Darwin Babino, PhD, a trained pharmacologist/electrophysiologist, has spent the last ten years working on several disciplines in the vision sciences. His proposal entitled ?Assessment of murine retinal acuity ex vivo by machine learning of multielectrode array recordings? presents his overarching goal to improve vision restoration approaches by developing methods to test the potential of these techniques thereby accelerating the development of effective interventions. Dr. Babino and his primary mentor, Dr. Russell Van Gelder, have assembled a strong team of co-mentors at the University of Washington SOM and collaborators to guide him through the proposed training and research. His previous training will be supplemented with goals to help his development as an independent investigator: 1) Study design and practical learning in performing panretinal (MEA) biological experiments; 2) Fundamental and advanced techniques of the proposed optogenetic and stem-cell restoration techniques; 3) Application of advanced machine learning techniques; 4) Develop leadership and professional skills to establish an independent group. The ability to assess the function of panretinal circuitry will foster our understanding of the advantages and weaknesses of different restoration techniques (Aim 1). The work proposed here will improve an existing retinal acuity assessment tool which combines machine learning techniques on novel, high-density multielectrode array recordings of ganglion cell responses in several mouse models. The utility of this system will be demonstrated in assessing visual potential of the mouse retina in three different approaches to vision restoration that are challenging for in vivo assessment (Aim 2). In collaboration with Dr. Deepak A. Lamba at UCSF, we will apply our system to animals which have undergone stem-cell replacement of retinal cells including photoreceptor cells. An optogenetics approach will also be evaluated in collaboration with Dr. John Flannery at UC Berkeley whose group has developed vectors for expressing rhodopsin and cone opsins in ganglion and bipolar cells. Finally, differences between native and restored vison with small molecule photoswitches, light-activated inhibitors of voltage-gated potassium channels, which confer light-dependent firing on treated cells, will be assessed. The resulting advanced electrophysiology application will help elucidate fundamental questions about the functional retina, mechanisms that lead to retinal degeneration and the potential of several therapeutics for the treatment of retinal diseases. Furthermore, this career development award will facilitate Dr. Babino?s development into an independent investigator by priming an R01 grant application.
Project Narrative: The prevalence of vision loss from retinal degeneration numbers in the millions world-wide and is expected to double by the year 2050, and despite the development of several promising approaches to restore vision in the blind, progress in developing these therapies has been hampered by challenges in analysis of these methods in animal models. We describe a novel system that analyzes, by machine learning, retinal ganglion cell output in native, degenerated and therapeutically treated blind retinas which can characterize the visual information content of the ?reanimated? blind retina and thereby facilitate the development of these technologies. The system developed through this grant, as well as the career development pursued by the investigator, will be readily applicable to the assessment of potential retinal acuity restoration by current and novel therapeutic approaches.
