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Hayun, M.

Publications and source records attributed to Hayun, M..

2 recordsLinked to original sources

Allele-specific correction of dominant Best vitelliform macular dystrophy in patient-derived retinal pigment epithelium

Autosomal dominant Best vitelliform macular dystrophy (BVMD) caused by variants in the BEST1 gene is characterized by dysfunction of the macular retinal pigment epithelium (RPE) and secondary degeneration of the photoreceptors. There are currently no approved treatments for BVMD, and owing to its dominant nature, there remains uncertainty regarding the utility of traditional gene augmentation. Here we evaluated whether a dominant pathogenic BEST1 allele can be corrected by base editing in differentiated RPE cells. We identified a patient with a likely pathogenic BEST1 c.851A>G (p. Tyr284Cys) variant that was amenable to cytidine base editing. After establishing patient-derived induced pluripotent stem cells (iPSCs), we corrected the pathogenic variant in the iPSCs to obtain corrected iPSCs with the same genetic background. Corrected iPSC-derived RPE exhibited normalized monolayer appearance, improved barrier integrity, reduced cell death, and restored RPE-specific transcriptome. We then used a dual adeno-associated virus (AAV) split-intein system to deliver a CRISPR-associated protein 9 cytidine base editor (SpCas9-CBE) to BEST1 c.851A>G mutant RPE monolayers and achieved editing of the pathogenic allele with a maximum efficiency of 13.42 {+/-} 3.64% (mean {+/-} SD). Together, these results demonstrate progress towards allele-specific base editing in a dominantly inherited retinal disorder.

neuroscience↗

High Resolution Comparison of Cancer-Related Developmental Processes Using Trajectory Alignment

Abnormal differentiation is a key feature of cancer, yet currently there is no framework that enables a comparative analysis of differentiation processes across patients while preserving their individual-level resolution. Here, we present devMap, an algorithm that uses high-dimensional trajectory alignment to anchor cancer-related developmental processes to a common backbone process, thus allowing for their systematic comparison. We applied devMap to bone marrow samples from healthy individuals and AML patients profiled by single-cell mass-cytometry at cancer diagnosis and following treatment. devMap standardization enabled us to infer the developmental status of the AML samples and characterize its evolution following treatment and in relapse. Application of devMap on an external dataset of AML bone marrow samples revealed conserved patterns of developmental signaling responses in AML that were obscured by traditional methodologies for developmental inference.

systems biology↗