A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung.

The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location.

Histone 3 lysine27-to-methionine (H3-K27M) mutations most frequently occur in diffuse midline gliomas (DMGs) of the childhood pons but are also increasingly recognized in adults. Their potential heterogeneity at different ages and midline locations is vastly understudied. Here, through dissecting the single-cell transcriptomic, epigenomic and spatial architectures of a comprehensive cohort of patient H3-K27M DMGs, we delineate how age and anatomical location shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We show that stem-like oligodendroglial precursor-like cells, present across all clinico-anatomical groups, display varying levels of maturation dependent on location. We reveal a previously underappreciated relationship between mesenchymal cancer cell states and age, linked to age-dependent differences in the immune microenvironment. Further, we resolve the spatial organization of H3-K27M DMG cell populations and identify a mitotic oligodendroglial-lineage niche. Collectively, our study provides a powerful framework for rational modeling and therapeutic interventions.

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.

With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.

Spatial Resolution of Mycobacterium tuberculosis Bacteria and Their Surrounding Immune Environments Based on Selected Key Transcripts in Mouse Lungs.

Mycobacterium tuberculosis (Mtb) bacilli are the causative agent of tuberculosis (TB), a major killer of mankind. Although it is widely accepted that local interactions between Mtb and the immune system in the tuberculous granuloma determine whether the outcome of infection is controlled or disseminated, these have been poorly studied due to methodological constraints. We have recently used a spatial transcriptomic technique, in situ sequencing (ISS), to define the spatial distribution of immune transcripts in TB mouse lungs. To further contribute to the understanding of the immune microenvironments of Mtb and their local diversity, we here present two complementary automated bacteria-guided analysis pipelines. These position 33 ISS-identified immune transcripts in relation to single bacteria and bacteria clusters. The analysis was applied on new ISS data from lung sections of Mtb-infected C57BL/6 and C3HeB/FeJ mice. In lungs from C57BL/6 mice early and late post infection, transcripts that define inflammatory macrophages were enriched at subcellular distances to bacteria, indicating the activation of infected macrophages. In contrast, expression patterns associated to antigen presentation were enriched in non-infected cells at 12 weeks post infection. T-cell transcripts were evenly distributed in the tissue. In Mtb-infected C3HeB/FeJ mice, transcripts characterizing activated macrophages localized in apposition to small bacteria clusters, but not in organized granulomas. Despite differences in the susceptibility to Mtb, the transcript patterns found around small bacteria clusters of C3HeB/FeJ and C57BL/6 mice were similar. Altogether, the presented tools allow us to characterize in depth the immune cell populations and their activation that interact with Mtb in the infected lung.

Direct RNA targeted in situ sequencing for transcriptomic profiling in tissue.

Highly multiplexed spatial mapping of transcripts within tissues allows for investigation of the transcriptomic and cellular diversity of mammalian organs previously unseen. Here we explore a direct RNA (dRNA) detection approach incorporating the use of padlock probes and rolling circle amplification in combination with hybridization-based in situ sequencing chemistry. We benchmark a High Sensitivity Library Preparation Kit from CARTANA that circumvents the reverse transcription needed for cDNA-based in situ sequencing (ISS) via direct RNA detection. We found a fivefold increase in transcript detection efficiency when compared to cDNA-based ISS and also validated its multiplexing capability by targeting a curated panel of 50 genes from previous publications on mouse brain sections, leading to additional data interpretation such as de novo cell clustering. With this increased efficiency, we also found to maintain specificity, multiplexing capabilities and ease of implementation. Overall, the dRNA chemistry shows significant improvements in target detection efficiency, closing the gap to other fluorescent in situ hybridization-based technologies and opens up possibilities to explore new biological questions previously not possible with cDNA-based ISS.

Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps.

BACKGROUND: A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a need for versatile user friendly tools that can be used for a de novo exploration of datasets. RESULTS: Here we present MATLAB-based Analysis toolbox for in situ sequencing (ISS) expression maps (Matisse). We demonstrate Matisse by characterizing the 2-dimensional spatial expression of 119 genes profiled in a mouse coronal section, exploring different levels of complexity. Additionally, in a comprehensive analysis, we further analyzed expression maps from a second technology, osmFISH, targeting a similar mouse brain region. CONCLUSION: Matisse proves to be a valuable tool for initial exploration of in situ sequencing datasets. The wide set of tools integrated allows for simple analysis, using the position of individual reads, up to more complex clustering and dimensional reduction approaches, taking cellular content into account. The toolbox can be used to analyze one or several samples at a time, even from different spatial technologies, and it includes different segmentation approaches that can be useful in the analysis of spatially resolved transcriptomic datasets.