A CRISPR Resource for Individual, Combinatorial, or Multiplexed Gene Knockout

Erard, Nicolas; Knott, Simon; Hannon, Gregory J.

doi:10.1016/j.molcel.2017.06.030

Cited by 45 publications

(39 citation statements)

References 17 publications

(27 reference statements)

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“…1c). The control gRNAs, which target exon 1 of the olfactory receptor OR2W5, were predicted by the CRoatan algrotihm 21 . The STAG2 gRNA was predicted with the same algorithm.…”

Section: Cord Blood Sortingmentioning

confidence: 99%

Functional Profiling of Single CRISPR/Cas9-Edited Human Long-Term Hematopoietic Stem Cells

Wagenblast

Azkanaz

Smith

et al. 2019

Preprint

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and Eric R. Lechman (elechman@uhnresearch.ca) regenerative therapies. Thus, LT-HSCs are essential for therapeutic genome editing to correct acquired and genetic hematopoietic disorders 3,4 . Furthermore, the pathogenesis of hematological malignancies like acute myeloid leukemia (AML) is associated with the presence of initiating mutations acquired in LT-HSCs, which lead to their competitive expansion 5,6 . Pre-leukemic LT-HSCs are a source of clonal evolution within blood malignancies and can act as a reservoir of relapse after chemotherapy treatment 7 . Thus, modeling and understanding the genetic complexity and cellular heterogeneity seen in human hematological malignancies requires novel methodologies that allow genome editing in LT-HSCs and their functional read-out 3,4 .Recently several studies have demonstrated efficient gene editing of bulk CD34 + populations that are enriched for human HSPCs [8][9][10][11][12][13][14][15][16] . Highly efficient non-homologous end joining (NHEJ) mediated gene disruption of up to 80-90% efficiency has been reported in CD34 + HSPCs 10,11,14,16 . In addition, homology-directed repair (HDR) mediated knock-ins, with or without selectable fluorescent reporter genes, have been established with an efficiency of up to 20% in CD34 + HSPCs 8,9,11,13,15 . Stable integration of a fluorescent reporter using rAAV6 combined with flow cytometrybased sorting enabled enrichment of CRISPR/Cas9 edited HSPCs 8,9,16 . Because LT-HSCs represent only 0.1 -1% of CD34 + populations, these studies did not address LT-HSC targeting in the most direct manner. Previous studies have reported long term engraftment of up to 16 weeks following xenotransplantation of CRISPR/Cas9 edited human CD34 + HSPCs 8,15,16 , suggesting that rare LT-HSCs within the CD34 +

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“…1c). The control gRNAs, which target exon 1 of the olfactory receptor OR2W5, were predicted by the CRoatan algrotihm 21 . The STAG2 gRNA was predicted with the same algorithm.…”

Section: Cord Blood Sortingmentioning

confidence: 99%

Functional Profiling of Single CRISPR/Cas9-Edited Human Long-Term Hematopoietic Stem Cells

Wagenblast

Azkanaz

Smith

et al. 2019

Preprint

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“…We therefore began with a highly complex set of synthetic sequences and filtered these against the human and mouse genomes. To select potential SmartCode sequences, we took advantage of an algorithm that we previously developed, CRoatan [13,14], designed to identify optimal CRISPR target sites in proteincoding genes. As part of the CRoatan workflow, we implemented an analysis of local microhomology to determine the most likely outcome of a repair event [13][14][15].…”

Section: A Computational Approach To Smartcode Designmentioning

confidence: 99%

“…To select potential SmartCode sequences, we took advantage of an algorithm that we previously developed, CRoatan [13,14], designed to identify optimal CRISPR target sites in proteincoding genes. As part of the CRoatan workflow, we implemented an analysis of local microhomology to determine the most likely outcome of a repair event [13][14][15]. We used this approach to identify synthetic sequences that would not only be predicted to be efficiently targeted by Cas9 but also be to generate definable deletions following cas9-mediated cleavage and repair, such that a marker downstream would be shifted from the +2 to the +1 reading frame.…”

Section: A Computational Approach To Smartcode Designmentioning

confidence: 99%

SmartCodes: functionalized barcodes that enable targeted retrieval of clonal lineages from a heterogeneous population

Rebbeck

et al. 2018

Preprint

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Molecular barcoding has provided means to link genotype to phenotype, to individuate cells in single-cell analyses, to enable the tracking of evolving lineages, and to facilitate the analysis of complex mixtures containing phenotypically distinct lineages.To date, all existing approaches enable retrospective associations to be made between characteristics and the lineage harbouring them, but provide no path toward isolating or manipulating those lineages within the complex mixture. Here, we describe a strategy for creating functionalized barcodes that enable straightforward manipulation of lineages within complex populations of cells, either marking and retrieval of selected lineages, or modification of their phenotype within the population, including their elimination. These "SmartCodes" rely on a simple CRISPR-based, molecular barcode reader that can switch measurable, or selectable markers, on or off in a binary fashion. While this approach could have broad impact, we envision initial approaches to the study of tumour heterogeneity, focused on issues of tumour progression, metastasis, and drug resistance.

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“…In particular, understanding the genes associated with the formation of anatomical structures, also termed ‘anatomical entities’, is essential in developmental biology [1–4]. The majority of genes associated with anatomical entities are obtained using wet-lab methods, such as gene knockout [5, 6], gene knockdown [7], and overexpression [8, 9]. These methods, however, are time-consuming and require significant resources, and thus only a few genes may be associated with the development of a particular anatomical entity, though there are likely many more genes involved.…”

Section: Introductionmentioning

confidence: 99%

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

Fernando

Mabee

Zeng

2020

Preprint

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Results 41According to candidate gene prediction performance evaluations tested under four different 42 semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated 43 networks showed better receiver operating characteristic (ROC) and precision-recall curve 44 performances than PPI networks for both zebrafish and mouse. 45 Conclusion 46Integration of existing experimental knowledge about gene-anatomical entity relationships with 47 PPI networks via anatomy ontology improves the network quality, which makes them better 48 optimized for predicting candidate genes for anatomical entities. 49 50 molecular and phenotypic functions of proteins is a cornerstone in molecular 68 biology. In particular, understanding the genes associated with the formation of anatomical 69 structures, also termed 'anatomical entities', is essential in developmental biology [1][2][3][4]. The 70 majority of genes associated with anatomical entities are obtained using wet-lab methods, such 71 as gene knockout [5, 6], gene knockdown [7], and overexpression [8, 9]. These methods, 72 however, are time-consuming and require significant resources, and thus only a few genes may 73 be associated with the development of a particular anatomical entity, though there are likely 74 many more genes involved. 75Alternatively, computational prediction methods for discovering gene-anatomical entity 76 associations can be employed because of their higher speed and low resource consumption. 77Sequence similarity-based function prediction is such an example, which is widely used to 78 predict the molecular functions of proteins [10, 11]. However, using it to predict anatomical 79 associations of genes is questionable, because anatomical entities develop from a combination of 80 several biological pathways that include proteins with diverse molecular functions and sequences 81[12]. On the other hand, protein-protein interaction (PPI) networks can be used to predict 82 candidate genes for anatomical entities, based on the assumption that proteins that regulate the 83 same term or function are more likely to physically interact with each other [13, 14]. PPI 84 networks represent such interactions as graphs where proteins are represented by nodes and their 85 interactions are represented by edges. PPI networks have been widely used in predicting 86 candidate genes for human disease phenotypes [15][16][17]. Therefore, PPI networks are suitable for 87 predicting candidate genes associated with anatomical entities. However, the challenge with PPI 88 network-based candidate gene prediction is improving the accuracy of the predictions [13,[18][19][20][21], which is low because of the poor quality of the large-scale PPI network data sets [14, 21-90 23]. PPI networks are generated by experimental methods such as yeast two-hybrid assay and 91 high-throughput mass-spectrometric protein complex identification (HMS-PCI), which can 92 generate false positive interactions [19]. Furthermore, PPI networks for model organisms are still 93incomplete ...

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A CRISPR Resource for Individual, Combinatorial, or Multiplexed Gene Knockout

Cited by 45 publications

References 17 publications

Functional Profiling of Single CRISPR/Cas9-Edited Human Long-Term Hematopoietic Stem Cells

Functional Profiling of Single CRISPR/Cas9-Edited Human Long-Term Hematopoietic Stem Cells

SmartCodes: functionalized barcodes that enable targeted retrieval of clonal lineages from a heterogeneous population

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

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