Abstract
CRISPR-Cas9 mutagenesis facilitates the investigation of gene function in a number of developmental and cellular contexts. Human pluripotent stem cells (hPSCs), either embryonic or induced, are a tractable cellular model to investigate molecular mechanisms involved in early human development and cell fate decisions. hPSCs also have broad potential in regenerative medicine to model, investigate, and ameliorate diseases. Here, we provide an optimized protocol for efficient CRISPR-Cas9 genome editing of hPSCs to investigate the functional role of genes by engineering null mutations. We emphasize the importance of screening single guide RNAs (sgRNAs) to identify those with high targeting efficiency for generation of clonally derived null mutant hPSC lines. We provide important considerations for targeting genes that may have a role in hPSC maintenance. We also present methods to evaluate the on-target mutation spectrum and unintended karyotypic changes. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Selecting and ligating sgRNAs into expression plasmids Basic Protocol 2: Validation of sgRNA via in vitro transcription and cleavage assay Basic Protocol 3: Nucleofection of primed human embryonic stem cells Basic Protocol 4: MiSeq analysis of indel mutations Basic Protocol 5: Single cell cloning of targeted hPSCs Basic Protocol 6: Karyotyping of targeted hPSCs.
Highlights
Human pluripotent stem cells are established either from early embryonic epiblast progenitor cells of pre-implantation-stage embryos (Thomson et al, 1998) or following reprogramming of fibroblasts to generate induced pluripotent stem cells (Takahashi et al, 2007). hPSCs have the ability to differentiate into cells of all three germ layers and exhibit unlimited self-renewal (Nichols & Smith, 2009).Appropriate spatial and temporal expression of genes is central to the regulation of pluripotency and early cell fate decisions (Martello & Smith, 2014; Ng & Surani, 2011)
While the function of various distinct pluripotency-associated factors has been well explored in mouse embryonic stem cells (Niwa, 2007) and mouse embryos, the precise functional roles of factors in hPSCs and human embryos remain unknown and necessitate further investigation
By aggregating hPSCs with null mutations in genes of interest to form these complex structures, their functional requirements can be assessed in a tractable model of early human development. hPSCs and their resulting structures are a cellular context that is highly informative for refining CRISPR-Cas9 editing techniques before testing the role of a gene directly in precious human embryos
Summary
Human pluripotent stem cells (hPSCs) are established either from early embryonic epiblast progenitor cells of pre-implantation-stage embryos (Thomson et al, 1998) or following reprogramming of fibroblasts to generate induced pluripotent stem cells (Takahashi et al, 2007). hPSCs have the ability to differentiate into cells of all three germ layers and exhibit unlimited self-renewal (Nichols & Smith, 2009). The generation of loss-of-function mutations and subsequent phenotyping of the resultant null mutant cells is an informative approach to understanding the role of genes in these processes. To engineer null mutations to investigate gene function, it can be especially useful to direct the CRISPR-Cas genome editing strategy to target functional domains. The presence of multiple pseudogenes for a target gene of interest can make successful editing of the intended target more challenging if there is significant sequence homology This is because off-target editing at the pseudogenes will result in a decrease in efficiency of the CRISPR-Cas9-mediated genome editing at the on-target site, and may lead to potential unintended off-target effects. SgRNAs are designed to target near the 5 end of the coding region In this way, frameshift mutations can either result in the introduction of PTCs early in the gene sequence, or alternatively the whole of the coding region can be BASIC PROTOCOL 1. Ontarget efficiencies vary based on the prediction algorithm, while off-targets are predicted
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