Biology subjects

Gandrillon, O.

Publications and source records attributed to Gandrillon, O..

At least 19 recordsLinked to original sources

bioRxiv2026

Gene Regulatory Network Inference reveals tcf4 as a key a player in neuroblastoma gene expression circuitry

Neuroblastoma (NB), a pediatric cancer arising from disrupted sympathetic neuron differentiation, exhibits marked heterogeneity and limited therapeutic options. To better understand its molecular circuitry dynamics, we applied CardamomOT, a novel Gene Regulatory Network (GRN) inference framework, to single-cell RNA-seq data from patient-derived tumoroids. This approach models gene regulation via piecewise deterministic Markov processes, capturing transcriptional bursting and protein-mediated feedback, overcoming limitations of RNA velocity (e.g., gene independence and lack of biological time). We identified a continuous chromaffin-to-sympathoblast differentiation trajectory along which we selected 85 dynamically relevant genes enriched in cell cycle and DNA replication functions. Notably, 9 genes overlapped with those driving normal sympathoadrenal differentiation, underscoring tumor-normal tissue similarity. The inferred 85-genes network reproduced quite well experimental gene expression patterns in silico, and allowed to predict protein-level dynamics. Furthermore, it allowed to predict the effect of perturbations (both knock-out and overexpression) of hub genes (e.g., tcf4 and PLK1). We show that those perturbations significantly altered cell fate proportions in silico, with tcf4 KO increasing chromaffin-like cells and reducing proliferative late sympathoblasts. Predictions regarding tcf4 were tested using drug inhibition as a proxy for the gene KO. Using the BET inhibitor JQ1 indeed induced profound effect on the transcriptomic identity of our tumoroids. All of the 50 predicted tcf4 target genes were found to be significantly altered by JQ1 treatment. Finally cell fate proportions were also altered ex vivo closely resembling the predicted output. Our work therefore demonstrates that NB tumoroids retain a dynamic, differentiation-like architecture amenable to GRN modeling. Predicted druggable targets offer testable therapeutic avenues, including repurposing BET inhibitors or PLK1 inhibitors, potentially in combination.

cancer biology↗

bioRxiv2025

Cell Trajectory Inference based on Optimal Transport and a mechanistic stochastic gene expression model

Cellular differentiation is the biological process that leads a cell to opt for a particular cellular identity. Recently, single-cell RNA-sequencing has enabled the simultaneous measurement of gene expression levels at specific times for a large number of individual cells and a large number of genes. Repeating such measurements at different time points gives then access to the temporal variation, or transport, of a distribution on a gene expression space. The whole temporal trajectory of distributions thus characterizes the differentiation process at population level, but trajectories of individual cells are still out of reach since most measurement techniques are destructive. The optimal transport theory that has been used so far to infer cellular differentiation trajectories from time-stamped single-cell RNA-seq data involves solving the so-called Schrodinger problem in its most common version. This implies assuming that cells move, in the gene expression space, by diffusion. Yet, real gene dynamics are much more complex. In the present work, we assume that mRNA dynamics are characterized by brief and important production of RNA, with long periods of inactivity in between, and consider the so-called Bursty model of gene dynamics. We use this model to define a reference process for the Schrodinger problem. By comparing the solutions of the Schrodinger problems with a Diffusive and a Bursty reference process, under different conditions, we show that the Bursty model provides a better approximation of the underlying gene dynamics than the standard Diffusive process when inferring cell trajectories.

systems biology↗

bioRxiv2025

Multiscale modeling of the spatial structure of stem cells in neuroblastoma patient-derived tumoroids reveals a critical role for a short-range diffusive process

Neuroblastomas are heterogeneous pediatric tumors of the sympathetic nervous system for which treatments are still limited. Fundamental and applied approaches have been enabled thanks to the generation of patient-derived tumoroids (PDTs), ex vivo 3D structures used as avatars of the original tumor. We generated neuroblastoma PDTs and quantified the spatial distribution of CD133+ cancer stem cells using immunohistochemistry. We observed that those cells tend to aggregate in the PDT. In order to better understand the set of rules needed for generating such structures, we implemented a multiscale agent-based neuroblastoma tumoroid model. Model rules specify single cells fate based on its intracellular content, which dynamically evolves according to a stochastic gene regulatory network. The state of this network can be modulated by cell-to-cell signalling through neighbor cells fate decisions and, possibly, spatial location. We first observed that in the absence of any spatial rules for inter-cellular interactions, no spatial structure emerged. The addition of simple rules (signalling by cell-to-cell contact or differential cell adhesion) only marginally improved the quantitative agreement to the experimental dataset. In sharp contrast, the addition of short-range pro-stem cell diffusive signalling among stem cells produced very realistic 3D PDT-like structures. This works highlights the power of our multiscale approach to discard too simplistic rules and to propose a minimal set of hypotheses required to reproduce qualitatively and quantitatively experimentally observed spatial structures. In the case of neuroblastomas-derived PDTs, short-range spatial diffusion of stem-to-stem cell signalling proved to play a key role in successfully reconstructing the spatial structure.

cancer biology↗

bioRxiv2025

Inferring and simulating a gene regulatory network for the sympathoadrenal differentiation from single-cell transcriptomics in human.

BackgroundNeuroblastoma is a malignant childhood cancer with significant inter- and intrapatient heterogeneity arising from the abnormal differentiation of neural crest cells into sympathetic neurons. The lack of actionable mutations limits therapeutic options, highlighting the need to better understand the molecular mechanisms that drive this differentiation. Although RNA velocity has provided some insights, modeling regulatory relationships is limited. MethodsTo address this, we applied our integrated gene regulatory network (GRNs) inference (CARDAMOM) and simulation (HARISSA) tools using a published single-cell RNAseq dataset from human sympathoadrenal differentiation. ResultsOur analysis identified a 97-gene GRN that drives the transition from Schwann cell precursors to chromaffin cells and sympathoblasts, highlighting dynamic interactions such as self-reinforcing loops and toggle switches. The simulation of that GRN was able to reproduce very satisfactorily the experimentally observed gene expression distributions. ConclusionsAltogether, these findings demonstrate the utility of our GRN model framework for inferring GRN structure, even in the absence of a time-resolved dataset.

systems biology↗

bioRxiv2025

A temporary challenge by tumor cells can lead to a permanent partial-impairment of memory CD8 T cell function

Memory CD8 T cells typically exhibit improved effector functions compared to their naive counterparts. However, under certain activation conditions, such as chronic viral infections or cancer, these cells may develop functional defects. In this study, we compared the functional quality of memory CD8 T cells generated following tumor rejection with those arising from an acute viral infection. We found that tumor-induced (Tum-CD8) memory cells exhibited a distinct phenotype and transcriptomic profile compared with viral-induced (Vir-CD8) memory cells. These memory cells are characterized by the expression of inhibitory receptors and displayed altered functions including reduced IFN{gamma} and TNF production as well as changes in integrin expression. Additionally, the protective capacity of Tum-CD8 memory cells was flawed relative to that of Vir-CD8 memory cells. Importantly, the functional defects of Tum-CD8 persisted upon viral recall. Together, these findings indicate that transient tumoral stimulation can imprint a stable partial exhaustion-like program on memory CD8 T cells.

immunology↗

bioRxiv2024

MYC shapes ER-mitochondria calcium transfer by directly targeting ITPR1: implications for MYC-induced safeguard mechanisms and cancer

The MYC and NMYC transcription factors (TFs) play a key role in cell proliferation and are overexpressed in most cancer cells. However, in normal cells their overexpression triggers safeguard mechanisms promoting cell death and cellular senescence, which are bypassed in cancer cells. The mechanisms of action of this TF family are only partially understood. Here, we reveal that in normal cells MYC binds to the Inositol 1,4,5-Trisphosphate Receptor type 1 (ITPR1) gene and upregulates its expression, triggering an ER-mitochondria calcium (Ca2+) transfer, which is involved in MYC-induced cell death and senescence. Supporting a tumor suppressive role of MYC/ITPR1 axis, ITPR1 expression is generally decreased in cancer and reactivation of this pathway induces cancer cell death. Nevertheless, some cancer cells, generally expressing high levels of MYCN and/or MYC, also express high level of ITPR1, which correlates with high expression of BCL2, encoding an inhibitor of ITPR1. Strikingly, in high-risk MYCN-amplified neuroblastoma, ITPR1 expression is controlled by NMYC and its level correlates with worse patient survival. In these cells, blocking the interaction between BCL2 and ITPR1 induces mitochondrial Ca2+ accumulation and cell death, and decreases tumor size. Collectively these data highlight a new function of MYC factors by controlling Ca2+ signaling, which could constitute an unsuspected vulnerability for some cancer cells, including high-risk MYCN-amplified neuroblastoma cells.

cancer biology↗

bioRxiv2024

Modeling relaxation experiments with a mechanistic model of gene expression

BackgroundIn the present work, we aimed at modeling a relaxation experiment which consists in selecting a subfraction of a cell population and observing the speed at which the entire initial distribution for a given marker is reconstituted. MethodsFor this we first proposed a modification of a previously published mechanistic two-state model of gene expression to which we added a state-dependent proliferation term. This results in a system of two partial differential equations. Under the assumption of a linear proliferation rate, we could derive the asymptotic profile of the solutions of this model. ResultsIn order to confront our model with experimental data, we generated a relaxation experiment of the CD34 antigen on the surface of TF1-BA cells, starting either from the highest or the lowest CD34 expression levels. We observed in both cases that after approximately 25 days the distribution of CD34 returns to its initial stationary state. Numerical simulations, based on parameter values estimated from the dataset, have shown that the model solutions closely align with the experimental data from the relaxation experiments. ConclusionAltogether our results strongly support the notion that cells should be seen and modeled as probabilistic dynamical systems.

systems biology↗

bioRxiv2024

In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics

The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior. In order to model this response, we devised a multiscale agent-based approach using Simuscale software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process (PDMP) formalism. Cell fate - differentiation, proliferation, death - was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell-cell contacts, influencing the GRN behavior. Results show the ability of the model to correctly capture both population behaviour and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing that a higher degradation rate for the protein controlling cell death induces a later peak in the response.

systems biology↗

bioRxiv2023

Multiscale chromatin dynamics and high entropy in plant iPSC cultures

Plant protoplasts constitute the starting material to induce pluripotent cell masses in vitro competent for tissue regeneration. Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterized by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what are the factors influencing chromatin transitions during culturing is largely unknown. High-throughput imaging and a custom, supervised image analysis protocol extracting over 100 chromatin features unravelled a rapid, multiscale dynamics of chromatin patterns which trajectory strongly depends on nutrients availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating an intrinsic entropy as hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.

molecular biology↗

bioRxiv2023

In silico single-cell metabolism analysis unravels a new transition stage of CD8 T cells 4 days post-infection

CD8 T cell proper differentiation during antiviral responses relies on metabolic adaptations. Herein, we investigated global metabolic activity in single CD8 T cells along an in vivo response by estimating metabolic fluxes from single-cell RNA-sequencing data. The approach was validated by the observation of metabolic variations known from experimental studies on global cell populations, while adding temporally detailed information and unravelling yet undescribed sections of CD8 T cell metabolism that are affected by cellular differentiation. Furthermore, inter-cellular variability in gene expression level, highlighted by single cell data, and heterogeneity of metabolic activity 4 days post-infection, revealed a new transition stage accompanied by a metabolic switch in activated cells differentiating into full-blown effectors.

immunology↗

bioRxiv2023

TopoDoE: A Design of Experiment strategy for selection and refinement in ensembles of executable Gene Regulatory Networks

BackgroundInference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data. ResultsWe apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions. ConclusionThese results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.

bioinformatics↗

bioRxiv2023

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

bioinformatics↗

bioRxiv2023

An Image-Guided Microfluidic System for Single-Cell Lineage Tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which is compatible with downstream single-cell RNA sequencing analysis.

cell biology↗

bioRxiv2022

scAN10: A reproducible and standardized pipeline for processing 10X single cell RNAseq data

1Single cell transcriptomics has recently seen a surge in popularity, leading to the need for data analysis pipelines that are reproducible, modular, and interoperable across different systems and institutions. To meet this demand, we introduce scAN1.0, a processing pipeline for analyzing 10X single cell RNA sequencing data. scAN1.0 is built using the Nextflow DSL2 and can be run on most computational systems. The modular design of Nextflow pipelines enables easy integration and evaluation of different blocks for specific analysis steps. We demonstrate the usefulness of scAN1.0 by showing its ability to examine the impact of the mapping step during the analysis of two datasets: (i) a 10X scRNAseq of a human pituitary gonadotroph tumor dataset and (ii) a murine 10X scRNAseq acquired on CD8 T cells during an immune response.

bioinformatics↗

bioRxiv2022

Differentiation is accompanied by a progressive loss in transcriptional memory

Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (sc-RT-qPCR and scRNA-seq). We identified that the gene transcription profiles of differentiating sister-cells are more similar to each-other than to those of non related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister-cells than between self-renewing sister-cells. Furthermore, a continuous increase in this divergence from first generation to second generation was observed when comparing differentiating cousin-cells to self renewing cousin-cells. Our results are in favor of a continuous and gradual erasure of transcriptional memory during the differentiation process.

cell biology↗

bioRxiv2022

One model fits all: combining inferenceand simulation of gene regulatorynetworks

The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation. Author summaryGene regulatory network (GRN) inference is an old problem, to which single-cell data has recently offered new challenges and breakthrough potential. Many GRN inference methods based on single-cell transcriptomic data have been developed over the last few years, while GRN simulation tools have also been proposed for generating synthetic datasets with realistic features. However, except for benchmarking purposes, these two fields remain largely disconnected. In this work, building on a combination of two methods we recently described, we show that a particular GRN model can be used simultaneously as an inference tool, to reconstruct a biologically relevant network from time-course single-cell gene expression data, and as a simulation tool, to generate realistic transcriptional profiles in a non-trivial way through gene interactions. This integrated strategy demonstrates the benefits of using the same executable model for both simulation and inference.

systems biology↗

bioRxiv2022

CD8 memory precursor cells generation is a continuous process

In this work, we studied the generation of memory precursor cells following an acute infection by analyzing single-cell RNA-seq data that contained CD8 T cells collected during the postinfection expansion phase. We used different tools to reconstruct the developmental trajectory that CD8 T cells followed after activation. Cells that exhibited a memory precursor signature were identified and positioned on this trajectory. We found that memory precursors are generated continuously with increasing numbers being generated over time. Similarly, expression of genes associated with effector functions was also found to be raised in memory precursors at later time points. The ability of cells to enter quiescence to differentiate into memory cells was confirmed by BrdU pulse-chase experiment in vivo. Analysis of cell counts indicates that the vast majority of memory cells are generated at later time points from cells that have extensively divided.

immunology↗

bioRxiv2022

Evidence for close molecular proximity between reverting and undifferentiated cells

According to Waddingtons epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or "attractor states" to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 hours the ability to self-renew when transferred back in self-renewal conditions. Despite those intriguing and promising results, the underlying molecular state of those "reverting" cells remains unexplored. The aim of the present study was therefore to molecularly characterize the T2EC reversion process by combining advanced statistical tools to make the most of single cell transcriptomic data. For this purpose, T2EC, initially maintained in a self-renewal medium (0H), were induced to differentiate for 24h (24H differentiating cells); then a part of these cells was transferred back to the self-renewal medium (48H reverting cells) and the other part was maintained in the differentiation medium for another 24h (48H differentiating cells). For each time point, cell transcriptomes were generated using scRT-qPCR and scRNAseq. Our results showed a strong overlap between 0H and 48H reverting cells when applying dimensional reduction. Moreover, the statistical comparison of cell distributions and differential expression analysis indicated no significant differences between these two cell groups. Interestingly, gene pattern distributions highlighted that, while 48H reverting cells have gene expression pattern more similar to 0H cells, they retained traces of their engagement in the differentiation process. Finally, Sparse PLS analysis showed that only the expression of 3 genes discriminates 48H reverting and 0H cells. Altogether, we show that reverting cells return to an earlier molecular state almost identical to undifferentiated cells and demonstrate a previously undocumented physiological and molecular plasticity during the differentiation process, which most likely results from the dynamic behavior of the underlying molecular network.

cell biology↗