GSE173774
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scRNA-Seq)
The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of  cell states, which can not be clearly demarcated and may reflect important differences in the cells' function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org). Overall design: Lymph nodes from mice treated with mycobacterium or PBS were digested in IMDM containing 100 µg/mL Liberase TL and 100 µg/mL DNase I (both from Roche, Germany) for 20 minutes at 37°C. Last 5 minutes of incubation, EDTA was added at a final concentration of 10 mM. Cells were collected, filtered through a 70mm cell strainer, washed with PBS + 0.04% BSA for a final concentration of 1000 cells/µL. before loading into the 10x genomics chromium instrument.