Biology subjects

Saeys, Y.

Publications and source records attributed to Saeys, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

BfBio: a graph-based tool for the prediction of Angiogenic Stalk Cell genes using a Personalized PageRank algorithm

Although most human protein coding genes have functional annotations in databases, such as GeneCards, many remain poorly characterized. To address this gap, computational tools can be leveraged to predict the functional roles of under-annotated genes by extracting patterns from complex biological networks. Here we introduce Brain-for-Biotech (BfBio), a framework designed to identify genes important for vascular endothelial cells (EC), which are crucial cells for vessel formation (angiogenesis), vascular homeostasis, hemostasis and blood/tissue barrier function but also critical mediators of immunity and cancer progression. BfBio utilizes a Personalized PageRank (PPR) algorithm on an integrated network of different omics datasets and publicly available gene-gene/protein-protein interaction databases. In this study, we apply the predictive capabilities of BfBio to infer angiogenic stalk cell phenotype function in genes for which this function was not known before. By leveraging a set of genes characterizing the stalk cell cluster in lung tumor EC models previously identified, we have achieved a high Area Under Receiver Operative Characteristic (AUC-ROC) performance (0.837). Enrichment analysis, coupled with a text mining application, further confirmed that among the 49 predicted genes four of them were poorly characterized yet possessed biologically relevant properties and were linked to cancer, thereby validating BfBio as a robust tool for prioritizing novel therapeutic targets in vascular biology.

cancer biology↗

bioRxiv2026

IL-1 instructs para-bronchial cuff fibroblasts to organize lung antibody secreting cell niches during continued antigen exposure

Secondary lymphoid organs (SLO) are prototypic sites of antibody production, yet mucosal sites also generate and maintain mucosal antibodies during continued inflammation. Using a murine model of house dust mite-induced airway inflammation, we show that prolonged allergen exposure induces tertiary lymphoid organs (TLO) within para-bronchial adventitial cuffs. Within these regions, IL-1 signaling instructs fibroblasts to form niches that recruit, retain, and sustain lung antibody-secreting cells (ASCs) through chemokine induction. These mucosal ASCs share immunoglobulin repertoires with TLO-derived germinal center B cells. Thus, continued allergen exposure reshapes the lung microenvironment by converting para-bronchial fibroblasts into IL-1-dependent supportive niches for non-IgE antibody production, revealing a fibroblast-mediated mechanism for local immune regulation in chronic inflammation. One sentence summaryContinued allergen exposure drives lung TLO to generate ASCs that home to IL1-instructed para-bronchial cuffs.

immunology↗

bioRxiv2025

CytoScan: Automated detection of technical anomalies for cytometry quality control

Studies evaluating cellular phenotypes by cytometry techniques are increasingly facing analytical challenges due to the multitudes of samples and parameters that are evaluated concurrently. Spurious technical effects resulting from a lack of standardization can affect marker distributions and further complicate multi-sample analyses. User-friendly tools for exploratory data analysis to identify such technical effects in large datasets are lacking. To fill this gap, we present a novel R package, CytoScan, that evaluates inter-measurement variation in cytometry datasets and allows for detecting anomalous measurements after data acquisition. CytoScan can detect two types of anomalies: files with limited similarity to others within a dataset (outliers) and files with limited similarity to previously acquired high-quality reference data (novelties). Using simulations of skewed marker distributions and real-life technical effects we demonstrate that CytoScan can accurately detect such anomalies. CytoScan can be applied to large cytometry datasets on consumer-grade hardware with informative visualizations, providing accessible quality-control for more reliable analyses.

bioinformatics↗

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↗

bioRxiv2025

anndataR improves interoperability between R and Python in single-cell transcriptomics

SummaryMany single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. AvailabilityanndataRs source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241. Contactrobrecht.cannoodt@gmail.com

bioinformatics↗

bioRxiv2025

Corelative light and electron microscopy (CLEM) image registration by blood vessel axes alignment

Correlative light and electron microscopy (CLEM) is a multi-modal microscopy approach that contains complementary information from light (LM) and electron microscopy (EM). CLEM image alignment can be a challenging task for both the biological expert as well as for existing automated methods. This work introduces a novel method that focuses on aligning the medial axes of blood vessels in both the LM and EM modalities. Evaluation of 3D CLEM image registration problems shows that this approach obtains an accurate alignment in an intuitive and robust way. This method holds potential as a valuable tool for global and local alignment of a subset of challenging CLEM image alignment problems, both as guidance in a manual setting or in a more automated workflow.

bioinformatics↗

bioRxiv2025

Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics

Automatic cell type annotation methods assign cell type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit feature distributions that diverge from it. These scenarios can significantly affect the reliability of annotation predictions, a factor often overlooked in current automatic annotation methods. The field of out-of-distribution detection (OOD), primarily focused on computer vision, addresses the identification of instances that differ from the training distribution. Implementing OOD methods in the context of novel cell type annotation and data shift detection for single-cell transcriptomics may enhance annotation accuracy and trustworthiness. We evaluate 6 OOD detection methods: LogitNorm, MC dropout, Ensembles, Energy- based OOD, Deep NN and Posterior networks, for their annotation and OOD detection performance in both synthetical and real-life application settings. We show that OOD detection methods are able to accurately detect novel cell types and show their promise to detect severe data shifts on non-integrated datasets. Moreover, integration of the OOD datasets improves annotation performance, but interferes with OOD detection, diminishing novel cell type capabilities of the OOD methods.

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

SPArrOW: a flexible, interactive and scalable pipeline for spatial transcriptomics analysis

Current spatial transcriptomics technologies are increasingly able to measure large gene panels at subcellular resolution, but a major bottleneck in this rapidly advancing field is the computational analysis and interpretation of the data. To bridge this gap, here we present SPArrOW, a flexible, modular and scalable pipeline for processing spatial transcriptomics data. SPArrOW improves cell segmentation and leads to better overall data quality, resulting in more accurate cell annotations at the single-cell level. Furthermore, it provides the users with numerous visual quality checks that are crucial for the correct interpretation of the data, offering users more control in processing their data. Our workflow is designed to accommodate the various available spatial transcriptomics platforms. Finally, SPArrOW offers interactive visualization and data exploration, enabling sample-specific pipeline optimization by various tuneable parameters and an efficient comparison of different staining and gene allocation strategies.

bioinformatics↗

bioRxiv2024

Epigenomic programming of peripheral monocytes determines their transcriptional response to the tumor microenvironment

Classical monocytes are recruited to tumors and undergo transcriptional reprogramming resulting in tumor-promoting functions. Epigenomic features, such as post-translational modification of histones and chromatin accessibility, are key determinants of transcription factor binding and thereby play an important role in determining transcriptional responses to the tissue environment. It is unknown to what extent the epigenetic landscape of peripheral monocytes is rewired by cancer and how this could shape their transcriptional response upon recruitment to the tumor microenvironment. Here we used a combination of genome-wide assays for mRNA expression, chromatin accessibility and multiple histone modifications (H3K4me1, H3K4me3, H3K27ac) in a mouse model to investigate changes in the epigenomic landscape of peripheral monocytes. We then linked these epigenetic alterations to gene expression changes in monocytes occurring in the periphery or during tumor infiltration. We found that the distal tumor caused extensive remodeling of both H3K4me3+ promoters and H3K4me1+ enhancers in peripheral monocytes. Specifically, this involved the repression of interferon-responsive promoters and enhancers as well as the establishment of enhancers harboring binding motifs for transcription factors downstream of inflammatory and cytokine signaling pathways. The enhancers altered in the periphery could be linked to sustained gene expression changes which were less likely to be reversed in the tumor microenvironment. In addition, genes activated upon tumor infiltration showed prior epigenetic priming in peripheral monocytes. Overall, these results indicate that the epigenomic landscape of peripheral monocytes is altered in response to a distal tumor, and this could shape the transcriptional response of monocytes when they encounter microenvironmental signals upon infiltrating the tumor.

immunology↗

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↗

bioRxiv2024

Transit amplifying cells balance growth and differentiation in above-ground meristems

In both animals and plants, stem cell niches balance between cell-renewal and the generation of progeny cells that differentiate into specialized tissues. The multipotent and highly proliferative transit amplifying cells (TACs) integrate signals from stem cells and their differentiating progeny cells. Here we used spatial transcriptomics mapped to individual cells to illustrate the localization of TACs in maize meristems based on transcriptional gradients. Through genetic interactions and fluctuations in the transcriptional gradients we show that the multiplicative cell divisions are independently controlled from the TAC cell divisions. The dynamic nature of transcriptional variation in response to cell state or environment, together with the potential to improve yield by their modulation highlights the importance of finetuned modulation of networks rather than constitutive perturbations for crop improvement.

plant biology↗

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↗

bioRxiv2023

Uncertainty-aware single-cell annotation with a hierarchical reject option

AbstractAutomatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty present in the label assignment. To enhance the reliability and robustness of annotation, most machine learning methods address this uncertainty by providing a full reject option, i.e. when the predicted confidence score of a cell type label falls below a user-defined threshold, no label is assigned and no prediction is made. As a better alternative, some methods deploy hierarchical models and consider a so-called partial rejection by returning internal nodes of the hierarchy as label assignment. However, because a detailed experimental analysis of various rejection approaches is missing in the literature, there is no consensus on best practices, superiority of certain methods, and potential drawbacks associated with rejection. We evaluate three annotation approaches (1) full rejection (2) partial rejection and (3) no rejection for both flat and hierarchical probabilistic classifiers. Our findings indicate that hierarchical classifiers are superior when rejection is applied, with partial rejection being the preferred rejection approach, as it preserves a significant amount of label information. For optimal rejection implementation, the rejection threshold should be determined through careful examination of a methods rejection behavior. Without rejection, flat and hierarchical annotation perform equally well, as long as the cell type hierarchy accurately captures transcriptomic relationships.

bioinformatics↗

bioRxiv2023

MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

Dysregulated cell-cell communication is a hallmark of many disease phenotypes. Due to recent advances in single-cell transcriptomics and computational approaches, it is now possible to study intercellular communication on a genome- and tissue-wide scale. However, most current cell-cell communication inference tools have limitations when analyzing data from multiple samples and conditions. Their main limitation is that they do not address inter-sample heterogeneity adequately, which could lead to false inference. This issue is crucial for analyzing human cohort scRNA-seq datasets, complicating the comparison between healthy and diseased subjects. Therefore, we developed MultiNicheNet (https://github.com/saeyslab/multinichenetr), a novel framework to better analyze cell-cell communication from multi-sample multi-condition single-cell transcriptomics data. The main goals of MultiNicheNet are inferring the differentially expressed and active ligand-receptor pairs between conditions of interest and predicting the putative downstream target genes of these pairs. To achieve this goal, MultiNicheNet applies the principles of state-of-the-art differential expression algorithms for multi-sample scRNA-seq data. As a result, users can analyze differential cell-cell communication while adequately addressing inter-sample heterogeneity, handling complex multifactorial experimental designs, and correcting for batch effects and covariates. Moreover, MultiNicheNet uses NicheNet-v2, our new and substantially improved version of NicheNets ligand-receptor network and ligand-target prior knowledge model. We applied MultiNicheNet to patient cohort data of several diseases (breast cancer, squamous cell carcinoma, multisystem inflammatory syndrome in children, and lung fibrosis). For these diseases, MultiNicheNet uncovered known and novel aberrant cell-cell signaling processes. We also demonstrated MultiNicheNets potential to perform non-trivial analysis tasks, such as studying between- and within-group differences in cell-cell communication dynamics in response to therapy. As a final example, we used MulitNicheNet to elucidate dysregulated intercellular signaling in idiopathic pulmonary fibrosis while correcting batch effects in integrated atlas data. Given the anticipated increase in multi-sample scRNA-seq datasets due to technological advancements and extensive atlas-building integration efforts, we expect that MultiNicheNet will be a valuable tool to uncover differences in cell-cell communication between healthy and diseased states.

bioinformatics↗

bioRxiv2023

Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) is an emerging field that aims to profile the transcriptome of a cell while keeping its spatial context. Although the resolution of non-targeted ST technologies has been rapidly improving in recent years, most commercial methods do not yet operate at single-cell resolution. To tackle this issue, computational methods such as deconvolution can be used to infer cell type proportions in each spot by learning cell type-specific expression profiles from reference single-cell RNA-sequencing (scRNA-seq) data. Here, we benchmarked the performance of 11 deconvolution methods using 63 silver standards, three gold standards, and two case studies on liver and melanoma tissues. The silver standards were generated using our novel simulation engine synthspot, where we used seven scRNA-seq datasets to create synthetic spots that followed one of nine different biological tissue patterns. The gold standards were generated using imaging-based ST technologies at single-cell resolution. We evaluated method performance based on the root-mean-squared error, area under the precision-recall curve, and Jensen-Shannon divergence. Our evaluation revealed that method performance significantly decreases in datasets with highly abundant or rare cell types. Moreover, we evaluated the stability of each method when using different reference datasets and found that having sufficient number of genes for each cell type is crucial for good performance. We conclude that while cell2location and RCTD are the top-performing methods, a simple off-the-shelf deconvolution method surprisingly outperforms almost half of the dedicated spatial deconvolution methods. Our freely available Nextflow pipeline allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

bioinformatics↗

bioRxiv2022

Cell type-specific attenuation of brassinosteroid signalling drives stomatal asymmetric cell division

In Arabidopsis thaliana, the negative brassinosteroid (BR) signalling regulator, BR INSENSITIVE2 (BIN2) promotes and restricts stomatal asymmetric cell division (ACD) depending on its subcellular localization, which is regulated by the stomatal lineage-specific scaffolding protein POLAR. BRs inactivate BIN2, but how they govern stomatal development remains unclear. Mapping the single-cell transcriptome of stomatal lineages with either exogenous BRs or the specific BIN2 inhibitor revealed that the two modes of BR activation triggered spatiotemporally distinct transcriptional responses. We established that when in a complex with POLAR and its closest homolog POLAR-LIKE1, BIN2 is insulated from BR-mediated inactivation, nevertheless, it remains sensitive to the inhibitor. Subsequently, BR signalling is attenuated in ACD precursors, whereas it remains active in epidermal cells that would undergo differentiation. Our study demonstrates how scaffold proteins contribute to cellular signal specificity of hormonal responses in plants.

plant biology↗

bioRxiv2022

A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data

Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing infrastructure. SCIP is scalable, flexible, open-source and enables reproducible image processing. It performs projection, illumination correction, segmentation, background masking and extensive morphological profiling on various imaging types. We showcase SCIPs capabilities on three large-scale image cytometry datasets. First, we process an imaging flow cytometry (IFC) dataset of human white blood cells and show how the obtained features are used to classify cells into 8 cell types based on bright- and darkfield imagery. Secondly, we process an automated microscopy dataset of human white blood cells to divide them into cell types in an unsupervised manner. Finally, a high-content screening dataset of breast cancer cells is processed to predict the mechanism-of-action of a large set of compound treatments. The software can be installed from the PyPi repository. Its source code is available at https://github.com/ScalableCytometryImageProcessing/SCIP under the GNU General Public License version 3. It has been tested on Unix operating systems. Issues with the software can be submitted at https://github.com/ScalableCytometryImageProcessing/SCIP/issues. 1 Author SummaryCytometry is a field of biology that studies cells by measuring their characteristics. In image cytometry, this is done by acquiring images of cells. In order to gain biological insight from a set of images, an extensive amount of measurements are derived from them describing the cells they contain. These measurements include, for instance, a cells area, diameter, or the average brightness of the cell image. These measurements can then be analyzed using automated software tools to understand, for example, how cells respond to drug treatments, or how cells differ between a healthy and a diseased person. In this work, we present a novel software tool that is able to efficiently compute image measurements on large datasets of images. We do this by harnessing the power of high performance computing infrastructure. By enabling image cytometry researchers to make use of more computational power, they can more efficiently process complex and large datasets, paving the way to novel, fascinating biological discoveries.

bioinformatics↗