Deep-learning based profiling of patient-derived cells as a tool for genomic and translational medicine

项目摘要/摘要： 罕见病和常见病的遗传格局呈现出异质性和复杂性。已经， 研究人员和临床医生面临着弄清病理生理机制和治疗的挑战 在罕见疾病中发现的数百种遗传亚型的机会，如遗传性 单独的神经病(INS)或线粒体疾病(MIDS)。尽管如此，很大一部分疾病部位仍有待于 发现--这是一项艰巨的任务，因为基因鉴定研究通常需要巨大的样本量，这是 即使是在更常见的情况下，也很难实现。同时，许多人的遗传性 疾病似乎是由可能数千种低影响变种的集体影响决定的， 在整个基因组中传播。理想情况下，可以在以下范围内评估给定候选变体集的影响 高通量框架，考虑到个体患者的遗传背景。利用高级 深度学习算法，我们开发了一种无偏见、可扩展的方法来快速识别疾病- 原发患者的高分辨率、多路传输的荧光显微镜图像中的相关表型 派生的细胞。反过来，发现的表型可以作为实验信号来利用， 通过基因互补实验，可以确认候选变异的疾病相关性。 同时，我们的方法的标准化和可伸缩性使其适合于测试潜力 治疗干预，例如测试潜在基因治疗的效果，或筛选小分子 库，同时保持患者特定的粒度。该提案的目标是将我们的方法应用于 扩大患者细胞队列，改进解释遗传学和药理学的方法 微扰。在这方面，我将得到一支杰出的、多学科的临床专家团队的支持， 分子和功能遗传学，以及计算机科学家，在世界级的科学环境中 由哥伦比亚大学和布罗德研究所提供。在精心设计的发展计划中，我将最终确定 我在机器学习和数据科学方面的培训，将我的专业知识扩展到单细胞RNA测序和 其他单细胞方法，并获得必要的领导力和学术技能所需的独立 研究生涯。在这个奖项的过程中，我将应用我们的细胞分析方法来生成一个 疾病相关细胞表型的深入、定量描述的标准化地图 INS、MID和神经退行性疾病的数量。我们将探索将RNA测序整合到 改进我们的方法。最后，我们将把我们的方法应用于新疾病的发现和确认 基因，并通过我们的方法筛选有限数量的药理干预。团结在一起， 建议的发展计划和研究战略将培养我领导独立研究的能力 计划，建立细胞图谱作为推动基因组和转化医学的强大平台。

Project Summary/Abstract: The genetic landscape of rare and common diseases has emerged as heterogeneous and complex. Already, researchers and clinicians face the challenge to discern pathophysiological mechanism and treatment opportunities for hundreds of genetic subtypes that have been identified in rare diseases, such as inherited neuropathies (INs) or mitochondrial diseases (MiDs) alone. Still, a large fraction of disease loci remains to be discovered – a daunting task, since gene-identification studies often require immense sample-sizes, which are difficult to achieve, even for more common conditions. Simultaneously, much of the heritability of many disorders appears to be determined by the collective impact of possibly thousands of low-impact variants, spread across the genome. Ideally, the impact of a given set of candidate variants could be assessed within high-throughput framework that accounts for the genetic context of individual patients. Leveraging advanced deep learning algorithms, we have developed an unbiased, scalable method to rapidly identify disease- associated phenotypes in high-resolution, multiplexed, fluorescent microscopy images of primary, patient derived cells. In turn, the discovered phenotypes can be exploited as experimental signals against which the disease relevance of candidate variants can be confirmed, by virtue of genetic complementation experiments. At the same time, the standardized and scalable nature of our method renders it suitable to test potential therapeutic interventions, e.g. to test the efficacy of potential gene-therapy, or to screen small molecule libraries, while maintaining patient-specific granularity. The goal of this proposal is to apply our approach to an expanded cohort of patient cells and to refine methods to interpret both genetic and pharmacological perturbations. In this, I will be supported by an exceptional and multidisciplinary team of experts in clinical, molecular and functional genetics, and computer scientists, within the world-class scientific environment offered by Columbia University and the Broad Institute. In a carefully designed development plan, I will finalize my training in machine learning and data science, expand my expertise to single-cell RNA-sequencing and other single-cell methods, and acquire essential leadership and scholarly skills required for an independent research career. Over the course of this award, I will apply our cellular profiling approach to generate a standardized map of deep, quantitative descriptions of disease-associated cellular phenotypes across a number of INs, MiDs and neurodegenerative conditions. We will explore the integration of RNA-sequencing to enhance our approach. Finally, we will apply our method to the discovery and confirmation of new disease genes, and screen a limited number of pharmacological interventions through our method. Together, the proposed developmental plan and research strategy will foster my ability to lead an independent research program, to establish cellular profiling as a powerful platform to advance genomic and translational medicine.