Biology subjects

Thomas-Chollier, M.

Publications and source records attributed to Thomas-Chollier, M..

12 recordsLinked to original sources

bioRxiv2025

Regeneration of distinct complex structures in the annelid Platynereis is partially based on common morphological, cellular, and molecular events

Many metazoan species harbor fascinating regenerative capabilities, but the underlying mechanisms remain poorly understood. Whether the capacity to regenerate complex structures successfully relies on common or divergent features is still an open question. To identify the key molecular and cellular elements necessary for successful regeneration, we investigated the efficient regeneration of two distinct complex structures in the annelid worm Platynereis dumerilli. By combining classical morphological and developmental approaches, with state-of-the-art single-cell RNA sequencing and analysis, we conducted a comprehensive comparison of locomotory appendage and posterior part regeneration. We uncovered the rich cell type diversity of this Platynereis appendage, over a third of which are nerve-related populations, highlighting the importance of its sensory function. We also described its regenerative process at the morphological and cellular levels, defining stages that include the formation of a proliferative blastema. We then compared posterior part and appendage blastemas, specifically assessing their cell diversity and cell differentiation trajectories. We found transcriptionally similar epithelial and mesodermal progenitors at play during posterior and appendage regeneration, although their specific trajectories differed to some extent. Our work, revealing partial morphological, molecular and cellular parallels between these two efficient regenerations within a single species, sets the foundation for addressing the fundamental question of regeneration success in animals.

developmental biology↗

bioRxiv2025

A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data

Alternative splicing plays a crucial role in transcriptomic complexity, yet remains difficult to resolve at the single-cell level due to the limitations of short-read technologies. Coupling single-cell with long-read sequencing offers full-length transcript coverage, enabling more accurate isoform detection. Multiple specialized computational tools tailored for single-cell and spatial long-read transcriptomics have been developed, with diverse strategies. To compare the effectiveness of these approaches, we generated paired short-read and Nanopore long-read single-cell datasets, tailored for benchmarking bioinformatics tools. We evaluated ten state-of-the-art methods, spanning four analytical dimensions: barcodes and UMI detection, demultiplexing and UMI clustering, gene-level expression profiling, and isoform detection and quantification. Using real and simulated datasets across different protocols, sequencing depths and chemistries, we assessed the accuracy, robustness, and scalability of each tool. Our results revealed method-specific trade-offs, and highlight the importance of sequencing quality and UMI correction strategies. This benchmark provides a practical resource for optimizing isoform analysis and accurate gene expression profiling in single-cell and spatial transcriptomics using long-read sequencing. Our benchmarking workflow is designed to be reusable, thereby enabling method developers to compare their own approaches against the set of reference methods evaluated in this work.

bioinformatics↗

bioRxiv2024

A low CDKN1c/p57kip2 expression in spinal progenitors drives the transition from proliferative to neurogenic modes of division

During vertebrate neurogenesis, a progressive transition from symmetric proliferative to asymmetric neurogenic divisions is critical to balance growth and differentiation. Using single-cell RNA-seq data from chick embryonic neural tube, we identify the cell cycle regulator Cdkn1c as a key regulator of this transition. While Cdkn1 is classically associated with neuronal cell cycle exit, we show that its expression initiates at low levels in neurogenic progenitors. Functionally targeting the onset of this expression impacts the course of neurogenesis: Cdkn1c knockdown impairs neuron production by favoring proliferative symmetric divisions. Conversely, inducing a low-level CDKN1c misexpression in self-expanding progenitors forces them to prematurely undergo neurogenic divisions. CDKN1c exerts this effect primarily by inhibiting the cyclin D-CDK complex and lengthening G1 phase duration. We propose that Cdkn1c acts as a dual driver of the neurogenic transition whose low level of expression first controls the progressive entry of progenitors into neurogenic modes of division before a higher expression mediates cell cycle exit in daughter cells. This highlights that the precise control of neurogenesis regulators expression levels sequentially imparts distinct functions, and is essential for proper neural development.

developmental biology↗

bioRxiv2024

AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow

MotivationThe combination of long-read sequencing technologies like Oxford Nanopore with single-cell RNA sequencing (scRNAseq) assays enables the detailed exploration of transcriptomic complexity, including isoform detection and quantification, by capturing full-length cDNAs. However, challenges remain, including the lack of advanced simulation tools that can effectively mimic the unique complexities of scRNAseq long-read datasets. Such tools are essential for the evaluation and optimization of isoform detection methods dedicated to single-cell long read studies. ResultsWe developed AsaruSim, a workflow that simulates synthetic single-cell long-read Nanopore datasets, closely mimicking real experimental data. AsaruSim employs a multi-step process that includes the creation of a synthetic UMI count matrix, generation of perfect reads, optional PCR amplification, introduction of sequencing errors, and comprehensive quality control reporting. Applied to a dataset of human peripheral blood mononuclear cells (PBMCs), AsaruSim accurately reproduced experimental read characteristics. Availability and implementationThe source code and full documentation are available at: https://github.com/GenomiqueENS/AsaruSim. Data availabilityThe 1,090 Human PBMCs count matrix and cell type annotation files are accessible on zenodo under DOI: 10.5281/zenodo.12731408.

bioinformatics↗

bioRxiv2023

Alternative splicing of the stress-sensitive regulator CIRBP sharpens the cellular response to bacterial infection

Cell autonomous responses to intracellular bacteria largely depend on gene expression reorganization. To gain isoform-level resolution into these regulations, we combined long- and short-read transcriptomic analyses of the response of intestinal epithelial cells to infection by the foodborne pathogen Listeria monocytogenes. Among the most striking isoform-based regulations, expression of the cellular stress response regulator CIRBP (cold-inducible RNA-binding protein) and of several SRSFs (serine/arginine-rich splicing factors) switched from canonical transcripts to nonsense-mediated decay-sensitive isoforms by inclusion of "poison exons". We showed that damage to host cell membranes caused by bacterial pore-forming toxins (listeriolysin O, perfringolysin, streptolysin, or aerolysin) led to the dephosphorylation of SRSF proteins via the inhibition of the kinase activity of CLK1, thereby driving CIRBP alternative splicing. CIRBP isoform usage was found to have consequences on infection, since selective repression of canonical CIRBP reduced intracellular bacterial load while that of the poison exon-containing isoform exacerbated it. Consistently, CIRBP-bound mRNAs were shifted towards stress-relevant transcripts in infected cells, with increased mRNA levels or reduced translation efficiency for some targets. Our results thus generalize the alternative splicing of CIRBP and SRSFs as a common response to biotic or abiotic stresses by extending its relevance to the context of bacterial infection.

molecular biology↗

bioRxiv2021

Eoulsan 2: an efficient workflow manager for reproducible bulk, long-read and single-cell transcriptomics analyses

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSCore sequencing facilities produce huge amounts of sequencing data that need to be analysed with automated workflows to ensure reproducibility and traceability. Eoulsan is a versatile open-source workflow engine meeting the needs of core facilities, by automating the analysis of a large number of samples. Its core design separates the description of the workflow from the actual commands to be run. This originality simplifies its usage as the user does not need to handle code, while ensuring reproducibility. Eoulsan was initially developed for bulk RNA-seq data, but the transcriptomics applications have recently widened with the advent of long-read sequencing and single-cell technologies, calling for the development of new workflows. ResultWe present Eoulsan 2, a major update that (i) enhances the workflow manager itself, (ii) facilitates the development of new modules, and (iii) expands its applications to long reads RNA-seq (Oxford Nanopore Technologies) and scRNA-seq (Smart-seq2 and 10x Genomics). The workflow manager has been rewritten, with support for execution on a larger choice of computational infrastructure (workstations, Hadoop clusters, and various job schedulers for cluster usage). Eoulsan now facilitates the development of new modules, by reusing wrappers developed for the Galaxy platform, with support for container images (Docker or Singularity) packaging tools to execute. Finally, Eoulsan natively integrates novel modules for bulk RNA-seq, as well as others specifically designed for processing long read RNA-seq and scRNA-seq. Eoulsan 2 is distributed with ready-to-use workflows and companion tutorials. Availability and implementationEoulsan is implemented in Java, supported on Linux systems and distributed under the LGPL and CeCILL-C licenses at: http://outils.genomique.biologie.ens.fr/eoulsan/. The source code and sample workflows are available on GitHub: https://github.com/GenomicParisCentre/eoulsan. A GitHub repository for modules using the Galaxy tool XML syntax is further provided at: https://github.com/GenomicParisCentre/eoulsan-tools Contacteoulsan@bio.ens.psl.eu

bioinformatics↗

bioRxiv2020

Logical modeling of dendritic cells in vitro differentiation from human monocytes unravels novel transcriptional regulatory interactions

Dendritic cells are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they have been used as targets for immunotherapy. Monocytes can differentiate into dendritic cells in vitro in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB, and PI3K). However, the transcriptional regulation responsible for dendritic cell differentiation from monocytes (moDCs) remains unknown. By curating scientific literature on moDCs differentiation, we established a preliminary logical model that helped us identify missing information for the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active and inactive promoters, together with differentially expressed genes in monocytes, moDCs, and macrophages (which correspond to an alternative cell fate). We then used this functional genomic information to predict novel targets for the identified TFs. We established a second logical model integrating this information, which enabled us to recapitulate the main established facts regarding moDCs differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies based on moDCs regulatory network.

immunology↗

bioRxiv2020

Cis acting variation is common, can propagates across multiple regulatory layers, but is often buffered in developmental programs

Precise patterns of gene expression are driven by interactions between transcription factors, regulatory DNA sequence, and chromatin. How DNA mutations affecting any one of these regulatory layers is buffered or propagated to gene expression remains unclear. To address this, we quantified allele-specific changes in chromatin accessibility, histone modifications, and gene expression in F1 embryos generated from eight Drosophila crosses, at three embryonic stages, yielding a comprehensive dataset of 240 samples spanning multiple regulatory layers. Genetic variation in cis-regulatory elements is common, highly heritable, and surprisingly consistent in its effects across embryonic stages. Much of this variation does not propagate to gene expression. When it does, it acts through H3K4me3 or alternatively through chromatin accessibility and H3K27ac. The magnitude and evolutionary impact of mutations is influenced by a genes regulatory complexity (i.e. enhancer number), with transcription factors being most robust to cis-acting, and most influenced by trans-acting, variation. Overall, the impact of genetic variation on regulatory phenotypes appears context-dependent even within the constraints of embryogenesis.

genomics↗

bioRxiv2020

Computational verification of large logical models - application to the prediction of T cell response to checkpoint inhibitors

At the crossroad between biology and mathematical modelling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalisation and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation", enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behaviour of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.

systems biology↗

bioRxiv2020

Deciphering and modelling the TGF-β signalling interplays specifying the dorsal-ventral axis of the sea urchin embryo.

During sea urchin development, secretion of Nodal and BMP2/4 ligands and their antagonists Lefty and Chordin from a ventral organizer region specifies the ventral and dorsal territories. This process relies on a complex interplay between the Nodal and BMP pathways through numerous regulatory circuits. To decipher the interplay between these pathways, we used a combination of treatments with recombinant Nodal and BMP2/4 proteins and a computational modelling approach. We assembled a logical model focusing on cell responses to signalling inputs along the dorsal-ventral axis, which was extended to cover ligand diffusion and enable multicellular simulations. Our model simulations accurately recapitulate gene expression in wild type embryos, accounting for the specification of ventral ectoderm, ciliary band and dorsal ectoderm. Our model simulations further recapitulate various morphant phenotypes, reveals a dominance of the BMP pathway over the Nodal pathway, and stresses the crucial impact of the rate of Smad activation in D/V patterning. These results emphasise the key role of the mutual antagonism between the Nodal and BMP2/4 pathways in driving early dorsal-ventral patterning of the sea urchin embryo. Summary StatementWe propose a predictive computational model of the regulatory network controlling the dorsal-ventral axis specification in sea urchin embryos, and highlight key features of Nodal and BMP antagonism.

developmental biology↗

bioRxiv2019

RSAT Var-tools: an accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding

Gene regulatory regions contain short and degenerated DNA sites recognized by transcription factors (TFs). When such regions harbor SNPs, the DNA motifs where TFs bind may be affected, thereby altering the transcriptional regulation of the target genes. Such regulatory SNPs have been implicated as causal variants in GWAS studies. In this study, we describe the application of the programs Var-tools designed to predict regulatory variants, and present four case studies to illustrate their usage and applications. In brief, Var-tools facilitate i) obtaining variation information, ii) interconversion of variation file formats, iii) retrieval of sequences surrounding variants, and iv) calculating the change on predicted TF affinity scores between alleles, using motif scanning approaches. Notably, the tools support the analysis of haplotypes. The tools are included within the well-maintained suite Regulatory Sequence Analysis Tools (RSAT, http://rsat.eu), and accessible through a web interface that currently enables analysis of five metazoa and ten plant genomes. Vart-tools can also be used in command-line with any locally-installed Ensembl genome. Users can input personal collections of variants and motifs, providing flexibility in the analysis.

bioinformatics↗

bioRxiv2018

Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise.

The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of dozens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GRs core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed distinct DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise.

systems biology↗