Cancer microenvironment

Cell type deconvolution in tumor spatial transcriptomics (ST) data remains challenging. Here, the authors develop Spatial Cellular Estimator for Tumors (SpaCET) to infer cell types and intercellular interactions from ST data in cancer across different platforms, with improved performance over similar methods.

Imaging mass cytometry of human brain tumours provides spatial information that, combined with existing transcriptomic data, reveals the existence of a cellular neighbourhood containing a rare macrophage population associated with prolonged survival.

Using imaging mass cytometry, the tumour and immunological spatial landscapes of 416 lung adenocarcinomas are characterized, which, when combined with deep learning, can predict clinical outcomes with high accuracy.

Fibroblast heterogeneity is a prominent but poorly understood feature of solid tumours. Here three major fibroblast subpopulations in non-small cell lung cancer are identified and characterised through single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry.