B - Gene Targeting and Gene Correction -> B2 - Gene Targeting and Gene Correction – In Vitro Studies (Basic development of novel technologies for genome editing, with or without site-specific endonuclease.
533: Outcomes from the First NIST Genome Editing Consortium Interlab Study
Type: Poster Session
Poster Board Number: 533
Presentation Details
Session Title: Wednesday Poster Session
Location:
Start Time: 5/17/2023 12:00
End Time: 5/17/2023 14:00
Genome editing is being actively pursued globally to transform medicine and bioscience to enable previously impossible advances in cell & gene therapy. Moving the promise of these technologies into production and medical practice requires robust quantitative assays, with associated controls and data tools. The U.S. National Institute of Standards and Technology (NIST) Genome Editing Consortium (GEC) convenes experts across academia, industry, and government to collaboratively address precompetitive genome editing measurements and standards needed to increase confidence in evaluating genome editing and utilizing these technologies in research and commercial products. As a result of NIST GEC led efforts there is now a first international standard for genome editing terminology: International Standards Organization- ISO 5058-1:2021 Biotechnology — Genome editing — Part 1: Vocabulary This presentation will detail the design and results of this first NIST GEC Interlab Study. The NIST GEC has completed a first of its kind interlab study with organizations in the genome editing field to understand the performance of DNA detection technologies of interest for confirming on- and off-target genome editing. For this interlab study, NIST designed mixture schemes consisting of DNA- or cell-based control samples. These control samples were qualified with ddPCR and NGS to evaluate the accuracy of the study participants' reporting on the size, sequence, and frequency of the DNA variants. DNA variant benchmarks within control samples ranged in size from single nucleotide variants (SNV) to insertions and deletions tens of kilobases long. Variants were present within the interlab study control samples at all of 5 primary frequency bins: 0%, >30%, 5 - 10%, 0.5 -2%, and 0.1 - 0.25%; with two optional samples of ~0.01% and ~0.001%. Participants were provided 5 required samples and a core list of 39 genomic positions of interest to be analyzed by any type of DNA detection process being utilized by the interlab participant. Interlab participants were blinded as to variant sequence and variant frequency within each sample and at each position of interest. 14 Interlab participants returned their assessments of the samples they were given, including metadata describing the assay and data analysis approach, raw data files, and a list of variants detected- complete with variant positions, sequences, and frequencies. One to two technologies were assessed per participant, with 1-4 replicates per technology. Technologies assessed include: bulk DNA short read NGS, bulk DNA long read NGS, single cell targeted NGS, genome wide DNA imaging, targeted DNA probe imaging, and capillary electrophoresis fragment analysis. NIST evaluated the results from individual technologies for accuracy of variant detection and frequency prediction. While no one technology had the capability to detect all of the variants at all of the frequencies and sizes, combined results from all of the technologies evaluated, confirmed the ability to measure a subset of variants down to 0.001% and variant sizes from SNV to >100kb. Trends in performance were identified as well as instances where variant signal was present but not reported by the bioinformatics process. The NIST GEC is working on a database where all datasets, metadata, and results could be made publicly accessible. Further in-progress NIST GEC work includes development of metadata norms and clonal engineered cell lines to be used as DNA/cell based controls supporting greater assay confidence for the genome editing community and development of genome edited cell based therapies.
Samantha Maragh1, Sierra Miller1, Ayah Shevchenko1, Simona Patange1, Justin Zook1, Nate Olson1, Jamie Almeida1, Hua-Jun He2, Natalia Kolmakova1, Patricia Kiesler1, NIST Genome Editing Consortium1
1NIST, Gaithersburg, MD,2NIST: National Institute of Standards and Technology, Gaithersburg, MD
S. Maragh: None.
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