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| Status |
Public on Jul 08, 2020 |
| Title |
Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer [single-cell RNA-seq] |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
Third-party reanalysis
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| Summary |
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 vitro analyses showed that whilst TGF-β1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo phenotypes. Similarly, IL-1β treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in 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.
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| Overall design |
mRNA profiles of primary human lung samples (D1-12; “TLDS_All Cells.txt”). Third-party stromal cell data (included in "Merged_Stromal Cells.txt") were downloaded as the "all cells" file from https://gbiomed.kuleuven.be/scRNAseq-NSCLC. Stromal cells were identified in these data using a random forest classifier trained on the TLDS dataset variable genes.
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| Contributor(s) |
Waise S, Hanley CJ |
| Citation(s) |
36720863 |
| |
| Submission date |
Jul 07, 2020 |
| Last update date |
Apr 23, 2024 |
| Contact name |
Sara Waise |
| E-mail(s) |
s.waise@soton.ac.uk
|
| Organization name |
University of Southampton
|
| Department |
Cancer Sciences
|
| Lab |
Experimental Pathology Group
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| Street address |
Somers Building MP284, Southampton General Hospital, Tremona Road
|
| City |
Southampton |
| State/province |
Hampshire |
| ZIP/Postal code |
SO16 6YD |
| Country |
United Kingdom |
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| Platforms (1) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
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| Samples (18)
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| Relations |
| BioProject |
PRJNA644572 |
| SRA |
SRP270656 |
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