The development of single-cell RNA sequencing enabled the high throughput characterization of cell populations with unprecedented detail. Yet, it failed in capturing the spatial localization of individual cells. Overcoming this, different spatial profiling methods have been developed in recent years, with in situ sequencing (ISS) being among the most powerful solutions

ISS is a targeted spatially-resolved transcriptomics method designed to detect the expression of hundreds of genes in situ in a single experiment. For this, ISS employs padlock probes, a type of oligonucleotide designed to specifically hybridize on the targeted regions, with rolling circle amplification and a combinatorial detection of the transcripts imaged. Due to its throughput and resolution, ISS is seen as a useful tool to create high content molecular maps of tissues, being of special use for building spatial atlases. However, due to its recent development, it’s still unclear how this should be done. The work presented in this thesis explores ISS as a tool for building large spatially-resolved atlases of cell types. 

In paper I, we compare the performance of cDNA-based ISS with the High Sensitivity Library Preparation Kit, developed by CARTANA AB. We identify this product to be fivefold more sensitive than cDNA-based ISS due to its improved chemistry. In addition, we show that this increased sensitivity enhances the analytical capabilities of the resulting data.    

In paper II, we build a topographic atlas of the developmental human lung. We identify 83 different cell types and states, including a novel type of GHRL-positive neuroendocrine cell. We further elucidate the developmental origin multiple populations, defining their location in situ and predicting potential interactions. 

In paper III, we create a topographic atlas of the adult human lung. We combine multiple spatial transcriptomic technologies to generate spatial maps of the populations found in the adult lung. We decipher regional differences in terms of cell type composition and cell type-specific expression. Finally, we also characterize the spatial context of rare cell types.

In paper IV, we employ large-scale data integration to construct a scRNA-seq-based cellular map of glioblastoma, an aggressive brain malignancy. In addition, we use ISS to generate single-cell resolution cell type maps of 13 glioblastoma patients, identifying consistent niches across patients and uncovering the cellular organization of these tumors. 

In paper V, we explore the quality of the data generated by the Xenium In Situ Platform, a product based on ISS and commercialized by 10X Genomics. We explore the main characteristics of the data and benchmark it against other technologies. Finally, we also define best practices for the most common analysis done using these datasets. 

Collectively, the studies presented in this thesis serve as evidence of the efficacy of ISS in constructing comprehensive cellular atlases with a single-cell resolution.

The classification of cancer has evolved over millennia, and centuries of work has laid the groundwork for modern cancer classification, which continues to evolve with advances in our understanding of cancer biology in tandem with improvement in the technologies, tools and frameworks used to characterize them. This thesis builds on the historical legacy of cancer classification by integrating single-cell transcriptomic approaches to explore the molecular complexity and intratumoral heterogeneity (ITH) of cancer. By defining and analyzing diverse transcriptomic states, known as metaprograms, in three aggressive cancer types—glioblastoma (GB), triple-negative breast cancer (TNBC), and diffuse midline glioma (DMG)—this work offers a more refined and precise lens through which to understand tumor progression and develop personalized therapeutic strategies. Using high-resolution single-cell RNA sequencing (scRNA-seq), spatially-resolved transcriptomics (SRT), and patient-derived organoid models, we identify distinct metaprograms that shape tumor progression, resistance, and patient outcomes.

Starting with DMG, we use spatial transcriptomics to map tumor-specific phenotypes, uncovering a novel neural stem cell-like population that interacts with the tumor microenvironment. This phenotype, defined by key progenitor markers, demonstrates plasticity, likely contributing to DMG’s resistance to therapy. By studying nonmalignant cells in the DMG microenvironment, we propose that specific cell types support tumor growth and evolution, highlighting potential therapeutic interventions. We then apply scRNA-seq to GB, revealing the presence of multiple metaprograms, including those linked to stem-like properties, invasion, and immune evasion. These metaprograms provide insights into how GB cells adapt and evolve in response to their microenvironment, uncovering potential therapeutic targets for this highly resistant cancer. In TNBC, we develop a comprehensive TNBC-Map by integrating single cell-datasets from patient biopsies, identifying nine core malignant metaprograms. These metaprograms encompass biological processes such as immune modulation, epithelial-to-mesenchymal transition (EMT), and vasculogenic mimicry. By correlating these metaprograms with patient survival, we identify distinct patterns of molecular activity that could guide the development of more personalized and effective treatments for TNBC.

Across these studies, we assess the power of metaprogram analysis to dissect cancer heterogeneity, offering a deeper understanding of the functional states driving tumor progression. This knowledge enables the identification of patient-specific molecular signatures, paving the way for precision medicine approaches. This thesis lays the groundwork for metaprogram-based cancer diagnostics and provides a foundation for the future integration of multi-omic precision medicine strategies that target specific cancer cell states, ultimately improving patient outcomes.