Biology subjects

Gandrillon, O.

Publications and source records attributed to Gandrillon, O..

7 recordsLinked to original sources

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↗

bioRxiv2018

Model-based assessment of the Role of Uneven Partitioning of Molecular Content on Heterogeneity and Regulation of Differentiation in CD8 T-cell Immune Responses

Activation of naive CD8 T-cells can lead to the generation of multiple effector and memory subsets. Multiple parameters associated with activation conditions are involved in generating this diversity that is associated with heterogeneous molecular contents of activated cells. Naive cell polarisation upon antigenic stimulation and the asymmetric division that results are known to be a major source of heterogeneity and cell fate regulation. The consequences of stochastic uneven partitioning of molecular content upon subsequent divisions remain unclear. Here we aim at studying the impact of uneven partitioning on molecular-content heterogeneity and then on the immune response dynamics at the cellular level. To do so, we introduce a multiscale mathematical model of the CD8 T-cell immune response in the lymph node. In the model, cells are described as agents evolving and interacting in a 2D environment while a set of differential equations, embedded in each cell, models the regulation of intra and extracellular proteins involved in cell differentiation. Based on the analysis of in silico data at the single cell level, we show that immune response dynamics can be explained by the molecular-content heterogeneity generated by uneven partitioning at cell division. In particular, uneven partitioning acts as a regulator of cell differentiation and induces the emergence of two coexisting subpopulations of cells exhibiting antagonistic fates. We show that the degree of unevenness of molecular partitioning, along all cell divisions, affects the outcome of the immune response and can promote the generation of memory cells.

immunology↗

bioRxiv2018

Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts

The in vivo erythropoiesis, which is the generation of mature red blood cells in the bone marrow of whole organisms, has been described by a variety of mathematical models in the past decades. However, the in vitro erythropoiesis, which produces red blood cells in cultures, has received much less attention from the modelling community. In this paper, we propose the first mathematical model of in vitro erythropoiesis. We start by formulating different models and select the best one at fitting experimental data of in vitro erythropoietic differentiation. It is based on a set of linear ODE, describing 3 hypothetical populations of cells at different stages of differentiation. We then compute confidence intervals for all of its parameters estimates, and conclude that our model is fully identifiable. Finally, we use this model to compute the effect of a chemical drug called Rapamycin, which affects all states of differentiation in the culture, and relate these effects to specific parameter variations. We provide the first model for the kinetics of in vitro cellular differentiation which is proven to be identifiable. It will serve as a basis for a model which will better account for the variability which is inherent to experimental protocol used for the model calibration.

systems biology↗

bioRxiv2018

WASABI: a dynamic iterative framework for gene regulatory network inference

Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. In conclusion, WASABI is a versatile algorithm which should help biologists to fully exploit the power of time-stamped single-cell data.

systems biology↗

bioRxiv2018

Deep proteomic analysis of chicken erythropoiesis

In contrast to mammalian erythroid cells that lost their nucleus at the end of the differentiation process, circulating chicken erythrocytes, like erythrocytes of most other non-mammalian vertebrates, are nucleated although their nucleus is believed to be transcriptionally silent. This major difference suggests that the erythroid differentiation process is likely to present both similarities and differences in mammals compared to other vertebrates. Since proteins are the major cellular effectors, analysis of the proteome is more prone to reflect true differences than analysis of the pattern of mRNA expression. We have previously reported the evolution of the proteome of human erythroid cells throughout their differentiation process. Here we report the analysis of the proteome of chicken erythroblasts during their terminal differentiation. We used the T2EC cellular model that allows to obtain homogenous populations of immature erythroblasts. Induction of their terminal differentiation led to their maturation and the possibility to obtain cells at different differentiation stages. Mass spectrometry analysis of these cell populations allowed the absolute quantification of 6167 proteins throughout the terminal differentiation process. Beside many proteins with similar expression patterns between chicken and human erythroblasts, like SLC4A1 (Band3), GATA1 or CD44, this analysis also revealed that other important proteins like Kit or other GATA transcription factors exhibit fully different patterns of expression.

developmental 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↗

bioRxiv2017

Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment.

Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

cell biology↗