Biology subjects

van Dongen, S.

Publications and source records attributed to van Dongen, S..

12 recordsLinked to original sources

bioRxiv2024

Malat1 regulates female Th2 cell cytokine expression through controlling early differentiation and response to IL2

Identifying cell intrinsic regulators of immune sexual dimorphism is critical for treatment of several immunopathologies. We show that Malat1 is required for appropriate cytokine expression in female but not male Th2 cells. Malat1 deficiency impairs in vitro Th2 differentiation of naive CD4+ T cells from female mice, characterised by transcriptome-wide effects and suppression of cytokine expression, particularly IL10. Upon IL10R blockade a pronounced effect is also seen on IL4 and IL13. Mechanistically, naive CD4+ T cells from Malat1-/- female mice demonstrate altered early activation kinetics and impaired early differentiation gene expression, including up-regulation of an interferon stimulated gene (ISG) module. This is followed by suppression of IL2R and IL2R{gamma} expression and IL2-mediated differentiation. Mimicking the effect of Malat1 loss by maintaining early ISG expression in WT cells with IFN{beta} treatment partially phenocopies the effects of Malat1 deficiency. A subset of the effects of Malat1 loss in female cells is also observed in male cells. However, this does not affect endpoint Th2 differentiation. Male CD4+ T cells demonstrate stronger early activation, higher ISG expression during early differentiation, maintenance of IL2R expression independently of Malat1, and lower sensitivity to exogenous IL2 during late differentiation compared to female cells. In vivo, female, but not male, Malat1-/- mice demonstrate altered Th2 cytokine expression characterised by a reduction in IL10+ Th2 cells in both lung and spleen following priming and challenge with Schistosoma mansoni eggs, a model of lung type 2 inflammation. Overall, these findings reveal Malat1 as a novel determinant of immune sexual dimorphism.

immunology↗

bioRxiv2022

Transcriptional signals of dedifferentiation in human cancer

As normal cells transform into cancers, their cell state changes (or "dedifferentiates"), which may drive cancer cells into a stem-like or more primordial, foetal or embryonic cell state. Here, we used single cell atlases to study dedifferentiation in transcriptional terms across a wide spectrum of adult and childhood cancers. At the level of the whole transcriptome, we find that adult cancers rarely return to an embryonic state, but rather that a foetal state is a near-universal feature of childhood cancers. We extend these bulk transcriptomic findings to a single cell resolution analysis of colorectal and liver cancers, confirming the lack of reversion to a primordial state in adult tumours and the retention of foetal signals in childhood cancers. Our findings provide a nuanced picture of dedifferentiation in these two groups of neoplasms, indicating cancer-specific rather than universal patterns of dedifferentiation pervade adult epithelial cancers.

cancer biology↗

bioRxiv2022

Fast multi-resolution consensus clustering

Clustering is a key tool for exploring results from large scale data collection efforts. It is especially useful to find clusterings (partitions of data into non-overlapping sets) at different levels of granularity, ranging from detailed to abstract. In order to be consistent such a set of clusterings should be nested, requiring that elements that group together in a given clustering group together in all coarser clusterings. Hierarchical clustering provides a tree for the data and naturally induces nested clusterings, but none of the methods have gained traction to provide such a multi-resolution view. Widely used methods such as Leiden and the Markov Cluster method (MCL) can produce clusterings at different scales controlled by a resolution parameter, but the results are generally not nested. I introduce Restricted Contingency Linkage (RCL), a parameter-free consensus method that uniquely integrates and reconciles a set of flat clusterings with potentially widely varying levels of granularity into a single multi-resolution view. A common starting point in consensus methods is the association matrix A, where Aij tallies the number of co-occurrences of i and j in the same cluster across all clusterings. RCL creates a matrix R where Rij tallies a measure that differentiates i-j pairs with respect to the amount that they co-cluster in pairs of clusterings while taking into account the subset relationship between the clusters involved. The entries in R are fully abstracted from the input data, richly differentiated and uniformly normalised, making it suitable for single linkage clustering. From the resulting tree a limited set of flat clusterings is obtained by varying a resolution criterion along a logarithmic scale. I validate the method using large-scale single-cell transcriptome data from human developing kidneys, along with visualisations and quality control measures of clustering ensembles. Marker gene expression is summarised in a heatmap across the multi-level RCL clusters, providing a coherent view of cell type and cell state hierarchies within the tissue. This reveals clusters corresponding to small populations of mast cells, plasma cells and interstitial cells, and shows segregation of cycling cells in small populations across different cell clades. Given an ensemble of input clusterings, RCL rapidly enables these analyses in a parameter-free manner. An RCL reference implementation is provided for clustering ensembles that are associated with a network G, further restricting the RCL matrix to entries that correspond to edges in G. For a network G with m edges this implementation has complexity [O](m(p2 + log(m))) where p is the number of input clusterings, taking less than a minute on a dataset with N = 27k elements, m = 1.5M edges and p = 24 clusterings. The implementation, including software for QC plots, marker gene heatmaps and other visualisations is available as part of the MCL software package (https://github.com/micans/mcl). O_TEXTBOXA suggestion for a quick guide to RCL is the pseudocode in Listing 1 and the multi-resolution cluster/marker-gene heatmap in Figures 11.1-11.2, supplemented with quality control plots for clustering granularity (Figures 8.1 and 8.1) and clustering discrepancy (Pages 5-6 and Figure 8.3). C_TEXTBOX O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/511493v1_fig11_1.gif" ALT="Figure 111"> View larger version (85K): org.highwire.dtl.DTLVardef@121dfecorg.highwire.dtl.DTLVardef@f43c0eorg.highwire.dtl.DTLVardef@92e4a7org.highwire.dtl.DTLVardef@1e1ba4b_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 11.1:C_FLOATNO ComplexHeatmap [37] of marker genes for RCL clustering of cell transcriptomes from 27203 developing kidney cells. RCL clustering was performed as described in Section 8. Heatmap columns correspond to the rows in the tabular representation of the most fine-grained clusters (Section 10). Coarser levels are visible in the column clustering and the associated dendrogram. Clusters of residual nodes (Section 10) can be recognised as horizontal bars without a vertical descent. (Heatmap continued on the following page). C_FIG O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/511493v1_fig11_2.gif" ALT="Figure 112"> View larger version (77K): org.highwire.dtl.DTLVardef@8da768org.highwire.dtl.DTLVardef@1e9ae91org.highwire.dtl.DTLVardef@c3b292org.highwire.dtl.DTLVardef@99e4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 11.2:C_FLOATNO Continuation of the heatmap of marker genes for RCL clusters across developing kidney cell transcriptomes in Figure 11.1. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/511493v1_fig8_1.gif" ALT="Figure 81"> View larger version (34K): org.highwire.dtl.DTLVardef@1776682org.highwire.dtl.DTLVardef@1eb0783org.highwire.dtl.DTLVardef@13a2f91org.highwire.dtl.DTLVardef@10913b0_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 8.1:C_FLOATNO Each graph corresponds to a clustering of the kidney data (Section 8) for the specified Leiden resolution value (also called gamma). The graphs show the fraction of elements that are part of a cluster of size at most x for varying x. C_FIG O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/511493v1_fig8_3.gif" ALT="Figure 83"> View larger version (78K): org.highwire.dtl.DTLVardef@ebc1caorg.highwire.dtl.DTLVardef@1ffba5forg.highwire.dtl.DTLVardef@24f292org.highwire.dtl.DTLVardef@1b1795f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 8.3:C_FLOATNO Partition discrepancy (expressed as percentage) for Leiden and mcl clusterings. The i, j value denotes the percentage of elements needed to split off in order to transform [P]i into gcs([P]i, [P]j). Low i, j value compared to j, i implies that [P]i is close to being a subclustering of [P]j. C_FIG

bioinformatics↗

bioRxiv2021

Single-Cell Atlas of Common Variable Immunodeficiency reveals germinal center-associated epigenetic dysregulation in B cell responses

Common variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, is characterized by impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and a wide range of phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. We used single-cell epigenomics and transcriptomics to create a cell census of naive-to-memory B cell differentiation in a pair of CVID-discordant MZ twins. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B cells that mirror defective cell-cell communication defects following activation. These findings were validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naive-to-memory B-cell transition in CVID and reveal links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, paves the way for future diagnosis and treatments of CVID patients.

immunology↗

bioRxiv2021

Cells of the human intestinal tract mapped across space and time

The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. To comprehensively map cell lineages in the healthy developing, pediatric and adult human gut from ten distinct anatomical regions, as well as draining lymph nodes, we used singlecell RNA-seq and VDJ analysis of roughly one third of a million cells. This reveals the presence of BEST4+ absorptive cells throughout the human intestinal tract, demonstrating the existence of this cell type beyond the colon for the first time. Furthermore, we implicate IgG sensing as a novel function of intestinal tuft cells, and link these cells to the pathogenesis of inflammatory bowel disease. We define novel glial and neuronal cell populations in the developing enteric nervous system, and predict cell-type specific expression of Hirschsprungs disease-associated genes. Finally, using a systems approach, we identify key cell players across multiple cell lineages driving secondary lymphoid tissue formation in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. These data provide an unprecedented catalogue of intestinal cells, and new insights into cellular programs in development, homeostasis and disease.

cell biology↗

bioRxiv2021

Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated single-cell and spatial reference maps of the human uterus and 3D endometrial organoid cultures. We dissect the signalling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids highlights common pathways regulating the differentiation of secretory and ciliated lineage in vivo and in vitro. We show in vitro that downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. These mechanistic insights provide a platform for future development of treatments for a range of common endometrial disorders including endometriosis and carcinoma.

genomics↗

bioRxiv2020

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.

bioinformatics↗

bioRxiv2020

Comparison of visualisation tools for single-cell RNAseq data

In the last decade, single cell RNAseq (scRNAseq) datasets have grown from a single cell to millions of cells. Due to its high dimensionality, the scRNAseq data contains a lot of valuable information, however, it is not always feasible to visualise and share it in a scientific report or an article publication format. Recently, a lot of interactive analysis and visualisation tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review and compare several of the currently available analysis and visualisation tools and benchmark those that allow to visualize the scRNAseq data on the web and share it with others. To address the problem of format compatibility for most visualisation tools, we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualisation.

bioinformatics↗

bioRxiv2019

Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation

BackgroundThe Human Cell Atlas is a large international collaborative effort to map all cell types of the human body. Single cell RNA sequencing can generate high quality data for the delivery of such an atlas. However, delays between fresh sample collection and processing may lead to poor data and difficulties in experimental design. Despite this, there has not yet been a systematic assessment of the effect of cold storage time on the quality of scRNAseq\n\nResultsThis study assessed the effect of cold storage on fresh healthy spleen, oesophagus and lung from [≥]5 donors over 72 hours. We collected 240,000 high quality single cell transcriptomes with detailed cell type annotations and whole genome sequences of donors, enabling future eQTL studies. Our data provide a valuable resource for the study of these three organs and will allow cross-organ comparison of cell types.\n\nWe see little effect of cold ischaemic time on cell viability, yield, total number of reads per cell and other quality control metrics in any of the tissues within the first 24 hours. However, we observed higher percentage of mitochondrial reads, indicative of cellular stress, and increased contamination by background \"ambient RNA\" reads in the 72h samples in spleen, which is cell type specific.\n\nConclusionsIn conclusion, we present robust protocols for tissue preservation for up to 24 hours prior to scRNAseq analysis. This greatly facilitates the logistics of sample collection for Human Cell Atlas or clinical studies since it increases the time frames for sample processing.

genomics↗

bioRxiv2018

Visualisation and analysis of RNA-Seq assembly graphs

RNA-sequencing (RNA-Seq) is a powerful transcriptome profiling technology enabling transcript discovery and quantification. RNA-Seq data are large, and most commonly used as a source of genelevel quantification measurements, whilst the underlying assemblies of reads, if inspected, are usually viewed as sequence reads mapped on to a reference genome. Whilst sufficient for many needs, when the underlying transcript assemblies are complex, this visualisation approach can be limiting; errors in assembly can be difficult to spot and interpretation of splicing events is challenging.\n\nHere we report on the development of a graph-based visualisation method as a complementary approach to understanding transcript diversity and read assembly from short-read RNA-Seq data. Following the mapping of reads to the reference genome, read-to-read comparison is performed on all reads mapping to a given gene, producing a matrix of weighted similarity scores between reads. This is used to produce an RNA assembly graph where nodes represent reads derived from a cDNA and edges similarity scores between reads, above a defined threshold. Visualisation of resulting graphs is performed using Graphia Professional. This tool can render the often large and complex graph topologies that result from DNA/RNA sequence assembly in 3D space and supports info rmatio no verlay on to nodes, e.g. transcript models. We have also implemented an analysis pipeline for the creation of RNA assembly graphs with both a command-line and web-based interface that allows users to create and visualise these data. Here we demonstrate the utility of this approach on RNA-Seq data, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

bioinformatics↗

bioRxiv2017

A screen for combination therapies in BRAF/NRAS wild type melanoma identifies nilotinib plus MEK inhibitor as a synergistic combination

Despite recent therapeutic advances in the management of BRAFV600-mutant melanoma, there is still a compelling need for more effective treatments for patients who developed BRAF/NRAS wild type disease. Since the activity of single targeted agents is limited by innate and acquired resistance, we performed a high-throughput drug screen using 180 drug combinations to generate over 18,000 viability curves, with the aim of identifying agents that synergise to kill BRAF/NRAS wild type melanoma cells. From this screen we observed strong synergy between the tyrosine kinase inhibitor nilotinib and MEK inhibitors and validated this combination in an independent cell line collection. We found that AXL expression was associated with synergy to the nilotinib/MEK inhibitor combination, and that both drugs work in concert to suppress pERK. This finding was supported by genome-wide CRISPR screening which revealed that resistance mechanisms converge on regulators of the MAPK pathway. Finally, we validated the synergy of nilotinib/trametinib combination in vivo using patient-derived xenografts. Our results indicate that a nilotinib/MEK inhibitor combination may represent an effective therapy in BRAF/NRAS wild type melanoma patients.

cancer biology↗

bioRxiv2017

CD95L mRNA And CD95L Derived si- and shRNAs Kill Cancer Cells Through An RNAi Mechanism By Targeting Survival Genes

Over 80% of multiple tested siRNAs and shRNAs targeting CD95 or CD95 ligand (CD95L) induce a form of cell death characterized by simultaneous activation of multiple cell death pathways preferentially killing transformed and cancer stem cells. We now show these si/shRNAs kill cancer cells through canonical RNAi by targeting the 3UTR of critical survival genes in a unique form of off-target effect we call DISE (death induced by survival gene elimination). Drosha and Dicer deficient cells, devoid of most miRNAs, are hypersensitive to DISE, suggesting cellular miRNAs protect cells from this form of cell death. By testing 4666 shRNAs derived from the CD95 and CD95L mRNA sequences and an unrelated control gene, Venus, we have identified many toxic sequences - most of them located in the open reading frame of CD95L. We propose that using specific toxic RNAi-active sequences present in the genome can kill cancer cells.

cancer biology↗