GSE173773
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scMARS-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: MCA205 fibrosarcoma tumor were injected into mice. After 14 days, to prepare tumor infiltrating leukocytes single cell suspensions, the tumors underwent mechanical (gentle-MACSTM C tube, Miltenyi Biotec Inc., San Diego, CA) and enzymatic digestion (0.1mg/ml DNase type I (Roche), and 1mg/ml Collagenase IV (Worthington) in RPMI-1640) for 10 minutes at 37°C and repeat one more time. Cells then filtered through 100 µm cell strainer, washed with an ice cold sorting buffer, centrifuged (5 min, 4°C, 350 g), and stained with fluorophores conjugated anti-mouse CD45 antibodies on ice 30 minutes avoid light.  After staining, cells were washed and resuspended in a cold washing buffer (0.5% BSA and 2 mM EDTA in PBS), filtered through a 70 µm cell strainer. Before sorting, cells were stained with propidium iodide to exclude dead/dying cells. Cell sorting was performed using a BD FACSAria Fusion flow cytometer (BD Biosciences), gating for CD45+ cells after exclusion of dead cells and doublets.