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Files of codes for master thesis project: qPCR data analysis (statistical significance) and graphs - using R programming

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Generation of an isogenic iPSC-based model of Alexander’s Disease using genome editing

Author: Kavya Kalpana Ganesh1

Supervisors: Henrik Ahlenius1, Oskar Zetterdahl1

Abstract

Alexander’s Disease is a progressive neurological disorder caused by a wide array of dominant, gain of function mutations in the GFAP gene. The disorder is characterized by the accumulation of GFAP in astrocytes, leading to the formation of protein inclusion bodies known as Rosenthal fibers. This results in a disruption of astrocytic function and the subsequent degeneration of white matter. However, a better understanding of the disease mechanisms is required to standardize therapy and to develop disease modifying treatments. Since the disease is caused by a wide array of mutations, it is important to study the phenotypic characteristics associated with specific mutations to collect and collate data that can define the disease more precisely. Use of iPSCs coupled with CRISPR-Cas9 genome editing to generate isogenic controls, in combination with protocols to generate astrocytes and neurons allows us to model the disease in vitro and work towards this goal. Here, progress has been made towards obtaining an isogenic control line for mutation K228E and isogenic controls have been generated for the mutation Q93P in the GFAP gene.

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Files of codes for master thesis project: qPCR data analysis (statistical significance) and graphs - using R programming

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