Biology subjects

Gonin-Giraud, S.

Publications and source records attributed to Gonin-Giraud, S..

9 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

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↗

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

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

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

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↗

bioRxiv2019

Erythroid differentiation displays a peak of energy consumption concomitant with glycolytic metabolism rearrangements

It has been suggested that a switch from glycolysis, with lactate production, toward mitochondrial oxidative phosphorylation (OXPHOS) could be a driving force during stem cell differentiation.\n\nBased upon initial results, from our previous work, showing a drop in LDHA mRNA level during the differentiation of chicken erythroid progenitors, we studied metabolism behavior to question whether such switch might also be operating in those cells.\n\nWe first analyzed the level of 9 enzymes, including LDHA, involved either in glycolysis or OXPHOS, in self-renewing and differentiating cells. Our results suggest that erythroid differentiation might be accompanied by an enhancement of the respiratory chains and glycolysis activities at 12h, followed by a strong decline of the glycolytic pathway and a stabilization of OXPHOS.\n\nTo confirm that OXPHOS might be increased and glycolysis decreased during erythroid differentiation, we measured lactate concentration and mitochondrial membrane potential (MMP) of self-renewing and differentiating cells. Our findings show that at 12h-24h of differentiation, a surge of energy is needed, which could be fueled jointly by glycolysis and OXPHOS. Then the energy demand comes back to normal and might be supplied by OXPHOS instead of lactate production through glycolysis.\n\nThese results support the hypothesis that erythroid differentiation is associated with a metabolic switch from glycolysis to OXPHOS.\n\nWe also assessed LDHA role in erythroid progenitors self-renewal and the metabolic status changes. Inhibition experiments showed that LDHA activity could be involved in the maintenance of erythroid progenitors self-renewal, and its decline could influence their metabolic status.\n\nFinally, we investigated whether these metabolic rearrangements were necessary for erythroid differentiation. The addition of an inhibitor of the respiratory chains affected progenitors ability to differentiate, suggesting that the metabolic switch from glycolysis toward OXPHOS might act as a driving force for erythroid differentiation.\n\nAuthor summarySingle-cell based gene expression data from one of our previous publication pointed out significant variations of LDHA level, an important metabolism player, during erythroid differentiation. Deeper investigations highlighted that a metabolic switch occurred along differentiation of erythroid cells as previously emphasized in stem cell differentiation. More precisely our finding showed that self-renewing progenitor cells relied mostly upon a glycolytic, lactate-productive, metabolism and required LDHA activity, whereas differentiating cells, mainly involved the aerobic mitochondrial oxidative phosphorylation (OXPHOS). However our careful kinetic study demonstrated that these metabolic rearrangements were coming along with a particular temporary event, occurring within the first 24h of erythroid differentiation. The activity of glycolytic metabolism and OXPHOS rose jointly with ATP production at 12-24h of the differentiation process before lactate-productive glycolysis sharply fall down and energy needs decline. Finally, our results showed that the metabolic switch mediated through LDHA drop and OXPHOS upkeep might be necessary for erythroid differentiation. We also discuss the possibility that metabolism, gene expression and epigenetics could act together in a circular manner as a driving force for differentiation.

cell biology↗

bioRxiv2018

Drugs modulating stochastic gene expression affect the erythroid differentiation process

BackgroundTo understand how a metazoan cell makes the decision to differentiate, we assessed the role of stochastic gene expression (SGE) during the erythroid differentiation process. Our hypothesis is that stochastic gene expression has a role in single-cell decision-making. In agreement with this hypothesis, we and others recently showed that SGE significantly increased during differentiation. However, evidence for the causative role of SGE is still lacking. Such demonstration would require being able to experimentally manipulate SGE levels and analyze the resulting impact of these variations on cell differentiation.\n\nResultWe identified three drugs that modulate SGE in primary erythroid progenitor cells. Artemisinin and Indomethacin simultaneously decreased SGE and reduced the amount of differentiated cells. Inversely, -methylene-{gamma}-butyrolactone-3 (MB-3) simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment.\n\nConclusionUsing single-cell analysis and modeling tools, we provide experimental evidence that in a physiologically relevant cellular system, control of SGE can directly modify differentiation, supporting a causal link between the two.

systems biology↗

bioRxiv2017

Automated cell cycle and cell size measurements for single-cell gene expression studies

Recent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

cell biology↗