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.

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

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.

Biome-specific gene catalogues have been recovered for many environments using shotgun metagenomics, followed by assembly and gene calling on the assembled contigs. We recently proposed a novel mix-assembly strategy, combining individual and co-assembly approaches, and used this approach to assemble an extensive non-redundant gene set from 124 Baltic Sea metagenome samples. The Baltic Sea Gene Set (BAGS v1.1) comprises 66.53 million functionally and taxonomically annotated genes. To enable interactive exploration of this gene catalogue we have developed an RShiny application, BAGS-Shiny, that allows users to perform searches by sequence similarity (BLAST) and/or taxonomic and functional annotation. The gene catalogue and web application will serve as valuable tools for exploring microbial gene functions in brackish ecosystems. In addition, we here make available a pipeline to create gene catalogues based on the mix-assembly approach.

Recent developments in spatially resolved omics have expanded studies linking gene expression, epigenetic alterations, protein levels, and metabolite intensity to tissue histology. The integration of multiple spatial measurements can offer new insights into alterations propagating across modalities, however, it also presents experimental and computational challenges. 

To set the multimodal data into a shared coordinate system for enhanced integration and analysis, we propose MAGPIE, a framework for co-registering spatially resolved transcriptomics and spatial metabolomics measurements on the same or consecutive tissue sections, present within their existing histological context. Further, we showcase the utility of the MAGPIE framework on spatial multi-omics data from lung tissue, an inherently heterogeneous tissue type with integrity challenges and for which we developed an experimental sampling strategy to allow multimodal data generation. In these case studies, we were able to link pharmaceutical co-detection with endogenous responses in rat lung tissue following inhalation of a small molecule, which had previously been stopped during preclinical development with findings of lung irritation, and to characterise the metabolic and transcriptomic landscape in a mouse model of drug-induced pulmonary fibrosis in conjunction with histopathology annotations.

The generalisability and scalability of the MAGPIE framework were further benchmarked on public datasets from multiple species and tissue types, demonstrating applicability to both DESI and MALDI mass spectrometry imaging together with Visium-enabled transcriptomic assessment. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in a pharmacological context, and offers a modular, accessible computational workflow for data integration.

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.