Mapping Transcriptomes in Tissues
Abstract: Over the past few decades, the advent of pioneering biotechnological methods has allowed scientists to analyze the molecular components of multicellular organisms with remarkable precision. The field of transcriptomics has witnessed a rapid development of technologies for gene expression profiling of biological samples. These gene expression profiles offer crucial insights into biological processes that underlie organism development, homeostasis, and disease-causing dysregulation. Modern transcriptomics technologies can profile samples at various degrees of precision and resolution, and when combined, they contribute to a comprehensive understanding of the complex molecular mechanisms that shape entire organisms. Some of these molecular mechanisms occur at the microscopic scale, controlled by communication between nearby cells. Other mechanisms depend on coordinated efforts between large networks of cells organized into tissues and organs. Cells, tissues and organs represent hierarchical levels of structural organization, and each level plays a vital role in the proper functioning of the organism. Gene expression profiling technologies yield comprehensive data that can be harnessed to explore and characterize biological phenomena within and across these structural levels. The central theme of this thesis revolves around the use of experimental technologies and computational methods in the field of transcriptomics to enhance our understanding of multicellular life. Particular attention is directed at a technology known as Visium, which has held an important position in the field in recent years. The research articles included in this thesis demonstrate the applications of Visium and related technologies in biological research.In article I, we present a computational toolbox for processing, analyzing, and visualizing Visium data, assembled into an open-source package written in the R programming language. The package facilitates the characterization of gene expression profiles in tissue sections and seamlessly integrates expression data with corresponding histological images. This computational framework was used extensively for the data analyses presented in articles II, III and IV and the articles listed in the extended list of publications.In article II, we report one of the first spatiotemporal, transcriptomics atlases of the developing human heart. The atlas encompasses three developmental time points during the first trimester, and is constructed from gene expression data from isolated cells and intact tissue sections. Joint analysis of this data enabled characterization of the transcriptomic profiles and the cellular composition of anatomical domains within the heart, illuminating biological processes that underlie cardiac morphogenesis in humans.Article III constitutes a study of the transcriptomic landscape of the murine colon generated using spatially resolved transcriptomics. By folding the organ into a roll, we successfully obtained tissue sections covering the entire colon, enabling organ-wide transcriptomic profiling. Sections were acquired from a healthy colon and a colon recovering from damage due to treatment with a tissue-damaging substance. Data-driven analysis of the healthy colon unveiled a previously undiscovered molecular regionalization from the proximal to distal parts. In the recovering colon, we observed dramatic alterations in the distal tissues, while the proximal parts remained more similar to the healthy colon. In the injured distal colon, we mapped multiple gene expression programs associated with distinct biological responses to tissue injury.In article IV, we introduce an experimental protocol that makes the Visium method compatible with fresh frozen tissue samples with low RNA quality. The protocol was tested on human prostate cancer, lung, colon, small intestine and pediatric brain tumor tissue samples, as well as mouse brain and cartilage tissue samples. Together, these tissue samples represented a wide selection of specimens with varying composition and RNA quality. Through comparative analyses, we demonstrated that the proposed experimental protocol surpassed the standard Visium protocol in performance for samples with low to moderate RNA integrity.Finally, in article V, we present an updated R package for Visium data analysis. This R package builds upon the work presented in article I, but offers a more versatile and efficient computational framework. The package features web-based tools for interactive data exploration, image processing methods and methods to map cell types in tissue sections. Additionally, it includes several spatially aware analysis methods that incorporate information about distances between measurements to investigate biological phenomena that exhibit spatial patterns.
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