Cell deconvolution to estimate cell type abundance from spatial transcriptomic data within heterogeneous tissues


Mapping cell types across a heterogenous tissue is a central issue of spatial biology as it helps understand cells relationship in the disease context and therefore their involvement in treatment response. The application of deconvolution to quantify cell populations defined by single cell sequencing within the regions of spatial gene expression studies has thus become an indispensable tool for in depth tissue biomarker questions. In this perspective, we highlight the use of the SpatialDecon R library by NanoString to quantify baseline cell type abundance defined by spatial transcriptomic data in non-small-cell lung cancer (NSCLC) samples that differentially responded to checkpoint inhibitor therapy.

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