Abstract
Human neurons expressing mutations associated with neurodegenerative disease are becoming more widely available. Hence, developing assays capable of accurately detecting changes that occur early in the disease process and identifying therapeutics able to slow these changes should become ever more important. Using automated live cell imaging, we studied human motor neurons in the process of dying following neurotrophic factor withdrawal. We tracked different neuronal features, including cell body size, neurite length and number of nodes. In particular, measuring the number of nodes in individual neurons proved to be an accurate predictor of relative health. Importantly, intermediate phenotypes were defined and could be used to distinguish between agents that could fully restore neurons and neurites and those only capable of maintaining neuronal cell bodies. Application of live cell imaging to disease modeling has the potential to uncover new classes of therapeutic molecules that intervene early in disease progression.
Significance Statement This study establishes a new automated live cell imaging method that was used to analyze human motor neurons in the process of dying after trophic factor withdrawal. Our aim was to provide an alternative to traditional endpoint survival assays by determining morphologic changes that predate death. This was accomplished by tracking large numbers of individual motor neurons over long periods of time. We identified features of motor neurons that distinguish between those neurons that can fully recover from the early stages of degeneration and those that are committed to death. Our research is of clear interest to neuroscientists interested in disease and improved methods for discovering effective therapeutics.
- Automated live time-lapse imaging instrument
- heterogeneity
- live cell time-lapse imaging
- morphometric analysis
- single cell tracking
Footnotes
Authors report no conflict of interest.
This research was supported by a sponsored research grant from Nikon Corporation to LLR.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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