• Michael Snyder, Stanford University, United States

• Cole Trapnell, University of Washington, 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.

• Nick Nystrom, Pittsburgh Supercomputing Center, United States

• Ziv Bar-Joseph, Carnegie Mellon University, United States

To reconstruct a 3D human map HuBMAP intends to profile large amounts of high throughput biological data spanning several different organs, modalities and technologies. In this talk I will discuss the guiding principals underlying the decisions for selecting specific pipelines. I will mention some challenges related to implanting these pipelines in the context of a large and diverse consortium and will show some preliminary results for using these methods to analyze genomics, proteomics and imaging data.

• Nils Gehlenborg, Harvard Medical School, United States

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.

• Katy Borner, Indiana University, United States

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”).

• 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
• Michael Snyder, Stanford University, United States
• Nils Gehlenborg, Harvard Medical School, United States