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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

基本信息

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
项目类别：
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

项目摘要

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