Integrated morphological and transcriptomic single-cell profiling of patient-derived cells as a platform for genomic and translational medicine

患者来源细胞的综合形态学和转录组单细胞分析作为基因组和转化医学的平台

• 批准号: 10802704
• 负责人: Wolfgang Maximilian Anton Pernice
• 金额: $ 24.9万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-02 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10802704
• 关键词: Address; Award; Benign; Biological; Cells; Cellular Morphology; Cellular biology; Classification; Clinical; Complement; Complex; Computers; Data; Data Science; Data Sources; Development; Development Plans; Diagnostic; Disease; Environment; Face; Fibroblasts; Fostering; Gene Expression Profiling; Genes; Genetic; Genetic Diseases; Genetic Variation; Genome; Genomic medicine; Genomics; Goals; Hand; Heritability; Image; Intervention; Lead; Leadership; Machine Learning; Malignant Neoplasms; Maps; Measures; Medical Genetics; Methods; Mitochondrial Diseases; Molecular; Morphology; Nature; Nerve Degeneration; Outcome; Pathogenicity; Patient imaging; Patients; Phenocopy; Phenotype; Privatization; Protocols documentation; Rare Diseases; Research; Research Personnel; Resolution; Sample Size; Scientist; Signal Transduction; Standardization; Techniques; Technology; Testing; Therapeutic Intervention; Time; Training; Universities; Validation; Variant; career; cellular imaging; cohort; cost efficient; deep learning; deep learning algorithm; design; efficacy testing; experimental study; gene therapy; genetic association; genome wide association study; genome-wide; genomic platform; hereditary neuropathy; individual patient; learning strategy; microscopic imaging; multidisciplinary; patient subsets; patient variability; personalized genomic medicine; pharmacologic; programs; single-cell RNA sequencing; skills; small molecule libraries; statistics; success; technology platform; tool; transcriptome sequencing; translational medicine

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.

项目概要/摘要： 罕见病和常见病的遗传格局呈现出异质性和复杂性。我已经， 研究人员和临床医生面临的挑战，以辨别病理生理机制和治疗 在罕见疾病中发现的数百种遗传亚型，如遗传性 神经病变（IN）或线粒体疾病（MiD）。尽管如此，仍有很大一部分疾病位点仍有待研究。 发现-一项艰巨的任务，因为基因鉴定研究往往需要巨大的样本量， 这是很难实现的，即使是在更常见的条件下。同时，许多人的遗传性 疾病似乎是由可能数千种低影响变体的集体影响决定的， 分布在基因组中。理想情况下，可以在给定的候选变体集合内评估给定的候选变体集合的影响。 高通量的框架，占个别患者的遗传背景。利用先进 通过深度学习算法，我们开发了一种无偏的、可扩展的方法来快速识别疾病， 原发性患者高分辨率多重荧光显微镜图像中的相关表型 衍生细胞反过来，发现的表型可以被利用作为实验信号， 候选变异体的疾病相关性可以通过遗传互补实验来确认。 同时，我们的方法的标准化和可扩展性使其适合于测试潜在的 治疗干预，例如测试潜在基因治疗的功效，或筛选小分子 库，同时保持患者特定的粒度。本提案的目标是将我们的方法应用于 扩大患者细胞的队列，并改进解释遗传和药理学的方法 扰动在这方面，我将得到一个杰出的多学科专家团队的支持， 分子和功能遗传学，计算机科学家，在世界一流的科学环境 由哥伦比亚大学和布罗德研究所提供。在一个精心设计的发展计划，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， 其他单细胞方法，并获得必要的领导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 标准化地图的深度，定量描述疾病相关的细胞表型在一个 IN、MiD和神经退行性疾病的数量。我们将探索RNA测序的整合， 加强我们的方法。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物干预。统称 提出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，建立细胞分析作为一个强大的平台，以推进基因组和转化医学。