Methodology for single cell profiling using spatially resolved gene expression data - Case study using a four-stage cancer model

The advent of single-cell transcriptomics has enabled the study of cellular heterogeneity within and among populations. Current methods are only able to process a small number of cells. A promising method for high-throughput spatially resolved gene expression analysis with close to single-cell resolution is currently being developed under the concept of spatial transcriptomics. Work is currently carried out to create bioinformatics tools to enable efficient analysis and integration of the data produced by the method. This thesis describes an automatized pipeline that has been developed for the integration and post-processing of spatial transcriptomics cell line imaging and sequencing data. The pipeline was applied to data from two different cell lines derived from a four-stage cancer model study. Suitable pipeline parameters for the analysis of these cell lines are proposed