Spatial and Single-Cell Analysis of MERSCOPE® Platform Data using the Python Library CORMERANTT

MERFISH (multiplexed error-robust fluorescence in situ hybridization) is a single-cell transcriptomics technology that captures spatial gene expression patterns across whole tissue sections.1 The Vizgen MERSCOPE® Platform is built on MERFISH technology and enables the direct profiling of the spatial distribution of hundreds of RNA species in intact tissue with subcellular resolution*. Despite the recent development of spatial transcriptomics software (e.g., Giotto, Squidpy, Seurat), it remains challenging for researchers to visually explore and analyze the vast amount of data in a typical MERSCOPE experiment. Such experiments consist of hundreds of thousands of segmented single cells, millions of transcripts from hundreds of genes, as well as large high bit-depth images of cellular structures. We developed CORMERANTT library, an open-source Python library, that enables researchers to perform single- cell and spatial analysis of MERSCOPE data in the context of a Jupyter notebook. CORMERANT library utilizes novel spatial analysis and visualization techniques to help researchers navigate these complex data. Users can interactively overlay single-cell gene expression clustering results with spatially resolved single-cell locations, enabling users to cross-check the spatial distribution of cell clusters and easily identify spatial patterns of cell distributions and/or gene expression. CORMERANT library allows users to identify cell cluster, cell type, gene, and tissue neighborhoods using two complementary approaches – hexagonal tiling and alpha shape. Hexagonal tiling assigns each region (e.g., tile) of a tissue to a distinct non-overlapping neighborhood based on tile attributes, such as transcript counts or cell type distributions. The alpha shape approach allows users to identify neighborhoods of cell types/cell clusters or genes/gene clusters and allows researchers to quantify the complex ecosystem of overlapping neighborhoods that form within tissues (such as immune cells infiltrating a tumor neighborhood). CORMERANT library simplifies the process of identifying tissue microarchitecture (e.g., blood vessels or tissue borders) within tissues and uses these structures to define novel spatial metadata for cells. Finally, we demonstrate how CORMERANT library can be utilized to perform and view the results of differential expression performed across spatially distinct groups of cells. See video showing interactive features https://twitter.com/vizgen_inc/status/1679113477573541894?s=20