Image and data analysis for understanding spatial transcriptomics and tissue architecture

The human body is a complicated system, with its complex functions interpreted through interactions that scale from tissues down to individual genes. To unravel this complexity, we must look at the smallest functional compartments, specifically, the spatial organization of gene expression within tissues. This thesis focuses on computational methods in spatial transcriptomics, to precisely map gene expression patterns, providing insights into how cellular neighborhoods and protein interactions drive tissue functionality. Central to this research are computational tools and methodologies that tackle the spatial transcriptomics pipeline from start to finish, addressing data acquisition, pre-processing, decoding, classification, and spatial statistics. The contributions are presented across two perspectives: the technical advancements in the pipeline and their application to real-world biological scenarios. On the technical side, this thesis introduces two novel classification methods. The first is a graph-based segmentation algorithm that groups molecular signals into cells without relying on nuclear stains, overcoming common imaging limitations. The second is a fast and interactive clustering method for imaging-based spatial omics data. Additionally, the thesis includes work on TissUUmaps 3, an interactive visualization tool designed for high-resolution exploration and quality assessment of large-scale spatial data, along with plugins to enhance the detailed analysis of spatial patterns in gene expression across tissue types. A comprehensive review of spatial statistics methods applicable to spatial omics data complements these technical contributions. The biological application side demonstrates the utility of these tools in uncovering insights from real-world datasets. In the mouse olfactory epithelium, spatial gene expression mapping reveals cellular patterns critical for sensory processing. In bladder cancer, spatial transcriptomics illuminates immune cell behavior and interactions within the tumor microenvironment, showcasing how these methods bridge computational advancements with biological discovery. Together, these contributions highlight the power of computational spatial transcriptomics to reveal critical insights into tissue architecture, gene co-expression, and cellular functionality. By refining and scaling analytical techniques, this thesis advances our understanding of tissue complexity and paves the way for discoveries in developmental biol