Background.  Glioblastoma (GB), particularly IDH-wild type, is the most aggressive brain malignancy with a dismal prognosis. Despite advances in molecular profiling, the complexity of its tumor microenvironment and spatial organization remains poorly understood. This study aimed to create a comprehensive single-cell and spatial atlas of GB to unravel its cellular heterogeneity, spatial architecture, and clinical relevance.

Methods.  We integrated single-cell RNA sequencing data from 26 datasets, encompassing over 1.1 million cells from 240 patients, to construct GBmap, a harmonized single-cell atlas. High-resolution spatial transcriptomics was employed to map the spatial organization of GB tissues. We developed the Tumor Structure Score (TSS) to quantify tumor organization and correlated it with patient outcomes.

Results.  We showcase the applications of GBmap for reference mapping, transfer learning, and biological discoveries. GBmap revealed extensive cellular heterogeneity, identifying rare populations such as tumor-associated neutrophils and homeostatic microglia. Spatial analysis uncovered 7 distinct tumor niches, with hypoxia-dependent niches strongly associated with poor prognosis. The TSS demonstrated that highly organized tumors, characterized by well-defined vasculature and hypoxic niches, correlated with worse survival outcomes.

Conclusions.  This study provides a comprehensive resource for understanding glioblastoma heterogeneity and spatial organization. GBmap and the TSS provide an integrative view of tumor architecture in GB, highlighting hypoxia-driven niches that may represent avenues for further investigation. Our resource can facilitate exploratory analyses and hypothesis generation to better understand disease progression.

The amniote pallium contains sensory circuits that are structurally and functionally equivalent, yet their evolutionary relationship remains unresolved. We used birthdating analysis, single-cell RNA and spatial transcriptomics, and mathematical modeling to compare the development and evolution of known pallial circuits across birds (chick), lizards (gecko), and mammals (mouse). We reveal that neurons within these circuits’ stations are generated at varying developmental times and brain regions across species and found an early developmental divergence in the transcriptomic progression of glutamatergic neurons. Our research highlights developmental distinctions and functional similarities in the sensory circuit between birds and mammals, suggesting the convergence of high-order sensory processing across amniote lineages.

Oligodendroglia are the responsible cells for myelination in the central nervous system and their involvement in Parkinson’s disease (PD) is poorly understood. We performed sn-RNA-seq and image-based spatial transcriptomics of human caudate nucleus and putamen (dorsal striatum) from PD and control brain donors to elucidate the diversity of oligodendroglia and how they are affected by the disease. We profiled a total of ~ 200.000 oligodendroglial nuclei, defining 15 subclasses, from precursor to mature cells, 4 of which are disease-associated. These PD-specific populations are characterized by the overexpression of heat shock proteins, as well as distinct expression signatures related to immune responses, myelination alterations, and disrupted cell signaling pathways. We have also identified impairments in cell communication and oligodendrocyte development, evidenced by changes in neurotransmitter receptors expression and cell adhesion molecules. In addition, we observed significant disruptions in oligodendrocyte development, with aberrant differentiation trajectories and shifts in cell proportions, particularly in the transition from mature oligodendrocytes to disease-associated states. Quantitative immunohistochemical analysis revealed decreased myelin levels in the PD striatum, which correlated with transcriptomic alterations. Furthermore, spatial transcriptomics mapping revealed the distinct localization of disease-associated populations within the striatum, with evidence of impaired myelin integrity. Thus, we uncover oligodendroglia as a critical cell type in PD and a potential new therapeutic target for myelin-based interventions.

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

Heart development relies on topologically orchestrated cellular transitions and interactions, many of which remain poorly characterized in humans. Here, we combined unbiased spatial and single-cell transcriptomics with imaging-based validation across postconceptional weeks 5.5 to 14 to uncover the molecular landscape of human early cardiogenesis. We present a high-resolution transcriptomic map of the developing human heart, revealing the spatial arrangements of 31 coarse-grained and 72 fine-grained cell states organized into distinct functional niches. Our findings illuminate key insights into the formation of the cardiac pacemaker-conduction system, heart valves and atrial septum, and uncover unexpected diversity among cardiac mesenchymal cells. We also trace the emergence of autonomic innervation and provide the first spatial account of chromaffin cells in the fetal heart. Our study, supported by an open-access spatially centric interactive viewer, offers a unique resource to explore the cellular and molecular blueprint of human heart development, offering links to genetic causes of heart disease.

There is an ever-growing choice of single-cell and spatial ’omics platforms for industry and academia. The scTrends Consortium provides a brief historical overview of the established platforms and companies, revealing market trends and presenting possible angles for how technologies may differentiate themselves.

Embryonic development is a complex and dynamic process that unfolds over time and involves the production and diversification of increasing numbers of cells. The impact of developmental time on the formation of the central nervous system is well documented, with evidence showing that time plays a crucial role in establishing the identity of neuronal subtypes. However, the study of how time translates into genetic instructions driving cell fate is limited by the scarcity of suitable experimental tools. We introduce BirthSeq, a new method for isolating and analyzing cells based on their birth date. This innovative technique allows for in vivo labeling of cells, isolation via fluorescence-activated cell sorting, and analysis using high-throughput techniques. We calibrated the BirthSeq method for developmental organs across three vertebrate species (mouse, chick and gecko), and utilized it for single-cell RNA sequencing and novel spatially resolved transcriptomic approaches in mouse and chick, respectively. Overall, BirthSeq provides a versatile tool for studying virtually any tissue in different vertebrate organisms, aiding developmental biology research by targeting cells and their temporal cues.

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 respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lung and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

Deciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangling the complex neurological and psychiatric diseases affecting this brain structure. We performed snRNA-seq and spatial transcriptomics of postmortem human caudate nucleus and putamen samples to elucidate the diversity and abundance of interneuron populations and their inherent transcriptional structure in the human dorsal striatum. We propose a comprehensive taxonomy of striatal interneurons with eight main classes and fourteen subclasses, providing their full transcriptomic identity and spatial expression profile as well as additional quantitative FISH validation for specific populations. We have also delineated the correspondence of our taxonomy with previous standardized classifications and shown the main transcriptomic and class abundance differences between caudate nucleus and putamen. Notably, based on key functional genes such as ion channels and synaptic receptors, we found matching known mouse interneuron populations for the most abundant populations, the recently described PTHLH and TAC3 interneurons. Finally, we were able to integrate other published datasets with ours, supporting the generalizability of this harmonized taxonomy.