Fundamental biological processes such as development and adaptation to various environmental contexts rely on the fine spatiotemporal regulation of gene expression at the cellular level. Historically, biologists have been limited to observing average transcriptomes in groups of cells within a tissue or organ, thereby overlooking the vast diversity of cellular forms and functions.

Recent advances in single-cell sequencing have revealed unexpected variability in gene expression between adjacent cells, uncovering previously inaccessible cellular subpopulations. The emergence of spatial transcriptomics technologies represents a major breakthrough: they now make it possible to associate a cell’s transcriptional identity with its precise position within a tissue, in situ, and in its native biological context.

Named “Method of the Year 2020” by Nature Methods, spatial transcriptomics is revolutionizing our understanding of living tissues by offering an integrated view of cellular landscapes—cell type origins, spatial organization, local interactions, molecular gradients, and more. More broadly, the field of spatial OMICs has expanded beyond transcriptomics to include approaches for mapping the distribution of proteins, chromatin architecture, and metabolites at high spatial resolution.

The main scientific objective of this school is to train a new generation of researchers capable of leveraging spatial OMICs approaches to address complex biological questions, by combining the acquisition of spatially resolved data with multi-scale molecular analyses. A key focus of the school will be to introduce participants to data analysis methods specific to spatial OMICs approaches, with an emphasis on current challenges: integration of heterogeneous data, spatial resolution, noise handling, normalization, visualization, and biological interpretation. The analysis of spatial OMICs data requires interdisciplinary skills at the interface of molecular biology, bioinformatics, mathematics, and physics. New tools are constantly being developed, many of which use AI/machine learning approaches. This school will thus contribute to the development of an interdisciplinary community around spatial OMICs by fostering exchanges between experimental researchers and data analysts, and by initiating collaborations on innovative research projects.

The organization of this thematic school responds to growing demand for training within the scientific community, particularly driven by the EquipEx+ Spatial-Cell-ID project (https://spatial-cell-id.ens-lyon.fr/), which is developing a dedicated spatial transcriptomics platform in Lyon. At the national level, the school will also build on the momentum of the PEPR programs Cell-ID (https://www.pepr-cell-id.fr/) and Digital Health (https://pepr-santenum.fr/), whose projects strongly rely on spatial OMICs technologies, as well as on the France-BioImaging network (https://france-bioimaging.org/), of which Spatial-Cell-ID is an active member.