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.

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.

Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle nonlinear deformations between tissue sections and are ineffective for z-stack alignment, other modalities beyond image data or multimodal data. We address these challenges by introducing effortless landmark detection, a new unsupervised landmark detection and registration method using neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets including histology and spatially resolved transcriptomics, demonstrating superior performance in both accuracy and stability compared to existing approaches.

We present a spatial omics approach that combines histology, mass spectrometry imaging and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular-weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using mouse and human brain samples in the context of dopamine and Parkinson’s disease.

Sountoulidis et al. provide a spatial gene expression atlas of human embryonic lung during the first trimester of gestation and identify 83 cell identities corresponding to stable cell types or transitional states. 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 authors created a comprehensive developmental cell atlas for spatiotemporal gene expression of the human spinal cord, revealed species-specific regulation during development and used the atlas to infer novel markers for pediatric ependymomas. The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.

Venous thromboembolism (VTE) is a leading cause of preventable deaths in hospitals, and its incidence is not decreasing despite extensive efforts in clinical and laboratory research. Venous thrombi are primarily formed in the valve pockets of deep veins, where activated monocytes play a crucial role in bridging innate immune activation and hemostatic pathways through the production of inflammatory cytokines, chemokines, and tissue factor (TF) – a principal initiator of coagulation. In the valve pocket inflammation and hypoxia (sustained/intermittent) coexist, however their combined effects on immunothrombotic processes are poorly understood. Inflammation is strongly associated with VTE, while the additional contribution of hypoxia remains largely unexplored. To investigate this, we modelled the intricate conditions of the venous valve pocket using a state-of-the-art hypoxia chamber with software-controlled oxygen cycling. We comprehensively studied the effects of sustained and intermittent hypoxia alone, and in combination with VTE-associated inflammatory stimuli on primary monocytes. TF expression and activity was measured in monocytes subjected to sustained and intermittent hypoxia alone, or in combination with IL-1β. Monocyte responses were further analyzed in detailed by RNA sequencing and validated by ELISA. Stimulation with IL-1β alone promoted both transcription and activity of TF. Interestingly, the stimulatory effect of IL-1β on TF was attenuated by sustained hypoxia, but not by intermittent hypoxia. Our transcriptome analysis further confirmed that sustained hypoxia limited the pro-inflammatory response induced by IL-1β, and triggered a metabolic shift in monocytes. Intermittent hypoxia alone had a modest effect on monocyte transcript. However, in combination with IL-1β intermittent hypoxia significantly altered the expression of 2207 genes and enhanced the IL-1β-stimulatory effects on several chemokine and interleukin genes (e.g., IL-19, IL-24, IL-32, MIF), as well as genes involved in coagulation (thrombomodulin) and fibrinolysis (VEGFA, MMP9, MMP14 and PAI-1). Increased production of CCL2, IL-6 and TNF following stimulation with intermittent hypoxia and IL-1β was confirmed by ELISA. Our findings provide valuable insights into how the different hypoxic profiles shape the immunothrombotic response of monocytes and shed new light on the early events in the pathogenesis of venous thrombosis.

The respiratory system, encompassing the lungs, trachea, and vasculature, is essential for terrestrial life. Although recent research has illuminated aspects of lung development, such as cell lineage origins and their molecular drivers, much of our knowledge is still based on animal models, or is deduced from transcriptome analyses. In this study, conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the spatiotemporal organization of lung during the first trimester of human gestation in situ and at protein level. We used high-parametric tissue imaging on human lung samples, aged 6 to 13 post-conception weeks, using a 30-plex antibody panel. Our approach yielded over 2 million individual lung cells across five developmental timepoints, with an in-depth analysis of nearly 1 million cells. We present a spatially resolved cell type composition of the developing human lung, with a particular emphasis on their proliferative states, spatial arrangement traits, and their temporal evolution throughout lung development. We also offer new insights into the emerging patterns of immune cells during lung development. To the best of our knowledge, this study is the most extensive protein-level examination of the developing human lung. The generated dataset is a valuable resource for further research into the developmental roots of human respiratory health and disease.

Heart development relies on a topologically defined interplay between a diverse array of cardiac cells. We finely curated spatial and single-cell measurements with subcellular imaging-based transcriptomics validation to explore spatial dynamics during early human cardiogenesis. Analyzing almost 80,000 individual cells and 70,000 spatially barcoded tissue regions between the 5.5th and 14th postconceptional weeks, we identified 31 coarse- and 72 fine-grained cell states and mapped them to highly resolved cardiac cellular niches. We provide novel insight into the development of the cardiac pacemaker-conduction system, heart valves, and atrial septum, and decipher heterogeneity of the hitherto elusive cardiac fibroblast population. Furthermore, we describe the formation of cardiac autonomic innervation and present the first spatial account of chromaffin cells in the fetal human heart. In summary, our study delineates the cellular and molecular landscape of the developing heart’s architecture, offering links to genetic causes of heart disease.