Gene correction in human long-term hematopoietic stem cells (LT-HSCs) could be an effective therapy for monogenic diseases of the blood and immune system. Here we describe an approach for X-linked sSevere cCombined iImmunodeficiency (SCID-X1) using targeted integration of a cDNA into the endogenous start codon to functionally correct disease-causing mutations throughout the gene. Using a CRISPR-Cas9/AAV6 based strategy, we achieve up to 20% targeted integration frequencies in LT-HSCs. As measures of the lack of toxicity we observe no evidence of abnormal hematopoiesis following transplantation and no evidence of off-target mutations using a high-fidelity Cas9 as a ribonucleoprotein complex. We achieve high levels of targeting frequencies (median 45%) in CD34+ HSPCs from six SCID-X1 patients and demonstrate rescue of lymphopoietic defect in a patient derived HSPC population in vitro and in vivo. In sum, our study provides specificity, toxicity and efficacy data supportive of clinical development of genome editing to treat SCID-Xl.

The original version of this Article omitted the following from the Acknowledgements: G.B. acknowledges the support from the Cancer Prevention and Research Institute of Texas (RR140081 and RR170721).This has now been corrected in both the PDF and HTML versions of the Article.

Gene correction in human long-term hematopoietic stem cells (LT-HSCs) could be an effective therapy for monogenic diseases of the blood and immune system. Here we describe an approach for X-linked sSevere cCombined iImmunodeficiency (SCID-X1) using targeted integration of a cDNA into the endogenous start codon to functionally correct disease-causing mutations throughout the gene. Using a CRISPR-Cas9/AAV6 based strategy, we achieve up to 20% targeted integration frequencies in LT-HSCs. As measures of the lack of toxicity we observe no evidence of abnormal hematopoiesis following transplantation and no evidence of off-target mutations using a high-fidelity Cas9 as a ribonucleoprotein complex. We achieve high levels of targeting frequencies (median 45%) in CD34⁺ HSPCs from six SCID-X1 patients and demonstrate rescue of lymphopoietic defect in a patient derived HSPC population in vitro and in vivo. In sum, our study provides specificity, toxicity and efficacy data supportive of clinical development of genome editing to treat SCID-Xl.

The original version of this Article omitted the following from the Acknowledgements: "G.B. acknowledges the support from the Cancer Prevention and Research Institute of Texas (RR140081 and RR170721)."This has now been corrected in both the PDF and HTML versions of the Article.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.