Label-Free Characterization of Emerging Human Neuronal Networks
Article Mar 25, 2014
Mustafa Mir, Taewoo Kim, Anirban Majumder, Mike Xiang, Ru Wang, S. Chris Liu, Martha U. Gillette, Steven Stice, Gabriel Popescu
Summary: The emergent self-organization of a neuronal network in a developing nervous system is the result of a remarkably orchestrated process involving a multitude of chemical, mechanical and electrical signals. Little is known about the dynamic behavior of a developing network (especially in a human model) primarily due to a lack of practical and non-invasive methods to measure and quantify the process. Here we demonstrate that by using a novel optical interferometric technique, we can non-invasively measure several fundamental properties of neural networks from the sub-cellular to the cell population level. We applied this method to quantify network formation in human stem cell derived neurons and show for the first time, correlations between trends in the growth, transport, and spatial organization of such a system. Quantifying the fundamental behavior of such cell lines without compromising their viability may provide an important new tool in future longitudinal studies.
The reproducibility crisis is holding back science. London-based Labstep, a start-up out of Oxford University, think that their tool can help make science more open and reproducible. That claim has now been given some concrete evidence with the announcement that the research contingent of the MRC Unit The Gambia at LSHTM will be trialling Labstep across their Banjul-based facility.READ MORE
Researchers have identified a mechanism by which brain-derived neurotrophic factor (BDNF) can suppress GABAergic transmission in hippocampus. In this article, Dr. Rajamani Selvam explains how he and his team achieved these results, and their potential impact on the treatment of neurological disease.READ MORE