Proneurogenic Ligands Defined by Modeling Developing Cortex Growth Factor Communication Networks

Abstract

The neural stem cell decision to self-renew or differentiate is tightly regulated by its microenvironment. Here, we have asked about this microenvironment, focusing on growth factors in the embryonic cortex at a time when it is largely comprised of neural precursor cells (NPCs) and newborn neurons. We show that cortical NPCs secrete factors that promote their maintenance, while cortical neurons secrete factors that promote differentiation. To define factors important for these activities, we used transcriptome profiling to identify ligands produced by NPCs and neurons, cell-surface mass spectrometry to identify receptors on these cells, and computational modeling to integrate these data. The resultant model predicts a complex growth factor environment with multiple autocrine and paracrine interactions. We tested this communication model, focusing on neurogenesis, and identified IFNγ, Neurturin (Nrtn), and glial-derived neurotrophic factor (GDNF) as ligands with unexpected roles in promoting neurogenic differentiation of NPCs in vivo.

Deciphering cell-cell communication in the developing mammalian brain

“…However, the predominant cells are the radial precursors and newborn neurons, and we therefore hypothesized that during this developmental window, the cortical growth factor environment is largely determined by these two cell types. In our very recently published work, 4 we provided support for this hypothesis, and in the course of our studies, obtained a global overview of the growth factor environment during cortical neurogenesis. Here, we will discuss those studies and their implications for our understanding of neural precursor cells and neural development.…”

“…During the early neurogenic period in the embryonic cortex, the predominant cell types are the radial precursor cells (RPs), which populate the Ventricular/Subventricular Zones adjacent to the lateral ventricles, and newborn cortical neurons (Neurons) that migrate along the radial precursor basal processes to populate the Cortical Plate. In our recent paper 4 we showed that radial precursors produce secreted factors that act in an autocrine fashion to promote their own self-renewal, while newborn cortical neurons produce secreted factors that act in a paracrine fashion to enhance the genesis of neurons from radial precursors. We modeled these potential cell-cell communication networks, and identified 3 growth factors, GDNF, NRTN and IFNg, that promote neurogenesis.…”

“…8 Answering these questions will require much future work but these answers have the potential to transform how we think about the extrinsic control of embryonic neural precursor cells, and about cell fate selection in general. In this regard, we suggest that it is the combination of functional biologic analyses together with the types of iterative computational and systems biology approaches that we deployed in our recent work 4 that will allow us to answer these questions and that will provide an unbiased, global platform for unraveling the cell-cell communication networks that underlie the genesis and function of neural circuitry.…”

CD24: A Rheostat That Modulates Cell Surface Receptor Signaling of Diverse Receptors

“…In some cases, transcriptomic data may be used to predict partners by their co-expression with CD24 (Ayre et al, 2016), similar to the approach that identified novel ligand-receptor interactions regulating neuronal stem cell renewal (Yuzwa et al, 2016). Alternatively, identifying common signaling outcomes between CD24 and cell specific surface receptors could be used to predict receptor partners.…”

The Contribution of Spatial and Temporal Molecular Networks in the Induction of Long-term Memory and Its Underlying Synaptic Plasticity

“…Moreover, this integration provides more information than any one signaling network alone, such as coincidence detection. Importantly, two recent publications utilizing transcriptomic profiling in development support this notion and demonstrate unexpected instances of interaction and integration [169,170]. The task of characterizing the temporal and spatial profiles of a molecular network is itself a difficult undertaking.…”