Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer

Autor: Christian H. Ottensmeier, Julian Taylor, Jonathan West, Gareth J. Thomas, Christopher J. Hanley, Lucy M Kimbley, M Rose-Zerilli, Rachel Parker, Sara Waise, Maria-Antoinette Lopez
Rok vydání: 2020
Předmět:
Popis: Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumour progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. This identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-β1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However,in vitroanalyses showed that whilst TGF-β1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulateex-vivophenotypes. Similarly, IL-1β treatment only induced upregulation of a subset of inflammatory fibroblast marker genes.In silicomodelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in fibroblast activation, which can be examined using publicly available R shiny applications (at the following links:myofibroblast activationandinflammatory fibroblast activation). This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC.
Databáze: OpenAIRE