“Performing RNA sequencing works fantastically well, and it does not cost very much, but the software for analyzing the giant datasets is very challenging and still evolving, making the informatics side really hard,” says Vance Lemmon, Ph.D., professor of neurological surgery at the University of Miami School of Medicine.
Dr. Lemmon and colleagues recently used RNA-Seq to compare isoforms expressed in peripheral neurons from dorsal root ganglia, which are able to regenerate after injury, with those from cerebellar granule neurons, which do not regenerate. This comparative approach unveiled over 8,000 differentially expressed isoforms between the two cell types.
“After comparing gene expression in different types of neurons, we hope to exploit this information and transfer it from neurons that regenerate to neurons that do not regenerate, to identify targets that promote neuronal regeneration in the central nervous system,” explains Dr. Lemmon.
In addition to being very economical, RNA-Seq has several additional advantages. “This approach offers the additional opportunity to obtain information about isoforms that have never been studied and might not be in any databases,” says Dr. Lemmon. The possibility to use very small amounts of starting material also helps gain insight into the biology of defined groups of cells.
“With the possibility to identify all the RNA species from such a small amount of starting material, we can define much more precisely the specific roles they play in the different cell types from different brain regions, or under many different conditions, such as during development, disease, or injury, and this represents more information than anyone was able to get in biology before, and it is all happening in real time,” says Jessica K. Lerch, Ph.D., first author of the study.