an initiative coordinating proteomics studies to characterise human and mouse brain proteomes. Proteins identified in human brain samples during the project's pilot phase were put into biological context through integration with various annotation sources followed by a bioinformatics analysis. The data set was related to the genome sequence via the genes encoding identified proteins including an assessment of splice variant identification as well as an analysis of tissue specificity of the respective transcripts. Proteins were furthermore categorised according to subcellular localisation, molecular function and biological process, grouped into protein families and mapped to biological pathways they are known to act in. Involvement in pathological conditions was examined based on association with entries in the online version of Mendelian Inheritance in Man and an interaction network was derived from curated protein-proteininteraction data. Overall a non-redundant set of 1804 proteins was identified in human brain samples. In the majority of cases splice variants could be unambiguously identified by unique peptides, including matches to several hypothetical transcripts of known as well as predicted genes.

Alternative Splicing↗

PubMed2006

A comparison of the HUPO Brain Proteome Project pilot with other proteomics studies.

The pilot phase of the Brain Proteome Project (BPP), the Human Proteome Organization (HUPO) initiative that focuses on studies of the brain of both humans and mice, has now been completed. Participating laboratories studied the proteomes of two human samples derived from biopsy and autopsy as well as three mouse samples from various developmental stages. With the combined and centrally reprocessed data now available, a comparison in terms of protein identifications and project organization is made between the HUPO BPP pilot and three other proteomics studies: the HUPO Plasma Proteome Project (PPP) pilot, a proteome of human blood platelets and a recently published comprehensive mouse proteome. Finally, as any comparison between large-scale proteomics datasets is decidedly non-trivial, we also evaluate and discuss several ways to go about comparing such different result sets.

Animals↗

PubMed2006

Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO Brain Proteome Project pilot phase.

The newly available techniques for sensitive proteome analysis and the resulting amount of data require a new bioinformatics focus on automatic methods for spectrum reprocessing and peptide/protein validation. Manual validation of results in such studies is not feasible and objective enough for quality relevant interpretation. The necessity for tools enabling an automatic quality control is, therefore, important to produce reliable and comparable data in such big consortia as the Human Proteome Organization Brain Proteome Project. Standards and well-defined processing pipelines are important for these consortia. We show a way for choosing the right database model, through collecting data, processing these with a decoy database and end up with a quality controlled protein list merged from several search engines, including a known false-positive rate.

Algorithms↗

PubMed2006

HUPO Brain Proteome Project: summary of the pilot phase and introduction of a comprehensive data reprocessing strategy.

The Human Proteome Organisation (HUPO) initiated several projects focusing on the proteome analysis of distinct human organs. The Brain Proteome Project (BPP) is the initiative dedicated to the brain, its development and correlated diseases. Two pilot studies have been performed aiming at the comparison of techniques, laboratories and approaches. With the help of the results gained, objective data submission, storage and reprocessing workflow have been established. The biological relevance of the data will be drawn from the inter-laboratory comparisons as well as from the re-calculation of all data sets submitted by the different groups. In the following, results of the single groups as well as the centralised reprocessing effort will be summarised and compared, showing the added value of this concerted work.

Animals↗

PubMed2006

The power of cooperative investigation: summary and comparison of the HUPO Brain Proteome Project pilot study results.

Within the pilot phase of the HUPO Brain Proteome Project, nine participating laboratories analysed human (epilepsy and/or post mortem material) and mouse brain samples (embryonic, juvenile and adult), respectively, using a variety of different state of the art techniques. Thirty-seven different analytical approaches were accomplished. Of these analyses, 17 were done differentially, i.e. the protein expression patterns of the different samples (human or mouse) were compared. A catalogue of all proteins present in the respective sample was built in 20 analyses (mapping). All data were collected in the Data Collection Center in Bochum, Germany, and were reprocessed according to thoroughly defined parameters. In this report, a summary of all results and inter-laboratory comparisons with respect to the number of identified proteins, the analysed organism, and the used techniques is presented.

Animals↗

PubMed2006

Four stage liquid chromatographic selection of methionyl peptides for peptide-centric proteome analysis: the proteome of human multipotent adult progenitor cells.

Serial application of strong cation-exchange and diagonal reversed-phase chromatography selecting methionyl peptides by stepwise shifting them from their reduced to their sulfoxide and sulfone forms generates a four-stage fractionation system, allowing high coverage analysis of complex proteome digests by LC-MALDI-MS/MS. Application to the proteome of a human multipotent adult progenitor cell line (MAPC) identified 2151 proteins with high confidence as on average four MS/MS-spectra were linked to each protein. Our dataset contains several novel, potential marker proteins that may be evaluated as affinity-anchors for isolating different adult stem cells in further studies. Furthermore, at least 2 tyrosine kinases that were previously linked to the self-renewal potential of stem cells were identified, validating the stemness of the analyzed cells. We also present data hinting at possible involvement of the ubiquitin/proteasome machinery in steering proliferation and/or differentiation of MAPC. Finally, following comparison of the MAPC proteome with proteomes of four human differentiated cell lines reveals differential usage of chromosomal information: compared to differentiated cells, MAPC do not appear to hold any preference for expressing genes located on specific chromosomes.

Adult↗

PubMed2006

Proteome-wide characterization of N-glycosylation events by diagonal chromatography.

A procedure to map N-glycosylation sites is presented here. It can be applied to purified proteins as well as to highly complex mixtures. The method exploits deglycosylation by PNGase F in a diagonal, reverse-phase chromatographic setup. When applied to 10 microL of mouse serum, affinity-depleted for its three most abundant components, 117 known or predicted sites were mapped in addition to 10 novel sites. Several sites were detected on soluble membrane or receptor components. Our method furthermore senses the nature of glycan structures and can detect differential glycosylation on a given site. These properties--high sensitivity and dependence on glycan imprinting--can be exploited for glycan-biomarker analysis.

Amino Acid Sequence↗