It is well known that cells in tissues display a large heterogeneity in gene expression due to differences in cell lineage origin and variation in the local environment at different sites in the tissue, a heterogeneity that is difficult to study by analyzing bulk RNA extracts from tissue. Recently, genome-wide transcriptome analysis technologies have enabled the analysis of this variation with single-cell resolution. In order to link the heterogeneity observed at molecular level with the morphological context of tissues, new methods are needed which achieve an additional level of information, such as spatial resolution. In this thesis I describe the development and application of padlock probes and rolling circle amplification (RCA) as molecular tools for spatially-resolved transcriptome analysis. Padlock probes allow in situ detection of individual mRNA molecules with single nucleotide resolution, visualizing the molecular information directly in the cell and tissue context. Detection of clinically relevant point mutations in tumor samples is achieved by using padlock probes in situ, allowing visualization of intra-tumor heterogeneity. To resolve more complex gene expression patterns, we developed in situ sequencing of RCA products combining padlock probes and next-generation sequencing methods. We demonstrated the use of this new method by, for the first time, sequencing short stretches of transcript molecules directly in cells and tissue. By using in situ sequencing as read-out for multiplexed padlock probe assays, we measured the expression of tens of genes in hundreds of thousands of cells, including point mutations, fusions transcripts and gene expression level. These molecular tools can complement genome-wide transcriptome analyses adding spatial resolution to the molecular information. This level of resolution is important for the understanding of many biological processes and potentially relevant for the clinical management of cancer patients. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>

Single-cell RNA sequencing and in situ sequencing were combined in a recent study of the developing human heart to explore the transcriptional landscape at three developmental stages. However, the method used in the study to create the spatial cellular maps has some limitations. It relies on image segmentation of the nuclei and cell types defined in advance by single-cell sequencing. In this study, we applied a new unsupervised approach based on graph neural networks on the in situ sequencing data of the human heart to find spatial gene expression patterns and detect novel cell and sub-cell types. In this thesis, we first introduce some relevant background knowledge about the sequencing techniques that generate our data, machine learning in single-cell analysis, and deep learning on graphs. We have explored several graph neural network models and algorithms to learn embeddings for spatial gene expression. Dimensionality reduction and cluster analysis were performed on the embeddings for visualization and identification of biologically functional domains. Based on the cluster gene expression profiles, locations of the clusters in the heart sections, and comparison with cell types defined in the previous study, the results of our experiments demonstrate that graph neural networks can learn meaningful representations of spatial gene expression in the human heart. We hope further validations of our clustering results could give new insights into cell development and differentiation processes of the human heart.

In situ sekvensering är en metod som kan användas för att lokalisera differentiellt uttryck av mRNA direkt i vävnadssnitt, vilket kan ge viktiga ledtrådar om många sjukdomstillstånd. Idag förloras många av sekvenserna från in situ sekvensering på grund av det kvalitetsmått man använder för att säkerställa att sekvenser är korrekta. Det finns troligtvis möjlighet att förbättra prestandan av den nuvarande base calling-metoden eftersom att metoden är i ett tidigt utvecklingsskede. Vi har genomfört explorativ dataanalys för att undersöka förekomst av systematiska fel och korrigerat för dessa med hjälp av statistiska metoder. Vi har framförallt undersökt tre metoder för att korrigera för systematiska fel: I) Korrektion av överblödning som sker på grund avöverlappande emissionsspektra mellan fluorescenta prober. II) En sannolikhetsbaserad tolkningav intensitetsdata som resulterar i ett nytt kvalitetsmått och en alternativ klassificerare baseradpå övervakad inlärning. III) En utredning om förekomst av cykelberoende effekter, exempelvisofullständig dehybridisering av fluorescenta prober. Vi föreslår att man gör följande saker: Implementerar och utvärderar det sannolikhetsbaserade kvalitetsmåttet Utvecklar och implementerar den föreslagna klassificeraren Genomför ytterligare experiment för att påvisa eller bestrida förekomst av ofullständigdehybridisering / In situ sequencing is a method that can be used to localize differential expression of mRNA directly in tissue sections, something that can give valuable insights to many statest of disease. Today, many of the registered sequences from in situ sequencing are lost due to a conservative quality measure used to filter out incorrect sequencing reads. There is room for improvement in the performance of the current method for base calling since the technology is in an early stage of development. We have performed exploratory data analysis to investigate occurrence of systematic errors, and corrected for these by using various statistical methods. The primary methods that have been investigated are the following: I) Correction of emission spectra overlap resulting in spillover between channels. II) A probability-based interpretation of intensity data, resulting in a novel quality measure and an alternative classifier based on supervised learning. III) Analysis of occurrence of cycle dependent effects, e.g. incomplete dehybridization of fluorescent probes. We suggest the following: Implementation and evaluation of the probability-based quality measure Development and implementation of the proposed classifier Additional experiments to investigate the possible occurrence of incomplete dehybridization