Robust decomposition of cell type mixtures in spatial transcriptomics

Abstract

Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus.We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTD's recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.

Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics

“…Moreover, cell2location revealed that 50% of Slide-Seq "beads" (i.e. 10-micron diameter spatial locations) contained more than 1 cell type ( Fig S10B), consistent with the previous observations 4 .…”

“…Emerging spatial genomics technologies hold considerable promise for characterising tissue architecture, providing key opportunities to map resident cell types and cell signalling in situ, thereby helping guide in vitro tissue engineering efforts. Despite existing proof of concept applications [1][2][3][4][5] , it remains a challenge to define versatile and broadly applicable spatial genomics technologies and workflows. One reason is the enormous variation in tissue architecture across organs, ranging from the brain with hundreds of cell types found across discrete anatomical regions to immune organs with continuous cellular gradients and dynamically modified microenvironments.…”

Giotto, a toolbox for integrative analysis and visualization of spatial expression data

“…5 and 6A, Supplementary Notes). We also compared it with RCTD 30 , which is a newly developed method for deconvolution. RCTD also performed well (median AUC value of 0.95, Supplementary Fig 5. and 6A), but it was considerably slower than the other methods ( Supplementary Fig.…”

Mapping the temporal and spatial dynamics of the human endometriumin vivoandin vitro

“…The spatial arrangement of cells is key to understanding a morphologically complex tissue such as the endometrium, where a cell's function may differ depending on signals it receives from neighbouring cells (16). Many spatiallyresolved transcriptomics methods are not quite at single-cell resolution, and rely on the computational integration of coupled single-cell (or single nuclei) transcriptomes to achieve this level of detail (17)(18)(19). These genomic technologies are the basis of the Human Cell Atlas initiative, which aims to map all cells in the human body (20).…”