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.

摘要 一种全面发现、表征和识别由正常基因产生的结构变体的简单方法 新陈代谢过程，以及细胞操作，将对基因编辑、肿瘤学和罕见的 疾病研究，以及其他应用。新的定向基因组杂交(DGH™)已经被 开发用于有效筛选数千个细胞的简单、复杂和异质性的存在 结构变体。在这个项目中，自动化、高通量识别基因结构变异 基因编辑和未诊断遗传病筛查，我们提出了一种扩展的K-带™DGH 方法。 K-Band DGH是一种原位杂交方法，它利用具有明显条带的高密度染色单体颜料 光谱。正常的染色体有明确的条带、光谱和探针密度模式。结构变体 通过改变信号模式来检测和识别。建议的K波段™DGH方法将提供 从头发现涉及同一基因座上的断裂点的平衡等位基因易位的手段， 倒置，姐妹染色单体重组和交换事件，这些都是现有方法看不到的 如测序和aCGH。K-带DGH还将表征缺失、复制、 易位、非整倍体、多倍体和更复杂的重排。 结构变异会导致从罕见疾病到癌症等广泛的疾病，并且可以精确和 明确的生物标志物。此外，因为变异是由于DNA双链断裂的错误修复而产生的， 意外的结构破坏是基因组编辑不可避免的、潜在的高风险副产品。这个 CRISPR-Cas9等基因组编辑方法在疾病治疗中的潜力是广泛的 承认和实现这种治疗方法的前景将依赖于准确的确认 存在和不存在潜在危险的结构性变异。出于这些原因，全面的 结构变异的检测和表征是理解、诊断 最终精确治疗遗传病。从同质或异质的细胞群体中， 在一个单独的实验中，K-Band DGH将识别结构正常表型的细胞，检测所有 类，并定位每个类中所有简单和复杂结构变体的断点 手机。K-Band DGH的检测限在5Kb以下，是确定基因结果的理想方法 编辑，发现未诊断的罕见疾病的原因，描绘基因组结构不稳定和 可变性，以及发现和验证以前未知的疾病的结构性遗传驱动因素。