Biology subjects

Van Gassen, S.

Publications and source records attributed to Van Gassen, S..

5 recordsLinked to original sources

bioRxiv2025

An optimal transport-based low-dimensional visualization framework for high-parameter flow cytometry

Conventional data visualization techniques in single-cell analysis (such as two-dimensional dot plots, SPADE, PCA, t-SNE, or UMAP) often fall short in providing an intuitive understanding of high-parameter flow cytometry data. These methods tend to oversimplify complex biological relationships, lack biologically meaningful interpretations, and offer no principled framework for downstream quantitative analysis. To address these limitations, we present a graph-based visualization framework grounded in optimal transport theory. In this framework, cell populations are defined by their marker-expression profiles, and inter-population similarity is quantified using an efficiently computable optimal transport formulation known as the Sinkhorn distance. Our approach produces biologically consistent two-dimensional graph layouts using a phenotypeaware Hamming distance. Structural differences between sample graphs are characterized through a customized graph-edit distance that captures changes in population size, marker expression, and relationships between populations. We demonstrate our methods on two flow cytometry datasets: one from a clinical trial of dendritic cell-based immunotherapy in malignant peritoneal mesothelioma, involving 14 patients sampled at three time points with 14-color panels, and another from FlowCAP-II, which involved 43 acute myeloid leukemia patient samples analyzed with 7-color panels. Our framework produces robust, quantitative visual summaries of cell populations and supports statistical analysis based on graph edit distances, thereby offering new insights into disease progression and treatment response. Ultimately, our method bridges the gap between flow cytometry data visualization and biological interpretation.

bioinformatics↗

bioRxiv2024

CytoNormPy enables a fast and scalable removal of batch effects in cytometry datasets.

MotivationWe present a python implementation of the widely used CytoNorm algorithm for the removal of batch effects. ResultsOur implementation ran up to 85% faster than its R counterpart, while being fully compatible with common single-cell data structures and -frameworks of python. We extend the previous functionality by adding common clustering algorithms and provide key visualizations of the algorithm and its evaluation. Availability and implementationThe CytoNormPy implementation is freely available on GitHub: https://github.com/TarikExner/CytoNormPy.

bioinformatics↗

bioRxiv2024

Systemic autoimmune disease patients' blood immunome reveals specificities and commonalities among different diagnostic entities

1BackgroundSystemic autoimmune diseases (SADs) are characterized by internal heterogeneity, overlapping clinical symptoms, and shared molecular pathways. Therefore, they are difficult to diagnose and new tools allowing precise diagnosis are needed. Molecular-based reclassification studies enable to find patterns in a diagnosis-independent way. ObjectiveTo evaluate the possibility of using high-content immunophenotyping for detecting patient subgroups in the context of precise treatment. MethodsWhole blood high-content immunophenotyping of 101 patients with 7 systemic autoimmune diseases and 22 controls was performed using 36-plex mass cytometry panel. Patients were compared across diagnostic entities and re-classified using Monte Carlo reference-based consensus clustering. Levels of 45-plex multiplexed cytokine were measured and used for cluster characterization. ResultsDifferential analysis by diagnosis did not reveal any disease-specific pattern in the cellular compositions and phenotypes but rather their relative similarities. Accordingly, patients were classified into phenotypically distinct groups composed of different diagnostic entities sharing common immunophenotypes and cytokine signatures. These features were mainly based on granulocyte activation and CD38 expression in discrete lymphocyte populations and were related to Th17 or IFN-dependent cytokines. ConclusionsOur data indicate that specific individuals could potentially benefit from the same line of treatment independently of their diagnosis and emphasize the possibility of using immunophenotyping as a stratification tool in precision rheumatology. 2 Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/594621v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1098351org.highwire.dtl.DTLVardef@18c45a1org.highwire.dtl.DTLVardef@2f5f42org.highwire.dtl.DTLVardef@170e28_HPS_FORMAT_FIGEXP M_FIG C_FIG Key messagesO_LIWhole blood immmunophenotyping could be used to stratify systemic autoimmune patients, thus it is a useful tool in precision medicine. C_LIO_LIPatients groups could benefit from the same line of treatment. C_LI

immunology↗

bioRxiv2023

A framework for quantifiable local and global structure preservation in single-cell dimensionality reduction

The ability to explore high-dimensional single-cell transcriptomics data efficiently is crucial in many biological studies. Dimensionality reduction techniques have therefore emerged as a basic building block of analytical workflows. They generate low-dimensional embeddings that capture important structures in the data, and are often used in discovery, quality control, and downstream analysis. However, the trustworthiness of current methods and the rigour of popular evaluation criteria are limited. We tackle this in an empirical study of structure-preserving data embeddings, delivering two tools. First, we introduce ViScore: a robust scoring framework that improves both unsupervised and supervised quality metrics, with emphasis on scalability and fairness. Second, we introduce ViVAE : a deep learning model that achieves better multi-scale structure preservation and is equipped with new tools for interpretability. We demonstrate the potential of these contributions to advance the trustworthiness of single-cell dimensionality reduction in a quantitative comparison and focused case studies.

bioinformatics↗

bioRxiv2022

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma

BackgroundFunctional profiling of freshly isolated glioblastoma cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity. MethodsHere, we describe the precision oncology by single-cell profiling using ex vivo readouts of functionality (PROSPERO) assay to evaluate the intrinsic susceptibility of high- grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution. ResultsThe PROSPERO assay was developed by correlating short-term single-cell molecular signatures using CyTOF to long-term cytotoxicity readouts in representative patient- derived glioblastoma cell cultures (n=14) that were exposed to radiotherapy and the small- molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n=34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in PDX models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal transitions, while in patients samples this was more heterogeneous. ConclusionPROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

cancer biology↗