Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

TLDR

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. The authors introduce SpatialDecon, an algorithm that quantifies cell populations defined by single‑cell sequencing within spatial gene expression studies, using log‑normal regression and background modeling to outperform classical least‑squares methods. SpatialDecon incorporates advanced deconvolution techniques, compiles cell‑profile matrices for 75 tissue types, identifies low‑expression genes suitable for immune deconvolution in tumors, and benchmarks its performance against marker proteins in lung‑tumor datasets. The tool delivers simple, flexible, spatially resolved, granular cell‑abundance estimates that are paired with highly multiplexed gene‑expression data.

Abstract

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.