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

通过多电极阵列记录的机器学习来评估离体小鼠视网膜敏锐度

基本信息

批准号：
10244896
负责人：
Darwin Babino
金额：
$ 11.31万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10244896
关键词：
Aftercare Amacrine Cells Animal Model Animals Applications Grants Assessment tool Behavioral Assay Biological Blindness Cells Collaborations Cone Contrast Sensitivity Data Development Discipline Dissection Doctor of Philosophy Ectopic Expression Electrophysiology (science)Electroretinography Evolution Feedback Fostering Ganglia Genetic Goals Grant Human Image In Vitro Individual Intervention K-Series Research Career Programs Knockout Mice Knowledge Lead Leadership Learning Light MW opsin Machine Learning Measurable Measurement Measures Mediating Mentors Methods Movement Mus Opsin Output Photoreceptors Prevalence Protocols documentation Psychophysics Research Research Design Research Personnel Resolution Retina Retinal Cone Retinal Degeneration Retinal Diseases Retinal Ganglion Cells Retinal gene therapy Rhodopsin Rod Rodent Saccades Sampling Scanning Science Scientist Specificity Stimulus Synapsins System Systems Analysis Systems Development Techniques Testing Therapeutic Training Transgenic Organisms Universities Vertebrate Photoreceptors Viral Vision Visual Visual Acuity Visual system structure Voltage-Gated Potassium Channel Washington Wild Type Mouse Work base behavior test blind career development cell type cost effective density effective intervention experimental study ganglion cell improved in vivo induced pluripotent stem cell inhibitor/antagonist interest light intensity mimicry mouse model multi-electrode arrays mutant nonhuman primate novel novel therapeutic intervention optogenetics promoter rapid eye movement response restoration scale up sight restoration skills small molecule stem cell replacement stem cells technology development therapy development tool vector vision science visual information

项目摘要

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.

项目摘要：训练有素的药物/电生理学家达尔文·巴比诺（Darwin Babino）在过去的十年中，在视觉科学中花了几个学科。他的提议标题为“通过机器学习多电极阵列评估鼠视网膜敏锐度的评估录音”提出了他的总体目标，即通过开发测试这些技术潜力的方法，从而加速开发有效的干预措施。 Babino博士及其主要导师Russell Van博士盖尔德（Gelder 合作者指导他完成拟议的培训和研究。他以前的训练补充目标，以帮助他作为独立研究者的发展：1）研究执行泛聚内度（MEA）生物学实验的设计和实践学习； 2）拟议的光遗传学和干细胞恢复的基本和先进技术技术； 3）应用高级机器学习技术； 4）发展领导力和建立独立团体的专业技能。评估功能的能力突出电路将促进我们对不同的优势和弱点的理解恢复技术（AIM 1）。这里提出的工作将改善现有的视网膜敏锐度评估工具结合了机器学习技术的新型高密度在几种鼠标模型中，神经节细胞响应的多电极阵列记录。实用程序该系统将在评估三个小鼠视网膜的视觉潜力时得到证明视力恢复的不同方法是为体内评估而质疑的（AIM 2）。在与UCSF的Deepak A. Lamba博士合作，我们将应用我们的系统经过了包括感光细胞在内的残留细胞的干细胞替代。一个光遗传学方法还将与UC的John Flannery博士合作评估伯克利的群体开发了用于表达视紫红质和锥蛋白的向量神经节和双极细胞。最后，本机和恢复的Vison之间的差异很小分子照片开关，电压门控钾通道的光激活抑制剂，该抑制剂会议对处理的细胞上的会议依赖性触发。由此产生的高级电生理应用程序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剂的潜力残留疾病的治疗。此外，该职业发展奖将有助于博士。 Babino通过启动R01赠款申请来发展成为独立研究人员。