Exploring the transcriptional space

Abstract: Transcriptomics promises biological insight into gene regulation, cell diversity,and mechanistic understanding of dysfunction. Driven by technologicaladvancements in sequencing technologies, the field has witnessed anexponential growth in data output. Not only has the amount of raw dataincreased tremendously but it’s granularity as well. From only being ableto obtain aggregated transcript information from large tissue samples, wecan now pinpoint the precise origin of transcripts within the tissue, sometimeseven within the confines of individual cells. This thesis focuses onthe different aspects of how to use these emergent technologies to obtain agreater understanding of biological mechanisms. The work conducted herespans only a few years of the much longer history of spatially resolved transcriptomics,which started with the early in situ hybridization techniquesand will continue to a potential future with complete molecular profiling ofevery cell in their natural, active state. Thus, at the same time the workpresented here introduces and demonstrates the use of the latest techniqueswithin spatial transcriptomics, it also deals with the shortcomings of the currentstate of the field, which undoubtedly will see extensive improvementsin the not too distant future. Article I is part of a series of articles wherewe mechanistically examine the biological underpinnings of a serendipitousfinding that single-stranded nucleic acids have immunomodulatory effects.In particular, we look at influenza-infected innate immune cells and theability of the oligonucleotide to inhibit viral entry. The oligonucleotidesprevent the cells from responding to certain types of pattern recognitionand cause a decrease in viral load. Our hypothesis is that the administrationof oligonucleotides blocks certain endocytic routes. While the invivo experiments suggest that the influenza virus is still able to infect andpromote disease in the host, changes in signaling response due to the inhibitionof the endocytotic routes could represent an avenue for future therapeutics.The conclusions were drawn by combining protein labeling andconventional methods for RNA profiling in the form of quantitative realtimePCR and bulk RNA sequencing. As a transition into the concept ofspatial RNA profiling, the thesis includes an Additional material reviewarticle on spatial transcriptomics, where we give an overview of the currentstate of the field, as it looked like in the beginning of 2020. In ArticleII, we report on the development of an R package for analyzing spatialtranscriptomics datasets. The package offers visualization features and anautomated pipeline for masking tissue images and aligning serially sectionedexperiments. The tool is extensively used throughout the rest of the articleswhere spatial transcript information is analyzed and is available for allscientists that use the supported spatial transcriptomics platforms in theirresearch. In Article III, we propose a method to spatially map long-readsequencing data. While previously described methods for high-throughputspatial transcriptomics produce short-read data, full-length transcript informationallows us to spatially profile alternatively spliced transcripts. Usingthe proposed method, we find alternatively spliced transcripts and find isoformsof the same gene to be differentially expressed in different regions ofthe mouse brain. Furthermore, we profile RNA editing across the full-lengthtranscripts and find certain parts of the mouse left hemisphere to displaya substantially higher degree of editing events compared to the rest of thebrain. The proposed method is based on readily available reagents anddoes not require advanced instrumentation. We believe full-length transcriptinformation obtained in this manner could help scientists obtain adeeper understanding from transcriptome data. Finally, in Article IV,we explore how the latest technologies for spatial transcriptomics can beused to characterize the expression landscape of respiratory syncytial virusinfections by comparing infected and non-infected mouse lungs. By integrationof annotated single-cell data and spatially resolved transcriptomics, wemap the location of the single cells onto the spatial grid to localize immunecell populations across the tissue sections. By correlating the locations togene expression, we profile locally confined cellular processes and immuneresponses. We believe that high-throughput spatial information obtainedwithout predefined targets will become an important tool for exploratoryanalysis and hypothesis generation, which in turn could unlock mechanisticknowledge of the differences between experimental models that are importantfor translational research.