Biology subjects

Rivaud, P.

Publications and source records attributed to Rivaud, P..

4 recordsLinked to original sources

bioRxiv2025

Designing signaling environments to modulate neural progenitor cell differentiation with regulatory network models

During development, combinatorial signals modulate progenitor differentiation to build populations of diverse cell types. Classical deterministic fate-control circuits focus on stable commitment but do not explain how a diverse cell population is generated and tuned by signals. Here, using single-cell RNA-seq profiling of neural progenitor cells (NPCs) upon combinatorial signal inputs, we identify a candidate circuit-level mechanism for controlling cell type compositions through a noise-gated toggle switch. With 40 signaling conditions, we show that combinatorial signal inputs modulate the neuronal-glial population proportions following a simple log-linear model. Regulatory network models built by D-SPIN quantitatively capture cell state distribution shifts induced by signal combinations, and indicate a circuit model for the probabilistic fate specification by a toggle switch with controlled transcriptional heterogeneity. Noise levels drive the system from highly plastic to committed states, allowing the relative level of signal inputs to shift population structures. The model further predicts an early bipotent state expressing lineage-specifying factors of both fates, which we verify in our single-cell profiling experiment and a mouse embryonic visual cortex development dataset. Our work identifies a design principle of noise-driven additive regulation in the logarithmic cell-fate probability space, providing a new strategy for population-level stem cell control.

systems biology↗

bioRxiv2023

Single-cell transcriptome landscape of developing fetal gonads defines somatic cell lineage specification in humans

Gonad development is an exciting model to study cell fate commitment. However, the specification and differentiation of somatic cell lineages within the testis and the ovary are incompletely characterized, especially in humans. In fact, a better understanding of sex determination first requires the identification of all the cell types involved and of their dynamic expression programs. Here we present a comprehensive analysis of approximately 128,000 single cells collected from 33 fetal testes and ovaries between 5 and 12 postconceptional weeks. In particular, a focused analysis of somatic cells allowed us to identify a common population of bipotential progenitors derived from the coelomic epithelium of both male and female gonads and capable of committing to either a steroidogenic or a supporting fate. Moreover, we have shown that early supporting cells, prior to further differentiation into Sertoli or granulosa cells, also give rise to the rete testis/ovarii lineage. Finally, we found that the ovary retains the capacity to feed the supporting cell pool for an extended period of time, directly from the surface epithelial cells and, bypassing the bipotential progenitor step. Altogether, our results provide an unprecedented revisiting of the human gonadal sex determination process.

developmental biology↗

bioRxiv2019

Designing signaling environments to steer transcriptional diversity in neural progenitor cell populations

Stem and progenitor populations within developing embryos are diverse, composed of different subpopulations of precursor cells with varying developmental potential. How these different subpopulations are coordinately regulated by their signaling environments is not well understood. In this paper we develop a framework for controlling progenitor population structure in cell culture using high-throughput single cell mRNA-seq and computational analysis. We find that the natural transcriptional diversity of neural stem cell populations from the developing mouse brain collapses during in vitro culture. Cell populations are depleted of committed neuroblast progenitors and become dominated by a single pre-astrocytic cell population. By analyzing the response of neural stem cell populations to forty combinatorial signaling conditions, we demonstrate that signaling environments can restructure cell populations by modulating the relative abundance of pre-astrocytic and pre-neuronal subpopulations according to a simple log-linear model. Our work demonstrates that single-cell RNA-seq can be applied to learn how to modulate the diversity of stem cell populations, providing a new strategy for population-level stem cell control. HighlightsO_LINatural progenitor diversity in the brain collapses during in vitro culture to a single progenitor type C_LIO_LILoss of progenitor diversity alters fate potential of cells during differentiation C_LIO_LILarge scale single-cell signaling screen identifies signals that reshape population structure towards neuronal cell types C_LIO_LISignals regulate population structure according to a simple log-linear model C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/890087v3_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@e5286corg.highwire.dtl.DTLVardef@c0e69borg.highwire.dtl.DTLVardef@640b20org.highwire.dtl.DTLVardef@16779e_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2018

Dissecting heterogeneous cell-populations across signaling and diseaseconditions with PopAlign

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture, and tracks gene expression and cell abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals or physiological change.

bioinformatics↗