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%0 Thesis
%A Graßhoff, Martin
%T A computational framework for the genotyping of single cell RNA sequencing data
%I RWTH Aachen University
%V Dissertation
%C Aachen
%M RWTH-2026-00479
%P 1 Online-Ressource : Illustrationen
%D 2025
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2026
%Z Dissertation, RWTH Aachen University, 2025
%X he development of single-cell RNA-sequencing (scRNA-seq) technologies allows the investigation of individual cells in large numbers. Investigating differences between conditions, such as disease and healthy cells, can be conducted at the single cell level, leading to a more accurate estimation of cell specific changes in a disease. By accounting for variants within measured transcripts, it is also possible to evaluate somatic variants and characterize clonal cell lineages. This is especially desirable in the case of cancers, as mutations are a major driver for the development of cancers. Especially in the case of cancers like myeloproliferative neoplasms (MPN), as MPNs are typically associated with mutually exclusive driver mutations and additional secondary mutations. However, the sparsity of the scRNA-seq data (very few reads per transcript) or a bias in protocols(favoring 3’ ends of the transcripts) makes the chance of capturing somatic variants very unlikely. These challenges can be overcome by locus-specific sequencing for known driver mutations and the use of mitochondrial variants as natural barcodes to identify clonal lineages. Currently, available computational tools focus on genotyping, but do not provide functionality for combined analysis of somatic and mitochondrial variants and functional analysis such as characterization of gene expression changes in detected clones. Therefore, we propose SIGURD, which is an R-based pipeline for the clonal analysis of scRNA-seqdata. Our approach allows to identify clones by leveraging both somatic and mitochondrial variants. SIGURD also allows for functional analysis after clonal detection: association of clones with cell populations, detection of differentially expressed genes across clones and association of somatic and mitochondrial variants. Here, we demonstrate the power of SIGURD by analyzing single-cell data from two case studies, both of patients with MPN. In the first case study, we analyze colony-forming units (CFU) derived from patients with MPN and healthy individuals. We genotype cells for the JAK2V617F driver mutation and for mitochondrial variants (mtVar). This allow us to determine the influence of the driver mutation on the cells and to quantify the clonal diversity in MPN and healthy samples by the use of mitochondrial variants. In the second case study, we analyze a myelofibrosis cell atlas. In this study, patients were genotyped before and after receiving an allogeneic bone marrow transplant. This provides us with the opportunity to track the presence of mutated cells over time and determine the presence of treatment-resistant clonal lineages. Our results demonstrate the usefulness of single cell genotyping for the analysis of clonal diseases and the power of SIGURD to facilitate this.
%F PUB:(DE-HGF)11
%9 Dissertation / PhD Thesis
%R 10.18154/RWTH-2026-00479
%U https://publications.rwth-aachen.de/record/1025026