Exploring the transcriptional space

Abstract

Transcriptomics promises biological insight into gene regulation, cell diversity, and mechanistic understanding of dysfunction. Driven by technological advancements in sequencing technologies, the field has witnessed an exponential growth in data output. Not only has the amount of raw data increased tremendously but it’s granularity as well. From only being able to obtain aggregated transcript information from large tissue samples, we can now pinpoint the precise origin of transcripts within the tissue, sometimes even within the confines of individual cells. This thesis focuses on the different aspects of how to use these emergent technologies to obtain a greater understanding of biological mechanisms. The work conducted here spans only a few years of the much longer history of spatially resolved transcriptomics, which started with the early in situ hybridization techniques and 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 techniques within spatial transcriptomics, it also deals with the shortcomings of the current state of the field, which undoubtedly will see extensive improvements in the not too distant future. Article I is part of a series of articles where we mechanistically examine the biological underpinnings of a serendipitous finding that single-stranded nucleic acids have immunomodulatory effects. In particular, we look at influenza-infected innate immune cells and the ability of the oligonucleotide to inhibit viral entry. The oligonucleotides prevent the cells from responding to certain types of pattern recognitionand cause a decrease in viral load. Our hypothesis is that the administration of oligonucleotides blocks certain endocytic routes. While the invivo experiments suggest that the influenza virus is still able to infect and promote disease in the host, changes in signaling response due to the inhibition of the endocytotic routes could represent an avenue for future therapeutics. The conclusions were drawn by combining protein labeling and conventional methods for RNA profiling in the form of quantitative realtime PCR and bulk RNA sequencing. As a transition into the concept of spatial RNA profiling, the thesis includes an Additional material review article on spatial transcriptomics, where we give an overview of the current state of the field, as it looked like in the beginning of 2020. In Article II, we report on the development of an R package for analyzing spatial transcriptomics datasets. The package offers visualization features and an automated pipeline for masking tissue images and aligning serially sectioned experiments. The tool is extensively used throughout the rest of the articles where spatial transcript information is analyzed and is available for all scientists that use the supported spatial transcriptomics platforms in their research. In Article III, we propose a method to spatially map long-read sequencing data. While previously described methods for high-throughput spatial transcriptomics produce short-read data, full-length transcript information allows us to spatially profile alternatively spliced transcripts. Using the proposed method, we find alternatively spliced transcripts and find isoforms of the same gene to be differentially expressed in different regions of the mouse brain. Furthermore, we profile RNA editing across the full-length transcripts and find certain parts of the mouse left hemisphere to display a substantially higher degree of editing events compared to the rest of the brain. The proposed method is based on readily available reagents and does not require advanced instrumentation. We believe full-length transcript information obtained in this manner could help scientists obtain a deeper understanding from transcriptome data. Finally, in Article IV, we explore how the latest technologies for spatial transcriptomics can be used to characterize the expression landscape of respiratory syncytial virus infections by comparing infected and non-infected mouse lungs. By integration of annotated single-cell data and spatially resolved transcriptomics, we map the location of the single cells onto the spatial grid to localize immune cell populations across the tissue sections. By correlating the locations to gene expression, we profile locally confined cellular processes and immune responses. We believe that high-throughput spatial information obtained without predefined targets will become an important tool for exploratory analysis and hypothesis generation, which in turn could unlock mechanistic knowledge of the differences between experimental models that are important for translational research.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289365