Cargando…

Metabolomics dataset of mouse optogenetic axon regeneration after optic nerve crush

This metabolite dataset was collected from transgenic murine retinal ganglion cells (RGC) expressing bacterial channelrhodopsin labeled with fluorescent protein. Mice were subjected to optic nerve crush (ONC) with subsequent RGC stimulation of channelrhodopsin with blue light (promoting regeneration...

Descripción completa

Detalles Bibliográficos
Autores principales: Jauregui, Alexa M., Liu, Yuan, Bhattacharya, Sanjoy K., Lee, Richard K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156864/
https://www.ncbi.nlm.nih.gov/pubmed/35664657
http://dx.doi.org/10.1016/j.dib.2022.108306
Descripción
Sumario:This metabolite dataset was collected from transgenic murine retinal ganglion cells (RGC) expressing bacterial channelrhodopsin labeled with fluorescent protein. Mice were subjected to optic nerve crush (ONC) with subsequent RGC stimulation of channelrhodopsin with blue light (promoting regeneration) or non-stimulation (control). ONC induces retinal ganglion cell degeneration over time with progressive loss of axons. In transgenic bacterial channelrhodopsin expressing RGC cells, light stimulation promotes regeneration of ONC axons. Genetically matched wild-type uninjured optic nerves were analyzed as controls for comparison. Metabolites were carefully extracted from finely minced optic nerve tissue using a solvent system (initial separation using 1:1 methanol and H(2)O and second extraction using 8:1:1 of acetonitrile:acetone:methanol). Untargeted liquid chromatography-mass spectrometry profiling was performed using fractionation on a Vanquish Horizon Binary UHPLC. Subsequent analyses were performed on an inline coupled Q-Exactive Orbitrap instrument. Metabolites were identified using Compound Discoverer(TM) software. Statistical analysis was performed using MetaboAnalyst 5.0. This data is available on Metabolomics Workbench, Study ID ST002111.