Phase I Amount
$1,088,162
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.
Public Health Relevance Statement: PROJECT NARRATIVE Modern genomics, driven by the demands of personalized medicine, needs higher throughput, higher integrity, and improved resolution to enable the discovery of new disease drivers and in the development of safer gene therapies. Rapid, accurate, efficient characterization of structural variants and their associated risks is critical to the discovery of potential therapeutic avenues for undiagnosed and rare diseases, and to the development of engineered cellular therapies, including stem cells, CAR-T and other gene therapies leveraging gene-editing tools such as CRISPR. K-Band dGH, proposed in Automated, High-Throughput Identification of Genetic Structural Variants for Gene Editing and Undiagnosed Genetic Diseases Screening, will enable single-cell, single-experiment identification of clinically important structural variants that cannot be discovered or identified by NGS or other available methods with broad applicability in genome engineering R&D, target discovery and personalized therapy development.
Project Terms: Algorithms; Alleles; Aneuploidy; Artificial Intelligence; automated image analysis; base; Bioinformatics; Biological Assay; Biological Markers; Biomedical Research; Cell Line; Cell Therapy; Cells; cellular engineering; Chromatids; Chromosomal Rearrangement; Chromosome Structures; Chromosomes; Clinical; Clinical assessments; clinically relevant; Clustered Regularly Interspaced Short Palindromic Repeats; Color; commercial application; comparative genomic hybridization; Complex; CRISPR screen; CRISPR therapeutics; CRISPR/Cas technology; Data; density; Detection; Development; Diagnosis; Disease; DNA; DNA Double Strand Break; DNA Repair; DNA Sequence; Double Strand Break Repair; Event; experimental study; Exposure to; Fingerprint; fluorophore; Funding; gene therapy; Genes; Genetic Diseases; Genetic Recombination; Genetic Structures; genetic variant; genome editing; Genome engineering; Genomic Hybridizations; genomic profiles; Genomics; genotoxicity; Goals; Government; high risk; Human; Image; Image Analysis; improved; In Situ Hybridization; Individual; intelligent algorithm; Knowledge; Location; Malignant Neoplasms; Maps; Marketing; Measurement; Measures; Medical; Metabolism; Methods; Modernization; new therapeutic target; Oncology; Outcome; Paint; Pattern; Pattern Recognition; personalized medicine; Pharmacologic Substance; Phase; Phenotype; Polyploidy; Population; Prevalence; Radiation; Rare Diseases; Reciprocal Translocation; reconstruction; Research; research and development; Research Personnel; Resolution; Risk; Sampling; screening; Signal Transduction; Sister Chromatid; Small Business Innovation Research Grant; Solid; stem cells; structural genomics; Structure; System; technological innovation; Technology; Testing; Therapeutic; therapeutic gene; therapeutic target; therapy development; tool; Validation; Variant; whole genome; Work
