A computational model for the coordination of neural progenitor self-renewal and differentiation through Hes1 dynamics

Pfeuty, Benjamin

doi:10.1242/dev.112649

Cited by 28 publications

(43 citation statements)

References 68 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3H). An early cell fate decision point tied to cell cycle is consistent with previous work indicating cell fate decisions in neurogenesis are made in G1 (36,37). However, previous models of asymmetric neurogenic divisions suggest that only a few key transcription factors of the "daughter" lineage are expressed in the asymmetrically dividing cell, whereas we observe early induction of more extensive cell type transcriptional programs (36,37).…”

supporting

confidence: 91%

“…An early cell fate decision point tied to cell cycle is consistent with previous work indicating cell fate decisions in neurogenesis are made in G1 (36,37). However, previous models of asymmetric neurogenic divisions suggest that only a few key transcription factors of the "daughter" lineage are expressed in the asymmetrically dividing cell, whereas we observe early induction of more extensive cell type transcriptional programs (36,37). Cells are producing transcriptomes representing two cell types from a single nucleus, prior to cytokinesis, demonstrating the usefulness of single-cell transcriptomics for furthering our understanding the transcriptional dynamics involved in early neural development (38,39).…”

supporting

confidence: 88%

See 1 more Smart Citation

A single cell transcriptomic analysis of human neocortical development

Polioudakis

Torre-Ubieta

Langerman³

et al. 2018

Preprint

View full text Add to dashboard Cite

Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We defined single cell transcriptomic profiles for 40,000 cells at mid-gestation to identify cell types in the developing human neocortex. We define expression profiles corresponding to all known major cell types at this developmental period and identify multiple transcription factors and co-factors expressed in specific cell types, providing an unprecedented resource for understanding human neocortical development including the first single-cell characterization of human subplate neurons. We characterize major developmental trajectories during early neurogenesis, showing that cell type differentiation occurs on a continuum that involves transitions that tie cell cycle progression with early cell fate decisions. We use these data to deconvolute regulatory networks and map neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.One Sentence Summary: Comprehensive single cell transcriptomes in developing human cortex inform models of cell diversity, differentiation and disease risk. Main text:The human cortex is composed of billions of cells estimated to encompass hundreds or thousands of distinct cell types each with unique functions (1, 2). It is reasonable to assume that to understand a system as complex as the human cortex, it is necessary to understand its components (3,4). Ground breaking work in mouse revealed the power of single-cell transcriptomics for providing a framework for understanding the complexity and heterogeneity of cell types in the brain (5-10). The availability of high quality tissue and advances in single-cell transcriptomic technologies now permit us to catalog the cell type diversity of the human cortex in a comprehensive and unbiased manner (11). Despite the enormous progress that has been made in characterizing early cortical development (1,(12)(13)(14)(15)(16), many of the molecular mechanisms underpinning the generation, differentiation, and development of the diverse types of cells remain largely unknown (17). Molecular taxonomies of cortical cell types from developing human brains enable us to understand the mechanisms of neurogenesis and how the remarkable cellular diversity found in the human cortex is achieved (18-22). Developing knowledge of neocortical cell types and their transcriptional programs during this epoch is a key step in understanding mechanistic dysregulation in neurodevelopmental disorders with cell type resolution. Several recent studies have taken a first step in this direction, analyzing several hundred, or a few thousand cells from developing human brain (18-22). However, advances in te...

show abstract

supporting

confidence: 91%

supporting

confidence: 88%

A single cell transcriptomic analysis of human neocortical development

Polioudakis

Torre-Ubieta

Langerman³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The spore and stalk phenotypes are later stabilized at the mound and fruiting body stages, presumably though positive feedback generated by intracellular or autocrine signaling mechanisms (12,13). Quite similar developmental features are observed in early mammalian embryos where the small pool of cells (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) cell stage) of the inner cell mass is equally segregated in two populations of primitive endoderm (Gata6-positive) and epiblast (Nanog-positive) lineage cells (14). Cells are first biased to a specific lineage in a reversible manner as Nanog-positive cells contribute to increase extracellular Fgf4 levels that influence Fgf4-bound cells by preventing accumulation of Nanog and promoting accumulation of Gata6 (15).…”

Section: Introductionmentioning

confidence: 76%

Requirements for efficient cell-type proportioning: regulatory timescales, stochasticity and lateral inhibition

Pfeuty¹,

Kaneko²

2016

Phys. Biol.

View full text Add to dashboard Cite

The proper functioning of multicellular organisms requires the robust establishment of precise proportions between distinct cell-types. This developmental differentiation process typically involves intracellular regulatory and stochastic mechanisms to generate cell-fate diversity as well as intercellular mechanisms to coordinate cell-fate decisions at tissue level. We thus surmise that key insights about the developmental regulation of cell-type proportion can be captured by the modeling study of clustering dynamics in population of inhibitory-coupled noisy bistable systems. This general class of dynamical system is shown to exhibit a very stable two-cluster state, but also frustrated relaxation, collective oscillations or steady-state hopping which prevents from timely and reliably reaching a robust and well-proportioned clustered state. To circumvent these obstacles or to avoid fine-tuning, we highlight a general strategy based on dual-time positive feedback loops, such as mediated through transcriptional versus epigenetic mechanisms, which improves proportion regulation by coordinating early and flexible lineage priming with late and firm commitment. This result sheds new light on the respective and cooperative roles of multiple regulatory feedback, stochasticity and lateral inhibition in developmental dynamics.

show abstract

“…The expression of cell-fate determination factors such as bHLH transcription factors like Hes1 and Ngn2, and proteins involved in cell-cell communication pathways such as Notch/Delta proteins, have been shown to be tightly coupled with each other 17,39 . Computational modeling studies have predicted that Notch-Hes1 intercellular signaling affects differentiation and cell cycle progression of individual cells and this signaling is important for the maintenance of an optimal balance between differentiating cells and self-renewing progenitor cells 40 . The spatial dynamics of cell-cell signaling and its impact on single-cell differentiation status is an intriguing subject for future study.…”

Section: Discussionmentioning

confidence: 99%

Living Neural Networks: Dynamic Network Analysis of Developing Neural Progenitor Cells

Mahadevan

et al. 2016

Preprint

View full text Add to dashboard Cite

12The architecture of the mammalian brain has been characterized through decades of innovation in the 13 field of network neuroscience. However, the assembly of the brain from progenitor cells is an immensely 14 complex process, and a quantitative understanding of how neural progenitor cells (NPCs) form neural 15 networks has proven elusive. Here, we introduce a method that integrates graph-theory with long-term 16 imaging of differentiating human NPCs to characterize the evolution of spatial and functional network 17 features in NPCs during the formation of neural networks in vitro. We find that the rise and fall in spatial 18 network efficiency is a characteristic feature of the transition from immature NPC networks to mature 19 neural networks. Furthermore, networks at intermediate stages of differentiation that display high spatial 20 network efficiency also show high levels of network-wide spontaneous electrical activity. These results 21 support the view that network-wide signaling in immature progenitor cells gives way to a hierarchical 22 form of communication in mature neural networks. We also leverage graph theory to study the spatial 23 features of individual cell types in developing cultures, uncovering spatial features of polarized 24 neuroepithelium. Finally, we employ our method to uncover aberrant network features in a 25 neurodevelopmental disorder using induced pluripotent stem cell (iPSC) models. The "Living Neural 26 Networks" method bridges the gap between developmental neurobiology and network neuroscience, and 27 offers insight into the relationship between developing and mature neural networks. 28 29 42 the fundamental architectural features of neural networks, no models have been available to study the 43 development of human neural networks from progenitor cells in a quantitative manner, nor have there 44 been tools to characterize the spatial and functional dynamics of network formation at the cellular level. 45 46 Cell-cell communication among neural progenitor cells (NPCs) is an essential aspect of human nervous 47 65 features of cortical neurogenesis 19,20 . In addition, iPSC models have been used to study aberrant 66 development in several neurodevelopmental disorders 21,22 . The ubiquity of stem cell differentiation 67 protocols provides a unique opportunity to study the self-assembly of neural networks in a dish. 69In this report, we introduce a method to study network features of developing human neural networks at 70 the global and single-cell levels. We use long-term imaging coupled with automated image analysis to 71 develop network representations of cell spatial topology and assign spatial coordinates to individual cells 72 (Figure 1). We use our method to demonstrate that two independent human NPC cell lines exhibit a 73 similar rise and fall in spatial network efficiency that characterizes the maturation of in vitro neural 74 networks. We demonstrate that high spatial network efficiencies at intermediate stages of neural 75 differentiation are linked with high levels of sp...

show abstract

A computational model for the coordination of neural progenitor self-renewal and differentiation through Hes1 dynamics

Cited by 28 publications

References 68 publications

A single cell transcriptomic analysis of human neocortical development

A single cell transcriptomic analysis of human neocortical development

Requirements for efficient cell-type proportioning: regulatory timescales, stochasticity and lateral inhibition

Living Neural Networks: Dynamic Network Analysis of Developing Neural Progenitor Cells

Contact Info

Product

Resources

About