Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering- or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes (AT) in a simplex-shaped neural network latent space. Using preclinical and clinical models of breast cancer, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism, and immune interactions. Primary tumor ATs are recapitulated in matched liver, lung, and lymph node metastases. Spatial transcriptomics reveals archetypal organization within the tumor and intra-archetypal mirroring between cancer and adjacent stromal cells. AAnet identifies GLUT3 within the hypoxic AT that proves critical for tumor growth and metastasis. AAnet is a powerful tool, capturing complex, functional cell states from multimodal data. SIGNIFICANCE: Defining critical cell states among cells that reside along a phenotypic continuum is a current biological and computational challenge. In this study, we present AAnet, a neural network that learns archetypal cell states of cancer cells. AAnet defines discrete spatially localized ATs that resolve intratumoral heterogeneity.

Breast cancer is a highly heterogeneous disease with diverse outcomes, and intra-tumoral heterogeneity plays a significant role in both diagnosis and treatment. Despite its importance, the spatial distribution of intra-tumoral heterogeneity is not fully elucidated. Spatial transcriptomics has emerged as a promising tool to study the molecular mechanisms behind many diseases. It offers accurate measurements of RNA abundance, providing powerful tools to correlate the morphologies of cellular neighborhoods with localized gene expression patterns. However, the spot-based spatial transcriptomic tools, including the most widely used platform, Visium, do not achieve single-cell resolution readouts, which hinders data interpretability. In this study, we present a computational pathology image analysis pipeline (i.e., computational tissue annotation, CTA) that utilizes machine learning algorithms to accurately map tumor, stroma, and immune compartments within Visium-assayed tumor sections. Using a cohort of 23 breast tumor sections from four patients, we demonstrate that CTA can provide high-resolution annotations on the hematoxylin-and-eosin-stained images alongside the paired sequencing data, support the evaluation of deconvolution methods, deepen insights into intra-tumoral heterogeneity by increasing data analysis resolution, assist with spatially resolved intrinsic subtyping, and enhance the visualization of lymphocyte clones at single-cell resolution. The proposed pipeline provides valuable insights into the complex spatial architecture of breast cancer, contributing to more personalized diagnostics and treatment strategies.

Melanoma brain metastases (MBM) exhibit extensive intertumor and intratumor heterogeneity (ITH), driven by a complex tumor microenvironment. The aim of this study was to perform a detailed analysis of individual MBM patient tumors using a multiomics approach, integrating spatial transcriptomics with multi-region bulk exome, proteome, and transcriptome profiling for a small group of four patient samples. We identified significant patient-specific variations in immune cell infiltration, particularly in B/plasma cells, myeloid cells, and cancerassociated fibroblasts (CAFs). Notably, immunotherapy-treated patients showed enriched pathways related to epithelial-mesenchymal transition (EMT), interferon-gamma (IFN-gamma) signaling, oxidative phosphorylation, T-cell signaling, inflammation and DNA damage, which aligned with distinct cellular compositions observed in the spatial analysis. We also uncovered considerable ITH, especially at the protein level, revealing differential

Concepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. The MEA-seqX platform, integrating high-density microelectrode arrays, spatial transcriptomics, optical imaging, and advanced computational strategies, enables the simultaneous recording and analysis of molecular and electrical network activities at mesoscale spatial resolution. Applied to a mouse hippocampal model of experience-dependent plasticity, MEA-seqX unveils massively enhanced nested dynamics between transcription and function. Graph-theoretic analysis reveals an increase in densely connected bimodal hubs, marking the first observation of coordinated hippocampal circuitry dynamics at molecular and functional levels. This platform also identifies different cell types based on their distinct bimodal profiles. Machine-learning algorithms accurately predict network-wide electrophysiological activity features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.

Tissue architecture is a product of a multilayered molecular landscape, where even subtle disruptions in the spatial context can initiate or reflect disease processes. Recent advances in high-throughput spatial omics technologies have enabled the investigation of this complexity in stunning detail, providing groundbreaking insights into how spatial molecular organization shapes health and disease. Spatial analysis empowers the discovery of developmental and disease-associated molecular signatures, cell states and multicellular niches, as well as the evaluation of disease heterogeneity within and across organs. This Review examines spatially resolved pathological molecular alterations in a wide range of disease processes, such as developmental disorders, tumorigenesis, fibrosis and injury responses, neurodegeneration, infection and inflammation, through the lens of these universal biological frameworks. We discuss challenges, opportunities and promising examples in advancing these technologies to clinical applications, including the increasing importance of artificial intelligence. Finally, we explore possible avenues for a more comprehensive, multidimensional assessment of tissues.

Integration of scRNA-seq data from millions of cells revealed a high diversity of cell types in the healthy and diseased human lung. In a large and complex organ, constantly exposed to external agents, it is crucial to understand the influence of lung tissue topography or external factors on gene expression variability within cell types. Here, we apply three spatial transcriptomics approaches, to: (i) localize the majority of lung cell types, including rare epithelial cells within the tissue topography, (ii) describe consistent anatomical and regional gene expression variability within and across cell types, and (iii) reveal distinct cellular neighborhoods in specific anatomical regions and examine gene expression variations in them. We thus provide a spatially resolved tissue reference atlas in three representative regions of the healthy human lung. We further demonstrate its utility by defining previously unknown imbalances of epithelial cell type compositions in chronic obstructive pulmonary disease lungs. Our topographic atlas enables a precise description of characteristic regional cellular responses upon experimental perturbations or during disease progression.

Introduction: RNA expression is modulated by tau. We used two mouse models, THY-Tau22 mice, which express pro-aggregation tau, and TauKO mice, which are null for tau, to improve our understanding of tau-altered mRNA expression in brain. Methods: Spatial transcriptomics on Tau22 and TauKO mice were used to interrogate regional mRNA expression changes. We focused on mRNA expression changes in the hippocampus and ventricles; two regions altered early in Alzheimer’s disease. Results: We identified the transthyretin mRNA, Ttr, as being dysregulated in a tau-dependent manner. Immunofluorescence (IF) revealed increased TTR protein expression in THY-Tau22 mice and lowered expression in TauKO mice in the choroid plexus epithelial cells. Conclusion: As TTR is involved in the clearance of Aβ and the prevention of Aβ aggregation, we evaluated endogenous mouse Aβ in TauKO mice and observed increased Aβ deposits. Our study reveals a hitherto unknown regulatory role of tau on Ttr mRNA and protein expression, which may participate in a feedback loop contributing to Aβ disease progression.

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.

Tissues are dynamic and complex biological systems composed of specialized cell types that interact with each other for proper biological function. To comprehensively characterize and understand the cell circuitry underlying biological processes within tissues, it is crucial to preserve their spatial information. Here we report a simple mounting technique to maximize the area of the tissue to be analyzed, encompassing the whole length of the murine gastrointestinal (GI) tract, from mouth to rectum. Using this method, analysis of the whole murine GI tract can be performed in a single slide not only by means of histological staining, immunohistochemistry and in situ hybridization but also by multiplexed antibody staining and spatial transcriptomic approaches. We demonstrate the utility of our method in generating a comprehensive gene and protein expression profile of the whole GI tract by combining the versatile tissue-rolling technique with a cutting-edge transcriptomics method (Visium) and two cutting-edge proteomics methods (ChipCytometry and CODEX-PhenoCycler) in a systematic and easy-to-follow step-by-step procedure. The entire process, including tissue rolling, processing and sectioning, can be achieved within 2–3 d for all three methods. For Visium spatial transcriptomics, an additional 2 d are needed, whereas for spatial proteomics assays (ChipCytometry and CODEX-PhenoCycler), another 3–4 d might be considered. The whole process can be accomplished by researchers with skills in performing murine surgery, and standard histological and molecular biology methods.