Biology subjects

Gilis, J.

Publications and source records attributed to Gilis, J..

9 recordsLinked to original sources

bioRxiv2026

Cell-Type-Resolved Pseudobulk Classification Across Independent Cohorts Identifies Microglial PTPRG as a Transcriptional Hub in Alzheimer's Disease

Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and widespread cerebral pathology. Understanding cell-type-specific molecular mechanisms underlying AD is critical for identifying precise therapeutic targets. We applied a supervised machine learning approach to single-nucleus RNA sequencing data from the ROSMAP cohort, aggregating gene expression profiles into pseudobulk representations across six major brain cell types. Systematic evaluation of all possible cell-type combinations identified microglia and astrocytes as the most discriminative cell types for AD classification. A logistic regression model trained on 228 highly variable genes achieved robust classification performance on held-out ROSMAP samples (balanced accuracy 0.87, AUC 0.89) and generalized to an independent cohort from the Seattle Alzheimers Disease Brain Cell Atlas (balanced accuracy 0.86, AUC 0.92), demonstrating cross-cohort reproducibility that remains uncommon in computational AD research. Among the 72 genes selected by the model, microglial PTPRG exhibited the highest absolute coefficient. Gene Set Enrichment Analysis (GSEA) revealed that microglia-expressed genes were enriched for chronic immune activation and inflammatory signaling, while astrocyte-associated genes implicated protein homeostasis stress and HSF1-mediated chaperone pathways. Weighted Gene Co-expression Network Analysis (WGCNA) further showed that PTPRG operates within fundamentally different gene network contexts in AD and NCI microglia, with AD networks characterized by inflammatory dysregulation and NCI networks reflecting homeostatic immune surveillance. Cell-cell communication analysis identified established AD risk genes including APOE, GRN, PSEN1, and CLU among the top neuronal ligands predicted to regulate microglial PTPRG, positioning it as a convergence point for disease-relevant neuronal signals. Correlation analysis further revealed that excitatory and inhibitory neurons couple to microglial PTPRG through distinct biological processes, implicating divergent mechanisms of AD-associated microglial dysregulation. Collectively, these findings establish microglial PTPRG as a central hub integrating neuronal signaling and inflammatory dysregulation in AD pathology.

systems biology↗

bioRxiv2025

IsoformSwitchAnalyzeR v2: Analysis of Functional Isoform Changes in Long-read and Single-cell Sequencing Data

Alternative splicing enables a single gene to produce a variety of mRNA transcripts, significantly enhancing protein diversity in higher eukaryotes. Isoform switching refers to the differential usage of a genes transcripts and occurs pervasively across physiological and pathological conditions. IsoformSwitchAnalyzeR was developed to identify these isoform switches and analyze their functional consequences. Advances in RNA-seq technology, including long-read and single-cell sequencing, along with state-of-the-art computational tools, enable unprecedented accuracy in isoform switch identification and its functional consequences, necessitating an update to IsoformSwitchAnalyzeR. Here we present IsoformSwitchAnalyzeR 2.0, with substantial improvements in the robustness of isoform switch detection, the incorporation of new functional annotation types, and interoperability with other bioinformatics tools. We showcase how IsoformSwitchAnalyzeRs standard workflow is now well-suited for analysis of both long-read RNA-seq and single-cell data through two case studies. Specifically, we analyze long-read data from patients with Alzheimers Disease and single-cell data from patients with glioblastoma. In both case studies, we find important isoform switches with disease-relevant functional consequences, showcasing the power of IsoformSwitchAnalyzeR v2. Taken together, these findings highlight the versatility and robustness of IsoformSwitchAnalyzeR in handling advanced sequencing technologies, thereby broadening its applicability across diverse research contexts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/693027v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f0ecd8org.highwire.dtl.DTLVardef@4d7285org.highwire.dtl.DTLVardef@999abeorg.highwire.dtl.DTLVardef@7e3e9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Strategies for addressing pseudoreplication in multi-patient scRNA-seq data

The rapidly evolving field of single-cell transcriptomics has provided a powerful means for understanding cellular heterogeneity. Large-scale studies with multiple biological samples hold promise for discovering differentially expressed biomarkers with a higher level of confidence through a better characterization of the target population. However, the hierarchical nature of these experiments introduces a significant challenge for downstream statistical analysis. Indeed, despite the availability of numerous differential expression methods, only a select few can accurately address the within-patient correlation of single-cell expression profiles. Furthermore, due to the high computational costs associated with some of these methods, their practical use is limited. In this manuscript, we undertake a comprehensive assessment of different strategies to address the hierarchical correlation structure in multi-sample scRNA-seq data. We employ synthetic data generated from a simulator that retains the original correlation structure of multi-patient data while making minimal assumptions, providing a robust platform for benchmarking method performance. Our analyses indicate that neglecting within-patient correlation jeopardizes type I error control. We show that, in line with some previous reports and in contrast with others, Poisson Generalized Estimation Equations provide a useful and flexible framework for addressing these issues. We also show that pseudobulk approaches outperform single-cell level methods across the board. In this work, we resolve the conflicting results regarding the utility of GEEs and their performance relative to pseudobulk approaches. As such, we provide valuable guidelines for researchers navigating the complex landscape of gene expression modeling, and offer insights on choosing the most appropriate methods based on the specific structure and design of their datasets.

bioinformatics↗

bioRxiv2023

Differential detection workflows for multi-sample single-cell RNA-seq data

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

bioinformatics↗

bioRxiv2023

Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses.

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and limitations. Indeed, tools for differential transcript usage or rare disease diagnosis through splicing and expression outliers, either lack performance, discard information, or do not scale to large datasets. We show that replacing normalization offsets unlocks bulk RNA-seq tools for differential usage and aberrant splicing, providing a single framework for various short- and long-read applications. We then introduce saseR, a tool for prioritizing expression and usage outliers that is much faster than state-of-the-art methods, and significantly outperforms these for aberrant splicing detection.

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↗

bioRxiv2022

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

bioinformatics↗

bioRxiv2021

satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications.

bioinformatics↗

bioRxiv2020

Pitfalls in re-analysis of observational omics studies: a post-mortem of the human pathology atlas.

Uhlen et al. (Reports, 18 august 2017) published an open-access resource with cancer-specific marker genes that are prognostic for patient survival in seventeen different types of cancer. However, their data analysis workflow is prone to the accumulation of false positives. A more reliable workflow with flexible Cox proportional hazards models employed on the same data highlights three distinct problems with such large-scale, publicly available omics datasets from observational studies today: (i) re-analysis results can not necessarily be taken forward by others, highlighting a need to cross-check important analyses with high impact outcomes; (ii) current methods are not necessarily optimal for the re-analysis of such data, indicating an urgent need to develop more suitable methods; and (iii) the limited availability of potential confounders in public metadata renders it very difficult (if not impossible) to adequately prioritize clinically relevant genes, which should prompt an in-depth discussion on how such information could be made more readily available while respecting privacy and ethics concerns.

bioinformatics↗