Bridging dataset generation to enable integrated data analysis and interpretation across HuBMAP tissues

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PROJECT SUMMARY The goal of this supplement is to generate bridging datasets across all HuBMAP tissues to allow the HuBMAP Consortium to aggregate and integrate data and metadata from each tissue with respect to anatomical references in a standardized manner to facilitate data integration, analysis and interpretation. We will perform the 10x Genomics Chromium Single Cell Multiome ATAC + Gene Expression assay on snap- frozen tissue samples for each organ across several Tissue Mapping Centers (TMC). This assay enables collection of RNAseq and ATACseq data that are linked at the single-cell/single-nucleus level. We will link the individual tissue 10x Multiome bridge datasets to each of the Anatomic, Structural, Cell Types and Biomarkers (ASCT+B) tables for each organ to build a framework to input into the HuBMAP HIVE’s Registration User Interface. We will ensure that these data (annotated with detailed biological and technical metadata) will be made available for broad use, including not only the building of 3D multiscale maps that are the primary goal of this Consortium, but also development of novel profiling technologies and computational approaches for analysis and integration of multiple data types across multiple tissue types. We will rapidly release these bridge datasets to the TMCs to be integrated with the organ-specific TMC spatial data to build spatially-resolved 3D tissue maps derived from this common bridging dataset. As the Co-Chair of the HuBMAP Integrated Data Analysis & Interpretation Working Group and through involvement with the Collaborative Projects and Publications Working Group planning the cross-tissue integrative data analysis manuscript, we identified this important gap in the HuBMAP Consortium datasets currently generated or planned to be generated at the HuBMAP 2022 Annual Meeting. Generating a bridging dataset across all HuBMAP tissues will create an anchor for all individual organ atlases and enable integrative analysis across consortium datasets to assess similarities and differences across tissues in cell count composition, cell types and states, neighborhoods and functional units.