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

Abstract

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...

On the nature and use of models in network neuroscience

“…Such models can be either data representations or first-principles theories and can either include extensive biophysical realism or explain functional phenomenology [80,108,118,129,130]. Network models at this scale can be used to ask questions about cellular mechanisms of growth and development or transitions from neural progenitor cells to neurons [131,132]. Recent work has extended these approaches to even smaller scales, for example to investigate specific sections of a cell such as neurite outgrowth [133].…”

cytoNet: Spatiotemporal Network Analysis of Cell Communities

“…Spatial type II graphs ( Figure 2a ) showed a rise and fall in global network efficiency during neural differentiation (compared to randomized null models in which edges were rewired while preserving degree distribution; Figure 2b) . We hypothesize that these trends, independently confirmed in multiple NPC lines (2), reflect a transition from topologies favoring global to hierarchical information flow. We further explored this possibility through calcium imaging.…”

“…In this case study, we describe data obtained using ReNCell VM human neural progenitor cells, in which spontaneous differentiation was triggered through withdrawal of growth factors, leading to rapid cell cycle exit and formation of dense neuronal networks in 5 days (2). We captured spontaneous calcium activity at days 1, 3, and 5 after withdrawal of growth factors.…”

“…(g) Neighborhood similarity score across time for medium‐density culture. Figures 2a-d adapted from reference (2).…”

“…Coupled with its ease-of-use, cytoNet’s versatility makes it accessible to researchers across domains. We originally designed the network modeling approach to study populations of developing neurons (2) and characterize how vascular cells respond to neurotrophic factors (16, 17). Here we extend the approach to case studies in a number of other biological systems.…”