Ferreira, Ricardo MeloFreije, Benjamin J.Eadon, Michael T.2023-04-282023-04-282022-01-13Melo Ferreira R, Freije BJ, Eadon MT. Deconvolution Tactics and Normalization in Renal Spatial Transcriptomics. Front Physiol. 2022;12:812947. Published 2022 Jan 13. doi:10.3389/fphys.2021.812947https://hdl.handle.net/1805/32686The kidney is composed of heterogeneous groups of epithelial, endothelial, immune, and stromal cells, all in close anatomic proximity. Spatial transcriptomic technologies allow the interrogation of in situ expression signatures in health and disease, overlaid upon a histologic image. However, some spatial gene expression platforms have not yet reached single-cell resolution. As such, deconvolution of spatial transcriptomic spots is important to understand the proportion of cell signature arising from these varied cell types in each spot. This article reviews the various deconvolution strategies discussed in the 2021 Indiana O’Brien Center for Microscopy workshop. The unique features of Seurat transfer score methodology, SPOTlight, Robust Cell Type Decomposition, and BayesSpace are reviewed. The application of normalization and batch effect correction across spatial transcriptomic samples is also discussed.en-USAttribution 4.0 InternationalSpatial transcriptomicsVisium gene expressionSingle nuclear RNA sequencingNephronAcute kidney injuryBiopsy specimenArticle