A. Mahadevan1, J. Robinson2, D. Wagner3, and A.A. Qutub1,*
1Departments of Bioengineering, 2Electrical and Computer Engineering, 3Biochemistry & Cell Biology, Rice University, Houston, TX
Decoding how neurons form can advance our understanding of how the brain makes new memories, degenerates in disease, and self-repairs after injury. Neural differentiation appears species-specific, and single neurons can take on unique functions. These observations make it critical to study neural differentiation in human cells at the single cell level. However the process is immensely complex, and dependent on spatial and temporal cues from neighboring cells. Neural progenitor cells transform into neurons through intricate coordination of chemical, mechanical and electrical cell-cell communication.
A quantitative understanding of how neural progenitor cells (NPCs) communicate can shed light on this complexity and point to new ways to optimize neural regenerative strategies. To that end, we characterized the dynamics of human embryonic NPC and human induced pluripotent NPC self-organization during differentiation by integrating cellular omics analysis (proteomics, multiplexed immunostaining) with a spatially-derived graphical image analysis, live reporter assays for cell cycle (Cdt1-Venus, Geminin-mCherry), and a functional Ca2+ network analysis that captured coordinated signaling between pairs of cells. This single cell profiling enabled us to develop a spatiotemporal model of how single human neurons form in the context of a developing neuronal network. We independently confirmed neural differentiation stages identified by the graph-based approach through patch-clamp electrophysiology and immunochemistry. We also captured neural differentiation events including the formation of rosette-like structures and neurite extension.
Results identified unique protein expression patterns (including changes in cell cycle proteins and transcription factors regulating responses to oxidative stress) predicting the progression of NPCs into functional neurons. Through the integration of the computational and experimental methods, we observed the synchronization of cell cycle among small ‘connected cliques’ of single cells that forecasted changes in calcium dynamics. At the multicellular level, we identified relationships between network efficiency and modularity in the networks, which we hypothesized indicate a shift in the communication mode of cells from one driven by localized, adjacency-dependent chemical signaling to electrical conduction.
These results provide insight into how functional neuronal networks develop from single cells, opening the door for controlled modulation of neural differentiation for therapeutic applications. Ongoing studies are characterizing the differentiation process for NPCs derived from patients with known gene mutations that affect brain development. Here we summarize these findings, presenting them in the context of the overall goal to map how single neural progenitor cells form functional neuronal networks through the tight coordination of morphological, chemical and electrical signaling.