Visualizing neurons one-by-one in vivo: Optical dissection and reconstruction of neural networks with reversible fluorescent proteins
Article first published online: 17 JUL 2006
Copyright © 2006 Wiley-Liss, Inc.
Volume 235, Issue 8, page spc1, August 2006
How to Cite
Aramaki, S. and Hatta, K. (2006), Visualizing neurons one-by-one in vivo: Optical dissection and reconstruction of neural networks with reversible fluorescent proteins. Dev. Dyn., 235: spc1. doi: 10.1002/dvdy.20913
- Issue published online: 17 JUL 2006
- Article first published online: 17 JUL 2006
- Manuscript Accepted: 15 MAR 2006
- Ministry of Education, Culture, Sports, Science, and Technology of Japan. Grant Numbers: 15029263, 15300117, 15657054, 17023052
- Cited By
- two-photon microscopy;
- neural network;
- confocal microscopy;
- Mauthner cell
A great many axons and dendrites intermingle to fasciculate, creating synapses as well as glomeruli. During live imaging in particular, it is often impossible to distinguish between individual neurons when they are contiguous spatially and labeled in the same fluorescent color. In an attempt to solve this problem, we have taken advantage of Dronpa, a green fluorescent protein whose fluorescence can be erased with strong blue light, and reversibly highlighted with violet or ultraviolet light. We first visualized a neural network with fluorescent Dronpa using the Gal4-UAS system. During the time-lapse imaging of axonal navigation, we erased the Dronpa fluorescence entirely; re-highlighted it in a single neuron anterogradely from the soma or retrogradely from the axon; then repeated this procedure for other single neurons. After collecting images of several individual neurons, we then recombined them in multiple pseudo-colors to reconstruct the network. We have also successfully re-highlighted Dronpa using two-photon excitation microscopy to label individual cells located inside of tissues and were able to demonstrate visualization of a Mauthner neuron extending an axon. These “optical dissection” techniques have the potential to be automated in the future and may provide an effective means to identify gene function in morphogenesis and network formation at the single cell level. Developmental Dynamics, 2006. © 2006 Wiley-Liss, Inc.