ABSTRACT
SUMMARY: Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools. AVAILABILITY AND IMPLEMENTATION: The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.
Subject(s)
Algorithms , Software , Gene Expression Profiling , Neural Networks, ComputerABSTRACT
In vitro studies have associated oxidative phosphorylation (OXPHOS) with anti-inflammatory macrophages, whereas pro-inflammatory macrophages rely on glycolysis. However, the metabolic needs of macrophages in tissues (TMFs) to fulfill their homeostatic activities are incompletely understood. Here, we identified OXPHOS as the highest discriminating process among TMFs from different organs in homeostasis by analysis of RNA-seq data in both humans and mice. Impairing OXPHOS in TMFs via Tfam deletion differentially affected TMF populations. Tfam deletion resulted in reduction of alveolar macrophages (AMs) due to impaired lipid-handling capacity, leading to increased cholesterol content and cellular stress, causing cell-cycle arrest in vivo. In obesity, Tfam depletion selectively ablated pro-inflammatory lipid-handling white adipose tissue macrophages (WAT-MFs), thus preventing insulin resistance and hepatosteatosis. Hence, OXPHOS, rather than glycolysis, distinguishes TMF populations and is critical for the maintenance of TMFs with a high lipid-handling activity, including pro-inflammatory WAT-MFs. This could provide a selective therapeutic targeting tool.
