Biology subjects

van Kampen, A. H. C.

Publications and source records attributed to van Kampen, A. H. C..

6 recordsLinked to original sources

bioRxiv2026

AbSolution: interactive exploration of sequence-derived features in AIRR-seq repertoires

High-throughput sequencing of B-cell and T-cell immune receptor repertoires provides unprecedented insight into adaptive immune responses. The data produced are structured by clonal relationships and somatic mutation signatures, and yield extremely rich information in sequence-derived features, including physicochemical properties and compositional patterns. However, integrated analysis across datasets, conditions, and time points remains challenging. Current analytical tools typically focus only on certain features within individual repertoires, without enabling integrated, multivariable comparisons across datasets, conditions, and time points to address their diversity and variability. Here we present AbSolution, a user-friendly and flexible interactive application for comprehensive exploration of immune repertoires and their sequence-based properties. AbSolution enables multiscale analysis of thousands of sequence-derived features across receptor regions, while accounting for V(D)J usage, clonal composition and experimental groupings. We demonstrate its utility by identifying distinct sequence-based profiles associated with dominant (highly abundant) and non-dominant B-cell clones in peripheral blood BCR repertoires from patients with idiopathic inflammatory myopathies, and with antigen-responsive T-cell populations over time in a longitudinal in vitro antigen-stimulation dataset. Through interactive, interlinked visualizations, statistical feature selection and multi-sample comparisons, AbSolution facilitates integrated feature profiling that supports the interpretation of immune selection processes and enables systematic analysis of complex repertoire datasets.

bioinformatics↗

bioRxiv2026

AbSolution and ENCORE: a proof-of-concept for automating computational reproducibility in interactive applications

Reproducibility and transparency in computational analyses are essential in science, although achieving these goals often requires significant knowledge and systematic organization. Graphical interactive applications simplify the conduct of analyses and make them accessible to a broader audience. However, there is currently no consensus on how to and to which extent implement reproducibility in interactive applications. We recently developed AbSolution, a user-friendly and flexible interactive web-based R Shiny application for exploring immune repertoires and their sequence-based features, and we established the ENCORE framework to enhance transparency and reproducibility by guiding researchers in structuring and documenting computational projects. In this work, as a proof-of-concept we integrate AbSolution, ENCORE and specific R packages to address reproducibility challenges. This enables a single-step export of raw, processed, and meta-data, the software environment, the underlying generated code and a HTML report containing results and figures, operating system, hardware and R session details, and researcher notes. Its reproducibility has been independently validated by the CODECHECK initiative. This paper demonstrates how the combination of several approaches can improve and automate reproducibility of interactive applications.

bioinformatics↗

bioRxiv2025

Systematic evaluation of robustness to cell type mismatch of deconvolution methods for spatial transcriptomics data

Sequencing-based spatial transcriptomics (ST) approaches preserve spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) techniques provide single-cell resolution but lose spatial context during tissue dissociation. Given these complementary strengths, computational tools have been developed to combine scRNA-seq and ST data. These methods use deconvolution techniques to identify cell types and estimate their proportions at each spatial location in ST data, using scRNA-seq reference data. However, these methods are sensitive to missing cell types in the scRNA-seq reference, a problem known as cell type mismatch. Using two reference datasets, we performed extensive simulations to systematically evaluate the robustness to cell type mismatch of six deconvolution methods (CARD, cell2location, RCTD, Seurat, SPOTlight, Stereoscope) tailored for ST data, and two designed for bulk RNA-seq data (MuSiC, SCDC). At baseline, that is, with no cell types missing from the reference datasets, cell2location showed the strongest performance, while Seurat performed the worst. By simulating different cell type mismatch scenarios, we found that the performance of deconvolution methods decreases proportionally to the number of cell types missing from the reference. Moreover, compared to baseline, for most methods the relative decrease in performance is similar. Additionally, methods that perform well at baseline tend to assign the proportions of a missing cell type to the transcriptionally most similar cell types present in the reference data. Our results highlight the adverse effects of cell type mismatch on the performance of deconvolution methods for ST data and stress the need for more robust approaches to this issue.

bioinformatics↗

bioRxiv2020

Metabolomics and lipidomics in C. elegans using a single sample preparation

Comprehensive metabolomic and lipidomic mass spectrometry methods are in increasing demand, for instance in research related to nutrition and aging. The nematode C. elegans is a key model organism in these fields, due to the large repository of available C. elegans mutants and their convenient natural lifespan. Here, we describe a robust and sensitive analytical method for the semi-quantitative analysis of >100 polar (metabolomics) and >1000 apolar (lipidomics) metabolites in C. elegans, using a single sample preparation. Our method is capable of reliably detecting a wide variety of biologically relevant metabolic aberrations in, for instance, glycolysis and the TCA cycle, pyrimidine metabolism and complex lipid biosynthesis. In conclusion, we provide a powerful analytical tool that maximizes metabolic data yield from a single sample.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

Time-resolved profiling reveals ATF3 as a novel mediator of endocrine resistance in breast cancer

Breast cancer is one of the leading causes of death for women worldwide. Patients whose tumors express Estrogen Receptor (ER) account for around 70% of cases and are mostly treated with targeted endocrine therapy. However, 40% of these tumors eventually relapse due to resistance development and further treatment of these patients is highly ineffective. In this study we profiled the early phases of the resistance development process to uncover drivers of this phenomenon. Time-resolved analysis revealed that ATF3, a member of the ATF/CREB family of transcription factors, acts as a novel regulator of the response to therapy via rewiring of central signaling processes towards the adaptation to endocrine treatment. ATF3 was found to be essential in controlling crucial processes such as proliferation, cell cycle and apoptosis during the early response to treatment through the regulation of MAPK/AKT signaling pathways. Its essential role was confirmed in vivo in a mouse model and elevated expression of ATF3 was verified in patient datasets, adding clinical relevance to our findings. This study proposes ATF3 as a novel mediator of endocrine resistance development in breast cancer and elucidates its role in the regulation of downstream pathways activities.

cancer biology↗

bioRxiv2019

Candidate methylation sites associated with endocrine therapy resistance in the TCGA ER+/HER2- breast cancer cohort

BackgroundEstrogen receptor (ER) positive breast cancer is often effectively treated with drugs that inhibit ER signaling, i.e., tamoxifen (TAM) and aromatase inhibitors (AIs). However, about 30% of ER+ breast cancer patients develop resistance to therapy leading to tumour recurrence. Changes in the methylation profile have been implicated as one of the mechanisms through which therapy resistance develops. Therefore, we aimed to identify methylation loci associated with endocrine therapy resistance. MethodsWe used genome-wide DNA methylation profiles of primary ER+ tumors from The Cancer Genome Atlas in combination with curated data on survival and treatment to predict development of endocrine resistance. Association of individual DNA methylation markers with survival was assessed using Cox proportional hazards models in a cohort of ER+/HER2- tumours (N=552) and two sub-cohorts corresponding to the endocrine treatment (AIs or TAM) that patients received (N=210 and N=172, respectively). Models were adjusted for clinical variables tumour stage, age, AI treatment and luminal subtype. We also identified signatures of multiple methylation loci associated with survival using Cox proportional hazards models with elastic net regularization. Individual markers and multivariable signatures were compared with DNA methylation profiles generated in a time course experiment using the T47D ER+ breast cancer cell line treated with tamoxifen or deprived from estrogen. ResultsWe identified 132, 9 and 1 CpGs for which DNA methylation is significantly associated with survival in the ER+/HER2-, TAM and AI cohorts respectively. Corresponding multi-locus signatures consisted of 171, 50 and 160 CpGs and showed a large overlap with the corresponding single-locus signatures. Single-locus signatures for the ER+/HER2- and TAM cohorts were conserved among the loci that were differentially methylated in endocrine-resistant T47D cells. Similarly, multi-locus signatures for the ER+/HER2- and AI cohorts were conserved in endocrine-resistant T47D cells. ConclusionsWe identified individual and multivariable DNA methylation markers associated with therapy resistance independently of luminal status. Our results suggest that these markers identified from primary tumours and prior to any endocrine treatment are associated with development of endocrine resistance.

bioinformatics↗