Biology subjects

Van den Berge, K.

Publications and source records attributed to Van den Berge, K..

At least 19 recordsLinked to original sources

bioRxiv2025

transfactor: Transcription factor activity estimation via probabilistic gene expression deconvolution

Gene expression is the primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely-used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene through prior distributions, and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies.

bioinformatics↗

bioRxiv2025

Immune signaling mediates stromal changes to support epithelial reprogramming in Celiac duodenum

Coeliac Disease (CeD) is a chronic autoimmune disorder affecting 0.5-1% of the general population with a wide geographical distribution. Despite recent efforts to deeply phenotype gluten-specific immune activation at the single cell level, recent clinical studies targeting gluten degradation and other immune tolerance mechanisms have been unsuccessful. To this end, a deeper understanding of immune and non-immune cellular dynamics and interactions are required to characterize tissue-specific mechanisms responsible for CeD pathogenesis, repair, and resolution. Here, we assembled the most comprehensive scRNAseq dataset in Coeliac Disease to date, including 203,555 cells across 21 active CeD and 11 control duodenal samples. Compared to control duodenum, CeD was characterized by single cell differential changes in abundance, gene expression and cell-cell interactions across cellular compartments. In the immune compartments, CeD samples showed expected increases in plasma cell abundance and shifts toward type 1 effector biology (e.g., increase in cycling CD8pos, {gamma}{delta} T cells and IFNG transcriptional shifts) and Tfh-related biology (e.g., increases in IL21 signaling to effector T cells). In addition, activated myeloid subsets, including DC2 and monocytes, were increased in disease and were characterized by increased pro-inflammatory pathway expression, including IL-1{beta}. Non-immune compartments showed increased stem/crypt and secretory enterocytes in CeD samples with a decrease in absorptive enterocytes, reflecting the villus atrophy and crypt hyperplasia hallmarks of CeD epithelial dysfunction. Accompanying the epithelial changes, distinct changes in stromal populations were identified, particularly with increases in abundance and transcriptional activity of NRG1 and SMOC2 fibroblasts. Cell-cell interaction analysis across multiple cellular compartments proposed a distinct increased role of fibroblasts to support the epithelial reprogramming of the increased stem/crypt epithelial fraction in CeD, mediated by myeloid derived IL-1{beta} signal and lymphoid-derived IFN-{gamma}. This dataset reveals a previously unknown role for T-myeloid-stromal-epithelial cell communication in CeD, highlighting key mechanisms of the tissue-level cellular dynamics in response to gluten ingestion.

immunology↗

bioRxiv2024

Assessing differential cell composition in single-cell studies using voomCLR

In single-cell studies, a common question is whether there is a change in cell composition between conditions. While ideally, one needs absolute cell counts (number of cells per volumetric unit in a sample) to address these questions, current experimentation typically obtains cell counts that only carry relative information. It is therefore crucial to account for the compositional nature of cell count data in the statistical analysis. While recently developed methods address compositionality using compositional transformations together with a bias correction, they do not account for the uncertainty involved in estimation of the bias term, nor do they accommodate the mean-variance structure of the counts. Here, we introduce a statistical method, voomCLR, for assessing differences in cell composition between conditions incorporating both uncertainty on the bias term as well as acknowledging the mean-variance structure of the transformed data, by leveraging developments from the differential gene expression literature. We demonstrate the performances of voomCLR, illustrate the benefit of all components and compare the methodology to the state-of-the-art on simulated and real single-cell gene expression datasets.

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

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify the transcriptional cascades and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around wound response and lineage-specific genes prior to their later expression in response to injury. Together these results provide evidence for a latent activated stem cell state, in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

genomics↗

bioRxiv2023

AAV-HBV mouse model replicates immune exhaustion patterns of chronic HBV patients at single-cell level

Background and AimsUnresolved hepatitis B virus (HBV) infection leads to a progressive state of immune exhaustion that impairs resolution of infection, leading to chronic infection (CHB). The immune-competent AAV-HBV mouse is a common HBV preclinical immune competent model, though a comprehensive characterization of the liver immune microenvironment and its translatability to human infection is still lacking. We investigated the intrahepatic immune profile of the AAV-HBV mouse model at a single-cell level and compared with data from CHB patients in immune tolerant (IT) and immune active (IA) clinical stages. MethodsImmune exhaustion was profiled through an iterative subclustering approach for cell-typing analyses of single-cell RNA-sequencing data in CHB donors and compared to the AAV-HBV mouse model 24-weeks post-transduction to assess its translatability. This was validated using an exhaustion flow cytometry panel at 40 weeks post-transduction. ResultsUsing single-cell RNA-sequencing, CD8 pre-exhausted T-cells with self-renewing capacity (TCF7+), and terminally exhausted CD8 T-cells (TCF7-) were detected in the AAV-HBV model. These terminally exhausted CD8 T-cells (expressing Pdcd1, Tox, Lag3, Tigit) were significantly enriched versus control mice and independently identified through flow cytometry. Importantly, comparison to CHB human data showed a similar exhausted CD8 T-cell population in IT and IA donors, but not in healthy individuals. ConclusionsLong term high titer AAV-HBV mouse liver transduction led to T-cell exhaustion, as evidenced by expression of classical immune checkpoint markers at mRNA and protein levels. In both IT and IA donors, a similar CD8 exhausted T-cell population was identified, with increased frequency observed in IA donors. These data support the use of the AAV-HBV mouse model to study T-cell exhaustion in HBV infection and the effect of immune-based therapeutic interventions. Lay SummaryThe AAV-HBV mouse model is used as a research tool to study hepatitis B infection. In this study we evaluated the translation value from mouse to human with regards to T-cell exhaustion. HighlightsO_LIAAV-HBV mice transduced with a high titer vector showed presence of CD8 exhausted T-cells after 24 weeks. C_LIO_LIHigh titer transduced mice, but not lower titer show increased expression of LAG-3, TOX, TIM-3 and TIGIT in CD8 T-cells. PD-1 was increased in CD8 T-cells, independent of HBV transduction titer. C_LIO_LIA similar exhausted CD8 T-cell population could be found in chronic HBV donors, but not in healthy individuals. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/552328v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@17db34dorg.highwire.dtl.DTLVardef@186a0b1org.highwire.dtl.DTLVardef@19fd39borg.highwire.dtl.DTLVardef@1fd9f64_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

A guide to trajectory inference and RNA velocity

Technological developments have led to an explosion of high-throughput single cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. Trajectory inference methods estimate a trajectory, a collection of differentiation paths of a dynamic system, by ordering cells along the paths of such a dynamic process. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs, for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use-case on real data.

bioinformatics↗

bioRxiv2021

Genotype-specific transcriptional responses overshadow salinity effects in a marine diatom sampled along the Baltic Sea salinity cline

The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of S. marinoi grown in a common garden. Our experiment included eight genotypes from source populations spanning the Baltic Sea salinity cline. Changes in gene expression revealed a shared response to salinity across genotypes, where low salinities induced profound changes in cellular metabolism, including upregulation of carbon fixation and storage compound biosynthesis, and increased nutrient demand and oxidative stress. Nevertheless, the genotype effect overshadowed the salinity effect, as genotypes differed significantly in their response, both in the magnitude and direction of gene expression. Intraspecific differences included regulation of transcription and translation, nitrogen metabolism, cell signaling, and aerobic respiration. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond and adapt to environmental change.

microbiology↗

bioRxiv2021

Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression.

In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectorys path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

bioinformatics↗

bioRxiv2021

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects

Modern assays have enabled high-throughput studies of epigenetic regulation of gene expression using DNA sequencing. In particular, the assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of chromatin configuration for an entire genome. Despite the gain in popularity of the assay, there have been limited studies investigating the analytical challenges related to ATAC-seq data, and most studies leverage tools developed for bulk transcriptome sequencing (RNA-seq). Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample-specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins, and evaluate it together with eleven different normalization procedures on eight public ATAC-seq datasets. Our work clearly shows that accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, leading to improved accuracy and interpretability of the results. Using two case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

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

Mating type specific transcriptomic response to sex inducing pheromone in the pennate diatom Seminavis robusta

Sexual reproduction is a fundamental phase in the life cycle of most diatoms. Despite its role as a source of genetic variation, it is rarely reported in nature and its molecular foundations remain largely unknown. Here, we integrate independent transcriptomic datasets, in order to prioritize genes responding to sex inducing pheromones (SIPs) in the pennate diatom Seminavis robusta. We observe marked gene expression changes associated with SIP treatment in both mating types, including an inhibition of S-phase progression, chloroplast division, mitosis and cell wall formation. Meanwhile, meiotic genes are upregulated in response to SIP, including a sexually induced diatom specific cyclin (dsCyc). Our data further suggest an important role for reactive oxygen species, energy metabolism and cGMP signaling during the early stages of sexual reproduction. In addition, we identify several genes with a mating type specific response to SIP, and link their expression pattern with physiological responses such as the production of the attraction pheromone diproline and mate-searching behaviour in MT+. Combined, our results provide a model for early sexual reproduction in pennate diatoms and significantly expand the suite of target genes to detect sexual reproduction events in natural diatom populations.

molecular biology↗

bioRxiv2020

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

Single-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable. Here, we propose a new method called Dune for optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets.

bioinformatics↗

bioRxiv2020

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

neuroscience↗

bioRxiv2019

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type differentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are differentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess differential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad differences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage differential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage differential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from droplet-based and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

bioinformatics↗

bioRxiv2018

Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell transcriptome sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial (ZINB) model, that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

bioinformatics↗

bioRxiv2017

zingeR: unlocking RNA-seq tools for zero-inflation and single cell applications

Dropout in single cell RNA-seq (scRNA-seq) applications causes many transcripts to go undetected. It induces excess zero counts, which leads to power issues in differential expression (DE) analysis and has triggered the development of bespoke scRNA-seq DE tools that cope with zero-inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce zingeR, a zero-inflated negative binomial model that identifies excess zero counts and generates observation weights to unlock bulk RNA-seq pipelines for zero-inflation, boosting performance in scRNA-seq differential expression analysis.

bioinformatics↗