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poster

Navigating Heterogeneity in Genome Editing Outcomes Using Single-cell Analysis


Brittany Enzmann

Genome editing has emerged as a revolutionary force within the life sciences, wielding transformative potential in applications such as cell and gene therapy development, disease modeling, and functional genomics. Despite the precision of advanced genome editors, editing outcomes remain largely unpredictable. Different cells subjected to the same editing regimen can yield distinct combinations of edits, varying not only across multiple on-target sites but also between on-target and off-target locations. In particular, from the perspective of the fundamental biological unit—a single cell— the zygosity disparity (monoallelic vs. bi-allelic), heterogeneity in variants (homozygous, heterozygous, compound heterozygous), and their phenotypic effects all contribute to the layer of complexity to the mosaicism of editing’s outcomes. Current genome editing analyses primarily rely on bulk methods which provide only an average editing efficiency of a population. The nuanced cell-to-cell variation of edits remains elusive within these traditional approaches. Here, we present compelling evidence that the Tapestri Genome Editing Solution offers a breakthrough in the analysis of knockout (KO) and base editing (BE) experiments. We demonstrate the technology’s unique single-cell multi-omics capability to furnish intricate details regarding zygosity and the co-occurrence of on- and off-target edits and immunophenotype, thereby affording researchers the granularity needed for precise experimental outcomes. We also illustrate the capability to multiplex samples via antibody hashing, which allows for economical and scalable analysis while maintaining performance.


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High Throughput Single Cell Analysis Workflow for Accurate Measurement of Genotoxicity Arising from Gene Editing Experiments
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Single-Cell Multi-Omic Correlation of Single Nucleotide Variants, Copy Number Variation, and Surface Epitopes for Clonal Profiling of Myeloma
Adam Sciambi; Indira Krishnan; Ben Geller; Daniel Mendoza; Chenchen Yang; Charlie Murphy; Cedric Dos Santos; Vivek S. Chopra; Habib Hamidi; Michael Nixon; Yann Nouet; Todd Druley; Herve Avet-Loiseau
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Decoding the mosaicism of genome editing with single-cell multi-omics analysis
Saurabh Gulaiti
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Single-Cell Multi-Omic Correlation of Single Nucleotide Variants, Copy Number Variation, and Surface Epitopes for Clonal Profiling of Myeloma
Adam Sciambi, Cedric Dos Santos, Vivek S. Chopra, Habib Hamidi, Michael Nixon, Yann Nouet, Todd Druley, Herve Avet-Loiseau
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