Assessment of murine retinal acuity ex vivo by machine learning of multielectrode array recordings

项目概述：达尔文·巴比诺博士，一位训练有素的药理学家/电生理学家， 在过去的十年里，他致力于视觉科学中的几个学科。他的建议 题为《基于多电极阵列机器学习的小鼠视网膜体外视力评估》 记录“提出了他的首要目标，以改善视力恢复方法 开发方法来测试这些技术的潜力，从而加速 制定有效的干预措施。Babino博士和他的主要导师Russell Van博士 已经在华盛顿大学SOM组建了一支强大的联合导师团队，并 合作者指导他完成拟议的培训和研究。他之前的训练将会 辅以帮助他发展为独立调查员的目标：1)研究 进行全视网膜(MEA)生物实验的设计和实践学习；2) 提出的光遗传和干细胞修复的基础和先进技术 技术；3)先进机器学习技术的应用；4)培养领导力和 具备专业技能，建立独立的团队。评估功能的能力 全视网膜电路将促进我们对不同 修复技术(目标1)。这里提出的工作将提高现有的视网膜敏感度 一种评估工具，结合了机器学习技术在新颖、高密度 几种小鼠模型神经节细胞反应的多电极阵列记录。该实用程序 该系统将在评估小鼠视网膜的视觉潜力中展示在三个 对活体评估具有挑战性的视力恢复的不同方法(目标2)。在……里面 与加州大学旧金山分校的迪帕克·A·兰巴博士合作，我们将把我们的系统应用于 已经接受了包括感光细胞在内的视网膜细胞的干细胞替换。一个 光遗传学方法也将与加州大学的约翰·弗兰纳里博士合作进行评估 伯克利的研究小组已经开发出表达视紫红质和视锥视蛋白的载体 神经节和双极细胞。最后，原生和修复后的Vison之间的差异很小 分子光开关，电压门控钾通道的光激活抑制剂，它 对处理过的细胞赋予光依赖的激发，将被评估。由此产生的高级 电生理学应用将有助于阐明有关功能的基本问题 视网膜，导致视网膜变性的机制以及几种治疗方法的潜力 视网膜疾病的治疗。此外，这一职业发展奖将为博士提供便利。 巴比诺通过启动R01拨款申请，发展成为一名独立调查人员。

Project Summary: 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.