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Leah A. Owen

Researcher at University of Utah

Publications -  47
Citations -  1273

Leah A. Owen is an academic researcher from University of Utah. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 13, co-authored 37 publications receiving 971 citations. Previous affiliations of Leah A. Owen include University at Buffalo.

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Expression profiling of EWS/FLI identifies NKX2.2 as a critical target gene in Ewing's sarcoma

TL;DR: It is shown that ongoing EWS/FLI expression is required for the tumorigenic phenotype of Ewing's sarcoma by combining retroviral-mediated RNA interference with reexpression studies, and a highly validated transcriptional profile for the EWS-FLI fusion protein is developed.
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EWS/FLI mediates transcriptional repression via NKX2.2 during oncogenic transformation in Ewing's sarcoma.

TL;DR: Microarray analysis demonstrated that the transcriptional repressive function of NKX2.2 is necessary, and sufficient, for the oncogenic phenotype of Ewing's sarcoma, and suggest a therapeutic approach to this disease.
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Generation, transcriptome profiling, and functional validation of cone-rich human retinal organoids.

TL;DR: C cone-rich retinal organoids are established and a reference of transcriptomes that are valuable resources for retinal studies are established, which resemble the macula/fovea based on the ratio of cones to rods and single-cell transcriptomes.
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Genetics of age-related macular degeneration (AMD).

TL;DR: If the goal of personalized medicine is to be realized and biomarkers are to have predictive value for determining the magnitude of risk for AMD at the genetic level, one will need to examine the relationships between these pathways across disease state and relative to modifiable risk factors such as hypertension, smoking, body mass index, and hypercholesterolemia.
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Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

TL;DR: It is demonstrated that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models, and the dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA- sequencing results.