Automated, high-throughput identification of genetic structural variants for gene editing and undiagnosed genetic diseases screening

自动化、高通量鉴定遗传结构变异，用于基因编辑和未确诊遗传病筛查

• 批准号: 10228763
• 负责人: Christopher John Tompkins
• 金额: $ 58.44万
• 依托单位: KROMATID, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-04 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228763
• 关键词: Algorithms; Alleles; Aneuploidy; Artificial Intelligence; Bioinformatics; Biological Assay; Biological Markers; Biomedical Research; CRISPR screen; CRISPR therapeutics; CRISPR/Cas technology; Cell Line; Cell Therapy; Cells; Chromatids; Chromosomal Rearrangement; Chromosome Structures; Chromosomes; Clinical; Clinical assessments; Clustered Regularly Interspaced Short Palindromic Repeats; Color; Complex; DNA; DNA Double Strand Break; DNA Repair; DNA Sequence; Data; Detection; Development; Diagnosis; Disease; Double Strand Break Repair; Event; Exposure to; Fingerprint; Funding; Genes; Genetic Diseases; Genetic Recombination; Genetic Structures; Genome engineering; Genomic Hybridizations; Genomics; Goals; Government; Human; Image; Image Analysis; In Situ Hybridization; Individual; Knowledge; Location; Malignant Neoplasms; Maps; Marketing; Measurement; Measures; Medical; Metabolism; Methods; Modernization; Oncology; Outcome; Paint; Pattern; Pattern Recognition; Pharmacologic Substance; Phase; Phenotype; Polyploidy; Population; Prevalence; Radiation; Rare Diseases; Reciprocal Translocation; Research; Research Personnel; Resolution; Risk; Sampling; Signal Transduction; Sister Chromatid; Small Business Innovation Research Grant; Solid; Structure; System; Technology; Testing; Therapeutic; Validation; Variant; Work; automated image analysis; base; cellular engineering; clinically relevant; commercial application; comparative genomic hybridization; density; detection limit; experimental study; fluorophore; gene therapy; genetic variant; genome editing; genotoxicity; high risk; improved; intelligent algorithm; new therapeutic target; personalized medicine; reconstruction; research and development; screening; stem cells; structural genomics; technological innovation; therapeutic gene; therapeutic target; therapy development; tool; whole genome

ABSTRACT A simple method to comprehensively discover, characterize and identify structural variants arising from normal metabolic processes, as well as cell manipulations, would have great utility for gene editing, oncology, and rare disease research, among other applications. De Novo Directional Genomic Hybridization (dGH™) has been developed to efficiently screen thousands of cells for the presence of simple, complex, and heterogenous structural variants. In this project, Automated, High-Throughput Identification of Genetic Structural Variants for Gene Editing and Undiagnosed Genetic Diseases Screening, we propose K-Band™ dGH, an expanded dGH method. K-Band dGH is an in-situ hybridization method that utilizes high-density chromatid paints with bands of distinct spectra. A normal chromosome has a definitive pattern of bands, spectra and probe density. Structural variants are detected and identified via changes to the signal pattern. The proposed K-Band™ dGH method will provide the means for de novo discovery of balanced allelic translocations involving breakpoints at the same loci, inversions, and sister chromatid recombination and exchange events that are invisible to existing methods such as sequencing and aCGH. K-Band dGH will additionally characterize deletions, duplications, translocations, aneuploidy, polyploidy and more complex rearrangements. Structural variations cause a wide range of disorders, from rare diseases to cancers, and can be precise and definitive biomarkers. Also, because variations arise from the mis-repair of DNA double-strand breaks, unintended structural damage is an inevitable and potentially high-risk byproduct of genome editing. The potential of genome editing approaches such as CRISPR-Cas9 in the treatment of diseases is widely recognized and the realization of the promise of such therapeutic approaches will rely on accurate confirmation of the presence and absence of potentially risky structural variants. For these reasons, comprehensive detection and characterization of structural variations is a necessary step toward understanding, diagnosing and ultimately precisely treating genetic diseases. From a homogeneous or heterogenous population of cells, and in a single experiment, K-Band dGH will identify cells with a structurally normal phenotype, detect all classes of structural variants, and locate the breakpoints of all simple and complex structural variants in each cell. With a limit of detection below 5Kb, K-Band dGH is an ideal method for determining the outcomes of gene editing, discovering the causes of undiagnosed rare diseases, profiling genomic structural instability and variability, and discovering and validating previously unknown structural genetic drivers of disease.

抽象的 一种简单的方法，可以全面发现，表征和识别正常的结构变体 代谢过程以及细胞操作将具有极大的基因编辑，肿瘤学和罕见的实用性 疾病研究以及其他应用。从头定向基因组杂交（DGH™）已经 开发为有效筛选数千个细胞，以实现简单，复杂和异质的存在 结构变体。在这个项目中，对遗传结构变异的自动化高通量识别 基因编辑和未诊断的遗传疾病筛查，我们提出了K-band™dGH，这是一种扩展的DGH 方法。 k频段DGH是一种原位杂交方法，它利用具有不同带的高密度染色单体油漆 正常染色体具有确定的带，光谱和探针密度的模式。结构变体 通过更改信号模式检测和识别。拟议的K-band™DGH方法将提供 从头发现平衡等位基因易位的手段，涉及同一基因座断点的平衡易位， 倒置，以及现有方法看不见的姐妹染色单体重组和交换事件 例如测序和ACGH。 K频段DGH还将描述删除，重复，重复， 易位，非整倍性，多倍体和更复杂的重排。 结构性变化会引起广泛的疾病，从罕见疾病到癌症，并且可能是精确的，并且 确定的生物标志物。而且，由于DNA双链断裂的错误修复引起的变化，所以 意外的结构损害是基因组编辑的不可避免的高危副产品。这 基因组编辑方法（例如CRISPR-CAS9）在疾病治疗中的潜力广泛 公认和实现这种治疗方法的承诺将依赖于准确的确认 存在和不存在潜在风险的结构变体。由于这些原因，全面 结构变化的检测和表征是理解诊断的必要步骤 并最终精确治疗遗传疾病。来自均质或异源细胞群， 在一个实验中，k频段DGH将识别具有结构正常表型的细胞，检测所有 类别的结构变体，并在每种中找到所有简单和复杂的结构变体的断点 细胞。 k波段DGH的检测极限低于5Kb，是确定基因结果的理想方法 编辑，发现未经诊断的罕见疾病的原因，分析基因组结构不稳定性和 可变性，并发现和验证以前未知的疾病结构遗传驱动因素。