Increasing CRISPR Efficiency and Measuring Its Specificity in HSPCs Using a Clinically Relevant System

Abstract

Genome editing of human cluster of differentiation 34 + (CD34 + ) hematopoietic stem and progenitor cells (HSPCs) holds great therapeutic potential. This study aimed to optimize on-target, ex vivo genome editing using the CRISPR-Cas9 system in CD34 + HSPCs and to create a clear workflow for precise identification of off-target effects. Modified synthetic guide RNAs (gRNAs), either 2-part gRNA or single-guide RNA (sgRNA), were delivered to CD34 + HSPCs as part of ribonucleoprotein (RNP) complexes, targeting therapeutically relevant genes. The addition of an Alt-R electroporation enhancer (EE), a short, single-stranded oligodeoxynucleotide (ssODN), significantly increased editing efficiency in CD34 + HSPCs. Notably, similar editing improvement was observed when excess gRNA over Cas9 protein was used, providing a DNAfree alternative suitable for therapeutic applications. Furthermore, we demonstrated that sgRNA may be preferable over 2-part gRNA in a locus-specific manner. Finally, we present a clear experimental framework suitable for the unbiased identification of bona fide off-target sites by Genome-Wide, Unbiased Identification of Double-Strand Breaks (DSBs) Enabled by Sequencing (GUIDE-seq), as well as subsequent editing quantification in CD34 + HSPCs using rhAmpSeq. These findings may facilitate the implementation of genome editing in CD34 + HSPCs for research and therapy and can be adapted for other hematopoietic cells.

CRISPAltRations: a validated cloud-based approach for interrogation of double-strand break repair mediated by CRISPR genome editing

“…To create a synthetic benchmarking dataset representing the ability to perform on-target HDR quantification, we took all of on- and off-targets from the RAG1 Cas9 GUIDE-Seq panel and separated these out as single targets (91 total) 25 . The RAG1 panel was chosen because 1) no target processing problems were found when using CRISPResso2 and 2) the genomic sequence around the targets included homopolymers and other events that represent challenging genomic regions to annotate.…”

“…To provide recommendations for target sequencing read depth requirements, we re-analyzed previously published CRISPR NGS data from a series of rhAmpSeq panels designed for on/off target sites of guides targeting the RAG1/RAG2 loci with a wide range of editing frequencies, obtainable at the Sequence Read Archive (SRA) under: PRJNA628100 25 . Reads from these samples were subsampled, without replacement, in triplicate with random seeds to a range between 5-3,000 reads pairs per site and quantified using CRISPAltRations with optimized parameters.…”

“…A number of methods have been developed to quantify the population of alleles after editing, including heteroduplex cleavage assays [14][15][16] , capillary electrophoresis 17 , sanger deconvolution (TIDE/ICE) 18,19 and next generation sequencing (NGS) [20][21][22][23] . Limitations have been described for non-NGS based detection methods, including: limited effective editing range 24 , low sensitivity 25,26 , indel size and type limitations 14,18 , low allelic frequency resolution 26 , and reliance on high quality sanger traces 19,26 . Thus, NGS has become the gold standard for high-throughput accurate genome editing detection 27 , and it is the only method capable of simultaneously quantifying editing at both on-and off-target locations in highly multiplexed samples.…”

Clinically Relevant Gene Editing in Hematopoietic Stem Cells for the Treatment of Pyruvate Kinase Deficiency Hemolytic Anemia

“…1A). To analyze the off-target (OT) activity, these guides were transfected into HEK293 cells stably expressing Cas9, to force OT effect, which allowed a stringent detection of OT by GUIDE-seq analysis in vivo (40) . OTs identified in this analysis are shown in Table S2.…”

Advances in Genome Editing and Application to the Generation of Genetically Modified Rat Models

“…Chemical modifications and gRNA optimization have been recently reviewed (Filippova et al, 2019) and offer a clear advantage for synthetic gRNA (Figure 2A and Table 4). Regarding their format, both dgRNA and single gRNA (sgRNA) display similar efficiency (Terao et al, 2016;Shapiro et al, 2020). Chromatin state can influence editing efficiency (Janssen et al, 2019;Verkuijl and Rots, 2019) and even prevent editing of gRNA with predicted high on target score.…”