|2:00 PM - 2:40 PM||HUBMAP Session Keynote: HuBMAP Data Collection Plans||Michael Snyder, Stanford University, United States|
|2:40 PM - 3:05 PM||Embryo-scale, single-cell spatial transcriptomics||Cole Trapnell, University of Washington, United States|
|3:20 PM - 3:45 PM||Infrastructure for Storing Massive Biological Data Sets||Nick Nystrom, Pittsburgh Supercomputing Center, United States|
|3:45 PM - 4:10 PM||Tools and Pipelines for the Analysis and Integration of HuBMAP data||Ziv Bar-Joseph, Carnegie Mellon University, United States|
|4:10 PM - 4:35 PM||Visualization and Exploration of Heterogeneous Human Tissue Data Sets||Nils Gehlenborg, Harvard Medical School, United States|
|4:35 PM - 5:00 PM||Common coordinates for registering human scale data at multiple scales||Katy Borner, Indiana University, United States|
|5:00 PM - 5:30 PM||How can I interact with, use, develop methods for and obtain funding to work with HuBMAP data?||Michael Snyder, Stanford University, United States|
Nils Gehlenborg, Harvard Medical School, United States
Nick Nystrom, Pittsburgh Supercomputing Center, United States
Ziv Bar-Joseph, Carnegie Mellon University, United States
Katy Borner, Indiana University, United States
Ajay Pillai, National Institutes of Health, United States
Spatial patterns of gene expression span many scales, and are shaped by both local (e.g. cell-cell interactions) and global (e.g. tissue, organ) context. However, most in situ methods for profiling gene expression either average local contexts or are restricted to limited fields of view. Here we introduce sci-Space, a scale-flexible method for spatial transcriptomics that retains single cell resolution while simultaneously capturing heterogeneity at larger scales. As a proof-of-concept, we apply sci-Space to the developing mouse embryo, capturing the approximate spatial coordinates of profiled cells from whole embryo serial sections. We identify genes including Hox-family transcription factors expressed in an anatomically patterned manner across excitatory neurons and other cell types. We also show that sci-Space can resolve the differential contribution of cell types to signalling molecules exhibiting spatially heterogeneous expression. Finally, we develop and apply a new statistical approach for quantifying the contribution of spatial context to variation in gene expression within cell types.
The HuBMAP consortium generates, integrates, and disseminates multi-modal, single-cell data from human tissues. The heterogeneity and scale of these data sets pose new challenges for data visualization, such as integrating diverse data types and scaling to enormous dataset sizes. To address these issues, we have designed and implemented Vitessce, a visualization tool for exploring spatial single-cell experiments. Vitessce can be used both as a standalone tool as well as a component for portal user interfaces. In my presentation, I will introduce the features and architecture of Vitessce and discuss our strategies for tight integration between the HuBMAP Data Portal and Vitessce visualizations.
The Common Coordinate System (CCF) consists of ontologies, reference object libraries, and computer software (e.g., user interfaces) that enable biomedical experts to semantically annotate tissue samples and to precisely describe their locations in the human body (“registration”), align multi-modal tissue data extracted from different individuals to a reference coordinate system (“mapping”) and, provide tools for searching and browsing HuBMAP data at multiple levels, from the whole body down to single cells (“exploration”).