Biology subjects

Argelaguet, R.

Publications and source records attributed to Argelaguet, R..

14 recordsLinked to original sources

bioRxiv2024

Epigenetic priming of embryonic enhancer elements coordinates developmental gene networks

Embryonic development requires the accurate spatiotemporal execution of cell lineage-specific gene expression programs, which are controlled by transcriptional enhancers. Developmental enhancers adopt a primed chromatin state prior to their activation; however how this primed enhancer state is established, maintained, and how it affects the regulation of developmental gene networks remains poorly understood. Here, we use comparative multi-omic analyses of human and mouse early embryonic development to identify subsets of post-gastrulation lineage-specific enhancers which are epigenetically primed ahead of their activation, marked by the histone modification H3K4me1 within the epiblast. We show that epigenetic priming occurs at lineage-specific enhancers for all three germ layers, and that epigenetic priming of enhancers confers lineage-specific regulation of key developmental gene networks. Surprisingly in some cases, lineage-specific enhancers are epigenetically marked already in the zygote, weeks before their activation during lineage specification. Moreover, we outline a generalisable strategy to use naturally occurring human genetic variation to delineate important sequence determinants of primed enhancer function. Our findings identify an evolutionarily conserved program of enhancer priming and begin to dissect the temporal dynamics and mechanisms of its establishment and maintenance during early mammalian development.

developmental biology↗

bioRxiv2024

Eomes directs the formation of spatially and functionally diverse extra-embryonic hematovascular tissues

During mouse gastrulation, extraembryonic mesoderm (ExEM) contributes to the extraembryonic yolk sac (YS) and allantois, both of which are essential for successful gestation. Although the genetic networks coordinating intra-embryonic mesodermal subtype specification are well-studied, the mechanisms driving ExEM diversification are poorly understood. Here, we reveal that embryoid body in vitro differentiation generates two distinct lineages of mesodermal cells matching YS and allantois respectively. Combining in vitro models with in vivo chimeric embryo analysis, we discover that Eomesodermin (Eomes) regulates the formation of a subset of YS-fated ExEM but is dispensable for allantois formation. Furthermore, simultaneous disruption of Eomes and T impedes the specification of any YS or allantois mesoderm, indicating compensatory roles for T during allantois formation when Eomes is disrupted. Our study highlights previously unrecognized functional and mechanistic diversity in ExEM diversification and endothelial development and introduces a tractable EB model to dissect the signaling pathways and transcriptional networks driving the formation of key extraembryonic tissues.

developmental biology↗

bioRxiv2023

Tracking Early Mammalian Organogenesis - Prediction and Validation of Differentiation Trajectories at Whole Organism Scale

Early organogenesis represents a key step in animal development, where pluripotent cells divide and diversify to initiate formation of all major organs. Here, we used scRNA-Seq to profile over 300,000 single cell transcriptomes sampled in 6 hour intervals from mouse embryos between E8.5 and E9.5. Combining this dataset with our previous E6.5 to E8.5 atlas resulted in a densely sampled time course of over 400,000 cells from early gastrulation to organogenesis. Computational lineage reconstruction at full organismal scale identified complex waves of blood and endothelial development, including a new molecular programme for somite-derived endothelium. To assess developmental fates across the primitive streak, we dissected the E7.5 primitive streak into four adjacent regions, performed scRNA- Seq and predicted cell fates computationally. We next defined early developmental state/fate relationships experimentally by a combination of orthotopic grafting, microscopic analysis of graft contribution as well as scRNA-Seq to transcriptionally determine cell fates of the grafted primitive streak regions after 24h of in vitro embryo culture. Experimentally determined fate outcomes were in good agreement with the fates predicted computationally, thus demonstrating how classical grafting experiments can be revisited to establish high-resolution cell state/fate relationships. Such interdisciplinary approaches will benefit future studies in both developmental biology as well as guide the in vitro production of cells for organ regeneration and repair.

developmental biology↗

bioRxiv2022

Inter-gastruloid heterogeneity revealed by single cell transcriptomics time course: implications for organoid based perturbation studies

Recent advances in organoid and genome editing technologies are allowing for perturbation experiments at an unprecedented scale. However, before doing such experiments it is important to understand the gene expression profile in each of the organoids cells, as well as how much heterogeneity there is between individual organoids. Here we characterise an organoid model of mouse gastrulation called gastruloids using single cell RNA-sequencing of individual organoids at half-day intervals between day 3 and day 5 of differentiation (roughly corresponding to E6.5-E8.75 in vivo). Our study reveals multiple differentiation trajectories that have hitherto not been characterised in gastruloids. Intriguingly, we observe that individual gastruloids displayed a strong bias towards producing either mesodermal (largely somitic) or ectodermal (specifically neural) cell types. This bifurcation is already seen at the earliest sampled time point, and is characterised by increased activity of WNT-associated pathways in mesodermally-biased gastruloids as compared to neurally-biased gastruloids. Notably, at day 5, mesodermal gastruloids show an increase in the proportion of neural cells, while neural gastruloids do not produce notably more mesodermal cells. This is in line with previous studies on how the balance between these cell types is regulated. We demonstrate using in silico simulations that without proper understanding of the inter-organoid heterogeneity, perturbation experiments have either very high false positive or negative rates, depending on the statistical model used. Thus in future studies, modelling of inter-organoid heterogeneity will be crucial when designing organoid-based perturbation studies. HighlightsO_LIA single cell RNA-sequencing time course of day 3 to day 5 mouse gastruloids reveals multiple mesodermal and neural differentiation trajectories hitherto uncharacterised in gastruloids C_LIO_LISingle gastruloid, single cell RNA-sequencing of mouse gastruloids reveals that gastruloids are either mesodermally- or neurally-biased C_LIO_LIThe two classes of gastruloid arise from differences in response strength to the WNT-agonist chiron C_LIO_LIAt day 5, mesodermal gastruloids start making more neural cells, while neural gastruloids do not make more mesodermal cells, aligning with previously studied in vivo feedback loops C_LIO_LIWe show using simulations that understanding interorganoid heterogeneity is a crucial consideration in the design and analysis of well-powered organoid-based perturbation studies C_LI

genomics↗

bioRxiv2022

Decoding gene regulation in the mouse embryo using single-cell multi-omics

Following gastrulation, the three primary germ layers develop into the major organs in a process known as organogenesis. Single-cell RNA sequencing has enabled the profiling of the gene expression dynamics of these cell fate decisions, yet a comprehensive map of the interplay between transcription factors and cis-regulatory elements is lacking, as are the underlying gene regulatory networks. Here we generate a multi-omics atlas of mouse early organogenesis by simultaneously profiling gene expression and chromatin accessibility from tens of thousands of single cells. We develop a computational method to leverage the multimodal readouts to predict transcription factor binding events in cis-regulatory elements, which we then use to infer gene regulatory networks that underpin lineage commitment events. Finally, we show that these models can be used to generate in silico predictions of the effect of transcription factor perturbations. We validate this experimentally by showing that Brachyury is essential for the differentiation of neuromesodermal progenitors to somitic mesoderm fate by priming cis-regulatory elements. The data set can be interactively explored at https://www.bioinformatics.babraham.ac.uk/shiny/shiny_multiome_organogenesis/

developmental biology↗

bioRxiv2021

Modelling human zygotic genome activation in 8C-like cells in vitro

The remodelling of the epigenome and transcriptome of the fertilised oocyte to establish totipotency in the zygote and developing embryo is one of the most critical processes in mammalian embryogenesis. Zygotic or embryonic genome activation (ZGA, EGA) in the 2-cell embryo in mouse, and the 8-cell embryo in humans, constitutes the first major wave of transcription. Failure to initiate ZGA leads to developmental defects, and contributes to the high attrition rates of human pre-implantation embryos. Due to limitations in cell numbers and experimental tractability, the mechanisms that regulate human embryonic genome activation in the totipotent embryo remain poorly understood. Here we report the discovery of human 8-cell like cells (8CLCs) specifically among naive embryonic stem cells, but not primed pluripotent cells. 8CLCs express ZGA marker genes such as ZSCAN4, LEUTX and DUXA and their transcriptome closely resembles that of the 8-cell human embryo. 8-cell like cells reactivate 8-cell stage specific transposable elements such as HERVL and MLT2A1 and are characterized by upregulation of the DNA methylation regulator DPPA3. 8CLCs show reduced SOX2 protein, and can be identified based on expression of the novel ZGA-associated protein markers TPRX1 and H3.Y in vitro. Overexpression of the transcription factor DUX4. as well as spliceosome inhibition increase ZGA-like transcription and enhance TPRX1+ 8CLCs formation. Excitingly, the in vitro identified 8CLC marker proteins TPRX1 and H3.Y are also expressed in 8-cell human embryos at the time of genome activation and may thus be relevant in vivo. The discovery of 8CLCs provides a unique opportunity to model and manipulate human ZGA-like transcriptional programs in vitro, and might provide critical functional insights into one of the earliest events in human embryogenesis in vivo. HighlightsO_LIZGA markers and transposable elements are expressed in 8CLCs among naive human stem cells C_LIO_LIThe transcription factor DUX4 and spliceosome inhibition induce ZGA-like transcription C_LIO_LI8CLC marker proteins TPRX1 and H3.Y are expressed in nuclei of 8-cell human embryos C_LIO_LI8CLCs serve as a novel in vitro model for human ZGA C_LI

molecular biology↗

bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

bioinformatics↗

bioRxiv2020

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Transcriptional and epigenetic profiling of single-cells has advanced our knowledge of the molecular bases of gastrulation and early organogenesis. However, current approaches rely on dissociating cells from tissues, thereby losing the crucial spatial context that is necessary for understanding cell and tissue interactions during development. Here, we apply an image-based single-cell transcriptomics method, seqFISH, to simultaneously and precisely detect mRNA molecules for 387 selected target genes in 8-12 somite stage mouse embryo tissue sections. By integrating spatial context and highly multiplexed transcriptional measurements with two single-cell transcriptome atlases we accurately characterize cell types across the embryo and demonstrate how spatially-resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain-hindbrain boundary and the developing gut tube. Our spatial atlas uncovers axes of resolution that are not apparent from single-cell RNA sequencing data - for example, in the gut tube we observe early dorsal-ventral separation of esophageal and tracheal progenitor populations. In sum, by computationally integrating high-resolution spatially-resolved gene expression maps with single-cell genomics data, we provide a powerful new approach for studying how and when cell fate decisions are made during early mammalian development.

developmental biology↗

bioRxiv2020

Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO

Factor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.

bioinformatics↗

bioRxiv2020

Intestinal transkingdom analysis on the impact of antibiotic perturbation in health and critical illness

Bacterial microbiota play a critical role in mediating local and systemic immunity, and shifts in these microbial communities have been linked to impaired outcomes in critical illness. Emerging data indicate that other intestinal organisms, including bacteriophages, viruses of eukaryotes, fungi, and protozoa, are closely interlinked with the bacterial microbiota and their host, yet their collective role during antibiotic perturbation and critical illness remains to be elucidated. Here, multi-omics factor analysis (MOFA), a novel computational strategy to systematically integrate viral, fungal and bacterial sequence data, we describe the functional impact of exposure to broad-spectrum antibiotics in healthy volunteers and critically ill patients. We observe that a loss of the anaerobic intestinal environment is directly correlated with an overgrowth of aerobic pathobionts and their corresponding bacteriophages, as well as an absolute enrichment of opportunistic yeasts capable of causing invasive disease. These findings further illustrate the complexity of transkingdom interactions within the intestinal environment, and show that modulation of the bacterial component of the microbiome has implications extending beyond this kingdom alone.

microbiology↗

bioRxiv2019

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

Technological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.\n\nTo illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.

bioinformatics↗

bioRxiv2019

Single cell multi-omics profiling reveals a hierarchical epigenetic landscape during mammalian germ layer specification

Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan. Recent studies employing single cell RNA-sequencing have identified major transcriptional changes associated with germ layer specification. Global epigenetic reprogramming accompanies these changes, but the role of the epigenome in regulating early cell fate choice remains unresolved, and the coordination between different epigenetic layers is unclear. Here we describe the first single cell triple-omics map of chromatin accessibility, DNA methylation and RNA expression during the exit from pluripotency and the onset of gastrulation in mouse embryos. We find dynamic dependencies between the different molecular layers, with evidence for distinct modes of epigenetic regulation. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of local lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements, driven by loss of methylation in enhancer marks and a concomitant increase of chromatin accessibility. In striking contrast, the epigenetic landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or epigenetically remodelled prior to overt cell fate decisions during gastrulation, providing the molecular logic for a hierarchical emergence of the primary germ layers.\n\nHighlightsO_LIFirst map of mouse gastrulation using comprehensive single cell triple-omic analysis.\nC_LIO_LIExit from pluripotency is associated with a global repressive epigenetic landscape, driven by a sharp gain of DNA methylation and a gradual decrease of chromatin accessibility.\nC_LIO_LIDNA methylation and chromatin accessibility changes in enhancers, but not in promoters, are associated with germ layer formation.\nC_LIO_LIMesoderm and endoderm enhancers become open and demethylated upon lineage commitment.\nC_LIO_LIEctoderm enhancers are primed in the early epiblast and protected from the global repressive dynamics, supporting a default model of ectoderm commitment in vivo.\nC_LI

developmental biology↗

bioRxiv2017

Multi-Omics factor analysis disentangles heterogeneity in blood cancer

Multi-omic studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous datasets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation, and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex-vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multiomics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

bioinformatics↗

bioRxiv2017

Joint Profiling Of Chromatin Accessibility, DNA Methylation And Transcription In Single Cells

Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

genomics↗