Spatialxe - Standard processing and analysis of spatial Xenium in situ data

Spatial omics technologies represent a transformative approach in biological research, enabling the comprehensive analysis of molecular profiles within their native spatial context. By preserving the spatial relationships between cells, spatial omics technologies, such as 10x Xenium, offer critical insights into tissue architecture, cellular heterogeneity, and the microenvironment's role in a healthy and disease context. As there is an increase in demand for understanding spatial patterns to study diseases, there is a need for standardized and reproducible workflows. The nf-core community thus presents spatialxe, a blueprint for the analysis of Xenium data. Spatialxe supports featured benchmarked tools, such as Xenium Ranger. It generates a spatial object data that includes the cell feature matrix that can be used for further downstream analysis. We would also implement a number of segmentation algorithms like Cellpose, Baysor and QuPath for image annotation. Spatialxe will be an extensive pipeline to cover not only standard processing but also single cell and spatial omics quality control, conversion of the data to be SpatialData ready, and automated image annotation. The workflow will be deployed within the German Human Genome-Phenome Archive (GHGA - www.ghga.de) as the default analysis workflow for incoming Xenium data. The pipeline will be used to process and analyze Xenium data from primary tumor and metastase samples of hard-to-treat entities of colorectal cancer. The standardization as well as the reproducibility of the Nextflow/nf-core pipelines combined with the infrastructure for FAIR omics data usage and ethico-legal framework offered by GHGA will enable cross-project analysis and hence promote new collaborations and research projects.