bioRxiv Science⌕ Search

Biology subjects

Aerts, S.

Publications and source records attributed to Aerts, S..

49 records · Page 3Linked to original sources
bioRxiv2020

Actuation Enhances Patterning in Human Neural Tube Organoids

Tissues achieve their complex spatial organization through an interplay between gene regulatory networks, cell-cell communication, and physical interactions mediated by mechanical forces. Current strategies to generate in-vitro tissues have largely failed to implement such active, dynamically coordinated mechanical manipulations, relying instead on extracellular matrices which respond to, rather than impose mechanical forces. Here we develop devices that enable the actuation of organoids. We show that active mechanical forces increase growth and lead to enhanced patterning in an organoid model of the neural tube derived from single human pluripotent stem cells (hPSC). Using a combination of single-cell transcriptomics and immunohistochemistry, we demonstrate that organoid mechanoregulation due to actuation operates in a temporally restricted competence window, and that organoid response to stretch is mediated extracellularly by matrix stiffness and intracellularly by cytoskeleton contractility and planar cell polarity. Exerting active mechanical forces on organoids using the approaches developed here is widely applicable and should enable the generation of more reproducible, programmable organoid shape, identity and patterns, opening avenues for the use of these tools in regenerative medicine and disease modelling applications.

bioengineering↗
bioRxiv2019

Genomic adaptations to aquatic and aerial life in mayflies and the origin of wings in insects

The first winged insects underwent profound morphological and functional transformations leading to the most successful animal radiations in the history of earth. Despite this, we still have a very incomplete picture of the changes in their genomes that underlay this radiation. Mayflies (Ephemeroptera) are one of the extant sister groups of all other winged insects and therefore are at a key phylogenetic position to understand this radiation. Here, we describe the genome of the cosmopolitan mayfly Cloeon dipterum and study its expression along development and in specific organs. We discover an expansion of odorant-binding proteins, some expressed specifically in the breathing gills of aquatic nymphs, suggesting a novel sensory role for gills. In contrast, as flying adults, mayflies make use of an enlarged set of opsins and utilise these visual genes in a sexually dimorphic manner, with some opsins expressed only in males. Finally, to illuminate the origin of wings, we identify a core set of deeply conserved wing-specific genes at the root of the pterygote insects. Globally, this is the first comprehensive study of the structure and expression of the genome of a paleopteran insect and shows how its genome has kept a record of its functional adaptations.

evolutionary biology↗
bioRxiv2019

Cross-species analysis of melanoma enhancer logic using deep learning

Genomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. Deciphering the combinatorial code by which these binding sites are assembled within enhancers is indispensable to understand their regulatory involvement in establishing a cells phenotype, especially within biological systems with dysregulated gene regulatory networks, such as melanoma. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators. To perform an in-depth analysis of the enhancer architecture, we trained a deep neural network, called DeepMEL, to classify melanoma enhancers not only in the human genome, but also in other species. DeepMEL revealed the presence, organisation and positional specificity of important transcription factor binding sites. Together, this extensive analysis of the melanoma enhancer code allowed us to propose the concept of a core regulatory complex binding to melanocytic enhancers, consisting of SOX10, TFAP2A, MITF and RUNX, and to disentangle their individual roles in regulating enhancer accessibility and activity.

genomics↗
bioRxiv2019

Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics

Single-cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single-cell RNA-seq and single-cell ATAC-seq atlases of the Drosophila eye-antennal disc and spatially integrate the data using a virtual latent space that mimics the organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify [~]85% of enhancers in which chromatin accessibility and enhancer activity are coupled. Next, we infer enhancer-to-gene relationships in the virtual space, finding that genes are regulated by multiple redundant enhancers. Exploiting cell-type specific enhancers, we deconvolute cell-type specific effects of bulk-derived chromatin accessibility QTLs. Finally, we discover that Prospero drives neuronal differentiation through the binding of a GGG motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue.

genomics↗
bioRxiv2019

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

Melanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this \"transition\" state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.

genomics↗
bioRxiv2018

Establishment of the mayfly Cloeon dipterum as a new model system to investigate insect evolution

The great capability of insects to adapt to new environments promoted their extraordinary diversification, resulting in the group of Metazoa with the largest number of species distributed worldwide. To understand this enormous diversity, it is essential to investigate lineages that would allow the reconstruction of the early events in the evolution of insects. However, research on insect ecology, physiology, development and evolution has mostly focused on few well-established model species. The key phylogenetic position of mayflies within Paleoptera, as the sister group of the rest of winged insects and life history traits of mayflies make them an essential order to understand insect evolution. Here, we describe the established of a continuous culture system of the mayfly Cloeon dipterum and a series of experimental protocols and -omics resources that allow the study of its development and its great regenerative capability. Thus, the establishment of Cloeon as an experimental platform paves the way to understand genomic and morphogenetic events that occurred at the origin of winged insects.

evolutionary biology↗
bioRxiv2018

ChIP-seq meta-analysis yields high quality training sets for enhancer classification

Genome-wide prediction of enhancers depends on high-quality positive and negative training sets. The use of ChIP-seq peaks as positive training data can be problematic due to high degrees of indirectly bound regions, and often poor overlap between experimental conditions.\n\nHere we explore meta-analysis of ChIP-seq data to generate high-quality training data for enhancer modeling. Our method is based on rank aggregation and identifies a core set of directly bound regions per transcription factor, exploiting between five and twenty ChIP-seq data sets per factor. We applied this method to six different transcription factors, namely TP53, REST, SOX2, GRHL2, HIF1A and PPARG. Sequence analysis and modeling of recurrently bound enhancers yielded distinct enhancer features for the different factors, whereby binding sites of REST and TP53 are strongly determined by their motif; binding of GRHL2 and SOX2 is determined by nucleosome positioning; and binding of PPARG and HIF1A depends on other transcription factors. In conclusion, meta-analysis of ChIP-seq peaks, and centering on motifs, allowed discovering new properties of transcription factor binding.

bioinformatics↗
bioRxiv2018

Bcl6 promotes neurogenic conversion through transcriptional repression of multiple self-renewal-promoting extrinsic pathways.

During neurogenesis, progenitors switch from self-renewal to differentiation through the interplay of intrinsic and extrinsic cues, but how these are integrated remains poorly understood. Here we combine whole genome transcriptional and epigenetic analyses with in vivo functional studies and show that Bcl6, a transcriptional repressor known to promote neurogenesis, acts as a key driver of the neurogenic transition through direct silencing of a selective repertoire of genes belonging to multiple extrinsic pathways promoting self-renewal, most strikingly the Wnt pathway. At the molecular level, Bcl6 acts through both generic and pathway-specific mechanisms. Our data identify a molecular logic by which a single cell-intrinsic factor ensures robustness of neural cell fate transition by decreasing responsiveness to the extrinsic pathways that favor self-renewal.

neuroscience↗
bioRxiv2018

Cis-topic modelling of single cell epigenomes

Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover \"cis-regulatory topics\" and stable cell states from sparse single-cell epigenomics data. After benchmarking cisTopic on single-cell ATAC-seq data, single-cell DNA methylation data, and semi-simulated single-cell ChIP-seq data, we use cisTopic to predict regulatory programs in the human brain and validate these by aligning them with co-expression networks derived from single-cell RNA-seq data. Next, we performed a time-series single-cell ATAC-seq experiment after SOX10 perturbations in melanoma cultures, where cisTopic revealed dynamic regulatory topics driven by SOX10 and AP-1. Finally, machine learning and enhancer modelling approaches allowed to predict cell type specific SOX10 and SOX9 binding sites based on topic specific co-regulatory motifs. cisTopic is available as an R/Bioconductor package at http://github.com/aertslab/cistopic.

bioinformatics↗
bioRxiv2017

A single-cell catalogue of regulatory states in the ageing Drosophila brain

The diversity of cell types and regulatory states in the brain, and how these change during ageing, remains largely unknown. Here, we present a single-cell transcriptome catalogue of the entire adult Drosophila melanogaster brain sampled across its lifespan. Both neurons and glia age through a process of \"regulatory erosion\", characterized by a strong decline of RNA content, and accompanied by increasing transcriptional and chromatin noise. We identify more than 50 cell types by specific transcription factors and their downstream gene regulatory networks. In addition to neurotransmitter types and neuroblast lineages, we find a novel neuronal cell state driven by datilografo and prospero. This state relates to neuronal birth order, the metabolic profile, and the activity of a neuron. Our single-cell brain catalogue reveals extensive regulatory heterogeneity linked to ageing and brain function and will serve as a reference for future studies of genetic variation and disease mutations.

genomics↗
bioRxiv2017

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

bioinformatics↗
bioRxiv2017

Identification Of cis-Regulatory Mutations Generating De Novo Edges In Personalized Cancer Gene Regulatory Networks

The identification of functional non-coding mutations is a key challenge in the field of genomics, where whole-genome re-sequencing can swiftly generate a set of all genomic variants in a sample, such as a tumor biopsy. The size of the human regulatory landscape places a challenge on finding recurrent cis-regulatory mutations across samples of the same cancer type. Therefore, powerful computational approaches are required to sift through the tens of thousands of non-coding variants, to identify potentially functional variants that have an impact on the gene expression profile of the sample. Here we introduce an integrative analysis pipeline, called - cisTarget, to filter, annotate and prioritize non-coding variants based on their putative effect on the underlying 'personal' gene regulatory network. We first validate -cisTarget by re-analyzing three cases of oncogenic non-coding mutations, namely the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genome of ten cancer cell lines of six different cancer types, and used matched transcriptome data and motif discovery to infer master regulators for each sample. We identified candidate functional non-coding mutations that generate de novo binding sites for these master regulators, and that result in the up-regulation of nearby oncogenic drivers. We finally validated the predictions using tertiary data including matched epigenome data. Our approach is generally applicable to re-sequenced cancer genomes, or other genomes, when a disease- or sample-specific gene signature is available for network inference. -cisTarget is available from http://mucistarget.aertslab.org.

genomics↗