This proposal seeks to develop a robust theory of how single neural cells form electrically active networks. The project integrates emerging methods in computer science, systems biology, neuroengineering and developmental biology to offer insight into the brain's organization. Results of experiments performed in this project have the potential to impact the design of new computing devices, a $300B industry. Methods introduced by the investigators can be used broadly by scientists to rapidly characterize brain cells, and can aid in the discovery of new therapies for neurological diseases, which affect 1 in 6 people worldwide.
Neural differentiation, the process of neural progenitor cells transforming into neurons, holds the key to understanding the brain's ability to self-repair. Understanding this complex process can inform us how the structure of neural networks relates to their function, which is an important unsolved problem in neuroengineering. This project's ultimate goal is a mechanistically-detailed theory of how neural networks form as a result of decisions made by single neural progenitor cells. Integrating methods from three disciplines (systems biology, nanotechnology and developmental biology), the investigators will identify single cell features critical to network formation, and predict how heterogeneity and noise in the cell population defines the network's function. The investigators will employ three emerging methods (proteomic barcoding, ImageOmics, and E-phFACS) to define neural cell phenotypes as a function of chemical signaling, morphology and electrical activity (Aim 1). Observed changes in these single cell phenotypes will be mapped to neural network formation by coupling a state machine model with a graph-based analysis (Aim 2). The effects of cell heterogeneity will be explored computationally using a new framework developed by the investigators and iteratively compared to in vitro live-imaging assays, and in vivo assays. The tight coupling of chemical, morphological and electrical measurements enabled by the technologies introduced here, can be broadly applicable paradigm for integrative scientific research. Understanding how cells interact to form neural networks has relevance to organism development and tissue engineering. State-machines can be adapted across tissues and species to develop theories of cell decision-making. The phenotyping tools that correlate electrophysiology and protein expression with multi-cellular network topology will provide a powerful resource for neurobiologists. Furthermore, the E-phFACS device provides a high-throughput way to record electrical activity and sort cells. To further engage the scientific community, the investigators will (1) provide an open source ImageOmics platform and (2) host an international crowd-sourced neuronal network Design Challenge.
