Recent advances in spatial transcriptomics, proteomics, metabolomics etc. assays have revolutionized our understanding of the tumor microenvironment (TME). As these methods mature, users are increasingly combining multiple readouts for a holistic view of TME heterogeneity and complexity. Spatial multi-omics presents specific bioinformatics issues, requiring specific workflows to make sense of the data.

We present software and a computational framework for integration and data analysis of spatial biology data. Weave software addresses several spatial biology bioinformatics challenges, such as joint visualization of different spatial omics assays, providing common downstream multimodal analysis pipelines, integration of derived results, and enabling communication of results between collaborators. Users can explore multicellular environments, and uncover novel discoveries within both new and existing datasets, in areas spanning research, drug discovery and biomarker discovery.

Our approach to spatial multi-omic data integration is technology agnostic, enabling combined analysis of data acquired from different spatial platforms, from the same or serial sections. This is demonstrated via two different use cases. In the first, multiplexed immunofluorescence-based spatial proteomics was combined with serial-section multimodal mass spectrometry imaging of peptides, glycans and endogenous metabolites to investigate cellular and molecular heterogeneity, and mechanisms of intrinsic chemoresistance of high-grade serous ovarian cancer. In the second, lung cancer biopsy sections were sequentially analyzed via spatial transcriptomics and spatial proteomics using different commercial platforms. The use of same-section acquisition enabled single-cell level comparisons of RNA and protein expression, revealing segmentation accuracy and transcript-protein correlation analyses within individual cells.